Multi-response Optimisation Applied on Friction Stir Processing to Enhanced Wear and Corrosion performance in Al-Si Alloys

Multi-response Optimisation Applied on Friction Stir Processing to Enhanced Wear and Corrosion performance in Al-Si Alloys | 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 Multi-response Optimisation Applied on Friction Stir Processing to Enhanced Wear and Corrosion performance in Al-Si Alloys Denner Traiano, Silvia Rosa Nascimento, Luciano Augusto Lourençato, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4085088/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Eutectic (Al12Si) and hyper-eutectic (Al14Si) Al–Si alloys were processed through friction-stir processing (FSP). These Al-Si alloys' microstructure, microhardness, wear, and corrosion behaviour were studied. FSP led to the fragmentation and uniform distribution of Si and Fe-rich intermetallic phases in the Al matrix. Multi-response optimisation of the friction-stir process of Al–Si alloys was investigated. The optimum FSP parameters were a tool rotation speed of 1100 rpm and a 16 mm/min travel speed for Al12Si alloy. The friction stir processed Al12Si samples exhibit the highest microhardness (93 H v ); the most considerable fragmentation of Si particles and Fe-rich intermetallic phases, with average sizes of 2.82 and 2.07 µm, respectively; and optimal values of coefficient of friction (0.60); and corrosion rate (1.28×10 − 4 mm/y). This work provided a mathematical model to obtain the optimum FSP parameters for producing surfaces in Al12Si alloys with excellent microstructural characteristics, high hardness, and better wear and corrosion resistance. Friction-stir processing Optimisation Al-Si alloys Microhardness Wear and Corrosion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Al–Si alloys have great potential for application in the automotive, aerospace, and marine industries due to their combined properties, such as strength, corrosion resistance, and lightweight properties [1,2]. Javidani and Larouche [1] published a critical review about using Al–Si based alloys in cylinder heads and engine blocks. They concluded that the use of Al–S alloys are a promising alternative in the fabrication of cylinder blocks. The role of aluminum alloys in future commercial aircraft due to their relatively low cost, light weight, the fact that they are materials that can be heat treated to fairly high-strength levels and that they are one of the most easily fabricated high performance materials was reported by Rambabu et al. [2]. The authors emphasize the importance of optimizing the production processes of aluminum alloys and their chemical composition. The microstructure significantly influences the mechanical properties of Al–Si alloys. The morphology and size of undesirable secondary phases present in the cast material are detrimental to its properties, such as fatigue resistance and corrosion resistance, leading to premature failure of the machine elements [3,4]. Atxaga et al. [3] studied the fatigue behaviour of a cast Al – 7Si – Mg alloy. They observed that the presence of a heterogeneous microstructure with casting defects significantly affects the fatigue life of specimens. The authors concluded that heat treatment can improve the fatigue behaviour of these alloys. Mbuya et al. [4] studied the influence of Fe phases on the properties of Al–Si alloys. They stated that the detrimental effects of Fe in Al-Si alloys outweigh its benefits and, therefore, its reduction or control is desirable. Different technologies have been applied to transform the size and morphology of undesirable phases in Al-Si alloy. These techniques include chemical treatment, rapid solidification, mould vibration and melt agitation [5]. In recent years, the friction-stir processing (FSP) technique has emerged as an alternative due to its potential to modify the microstructure and improve the mechanical properties of different alloys, particularly Al alloys [6]. When the FSP process is applied to cast Al–Si alloys, intense fragmentation of dendrites, Si particles, and other intermetallic phases occurs. In addition, the reduction of grain size in the matrix, the dissolution of precipitates, and the decrease of porosity lead to a finer and more homogeneous microstructure and, as a result, a significant improvement in the mechanical properties, wear resistance and corrosion resistance [7]. Several studies have been published on applying the FSP process to achieve optimum morphology and particle size of secondary phases in Al–Si alloys to enhance their mechanical properties. [8–10]. Golafshani et al. [8] investigated the mechanical behaviour and microstructural evolution of A390 aluminium alloy processed by FSP. They reported a more uniform and dense particle distribution in the stirred zone compared to the as-cast A390 alloy. The authors concluded that FSP can increase the as-cast strain sensitivity index by eight times after three passes. Jiang et al. [9] conducted a friction stir processing on an Al–Si casting alloy. They affirmed that FSP promoted the grain refinement of Al–Si alloys, and mechanical properties were significantly improved by FSP. The authors reported that the Si phase and Al grains of an Al–Si casting alloy were refined through FSP. However, no reference is made in the research to the influence of Fe intermetallic phases. Soleymanpour et al. [10] studied the influence of heat treatment, rolling, and FSP on the microstructure, hardness, tensile behaviour and Al–Si alloy fracture mechanism. They reported that the FSP reduced the aspect ratio and increased the sphericity of Si and Fe-rich particles owing to the severe shear deformation at elevated temperatures, producing a uniform distribution of the particles in the matrix. The tensile properties of FSPed samples were much higher than the initial samples. Most of the publications consulted, which aim to study the influence of the microstructure on the mechanical properties of cast aluminium alloys, pay more attention to the effect of the size and aspect ratio of the Si particles in the different forms in which it is presented. However, the secondary intermetallic phases, depending on their condition and quantity, can negatively influence the mechanical properties of the alloys [11]. It is, therefore, important that the parameters of the FSP process are optimally selected to ensure that a fine microstructure with a homogeneous distribution of all phases present in the cast alloy is obtained. The optimisation of the FSP process parameters of Al–Si alloys to improve their microstructure and mechanical and tribological properties has been investigated by several authors [12–14]. Alzahrani et al. [12] investigated the effects of FSP parameters on the microstructure and mechanical properties of an Al–Si 7 Mg 0.2 alloy. The authors stated that optimisation of heat generation inside the stirred zone plays a vital role in managing the thermal profile and preventing adverse effects that impact the microstructure and properties of the alloy. They affirmed that the intense plastic deformation and dynamic recrystallisation during FSP resulted in a finer microstructure. As a result, hardness, wear rate, and corrosion resistance were improved. They developed a fitting model to predict the temperature distribution along the workpiece that can be used to optimise process parameters for different applications. Still, the optimal parameters for these experimental conditions were not proposed. Mohamadigangaraj et al. [14] used the response surface methodology to assess the effect of FSP parameters on the microstructure and mechanical properties of A390-10 wt% SiC compo-cast composite. They concluded that FSP positively affected the mechanical properties of the as-cast composite, and the microstructure modification after FSP decreased the friction coefficient and wear loss of the processed composite. The authors propose the optimal FSP parameter values that guarantee maximum hardness, ultimate tensile strength, and toughness. Ma et al. [13] applied conventional FSP and ultrasonic-assisted FSP (Ua-FSP) to as-cast Al–Si alloy under different processing parameters. They compared forming characteristics, grain refinement, and mechanical properties obtained by FSP and Ua-FSP to determine the effect of ultrasound. They demonstrated that the ultrasonic vibration improved surface formability and enlarged the processing regions due to the enhancement of Al–Si alloy flowability. The mechanical properties of A356 Al alloy were enhanced by applying ultrasonic vibration in FSP. The process parameters for which the optimum mechanical properties were obtained are recommended. However, no optimisation methodology was used for their determination. The present research aims to optimise the parameters of FSP, travel speed, and tool rotation speed to reach multiple objectives, such as maximising the hardness, friction coefficient, and corrosion rate of Al12Si and Al14Si alloys. To achieve this, experimental work was carried out to obtain the FSPed alloy surfaces, and the response surface method (RSM) was used for optimisation purposes. 2. Experimental procedure The base materials used in this research are cast ingots of Al12Si and Al14Si alloys with dimensions of 250 mm length and 75 mm height. The ingots were cut to the final dimensions of 90 mm × 45 mm × 5 mm to obtain the samples. The base materials were characterised by chemical analysis by X-ray fluorescence (XRF). The chemical composition of the base materials is presented in Table 1 . Table 1 Chemical composition of base materials Material Chemical composition, wt% Si Fe Cu Mn Mg Cr Zn Ti Al Al12Si 11,61 0,41 1,22 0,34 0,88 0,05 0,06 0,12 84,35 Al14Si 14,49 0,36 1,25 0,38 0,25 0,02 0,05 0,12 81,84 The surface modification was done on a ROMI machining center, model D600, with FANUC computerised numerical control and a maximum rotation speed of 8000 rpm and 20 HP. The tool used is made of H13 tool steel. The shoulder profile of the tool is concave with a diameter of 15 mm, with a conical pin of 5 mm diameter at the tip, a maximum diameter of 6 mm (at the base), and a length of 3 mm. Considering that this research aims to determine the optimum parameters of the FSP process to obtain a fine microstructure with good properties, a 2^3 factorial experimental design was selected, i.e., three factors with two levels plus a central or medium level. Friction-stir process parameters used are shown in Table 2 . Table 2 Parameters of the friction-stir process. Parameters Notation Unit Levels (-1) (0) (+ 1) Material M - Al12Si - Al14Si Travel speed TS mm/min 16 22 28 Tool rotation speed RS rpm 700 850 1100 The response variables analysed in this research are microhardness (Hv), Fe intermetallic particle size (Fe_IPS), Si particle size (Si_PS), coefficient of friction (COF), and corrosion rate (CR). The process parameters were selected from preliminary tests. All conditions were performed three times and randomly to reduce the experimental error; the average of the three runs of results obtained for the dependent variables is reported in this paper. In this study, constant plunge depth is used for each condition. 2.1. Macro and microstructural characterization Samples were cross-sectioned for metallographic studies, embedded in Bakelite, sanded with 220, 320, 400, 600, and 1200 grit sandpaper and polished to a mirror finish. The samples were prepared and etched using Osmond reagent (25 ml of distilled water, 25 ml of nitric acid, 25 ml of hydrochloric acid, and 2.5 ml of hydrofluoric acid) for 3 sec. Macrostructural images were evaluated using an Olympus UC30 optical microscope and processed using Stream Essentials software. A Tescan Model Vega3 Scanning Electron Microscope (SEM) was also used to obtain images of the microstructure and map the chemical composition. The microstructural MEV images were processed using Image J software to determine the size and distribution of the silicon and Fe intermetallic particles. Feret diameter was used in the particle size analysis, and the average distance between two parallel lines tangent to the particle projection was measured. 2.2. Microhardness, corrosion rate, and coefficient of friction analysis The microhardness measurements (Hv) were carried out using a Shimadzu HMV-G20 Series Equipment microhardness tester. Microhardness profiles were made on the cross sections of the samples, obtained from 36 indentations, distributed over all the modified zones (Nugget, TMAZ, HAZ) and in the substrate. The tests were carried out with a load of 300 gf and a time of 15 seconds. A high-performance AutoLab PGSTAT204 potentiostat/galvanostat instrument was used to evaluate the corrosion behaviour on friction-stirred and as-casting alloy samples. The studies used two electrode cells, including a reference H 2 SO 4 /KCl electrode, to obtain the corrosion potential (Ecorr) and polarisation. Open Circuit Potential (OCP) was measured according to ASTM G59-97 [15]. The polarisation was obtained following two stages: in the first one, a micro polarisation was performed with a voltage variation between ± 10 mV around E corr, and in the second stage, a macro polarisation was performed with a variation of ± 100 mV around Ecorr. With the results obtained, using the potentiostat/galvanostat tests, the corrosion current (I corr ) and the corrosion rate (CR) were calculated. A TTP Tribo1, linear wear tribometer, was used to evaluate wear behaviour, using a 52100 steel indenter and the following parameters: a test time of 300 s, a load of 5 N at a frequency of 5 Hz, and a track length of 2 mm. These tests were performed three times on friction-stirred and as-casting alloy samples. The mass losses for each condition were determined using a Shimadzu ATX 224 analytical balance. 2.3. Optimisation of the FSP process parameters methodology The FSP process parameters were optimised using the statistical response surface method (RSM), specifically, the method of optimising multiple responses. It allows determining the setup of the experimental factors for the characteristics desired of one or more responses through constructing the function of desirability. The response function y , which includes the dependent variables Hv, Fe_IPS, Si_PS, COF, and CR, will be a function of the material (M), tool rotation speed (RS), and travel speed (TS) and can be expressed by Eq. ( 1 ): $$\text{y}\left({\text{H}}_{\text{v}}, \text{F}\text{e}\_\text{I}\text{P}\text{S},\text{S}\text{i}\_\text{P}\text{S},\text{C}\text{O}\text{F},\text{C}\text{R}\right)=\text{f}(\text{R}\text{S}, \text{T}\text{S}, \text{M})$$ 1 To combine the multiple responses in a single function, the first step is to define the desirability function for each response d i , corresponding with the dependent variables. This function expresses the desirability of a response value equal to y on a scale of 0 to 1. This function takes three forms, depending on how maximised or minimised the response must be or if an objective value must be reached. In this specific case, the idea is to optimise microhardness; therefore, the desirability function is defined by Eq. ( 2 ): $${\text{d}}_{\text{i}}=\left\{\begin{array}{ccc}0& ,& \widehat{\text{y}}<\text{l}\text{o}\text{w}\\ {\left(\frac{\widehat{\text{y}}-\text{l}\text{o}\text{w}}{\text{h}\text{i}\text{g}\text{h}-\text{l}\text{o}\text{w}}\right)}^{\text{s}}& ,& \text{l}\text{o}\text{w}\le \widehat{\text{y}}\le \text{h}\text{i}\text{g}\text{h}\\ 1& ,& \widehat{\text{y}}>\text{h}\text{i}\text{g}\text{h}\end{array}\right.$$ 2 Where ŷ is the predicted value of the response variable; low is the value below which the response is completely unacceptable; and high is the value above which the desirability function is a maximum. The parameter s defines the shape of the process; for s = 1, the desirability function behaves linearly with low = 0 and high = 1. A i corresponds with the number of responses; in this case, i = 1 (H v ). In the case of the other four responses, Fe_IPS, Si_PS, COF and CR, the lower their value, the higher the surface quality obtained; therefore, the objective is to minimise them. The i values correspond to the number of the other four responses; i = 2, 3, 4, 5 (Fe_IPS, Si_PS, COF and CR, respectively). The desirability function is defined by Eq. ( 3 ): $${\text{d}}_{\text{i}}=\left\{\begin{array}{ccc}1& ,& \widehat{\text{y}}<\text{l}\text{o}\text{w}\\ {\left(\frac{\widehat{\text{y}}-\text{h}\text{i}\text{g}\text{h}}{\text{l}\text{o}\text{w}-\text{h}\text{i}\text{g}\text{h}}\right)}^{\text{s}}& ,& \text{l}\text{o}\text{w}\le \widehat{\text{y}}\le \text{h}\text{i}\text{g}\text{h}\\ 1& ,& \widehat{\text{y}}>\text{h}\text{i}\text{g}\text{h}\end{array}\right.$$ 3 A single composite desirability function D was created to combine the desirability function of n responses. If all the response variables are considered equally important, then the composite function is the geometric mean of the separated desirability functions, Eq. ( 4 ). $$\text{D}=\sqrt[\text{n}]{{\prod }_{\text{i}=1}^{\text{n}}{\text{d}}_{\text{i}}}=\sqrt[5]{{\text{d}}_{1}.{\text{d}}_{2}.{\text{d}}_{3}.{\text{d}}_{4}.{\text{d}}_{5}}$$ 4 3. Results and Discussion 3.1. Macro and microstructural analysis In this research, modified surfaces of Al12Si and Al14Si alloys with a fine and homogeneous microstructure were obtained by the FSP process. Figure 1 shows typical cross-sectional macrographs of the modified regions for the experimental condition of 700 rpm and 16 mm/min of tool rotational speed and travel speed, respectively. In all cases, it was possible to identify the nugget zone, deformation bands and particle size refinement as a result of the FSP process (Fig. 1 ). In addition; it was verified that a nugget region free of pores was obtained in comparison with the base materials (Al12Si and Al14Si) as casting. Figure 2 shows the EDS map obtained from the energy-dispersive X-ray spectrometry done to analyse the chemical composition of the Al12Si alloy surface before and after being modified by the FSP process. The analysed sample of the Al12Si alloy as casting, showed the presence of copper and iron in the form of the intermetallic phases Al 2 Cu-θ, AlFeMnSi-Feα, and AlFeSi-Feβ (Fig. 2 a). As seen in Fig. 2 a, the intermetallic phase AlFeMnSi-Feα, it´s present in Chinese script morphology. Despite its larger size, this phase (Feα) has a relatively small presence in the microstructure, unlike the AlFeSi-Feβ phase. The Feβ phase is presented in the form of smaller needles. Still, with a more significant presence in the microstructure, it may be responsible for the deterioration of the mechanical properties. On the other hand, Si is present in two forms: in the eutectic phase and the form of free particles. Depending on their condition and quantity, these phases can negatively influence the mechanical properties of the alloy [7,11]. Figure 2 b shows that as a result of the surface modification process, refining and recrystallisation of the microstructure is obtained, with the breakage and dispersion of silicon particles and intermetallic phases. Similar results have been reported by other authors [7,10,16]. These modifications occur due to severe plastic deformation, the localised temperature gradient, and mechanical mixing during the friction stir process. Compared to Al12Si as casting (Fig. 2 a), where the formation of intermetallics, mainly AlFeMnSiFe-α, is visible, the FSP causes these undesirable significant phases to be modified. The silicon particles provide a minor size and a better distribution (Fig. 2 b), therefore improving the mechanical properties, evidenced by a 15% increase in microhardness. Similar behaviour was observed for the Al14Si alloy, where the rise in microhardness reached 19% on average. Table 3 shows the average experimental values obtained for each tested condition. For the analysis of the influence of the process parameters on the particle diameter, Si particles (Si_PS) and Fe intermetallic phases, AlFeMnSi-Fe-α and AlFeSi-Fe-β (Fe_IPS), were chosen due to their more significant influence on the variation of the mechanical properties. These Si and intermetallic particles present a higher hardness, directly affecting the wear resistance. In addition, they constitute points of galvanic coupling, generating points of oxidation, affecting the corrosion resistance of the alloys studied [17]. Table 3 Average values of experimental results of the dependent variables M TS (mm/min) RS (rpm) H v Fe_IPS (µm) Si_PS (µm) COF CR×10 − 4 (mm/y) Al12Si 16 700 93.43 ± 0.01 1.65 ± 0.01 2.63 ± 0.01 0.632 ± 0.007 1.44 ± 0.04 28 700 96.30 ± 0.46 2.26 ± 0.01 1.76 ± 0.04 0.817 ± 0.006 1.59 ± 0.01 22 850 87.76 ± 1.19 1.88 ± 0.02 2.42 ± 0.02 0.707 ± 0.028 1.34 ± 0.01 16 1100 95.88 ± 0.32 2.53 ± 0.21 3.71 ± 0.03 0.503 ± 0.049 1.55 ± 0.09 28 1100 92.77 ± 0.39 2.21 ± 0.01 1.89 ± 0.01 0.663 ± 0.012 1.61 ± 0.07 Al14Si 16 700 84.40 ± 0.01 6.12 ± 0.01 4.92 ± 0.01 0.681 ± 0.009 0.71 ± 0.01 28 700 81.31 ± 1.39 5.64 ± 0.20 5.13 ± 0.03 0.767 ± 0.006 3.16 ± 0.18 22 850 82.86 ± 1.52 6.40 ± 0.01 6.80 ± 0.04 0.760 ± 0.010 1.79 ± 0.03 16 1100 82.76 ± 1.45 3.65 ± 0.34 7.73 ± 0.14 0.686 ± 0.010 1.37 ± 0.01 28 1100 84.07 ± 0.08 6.30 ± 0.01 5.95 ± 0.02 0.668 ± 0.012 1.61 ± 0.01 The graph in Fig. 3 demonstrates the microstructural refinement due to the FSP. Comparatively, the average value of Si_PS for both alloys decreased by approximately 77%. In the case of the average value of Fe_IPS, including the two phases present, the decrease was 81%. From this analysis, it can be concluded that FSP resulted in significant fragmentation of AlFeMnSi-Fe-α and AlFeSi-Fe-β intermetallic phases, as well as of the Si particles in their different forms, in both Al12Si and Al14Si alloys. 3.2. Optimisation of FSP process parameters The first step in applying the multiple response optimisation method corresponds to obtaining the response surfaces for each dependent variable analysed. The mathematical models for predicting the values of Hv, Fe_IPS, Si_PS, COF and CR, respectively, are listed as follows: $${\text{H}}_{\text{v}}=85.0414-4.86008\bullet \text{M}+0.105115\bullet \text{T}\text{S}+0.00450823\bullet \text{R}\text{S}-0.0318264\bullet \text{M}\bullet \text{T}\text{S}+0.000547973\bullet \text{M}\bullet \text{R}\text{S}-0.000163993\bullet \text{T}\text{S}\bullet \text{R}\text{S}$$ 5 $$\text{F}\text{e}\_\text{I}\text{P}\text{S}=7.85914+2.4778\bullet \text{M}-0.153944\bullet \text{T}\text{S}-0.00570047\bullet \text{R}\text{S}+0.0393889\bullet \text{M}\bullet \text{T}\text{S}-0.00178198\bullet \text{M}\bullet \text{R}\text{S}+0.000228056\bullet \text{T}\text{S}\bullet \text{R}\text{S}$$ 6 $$\text{S}\text{i}\_\text{P}\text{S}=-2.41025+0.0693138\bullet \text{M}+0.187885\bullet \text{T}\text{S}+0.00965551\bullet \text{R}\text{S}+0.0234792\bullet \text{M}\bullet \text{T}\text{S}+0.00137795\bullet \text{M}\bullet \text{R}\text{S}-0.000307535\bullet \text{T}\text{S}\bullet \text{R}\text{S}$$ 7 $$\text{C}\text{O}\text{F}=0.449947+0.0475638\bullet \text{M}+0.0207465\bullet \text{T}\text{S}+0.0000522042\bullet \text{R}\text{S}-0.00577083\bullet \text{M}\bullet \text{T}\text{S}+0.000116286\bullet \text{M}\bullet \text{R}\text{S}-0.0000135069\bullet \text{T}\text{S}\bullet \text{R}\text{S}$$ 8 $$\text{C}\text{R}=-0.000401874-0.0000437058\bullet M+0.0000274003\bullet TS+4.78673E-7\bullet RS+0.00000516875\bullet M\bullet TS-6.60113e-8\bullet M\bullet RS-2.37604e-8\bullet TS\bullet RS$$ 9 Analysis of variance (ANOVA) was performed to investigate the significance of the influence of each FSP process parameter and their interactions with the dependent variables. The study was done with a confidence level of 95%; Table 4 shows a summary of the ANOVA. Table 4 ANOVA summary Model R-squared Adjusted R-squared Average value Standard error of estimate Durbin-Watson statistic H v 82.499 77.933 88.164 2.8021 2.01984 Fe_IPS 92.358 90.365 3.857 0.6331 2.37817 Si_PS 97.319 96.620 4.324 0.3952 2.11749 COF 90.746 88.331 0.686 0.0302 2.02607 CR (×10 − 4 ) 81.563 76.753 1. 612 0.3005 2.46898 As shown in Table 4 , the adjusted coefficient of determination R-squared values for the models of the five dependent variables under study are between 77% and 96%. The use of adjusted coefficient of determination R-squared is more suitable in the case of models with several independent factors [18]. This implies that in the worst cases (Hv and CR), where the adjusted coefficient of determination R-squared is approximately 77%, the model does not explain only 23% of the total variations of these responses. The overall adequacy of the models is satisfactory. On the other hand, the Durbin-Watson test statistic values are in the range of 2.0 to 2.5, which indicates that no autocorrelation was detected in the sample. Predicted versus actual values are given in Fig. 4 . Data points are scattered around the reference line. Predicted values of dependent variables are in good agreement with original values. As seen in Fig. 4 , there is a practically linear relationship between the values of the experimental data and those obtained by the regression model for each response. Figure 5 shows the response surface graphs for all dependent variables. As shown in Fig. 5 a, the most significant microhardness values were obtained for the Al12Si alloy. The average microhardness value of the nugget zone for the Al12Si samples (93.2 Hv) was 12% bigger than the average microhardness value obtained for the Al14Si samples (83.1 Hv) and, at the same time, 22% bigger than the average microhardness value for the as-cast Al12Si alloy. This microhardness behaviour is directly related to the refined and homogeny microstructure obtained after applying the FSP and to the final particle size of the Fe-based intermetallic phases and the Si particles (Figs. 5 b and c). As can be seen in Fig. 5 b, by using the FSP, it was possible to achieve a high fragmentation of the Fe-based intermetallic phases. Regardless, the particle size of these phases (Fe_IPS) is 2.7 times higher for the Al14Si alloy, which is directly related to the size of these phases in the as-cast Al14Si alloy. Similar behaviour was observed for the Si particles (Si_PS), as shown in Fig. 5 c. A comparison of the coefficient of friction (COF) behaviour of the nugget zone and as cast alloys is shown in Fig. 5 d. The average COF value of the nugget zone for Al12Si alloy (0.66) was slightly lower than the average COF value obtained for Al14Si ally (0.71); however, decreased by 20% concerning the average. Similarly, a decrease in the average COF value of the FSPed Al14Si alloy compared to the as-cast Al14Si alloy was observed (17% lower). It can be concluded that FSP significantly enhances the wear resistance of Al12Si and Al14Si as-cast alloys due to the refinement of the microstructure and the increase of the hardness of the nugget zone. Similar results have been obtained by other authors [19]. FSP applied to cast Al–Si alloy increased its corrosion resistance. The corrosion rate of both alloys showed a similar behaviour (Fig. 5 e), decreasing concerning the as-cast alloys due to the reduction of Si and Fe-rich particle sizes and increase in microstructure homogeneity. Results such as these have been reported previously by other authors [19,17]. The composite function of desirability (D) allows obtaining the combination of the experimental factors (M, TS and RS), which guarantees the simultaneous optimisation of all responses (Hv, Fe_IPS, Si_PS, COF and CR). Figure 6 shows the response surface graph for D. The combination of factor levels that maximises the desirability function (D = 0.816), that is, the set of process parameter values at which the optimal global values for the response variables are achieved, is as follows: M = Al12Si, TS = 16 mm/min and RS = 1100 rpm. The higher composite desirability value implies better FSPed alloy characteristics; a summary of the model's optimal solution of process parameters and its calculated responses is shown in Table 5 . Table 5 Optimal solution and average confirmation test results Material TS (mm/min) RS (rpm) H v Fe_IPS (µm) Si_PS (µm) COF CR×10 − 4 (mm/y) Optimal solution Al12Si 16 1100 93.1 ± 1.7 2.07 ± 0.2 2.82 ± 0.05 0.60 ± 0.01 1.28 ± 0.04 Confirmation test results Al12Si 16 1100 90.3 ± 1.4 2.13 ± 0.2 2.96 ± 0.03 0.58 ± 0.02 1.33 ± 0.01 Percentage error 3.01 2.82 4.7 3.81 3.76 Confirmation test Confirmation tests using the optimal process parameters were done. Three runs were performed with the optimal combination of TS and RS for Al12Si alloy. The average values of the three confirmation tests of the dependent variables are shown in Table 5 . From the results, it is found that the optimal experimental results of microhardness, Fe-rich intermetallic phase size, Si phase size, coefficient of friction, and corrosion rate were very similar to those provided by the mathematical model, and the percentage of error was within the range of acceptance (Table 5 ). 4. Conclusions Multi-response optimisation on friction-stir process of Al–Si alloys was investigated in this study. Optimum FSP parameters of Al–Si alloys with Si 12–14 wt% could be chosen as tool rotation speed = 1100 rpm and travel speed = 16 mm/min for Al12Si alloy. The friction-stir processed Al12Si samples exhibit the highest microhardness, 93.15 H v ; the most considerable fragmentation of Si phases and Fe-rich intermetallic phases, with average sizes of 2.82 and 2.07 µm, respectively; and optimal values of coefficient of friction, 0.604 and corrosion rate 1.28×10 − 4 mm/y. The optimum FSP parameters obtained allowed the production of a surface with a fine structure, with a fragmented and uniform distribution of Si phases and other secondary intermetallic phases in the aluminium matrix, which is why the FSPed samples showed higher hardness and better wear and corrosion behaviour. This work provided a mathematical model to obtain the optimum FSP parameters for producing Al12Si alloys with excellent microstructural and mechanical characteristics, high hardness, and good wear and corrosion resistance. Declarations Acknowledgements The authors would like to thank to Coordenação de Aperfeicoamento de Pessoal de Nıvel Superior—Brazil (CAPES) for providing financial and technical support and C2MMa Lab of UTFPR for the analyses conducted. Funding: This research was supported by the Coordenação de Aperfeicoamento de Pessoal de Nıvel Superior—Brazil (CAPES). Conflicts of interest / Competing interests The authors declare that there are no conflicts of interest and they have no relevant financial or non-financial interests to disclose. Authors' contributions Traiano, Denner : Investigation, Methodology, Formal analysis; Nascimento, Silvia Rosa : Investigation, Methodology, Formal analysis; Lourençato, Luciano Augusto : Investigation, Methodology, Formal analysis; Sánchez Roca, Angel : Software, Validation, Visualization; Sánchez Orozco, Mario César : Software, Validation, Visualization, Writing - Original Draft, Writing - Review & Editing; Carvajal Fals, Hipólito Domingo : Conceptualization, Investigation, Methodology, Supervision, Writing - Review & Editing. All authors have read and agreed to the published version of the manuscript. References Javidani M, Larouche D (2014) Application of cast Al–Si alloys in internal combustion engine components. Int Mater Rev 59 (3): 132-158. https://doi.org/10.1179/1743280413Y.0000000027 Rambabu, P., Eswara Prasad, N., Kutumbarao, V.V., Wanhill, R.J.H. (2017). Aluminium Alloys for Aerospace Applications. In: Prasad, N., Wanhill, R. (eds) Aerospace Materials and Material Technologies . Indian Institute of Metals Series. Springer, Singapore. https://doi.org/10.1007/978-981-10-2134-3_2 Atxaga G, Pelayo A, Irisarri A (2001) Effect of microstructure on fatigue behaviour of cast Al–7Si–Mg alloy. Mater Sci Tech-Lond 17 (4):446-450. https://doi.org/10.1179/026708301101510023 Mbuya T, Odera B, Ng'ang'a S (2003) Influence of iron on castability and properties of aluminium silicon alloys: literature review. Int. J Cast Metal Res 16 (5):451-465. https://doi.org/10.1080/13640461.2003.11819622 S Nallusamy S (2016) A review on the effects of casting quality, microstructure and mechanical properties of cast Al-Si-0.3 Mg alloy. Int J Performability Eng 12 (2):143. https://doi.org/10.23940/ijpe.16.2.p143.mag Patel V, Li W, Vairis A, Badheka V (2019) Recent development in friction stir processing as a solid-state grain refinement technique: microstructural evolution and property enhancement. Crit Rev Solid State. 44 (5):378-426. https://doi.org/10.1080/10408436.2018.1490251 Guru P, Khan F, Panigrahi S, Ram GJ (2015) Enhancing strength, ductility and machinability of a Al–Si cast alloy by friction stir processing. J Manuf Process. 18:67-74. https://doi.org/10.1016/j.jmapro.2015.01.005 Golafshani KB, Nourouzi S, Jamshidi Aval H (2019) Evaluating the microstructure and mechanical properties of friction stir processed Al–Si alloy. Mater Sci Tech-Lond. 35 (9): 1061-1070. https://doi.org/10.1080/02670836.2019.1612577 Jiang H, Liu C, Yang Z, Li Y, Huang H, Qin F (2019) Effect of friction stir processing on the microstructure, damping capacity, and mechanical properties of Al-Si alloy. J Mater Eng Perform. 28:1173-1179. https://doi.org/10.1007/s11665-018-3844-2 Soleymanpour M, Aval HJ, Jamaati R (2022) Achieving high strength and superior ductility in Al–Si alloy by cold rolling and friction stir processing. J Alloy Compd. 896:163102. https://doi.org/10.1016/j.jallcom.2021.163102 Soleymanpour M, Aval HJ, Jamaati R (2022) Manufacturing of high-toughness Al–Si alloy by rolling and friction stir processing: Effect of traverse speed. CIRP J Manuf Sci Tec. 37: 19-36. https://doi.org/10.1016/j.cirpj.2021.12.007 Alzahrani MA, Alsoruji G, Moustafa EB, Mosleh AO, Mohamed SS (2023) Optimization of Friction Stir Processing Parameters for Improvement of Mechanical Properties of AlSi7Mg0. 2 Alloy. Coatings 13 (10):1667. https://doi.org/10.3390/coatings13101667 Ma L, Zhou C, Shi Y, Cui Q, Ji S, Yang K (2021) Grain-refinement and mechanical properties optimisation of A356 casting Al by ultrasonic-assisted friction stir processing. Met Mater Int 27 (12):5374-5388. https://doi.org/10.1007/s12540-020-00952-x Mohamadigangaraj J, Nourouzi S, Aval HJ (2020) Statistical modelling and optimization of friction stir processing of A390-10 wt% SiC compo-cast composites. Measurement 165:108166. https://doi.org/10.1016/j.measurement.2020.108166 ASTM International, ASTM G59-97 - Standard Test Method for Conducting Potentiodynamic Polarization Resistance Measurements, 2014. Zykova AP, Tarasov SY, Chumaevskiy AV, Kolubaev EA (2020) A review of friction stir processing of structural metallic materials: Process, properties, and methods. Metals 10 (6):772. https://doi.org/10.3390/met10060772 Rao A, Katkar V, Gunasekaran G, Deshmukh V, Prabhu N, Kashyap B (2014) Effect of multipass friction stir processing on corrosion resistance of hypereutectic Al–30Si alloy. Corros Sci. 83:198-208. https://doi.org/10.1016/j.corsci.2014.02.013 D.C. Montgomery, Design and analysis of experiments Joh,n wiley & sons, 2017 Madhusudhan Reddy G, Srinivasa Rao K (2010) Enhancement of wear and corrosion resistance of cast A356 aluminium alloy using friction stir processing. T Indian I Metals. 63: 793-798. https://doi.org/10.1007/s12666-010-0121-y Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4085088","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280962657,"identity":"7611ee83-b11b-430c-817b-ebc4a3f1997a","order_by":0,"name":"Denner Traiano","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Denner","middleName":"","lastName":"Traiano","suffix":""},{"id":280962658,"identity":"795e0eee-5b00-4758-bd2a-b28d6ce1bf18","order_by":1,"name":"Silvia Rosa Nascimento","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"Rosa","lastName":"Nascimento","suffix":""},{"id":280962659,"identity":"d204ddbd-2ec8-4175-b96d-cfaccad0620e","order_by":2,"name":"Luciano Augusto Lourençato","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Luciano","middleName":"Augusto","lastName":"Lourençato","suffix":""},{"id":280962660,"identity":"441c63cb-f5aa-4697-aea5-cc3d205438da","order_by":3,"name":"Angel Sánchez Roca","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Angel","middleName":"Sánchez","lastName":"Roca","suffix":""},{"id":280962661,"identity":"f7f50af3-a9a6-4a52-842d-d22c2945a6d9","order_by":4,"name":"Mario César Sánchez Orozco","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYJCCA3DWBxsQyUOCFsYZaURqgQNmHmK06LaffXjgA4NNPj//4YePbRIO58s78B77gE+L2Zl0g4MzGNIsZzYcMzbOSThsufEAX/IMvFoOpDEc5mE4bGBwsIdNOvfHYQPDBh5jvA4zO/8MquUwD5u0RQIxWm7AbDkG1MIA1CLPQFDLMwaQXwwke9iMDXsS0g0MmPmSCTgsjfkDMMQMQCH24EeCtYF8e+9hvFrAgPEfEseACA1oQL6BZC2jYBSMglEwzAEA7flCxIktSm8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1390-9582","institution":"Univeridad de Oriente","correspondingAuthor":true,"prefix":"","firstName":"Mario","middleName":"César Sánchez","lastName":"Orozco","suffix":""},{"id":280962662,"identity":"b8ae8295-63ee-4f53-a663-85c512f3280d","order_by":5,"name":"Hipólito Domingo Carvajal Fals","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hipólito","middleName":"Domingo Carvajal","lastName":"Fals","suffix":""}],"badges":[],"createdAt":"2024-03-12 15:21:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4085088/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4085088/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53080694,"identity":"f7d840fc-a513-4c05-af33-4233f7c8b8df","added_by":"auto","created_at":"2024-03-20 10:39:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10137271,"visible":true,"origin":"","legend":"\u003cp\u003eCross-sectional macrograph of FSPed surfaces at 700 rpm and 16 mm/min. a) Al12Si and b) Al14Si\u003c/p\u003e","description":"","filename":"FIG1N.png","url":"https://assets-eu.researchsquare.com/files/rs-4085088/v1/4eeb1b18276010a19284ba47.png"},{"id":53081419,"identity":"721aa518-189c-46bd-9c68-c6fb821b5be1","added_by":"auto","created_at":"2024-03-20 10:47:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":813850,"visible":true,"origin":"","legend":"\u003cp\u003eEDS analysis. a) Al12Si alloy as casting and b) FSPed Al12Si alloy\u003c/p\u003e","description":"","filename":"FIG2N.png","url":"https://assets-eu.researchsquare.com/files/rs-4085088/v1/21abc7ba745f7cc9539d4109.png"},{"id":53080692,"identity":"f88ed27b-c1a7-4a12-9d21-547421ab6ac2","added_by":"auto","created_at":"2024-03-20 10:39:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":174784,"visible":true,"origin":"","legend":"\u003cp\u003eComparative graph of particle size before and after the FSP process. a) Si particles and b) Fe intermetallic particles.\u003c/p\u003e","description":"","filename":"FIG3N.png","url":"https://assets-eu.researchsquare.com/files/rs-4085088/v1/57b216aec16f2966d34a80c8.png"},{"id":53080690,"identity":"93210f86-5e32-40f5-9a8e-3f1a0b2b7bbd","added_by":"auto","created_at":"2024-03-20 10:39:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":373031,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted versus actual values. a) H\u003csub\u003ev\u003c/sub\u003e, b) Fe_IPS, c) Si_PS, d) COF and e) CR\u003c/p\u003e","description":"","filename":"FIG4N.png","url":"https://assets-eu.researchsquare.com/files/rs-4085088/v1/ac8999968dde8e4c24107c11.png"},{"id":53080693,"identity":"b4555b01-98c6-4eeb-9e8c-c1044bc6942d","added_by":"auto","created_at":"2024-03-20 10:39:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1526429,"visible":true,"origin":"","legend":"\u003cp\u003eResponse surface graphs. a) H\u003csub\u003ev\u003c/sub\u003e, b) Fe_IPS, c) Si_PS, d) COF and e) CR\u003c/p\u003e","description":"","filename":"FIG5N.png","url":"https://assets-eu.researchsquare.com/files/rs-4085088/v1/e13e23c0f42458d4e5640f5b.png"},{"id":53080695,"identity":"1f640a01-4bdc-4eb9-9e95-c1f733dae584","added_by":"auto","created_at":"2024-03-20 10:39:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":272464,"visible":true,"origin":"","legend":"\u003cp\u003eComposite function of desirability\u003c/p\u003e","description":"","filename":"FIG6N.png","url":"https://assets-eu.researchsquare.com/files/rs-4085088/v1/c6df3175f00a5cad40782349.png"},{"id":56113475,"identity":"592db538-e12a-401c-948b-3c4b2619ab65","added_by":"auto","created_at":"2024-05-08 17:09:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3791675,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4085088/v1/a5049050-56c2-476a-87ed-987fc16ffa64.pdf"}],"financialInterests":"","formattedTitle":"Multi-response Optimisation Applied on Friction Stir Processing to Enhanced Wear and Corrosion performance in Al-Si Alloys","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAl\u0026ndash;Si alloys have great potential for application in the automotive, aerospace, and marine industries due to their combined properties, such as strength, corrosion resistance, and lightweight properties [1,2]. Javidani and Larouche [1] published a critical review about using Al\u0026ndash;Si based alloys in cylinder heads and engine blocks. They concluded that the use of Al\u0026ndash;S alloys are a promising alternative in the fabrication of cylinder blocks. The role of aluminum alloys in future commercial aircraft due to their relatively low cost, light weight, the fact that they are materials that can be heat treated to fairly high-strength levels and that they are one of the most easily fabricated high performance materials was reported by Rambabu et al. [2]. The authors emphasize the importance of optimizing the production processes of aluminum alloys and their chemical composition.\u003c/p\u003e \u003cp\u003eThe microstructure significantly influences the mechanical properties of Al\u0026ndash;Si alloys. The morphology and size of undesirable secondary phases present in the cast material are detrimental to its properties, such as fatigue resistance and corrosion resistance, leading to premature failure of the machine elements [3,4]. Atxaga et al. [3] studied the fatigue behaviour of a cast Al \u0026ndash; 7Si \u0026ndash; Mg alloy. They observed that the presence of a heterogeneous microstructure with casting defects significantly affects the fatigue life of specimens. The authors concluded that heat treatment can improve the fatigue behaviour of these alloys. Mbuya et al. [4] studied the influence of Fe phases on the properties of Al\u0026ndash;Si alloys. They stated that the detrimental effects of Fe in Al-Si alloys outweigh its benefits and, therefore, its reduction or control is desirable.\u003c/p\u003e \u003cp\u003eDifferent technologies have been applied to transform the size and morphology of undesirable phases in Al-Si alloy. These techniques include chemical treatment, rapid solidification, mould vibration and melt agitation [5]. In recent years, the friction-stir processing (FSP) technique has emerged as an alternative due to its potential to modify the microstructure and improve the mechanical properties of different alloys, particularly Al alloys [6]. When the FSP process is applied to cast Al\u0026ndash;Si alloys, intense fragmentation of dendrites, Si particles, and other intermetallic phases occurs. In addition, the reduction of grain size in the matrix, the dissolution of precipitates, and the decrease of porosity lead to a finer and more homogeneous microstructure and, as a result, a significant improvement in the mechanical properties, wear resistance and corrosion resistance [7].\u003c/p\u003e \u003cp\u003eSeveral studies have been published on applying the FSP process to achieve optimum morphology and particle size of secondary phases in Al\u0026ndash;Si alloys to enhance their mechanical properties. [8\u0026ndash;10]. Golafshani et al. [8] investigated the mechanical behaviour and microstructural evolution of A390 aluminium alloy processed by FSP. They reported a more uniform and dense particle distribution in the stirred zone compared to the as-cast A390 alloy. The authors concluded that FSP can increase the as-cast strain sensitivity index by eight times after three passes.\u003c/p\u003e \u003cp\u003eJiang et al. [9] conducted a friction stir processing on an Al\u0026ndash;Si casting alloy. They affirmed that FSP promoted the grain refinement of Al\u0026ndash;Si alloys, and mechanical properties were significantly improved by FSP. The authors reported that the Si phase and Al grains of an Al\u0026ndash;Si casting alloy were refined through FSP. However, no reference is made in the research to the influence of Fe intermetallic phases.\u003c/p\u003e \u003cp\u003eSoleymanpour et al. [10] studied the influence of heat treatment, rolling, and FSP on the microstructure, hardness, tensile behaviour and Al\u0026ndash;Si alloy fracture mechanism. They reported that the FSP reduced the aspect ratio and increased the sphericity of Si and Fe-rich particles owing to the severe shear deformation at elevated temperatures, producing a uniform distribution of the particles in the matrix. The tensile properties of FSPed samples were much higher than the initial samples.\u003c/p\u003e \u003cp\u003eMost of the publications consulted, which aim to study the influence of the microstructure on the mechanical properties of cast aluminium alloys, pay more attention to the effect of the size and aspect ratio of the Si particles in the different forms in which it is presented. However, the secondary intermetallic phases, depending on their condition and quantity, can negatively influence the mechanical properties of the alloys [11]. It is, therefore, important that the parameters of the FSP process are optimally selected to ensure that a fine microstructure with a homogeneous distribution of all phases present in the cast alloy is obtained.\u003c/p\u003e \u003cp\u003eThe optimisation of the FSP process parameters of Al\u0026ndash;Si alloys to improve their microstructure and mechanical and tribological properties has been investigated by several authors [12\u0026ndash;14]. Alzahrani et al. [12] investigated the effects of FSP parameters on the microstructure and mechanical properties of an Al\u0026ndash;Si \u003csub\u003e7\u003c/sub\u003eMg\u003csub\u003e0.2\u003c/sub\u003e alloy. The authors stated that optimisation of heat generation inside the stirred zone plays a vital role in managing the thermal profile and preventing adverse effects that impact the microstructure and properties of the alloy. They affirmed that the intense plastic deformation and dynamic recrystallisation during FSP resulted in a finer microstructure. As a result, hardness, wear rate, and corrosion resistance were improved. They developed a fitting model to predict the temperature distribution along the workpiece that can be used to optimise process parameters for different applications. Still, the optimal parameters for these experimental conditions were not proposed.\u003c/p\u003e \u003cp\u003eMohamadigangaraj et al. [14] used the response surface methodology to assess the effect of FSP parameters on the microstructure and mechanical properties of A390-10 wt% SiC compo-cast composite. They concluded that FSP positively affected the mechanical properties of the as-cast composite, and the microstructure modification after FSP decreased the friction coefficient and wear loss of the processed composite. The authors propose the optimal FSP parameter values that guarantee maximum hardness, ultimate tensile strength, and toughness.\u003c/p\u003e \u003cp\u003eMa et al. [13] applied conventional FSP and ultrasonic-assisted FSP (Ua-FSP) to as-cast Al\u0026ndash;Si alloy under different processing parameters. They compared forming characteristics, grain refinement, and mechanical properties obtained by FSP and Ua-FSP to determine the effect of ultrasound. They demonstrated that the ultrasonic vibration improved surface formability and enlarged the processing regions due to the enhancement of Al\u0026ndash;Si alloy flowability. The mechanical properties of A356 Al alloy were enhanced by applying ultrasonic vibration in FSP. The process parameters for which the optimum mechanical properties were obtained are recommended. However, no optimisation methodology was used for their determination.\u003c/p\u003e \u003cp\u003eThe present research aims to optimise the parameters of FSP, travel speed, and tool rotation speed to reach multiple objectives, such as maximising the hardness, friction coefficient, and corrosion rate of Al12Si and Al14Si alloys. To achieve this, experimental work was carried out to obtain the FSPed alloy surfaces, and the response surface method (RSM) was used for optimisation purposes.\u003c/p\u003e"},{"header":"2. Experimental procedure","content":"\u003cp\u003eThe base materials used in this research are cast ingots of Al12Si and Al14Si alloys with dimensions of 250 mm length and 75 mm height. The ingots were cut to the final dimensions of 90 mm \u0026times; 45 mm \u0026times; 5 mm to obtain the samples. The base materials were characterised by chemical analysis by X-ray fluorescence (XRF). The chemical composition of the base materials is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" 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\u003eChemical composition of base materials\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMaterial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003eChemical composition, wt%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl12Si\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e84,35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl14Si\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14,49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e81,84\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 surface modification was done on a ROMI machining center, model D600, with FANUC computerised numerical control and a maximum rotation speed of 8000 rpm and 20 HP. The tool used is made of H13 tool steel. The shoulder profile of the tool is concave with a diameter of 15 mm, with a conical pin of 5 mm diameter at the tip, a maximum diameter of 6 mm (at the base), and a length of 3 mm.\u003c/p\u003e \u003cp\u003eConsidering that this research aims to determine the optimum parameters of the FSP process to obtain a fine microstructure with good properties, a 2^3 factorial experimental design was selected, i.e., three factors with two levels plus a central or medium level. Friction-stir process parameters used are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters of the friction-stir process.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNotation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(+\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaterial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAl12Si\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAl14Si\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTravel speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emm/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTool rotation speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003erpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1100\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 response variables analysed in this research are microhardness (Hv), Fe intermetallic particle size (Fe_IPS), Si particle size (Si_PS), coefficient of friction (COF), and corrosion rate (CR). The process parameters were selected from preliminary tests. All conditions were performed three times and randomly to reduce the experimental error; the average of the three runs of results obtained for the dependent variables is reported in this paper. In this study, constant plunge depth is used for each condition.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Macro and microstructural characterization\u003c/h2\u003e \u003cp\u003eSamples were cross-sectioned for metallographic studies, embedded in Bakelite, sanded with 220, 320, 400, 600, and 1200 grit sandpaper and polished to a mirror finish. The samples were prepared and etched using Osmond reagent (25 ml of distilled water, 25 ml of nitric acid, 25 ml of hydrochloric acid, and 2.5 ml of hydrofluoric acid) for 3 sec.\u003c/p\u003e \u003cp\u003eMacrostructural images were evaluated using an Olympus UC30 optical microscope and processed using Stream Essentials software. A Tescan Model Vega3 Scanning Electron Microscope (SEM) was also used to obtain images of the microstructure and map the chemical composition.\u003c/p\u003e \u003cp\u003eThe microstructural MEV images were processed using Image J software to determine the size and distribution of the silicon and Fe intermetallic particles. Feret diameter was used in the particle size analysis, and the average distance between two parallel lines tangent to the particle projection was measured.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Microhardness, corrosion rate, and coefficient of friction analysis\u003c/h2\u003e \u003cp\u003eThe microhardness measurements (Hv) were carried out using a Shimadzu HMV-G20 Series Equipment microhardness tester. Microhardness profiles were made on the cross sections of the samples, obtained from 36 indentations, distributed over all the modified zones (Nugget, TMAZ, HAZ) and in the substrate. The tests were carried out with a load of 300 gf and a time of 15 seconds.\u003c/p\u003e \u003cp\u003eA high-performance AutoLab PGSTAT204 potentiostat/galvanostat instrument was used to evaluate the corrosion behaviour on friction-stirred and as-casting alloy samples. The studies used two electrode cells, including a reference H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e/KCl electrode, to obtain the corrosion potential (Ecorr) and polarisation. Open Circuit Potential (OCP) was measured according to ASTM G59-97 [15]. The polarisation was obtained following two stages: in the first one, a micro polarisation was performed with a voltage variation between \u0026plusmn;\u0026thinsp;10 mV around E\u003csub\u003ecorr,\u003c/sub\u003e and in the second stage, a macro polarisation was performed with a variation of \u0026plusmn;\u0026thinsp;100 mV around Ecorr. With the results obtained, using the potentiostat/galvanostat tests, the corrosion current (I\u003csub\u003ecorr\u003c/sub\u003e) and the corrosion rate (CR) were calculated.\u003c/p\u003e \u003cp\u003eA TTP Tribo1, linear wear tribometer, was used to evaluate wear behaviour, using a 52100 steel indenter and the following parameters: a test time of 300 s, a load of 5 N at a frequency of 5 Hz, and a track length of 2 mm. These tests were performed three times on friction-stirred and as-casting alloy samples. The mass losses for each condition were determined using a Shimadzu ATX 224 analytical balance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Optimisation of the FSP process parameters methodology\u003c/h2\u003e \u003cp\u003eThe FSP process parameters were optimised using the statistical response surface method (RSM), specifically, the method of optimising multiple responses. It allows determining the setup of the experimental factors for the characteristics desired of one or more responses through constructing the function of desirability. The response function \u003cem\u003ey\u003c/em\u003e, which includes the dependent variables Hv, Fe_IPS, Si_PS, COF, and CR, will be a function of the material (M), tool rotation speed (RS), and travel speed (TS) and can be expressed by 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$$\\text{y}\\left({\\text{H}}_{\\text{v}}, \\text{F}\\text{e}\\_\\text{I}\\text{P}\\text{S},\\text{S}\\text{i}\\_\\text{P}\\text{S},\\text{C}\\text{O}\\text{F},\\text{C}\\text{R}\\right)=\\text{f}(\\text{R}\\text{S}, \\text{T}\\text{S}, \\text{M})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo combine the multiple responses in a single function, the first step is to define the desirability function for each response \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, corresponding with the dependent variables. This function expresses the desirability of a response value equal to \u003cem\u003ey\u003c/em\u003e on a scale of 0 to 1. This function takes three forms, depending on how maximised or minimised the response must be or if an objective value must be reached. In this specific case, the idea is to optimise microhardness; therefore, the desirability function is defined by Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${\\text{d}}_{\\text{i}}=\\left\\{\\begin{array}{ccc}0\u0026amp; ,\u0026amp; \\widehat{\\text{y}}\u0026lt;\\text{l}\\text{o}\\text{w}\\\\ {\\left(\\frac{\\widehat{\\text{y}}-\\text{l}\\text{o}\\text{w}}{\\text{h}\\text{i}\\text{g}\\text{h}-\\text{l}\\text{o}\\text{w}}\\right)}^{\\text{s}}\u0026amp; ,\u0026amp; \\text{l}\\text{o}\\text{w}\\le \\widehat{\\text{y}}\\le \\text{h}\\text{i}\\text{g}\\text{h}\\\\ 1\u0026amp; ,\u0026amp; \\widehat{\\text{y}}\u0026gt;\\text{h}\\text{i}\\text{g}\\text{h}\\end{array}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eŷ\u003c/em\u003e is the predicted value of the response variable; \u003cem\u003elow\u003c/em\u003e is the value below which the response is completely unacceptable; and \u003cem\u003ehigh\u003c/em\u003e is the value above which the desirability function is a maximum. The parameter \u003cem\u003es\u003c/em\u003e defines the shape of the process; for \u003cem\u003es\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, the desirability function behaves linearly with \u003cem\u003elow\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0 and \u003cem\u003ehigh\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1. A \u003cem\u003ei\u003c/em\u003e corresponds with the number of responses; in this case, \u003cem\u003ei\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 (H\u003csub\u003ev\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003eIn the case of the other four responses, Fe_IPS, Si_PS, COF and CR, the lower their value, the higher the surface quality obtained; therefore, the objective is to minimise them. The i values correspond to the number of the other four responses; i\u0026thinsp;=\u0026thinsp;2, 3, 4, 5 (Fe_IPS, Si_PS, COF and CR, respectively). The desirability function is defined by Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${\\text{d}}_{\\text{i}}=\\left\\{\\begin{array}{ccc}1\u0026amp; ,\u0026amp; \\widehat{\\text{y}}\u0026lt;\\text{l}\\text{o}\\text{w}\\\\ {\\left(\\frac{\\widehat{\\text{y}}-\\text{h}\\text{i}\\text{g}\\text{h}}{\\text{l}\\text{o}\\text{w}-\\text{h}\\text{i}\\text{g}\\text{h}}\\right)}^{\\text{s}}\u0026amp; ,\u0026amp; \\text{l}\\text{o}\\text{w}\\le \\widehat{\\text{y}}\\le \\text{h}\\text{i}\\text{g}\\text{h}\\\\ 1\u0026amp; ,\u0026amp; \\widehat{\\text{y}}\u0026gt;\\text{h}\\text{i}\\text{g}\\text{h}\\end{array}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eA single composite desirability function D was created to combine the desirability function of n responses. If all the response variables are considered equally important, then the composite function is the geometric mean of the separated desirability functions, Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\text{D}=\\sqrt[\\text{n}]{{\\prod }_{\\text{i}=1}^{\\text{n}}{\\text{d}}_{\\text{i}}}=\\sqrt[5]{{\\text{d}}_{1}.{\\text{d}}_{2}.{\\text{d}}_{3}.{\\text{d}}_{4}.{\\text{d}}_{5}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Macro and microstructural analysis\u003c/h2\u003e \u003cp\u003eIn this research, modified surfaces of Al12Si and Al14Si alloys with a fine and homogeneous microstructure were obtained by the FSP process. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows typical cross-sectional macrographs of the modified regions for the experimental condition of 700 rpm and 16 mm/min of tool rotational speed and travel speed, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn all cases, it was possible to identify the nugget zone, deformation bands and particle size refinement as a result of the FSP process (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition; it was verified that a nugget region free of pores was obtained in comparison with the base materials (Al12Si and Al14Si) as casting. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the EDS map obtained from the energy-dispersive X-ray spectrometry done to analyse the chemical composition of the Al12Si alloy surface before and after being modified by the FSP process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysed sample of the Al12Si alloy as casting, showed the presence of copper and iron in the form of the intermetallic phases Al\u003csub\u003e2\u003c/sub\u003eCu-θ, AlFeMnSi-Feα, and AlFeSi-Feβ (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). As seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, the intermetallic phase AlFeMnSi-Feα, it\u0026acute;s present in Chinese script morphology. Despite its larger size, this phase (Feα) has a relatively small presence in the microstructure, unlike the AlFeSi-Feβ phase. The Feβ phase is presented in the form of smaller needles. Still, with a more significant presence in the microstructure, it may be responsible for the deterioration of the mechanical properties. On the other hand, Si is present in two forms: in the eutectic phase and the form of free particles. Depending on their condition and quantity, these phases can negatively influence the mechanical properties of the alloy [7,11].\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb shows that as a result of the surface modification process, refining and recrystallisation of the microstructure is obtained, with the breakage and dispersion of silicon particles and intermetallic phases. Similar results have been reported by other authors [7,10,16]. These modifications occur due to severe plastic deformation, the localised temperature gradient, and mechanical mixing during the friction stir process.\u003c/p\u003e \u003cp\u003eCompared to Al12Si as casting (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), where the formation of intermetallics, mainly AlFeMnSiFe-α, is visible, the FSP causes these undesirable significant phases to be modified. The silicon particles provide a minor size and a better distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), therefore improving the mechanical properties, evidenced by a 15% increase in microhardness. Similar behaviour was observed for the Al14Si alloy, where the rise in microhardness reached 19% on average.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the average experimental values obtained for each tested condition. For the analysis of the influence of the process parameters on the particle diameter, Si particles (Si_PS) and Fe intermetallic phases, AlFeMnSi-Fe-α and AlFeSi-Fe-β (Fe_IPS), were chosen due to their more significant influence on the variation of the mechanical properties. These Si and intermetallic particles present a higher hardness, directly affecting the wear resistance. In addition, they constitute points of galvanic coupling, generating points of oxidation, affecting the corrosion resistance of the alloys studied [17].\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\u003eAverage values of experimental results of the dependent variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003cp\u003e(mm/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRS\u003c/p\u003e \u003cp\u003e(rpm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003csub\u003ev\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFe_IPS\u003c/p\u003e \u003cp\u003e(\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSi_PS (\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCOF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCR\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(mm/y)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAl12Si\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e93.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.632\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e96.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.817\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e87.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.707\u0026thinsp;\u0026plusmn;\u0026thinsp;0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e95.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.503\u0026thinsp;\u0026plusmn;\u0026thinsp;0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e92.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.663\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAl14Si\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e84.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e6.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e4.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.681\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e81.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e5.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e5.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.767\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e3.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e82.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e6.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e6.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.760\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e82.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e7.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.686\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e84.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e6.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e5.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.668\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\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 graph in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates the microstructural refinement due to the FSP. Comparatively, the average value of Si_PS for both alloys decreased by approximately 77%. In the case of the average value of Fe_IPS, including the two phases present, the decrease was 81%. From this analysis, it can be concluded that FSP resulted in significant fragmentation of AlFeMnSi-Fe-α and AlFeSi-Fe-β intermetallic phases, as well as of the Si particles in their different forms, in both Al12Si and Al14Si alloys.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Optimisation of FSP process parameters\u003c/h2\u003e \u003cp\u003eThe first step in applying the multiple response optimisation method corresponds to obtaining the response surfaces for each dependent variable analysed. The mathematical models for predicting the values of Hv, Fe_IPS, Si_PS, COF and CR, respectively, are listed as follows:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${\\text{H}}_{\\text{v}}=85.0414-4.86008\\bullet \\text{M}+0.105115\\bullet \\text{T}\\text{S}+0.00450823\\bullet \\text{R}\\text{S}-0.0318264\\bullet \\text{M}\\bullet \\text{T}\\text{S}+0.000547973\\bullet \\text{M}\\bullet \\text{R}\\text{S}-0.000163993\\bullet \\text{T}\\text{S}\\bullet \\text{R}\\text{S}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\text{F}\\text{e}\\_\\text{I}\\text{P}\\text{S}=7.85914+2.4778\\bullet \\text{M}-0.153944\\bullet \\text{T}\\text{S}-0.00570047\\bullet \\text{R}\\text{S}+0.0393889\\bullet \\text{M}\\bullet \\text{T}\\text{S}-0.00178198\\bullet \\text{M}\\bullet \\text{R}\\text{S}+0.000228056\\bullet \\text{T}\\text{S}\\bullet \\text{R}\\text{S}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\text{S}\\text{i}\\_\\text{P}\\text{S}=-2.41025+0.0693138\\bullet \\text{M}+0.187885\\bullet \\text{T}\\text{S}+0.00965551\\bullet \\text{R}\\text{S}+0.0234792\\bullet \\text{M}\\bullet \\text{T}\\text{S}+0.00137795\\bullet \\text{M}\\bullet \\text{R}\\text{S}-0.000307535\\bullet \\text{T}\\text{S}\\bullet \\text{R}\\text{S}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$\\text{C}\\text{O}\\text{F}=0.449947+0.0475638\\bullet \\text{M}+0.0207465\\bullet \\text{T}\\text{S}+0.0000522042\\bullet \\text{R}\\text{S}-0.00577083\\bullet \\text{M}\\bullet \\text{T}\\text{S}+0.000116286\\bullet \\text{M}\\bullet \\text{R}\\text{S}-0.0000135069\\bullet \\text{T}\\text{S}\\bullet \\text{R}\\text{S}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$\\text{C}\\text{R}=-0.000401874-0.0000437058\\bullet M+0.0000274003\\bullet TS+4.78673E-7\\bullet RS+0.00000516875\\bullet M\\bullet TS-6.60113e-8\\bullet M\\bullet RS-2.37604e-8\\bullet TS\\bullet RS$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAnalysis of variance (ANOVA) was performed to investigate the significance of the influence of each FSP process parameter and their interactions with the dependent variables. The study was done with a confidence level of 95%; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows a summary of the ANOVA.\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\u003eANOVA summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted R-squared\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandard error of estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDurbin-Watson statistic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003ev\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.8021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.01984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe_IPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.37817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSi_PS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.11749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.02607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR (\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1. 612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.46898\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\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the adjusted coefficient of determination R-squared values for the models of the five dependent variables under study are between 77% and 96%. The use of adjusted coefficient of determination R-squared is more suitable in the case of models with several independent factors [18]. This implies that in the worst cases (Hv and CR), where the adjusted coefficient of determination R-squared is approximately 77%, the model does not explain only 23% of the total variations of these responses. The overall adequacy of the models is satisfactory. On the other hand, the Durbin-Watson test statistic values are in the range of 2.0 to 2.5, which indicates that no autocorrelation was detected in the sample.\u003c/p\u003e \u003cp\u003ePredicted versus actual values are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Data points are scattered around the reference line. Predicted values of dependent variables are in good agreement with original values. As seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, there is a practically linear relationship between the values of the experimental data and those obtained by the regression model for each response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the response surface graphs for all dependent variables. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, the most significant microhardness values were obtained for the Al12Si alloy. The average microhardness value of the nugget zone for the Al12Si samples (93.2 Hv) was 12% bigger than the average microhardness value obtained for the Al14Si samples (83.1 Hv) and, at the same time, 22% bigger than the average microhardness value for the as-cast Al12Si alloy. This microhardness behaviour is directly related to the refined and homogeny microstructure obtained after applying the FSP and to the final particle size of the Fe-based intermetallic phases and the Si particles (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb and c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, by using the FSP, it was possible to achieve a high fragmentation of the Fe-based intermetallic phases. Regardless, the particle size of these phases (Fe_IPS) is 2.7 times higher for the Al14Si alloy, which is directly related to the size of these phases in the as-cast Al14Si alloy. Similar behaviour was observed for the Si particles (Si_PS), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec.\u003c/p\u003e \u003cp\u003eA comparison of the coefficient of friction (COF) behaviour of the nugget zone and as cast alloys is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed. The average COF value of the nugget zone for Al12Si alloy (0.66) was slightly lower than the average COF value obtained for Al14Si ally (0.71); however, decreased by 20% concerning the average. Similarly, a decrease in the average COF value of the FSPed Al14Si alloy compared to the as-cast Al14Si alloy was observed (17% lower). It can be concluded that FSP significantly enhances the wear resistance of Al12Si and Al14Si as-cast alloys due to the refinement of the microstructure and the increase of the hardness of the nugget zone. Similar results have been obtained by other authors [19].\u003c/p\u003e \u003cp\u003eFSP applied to cast Al\u0026ndash;Si alloy increased its corrosion resistance. The corrosion rate of both alloys showed a similar behaviour (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee), decreasing concerning the as-cast alloys due to the reduction of Si and Fe-rich particle sizes and increase in microstructure homogeneity. Results such as these have been reported previously by other authors [19,17].\u003c/p\u003e \u003cp\u003eThe composite function of desirability (D) allows obtaining the combination of the experimental factors (M, TS and RS), which guarantees the simultaneous optimisation of all responses (Hv, Fe_IPS, Si_PS, COF and CR). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the response surface graph for D.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe combination of factor levels that maximises the desirability function (D\u0026thinsp;=\u0026thinsp;0.816), that is, the set of process parameter values at which the optimal global values for the response variables are achieved, is as follows: M\u0026thinsp;=\u0026thinsp;Al12Si, TS\u0026thinsp;=\u0026thinsp;16 mm/min and RS\u0026thinsp;=\u0026thinsp;1100 rpm. The higher composite desirability value implies better FSPed alloy characteristics; a summary of the model's optimal solution of process parameters and its calculated responses is shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\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\u003eOptimal solution and average confirmation test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaterial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTS (mm/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRS (rpm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u003csub\u003ev\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFe_IPS\u003c/p\u003e \u003cp\u003e(\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eSi_PS (\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCOF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCR\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(mm/y)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimal solution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl12Si\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfirmation test results\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl12Si\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.76\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 \u003cb\u003eConfirmation test\u003c/b\u003e \u003c/p\u003e \u003cp\u003eConfirmation tests using the optimal process parameters were done. Three runs were performed with the optimal combination of TS and RS for Al12Si alloy. The average values of the three confirmation tests of the dependent variables are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. From the results, it is found that the optimal experimental results of microhardness, Fe-rich intermetallic phase size, Si phase size, coefficient of friction, and corrosion rate were very similar to those provided by the mathematical model, and the percentage of error was within the range of acceptance (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMulti-response optimisation on friction-stir process of Al\u0026ndash;Si alloys was investigated in this study. Optimum FSP parameters of Al\u0026ndash;Si alloys with Si 12\u0026ndash;14 wt% could be chosen as tool rotation speed\u0026thinsp;=\u0026thinsp;1100 rpm and travel speed\u0026thinsp;=\u0026thinsp;16 mm/min for Al12Si alloy.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe friction-stir processed Al12Si samples exhibit the highest microhardness, 93.15 H\u003csub\u003ev\u003c/sub\u003e; the most considerable fragmentation of Si phases and Fe-rich intermetallic phases, with average sizes of 2.82 and 2.07 \u0026micro;m, respectively; and optimal values of coefficient of friction, 0.604 and corrosion rate 1.28\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mm/y.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe optimum FSP parameters obtained allowed the production of a surface with a fine structure, with a fragmented and uniform distribution of Si phases and other secondary intermetallic phases in the aluminium matrix, which is why the FSPed samples showed higher hardness and better wear and corrosion behaviour.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThis work provided a mathematical model to obtain the optimum FSP parameters for producing Al12Si alloys with excellent microstructural and mechanical characteristics, high hardness, and good wear and corrosion resistance.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank to Coordena\u0026ccedil;\u0026atilde;o de Aperfeicoamento de Pessoal de Nıvel Superior\u0026mdash;Brazil (CAPES) for providing financial and technical support and C2MMa Lab of UTFPR for the analyses conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Coordena\u0026ccedil;\u0026atilde;o de Aperfeicoamento de Pessoal de Nıvel Superior\u0026mdash;Brazil (CAPES).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e/\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest and they have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTraiano, Denner\u003c/strong\u003e: Investigation, Methodology, Formal analysis;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNascimento, Silvia Rosa\u003c/strong\u003e: Investigation, Methodology, Formal analysis;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLouren\u0026ccedil;ato, Luciano Augusto\u003c/strong\u003e: Investigation, Methodology, Formal analysis;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS\u0026aacute;nchez Roca, Angel\u003c/strong\u003e: Software, Validation, Visualization;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS\u0026aacute;nchez Orozco, Mario C\u0026eacute;sar\u003c/strong\u003e: Software, Validation, Visualization, Writing - Original Draft, Writing - Review \u0026amp; Editing;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCarvajal Fals, Hip\u0026oacute;lito Domingo\u003c/strong\u003e: Conceptualization, Investigation, Methodology, Supervision, Writing - Review \u0026amp; Editing. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJavidani M, Larouche D (2014) Application of cast Al\u0026ndash;Si alloys in internal combustion engine components. Int Mater Rev 59 (3): 132-158. https://doi.org/10.1179/1743280413Y.0000000027\u003c/li\u003e\n\u003cli\u003eRambabu, P., Eswara Prasad, N., Kutumbarao, V.V., Wanhill, R.J.H. (2017). Aluminium Alloys for Aerospace Applications. In: Prasad, N., Wanhill, R. (eds) Aerospace Materials and Material Technologies . Indian Institute of Metals Series. Springer, Singapore. https://doi.org/10.1007/978-981-10-2134-3_2\u003c/li\u003e\n\u003cli\u003eAtxaga G, Pelayo A, Irisarri A (2001) Effect of microstructure on fatigue behaviour of cast Al\u0026ndash;7Si\u0026ndash;Mg alloy. Mater Sci Tech-Lond 17 (4):446-450. https://doi.org/10.1179/026708301101510023\u003c/li\u003e\n\u003cli\u003eMbuya T, Odera B, Ng\u0026apos;ang\u0026apos;a S (2003) Influence of iron on castability and properties of aluminium silicon alloys: literature review. Int. J Cast Metal Res 16 (5):451-465. https://doi.org/10.1080/13640461.2003.11819622\u003c/li\u003e\n\u003cli\u003eS Nallusamy S (2016) A review on the effects of casting quality, microstructure and mechanical properties of cast Al-Si-0.3 Mg alloy. Int J Performability Eng 12 (2):143. https://doi.org/10.23940/ijpe.16.2.p143.mag\u003c/li\u003e\n\u003cli\u003ePatel V, Li W, Vairis A, Badheka V (2019) Recent development in friction stir processing as a solid-state grain refinement technique: microstructural evolution and property enhancement. Crit Rev Solid State. 44 (5):378-426. https://doi.org/10.1080/10408436.2018.1490251\u003c/li\u003e\n\u003cli\u003eGuru P, Khan F, Panigrahi S, Ram GJ (2015) Enhancing strength, ductility and machinability of a Al\u0026ndash;Si cast alloy by friction stir processing. J Manuf Process. 18:67-74. https://doi.org/10.1016/j.jmapro.2015.01.005\u003c/li\u003e\n\u003cli\u003eGolafshani KB, Nourouzi S, Jamshidi Aval H (2019) Evaluating the microstructure and mechanical properties of friction stir processed Al\u0026ndash;Si alloy. Mater Sci Tech-Lond. 35 (9): 1061-1070. https://doi.org/10.1080/02670836.2019.1612577\u003c/li\u003e\n\u003cli\u003eJiang H, Liu C, Yang Z, Li Y, Huang H, Qin F (2019) Effect of friction stir processing on the microstructure, damping capacity, and mechanical properties of Al-Si alloy. J Mater Eng Perform. 28:1173-1179. https://doi.org/10.1007/s11665-018-3844-2\u003c/li\u003e\n\u003cli\u003eSoleymanpour M, Aval HJ, Jamaati R (2022) Achieving high strength and superior ductility in Al\u0026ndash;Si alloy by cold rolling and friction stir processing. J Alloy Compd. 896:163102. https://doi.org/10.1016/j.jallcom.2021.163102\u003c/li\u003e\n\u003cli\u003eSoleymanpour M, Aval HJ, Jamaati R (2022) Manufacturing of high-toughness Al\u0026ndash;Si alloy by rolling and friction stir processing: Effect of traverse speed. CIRP J Manuf Sci Tec. 37: 19-36. https://doi.org/10.1016/j.cirpj.2021.12.007\u003c/li\u003e\n\u003cli\u003eAlzahrani MA, Alsoruji G, Moustafa EB, Mosleh AO, Mohamed SS (2023) Optimization of Friction Stir Processing Parameters for Improvement of Mechanical Properties of AlSi7Mg0. 2 Alloy. Coatings 13 (10):1667. https://doi.org/10.3390/coatings13101667\u003c/li\u003e\n\u003cli\u003eMa L, Zhou C, Shi Y, Cui Q, Ji S, Yang K (2021) Grain-refinement and mechanical properties optimisation of A356 casting Al by ultrasonic-assisted friction stir processing. Met Mater Int 27 (12):5374-5388. https://doi.org/10.1007/s12540-020-00952-x\u003c/li\u003e\n\u003cli\u003eMohamadigangaraj J, Nourouzi S, Aval HJ (2020) Statistical modelling and optimization of friction stir processing of A390-10 wt% SiC compo-cast composites. Measurement 165:108166. https://doi.org/10.1016/j.measurement.2020.108166\u003c/li\u003e\n\u003cli\u003eASTM International, ASTM G59-97 - Standard Test Method for Conducting Potentiodynamic Polarization Resistance Measurements, 2014. \u003c/li\u003e\n\u003cli\u003eZykova AP, Tarasov SY, Chumaevskiy AV, Kolubaev EA (2020) A review of friction stir processing of structural metallic materials: Process, properties, and methods. Metals 10 (6):772. https://doi.org/10.3390/met10060772\u003c/li\u003e\n\u003cli\u003eRao A, Katkar V, Gunasekaran G, Deshmukh V, Prabhu N, Kashyap B (2014) Effect of multipass friction stir processing on corrosion resistance of hypereutectic Al\u0026ndash;30Si alloy. Corros Sci. 83:198-208. https://doi.org/10.1016/j.corsci.2014.02.013\u003c/li\u003e\n\u003cli\u003eD.C. Montgomery, Design and analysis of experiments Joh,n wiley \u0026amp; sons, 2017\u003c/li\u003e\n\u003cli\u003eMadhusudhan Reddy G, Srinivasa Rao K (2010) Enhancement of wear and corrosion resistance of cast A356 aluminium alloy using friction stir processing. T Indian I Metals. 63: 793-798. https://doi.org/10.1007/s12666-010-0121-y\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Friction-stir processing, Optimisation, Al-Si alloys, Microhardness, Wear and Corrosion","lastPublishedDoi":"10.21203/rs.3.rs-4085088/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4085088/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEutectic (Al12Si) and hyper-eutectic (Al14Si) Al\u0026ndash;Si alloys were processed through friction-stir processing (FSP). These Al-Si alloys' microstructure, microhardness, wear, and corrosion behaviour were studied. FSP led to the fragmentation and uniform distribution of Si and Fe-rich intermetallic phases in the Al matrix. Multi-response optimisation of the friction-stir process of Al\u0026ndash;Si alloys was investigated. The optimum FSP parameters were a tool rotation speed of 1100 rpm and a 16 mm/min travel speed for Al12Si alloy. The friction stir processed Al12Si samples exhibit the highest microhardness (93 H\u003csub\u003ev\u003c/sub\u003e); the most considerable fragmentation of Si particles and Fe-rich intermetallic phases, with average sizes of 2.82 and 2.07 \u0026micro;m, respectively; and optimal values of coefficient of friction (0.60); and corrosion rate (1.28\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mm/y). This work provided a mathematical model to obtain the optimum FSP parameters for producing surfaces in Al12Si alloys with excellent microstructural characteristics, high hardness, and better wear and corrosion resistance.\u003c/p\u003e","manuscriptTitle":"Multi-response Optimisation Applied on Friction Stir Processing to Enhanced Wear and Corrosion performance in Al-Si Alloys","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-20 10:39:50","doi":"10.21203/rs.3.rs-4085088/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"98a3fbb2-644d-48cd-ba84-35649acf463f","owner":[],"postedDate":"March 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-08T16:32:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-20 10:39:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4085088","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4085088","identity":"rs-4085088","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}