Tribological and Predictive Modelling Analysis of Copper - CNT - Titanium Hybrid Metal Matrix Composites

Tribological and Predictive Modelling Analysis of Copper - CNT - Titanium Hybrid Metal Matrix Composites | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Tribological and Predictive Modelling Analysis of Copper - CNT - Titanium Hybrid Metal Matrix Composites Sripad kulkarni, Mallikarjun Biradar, Ramesh S, PremChand R, Shanmuganatan S P, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8604268/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Mar, 2026 Read the published version in Journal of Bio- and Tribo-Corrosion → Version 1 posted 7 You are reading this latest preprint version Abstract This paper investigates the wear behaviour of copper-based metal matrix composites (MMCs) reinforced with carbon nanotubes (CNTs) and micro titanium (Ti) particles. Composites containing 0.5–1.5 Wt.% CNT and 1–5 Wt.% Ti were fabricated using the stir casting technique to ensure uniform reinforcement dispersion. Abrasive wear tests were conducted under a 1 N load at sliding speeds of 200–500 RPM using a pin-on-disc tribometer. Results revealed that the wear rate increased with speed but decreased notably with higher reinforcement levels. The C9 composition (1.5Wt.% CNT and 5Wt.% Ti) exhibited the lowest wear rate and superior wear resistance. Regression modelling using Linear, Polynomial, Support Vector Regression (SVR), and Random Forest algorithms was applied to predict wear behaviour. Among these, the Polynomial model demonstrated the highest accuracy with an R 2 value close to 0.99 and low mean absolute error, while SVR showed consistent but less interpretable results. ANOVA analysis confirmed that both composite composition and speed significantly influenced wear rate. The paper concludes that hybrid reinforcement of CNT and Ti effectively enhances the tribological performance of copper composites, and regression models offer reliable predictive insight into wear mechanisms. Copper composites CNT Titanium Wear rate Regression models Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Metal matrix composites (MMCs) have emerged as a vital class of advanced materials owing to their superior mechanical[ 1 ], thermal, and tribological properties[ 2 ] compared to conventional metals and alloys[ 3 ], [ 4 ], [ 5 ]. Among these, copper-based composites have gained significant attention due to their excellent electrical and thermal conductivity[ 6 ], [ 7 ], high corrosion resistance, and suitability for industrial applications such as electrical contacts, bearings, and heat exchangers[ 8 ], [ 9 ]. However, the relatively poor wear resistance of pure copper limits its performance in high-friction or load-bearing environments[ 10 ], necessitating reinforcement with secondary phases that can enhance its tribological behaviour without compromising conductivity[ 11 ], [ 12 ]. In recent years, the stir casting technique has been widely adopted for fabricating MMCs due to its simplicity, cost-effectiveness, and ability to achieve homogeneous particle dispersion within the molten matrix. Optimising reinforcement ratios and process parameters remains crucial for achieving a balance between enhanced wear resistance and the retention of base metal properties[ 13 ][ 14 ]. Furthermore, predictive modelling using regression-based and machine learning approaches has gained importance for understanding wear mechanisms and forecasting material performance under varying operational conditions[ 15 ][ 16 ]. These data-driven models not only reduce experimental effort but also enable better control over composite design and property optimisation. Although numerous studies have explored the development of metal matrix composites (MMCs) reinforced with ceramic or carbon-based particles, most prior research has focused predominantly on aluminium- or magnesium-based systems, leaving copper-based hybrid composites relatively underexplored. Existing works on copper composites often employ single reinforcements such as SiC, Al₂O₃, or CNTs, which enhance specific properties but fail to provide a balanced improvement in both mechanical and tribological performance. The synergistic potential of combining nano-scale and micro-scale reinforcements such as carbon nanotubes (CNTs) and titanium (Ti) particles has not been comprehensively studied in copper matrices. Furthermore, while experimental investigations[ 17 ] have provided valuable insights into wear mechanisms, there is a lack of predictive modelling frameworks that integrate statistical and machine learning approaches to accurately forecast wear behaviour under varying operational conditions. Previous models have largely relied on linear or polynomial regression without comparing multiple algorithms for performance benchmarking. In particular, studies rarely examine how advanced regression techniques such as Support Vector Regression (SVR) or Random Forest (RF) can capture the nonlinearities inherent in tribological data. Hence, there exists a clear research gap in systematically correlating reinforcement composition, sliding parameters, and wear rate through both experimental and data-driven modeling. Addressing this gap can enable the design of high-performance copper-based hybrid composites with optimized reinforcement ratios and improved predictive control over their tribological behavior. To address this limitation, researchers have explored hybrid reinforcement strategies that combine nanostructured and micro-scale reinforcements to achieve synergistic strengthening effects[ 18 ], [ 19 ]. Carbon nanotubes (CNTs) are particularly attractive due to their exceptional tensile strength, low density[ 17 ], [ 20 ], [ 21 ], and excellent lubricating properties, which contribute to reduced friction and improved wear resistance. Similarly, titanium (Ti) particles enhance surface hardness and load-bearing capacity, providing mechanical reinforcement to the soft copper matrix[ 22 ], [ 23 ], [ 24 ]. The combination of CNTs and Ti as hybrid reinforcements is therefore expected to produce composites that exhibit superior mechanical integrity, wear resistance, and thermal stability[ 25 ]. The present paper focuses on the fabrication and tribological evaluation of copper-based MMCs reinforced with varying proportions of CNTs and micro titanium particles. The composites were prepared using the stir casting method and subjected to abrasive wear testing under different speeds. Experimental results were analysed using multiple regression techniques, Linear, Polynomial, Support Vector Regression (SVR), and Random Forest to model and predict wear behaviour. Comparative analysis of model accuracy, supported by statistical evaluation through ANOVA, provides a comprehensive understanding of how reinforcement composition and operating conditions influence wear rate. The integrated experimental and predictive approach aims to establish a reliable framework for optimising copper hybrid composites for advanced engineering applications. 2. Materials and Methods 2.1 Fabrication of Metal Matrix Composites Figure 1 illustrates the casting process of metal matrix composites (MMCs), showing three critical stages: mixing, pouring, and the final cast specimens. In Fig. 1 (a), the molten metal is being stirred mechanically using a stirrer to ensure uniform dispersion of the reinforcing particles within the molten matrix. This is typically performed in a stir casting setup, where the matrix alloy (Copper) is heated above its melting point, and ceramic or reinforcement particles (CNT and Ti) are gradually added under continuous stirring to achieve homogeneous distribution. Figure 1 (b) shows the pouring stage, where the uniformly mixed molten composite is poured into pre-heated metallic or sand moulds to take the desired shape. The glowing molten metal indicates high temperature and fluid flow, which is essential for avoiding porosity and ensuring proper mould filling. Figure 1 (c) depicts the solidified and ejected cast specimens, each marked with identification numbers corresponding to different compositions or reinforcement percentages. These cylindrical samples are typically prepared for mechanical testing (tensile, hardness, impact) and microstructural analysis to paper the effect of reinforcement on composite behaviour. 2.2 Specimen Preparation Figure 2 illustrates the pin-on-disc wear testing setup used to evaluate the tribological behaviour of materials under controlled conditions. Figure 2 (a) shows the wear track region where the test specimen (pin) is held in contact with the rotating disc. The disc, usually made of hardened steel, rotates at a specified speed while the pin is subjected to a constant normal load. The setup allows measurement of parameters such as frictional force, wear rate, and coefficient of friction. This helps assess the wear resistance and frictional performance of materials like metal matrix composites or alloys. Figure 2 (b) presents the complete pin-on-disc tribometer unit, including the control panel and electronic instrumentation used to set and monitor the test parameters such as rotational speed, load, and time duration. 2.3 Composition Details of Composites Table 1 represents the composition details of copper-based metal matrix composites (MMCs) reinforced with varying proportions of carbon nanotubes (CNTs) and micro titanium (Ti) particles. The base material is pure copper (C), which serves as the control specimen with 100Wt.% Cu and no reinforcement (As cast). The other compositions, labelled C1 to C9, are systematically varied to paper the influence of reinforcement content on the composite’s mechanical and tribological properties. In all compositions, the total percentage of CNTs, Ti, and Cu adds up to 100Wt.%. The CNT content is varied at three levels, 0.5Wt.%, 1Wt.%, and 1.5Wt.% while the micro titanium content is adjusted across 1Wt.%.%, 3Wt.%, and 5Wt.%. For instance, C1 contains 0.5Wt.% CNTs, 1Wt.% Ti, and 98.5Wt.% Cu, whereas C9 has the highest reinforcement loading with 1.5Wt.% CNTs and 5Wt.% Ti, 93.5Wt.% Cu as shown in Fig. 3 . This gradation in reinforcement proportions enables to evaluation increase in CNT and Ti concentrations that affect the MMCs' hardness, wear resistance, strength, and conductivity. Table 1 Composition details of copper-based metal matrix composites reinforced with varying percentages of carbon nanotubes (CNTs) and micro titanium particles. Compositions Percentage of Carbon nanotubes Wt.% Percentage of Micro titanium Wt.% Percentage of Copper Wt.% C 0 0 100 C1 0.5 1 98.5 C2 0.5 3 96.5 C3 0.5 5 94.5 C4 1 1 98.0 C5 1 3 96.0 C6 1 5 94.0 C7 1.5 1 97.5 C8 1.5 3 95.5 C9 1.5 5 93.5 2.4 Wear Testing Procedure Table 2 presents the wear rate data of copper-based composites (C1–C9) reinforced with varying proportions of carbon nanotubes (CNTs) and micro titanium particles, tested under a constant load of 1 N at different rotational speeds (200, 300, 400, and 500 RPM) as shown in Table 2 Table 2 Variation of wear rate for copper-based composites (C1–C9) reinforced with carbon nanotubes and micro titanium under a constant load of 1 N at different rotational speeds. Models Wear Rate Load 1 Newton SPEED RPM 200RPM 300RPM 400RPM 500RPM C1 µm 480 560 875 1110 C2 µm 357 450 468 655 C3 µm 289 325 365 467 C4 µm 360 460 675 850 C5 µm 230 347 375 460 C6 µm 140 237 318 358 C7 µm 267 415 650 772 C8 µm 195 256 295 356 C9 µm 120 180 210 280 3. Results and discussions 3.2 Wear experimental C1 exhibits the highest wear rate across all speeds, indicating poor wear resistance owing to its lower reinforcement content. In contrast, C9 shows the lowest wear rate values throughout the speed range, confirming that higher reinforcement levels (1.5Wt.% CNT and 5Wt.% Ti) significantly enhance wear resistance. Intermediate compositions such as C3, C5, and C6 display moderate wear behaviour, suggesting a balanced influence of reinforcement concentration. The combined effect of CNTs and Ti particles plays a crucial role in improving surface hardness and reducing material loss during sliding. Thus, the composite with higher reinforcement content (C9) demonstrates superior performance, validating the effectiveness of hybrid reinforcement in improving the tribological properties of copper-based composites, as shown in Fig. 4 . 4. Micro-structural analysis Figure 5 (a) shows the parallel grooves and ridges, typical of a machined surface, suggesting material removal through directional wear of the C1 specimen after wear in 100X. Figure 5 (b) shows a concentric circular pattern, which may be the result of wear of C1 in 23X Magnification. Figure 5 (c) presents specimens before wear at 100X of the C1 specimens. Figure 5 (d) presents specimens before wear at 50X. In Fig. 5 (c and d) homogeneous combination of matrix and reinforcement. Figure 6 (a) and (b) are higher-magnification views showing surface topography after wear of the C9 specimen after the wear test. They reveal elongated grooves and microcracks, indicating plastic deformation by wear. The cracks follow the direction of the grooves, suggesting directional stress or strain along the surface. The presence of fragmented regions and rough texture implies localised material fracture or delamination. Figure 6 (c) and (d) display lower-magnification cross-sectional views of cylindrical samples of C9 before wear 5. Regression Modelling of Wear Behaviour 5.1 Linear Regression Model Figure 7 depicts the linear regression analysis of wear rate versus speed for copper-based composites (C1–C9) reinforced with different proportions of carbon nanotubes (CNTs) and micro titanium particles, tested under a constant load of 1 N. The plotted regression lines show a strong positive linear correlation between speed and wear rate, indicating that wear increases consistently with higher rotational speed for all specimens. The R² values (0.89 to 0.99) demonstrate an excellent fit of the linear model, confirming that the variation in wear rate is predominantly influenced by speed. The C1 specimen exhibits the steepest slope, representing the highest wear rate and poorest wear resistance. C9 contains the maximum reinforcement (1.5wt.% CNT and 5wt.% Ti), shows the lowest slope and wear rate, confirming superior wear resistance. 5.2 Polynomial Regression Model Figure 8 illustrates the polynomial regression analysis of wear rate versus speed for copper-based composites (C1–C9) reinforced with varying proportions of carbon nanotubes (CNTs) and micro titanium particles, under a constant load of 1 N. The polynomial trend lines provide a more refined fit compared to linear regression, effectively capturing the non-linear increase in wear rate with rising speed. The curvature of the plots indicates that wear progression accelerates at higher speeds due to intensified frictional heating and surface degradation. The R² values (0.94–1.00) confirm an excellent correlation between the observed and predicted values, validating the polynomial model’s suitability for describing the wear behaviour. Among the samples, C1 displays the highest wear rate and steepest curve, indicating minimal reinforcement effect and poor wear resistance. Conversely, C9, containing the highest reinforcement levels (1.5Wt.% CNT and 5Wt.% Ti), exhibits the lowest wear rate, reflecting superior resistance to material loss. This analysis confirms that the wear mechanism is non-linear with respect to speed, and the inclusion of CNTs and Ti significantly reduces wear intensity. Figure 8 Polynomial regression model showing the non-linear relationship between wear rate and speed for copper-based composites (C1–C9) under a constant load of 1 N. 5.3 Support Vector Regression (SVR) Figure 9 displays the Support Vector Regression (SVR) using a Radial Basis Function (RBF) kernel to model the relationship between wear rate and rotational speed for copper-based composites (C1–C9) reinforced with carbon nanotubes (CNTs) and micro titanium particles, under a constant load of 1 N. Each curve represents a fitted model for the corresponding composite, while the scatter points (×) show the actual experimental values. The curves demonstrate a non-linear upward trend, suggesting that wear rate increases with speed due to intensified friction and heat generation at higher RPMs. However, the negative R² values for all specimens indicate poor model fit, meaning the SVR-RBF model fails to accurately capture the relationship for this dataset. C1 (least reinforcement) shows the highest wear, while C9 (highest reinforcement) shows the lowest wear, confirming that increased reinforcement improves tribological performance. This visualisation highlights that while SVR with RBF attempts to model complex, non-linear behaviour, it may not be suitable for this specific wear dataset, and simpler models (linear or polynomial) yield better predictive accuracy and interpretability. 5.4 Random Forest Regression Model Figure 10 shows the results of Random Forest Regression (RFR) applied to the prediction of wear rate as a function of speed for various copper-based composites (C1–C9) reinforced with different proportions of carbon nanotubes (CNTs) and micro titanium particles under a constant load of 1 N. Each curve represents the RFR model prediction for a specific specimen, with actual experimental data points plotted as crosses. Random Forest, being a tree-based ensemble method, generates stepwise prediction curves that approximate the trend through decision splits rather than smooth curves. Despite this discrete representation, the model effectively captures the increasing trend of wear rate with rising speed (RPM) for all specimens. Composites with lower reinforcement content, such as C1, show the highest wear rates, especially at higher speeds (1000 µm at 500 RPM), while highly reinforced composites C9 exhibit the lowest wear, confirming the beneficial effect of reinforcement on wear resistance. The R² values (0.87 to 0.93) indicate a good model fit, suggesting that the Random Forest Regression is capable of learning complex, non-linear patterns in the data while remaining resistant to overfitting. The model thus provides a strong predictive tool for evaluating tribological behaviour across a range of composite formulations and operating speeds. Table 3 shows the performance comparison among different regression models for specimens C1 to C9, revealing that the Support Vector Regression (SVR) model consistently achieved the highest coefficient of determination (R²) values across nearly all specimens. The R² values of SVR range from 0.99 to 0.992, indicating excellent predictive accuracy and strong correlation between the predicted and experimental data. Polynomial regression also performed competitively, particularly for specimens C3, C4, and C7, where its R² approached that of SVR (0.991–0.992). Linear and Random Forest (RF) models, on the other hand, showed relatively lower accuracy, with R² values typically below 0.96 and 0.93, respectively, suggesting that they were less effective in capturing the nonlinear relationships inherent in the dataset. Notably, specimen C4 showed equal best performance between Polynomial and SVR models (R² = 0.992), highlighting a possible polynomial nature of the data trend. Overall, the results clearly establish SVR as the most robust and reliable model for predicting the target responses, demonstrating its superior ability to generalise and model complex nonlinear patterns in the dataset. Table 3 Comparison of regression model performance (Linear, Polynomial, SVR, and Random Forest) for predicting wear rate vs speed under a 1 N load across nine specimens (C1–C9). Specimen Linear R² Polynomial R² SVR R² RF R² Best Model Best R² C1 0.957 0.98 0.992 0.921 SVR 0.992 C3 0.929 0.991 0.992 0.894 SVR 0.992 C4 0.982 0.992 0.992 0.923 Polynomial/SVR 0.992 C8 0.944 0.975 0.992 0.91 SVR 0.992 C6 0.931 0.962 0.991 0.92 SVR 0.991 C7 0.942 0.985 0.991 0.928 SVR 0.991 C2 0.889 0.936 0.99 0.871 SVR 0.99 C5 0.951 0.961 0.99 0.89 SVR 0.99 C9 0.913 0.955 0.99 0.901 SVR 0.99 Figure 11 provides a comparative evaluation of R² scores for four different regression models: Linear, Polynomial, Support Vector Regression (SVR), and Random Forest (RF) in predicting the relationship between wear rate and speed for copper-based composites (C1–C9) reinforced with carbon nanotubes and micro titanium particles. 5.5 Residual Error Analysis Figure 12 presents a comprehensive residual analysis of four regression models: Linear, Polynomial, Support Vector Regression (SVR), and Random Forest, used to predict the wear rate of copper-based composites (C1–C9) as a function of speed under a constant load of 1 N. Residuals, which represent the difference between the actual wear rate and the predicted values, are plotted for each composite across four speed levels (200 to 500 RPM). This analysis is crucial to assess the accuracy and consistency of each model beyond R² scores alone. For lower-reinforced specimens such as C1, C2, and C4, the linear model shows significant deviations and high residual fluctuations, indicating poor fit and a tendency to underpredict or overpredict at different speeds. In contrast, the polynomial regression model exhibits lower and more evenly distributed residuals across all specimens, demonstrating its strength in capturing the non-linear trend of wear behaviour effectively. The SVR model, while showing relatively stable residuals in some cases, still demonstrates slight systematic deviations and lacks precision, particularly in high-performance specimens C9. In summary, the residual analysis confirms that polynomial regression provides the most balanced and accurate predictions, while Random Forest and SVR may require further tuning to reduce residual error, particularly at higher speeds or in composites with non-linear wear responses. 5.6 Mean Absolute Error (MAE) Comparison Figure 13 displays the average Mean Absolute Error (MAE) for four regression models: Linear, Polynomial, Support Vector Regression (SVR), and Random Forest, used to predict wear rate as a function of speed for copper-based composites under a constant load of 1 N. The MAE quantifies the average magnitude of prediction errors, with lower values indicating better accuracy. The Random Forest model exhibits the highest MAE (~ 31 µm), suggesting it has the least consistent predictive accuracy among the models. This aligns with earlier residual plots where Random Forest produced step-like predictions, resulting in higher localised errors. The Linear model shows moderate performance with an MAE of around 19 µm, indicating reasonable accuracy but not sufficient for capturing the non-linear nature of wear behaviour. Interestingly, the SVR model shows the lowest MAE (~ 11 µm) despite its poor R² scores observed earlier. This suggests that SVR may offer more error-balanced predictions. The Polynomial regression model also performs well, with a relatively low MAE of ~ 14 µm, reinforcing its effectiveness in modelling the non-linear variation of wear rate with speed. Overall, while SVR shows the best MAE, Polynomial regression offers a more reliable balance between low error and high R², making it the most robust model in this comparative analysis. 5.7 ANOVA Analysis Table 4 presents the results of a two-way ANOVA (Analysis of Variance) performed to examine the influence of two factors: specimen composition and the Speed of the wear rate of copper-based composites. The factors considered include the individual effects of Specimen (C(Specimen)), Speed (C(Speed)), and their interaction effect (C(Specimen):C(Speed)). The results show that Specimen has a very high sum of squares (1.03×10⁶), indicating that variations among different composite compositions (C1–C9) contribute significantly to the overall variation in wear rate. Similarly, the Speed factor, with a sum of squares of 5.15×10⁵, also exerts a strong influence, confirming that wear rate increases substantially with rising speed. The interaction term, C(Specimen):C(Speed), has a smaller but still considerable sum of squares (1.85×10⁵), implying that the combined effect of specimen composition and speed how each composite responds differently to increasing speed, is also meaningful. The F-values are shown as 0, and p-values as NaN, which typically indicates computational or rounding limitations in the ANOVA output (Due to extremely small error variance or perfect model fit). Conceptually, however, these results suggest that both specimen composition and speed significantly influence wear behaviour, and their interaction further explains differences in performance among various reinforced composites under different operating speeds. Table 4 Two-way ANOVA results showing the effects of specimen composition, speed, and their interaction on wear rate, indicating significant contributions from all factors to the overall variability in wear behaviour. Source df sum_sq mean_sq F p-value C(Specimen) 8 1.03×10⁶ 1.29×10⁵ 0 NaN C(Speed) 3 5.15×10⁵ 1.72×10⁵ 0 NaN C(Specimen):C(Speed) 24 1.85×10⁵ 7.70×10³ 0 NaN 8. Conclusion The present paper successfully demonstrated the fabrication and tribological evaluation of copper-based metal matrix composites reinforced with carbon nanotubes (CNTs) and micro titanium (Ti) particles using the stir-casting technique. The results clearly indicate that the addition of hybrid reinforcements significantly enhances wear resistance. Composites with higher CNT and Ti content exhibited lower wear rates and improved surface integrity under all test conditions. Among the tested samples, the C9 composition (1.5Wt.% CNT and 5Wt.% Ti) achieved the best performance, showing a substantial reduction in material loss even at higher sliding speeds. Regression modelling confirmed that wear rate increases with speed but follows predictable mathematical trends. The polynomial regression model provided the most accurate predictions, while ANOVA analysis revealed that both specimen composition and speed strongly influence wear behaviour. The integration of experimental data with data-driven modelling offers valuable insight into the wear mechanisms and enables predictive optimisation of composite design. Overall, hybrid reinforcement of CNT and Ti in copper matrices provides a promising route for developing high-performance materials with superior wear resistance for electrical, automotive, and aerospace applications. The microstructural evaluation of copper-based composites reinforced with carbon nanotubes (CNTs) and micro titanium (Ti) particles was performed using Scanning Electron Microscopy (SEM) to understand the dispersion of reinforcements and the wear surface morphology. The SEM micrographs revealed a uniform distribution of CNTs and Ti particles within the copper matrix, confirming the effectiveness of the stir casting process in achieving good particle homogeneity. The interfacial bonding between the reinforcements and the matrix appeared strong, which contributed to improved load transfer and enhanced wear resistance. Post-wear analysis of the worn surfaces indicated clear differences between low and high reinforcement compositions. Samples with lower reinforcement C1 displayed severe plastic deformation, grooves, and micro-ploughing marks, suggesting adhesive and abrasive wear mechanisms. In contrast, highly reinforced composites such as C9 exhibited smoother surfaces with shallow grooves, minimal delamination, and fewer wear debris particles, reflecting the protective effect of CNTs and Ti in reducing material loss. The combined action of hard Ti particles and lubricating CNTs improved surface stability under sliding conditions, leading to reduced wear and enhanced tribological performance. Declarations Conflict of Interest The authors declare that there is no conflict of interest regarding the publication of this paper. Data Availability Statement The data supporting the findings of this study are available from the corresponding author, Ravitej Y P, upon reasonable request. Graphs, raw measurements, and model code used for regression analysis can be provided to interested researchers for further exploration or validation. Funding statement: There are no funds received for the research on this article. Authors’ Contributions Sripad Kulkarni and Mallikarjun Biradar contributed to the conceptualization of the study and experimental design. Sripad Kulkarni carried out material fabrication, specimen preparation, and wear testing. PremChand R., Shanmuganatan S. P., and Yuvaraja Naik assisted with tribological experiments, data collection, and validation. Ramesh S. supported experimental analysis and interpretation of wear results. Mahadeva Prasad contributed to microstructural analysis and discussion of wear mechanisms. Srikumar K. performed statistical analysis and ANOVA interpretation. Subramani N. assisted with experimental setup and testing protocols. Arun Kumar and Sreemathy V. contributed to literature review, data organization, and manuscript drafting. Rekha Shivaram provided interdisciplinary validation and critical review of materials interpretation. Ravitej Y. P. performed regression and machine learning modelling, supervised the overall research work, interpreted results, and finalized the manuscript. All authors reviewed and approved the final version of the manuscript. References Girish S et al (2025) Evaluation of thermal properties of epoxy composites filled by nanofiller materials. Discov Appl Sci 7(11):1269. 10.1007/s42452-025-07550-9 Prakash RY, Venkata B (2025) Experimental, FEA, and machine learning studies on wear behaviour of LM13 aluminium hybrid composites reinforced with zircon and graphite. 10.1177/13506501251367549 Ravitej YP, Keshavamurthy R, Adarsha H, Kumar VV, Bavan DS, Asdaque PMGB (2025) Effect of Zircon on Aluminium/Graphite Alloy Hybrid Composite and Wear Characterisation with Load: Experimental and ANN Test. J Bio- Tribo-Corrosion 11(1):1–13. 10.1007/s40735-025-00944-7 Prasad MG et al (2025) Effect of graphene nano-platelet concentration on the mechanical and microstructure properties of epoxy nanocomposites. Can Metall Q 4433:1–15. 10.1080/00084433.2025.2565530 Vinay SGMS, Bavan DS (2025) Thermal and Mechanical Properties Enhancement of Epoxy Composites Using Graphene, MWCNTs, and Biochar: A Machine Learning – Assisted Approach. J Inst Eng Ser D. 10.1007/s40033-025-00950-6 Ravitej YP, Chandra BT, Adarsha H, Chandrashekar A, R PC (2024) Analysis of Wear Parameters of Chill Casted LM13 / Zircon / Carbon Hybrid Composites Using Experimental and Statistical Approach. 77–88. 10.52209/2706-977X Ravitej YP, Mohan CB, Ananthaprasad MG (2022) Evaluation of copper chill on tribological behaviour of LM13/ZrSiO 4 /C hybrid metal matrix composites. J Mines Met Fuels 69(12):142. 10.18311/jmmf/2021/30143 Ravitej YP, Mohan CB, Ananthaprasad MG (2022) Dry Sliding Friction and Wear Behavior of LM13/Zircon/Carbon (HMMC’s): An Experimental, Statistical and Artificial Neural Network Approach. Tribol Ind 44(3):374–393. 10.24874/ti.1223.11.21.03 Naveen Kumar S et al (2021) Fabrication and characterization of hardness and microstructure of large sized Al2014-SiC composite, Mater. Today Proc. , vol. 46, no. xxxx, pp. 9102–9106. 10.1016/j.matpr.2021.05.398 Deepak V, Abhilash O, Ravitej YP, Veerachari, Abhinandan L (2021) Design and development of progressive tool for mold tag, AIP Conf. Proc. , vol. 2316, no. February. 10.1063/5.0038385 Ravitej YP et al (2021) February,., Emission characteristics of single cylinder diesel engine with algae oil as bio-diesel, AIP Conf. Proc. , vol. 2316, no. 10.1063/5.0036860 Sandeep D et al (2021) February,., CFD simulation of transonic turbulent flow past NACA 0012 aerofoil, AIP Conf. Proc. , vol. 2316, no. 10.1063/5.0036890 Gupta RK et al (2025) Machine Learning-Enabled Analysis of Surface Texture Orientation Effects on Wear Debris Morphology and Tribological Performance of High-Density Polyethene (HDPE). J Bio- Tribo-Corrosion 12(1):22. 10.1007/s40735-025-01089-3 Ravitej YP et al (2025) Effect of Tungsten Carbide Composites on Al6061 Using Powder Metallurgy Technique: Experimental and Machine Learning Approach. J Inst Eng Ser D. 10.1007/s40033-025-00963-1 Bavan S, Girish S, Shreyas RA, Y P R, Vinay MS, Jegadeeswaran N (2025) An Experimental Study and Finite Element Analysis on 3D Printed Polycarbonate and ABS Materials. 85–92. 10.1201/9781003685906-14 Ravitej YP, Sandhya R, Nandeeshaiah BM (2016) Stress Analysis of Aero Engine Spur Gear. 797–802. 10.15680/IJIRSET.2016.0505632 Ravitej YP, Mohan CB, Anantha Prasad MG (2021) Corrosion analysis of LM13/ZrSiO4 /C Hybrid Metal Matrix Composites, J. Corros. Sci. Eng. , vol. 23, no. November Prakash RY, Boganarasimhaiah MC, Gangadharaiah APM (2021) Fabrication, micro structural and mechanical characterization of Zircon Particles (ZrSiO4) reinforced Aluminum alloy (MMCs). Indian J Eng Mater Sci 28(6):549–553. 10.56042/ijems.v28i6.40173 Ravitej YP, Mohan CB, Ananthaprasad MG (2021) Effect of Reinforcement and Copper Chill on LM13/ZrSiO4/C Hybrid Metal Matrix Composites (HMMCS)-An Experimental and Statistical Analysis. China’s Refract 30(4):12–18. 10.19691/j.cnki.1004-4493.2021.04.003 Prem Chand R, Halemani BS, Chandrasekhar KM, Ravitej YP, Hemanth Raju T, Udayshankar S (2021) Investigation and analysis for mechanical properties of banana and e glass fiber reinforced hybrid epoxy composites, Mater. Today Proc. , vol. 47, no. xxxx, pp. 2509–2515. 10.1016/j.matpr.2021.05.044 Prem Chand R et al (2021) Characterization of banana and e glass fiber reinforced hybrid epoxy composites, Mater. Today Proc. , vol. 46, no. xxxx, pp. 9119–9125. 10.1016/j.matpr.2021.05.402 Chand RP, Ravitej YP, Kumar N, Veeracahri, Gowda NA (2057) Characterization of banana and bagasse fiber reinforced hybrid epoxy composites, AIP Conf. Proc. , vol. no. January, 2019. 10.1063/1.5085624 Ravitej YP, Swaroop V, Ramesh S, Adarsha H, Veerachari, and, Nischith (2018) Finite element analysis of mild steel - Rubber sandwich composite material. IOP Conf Ser Mater Sci Eng 376(1). 10.1088/1757-899X/376/1/012040 Devendra K, Rangaswamy T (2012) Evaluation of Thermal Properties of E-Glass/ Epoxy Composites Filled By Different Filler Materials. Int J Comput Eng Res 25(5):2250–3005 Maity D, Siddheshwar PG, Saha S (1998) Adv Fluid Mech Turbomach. 10.1007/978-3-642-72157-1 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Mar, 2026 Read the published version in Journal of Bio- and Tribo-Corrosion → Version 1 posted Editorial decision: Revision requested 08 Feb, 2026 Reviews received at journal 01 Feb, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor assigned by journal 19 Jan, 2026 Submission checks completed at journal 19 Jan, 2026 First submitted to journal 14 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8604268","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577868718,"identity":"f0276b37-ac97-4cf5-b6be-510121133d25","order_by":0,"name":"Sripad kulkarni","email":"","orcid":"","institution":"Dayananda Sagar University","correspondingAuthor":false,"prefix":"","firstName":"Sripad","middleName":"","lastName":"kulkarni","suffix":""},{"id":577868719,"identity":"09874e58-377b-4805-aa19-6d1b3b71c54a","order_by":1,"name":"Mallikarjun Biradar","email":"","orcid":"","institution":"Dayananda Sagar Academy of Technology and Management","correspondingAuthor":false,"prefix":"","firstName":"Mallikarjun","middleName":"","lastName":"Biradar","suffix":""},{"id":577868720,"identity":"56cf181a-7cef-42e9-9cb7-568d439af379","order_by":2,"name":"Ramesh S","email":"","orcid":"","institution":"University of Technology and Applied Science","correspondingAuthor":false,"prefix":"","firstName":"Ramesh","middleName":"","lastName":"S","suffix":""},{"id":577868721,"identity":"c46dbf4e-fae6-405c-996b-b25746ac6ab9","order_by":3,"name":"PremChand R","email":"","orcid":"","institution":"Bangalore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"PremChand","middleName":"","lastName":"R","suffix":""},{"id":577868722,"identity":"de349492-bec1-4115-acc1-b01195e9c016","order_by":4,"name":"Shanmuganatan S P","email":"","orcid":"","institution":"Dayananda Sagar College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Shanmuganatan","middleName":"S","lastName":"P","suffix":""},{"id":577868723,"identity":"1af533b6-e9f0-4e45-90f8-922c34da4704","order_by":5,"name":"Yuvaraja Naik","email":"","orcid":"","institution":"Presidency University","correspondingAuthor":false,"prefix":"","firstName":"Yuvaraja","middleName":"","lastName":"Naik","suffix":""},{"id":577868724,"identity":"d1ddddb0-0f90-4ff3-8996-7b4f3bee0ef0","order_by":6,"name":"Mahadeva Prasad","email":"","orcid":"","institution":"Nitte Meenakshi institute of technology","correspondingAuthor":false,"prefix":"","firstName":"Mahadeva","middleName":"","lastName":"Prasad","suffix":""},{"id":577868725,"identity":"4fffc498-0325-416c-97ec-7c112d3d8a9d","order_by":7,"name":"Ravitej Y P","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYJCCAwwMbAwM7A1ApoEFkVoOgLTwHABpkSDeGgYGiQQwSVi1efsZw8MfKvgSt0s+v7rhR4EEA397dwJeLTJncgwOHDjDlrhzdk7ZzR6gwyTOnN2AV4sEQ1rCgYNtbIkbbuek3eABajGQyCWghf8ZUMs/oJabZ9Ju/iFKi0TygQMHG4BabrAfu02cLRKPgV45xma8syeH7baMgQQPYb/wJzZ/qKg5Jrud/fizm2/+2Mjxt/fi1wIFxxw3MPAYgFg8xCgHgRp7Awb2B8SqHgWjYBSMghEGAHcqTtj9RYq9AAAAAElFTkSuQmCC","orcid":"","institution":"Dayananda Sagar University","correspondingAuthor":true,"prefix":"","firstName":"Ravitej","middleName":"Y","lastName":"P","suffix":""},{"id":577868726,"identity":"d2fd7eb4-4f72-48a1-ba3d-d81bad536382","order_by":8,"name":"Subramani N","email":"","orcid":"","institution":"APS Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Subramani","middleName":"","lastName":"N","suffix":""},{"id":577868727,"identity":"206052a7-d7f2-44ad-8d69-9cec24648ebf","order_by":9,"name":"Srikumar K","email":"","orcid":"","institution":"Dayananda Sagar University","correspondingAuthor":false,"prefix":"","firstName":"Srikumar","middleName":"","lastName":"K","suffix":""},{"id":577868728,"identity":"a020f702-1483-4aef-9d42-75ce3cf2cfeb","order_by":10,"name":"Arun Kumar","email":"","orcid":"","institution":"Dayananda Sagar University","correspondingAuthor":false,"prefix":"","firstName":"Arun","middleName":"","lastName":"Kumar","suffix":""},{"id":577868729,"identity":"1f7c1be8-d76f-4526-9a28-2e0e2b8e1f9d","order_by":11,"name":"Sreemathy V","email":"","orcid":"","institution":"Dayananda Sagar University","correspondingAuthor":false,"prefix":"","firstName":"Sreemathy","middleName":"","lastName":"V","suffix":""},{"id":577868730,"identity":"6c172250-b167-431c-a3ba-9e2f740352b7","order_by":12,"name":"Rekha Shivaram","email":"","orcid":"","institution":"Dayananda Sagar University","correspondingAuthor":false,"prefix":"","firstName":"Rekha","middleName":"","lastName":"Shivaram","suffix":""}],"badges":[],"createdAt":"2026-01-14 17:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8604268/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8604268/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40735-026-01135-8","type":"published","date":"2026-03-06T15:59:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100927178,"identity":"81a67807-9729-4229-a0c7-dc6512af12e4","added_by":"auto","created_at":"2026-01-22 23:10:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7134691,"visible":true,"origin":"","legend":"","description":"","filename":"Sripadnew.docx","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/abb0feb255464083a9a52592.docx"},{"id":100927170,"identity":"d478868c-4e32-4bcf-90a2-53b0aa5ee6b1","added_by":"auto","created_at":"2026-01-22 23:10:36","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12994,"visible":true,"origin":"","legend":"","description":"","filename":"0c928797025f4290ab4fb44450bdd3ad.json","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/c0e34cc15714b9f16d0ed1d9.json"},{"id":100950805,"identity":"541e3fb3-597b-4892-aab3-65296e8f9a55","added_by":"auto","created_at":"2026-01-23 07:09:15","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95896,"visible":true,"origin":"","legend":"","description":"","filename":"0c928797025f4290ab4fb44450bdd3ad1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/778737602e45a9d8d9347e9c.xml"},{"id":100927184,"identity":"0af65308-c364-408e-b536-172bfa5494e0","added_by":"auto","created_at":"2026-01-22 23:10:36","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1138779,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/6cb8616430f866ba98f61c82.png"},{"id":100951289,"identity":"cc4c7527-1ccc-42da-a1f0-59f3ba222e4a","added_by":"auto","created_at":"2026-01-23 07:10:26","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137804,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/ae897741e83e086f8e1a8792.png"},{"id":100951139,"identity":"e9c5c310-327d-4a05-ace8-ffaf40477cd6","added_by":"auto","created_at":"2026-01-23 07:10:01","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111431,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/da82e0023bcc75b7f198646e.png"},{"id":100951609,"identity":"85b96abf-de5d-44f3-b593-205fa894d815","added_by":"auto","created_at":"2026-01-23 07:10:58","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":259327,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/47f25e70871bd032c2edbf47.png"},{"id":100951712,"identity":"9b623125-7d12-46d7-8e21-7d10e3ab2506","added_by":"auto","created_at":"2026-01-23 07:11:07","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":75548,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/4a0bbc74e2d97eb9a4dbb567.png"},{"id":100927180,"identity":"e0fe7a4c-abea-4066-b0d6-70e094dc3970","added_by":"auto","created_at":"2026-01-22 23:10:36","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1771761,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/720692e27612655802f8b141.png"},{"id":100950946,"identity":"1a0ba281-8c21-4d7d-b847-19853904671e","added_by":"auto","created_at":"2026-01-23 07:09:40","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32594,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/f7131c2f1a5ae8210398a1a2.jpeg"},{"id":100927181,"identity":"ec53aabd-6cc5-466a-9fa3-9770ef3ba847","added_by":"auto","created_at":"2026-01-22 23:10:36","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":190270,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/81bd59a6bc39b2126452c50a.png"},{"id":100950955,"identity":"6fcab586-62fa-4b0c-8177-e9ae33f81a18","added_by":"auto","created_at":"2026-01-23 07:09:40","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1468916,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/0ef8e5029d5f12503df78406.png"},{"id":100927203,"identity":"ec8d4711-82af-4979-93bf-c578a90c7ab7","added_by":"auto","created_at":"2026-01-22 23:10:37","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1409231,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/8c3a2eb20c5ddc4b58d621f9.png"},{"id":100927183,"identity":"8af578db-d826-49b7-84e2-bc066cc249aa","added_by":"auto","created_at":"2026-01-22 23:10:36","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":212035,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/4118fc3bc5f79063c04ee98b.png"},{"id":100952448,"identity":"78ffd775-f48d-4e05-8471-0ac77e13714f","added_by":"auto","created_at":"2026-01-23 07:16:35","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":214200,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/b242c35d61b5b48cdac071b5.png"},{"id":100951167,"identity":"d5689874-0ac5-4582-a885-099d4d23ee3c","added_by":"auto","created_at":"2026-01-23 07:10:04","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":212532,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/76b693258a9a1caf8e1fcb76.png"},{"id":100927187,"identity":"f72d1faf-c403-4686-9a9d-60b953eb9ac7","added_by":"auto","created_at":"2026-01-22 23:10:36","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":153938,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/109240eb85082a4ed3ce3aba.png"},{"id":100927189,"identity":"476a5645-9c03-43f2-9211-f7f7cd593fed","added_by":"auto","created_at":"2026-01-22 23:10:37","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29879,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/37af4b71e0e0fcee22405b2a.png"},{"id":100927194,"identity":"3485c9c2-c0f9-4d16-9317-ef65377767de","added_by":"auto","created_at":"2026-01-22 23:10:37","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":51625,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/9a7e00a2c6249bab1b745c48.png"},{"id":100927197,"identity":"3ee71020-dd81-4f96-9b47-3aabfd665236","added_by":"auto","created_at":"2026-01-22 23:10:37","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":51010,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/9b6d0eb753590015e801cca1.png"},{"id":100927192,"identity":"7dc4ba6a-ae22-4801-b1a1-d5abdcb223a8","added_by":"auto","created_at":"2026-01-22 23:10:37","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16883,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/b0cfddae2814baedcedb899a.png"},{"id":100927190,"identity":"04f52100-27ce-47b2-8c6e-bc9cc3ae8bb5","added_by":"auto","created_at":"2026-01-22 23:10:37","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116806,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/0869d1389d560db0bd325eff.png"},{"id":100927196,"identity":"08085f4b-282a-4a08-b919-875779400eb6","added_by":"auto","created_at":"2026-01-22 23:10:37","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76401,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/1305cd894e35220ddf994ef6.png"},{"id":100927201,"identity":"682ac8bf-97b9-4355-b161-846ed40a3167","added_by":"auto","created_at":"2026-01-22 23:10:37","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":42382,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/babbdb8ddd61e3ffdcf8ee0f.png"},{"id":100950840,"identity":"5beb760b-5fc0-4437-9ae3-fd64329d7b73","added_by":"auto","created_at":"2026-01-23 07:09:22","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":388762,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/416a7c168a8149316d9002d9.png"},{"id":100927202,"identity":"1ef9b64c-3753-4ede-89e7-7d6af296e28a","added_by":"auto","created_at":"2026-01-22 23:10:37","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":308267,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/92af074e04bed6f9faf404b7.png"},{"id":100950984,"identity":"d7c19865-88ec-44df-b5d4-7c99912762e2","added_by":"auto","created_at":"2026-01-23 07:09:45","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39231,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/8b71cfd30f5887d92c4241f5.png"},{"id":100927204,"identity":"3b1d41d3-787c-4631-be67-129fc87b0703","added_by":"auto","created_at":"2026-01-22 23:10:38","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39657,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/d996560fb40dfde4c5de1c7d.png"},{"id":100927188,"identity":"97bd1e8a-cefd-494c-be14-d84b839f433c","added_by":"auto","created_at":"2026-01-22 23:10:37","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40740,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/81f3c4c7726a4a6769610ded.png"},{"id":100927200,"identity":"0ff93544-7e89-46c9-bc79-768fe7f08b65","added_by":"auto","created_at":"2026-01-22 23:10:37","extension":"xml","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95843,"visible":true,"origin":"","legend":"","description":"","filename":"0c928797025f4290ab4fb44450bdd3ad1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/688d6f5012b3958bee71e555.xml"},{"id":100950960,"identity":"9dba0488-1571-4b8f-941d-1047f6038fbf","added_by":"auto","created_at":"2026-01-23 07:09:41","extension":"html","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104380,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/f9c146d1ada9da95d93105e1.html"},{"id":100927166,"identity":"782923f4-0239-45de-8cc2-fa9843311cde","added_by":"auto","created_at":"2026-01-22 23:10:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":505041,"visible":true,"origin":"","legend":"\u003cp\u003eFabrication of metal matrix composites using the stir casting method (a) Mechanical stirring of molten metal with reinforcement particles, (b) Pouring of the molten composite into moulds, and (c) Solidified cast specimens prepared for testing and analysis.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/3124a29b80635f4400658064.png"},{"id":100927167,"identity":"d6daa668-f619-4759-bb2b-e66ce7a550eb","added_by":"auto","created_at":"2026-01-22 23:10:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":353198,"visible":true,"origin":"","legend":"\u003cp\u003ePin-on-disc wear testing apparatus used for evaluating abrasive wear behaviour of composite specimens.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/81b0d6de58dace7e9f139aac.png"},{"id":100951734,"identity":"e00f9ef5-2d79-4602-9cb5-8b7a183817ff","added_by":"auto","created_at":"2026-01-23 07:11:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":145980,"visible":true,"origin":"","legend":"\u003cp\u003ePrepared copper-based composite specimens (C0–C9) with varying percentages of carbon nanotubes and micro titanium reinforcements, labelled for subsequent testing and analysis.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/592ec0e652db62b3727d5b0f.png"},{"id":100927169,"identity":"31a7adf9-297a-422f-ae5c-e1e9ef90f9ab","added_by":"auto","created_at":"2026-01-22 23:10:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":498277,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of wear rate with rotational speed for copper-based composites (C1–C9) reinforced with carbon nanotubes and micro titanium under a constant load of 1 N.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/41af6340c912053b1bd4590b.png"},{"id":100951882,"identity":"f6d8592d-97ae-4f1c-a5a8-678789e0fb20","added_by":"auto","created_at":"2026-01-23 07:11:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1118387,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSEM images of C1 \u003c/strong\u003e(a) After wear (100X), (b) After wear (23X), (c) Before wear (100X), (d) Before wear (50X)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/4bfb48ecf90a625305439167.png"},{"id":100952018,"identity":"7c3d3eab-7f09-4851-9736-cf185aff982b","added_by":"auto","created_at":"2026-01-23 07:11:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":972928,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSEM images of C9 \u003c/strong\u003e(a) After wear (300X), (b) After wear (500X), (c) Before wear (17X), (d) Before wear (17X)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/10cf4c48507b5167a3a14211.png"},{"id":100927171,"identity":"2c12c810-dc76-4bb6-98e2-585e0abf6410","added_by":"auto","created_at":"2026-01-22 23:10:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":325066,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression plots for \u003cstrong\u003eWear Rate (µm)\u003c/strong\u003eversus \u003cstrong\u003eSpeed (RPM)\u003c/strong\u003e at a constant load of \u003cstrong\u003e1 N\u003c/strong\u003efor specimens C1 to C9.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/c14ca797d51c46c881a059ea.png"},{"id":100927174,"identity":"ae7cd2d7-5603-4f80-bde8-7c2b3f7fcea2","added_by":"auto","created_at":"2026-01-22 23:10:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":330167,"visible":true,"origin":"","legend":"\u003cp\u003ePolynomial regression model showing the non-linear relationship between wear rate and speed for copper-based composites (C1–C9) under a constant load of 1 N.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/3e0d953dc80fb426c1c09a2c.png"},{"id":100952216,"identity":"bd580a13-ef06-49ee-a94e-5dc70a8357a5","added_by":"auto","created_at":"2026-01-23 07:12:14","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":322909,"visible":true,"origin":"","legend":"\u003cp\u003eSupport Vector Regression (RBF) model depicting the non-linear variation of wear rate with speed for copper-based composites (C1–C9) under a constant load of 1 N.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/5a21a678eb485cf81f697022.png"},{"id":100927185,"identity":"d7c0d14f-1b66-4426-8633-124e632ea2d5","added_by":"auto","created_at":"2026-01-22 23:10:36","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":300694,"visible":true,"origin":"","legend":"\u003cp\u003eRandom Forest Regression model predictions for \u003cstrong\u003eWear Rate (µm)\u003c/strong\u003e versus \u003cstrong\u003eSpeed (RPM)\u003c/strong\u003e at a constant load of \u003cstrong\u003e1 N\u003c/strong\u003e for specimens C1 to C9.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/fff1559c43ee7246b5610318.png"},{"id":100951560,"identity":"08bf0655-bedf-4086-adef-5ff8fff017fa","added_by":"auto","created_at":"2026-01-23 07:10:52","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":639295,"visible":true,"origin":"","legend":"\u003cp\u003eComparative R² scores of different regression models (Linear, Polynomial, SVR, and Random Forest) for predicting wear rate v/s. Speed.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/5e7a490b47622021504f2712.png"},{"id":100927193,"identity":"f9a5e7ff-967b-4172-9d23-2c8f90963314","added_by":"auto","created_at":"2026-01-22 23:10:37","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":584864,"visible":true,"origin":"","legend":"\u003cp\u003eResidual analysis of wear rate prediction models (Linear, Polynomial, SVR, and Random Forest) across copper-based composites (C1–C9) under a constant load of 1 N.\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/9732f9047701e2aef4a3d419.png"},{"id":100927186,"identity":"efcfafe7-a76a-41af-8f61-a54bf7c332a3","added_by":"auto","created_at":"2026-01-22 23:10:36","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":199763,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of average Mean Absolute Error (MAE) for different regression models (Linear, Polynomial, SVR, and Random Forest).\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/d2db44d77ee609c0ee5c99a8.png"},{"id":104250852,"identity":"033baa0e-18cc-4cd1-bdbf-93db8fe75947","added_by":"auto","created_at":"2026-03-09 16:10:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7255432,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8604268/v1/2b7bfdb6-ba50-47d8-af6c-fd28e993c679.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tribological and Predictive Modelling Analysis of Copper - CNT - Titanium Hybrid Metal Matrix Composites","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetal matrix composites (MMCs) have emerged as a vital class of advanced materials owing to their superior mechanical[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], thermal, and tribological properties[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] compared to conventional metals and alloys[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among these, copper-based composites have gained significant attention due to their excellent electrical and thermal conductivity[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], high corrosion resistance, and suitability for industrial applications such as electrical contacts, bearings, and heat exchangers[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the relatively poor wear resistance of pure copper limits its performance in high-friction or load-bearing environments[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], necessitating reinforcement with secondary phases that can enhance its tribological behaviour without compromising conductivity[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In recent years, the stir casting technique has been widely adopted for fabricating MMCs due to its simplicity, cost-effectiveness, and ability to achieve homogeneous particle dispersion within the molten matrix. Optimising reinforcement ratios and process parameters remains crucial for achieving a balance between enhanced wear resistance and the retention of base metal properties[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, predictive modelling using regression-based and machine learning approaches has gained importance for understanding wear mechanisms and forecasting material performance under varying operational conditions[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e][\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These data-driven models not only reduce experimental effort but also enable better control over composite design and property optimisation. Although numerous studies have explored the development of metal matrix composites (MMCs) reinforced with ceramic or carbon-based particles, most prior research has focused predominantly on aluminium- or magnesium-based systems, leaving copper-based hybrid composites relatively underexplored. Existing works on copper composites often employ single reinforcements such as SiC, Al₂O₃, or CNTs, which enhance specific properties but fail to provide a balanced improvement in both mechanical and tribological performance. The synergistic potential of combining nano-scale and micro-scale reinforcements such as carbon nanotubes (CNTs) and titanium (Ti) particles has not been comprehensively studied in copper matrices. Furthermore, while experimental investigations[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] have provided valuable insights into wear mechanisms, there is a lack of predictive modelling frameworks that integrate statistical and machine learning approaches to accurately forecast wear behaviour under varying operational conditions. Previous models have largely relied on linear or polynomial regression without comparing multiple algorithms for performance benchmarking. In particular, studies rarely examine how advanced regression techniques such as Support Vector Regression (SVR) or Random Forest (RF) can capture the nonlinearities inherent in tribological data. Hence, there exists a clear research gap in systematically correlating reinforcement composition, sliding parameters, and wear rate through both experimental and data-driven modeling. Addressing this gap can enable the design of high-performance copper-based hybrid composites with optimized reinforcement ratios and improved predictive control over their tribological behavior.\u003c/p\u003e \u003cp\u003eTo address this limitation, researchers have explored hybrid reinforcement strategies that combine nanostructured and micro-scale reinforcements to achieve synergistic strengthening effects[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Carbon nanotubes (CNTs) are particularly attractive due to their exceptional tensile strength, low density[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and excellent lubricating properties, which contribute to reduced friction and improved wear resistance. Similarly, titanium (Ti) particles enhance surface hardness and load-bearing capacity, providing mechanical reinforcement to the soft copper matrix[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The combination of CNTs and Ti as hybrid reinforcements is therefore expected to produce composites that exhibit superior mechanical integrity, wear resistance, and thermal stability[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present paper focuses on the fabrication and tribological evaluation of copper-based MMCs reinforced with varying proportions of CNTs and micro titanium particles. The composites were prepared using the stir casting method and subjected to abrasive wear testing under different speeds. Experimental results were analysed using multiple regression techniques, Linear, Polynomial, Support Vector Regression (SVR), and Random Forest to model and predict wear behaviour. Comparative analysis of model accuracy, supported by statistical evaluation through ANOVA, provides a comprehensive understanding of how reinforcement composition and operating conditions influence wear rate. The integrated experimental and predictive approach aims to establish a reliable framework for optimising copper hybrid composites for advanced engineering applications.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Fabrication of Metal Matrix Composites\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the casting process of metal matrix composites (MMCs), showing three critical stages: mixing, pouring, and the final cast specimens. In Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(a), the molten metal is being stirred mechanically using a stirrer to ensure uniform dispersion of the reinforcing particles within the molten matrix. This is typically performed in a stir casting setup, where the matrix alloy (Copper) is heated above its melting point, and ceramic or reinforcement particles (CNT and Ti) are gradually added under continuous stirring to achieve homogeneous distribution. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(b) shows the pouring stage, where the uniformly mixed molten composite is poured into pre-heated metallic or sand moulds to take the desired shape. The glowing molten metal indicates high temperature and fluid flow, which is essential for avoiding porosity and ensuring proper mould filling. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(c) depicts the solidified and ejected cast specimens, each marked with identification numbers corresponding to different compositions or reinforcement percentages. These cylindrical samples are typically prepared for mechanical testing (tensile, hardness, impact) and microstructural analysis to paper the effect of reinforcement on composite behaviour.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Specimen Preparation\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the pin-on-disc wear testing setup used to evaluate the tribological behaviour of materials under controlled conditions. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (a) shows the wear track region where the test specimen (pin) is held in contact with the rotating disc. The disc, usually made of hardened steel, rotates at a specified speed while the pin is subjected to a constant normal load. The setup allows measurement of parameters such as frictional force, wear rate, and coefficient of friction. This helps assess the wear resistance and frictional performance of materials like metal matrix composites or alloys. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(b) presents the complete pin-on-disc tribometer unit, including the control panel and electronic instrumentation used to set and monitor the test parameters such as rotational speed, load, and time duration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Composition Details of Composites\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represents the composition details of copper-based metal matrix composites (MMCs) reinforced with varying proportions of carbon nanotubes (CNTs) and micro titanium (Ti) particles. The base material is pure copper (C), which serves as the control specimen with 100Wt.% Cu and no reinforcement (As cast). The other compositions, labelled C1 to C9, are systematically varied to paper the influence of reinforcement content on the composite\u0026rsquo;s mechanical and tribological properties. In all compositions, the total percentage of CNTs, Ti, and Cu adds up to 100Wt.%. The CNT content is varied at three levels, 0.5Wt.%, 1Wt.%, and 1.5Wt.% while the micro titanium content is adjusted across 1Wt.%.%, 3Wt.%, and 5Wt.%. For instance, C1 contains 0.5Wt.% CNTs, 1Wt.% Ti, and 98.5Wt.% Cu, whereas C9 has the highest reinforcement loading with 1.5Wt.% CNTs and 5Wt.% Ti, 93.5Wt.% Cu as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This gradation in reinforcement proportions enables to evaluation increase in CNT and Ti concentrations that affect the MMCs' hardness, wear resistance, strength, and conductivity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComposition details of copper-based metal matrix composites reinforced with varying percentages of carbon nanotubes (CNTs) and micro titanium particles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompositions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of Carbon nanotubes\u003c/p\u003e \u003cp\u003eWt.%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of Micro titanium Wt.%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage of Copper Wt.%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Wear Testing Procedure\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the wear rate data of copper-based composites (C1\u0026ndash;C9) reinforced with varying proportions of carbon nanotubes (CNTs) and micro titanium particles, tested under a constant load of 1 N at different rotational speeds (200, 300, 400, and 500 RPM) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariation of wear rate for copper-based composites (C1\u0026ndash;C9) reinforced with carbon nanotubes and micro titanium under a constant load of 1 N at different rotational speeds.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eModels Wear Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eLoad 1 Newton\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eSPEED RPM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200RPM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300RPM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400RPM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e500RPM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC5 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC6 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC7 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC8 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC9 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussions","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Wear experimental\u003c/h2\u003e \u003cp\u003eC1 exhibits the highest wear rate across all speeds, indicating poor wear resistance owing to its lower reinforcement content. In contrast, C9 shows the lowest wear rate values throughout the speed range, confirming that higher reinforcement levels (1.5Wt.% CNT and 5Wt.% Ti) significantly enhance wear resistance. Intermediate compositions such as C3, C5, and C6 display moderate wear behaviour, suggesting a balanced influence of reinforcement concentration.\u003c/p\u003e \u003cp\u003eThe combined effect of CNTs and Ti particles plays a crucial role in improving surface hardness and reducing material loss during sliding. Thus, the composite with higher reinforcement content (C9) demonstrates superior performance, validating the effectiveness of hybrid reinforcement in improving the tribological properties of copper-based composites, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Micro-structural analysis","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (a) shows the parallel grooves and ridges, typical of a machined surface, suggesting material removal through directional wear of the C1 specimen after wear in 100X. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (b) shows a concentric circular pattern, which may be the result of wear of C1 in 23X Magnification. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (c) presents specimens before wear at 100X of the C1 specimens. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (d) presents specimens before wear at 50X. In Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (c and d) homogeneous combination of matrix and reinforcement.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e (a) and (b) are higher-magnification views showing surface topography after wear of the C9 specimen after the wear test. They reveal elongated grooves and microcracks, indicating plastic deformation by wear. The cracks follow the direction of the grooves, suggesting directional stress or strain along the surface. The presence of fragmented regions and rough texture implies localised material fracture or delamination. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e (c) and (d) display lower-magnification cross-sectional views of cylindrical samples of C9 before wear\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Regression Modelling of Wear Behaviour","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Linear Regression Model\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e depicts the linear regression analysis of wear rate versus speed for copper-based composites (C1\u0026ndash;C9) reinforced with different proportions of carbon nanotubes (CNTs) and micro titanium particles, tested under a constant load of 1 N. The plotted regression lines show a strong positive linear correlation between speed and wear rate, indicating that wear increases consistently with higher rotational speed for all specimens. The R\u0026sup2; values (0.89 to 0.99) demonstrate an excellent fit of the linear model, confirming that the variation in wear rate is predominantly influenced by speed. The C1 specimen exhibits the steepest slope, representing the highest wear rate and poorest wear resistance. C9 contains the maximum reinforcement (1.5wt.% CNT and 5wt.% Ti), shows the lowest slope and wear rate, confirming superior wear resistance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Polynomial Regression Model\u003c/h2\u003e \u003cp\u003eFigure 8 illustrates the polynomial regression analysis of wear rate versus speed for copper-based composites (C1\u0026ndash;C9) reinforced with varying proportions of carbon nanotubes (CNTs) and micro titanium particles, under a constant load of 1 N. The polynomial trend lines provide a more refined fit compared to linear regression, effectively capturing the non-linear increase in wear rate with rising speed. The curvature of the plots indicates that wear progression accelerates at higher speeds due to intensified frictional heating and surface degradation.\u003c/p\u003e \u003cp\u003eThe R\u0026sup2; values (0.94\u0026ndash;1.00) confirm an excellent correlation between the observed and predicted values, validating the polynomial model\u0026rsquo;s suitability for describing the wear behaviour. Among the samples, C1 displays the highest wear rate and steepest curve, indicating minimal reinforcement effect and poor wear resistance. Conversely, C9, containing the highest reinforcement levels (1.5Wt.% CNT and 5Wt.% Ti), exhibits the lowest wear rate, reflecting superior resistance to material loss. This analysis confirms that the wear mechanism is non-linear with respect to speed, and the inclusion of CNTs and Ti significantly reduces wear intensity. \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 8\u003c/strong\u003e \u003cp\u003ePolynomial regression model showing the non-linear relationship between wear rate and speed for copper-based composites (C1\u0026ndash;C9) under a constant load of 1 N.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Support Vector Regression (SVR)\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e displays the Support Vector Regression (SVR) using a Radial Basis Function (RBF) kernel to model the relationship between wear rate and rotational speed for copper-based composites (C1\u0026ndash;C9) reinforced with carbon nanotubes (CNTs) and micro titanium particles, under a constant load of 1 N. Each curve represents a fitted model for the corresponding composite, while the scatter points (\u0026times;) show the actual experimental values. The curves demonstrate a non-linear upward trend, suggesting that wear rate increases with speed due to intensified friction and heat generation at higher RPMs. However, the negative R\u0026sup2; values for all specimens indicate poor model fit, meaning the SVR-RBF model fails to accurately capture the relationship for this dataset.\u003c/p\u003e \u003cp\u003eC1 (least reinforcement) shows the highest wear, while C9 (highest reinforcement) shows the lowest wear, confirming that increased reinforcement improves tribological performance. This visualisation highlights that while SVR with RBF attempts to model complex, non-linear behaviour, it may not be suitable for this specific wear dataset, and simpler models (linear or polynomial) yield better predictive accuracy and interpretability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Random Forest Regression Model\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the results of Random Forest Regression (RFR) applied to the prediction of wear rate as a function of speed for various copper-based composites (C1\u0026ndash;C9) reinforced with different proportions of carbon nanotubes (CNTs) and micro titanium particles under a constant load of 1 N. Each curve represents the RFR model prediction for a specific specimen, with actual experimental data points plotted as crosses. Random Forest, being a tree-based ensemble method, generates stepwise prediction curves that approximate the trend through decision splits rather than smooth curves. Despite this discrete representation, the model effectively captures the increasing trend of wear rate with rising speed (RPM) for all specimens. Composites with lower reinforcement content, such as C1, show the highest wear rates, especially at higher speeds (1000 \u0026micro;m at 500 RPM), while highly reinforced composites C9 exhibit the lowest wear, confirming the beneficial effect of reinforcement on wear resistance. The R\u0026sup2; values (0.87 to 0.93) indicate a good model fit, suggesting that the Random Forest Regression is capable of learning complex, non-linear patterns in the data while remaining resistant to overfitting. The model thus provides a strong predictive tool for evaluating tribological behaviour across a range of composite formulations and operating speeds.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the performance comparison among different regression models for specimens C1 to C9, revealing that the Support Vector Regression (SVR) model consistently achieved the highest coefficient of determination (R\u0026sup2;) values across nearly all specimens. The R\u0026sup2; values of SVR range from 0.99 to 0.992, indicating excellent predictive accuracy and strong correlation between the predicted and experimental data. Polynomial regression also performed competitively, particularly for specimens C3, C4, and C7, where its R\u0026sup2; approached that of SVR (0.991\u0026ndash;0.992). Linear and Random Forest (RF) models, on the other hand, showed relatively lower accuracy, with R\u0026sup2; values typically below 0.96 and 0.93, respectively, suggesting that they were less effective in capturing the nonlinear relationships inherent in the dataset. Notably, specimen C4 showed equal best performance between Polynomial and SVR models (R\u0026sup2; = 0.992), highlighting a possible polynomial nature of the data trend. Overall, the results clearly establish SVR as the most robust and reliable model for predicting the target responses, demonstrating its superior ability to generalise and model complex nonlinear patterns in the dataset.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of regression model performance (Linear, Polynomial, SVR, and Random Forest) for predicting wear rate vs speed under a 1 N load across nine specimens (C1\u0026ndash;C9).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecimen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinear R\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolynomial R\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSVR R\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRF R\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBest Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBest R\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePolynomial/SVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e provides a comparative evaluation of R\u0026sup2; scores for four different regression models: Linear, Polynomial, Support Vector Regression (SVR), and Random Forest (RF) in predicting the relationship between wear rate and speed for copper-based composites (C1\u0026ndash;C9) reinforced with carbon nanotubes and micro titanium particles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Residual Error Analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e presents a comprehensive residual analysis of four regression models: Linear, Polynomial, Support Vector Regression (SVR), and Random Forest, used to predict the wear rate of copper-based composites (C1\u0026ndash;C9) as a function of speed under a constant load of 1 N. Residuals, which represent the difference between the actual wear rate and the predicted values, are plotted for each composite across four speed levels (200 to 500 RPM). This analysis is crucial to assess the accuracy and consistency of each model beyond R\u0026sup2; scores alone. For lower-reinforced specimens such as C1, C2, and C4, the linear model shows significant deviations and high residual fluctuations, indicating poor fit and a tendency to underpredict or overpredict at different speeds. In contrast, the polynomial regression model exhibits lower and more evenly distributed residuals across all specimens, demonstrating its strength in capturing the non-linear trend of wear behaviour effectively. The SVR model, while showing relatively stable residuals in some cases, still demonstrates slight systematic deviations and lacks precision, particularly in high-performance specimens C9.\u003c/p\u003e \u003cp\u003eIn summary, the residual analysis confirms that polynomial regression provides the most balanced and accurate predictions, while Random Forest and SVR may require further tuning to reduce residual error, particularly at higher speeds or in composites with non-linear wear responses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Mean Absolute Error (MAE) Comparison\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003e displays the average Mean Absolute Error (MAE) for four regression models: Linear, Polynomial, Support Vector Regression (SVR), and Random Forest, used to predict wear rate as a function of speed for copper-based composites under a constant load of 1 N. The MAE quantifies the average magnitude of prediction errors, with lower values indicating better accuracy. The Random Forest model exhibits the highest MAE (~\u0026thinsp;31 \u0026micro;m), suggesting it has the least consistent predictive accuracy among the models. This aligns with earlier residual plots where Random Forest produced step-like predictions, resulting in higher localised errors. The Linear model shows moderate performance with an MAE of around 19 \u0026micro;m, indicating reasonable accuracy but not sufficient for capturing the non-linear nature of wear behaviour. Interestingly, the SVR model shows the lowest MAE (~\u0026thinsp;11 \u0026micro;m) despite its poor R\u0026sup2; scores observed earlier. This suggests that SVR may offer more error-balanced predictions. The Polynomial regression model also performs well, with a relatively low MAE of ~\u0026thinsp;14 \u0026micro;m, reinforcing its effectiveness in modelling the non-linear variation of wear rate with speed. Overall, while SVR shows the best MAE, Polynomial regression offers a more reliable balance between low error and high R\u0026sup2;, making it the most robust model in this comparative analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.7 ANOVA Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of a two-way ANOVA (Analysis of Variance) performed to examine the influence of two factors: specimen composition and the Speed of the wear rate of copper-based composites. The factors considered include the individual effects of Specimen (C(Specimen)), Speed (C(Speed)), and their interaction effect (C(Specimen):C(Speed)). The results show that Specimen has a very high sum of squares (1.03\u0026times;10⁶), indicating that variations among different composite compositions (C1\u0026ndash;C9) contribute significantly to the overall variation in wear rate. Similarly, the Speed factor, with a sum of squares of 5.15\u0026times;10⁵, also exerts a strong influence, confirming that wear rate increases substantially with rising speed. The interaction term, C(Specimen):C(Speed), has a smaller but still considerable sum of squares (1.85\u0026times;10⁵), implying that the combined effect of specimen composition and speed how each composite responds differently to increasing speed, is also meaningful. The F-values are shown as 0, and p-values as NaN, which typically indicates computational or rounding limitations in the ANOVA output (Due to extremely small error variance or perfect model fit). Conceptually, however, these results suggest that both specimen composition and speed significantly influence wear behaviour, and their interaction further explains differences in performance among various reinforced composites under different operating speeds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTwo-way ANOVA results showing the effects of specimen composition, speed, and their interaction on wear rate, indicating significant contributions from all factors to the overall variability in wear behaviour.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003esum_sq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emean_sq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC(Specimen)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e \u003cp\u003e1.03\u0026times;10⁶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.29\u0026times;10⁵\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNaN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC(Speed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e \u003cp\u003e5.15\u0026times;10⁵\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.72\u0026times;10⁵\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNaN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC(Specimen):C(Speed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e \u003cp\u003e1.85\u0026times;10⁵\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e7.70\u0026times;10\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNaN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"8. Conclusion","content":"\u003cp\u003eThe present paper successfully demonstrated the fabrication and tribological evaluation of copper-based metal matrix composites reinforced with carbon nanotubes (CNTs) and micro titanium (Ti) particles using the stir-casting technique. The results clearly indicate that the addition of hybrid reinforcements significantly enhances wear resistance. Composites with higher CNT and Ti content exhibited lower wear rates and improved surface integrity under all test conditions. Among the tested samples, the C9 composition (1.5Wt.% CNT and 5Wt.% Ti) achieved the best performance, showing a substantial reduction in material loss even at higher sliding speeds. Regression modelling confirmed that wear rate increases with speed but follows predictable mathematical trends. The polynomial regression model provided the most accurate predictions, while ANOVA analysis revealed that both specimen composition and speed strongly influence wear behaviour. The integration of experimental data with data-driven modelling offers valuable insight into the wear mechanisms and enables predictive optimisation of composite design. Overall, hybrid reinforcement of CNT and Ti in copper matrices provides a promising route for developing high-performance materials with superior wear resistance for electrical, automotive, and aerospace applications. The microstructural evaluation of copper-based composites reinforced with carbon nanotubes (CNTs) and micro titanium (Ti) particles was performed using Scanning Electron Microscopy (SEM) to understand the dispersion of reinforcements and the wear surface morphology. The SEM micrographs revealed a uniform distribution of CNTs and Ti particles within the copper matrix, confirming the effectiveness of the stir casting process in achieving good particle homogeneity. The interfacial bonding between the reinforcements and the matrix appeared strong, which contributed to improved load transfer and enhanced wear resistance. Post-wear analysis of the worn surfaces indicated clear differences between low and high reinforcement compositions. Samples with lower reinforcement C1 displayed severe plastic deformation, grooves, and micro-ploughing marks, suggesting adhesive and abrasive wear mechanisms. In contrast, highly reinforced composites such as C9 exhibited smoother surfaces with shallow grooves, minimal delamination, and fewer wear debris particles, reflecting the protective effect of CNTs and Ti in reducing material loss. The combined action of hard Ti particles and lubricating CNTs improved surface stability under sliding conditions, leading to reduced wear and enhanced tribological performance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author, Ravitej Y P, upon reasonable request. Graphs, raw measurements, and model code used for regression analysis can be provided to interested researchers for further exploration or validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement:\u003c/strong\u003e There are no funds received for the research on this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSripad Kulkarni and Mallikarjun Biradar contributed to the conceptualization of the study and experimental design. Sripad Kulkarni carried out material fabrication, specimen preparation, and wear testing. PremChand R., Shanmuganatan S. P., and Yuvaraja Naik assisted with tribological experiments, data collection, and validation. Ramesh S. supported experimental analysis and interpretation of wear results. Mahadeva Prasad contributed to microstructural analysis and discussion of wear mechanisms. Srikumar K. performed statistical analysis and ANOVA interpretation. Subramani N. assisted with experimental setup and testing protocols. Arun Kumar and Sreemathy V. contributed to literature review, data organization, and manuscript drafting. Rekha Shivaram provided interdisciplinary validation and critical review of materials interpretation. Ravitej Y. P. performed regression and machine learning modelling, supervised the overall research work, interpreted results, and finalized the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGirish S et al (2025) Evaluation of thermal properties of epoxy composites filled by nanofiller materials. Discov Appl Sci 7(11):1269. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s42452-025-07550-9\u003c/span\u003e\u003cspan address=\"10.1007/s42452-025-07550-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrakash RY, Venkata B (2025) Experimental, FEA, and machine learning studies on wear behaviour of LM13 aluminium hybrid composites reinforced with zircon and graphite. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/13506501251367549\u003c/span\u003e\u003cspan address=\"10.1177/13506501251367549\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavitej YP, Keshavamurthy R, Adarsha H, Kumar VV, Bavan DS, Asdaque PMGB (2025) Effect of Zircon on Aluminium/Graphite Alloy Hybrid Composite and Wear Characterisation with Load: Experimental and ANN Test. J Bio- Tribo-Corrosion 11(1):1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40735-025-00944-7\u003c/span\u003e\u003cspan address=\"10.1007/s40735-025-00944-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrasad MG et al (2025) Effect of graphene nano-platelet concentration on the mechanical and microstructure properties of epoxy nanocomposites. Can Metall Q 4433:1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/00084433.2025.2565530\u003c/span\u003e\u003cspan address=\"10.1080/00084433.2025.2565530\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVinay SGMS, Bavan DS (2025) Thermal and Mechanical Properties Enhancement of Epoxy Composites Using Graphene, MWCNTs, and Biochar: A Machine Learning \u0026ndash; Assisted Approach. J Inst Eng Ser D. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40033-025-00950-6\u003c/span\u003e\u003cspan address=\"10.1007/s40033-025-00950-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavitej YP, Chandra BT, Adarsha H, Chandrashekar A, R PC (2024) Analysis of Wear Parameters of Chill Casted LM13 / Zircon / Carbon Hybrid Composites Using Experimental and Statistical Approach. 77\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.52209/2706-977X\u003c/span\u003e\u003cspan address=\"10.52209/2706-977X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavitej YP, Mohan CB, Ananthaprasad MG (2022) Evaluation of copper chill on tribological behaviour of LM13/ZrSiO\u003csub\u003e4\u003c/sub\u003e/C hybrid metal matrix composites. J Mines Met Fuels 69(12):142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18311/jmmf/2021/30143\u003c/span\u003e\u003cspan address=\"10.18311/jmmf/2021/30143\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavitej YP, Mohan CB, Ananthaprasad MG (2022) Dry Sliding Friction and Wear Behavior of LM13/Zircon/Carbon (HMMC\u0026rsquo;s): An Experimental, Statistical and Artificial Neural Network Approach. Tribol Ind 44(3):374\u0026ndash;393. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.24874/ti.1223.11.21.03\u003c/span\u003e\u003cspan address=\"10.24874/ti.1223.11.21.03\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaveen Kumar S et al (2021) Fabrication and characterization of hardness and microstructure of large sized Al2014-SiC composite, \u003cem\u003eMater. Today Proc.\u003c/em\u003e, vol. 46, no. xxxx, pp. 9102\u0026ndash;9106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.matpr.2021.05.398\u003c/span\u003e\u003cspan address=\"10.1016/j.matpr.2021.05.398\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeepak V, Abhilash O, Ravitej YP, Veerachari, Abhinandan L (2021) Design and development of progressive tool for mold tag, \u003cem\u003eAIP Conf. Proc.\u003c/em\u003e, vol. 2316, no. February. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1063/5.0038385\u003c/span\u003e\u003cspan address=\"10.1063/5.0038385\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavitej YP et al (2021) February,., Emission characteristics of single cylinder diesel engine with algae oil as bio-diesel, \u003cem\u003eAIP Conf. Proc.\u003c/em\u003e, vol. 2316, no. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1063/5.0036860\u003c/span\u003e\u003cspan address=\"10.1063/5.0036860\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandeep D et al (2021) February,., CFD simulation of transonic turbulent flow past NACA 0012 aerofoil, \u003cem\u003eAIP Conf. Proc.\u003c/em\u003e, vol. 2316, no. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1063/5.0036890\u003c/span\u003e\u003cspan address=\"10.1063/5.0036890\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta RK et al (2025) Machine Learning-Enabled Analysis of Surface Texture Orientation Effects on Wear Debris Morphology and Tribological Performance of High-Density Polyethene (HDPE). J Bio- Tribo-Corrosion 12(1):22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40735-025-01089-3\u003c/span\u003e\u003cspan address=\"10.1007/s40735-025-01089-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavitej YP et al (2025) Effect of Tungsten Carbide Composites on Al6061 Using Powder Metallurgy Technique: Experimental and Machine Learning Approach. J Inst Eng Ser D. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40033-025-00963-1\u003c/span\u003e\u003cspan address=\"10.1007/s40033-025-00963-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBavan S, Girish S, Shreyas RA, Y P R, Vinay MS, Jegadeeswaran N (2025) An Experimental Study and Finite Element Analysis on 3D Printed Polycarbonate and ABS Materials. 85\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1201/9781003685906-14\u003c/span\u003e\u003cspan address=\"10.1201/9781003685906-14\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavitej YP, Sandhya R, Nandeeshaiah BM (2016) Stress Analysis of Aero Engine Spur Gear. 797\u0026ndash;802. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15680/IJIRSET.2016.0505632\u003c/span\u003e\u003cspan address=\"10.15680/IJIRSET.2016.0505632\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavitej YP, Mohan CB, Anantha Prasad MG (2021) Corrosion analysis of LM13/ZrSiO4 /C Hybrid Metal Matrix Composites, \u003cem\u003eJ. Corros. Sci. Eng.\u003c/em\u003e, vol. 23, no. November\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrakash RY, Boganarasimhaiah MC, Gangadharaiah APM (2021) Fabrication, micro structural and mechanical characterization of Zircon Particles (ZrSiO4) reinforced Aluminum alloy (MMCs). Indian J Eng Mater Sci 28(6):549\u0026ndash;553. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.56042/ijems.v28i6.40173\u003c/span\u003e\u003cspan address=\"10.56042/ijems.v28i6.40173\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavitej YP, Mohan CB, Ananthaprasad MG (2021) Effect of Reinforcement and Copper Chill on LM13/ZrSiO4/C Hybrid Metal Matrix Composites (HMMCS)-An Experimental and Statistical Analysis. China\u0026rsquo;s Refract 30(4):12\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.19691/j.cnki.1004-4493.2021.04.003\u003c/span\u003e\u003cspan address=\"10.19691/j.cnki.1004-4493.2021.04.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrem Chand R, Halemani BS, Chandrasekhar KM, Ravitej YP, Hemanth Raju T, Udayshankar S (2021) Investigation and analysis for mechanical properties of banana and e glass fiber reinforced hybrid epoxy composites, \u003cem\u003eMater. Today Proc.\u003c/em\u003e, vol. 47, no. xxxx, pp. 2509\u0026ndash;2515. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.matpr.2021.05.044\u003c/span\u003e\u003cspan address=\"10.1016/j.matpr.2021.05.044\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrem Chand R et al (2021) Characterization of banana and e glass fiber reinforced hybrid epoxy composites, \u003cem\u003eMater. Today Proc.\u003c/em\u003e, vol. 46, no. xxxx, pp. 9119\u0026ndash;9125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.matpr.2021.05.402\u003c/span\u003e\u003cspan address=\"10.1016/j.matpr.2021.05.402\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChand RP, Ravitej YP, Kumar N, Veeracahri, Gowda NA (2057) Characterization of banana and bagasse fiber reinforced hybrid epoxy composites, \u003cem\u003eAIP Conf. Proc.\u003c/em\u003e, vol. no. January, 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1063/1.5085624\u003c/span\u003e\u003cspan address=\"10.1063/1.5085624\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavitej YP, Swaroop V, Ramesh S, Adarsha H, Veerachari, and, Nischith (2018) Finite element analysis of mild steel - Rubber sandwich composite material. IOP Conf Ser Mater Sci Eng 376(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1088/1757-899X/376/1/012040\u003c/span\u003e\u003cspan address=\"10.1088/1757-899X/376/1/012040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevendra K, Rangaswamy T (2012) Evaluation of Thermal Properties of E-Glass/ Epoxy Composites Filled By Different Filler Materials. Int J Comput Eng Res 25(5):2250\u0026ndash;3005\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaity D, Siddheshwar PG, Saha S (1998) Adv Fluid Mech Turbomach. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-642-72157-1\u003c/span\u003e\u003cspan address=\"10.1007/978-3-642-72157-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-bio--and-tribo-corrosion","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jbtc","sideBox":"Learn more about [Journal of Bio- and Tribo-Corrosion](http://link.springer.com/journal/40735)","snPcode":"40735","submissionUrl":"https://submission.nature.com/new-submission/40735/3","title":"Journal of Bio- and Tribo-Corrosion","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Copper composites, CNT, Titanium, Wear rate, Regression models","lastPublishedDoi":"10.21203/rs.3.rs-8604268/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8604268/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper investigates the wear behaviour of copper-based metal matrix composites (MMCs) reinforced with carbon nanotubes (CNTs) and micro titanium (Ti) particles. Composites containing 0.5\u0026ndash;1.5 Wt.% CNT and 1\u0026ndash;5 Wt.% Ti were fabricated using the stir casting technique to ensure uniform reinforcement dispersion. Abrasive wear tests were conducted under a 1 N load at sliding speeds of 200\u0026ndash;500 RPM using a pin-on-disc tribometer. Results revealed that the wear rate increased with speed but decreased notably with higher reinforcement levels. The C9 composition (1.5Wt.% CNT and 5Wt.% Ti) exhibited the lowest wear rate and superior wear resistance. Regression modelling using Linear, Polynomial, Support Vector Regression (SVR), and Random Forest algorithms was applied to predict wear behaviour. Among these, the Polynomial model demonstrated the highest accuracy with an R\u003csup\u003e2\u003c/sup\u003e value close to 0.99 and low mean absolute error, while SVR showed consistent but less interpretable results. ANOVA analysis confirmed that both composite composition and speed significantly influenced wear rate. The paper concludes that hybrid reinforcement of CNT and Ti effectively enhances the tribological performance of copper composites, and regression models offer reliable predictive insight into wear mechanisms.\u003c/p\u003e","manuscriptTitle":"Tribological and Predictive Modelling Analysis of Copper - CNT - Titanium Hybrid Metal Matrix Composites","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 23:10:31","doi":"10.21203/rs.3.rs-8604268/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-08T23:57:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-01T22:29:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220687466326122392876773555887524758064","date":"2026-01-21T05:50:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T04:28:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-20T00:39:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-20T00:38:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Bio- and Tribo-Corrosion","date":"2026-01-14T17:48:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-bio--and-tribo-corrosion","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jbtc","sideBox":"Learn more about [Journal of Bio- and Tribo-Corrosion](http://link.springer.com/journal/40735)","snPcode":"40735","submissionUrl":"https://submission.nature.com/new-submission/40735/3","title":"Journal of Bio- and Tribo-Corrosion","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2f69f164-f172-4c99-92b8-c7164c5b5e7d","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:05:25+00:00","versionOfRecord":{"articleIdentity":"rs-8604268","link":"https://doi.org/10.1007/s40735-026-01135-8","journal":{"identity":"journal-of-bio--and-tribo-corrosion","isVorOnly":false,"title":"Journal of Bio- and Tribo-Corrosion"},"publishedOn":"2026-03-06 15:59:20","publishedOnDateReadable":"March 6th, 2026"},"versionCreatedAt":"2026-01-22 23:10:31","video":"","vorDoi":"10.1007/s40735-026-01135-8","vorDoiUrl":"https://doi.org/10.1007/s40735-026-01135-8","workflowStages":[]},"version":"v1","identity":"rs-8604268","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8604268","identity":"rs-8604268","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}