Research on Milling Parameter Optimization and Wear of PCD Tools for SiCp/Al Composites

Research on Milling Parameter Optimization and Wear of PCD Tools for SiCp/Al 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 Research on Milling Parameter Optimization and Wear of PCD Tools for SiC p /Al Composites Qingqing Lü, Kun Zhao, Liquan Yang, Erbo Liu, Guangxi Li, Daohui Xiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8259762/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract To investigate the wear behavior of Polycrystalline Diamond (PCD) tools during the milling of high volume fraction silicon carbide particle-reinforced aluminum matrix composites (high volume fraction SiC p /Al) and realize the prediction of tool wear, an orthogonal experiment and multiple linear regression analysis were adopted. The factors influencing tool wear and a tool wear prediction model were obtained, followed by the F-test and R² test for the model. The test results indicate that the model is statistically significant overall. Through orthogonal experiments, the effects of milling parameters on cutting force, workpiece surface roughness, and tool wear amount were determined. By comparing the relative error between the actual tool wear amount and the wear amount predicted by the model, it was found that the average error is within 5%, which verifies that the model possesses the preliminary capability of predicting tool wear. The experimental results show that in practical milling operations, a reasonable selection of milling parameters can effectively reduce workpiece surface roughness and tool wear, thereby prolonging tool life. Aluminum matrix silicon carbide Milling Tool wear Tool life Surface quality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Metal Matrix Composites (MMC) refer to a composite material system that uses metals or alloys as the matrix material and is reinforced with reinforcing phases such as fibers, whiskers, and particles. As an important component in the field of advanced materials, MMCs exhibit unparalleled advantages over other materials due to their wide application range and outstanding comprehensive properties. Therefore, MMC occupies an important position in key fields such as aerospace engineering, national defense technology, and electronic information technology [ 1 – 3 ]. In recent years, with the rapid development of science and technology in China, industries across the board have imposed increasingly stringent requirements on the performance of materials. SiC p /Al composites are high-performance MMCs that integrate the characteristics of light weight, high electrical conductivity, and high thermal conductivity. By combining the light weight, excellent electrical conductivity, and thermal conductivity of aluminum with the high hardness, outstanding wear resistance, and low thermal expansion properties of silicon carbide, this material exhibits unique material advantages. Its main performance characteristics include high strength and hardness, a coefficient of thermal expansion matching that of chips, excellent thermal conductivity, outstanding wear resistance, and significant lightweight properties. These performance characteristics endow SiC p /Al composites with broad application prospects in numerous fields such as aerospace engineering, automotive industry, electronic packaging technology, and thermal management [ 4 – 6 ]. PCD tools are usually used for the machining of silicon carbide particle-reinforced aluminum matrix composites. However, the silicon carbide particles inside the material lead to severe tool wear, which affects the machining accuracy and surface quality [ 7 – 9 ]. This not only affects machining efficiency but also significantly increases machining costs. Therefore, exploring high-efficiency and high-performance machining methods and technologies for silicon carbide particle-reinforced aluminum matrix composites has long been a research focus in both industrial and academic fields. Wang et al. conducted a detailed study on the wear morphology and mechanism of cutting tools, and their research provides important theoretical and practical guidance for the machining of silicon carbide particle-reinforced aluminum matrix composites [ 10 ]. Hao et al. reviewed the machining mechanisms of aluminum matrix composites and pointed out that polycrystalline diamond tools exhibit excellent wear resistance when machining high-volume-fraction silicon carbide particle-reinforced aluminum matrix composites. However, tool wear has a significant impact on the machined surface quality and cutting force [ 11 ]. Chen et al. investigated the wear modes of polycrystalline diamond tools during the machining of silicon carbide particle-reinforced aluminum matrix composites, as well as the effects of such wear on cutting force, cutting temperature, and surface residual stress, through experiments and finite element simulation [ 12 ]. Peter's research indicates that polycrystalline diamond tools are the most ideal choice for MMC, especially those containing silicon carbide particles. These tools can achieve longer tool life and higher machining quality at relatively high cutting speeds [ 13 ]. Ye et al. analyzed the influence of cutting parameters on the surface roughness of silicon carbide particle-reinforced aluminum matrix composites during milling through experiments [ 14 ]. Yang et al. established a prediction model for the relationship between tool cutting parameters and tool life via Python programming, and verified the accuracy of this model through experiments [ 15 ]. Zhang et al. conducted experiments on the wear of PCD tools during the ultrasonic elliptical vibration cutting of SiC p /Al composites, and revealed the wear mechanism of PCD tools [ 16 ]. Huang et al. found through their research that the wear form of PCD tools with different grain sizes is dominated by flank wear. No obvious wear marks were observed in the cutting area of the rake face; however, the cutting edges exhibited micro-chipping to varying degrees, and this phenomenon intensified as the diamond grain size increased [ 17 ]. Wu et al. used ABAQUS to simulate the cutting process of SiC p /Al composites, and concluded that the damage modes of silicon carbide (SiC) particles mainly include complete fracture, partial fragmentation, overall pull-out, and local debonding [ 18 ]. Mao et al. obtained the influence of milling parameters on cutting force and surface quality through precision milling experiments [ 19 ]. Laghari et al. studied the influence of cutting parameters on the cutting force during the milling of SiC p /Al composites with different volume fractions [ 20 ]. To improve tool wear resistance and enhance tool life, Zha et al. studied the tool wear of circular saw blades during the ultrasonic-assisted cutting of Nomex honeycomb composites [ 21 ]. In summary, current research generally holds that PCD tools exhibit significant advantages in the milling of SiC p /Al composites, and their wear mechanism is comprehensively influenced by material properties, machining parameters, and other factors [ 22 – 25 ]. At present, most domestic and international studies on SiC p /Al composites focus on materials with low volume fractions, while there are relatively few studies on the machining process of composites with high volume fractions. However, research on tool life prediction during the milling of high-volume-fraction SiC p /Al composites is rather insufficient. Therefore, this study focuses on tool life prediction and optimization of process parameters, aiming to explore the mechanisms and methods for high-efficiency and high-precision machining of high-volume-fraction SiC p /Al composites. 2 Materials and Methods 2.1 Experimental Equipment The experimental machine tool adopted the HFM-600V machining center (Guizhou Xingfuxiang Lijian Machinery Co., Ltd.). The milling force acquisition equipment is a 9119AA2 dynamometer (Swiss Kistler Instruments Co., Ltd., Winterthur, Switzerland, the milling force-acquisition system type 9119AA2, and the signal amplifier type 5080A). The experimental cutting tool was a PCD circular arc end mill with a rake angle of 0°, a relief angle of 10°, and dimensions specified as D8*6H*D8*75L*2F (i.e., diameter 8 mm, cutting edge length 6 mm, total tool length 75 mm, and 2 flutes), manufactured by Zhuzhou Cemented Carbide Cutting Tools Co, Ltd. In this experiment, an aluminum matrix silicon carbide composite material with a volume fraction of 70% was adopted, and its mechanical properties are presented in Table 1 . the scene of the experimental equipment is shown in Fig. 1 . Table 1 Mechanical Properties of A356 Aluminum Alloy Density (g/m 3 ) Poisson's ratio Shear modulus (GPa) Flexural strength (GPa) 3.04 0.28 99.6 393 2.2 Experimental Scheme Design During the milling process, tool wear is affected by a variety of factors. This study focuses on investigating the effects of spindle speed, feed per Tooth and milling depth on the wear amount of PCD tools. The parameter levels of each influencing factor are set as presented in Table 2 , and 16 groups of orthogonal experiments are designed accordingly. During the milling of the workpiece, the milling force was collected and recorded by the cutting force measurement system installed beneath the workpiece. After each group of experiments, an ultra-depth-of-field microscope (EasyZoom 5 3D, MOTIC CHINA GROUP CO., LTD.) was used to observe the flank wear amount (VB) of the PCD tool and the machined surface roughness of the workpiece, with the data recorded simultaneously. Table 2 Test Parameters Levels of Each Factor Spindle speed n/r∙min − 1 Feed per tooth f z /mm∙z − 1 Milling depth a p /mm K1 1000 50 0.2 K2 1500 100 0.4 K3 2000 150 0.6 K4 2500 200 0.8 3 Results and Discussion The cumulative milling distance for each group of experiments was 1000 mm, and the results of the orthogonal experiments are presented in Table 3 . Table 3 Experimental Results Item Number Spindle speed n/r∙min − 1 Feed per tooth f z /mm∙z − 1 Milling depth a p /mm Flank Wear Land Width VB/µm F x /N F y /N F z /N Surface RoughnessRa/µm 1 1000 0.025 0.2 44.63 22.9 9.7 30.7 0.37 2 1000 0.05 0.4 41.52 28.7 12.8 40.3 0.39 3 1000 0.075 0.6 39.21 36.8 15.2 44.2 0.55 4 1000 0.1 0.8 35.63 43.3 18.6 48.7 0.67 5 1500 0.025 0.4 60.75 25.8 10.3 44.3 0.36 6 1500 0.05 0.2 44.79 21.6 10.6 36.6 0.45 7 1500 0.075 0.8 49.51 43.2 15.9 54.2 0.31 8 1500 0.1 0.6 42.86 40.8 16.7 52.3 0.48 9 2000 0.025 0.6 83.14 27.6 11.5 39.7 0.25 10 2000 0.05 0.8 72.57 36.8 13.3 53.2 0.34 11 2000 0.075 0.2 48.42 24.4 10.5 47.3 0.39 12 2000 0.1 0.4 50.27 34.5 14.8 51.8 0.50 13 2500 0.025 0.8 104.75 32.2 10.2 53.6 0.35 14 2500 0.05 0.6 80.05 30.7 12.3 59.9 0.33 15 2500 0.075 0.4 65.47 31.5 11.8 52.4 0.34 16 2500 0.1 0.2 52.64 24.3 13.6 51.7 0.38 3.1 The Influence of Milling Parameters on Cutting Force The influence trend of milling parameters on the variation of cutting force is illustrated in Fig. 3 . As can be seen from Fig. 2 (a), the cutting force in the Fz direction increases with the rise of spindle speed, which is opposite to the variation trend of cutting forces in the F x and F y directions. This phenomenon occurs because an increase in spindle speed leads to a higher frictional force between the cutting tool and the aluminum matrix silicon carbide composite, thereby increasing by cutting force [ 26 ]. It can be observed from Fig. 2 (b) and Fig. 2 (c) that the three-directional cutting forces increase with the increase of feed rate and milling depth. (a) The Relationship Between Spindle Speed and Cutting Force (b) The Relationship Between Feed Per Tooth and Cutting Force The range analysis table of the cutting force in the F x direction is shown in Table 4 . It can be seen from Table 4 that the order of the influence of cutting parameters on the cutting force, from the greatest to the smallest, is as follows: feed rate, milling depth, and spindle speed. With the minimum milling force as the optimization objective, the optimal parameter combination is as follows: spindle speed of 2500 r/min, feed rate of 50 mm/min, and milling depth of 0.2 mm. Experimental verification was carried out using the aforementioned milling parameters, and the results indicate that under the above optimal parameters, the F x is 19.3 N, which is lower than the F x of any test in the orthogonal experiment. Among them, the minimum reduction range of F x is 10.65% and the maximum reduction range is 55.43%, which verifies the rationality of the optimal parameters obtained from the orthogonal experiment. Table 4 Range Analysis of F x Serial number F x (Spindle speed) F x (Feed per tooth) F x (Milling depth) K1 27.9250 20.3750 19.8000 K2 25.6000 22.7000 24.6250 K3 25.3250 26.4750 28.4750 K4 23.6750 32.9750 29.6250 Range 4.2500 12.6000 9.8250 The range analysis table of the cutting force in the F y direction is shown in Table 5 . It can be observed from Table 5 that the order of the influence of cutting parameters on the cutting force, from the strongest to the weakest, is as follows: milling depth, feed rate, and spindle speed. With the minimum milling force set as the optimization objective, the optimal parameter combination is as follows: a spindle speed of 2500 r/min, a feed rate of 50 mm/min, and a milling depth of 0.2 mm. Experimental verification was conducted using the aforementioned milling parameters, and the results show that under the above optimal parameters, the F y is 8.5 N, which is lower than the F y of any test in the orthogonal experiment. Among them, the minimum reduction range of F y is 12.37% and the maximum reduction range is 57.6%, which verifies the rationality of the optimal parameters obtained from the orthogonal experiment. Table 5 Range Analysis of F y Serial number F y (Spindle speed) F y (Feed per tooth) F y (Milling depth) K1 14.0750 10.9250 10.8500 K2 13.1000 12.7500 12.4250 K3 12.7750 14.0500 13.7000 K4 12.4750 14.7000 15.4500 Range 1.6000 3.7750 4.6000 The range analysis table of the cutting force in the F z direction is shown in Table 6 . It can be seen from Table 6 that the order of the influence of cutting parameters on the cutting force, from the greatest to the smallest, is as follows: spindle speed, milling depth, and feed rate. With the minimum milling force as the optimization objective, the optimal parameter combination is as follows: spindle speed of 1000 r/min, feed rate of 50 mm/min, and milling depth of 0.2 mm. Experimental verification was performed using the aforementioned milling parameters, and the results indicate that under the above optimal parameters, the F z is 25.4 N, which is lower than the F z of any test run in the orthogonal experiment. Among them, the minimum reduction range of F z is 17.26% and the maximum reduction range is 57.73%, which verifies the rationality of the optimal parameters obtained from the orthogonal experiment. Table 6 Range Analysis of F z Serial number F z (Spindle speed) F z (Feed per tooth) F z (Milling depth) K1 30.9750 32.0750 31.5750 K2 36.8500 37.4500 37.2000 K3 38.0000 39.5250 38.9750 K4 44.3500 41.1250 42.4250 Range 13.3750 9.0500 10.8500 3.2 The Influence of Milling Parameters on Surface Roughness Surface roughness is a crucial factor for evaluating the machining quality of workpiece surfaces, and its magnitude directly affects the performance, service life, and other properties of the workpiece. To reduce the deviation caused by measurement, in this experiment, six different regions were selected on the machined surface to measure the roughness values, and the average value was taken as the basis for analysis. The variation trend of surface roughness is shown in Fig. 3 . It can be observed that the surface roughness of the workpiece decreases with the increase in spindle speed, while it increases with the increase in feed rate and milling depth. The range values of surface roughness derived from Table 3 are presented in Table 7 . Among them, the range of feed rate is 0.175 µm, making it the primary influencing factor; the range of spindle speed is 0.145 µm, classified as a secondary influencing factor; and the range of milling depth is 0.02 µm, which has almost no impact on surface roughness. With the minimum surface roughness set as the optimization objective, the optimal parameter combination is as follows: a spindle speed of 2500 r/min, a feed rate of 0.025 mm/z, and a milling depth of 0.2 mm. Experimental verification was conducted under the above parameter conditions, and the results show that the surface roughness of the workpiece is 0.16 µm, which is lower than any set of data in the orthogonal experiment. Among them, the minimum reduction range is 36% and the maximum reduction range is 76.12%, which verifies the rationality of the optimal parameters obtained from the orthogonal experiment. Table 7 Range Analysis of Surface Roughness Serial number Surface Roughness (Spindle speed) Surface Roughness (Feed per tooth) Surface Roughness (Milling depth) K1 0.495 0.3325 0.3975 K2 0.4 0.3775 0.3975 K3 0.37 0.3975 0.4025 K4 0.35 0.5075 0.4175 Range 0.145 0.175 0.02 3.3 The Influence of Cutting Parameters on Tool Wear Amount By analyzing the wear amount per unit time and fixed-stroke wear amount in Table 3 , it can be observed that the wear amount per unit time and fixed-stroke wear amount under the 13th group of milling parameters are both the largest, while those under the 4th group of milling parameters are both the smallest. This indicates that during milling, the 4th group of milling parameters can effectively reduce tool wear during machining and extend the tool service life. From the perspective of tool service life, under actual milling conditions, the 13th group of milling parameters should be avoided as much as possible to prevent excessive tool damage. As shown in Fig. 4 , the influence of milling parameters on the flank wear amount of the cutting tool is presented. When the spindle speed increases from 1000 m/min to 2500 m/min, the wear amount of the tool per unit time shows a significant upward trend. This is because the increase in spindle speed leads to a rise in cutting temperature, which promotes the adhesive wear on the flank face. Additionally, a higher rotational speed intensifies the friction between the flank face and the workpiece, thereby resulting in an increase in the tool wear amount. With the increase in feed per tooth, the flank wear amount shows a decreasing trend. This is because a lower feed per tooth leads to a reduction in feed rate, which in turn prolongs the contact time between the cutting tool and the SiC particles in the workpiece. When the feed per tooth increases, the local temperature at the tool tip rises, which in turn softens the aluminum matrix. During the milling process, the SiC particles are pressed into the Al matrix, reducing the contact between the cutting tool and the SiC particles and thereby decreasing the flank wear amount [ 10 ]. An increase in milling depth also intensifies the flank wear. This is because a larger milling depth leads to a higher frequency of collisions between the SiC particles inside the workpiece and the cutting tool, which in turn results in an increase in the flank wear amount of the tool. The range values of flank wear amount derived from Table 3 are presented in Table 8 . From the range analysis results of flank wear amount, it can be concluded that the influence of milling parameters on flank wear amount, in descending order of significance, is as follows: spindle speed, feed per tooth, and cutting depth. With the minimum flank wear amount as the criterion, the optimal parameter combination is determined as follows: spindle speed n = 1000 r/min, feed per tooth f z =0.1 mm/z, milling depth a p =0.2 mm. Experimental verification was conducted under the aforementioned parameter conditions, and the resulting flank wear amount was 29.47 µm, which is lower than that of any group of data in the orthogonal experiment. Among them, the minimum reduction rate was 17.29% and the maximum reduction rate was 71.87%, which verifies the rationality of the optimal parameters obtained from the orthogonal experiment. Table 8 Flank Wear Amount Range Analysis Serial number Flank Wear Amount (Spindle speed) Flank Wear Amount (Feed per tooth) Flank Wear Amount (Milling depth) K1 40.2475 73.3175 47.6200 K2 49.4775 59.7325 54.5025 K3 63.6000 50.6525 61.3150 K4 75.7275 45.3500 65.6150 Range 35.4800 27.9675 17.9950 3.4 Multiple Linear Regression Model for PCD Tool Wear In the milling process, three cutting variables—spindle speed, feed rate, and milling depth—exert a significant influence on tool life. According to international standards, the effective life of a cutting tool terminates when it can no longer machine workpieces that meet the required shape and surface quality. In this study, when measuring the flank wear amount of PCD tools, a value of △VB = 300 µm was adopted as the blunting criterion for PCD milling cutters. Specifically, the tool life is deemed to end when the flank wear amount exceeds 300 µm [ 27 ]. Based on the experimental results in Table 3 , the principle of least squares was applied to establish a tool life prediction model, which is shown in Eq. ( 1 ). $$S=0.185 \cdot v_{c}^{{0.6631}} \cdot f_{{\text{z}}}^{{ - 0.3169}} \cdot a_{p}^{{0.1758}}$$ 1 In the formula, represents the flank wear amount with the unit of µ m; Vc represents the spindle speed with the unit of r/min; fz represents the feed per tooth with the unit of mm/z; a p represents the milling depth with the unit of mm. The exponents of each term indicate the degree of influence of the corresponding cutting factors on the tool's flank wear amount. Equation ( 1 ) was verified using the R-squared (R²) test method, and the R² value of the model was found to be 0.9916. Since this value is close to 1, it indicates that the equation has a high goodness of fit and the model exhibits good accuracy, which can explain approximately 99.16% of the variation in tool wear amount. The F-test was performed on Eq. ( 1 ). When α = 0.05, the F-statistic is greater than the critical F-value, indicating that the model is overall significant. It can be seen from Eq. ( 1 ) that the most significant factor affecting the flank wear amount of the tool is the spindle speed, followed by the feed per tooth. Moreover, the feed per tooth has a negative correlation with the tool flank wear amount, which is consistent with the influence trend of milling parameters on the flank wear amount obtained in the previous section. The data comparing the actual tool wear conditions with the model prediction results are presented in Table 8 . It can be observed that the relative error between the results of the empirical formula and the experimental data is basically within 5%, which indicates that the empirical formula derived in this paper has a relatively accurate predictive ability for the tool wear amount. Table 9 Comparison between the Actual Measured Values and the Predicted Values Item Number Actual Value Predicted Value Relative Error 1 44.630000 43.779539 1.91% 2 41.520000 39.699970 4.38% 3 39.210000 37.492153 4.38% 4 35.630000 36.000553 1.04% 5 60.750000 64.707451 6.51% 6 44.790000 45.988380 2.68% 7 49.510000 51.602603 4.23% 8 42.860000 44.783598 4.49% 9 83.140000 84.092288 1.15% 10 70.710000 71.010617 2.15% 11 48.420000 48.943660 1.08% 12 50.270000 50.468118 0.39% 13 104.750000 102.560548 2.09% 14 80.050000 78.275533 2.22% 15 65.470000 64.102133 2.09% 16 52.640000 51.804558 1.59% There exists a certain deviation between the predicted results of the models in Table 9 and the experimental results. This can be attributed to the fact that during actual machining and production processes, variations in factors such as temperature and mechanical vibration cause the tool wear amount to exhibit a non-linear change. Consequently, a certain discrepancy arises between the model-predicted values and the experimental data. As concluded in the previous section, the relative error between the tool wear amount under the optimal milling parameters and the model-predicted value is 4.3%. 3.5 Analysis on Wear Results of PCD Cutting Tools To observe the tool wear morphology more intuitively, the tool set with the most severe wear in the experiment-specifically the thirteenth tool set was selected as the reference for result analysis. As shown in Fig. 5(a), there are a large number of scratch wear marks on the tool face, and these scratches are in inconsistent directions. This phenomenon is caused by the severe friction between the cutting surface and the tool during the milling process. When the chips flow out from the rake face, the silicon carbide particles contained therein continuously scrape the rake face of the tool, and these scratches are gradually formed as a result. However, some local areas on the rake face appear relatively rough, with tiny spalling and damage points. This is because during the cutting process, the tool bears high temperature and high pressure, which leads to fatigue damage of the material on the rake face. When the stress exceeds the strength limit of the material, the material in some tiny areas will peel off, resulting in damage to the surface integrity and thus exerting an adverse effect on the milling process. The rough surfaces caused by such wear will impede the smooth flow of chips, further exacerbating tool wear and adversely affecting machining quality. As shown in Fig. 5(b), the flank face of the tool used in the experiment exhibits groove morphologies with inconsistent lengths and depths. The cause of this phenomenon can be attributed to the irregular distribution of silicon carbide particles within the aluminum matrix during the milling process. When the aluminum matrix softens due to milling heat, some silicon carbide particles become detached and pulled out from the aluminum matrix or pressed into its interior under the action of cutting impact. In the subsequent cutting process, these silicon carbide particles further exert extrusion and friction effects on the polycrystalline diamond tool, which in turn triggers intense abrasive wear behavior. Eventually, such surface morphologies with fragmented characteristics are formed on the tool's flank face, and the degree of abrasive wear on the flank face is far greater than that on the rake face [ 28 ]. When machining aluminum matrix silicon carbide composites with a high volume fraction, an intermittent cutting mode tends to occur. Under these working conditions, the cutting edge of the polycrystalline diamond tool is required to cut off silicon carbide particles. Due to the high hardness and discrete distribution of silicon carbide particles, the tool edge bears more intense impact loads during the cutting process, which increases the stress level on the edge and raises the risk of wear. As shown in Fig. 5(c), the tool's cutting edge exhibits a certain degree of micro-chipping. These small notches may be caused by the fatigue and detachment of local material at the cutting edge, which results from the impact of silicon carbide particles during the milling process. 4 Conclusions This study conducts a systematic investigation focusing on the milling wear characteristics and life prediction of PCD tools when machining aluminum matrix silicon carbide composites. By employing methods such as orthogonal experiments and multiple linear regression analysis, the factors influencing tool wear and their corresponding rules during the milling process are revealed, and a tool life prediction model is established. The main conclusions are as follows: (1)The surface roughness is mainly affected by the spindle speed and feed rate, with the least influence from the cutting depth. It decreases with the increase of spindle speed and increases with the increase of feed rate. Although it also increases with the increase of cutting depth, the degree of influence on surface roughness is far lower than that of the other two parameters. Therefore, it is difficult to improve the surface quality of workpieces by reducing the milling depth in actual machining. (2)The range analysis of the multi-factor experiment shows that the feed per tooth and milling depth are the main factors affecting the tool milling force. Verification experiments were conducted under the optimal milling parameters. For the cutting forces in the F x , F y and F z directions, the minimum reduction rates were 10.65%, 12.37%, and 17.26% respectively, while the maximum reduction rates were 55.43%, 57.6%, and 57.73% respectively. (3)The multi-factor orthogonal experiment shows that the spindle speed and feed per tooth are the main factors affecting tool wear, and the obtained optimal parameter combination is as follows: spindle speed n = 1000 r/min, feed per tooth f z =0.1 mm/z, milling depth a p =0.2 mm. Under the optimal milling parameters, compared with the data obtained from the orthogonal experiment, the minimum reduction rate of the tool wear amount is 17.29%, and the maximum reduction rate is 71.87%. Meanwhile, combined with the experimental data, a tool wear prediction model was established using the multiple linear regression analysis method. The error between the experimental results after parameter optimization and the model-predicted values is only 4.3%. (4)Through milling experiments and analysis of the tool wear mechanism, it is found that when milling aluminum matrix silicon carbide composites with high volume fraction, abrasive wear is the main cause of PCD tool wear. Secondly, factors such as micro-chipping of the tool edge and grain spalling also contribute to the tool blunting and failure. Declarations Author contribution: Qingqing Lü: validation，data curation，writing—original draft preparation, writing—review and editing; Kun Zhao: validation，data curation，writing—original draft preparation, writing—review and editing; Liquan Yang: validation, writing—original draft preparation, writing—review and editing; Erbo Liu: writing-review and editing; Guangxi Li: writing-review, editing and funding acquisition; Daohui Xiang: Study conception, methodology and funding acquisition. Funding: This research was funded by Key R&D and Promotion Special/Tackling Key Problems in Science and Technology in Henan Province, China (grant No. 252102241063, 252102220130); Henan Province Science and Technology Research and Development Joint Fund (Industrial Category) (grant No. 202324119); China Iron and Steel Education Society Fund Project (grant No. SYJX2024033); Key Scientific Research Fund of Pingdingshan University (grant No. 2023-JYZD01). Data availability: Not applicable. Code availability: Not applicable. Ethics approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable. Conflict of interest: The authors declare no competing interests. References Cong P, Xie L, Peng S (2015) Experimental Study on High-speed Milling of High Volume Fraction SiC p /Al Composites by PCD Tools. 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Superhard Mater Eng 37:43–51. 10.3969/j.issn.1673-1433.2025.01.007 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 15 Dec, 2025 Reviewers invited by journal 10 Dec, 2025 Editor assigned by journal 08 Dec, 2025 First submitted to journal 05 Dec, 2025 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. 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2","display":"","copyAsset":false,"role":"figure","size":88562,"visible":true,"origin":"","legend":"\u003cp\u003eThe Relationship Between Milling Parameters and Cutting Force\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8259762/v1/0b491a6c1946d3de7b1fe6c9.png"},{"id":98433607,"identity":"ac1f4c9a-aca1-4b88-a432-f15e24192719","added_by":"auto","created_at":"2025-12-17 16:50:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45614,"visible":true,"origin":"","legend":"\u003cp\u003eThe Influence of Milling Parameters on Surface Roughness\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8259762/v1/f89801e33fa69adc17c47b73.png"},{"id":98215413,"identity":"8b1e93a7-e72e-4383-9119-5f32303ccdad","added_by":"auto","created_at":"2025-12-15 10:26:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49459,"visible":true,"origin":"","legend":"\u003cp\u003eThe Influence of Milling Parameters on Flank Wear Amount\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8259762/v1/bad2cc00b033a660ccb113b9.png"},{"id":98433155,"identity":"67f3df1f-1987-462b-bcdc-639fa238dd28","added_by":"auto","created_at":"2025-12-17 16:50:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":573386,"visible":true,"origin":"","legend":"\u003cp\u003eTool Wear\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8259762/v1/9aab9ba28a6d49a8f9d3b7ee.png"},{"id":98774966,"identity":"68e8759e-b49d-4f2e-af8c-4c472f8cbdea","added_by":"auto","created_at":"2025-12-22 12:17:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2801470,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8259762/v1/8bc81eef-274c-40cc-aef5-8ffc93dbc8d0.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003e\u003cstrong\u003eResearch on Milling Parameter Optimization and Wear of PCD Tools for SiC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e/Al Composites\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMetal Matrix Composites (MMC) refer to a composite material system that uses metals or alloys as the matrix material and is reinforced with reinforcing phases such as fibers, whiskers, and particles. As an important component in the field of advanced materials, MMCs exhibit unparalleled advantages over other materials due to their wide application range and outstanding comprehensive properties. Therefore, MMC occupies an important position in key fields such as aerospace engineering, national defense technology, and electronic information technology [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In recent years, with the rapid development of science and technology in China, industries across the board have imposed increasingly stringent requirements on the performance of materials.\u003c/p\u003e\u003cp\u003eSiC\u003csub\u003ep\u003c/sub\u003e/Al composites are high-performance MMCs that integrate the characteristics of light weight, high electrical conductivity, and high thermal conductivity. By combining the light weight, excellent electrical conductivity, and thermal conductivity of aluminum with the high hardness, outstanding wear resistance, and low thermal expansion properties of silicon carbide, this material exhibits unique material advantages. Its main performance characteristics include high strength and hardness, a coefficient of thermal expansion matching that of chips, excellent thermal conductivity, outstanding wear resistance, and significant lightweight properties. These performance characteristics endow SiC\u003csub\u003ep\u003c/sub\u003e/Al composites with broad application prospects in numerous fields such as aerospace engineering, automotive industry, electronic packaging technology, and thermal management [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePCD tools are usually used for the machining of silicon carbide particle-reinforced aluminum matrix composites. However, the silicon carbide particles inside the material lead to severe tool wear, which affects the machining accuracy and surface quality [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This not only affects machining efficiency but also significantly increases machining costs. Therefore, exploring high-efficiency and high-performance machining methods and technologies for silicon carbide particle-reinforced aluminum matrix composites has long been a research focus in both industrial and academic fields. Wang et al. conducted a detailed study on the wear morphology and mechanism of cutting tools, and their research provides important theoretical and practical guidance for the machining of silicon carbide particle-reinforced aluminum matrix composites [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Hao et al. reviewed the machining mechanisms of aluminum matrix composites and pointed out that polycrystalline diamond tools exhibit excellent wear resistance when machining high-volume-fraction silicon carbide particle-reinforced aluminum matrix composites. However, tool wear has a significant impact on the machined surface quality and cutting force [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Chen et al. investigated the wear modes of polycrystalline diamond tools during the machining of silicon carbide particle-reinforced aluminum matrix composites, as well as the effects of such wear on cutting force, cutting temperature, and surface residual stress, through experiments and finite element simulation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Peter's research indicates that polycrystalline diamond tools are the most ideal choice for MMC, especially those containing silicon carbide particles. These tools can achieve longer tool life and higher machining quality at relatively high cutting speeds [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Ye et al. analyzed the influence of cutting parameters on the surface roughness of silicon carbide particle-reinforced aluminum matrix composites during milling through experiments [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Yang et al. established a prediction model for the relationship between tool cutting parameters and tool life via Python programming, and verified the accuracy of this model through experiments [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Zhang et al. conducted experiments on the wear of PCD tools during the ultrasonic elliptical vibration cutting of SiC\u003csub\u003ep\u003c/sub\u003e/Al composites, and revealed the wear mechanism of PCD tools [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Huang et al. found through their research that the wear form of PCD tools with different grain sizes is dominated by flank wear. No obvious wear marks were observed in the cutting area of the rake face; however, the cutting edges exhibited micro-chipping to varying degrees, and this phenomenon intensified as the diamond grain size increased [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Wu et al. used ABAQUS to simulate the cutting process of SiC\u003csub\u003ep\u003c/sub\u003e/Al composites, and concluded that the damage modes of silicon carbide (SiC) particles mainly include complete fracture, partial fragmentation, overall pull-out, and local debonding [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Mao et al. obtained the influence of milling parameters on cutting force and surface quality through precision milling experiments [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Laghari et al. studied the influence of cutting parameters on the cutting force during the milling of SiC\u003csub\u003ep\u003c/sub\u003e/Al composites with different volume fractions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To improve tool wear resistance and enhance tool life, Zha et al. studied the tool wear of circular saw blades during the ultrasonic-assisted cutting of Nomex honeycomb composites [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In summary, current research generally holds that PCD tools exhibit significant advantages in the milling of SiC\u003csub\u003ep\u003c/sub\u003e/Al composites, and their wear mechanism is comprehensively influenced by material properties, machining parameters, and other factors [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. At present, most domestic and international studies on SiC\u003csub\u003ep\u003c/sub\u003e/Al composites focus on materials with low volume fractions, while there are relatively few studies on the machining process of composites with high volume fractions. However, research on tool life prediction during the milling of high-volume-fraction SiC\u003csub\u003ep\u003c/sub\u003e/Al composites is rather insufficient. Therefore, this study focuses on tool life prediction and optimization of process parameters, aiming to explore the mechanisms and methods for high-efficiency and high-precision machining of high-volume-fraction SiC\u003csub\u003ep\u003c/sub\u003e/Al composites.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Experimental Equipment\u003c/h2\u003e\u003cp\u003eThe experimental machine tool adopted the HFM-600V machining center (Guizhou Xingfuxiang Lijian Machinery Co., Ltd.). The milling force acquisition equipment is a 9119AA2 dynamometer (Swiss Kistler Instruments Co., Ltd., Winterthur, Switzerland, the milling force-acquisition system type 9119AA2, and the signal amplifier type 5080A). The experimental cutting tool was a PCD circular arc end mill with a rake angle of 0\u0026deg;, a relief angle of 10\u0026deg;, and dimensions specified as D8*6H*D8*75L*2F (i.e., diameter 8 mm, cutting edge length 6 mm, total tool length 75 mm, and 2 flutes), manufactured by Zhuzhou Cemented Carbide Cutting Tools Co, Ltd. In this experiment, an aluminum matrix silicon carbide composite material with a volume fraction of 70% was adopted, and its mechanical properties are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. the scene of the experimental equipment is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eMechanical Properties of A356 Aluminum Alloy\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=\"left\" 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\u003eDensity (g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoisson's ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShear modulus (GPa)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFlexural strength (GPa)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e393\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=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Experimental Scheme Design\u003c/h2\u003e\u003cp\u003eDuring the milling process, tool wear is affected by a variety of factors. This study focuses on investigating the effects of spindle speed, feed per Tooth and milling depth on the wear amount of PCD tools. The parameter levels of each influencing factor are set as presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and 16 groups of orthogonal experiments are designed accordingly. During the milling of the workpiece, the milling force was collected and recorded by the cutting force measurement system installed beneath the workpiece. After each group of experiments, an ultra-depth-of-field microscope (EasyZoom 5 3D, MOTIC CHINA GROUP CO., LTD.) was used to observe the flank wear amount (VB) of the PCD tool and the machined surface roughness of the workpiece, with the data recorded simultaneously.\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\u003eTest Parameters\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevels of Each Factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpindle speed n/r∙min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFeed per tooth \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e/mm∙z\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMilling depth a\u003csub\u003ep\u003c/sub\u003e/mm\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\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 Discussion","content":"\u003cp\u003eThe cumulative milling distance for each group of experiments was 1000 mm, and the results of the orthogonal experiments are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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\u003eExperimental Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem Number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpindle speed n/r∙min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFeed per tooth \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e/mm∙z\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMilling depth a\u003csub\u003ep\u003c/sub\u003e/mm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFlank Wear Land Width VB/\u0026micro;m\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eF\u003csub\u003ex\u003c/sub\u003e/N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF\u003csub\u003ey\u003c/sub\u003e/N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eF\u003csub\u003ez\u003c/sub\u003e/N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSurface RoughnessRa/\u0026micro;m\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e30.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e41.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e28.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e40.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e36.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e44.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e43.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e18.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e48.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e44.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e36.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e43.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e54.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e42.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e40.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e52.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e83.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e39.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e36.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e53.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e47.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e51.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e104.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e32.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e53.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e59.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e52.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e51.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1 The Influence of Milling Parameters on Cutting Force\u003c/h2\u003e\u003cp\u003eThe influence trend of milling parameters on the variation of cutting force is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a), the cutting force in the Fz direction increases with the rise of spindle speed, which is opposite to the variation trend of cutting forces in the F\u003csub\u003ex\u003c/sub\u003e and F\u003csub\u003ey\u003c/sub\u003e directions. This phenomenon occurs because an increase in spindle speed leads to a higher frictional force between the cutting tool and the aluminum matrix silicon carbide composite, thereby increasing by cutting force [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It can be observed from Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(b) and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(c) that the three-directional cutting forces increase with the increase of feed rate and milling depth.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\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\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(a) The Relationship Between Spindle Speed and Cutting Force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(b) The Relationship Between Feed Per Tooth and Cutting Force\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\u003cp\u003eThe range analysis table of the cutting force in the F\u003csub\u003ex\u003c/sub\u003e direction is shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. It can be seen from Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e that the order of the influence of cutting parameters on the cutting force, from the greatest to the smallest, is as follows: feed rate, milling depth, and spindle speed. With the minimum milling force as the optimization objective, the optimal parameter combination is as follows: spindle speed of 2500 r/min, feed rate of 50 mm/min, and milling depth of 0.2 mm.\u003c/p\u003e\u003cp\u003eExperimental verification was carried out using the aforementioned milling parameters, and the results indicate that under the above optimal parameters, the F\u003csub\u003ex\u003c/sub\u003e is 19.3 N, which is lower than the F\u003csub\u003ex\u003c/sub\u003e of any test in the orthogonal experiment. Among them, the minimum reduction range of F\u003csub\u003ex\u003c/sub\u003e is 10.65% and the maximum reduction range is 55.43%, which verifies the rationality of the optimal parameters obtained from the orthogonal experiment.\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\u003eRange Analysis of F\u003csub\u003ex\u003c/sub\u003e\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerial number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003csub\u003ex\u003c/sub\u003e (Spindle speed)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003csub\u003ex\u003c/sub\u003e (Feed per tooth)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF\u003csub\u003ex\u003c/sub\u003e (Milling depth)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.9250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.3750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.8000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.6000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.7000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.6250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.3250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.4750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.4750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.6750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.9750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.6250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.6000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.8250\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\u003eThe range analysis table of the cutting force in the F\u003csub\u003ey\u003c/sub\u003e direction is shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. It can be observed from Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e that the order of the influence of cutting parameters on the cutting force, from the strongest to the weakest, is as follows: milling depth, feed rate, and spindle speed. With the minimum milling force set as the optimization objective, the optimal parameter combination is as follows: a spindle speed of 2500 r/min, a feed rate of 50 mm/min, and a milling depth of 0.2 mm.\u003c/p\u003e\u003cp\u003eExperimental verification was conducted using the aforementioned milling parameters, and the results show that under the above optimal parameters, the F\u003csub\u003ey\u003c/sub\u003e is 8.5 N, which is lower than the F\u003csub\u003ey\u003c/sub\u003e of any test in the orthogonal experiment. Among them, the minimum reduction range of F\u003csub\u003ey\u003c/sub\u003e is 12.37% and the maximum reduction range is 57.6%, which verifies the rationality of the optimal parameters obtained from the orthogonal experiment.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRange Analysis of F\u003csub\u003ey\u003c/sub\u003e\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerial number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003csub\u003ey\u003c/sub\u003e (Spindle speed)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003csub\u003ey\u003c/sub\u003e (Feed per tooth)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF\u003csub\u003ey\u003c/sub\u003e (Milling depth)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.0750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.9250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.8500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.7500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.4250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.7750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.0500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.7000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.4750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.7000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.4500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.6000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.7750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.6000\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\u003eThe range analysis table of the cutting force in the F\u003csub\u003ez\u003c/sub\u003e direction is shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. It can be seen from Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e that the order of the influence of cutting parameters on the cutting force, from the greatest to the smallest, is as follows: spindle speed, milling depth, and feed rate. With the minimum milling force as the optimization objective, the optimal parameter combination is as follows: spindle speed of 1000 r/min, feed rate of 50 mm/min, and milling depth of 0.2 mm.\u003c/p\u003e\u003cp\u003eExperimental verification was performed using the aforementioned milling parameters, and the results indicate that under the above optimal parameters, the F\u003csub\u003ez\u003c/sub\u003e is 25.4 N, which is lower than the F\u003csub\u003ez\u003c/sub\u003e of any test run in the orthogonal experiment. Among them, the minimum reduction range of F\u003csub\u003ez\u003c/sub\u003e is 17.26% and the maximum reduction range is 57.73%, which verifies the rationality of the optimal parameters obtained from the orthogonal experiment.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRange Analysis of F\u003csub\u003ez\u003c/sub\u003e\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerial number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003csub\u003ez\u003c/sub\u003e (Spindle speed)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003csub\u003ez\u003c/sub\u003e (Feed per tooth)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF\u003csub\u003ez\u003c/sub\u003e (Milling depth)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.9750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.0750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.5750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36.8500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.4500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.2000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.5250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.9750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44.3500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.4250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.3750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.0500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.8500\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\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2 The Influence of Milling Parameters on Surface Roughness\u003c/h2\u003e\u003cp\u003eSurface roughness is a crucial factor for evaluating the machining quality of workpiece surfaces, and its magnitude directly affects the performance, service life, and other properties of the workpiece. To reduce the deviation caused by measurement, in this experiment, six different regions were selected on the machined surface to measure the roughness values, and the average value was taken as the basis for analysis. The variation trend of surface roughness is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. It can be observed that the surface roughness of the workpiece decreases with the increase in spindle speed, while it increases with the increase in feed rate and milling depth. The range values of surface roughness derived from Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Among them, the range of feed rate is 0.175 \u0026micro;m, making it the primary influencing factor; the range of spindle speed is 0.145 \u0026micro;m, classified as a secondary influencing factor; and the range of milling depth is 0.02 \u0026micro;m, which has almost no impact on surface roughness. With the minimum surface roughness set as the optimization objective, the optimal parameter combination is as follows: a spindle speed of 2500 r/min, a feed rate of 0.025 mm/z, and a milling depth of 0.2 mm.\u003c/p\u003e\u003cp\u003eExperimental verification was conducted under the above parameter conditions, and the results show that the surface roughness of the workpiece is 0.16 \u0026micro;m, which is lower than any set of data in the orthogonal experiment. Among them, the minimum reduction range is 36% and the maximum reduction range is 76.12%, which verifies the rationality of the optimal parameters obtained from the orthogonal experiment.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRange Analysis of Surface Roughness\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerial number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurface Roughness (Spindle speed)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSurface Roughness\u003c/p\u003e\u003cp\u003e(Feed per tooth)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSurface Roughness (Milling depth)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3975\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3975\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.5075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\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=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3 The Influence of Cutting Parameters on Tool Wear Amount\u003c/h2\u003e\u003cp\u003eBy analyzing the wear amount per unit time and fixed-stroke wear amount in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, it can be observed that the wear amount per unit time and fixed-stroke wear amount under the 13th group of milling parameters are both the largest, while those under the 4th group of milling parameters are both the smallest. This indicates that during milling, the 4th group of milling parameters can effectively reduce tool wear during machining and extend the tool service life. From the perspective of tool service life, under actual milling conditions, the 13th group of milling parameters should be avoided as much as possible to prevent excessive tool damage.\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the influence of milling parameters on the flank wear amount of the cutting tool is presented. When the spindle speed increases from 1000 m/min to 2500 m/min, the wear amount of the tool per unit time shows a significant upward trend. This is because the increase in spindle speed leads to a rise in cutting temperature, which promotes the adhesive wear on the flank face. Additionally, a higher rotational speed intensifies the friction between the flank face and the workpiece, thereby resulting in an increase in the tool wear amount. With the increase in feed per tooth, the flank wear amount shows a decreasing trend. This is because a lower feed per tooth leads to a reduction in feed rate, which in turn prolongs the contact time between the cutting tool and the SiC particles in the workpiece. When the feed per tooth increases, the local temperature at the tool tip rises, which in turn softens the aluminum matrix. During the milling process, the SiC particles are pressed into the Al matrix, reducing the contact between the cutting tool and the SiC particles and thereby decreasing the flank wear amount [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. An increase in milling depth also intensifies the flank wear. This is because a larger milling depth leads to a higher frequency of collisions between the SiC particles inside the workpiece and the cutting tool, which in turn results in an increase in the flank wear amount of the tool.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe range values of flank wear amount derived from Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e are presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. From the range analysis results of flank wear amount, it can be concluded that the influence of milling parameters on flank wear amount, in descending order of significance, is as follows: spindle speed, feed per tooth, and cutting depth. With the minimum flank wear amount as the criterion, the optimal parameter combination is determined as follows: spindle speed n\u0026thinsp;=\u0026thinsp;1000 r/min, feed per tooth \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e=0.1 mm/z, milling depth a\u003csub\u003ep\u003c/sub\u003e=0.2 mm.\u003c/p\u003e\u003cp\u003eExperimental verification was conducted under the aforementioned parameter conditions, and the resulting flank wear amount was 29.47 \u0026micro;m, which is lower than that of any group of data in the orthogonal experiment. Among them, the minimum reduction rate was 17.29% and the maximum reduction rate was 71.87%, which verifies the rationality of the optimal parameters obtained from the orthogonal experiment.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFlank Wear Amount Range Analysis\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerial number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFlank Wear Amount\u003c/p\u003e\u003cp\u003e(Spindle speed)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFlank Wear Amount\u003c/p\u003e\u003cp\u003e(Feed per tooth)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFlank Wear Amount\u003c/p\u003e\u003cp\u003e(Milling depth)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40.2475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73.3175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.6200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49.4775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.7325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.5025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63.6000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50.6525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.3150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75.7275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.3500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65.6150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.4800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.9675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.9950\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\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Multiple Linear Regression Model for PCD Tool Wear\u003c/h2\u003e\u003cp\u003eIn the milling process, three cutting variables\u0026mdash;spindle speed, feed rate, and milling depth\u0026mdash;exert a significant influence on tool life. According to international standards, the effective life of a cutting tool terminates when it can no longer machine workpieces that meet the required shape and surface quality. In this study, when measuring the flank wear amount of PCD tools, a value of △VB\u0026thinsp;=\u0026thinsp;300 \u0026micro;m was adopted as the blunting criterion for PCD milling cutters. Specifically, the tool life is deemed to end when the flank wear amount exceeds 300 \u0026micro;m [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Based on the experimental results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the principle of least squares was applied to establish a tool life prediction model, which is shown in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$S=0.185 \\cdot v_{c}^{{0.6631}} \\cdot f_{{\\text{z}}}^{{ - 0.3169}} \\cdot a_{p}^{{0.1758}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the formula, \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e represents the flank wear amount with the unit of \u003cem\u003e\u0026micro;\u003c/em\u003em; \u003cem\u003eVc\u003c/em\u003e represents the spindle speed with the unit of r/min; \u003cem\u003efz\u003c/em\u003e represents the feed per tooth with the unit of mm/z; \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e represents the milling depth with the unit of mm. The exponents of each term indicate the degree of influence of the corresponding cutting factors on the tool's flank wear amount.\u003c/p\u003e\u003cp\u003eEquation (\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was verified using the R-squared (R\u0026sup2;) test method, and the R\u0026sup2; value of the model was found to be 0.9916. Since this value is close to 1, it indicates that the equation has a high goodness of fit and the model exhibits good accuracy, which can explain approximately 99.16% of the variation in tool wear amount. The F-test was performed on Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). When α\u0026thinsp;=\u0026thinsp;0.05, the F-statistic is greater than the critical F-value, indicating that the model is overall significant. It can be seen from Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) that the most significant factor affecting the flank wear amount of the tool is the spindle speed, followed by the feed per tooth. Moreover, the feed per tooth has a negative correlation with the tool flank wear amount, which is consistent with the influence trend of milling parameters on the flank wear amount obtained in the previous section.\u003c/p\u003e\u003cp\u003eThe data comparing the actual tool wear conditions with the model prediction results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. It can be observed that the relative error between the results of the empirical formula and the experimental data is basically within 5%, which indicates that the empirical formula derived in this paper has a relatively accurate predictive ability for the tool wear amount.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison between the Actual Measured Values and the Predicted Values\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem Number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eActual Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePredicted Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRelative Error\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44.630000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.779539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.91%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41.520000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.699970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.38%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39.210000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.492153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.38%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.630000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.000553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.04%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60.750000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64.707451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.51%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44.790000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.988380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.68%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49.510000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.602603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.23%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42.860000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44.783598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.49%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83.140000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.092288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.15%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70.710000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.010617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.15%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.420000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.943660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.08%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.270000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50.468118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.39%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e104.750000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102.560548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.09%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80.050000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.275533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.22%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.470000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64.102133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.09%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52.640000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.804558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.59%\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\u003eThere exists a certain deviation between the predicted results of the models in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and the experimental results. This can be attributed to the fact that during actual machining and production processes, variations in factors such as temperature and mechanical vibration cause the tool wear amount to exhibit a non-linear change. Consequently, a certain discrepancy arises between the model-predicted values and the experimental data. As concluded in the previous section, the relative error between the tool wear amount under the optimal milling parameters and the model-predicted value is 4.3%.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Analysis on Wear Results of PCD Cutting Tools\u003c/h2\u003e\u003cp\u003eTo observe the tool wear morphology more intuitively, the tool set with the most severe wear in the experiment-specifically the thirteenth tool set was selected as the reference for result analysis.\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;5(a), there are a large number of scratch wear marks on the tool face, and these scratches are in inconsistent directions. This phenomenon is caused by the severe friction between the cutting surface and the tool during the milling process. When the chips flow out from the rake face, the silicon carbide particles contained therein continuously scrape the rake face of the tool, and these scratches are gradually formed as a result. However, some local areas on the rake face appear relatively rough, with tiny spalling and damage points. This is because during the cutting process, the tool bears high temperature and high pressure, which leads to fatigue damage of the material on the rake face. When the stress exceeds the strength limit of the material, the material in some tiny areas will peel off, resulting in damage to the surface integrity and thus exerting an adverse effect on the milling process. The rough surfaces caused by such wear will impede the smooth flow of chips, further exacerbating tool wear and adversely affecting machining quality.\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;5(b), the flank face of the tool used in the experiment exhibits groove morphologies with inconsistent lengths and depths. The cause of this phenomenon can be attributed to the irregular distribution of silicon carbide particles within the aluminum matrix during the milling process. When the aluminum matrix softens due to milling heat, some silicon carbide particles become detached and pulled out from the aluminum matrix or pressed into its interior under the action of cutting impact. In the subsequent cutting process, these silicon carbide particles further exert extrusion and friction effects on the polycrystalline diamond tool, which in turn triggers intense abrasive wear behavior. Eventually, such surface morphologies with fragmented characteristics are formed on the tool's flank face, and the degree of abrasive wear on the flank face is far greater than that on the rake face [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhen machining aluminum matrix silicon carbide composites with a high volume fraction, an intermittent cutting mode tends to occur. Under these working conditions, the cutting edge of the polycrystalline diamond tool is required to cut off silicon carbide particles. Due to the high hardness and discrete distribution of silicon carbide particles, the tool edge bears more intense impact loads during the cutting process, which increases the stress level on the edge and raises the risk of wear. As shown in Fig.\u0026nbsp;5(c), the tool's cutting edge exhibits a certain degree of micro-chipping. These small notches may be caused by the fatigue and detachment of local material at the cutting edge, which results from the impact of silicon carbide particles during the milling process.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThis study conducts a systematic investigation focusing on the milling wear characteristics and life prediction of PCD tools when machining aluminum matrix silicon carbide composites. By employing methods such as orthogonal experiments and multiple linear regression analysis, the factors influencing tool wear and their corresponding rules during the milling process are revealed, and a tool life prediction model is established. The main conclusions are as follows:\u003c/p\u003e\u003cp\u003e(1)The surface roughness is mainly affected by the spindle speed and feed rate, with the least influence from the cutting depth. It decreases with the increase of spindle speed and increases with the increase of feed rate. Although it also increases with the increase of cutting depth, the degree of influence on surface roughness is far lower than that of the other two parameters. Therefore, it is difficult to improve the surface quality of workpieces by reducing the milling depth in actual machining.\u003c/p\u003e\u003cp\u003e(2)The range analysis of the multi-factor experiment shows that the feed per tooth and milling depth are the main factors affecting the tool milling force. Verification experiments were conducted under the optimal milling parameters. For the cutting forces in the F\u003csub\u003ex\u003c/sub\u003e, F\u003csub\u003ey\u003c/sub\u003e and F\u003csub\u003ez\u003c/sub\u003e directions, the minimum reduction rates were 10.65%, 12.37%, and 17.26% respectively, while the maximum reduction rates were 55.43%, 57.6%, and 57.73% respectively.\u003c/p\u003e\u003cp\u003e(3)The multi-factor orthogonal experiment shows that the spindle speed and feed per tooth are the main factors affecting tool wear, and the obtained optimal parameter combination is as follows: spindle speed \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1000 r/min, feed per tooth \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e=0.1 mm/z, milling depth \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e=0.2 mm. Under the optimal milling parameters, compared with the data obtained from the orthogonal experiment, the minimum reduction rate of the tool wear amount is 17.29%, and the maximum reduction rate is 71.87%. Meanwhile, combined with the experimental data, a tool wear prediction model was established using the multiple linear regression analysis method. The error between the experimental results after parameter optimization and the model-predicted values is only 4.3%.\u003c/p\u003e\u003cp\u003e(4)Through milling experiments and analysis of the tool wear mechanism, it is found that when milling aluminum matrix silicon carbide composites with high volume fraction, abrasive wear is the main cause of PCD tool wear. Secondly, factors such as micro-chipping of the tool edge and grain spalling also contribute to the tool blunting and failure.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution:\u003c/strong\u003e Qingqing Lü: validation，data curation，writing—original draft preparation, writing—review and editing;\u003c/p\u003e\n\u003cp\u003eKun Zhao: validation，data curation，writing—original draft preparation, writing—review and editing;\u003c/p\u003e\n\u003cp\u003eLiquan Yang: validation, writing—original draft preparation, writing—review and editing;\u003c/p\u003e\n\u003cp\u003eErbo Liu: writing-review and editing;\u003c/p\u003e\n\u003cp\u003eGuangxi Li: writing-review, editing and funding acquisition;\u003c/p\u003e\n\u003cp\u003eDaohui Xiang: Study conception, methodology and funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was funded by Key R\u0026amp;D and Promotion Special/Tackling Key Problems in Science and Technology in Henan Province, China (grant No. 252102241063, 252102220130); Henan Province Science and Technology Research and Development Joint Fund (Industrial Category) (grant No. 202324119); China Iron and Steel Education Society Fund Project (grant No. SYJX2024033); Key Scientific Research Fund of Pingdingshan University (grant No. 2023-JYZD01).\u003c/p\u003e\n\u003cp\u003eData availability: Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCode availability: Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics approval: Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent to participate: Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConflict of interest: The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCong P, Xie L, Peng S (2015) Experimental Study on High-speed Milling of High Volume Fraction SiC\u003csub\u003ep\u003c/sub\u003e/Al Composites by PCD Tools. 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Superhard Mater Eng 37:43\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3969/j.issn.1673-1433.2025.01.007\u003c/span\u003e\u003cspan address=\"10.3969/j.issn.1673-1433.2025.01.007\" 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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Aluminum matrix silicon carbide, Milling, Tool wear, Tool life, Surface quality","lastPublishedDoi":"10.21203/rs.3.rs-8259762/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8259762/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo investigate the wear behavior of Polycrystalline Diamond (PCD) tools during the milling of high volume fraction silicon carbide particle-reinforced aluminum matrix composites (high volume fraction SiC\u003csub\u003ep\u003c/sub\u003e/Al) and realize the prediction of tool wear, an orthogonal experiment and multiple linear regression analysis were adopted. The factors influencing tool wear and a tool wear prediction model were obtained, followed by the F-test and R\u0026sup2; test for the model. The test results indicate that the model is statistically significant overall. Through orthogonal experiments, the effects of milling parameters on cutting force, workpiece surface roughness, and tool wear amount were determined. By comparing the relative error between the actual tool wear amount and the wear amount predicted by the model, it was found that the average error is within 5%, which verifies that the model possesses the preliminary capability of predicting tool wear. The experimental results show that in practical milling operations, a reasonable selection of milling parameters can effectively reduce workpiece surface roughness and tool wear, thereby prolonging tool life.\u003c/p\u003e","manuscriptTitle":"Research on Milling Parameter Optimization and Wear of PCD Tools for SiCp/Al Composites","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 10:26:26","doi":"10.21203/rs.3.rs-8259762/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-12-15T13:31:53+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-10T10:11:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T03:12:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2025-12-05T09:21:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3a0420d3-6dac-4578-a344-189f09c6ab63","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T05:13:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 10:26:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8259762","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8259762","identity":"rs-8259762","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}