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Abstract

Aluminum alloys are known for their extensive use in aerospace, automobile, marine, etc., industries due to their excellent inherent properties. Recent studies have developed different methods to modify the surface properties of aluminum by producing surface composites, such as the friction stir processing (FSP) method. The current study made an effort to develop a new hybrid surface composite of AA5083/(SiC-Gr) using the FSP method. For FSP process optimization, the response surface methodology (RSM) has been used. For creating the mathematical model using RSM, various input process parameters of the FSP are selected to predict the output characteristics of the prepared hybrid composite. A Box–Behnken design was used for the process with four factors, each factor was used with three levels, and the RSM was utilized to form a regression model to predict the responses. The ANOVA analysis suggests that NoP (number of passes): 3 and RV (reinforcement volume): 75:25 (SiC: Gr) ratio are the significant parameters of the study with a p-value less than .05. The novelty of this study lies in the development of a new hybrid surface composite of AA5083/(SiC-Gr) using the friction stir processing (FSP) method, with optimization achieved through the response surface methodology (RSM) and multi-objective selection criteria, resulting in predicted outcomes within a range of ±10% of the experimental observations.

Keywords

friction stir processing hybrid surface composite multi-response optimization response surface methodology Box-Behnken design analysis of variance

Introduction

The friction stir processing (FSP) is a novel method that has been developed over a period to produce hybrid surface composites which may offer altered and improved properties of the base metals.^1,2 The FSP method was developed in 1999, based on similar characteristics of FS-Welding (FSW).^3–6 Earlier FSP was used to produce superplasticity in the material. However with the advancement in the FSP technique, it was used to produce the surface metal matrix composites. Composite materials provide the material with enhanced microstructural, mechanical, and tribological properties.⁷ The FSP helps to enhance the properties by enhancing only the surface properties of the material. During the FSP method, a cylindrical tool works on the surface of the materials, creating frictional force, thus generating a high-temperature zone leading to material softening and easy new composite surface formation by immersion of foreign particles in the heat-affected zone.⁸ Scientists have used different reinforcements of the foreign particle during the FSP process for aluminum alloy.^9,10,11 The study has highlighted the correlation between mechanical properties with the surface properties by deriving proportionality constants for tensile strength and yield strength properties. Mazaheri et al.¹² used the FSP to create surface composites of magnesium alloy (AZ31 grade), with zirconium oxide (ZrO₂) as foreign particles. They have highlighted that with the addition of ZrO2 particles on the surface, the surface hardness improved significantly. Also, they have reported improvement in the homogeneity of the composite by an increased number of passes.

The number of passes for the FSP process has been observed as one of the key factors that leads to improvement in the wear properties of the prepared surface composite. Mehta and Badheka¹³ produced work with boron carbide (B₄C) particles on the aluminum surface through the FSP. They have observed that the developed FSP-based composite resulted in a 78% reduction in wear rate in comparison to normal metal. Reversal of the FSP with a different number of passes has led to increased surface properties, such as homogeneity along with improved wear characteristics. Various studies have reviewed the recent developments in the FSP technology and its applications in producing superplastic materials. The authors have discussed the challenges such as tool wear, deformation, and cost inclusion and have also highlighted the need for further research to fully understand the underlying mechanisms of superplastic behavior in FSP-processed materials.^14,15,16,17

Mahmoud et al.¹⁸ have used SiC- and Al₂O₃-based particles to prepare FSP composites of aluminum alloy. Their work reported the development of certain voids near the Al₂O₃ particle in the matrix. A hybrid composite of 80% SiC and 20% Al₂O₃ has been observed to be an optimized composite ratio than any other tested matrix in the study. Soleymani et al.¹⁹ employed SiC and MoS₂ particles on the surface of AA5083 aluminum alloys. They have found that the addition of foreign particles has led to improved surface and tribological characteristics of hybrid composite. Pragada et al.²⁰ produced AA2014/SiC-CNT hybrid surface composites by varying the number of FSP passes on the composite surface. Their study has reported improved wear and surface properties, such as homogeneity of reinforced particles on composite surface with altering the direction of FSP through different number of passes.

Other than the number of passes, tool material, workpiece material, and foreign reinforcement matrix, the tool rotating speed (V_TRS) and tool traverse speed (V_TS) are also significant factors that may have an important say in deciding the composite behavior.²¹ For instance, Alidokht et al.²² used a tool rotating speed of 1600 r/min and a tool traverse speed of 50 mm/min and reported an exposed MoS₂-based composite layer after the FSP. The authors have given significant attention to the MoS₂-based layer for improved tribological behavior. Mahmoud et al.²³ have worked with the tool rotation speed of 1500 r/min and traverse speed of 100 mm/min while reinforcing the Al base matrix with SiC and Al₂O₃ particles. They have reported a strong relationship between the number of input processing factors of the FSP process with the output characteristics of prepared hybrid composites.

Singh et al.²⁴ used 3 passes and 2 passes of the FSP. The study has observed the best wear resistance in metallic wear cases for 3 passes of FSP proceed composite. Whereas for abrasive wear conditions, 2 pass-based FSP-processed material has shown the lowest wear rate. Dolatkhah et al.²⁵ studied the impact of different input processing parameters of the FSP process on wear characteristics and noted that with an increased particle size of the SiC on the Al base matrix, the wear rate increases. An additional number of passes and high tool rotation speed lead to a low wear rate of the hybrid surface composite. Dieguez et al.²⁶ have reported the superplastic elongation of the 7075-T651 aluminum alloy. They have highlighted the significance of a 900% improvement in superelasticity of the samples at an elevated temperature of 400°C and pre-strain values.

Scope for the present study

The literature review has revealed that several studies exist which discuss the effect of various reinforcements over the base matrix of different Al alloy matrices. The literature review has highlighted the significance of FSP in improving the mechanical, rheological, and tribological behaviors of the prepared hybrid surface composites.^27,28,29 But few studies exist which provide the relevant base for an optimum running condition for the FSP process while using tool rotating speed (V_TRS), tool traverse speed (V_TS), number of passes (NoP), and reinforcement volume fraction (RV) together.^30,31 In addition, a statistical model for multi-response selection criteria for various input process parameters along with the optimization of the process parameters using the response surface methodology (RSM) rarely exists. Therefore, an effort has been made to develop AA5083/(SiC-Gr) composite and optimize the different process parameters of the FSP set up in a single study using the multi-response criteria method for outputs such as surface hardness (H), wear rate (WR), and coefficient of friction (CoF).

Materials and Method

The study made an effort to the development of AA5083/SiC-Gr composite using the FSP process. The present work explored the effect of various input processing parameters together on mechanical (CoF, H) and tribological (WR) and outputs of the prepared composite by utilizing the RSM on the generated experimental data.

Design of the experiment

Design of the experiment (DOE) typically involves two variables that influence the experiment, namely, the input, which in our case refers to the process parameters acting as an independent variable, and the output, which in our case refers to the desired properties acting as a dependent variable, also known as the response. The DOE approach involves assigning various levels to the input variables, such as three different levels for each processing variable that has been selected for the present study. Further based on Box–Behnken design, experiments have been performed and analysis of variance (ANOVA) was used as a statistical approach to make conclusions for the prepared hybrid surface composite of Al alloy.

Response surface methodology

The RSM is a well-established technique of data visualization and analysis, which generates interdependent relationships between the different processing variables and our characteristics in focus. Further, it may also provide sufficient graphs, regression model tables, and other data that may be used as sufficient ground to explain the findings of the study.³² Equation (1) displays the regression equation employed in developing the model presently used for this study.

Y = C_{0} + C_{1} Z_{1} + C_{2} Z_{2} + C_{3} Z_{3} + C_{4} Z_{4} + C_{11} Z_{12} + C_{22} Z_{22} + C_{33} Z_{32} + C_{44} Z_{42} + C_{12} Z_{1} Z_{2} + C_{13} Z_{1} Z_{3} + C_{14} Z_{1} Z_{4} + C_{23} Z_{2} Z_{3} + C_{24} Z_{2} Z_{4} + C_{34} Z_{3} Z_{4}

(1)where Y is the response, Z₁ is V_TRS, Z₂ is V_TS, Z₃ is NoP, and Z₄ is RV.

Experimental work

Selection of the base material

In this study, an Al alloy grade AA5083 has been taken as the base matrix material. For FSP process utilization, 5 mm thick aluminum sheets are selected based on the previous studies.^33,34 Chemical spectroscopy testing was conducted to analyze the chemical composition of the AA5083 aluminum prior to processing. Table 1 shows the spectroscopic analysis based on the chemical composition of the Al-grade sheets. The 5 mm thick sheets of selected Al grade alloy were cut into square shapes of length (L)100 mm and width (B)100 mm. A special-purpose vertical milling machine (Universal Milling Machine - BFW Wagner - UF 1, Germany) has been used for the preparation of hybrid surface composite, and a specially customized fixture was attached to the machine bed to securely hold the aluminum sheets during processing. Figure 1 depicts the customized special-purpose vertical milling center with the fixture used in this study.

Table 1.

Spectrographic analysis for the selected grade of Al alloy.

S.No.	Elements	Percentage (wt%)
1	Magnesium	4.27
2	Nickle	0.004
3	Al	94.59
4	Copper	0.0001
5	Titanium	0.021
6	Iron (Fe)	0.23
7	Chromium (Cr)	0.089
8	Lead (Pb)	0.002
9	SiC	0.066
10	Tin (Sn)	0.0001
11	Manganese (Mn)	0.61
12	Zinc (Zn)	0.005

Figure 1.

Customized special-purpose vertical milling center with fixture.

Reinforcement and tool selection

The foreign particles of SiC and Gr were selected for the FSP process with varying levels of wt%. The average 50 μm size particles of SiC and Gr were used for the preparation of the composite. Further, for composite preparation hardened steel of H13 grade FSP tool was employed, with two different characteristics: (a) pinless tool for the capping pass and (b) pin-based tool (3 mm in length and height) for actual FSP with a tool shoulder diameter of 18 mm. These parameters were selected based upon the previous published work.^{35,36,37,38,39,40} Figure 2 shows the FSP tool used in this study.

Figure 2.

(a) Pin and (b) pinless tool used in the study.

Box–Behnken design

Many studies in the literature discussed the effect of various reinforcements over the base matrix of different Al alloy matrices.^41,42,43,44 They have highlighted the significance of FSP in improving the mechanical, rheological, and tribological behaviors of the prepared hybrid surface composites. Results of those studies have provided sufficient information on the utilization of V_TRS, V_TS, NoP, and RV as processing variables, which have the maximum impact on prepared surface composite. For this particular study, Table 2 lists the specific process parameters that were selected. A Box–Behnken design (BBD) technique-based DOE was used to explore the impact of process parameters on various output properties of composite such as H (hardness), CoF, and WR. By choosing three processing input levels for 4 parameters, the Box–Behnken design has resulted in 29 sets of experimental conditions. The present study utilized the obtained DOE 29 experimental conditions to determine the possible model for how the process parameters interact with the selected responses, which were hardness H (HRB), WR (μm/mm), and coefficient of friction (COF). Table 3 provides further details on the experiments conducted and the responses obtained.

Table 2.

Input processing variable for the FSP process and their levels.

Parameters	Symbol	Coded levels
—	—	−1	0	+1
V_TRS (rpm)	A	500	1400	1000
V_T (mm/min)	B	20	50	80
NoP	C	1	2	3
RV	D	25:75	50:50	75:25

Table 3.

DOE based on the Box–Behnken model.

Exp. No	Process parameters								Responses
Exp. No	V_TRS (rpm)		V_T (mm/min)		NoP			RV	H (HRB)	WR (micron/mm)	COF
Std	Co	Act	Co	Act	Co	Act	Co	Act	H (HRB)	WR (micron/mm)	COF
1	−1	500	−1	20	0	2	0	50:50	43	98.5	0.271
2	1	1000	−1	20	0	2	0	50:50	41	108.301	0.279
3	−1	500	1	80	0	2	0	50:50	42	119.6	0.292
4	1	1000	1	80	0	2	0	50:50	44	95.3	0.268
5	0	1400	0	50	−1	1	−1	25:75	35	213.88	0.369
6	0	1400	0	50	1	3	−1	25:75	32	363.07	0.462
7	0	1400	0	50	−1	1	1	75:25	30	305.19	0.454
8	0	1400	0	50	1	3	1	75:25	41	134.9	0.295
9	−1	500	0	50	0	2	−1	25:75	35	252.5	0.387
10	1	1000	0	50	0	2	−1	25:75	38	160.88	0.326
11	−1	500	0	50	0	2	1	75:25	38	145.1	0.321
12	1	1000	0	50	0	2	1	75:25	40	199.24	0.352
13	0	1400	−1	20	−1	1	0	50:50	37	161.23	0.328
14	0	1400	1	80	−1	1	0	50:50	36	182.56	0.341
15	0	1400	−1	20	1	3	0	50:50	40	138.98	0.301
16	0	1400	1	80	1	3	0	50:50	40	139	0.311
17	−1	500	0	50	−1	1	0	50:50	44	102.26	0.272
18	1	1000	0	50	−1	1	0	50:50	28	302.85	0.414
19	−1	500	0	50	1	3	0	50:50	30	250.045	0.381
20	1	1000	0	50	1	3	0	50:50	47	82.35	0.267
21	0	1400	−1	20	0	2	−1	25:75	38	146	0.322
22	0	1400	1	80	0	2	−1	25:75	35	256.046	0.401
23	0	1400	−1	20	0	2	1	75:25	43	180.71	0.339
24	0	1400	1	80	0	2	1	75:25	40	144	0.317
25	0	1400	0	50	0	2	0	50:50	38	146.395	0.325
26	0	1400	0	50	0	2	0	50:50	36	191.5	0.344
27	0	1400	0	50	0	2	0	50:50	37	174.481	0.328
28	0	1400	0	50	0	2	0	50:50	36	203.9	0.355
29	0	1400	0	50	0	2	0	50:50	37	175.9	0.335

Co: coded value in DOE; Act: actual value for process parameters.

Mechanical and wear properties analyses

To examine the mechanical characteristics of the surface composite, all 29 samples were subjected to hardness testing (HRB). For surface hardness testing, ASTM E18-15 standards were used with notified 200 g load conditions.

For wear analysis to investigate WR, a pin-on-disc tribometer was used with standard testing conditions (10 N of load and 1000 mm sliding distance). For micrographic analysis of the morphological property, ASTM G-99 standards were used by cutting the samples from the processed zone. Figure 3 shows the FSP-based specimens from the present study.

Figure 3.

Processed samples of the present study based on the DOE of Box–Behnken design.

Results and Discussion

Table 3 shows the results obtained for the processed composites. The tabular data has been further processed with a regression tool to obtain the regression equations that drive the output values for H, WR, and CoF. The data obtained were used again with ANOVA analysis to optimize the multi-responses obtained for the input processing parameters. Using the regression models developed by equation (1), further equations for the output characteristics were obtained using a suitable design expert tool.

Regression model for chosen responses

H a r d n e s s = 36.8 + 0.5 A - 0.416667 B + 1.66667 C + 1.58333 D + A B + 8.25 A C - 0.25 A D + 0.25 B C - {8.9}^{*} 10^{- 17} B D + 3.5 C D + {2.14167 A}^{2} + {3.26667 B}^{2} - {1.60833 C}^{2} - {0.983333 D}^{2}

(2)

W e a r R a t e = 178.435 - 1.59033 A + 8.56542 B - 13.3021 C - 23.603 D - 8.52525 A B - 92.0712 A C + 36.44 A D - 5.3275 B C - 36.689 B D - 79.87 C D - {28.039 A}^{2} - {46.3764}^{*} B^{2} + {29.3846 C}^{2} + {45.0347 D}^{2}

(3)

C O F = 0.3374 - 0.0015 A + 0.0075 B - 0.0134167 C - 0.01575 D - 0.008 A B - 0.064 A C + 0.023 A D - 0.00075 B C - 0.02525 B D - 0.063 C D - {0.0262 A}^{2} - {0.0337 B}^{2} + {0.019425 C}^{2} + {0.038175 D}^{2}

(4)

Effect of processing parameters using ANOVA analysis

To generate ANOVA tables and regression model coefficients, a design expert tool has been utilized. The aim of the model was to determine how closely the predicted results match the proposed input variables and to select the single optimal condition for combined processing variables. The significance of the RSM model was assessed using the significance level (P) and the Fisher (F) variation ratio, both of which were provided in the tables.

Analysis for hardness

Table 4 displays the statistical model that provides fit values for the selected model related to the output property, especially hardness. Further, Table 5 shows the analysis data for the same parameter. The model has demonstrated significance, with F-values of 24.54 and a noise (error) of 0.01%. However, the model has shown an F-value of only 2.73 (lack of fit), indicating the insignificant model, with a probability fit value of 17.30%. The R² value for hardness is 0.9609, indicating the model relevancy as the data has a close fit to the regression plot. Figure 4 displays the relevancy of the selected regression model from regression equation (2). The results suggest that the predicted values through the regression technique have a strong connection with experimental observation with a valid model.

Table 4.

Model stats for hardness as output characteristics.

SD	1.25	R ²	0.9609
M	37.97	Adjusted R²	0.9217
Coefficient of variation (%)	3.29	Predicted R²	0.7955
—	—	A.P	22.0575

S.D.: standard deviation; M: mean; A.P: adequate precision.

Table 5.

Quadratic ANOVA model analysis table for hardness.

Source	SS	DoF	Msquare	Fisher value	p-Value
Model	537.08	14	38.36	24.54	<.0001	Significant
A	3.00	1	3.00	1.92	.1876	—
B	2.08	1	2.08	1.33	.2676	—
C	33.33	1	33.33	21.33	.0004	—
D	30.08	1	30.08	19.25	.0006	—
AB	4.00	1	4.00	2.56	.1320	—
AC	272.25	1	272.25	174.17	<.0001	—
AD	0.2500	1	0.2500	0.1599	.6952	—
BC	0.2500	1	0.2500	0.1599	.6952	—
BD	0.0000	1	0.0000	0.0000	1.0000	—
CD	49.00	1	49.00	31.35	<.0001	—
A²	29.75	1	29.75	19.03	.0006	—
B²	69.22	1	69.22	44.28	<.0001	—
C²	16.78	1	16.78	10.73	.0055	—
D²	6.27	1	6.27	4.01	.0649	—
Residual	21.88	14	1.56	—	—	—
LoF	19.08	10	1.91	2.73	.1730	—
PE	2.80	4	0.7000	—	—	—
Cor total	558.97	28	—	—	—	—

SS: sum of square; DoF: degree of freedom; Msquare: mean of square; A: V_TRS; B: V_TS; C: NoP; D: RV; LoF: lack of fit; PE: pure error.

Figure 4.

Relevancy plot for the selected regression model from the regression equation of hardness (HRB).

From Table 4, it may be observed that the expected R² value of 0.7955 corresponds well with the adjusted R² value of 0.9217, where very less difference (<0.2) was observed between the two values. From Table 4, it may also be observed that the A.P value of the model (ratio 22.0575) indicates the level of clarity in the model, and a ratio above four is preferred; thus, it provides a clear signal that the selected model can effectively be utilized for navigating the design space.

Analysis for WR

Table 6 displays the fit statistics pertaining to the WR parameter. Table 7 shows the ANOVA data for the wear rate. The model indicates significance, as demonstrated by the F-value of 27.25 and noise (error) of 0.01%. However, the lack of fit F-value is 0.597, indicating an insignificant mode with a lack of fit probability of 76.85%. The wear rate has an R² value of 0.9646, indicating the model relevancy as the data has a close fit to the regression plot. Figure 5 displays the relevancy of the selected regression model from regression equation (3). The results suggest that the predicted values through the regression technique have a strong connection with experimental observation with a valid model.

Table 6.

Model stats for WR as output characteristics.

S. D	18.24	R ²	0.9646
M	178.44	Adjusted R²	0.9292
Coefficient of variation (%)	10.22	Predicted R²	0.8557
—	—	A.P	20.6023

Table 7.

Quadratic ANOVA model analysis table for WR.

Source	SS	DoF	Msquare	Fisher value	p-Value
Model	1.269 × 10⁵	14	9062.36	27.25	<.0001	Significant
A	30.35	1	30.35	0.0913	.7670	—
B	880.40	1	880.40	2.65	.1260	—
C	2123.35	1	2123.35	6.38	.0242	—
D	6685.22	1	6685.22	20.10	.0005	—
AB	290.72	1	290.72	0.8742	.3656	—
AC	33,908.46	1	33,908.46	101.96	<.0001	—
AD	5311.49	1	5311.49	15.97	.0013	—
BC	113.53	1	113.53	0.3414	.5683	—
BD	5384.33	1	5384.33	16.19	.0013	—
CD	25,516.87	1	25,516.87	76.73	<.0001	—
A²	5099.59	1	5099.59	15.33	.0016	—
B²	13,950.94	1	13,950.94	41.95	<.0001	—
C²	5600.79	1	5600.79	16.84	.0011	—
D²	13,155.42	1	13,155.42	39.56	<.0001	—
Residual	4655.89	14	332.56	—	—	—
LoF	2788.10	10	278.81	0.5971	.7685	—
PE	1867.78	4	466.95	—	—	—
Cor total	1.315 × 10⁵	28	—	—	—	—

Figure 5.

Relevancy plot for the selected regression model from the regression equation of WR (microns/mm).

From Table 6, it may be observed that the predicted R² value of 0.8557 corresponds well with the adjusted R² value of 0.9292, where very less difference (<0.2) was observed between the two values. From Table 6, it may also be observed that the A.P value of the model (ratio 20.6023) indicates the level of clarity in the model, and a ratio above four is preferred; thus, it provides a clear signal that the selected model can effectively be utilized for navigating the design space.

Analysis for CoF

Table 8 displays the fit statistics pertaining to the WR parameter. Table 9 shows the ANOVA data for the CoF. The model indicates significance, as demonstrated by the F-value of 39.83 and noise (error) of 0.01%. However, the LoF F-value is 0.8073, indicating an insignificant mode with a lack of fit probability of 64.48%. The CoF has an R² value of 0.9755, indicating the model relevancy as the data has a close fit to the regression plot. Figure 6 displays the relevancy of the selected regression model from regression equation (3). The results suggest that the predicted values through the regression technique have a strong connection with experimental observation with the valid model.

Table 8.

Model stats for CoF as output characteristics.

SD	0.0114	R ²	0.9755
M	0.3364	Adjusted R²	0.9510
Coefficient of variation (%)	3.38	Predicted R²	0.8930
—	—	A.P	25.4795

Table 9.

Quadratic ANOVA model analysis table for WR.

Source	SS	DoF	Msquare	Fisher value	p-Value
Model	0.0723	14	0.0052	39.83	<.0001	Significant
A	0.0000	1	0.0000	0.2083	.6551	—
B	0.0007	1	0.0007	5.21	.0386	—
C	0.0022	1	0.0022	16.67	0.0011	—
D	0.0030	1	0.0030	22.97	.0003	—
AB	0.0003	1	0.0003	1.98	.1817	—
AC	0.0164	1	0.0164	126.40	<.0001	—
AD	0.0021	1	0.0021	16.33	.0012	—
BC	2.250 × 10⁻⁶	1	2.250 × 10⁻⁶	0.0174	.8971	—
BD	0.0026	1	0.0026	19.68	.0006	—
CD	0.0159	1	0.0159	122.49	<.0001	—
A²	0.0045	1	0.0045	34.35	<.0001	—
B²	0.0074	1	0.0074	56.83	<.0001	—
C²	0.0024	1	0.0024	18.88	.0007	—
D²	0.0095	1	0.0095	72.93	<.0001	—
Residual	0.0018	14	0.0001	—	—	—
LoF	0.0012	10	0.0001	0.8073	.6448	Not significant
PE	0.0006	4	0.0002	—	—	—
Cor total	0.0741	28	—	—	—	—

Figure 6.

Relevancy plot for the selected regression model from the regression equation for COF.

From Table 8, it may be observed that the predicted R² value of 0.8930 corresponds well with the adjusted R² value of 0.9510, where very less difference (<0.2) was observed between the two values. From Table 8, it may also be observed that the A.P value of the model (ratio 25.4795) indicates the level of clarity in the model, and a ratio above four is preferred; thus, it provides a clear signal that the selected model can effectively be utilized for navigating the design space.

Process multi-response optimization

For multi-response optimization to get a conclusion regarding the optimized condition of FSP processing of Al composite, a well-explored multi-response optimization statistical tool has been used with the desirability concept.⁴⁵ In the present study, the desirability of the output has been selected from 0 to 1, as discussed (Table 10), with 1 being the most desirable. As the work has included four different input process parameters of the FSP process, therefore multi-response optimization for four input parameters has been prepared as reflected in Table 10. Table 10 shows the constraints used to obtain the optimized results. The result of the present work targets to satisfy all the selected processing variables toward their best output by reducing the WR and CoF with improving the surface hardness. Figure 7 shows the optimization plots and optimization results for the four responses. Using design expert software, it is recommended to set the corresponding parameters according to Table 11.

Table 10.

Constraints used for the optimization in multi-response optimization.

Details	Lower limit	Upper limit	Lower weight	Upper weight	Importance
A: A	−1	1	1	1	3
B: B	−1	1	1	1	3
C: C	−1	1	1	1	3
D: D	−1	1	1	1	3
H	28	47	1	1	3
WR	82.35	363.07	1	1	3
CoF	0.267	0.462	1	1	3

Aim: reducing the WR and CoF; improving the H (surface hardness).

Figure 7.

Multi-response optimization plot.

Table 11.

Optimized result from multi-response technique.

No.	Process parameter	Optimum conditions
1	NoP	3 passes
2	V_T	50 mm/min
3	V_TRS	1000 r/min
4	RV	75:25 (SiC: Gr)

Validation of the optimization process

To validate the optimized process parameters, experimental confirmation tests were performed to verify their accuracy. The average wear rate, COF, and hardness for the confirmation experiment are observed in Table 12. The experimental results fall between the confidence intervals and the predicted optimal values, as shown in Table 13.

Table 12.

Confirmatory test observations.

No.	Output	Value
1	Hardness	46.115
2	Wear rate	80.525
3	COF	0.245

Table 13.

Confirmation of optimal FSP conditions.

Property	P_median	P_mean	SD	n	SE. Pred	95% PI low	95% PI high	Observed data mean
Hardness	47.75	47.75	1.25024	2	1.30129	44.959	50.541	46.115
Wear rate	72.8171	72.8171	18.2363	2	18.981	32.107	113.527	80.525
COF	0.251708	0.251708	0.0113849	2	0.0118498	0.226293	0.277124	0.245

P_median: predicted median; P_mean: predicted mean.

Optical microscopy analysis

The optical microscopy analysis was conducted to check the microstructure of the samples with optimized process parameters. Figure 8 shows the microscopic image of the specimens. The analysis has suggested homogeneous distribution of foreign particles on the surface of Al composite alloy. Results indicated that the processed region shows better grain refinement in comparison to the base metal AA5083. The formation of onion rings in the structure shows a good uniform dispersion of the reinforcement particles in the matrix. The interface between the nugget and the thermomechanical affected zone (TMAZ) can be seen in the figure which shows the material processing during the FSP. The microstructure showed a better distribution of the reinforcement particles along with the grain refinement in the processed region.

Figure 8.

Optical microscopy of the confirmation test samples from the nugget region.

Discussion

In this study, a hybrid surface composite of AA5083/(SiC-Gr) was developed using the friction stir processing (FSP) method, and the process parameters were optimized through the response surface methodology (RSM). The composite exhibited notable enhancements in hardness, tribological properties, and microstructural characteristics. The RSM allowed to create a mathematical model based on different input process parameters of the FSP method, enabling them to predict the output characteristics of the hybrid composite. The significant parameters were identified through ANOVA analysis as the number of passes (NoP) and reinforcement volume (RV). The study demonstrated a close agreement between the predicted outcomes and experimental observations, with a range of ±10%. The regression equation generated using experimental data accurately predicted the hardness, wear rate, and coefficient of friction (COF) properties of the composite. The optimization process using the RSM and multi-objective selection criteria successfully determined the optimal FSP process parameters.

The optimized parameters for AA5083/(SiC-Gr) were found to be a rotational speed of 1000 r/min, a tool traverse speed of 50 mm/min, three FSP passes, and a reinforcement volume fraction of 75:25 (SiC:Gr). Microstructural analysis confirmed the improved microstructure and uniform dispersion of the reinforcement material in the FSP specimen with these parameters. Overall, this study provided valuable insights into the development and optimization of hybrid surface composites using the FSP method. The findings contribute to the understanding of surface modification techniques for aluminum alloys and have implications for various industries, including aerospace, automobile, and marine. Further research could explore additional properties and applications of the developed composite, as well as scalability and industrial feasibility.

Conclusion

In this study, the response surface methodology (RSM) was employed to optimize the friction stir processing (FSP) parameters for enhancing the hardness, tribological, and microstructural properties of the AA5083 aluminum alloy with SiC-Gr hybrid reinforcements. The regression equation generated using experimental data on hardness, wear rate, and coefficient of friction (COF) proved effective in predicting outcomes. The predicted results showed a close agreement with the experimental observations within a range of ±10%. The optimal FSP process parameters for AA5083/(SiC-Gr) were determined as a rotational speed of 1000 r/min, a tool traverse speed of 50 mm/min, three FSP passes, and a reinforcement volume fraction of 75:25 (SiC:Gr). Microstructural analysis using optical microscopy revealed improved microstructure and uniform dispersion of the reinforcement material in the FSP specimen with these optimized parameters.

Footnotes

Acknowledgments

The authors express their gratitude to Mr Abhijat Joshi, Dharmendra Naik, Jayesh Panchal, and Ramjibhai Patel for their help during the experimental phase. The author also express gratitude twoards D Y Patil International University for technical support provided for the work.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors appreciate the Department of Mechanical Engineering, Nirma University, for the financial assistance to implement the research (Research No. E1A07A). The Open Access fee for publication of the manuscript is supported by the Universität der Bundeswehr München.

ORCID iDs

Sudhir Kumar

Seyed Saeid Rahimian Koloor

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An insight on optimization of FSP process parameters for the preparation of AA5083/(SiC-Gr) hybrid surface composites using the response surface methodology

Abstract

Keywords

Introduction

Scope for the present study

Materials and Method

Design of the experiment

Response surface methodology

Experimental work

Selection of the base material

Reinforcement and tool selection

Box–Behnken design

Mechanical and wear properties analyses

Results and Discussion

Regression model for chosen responses

Effect of processing parameters using ANOVA analysis

Analysis for hardness

Analysis for WR

Analysis for CoF

Process multi-response optimization

Validation of the optimization process

Optical microscopy analysis

Discussion

Conclusion

Footnotes

Acknowledgments

Declaration of conflicting interests

Funding

ORCID iDs

References