Turning Parameters Optimizing for Surface Quality of Al- MMCs: A Comparative Study of Cutting Environments

doi:10.21203/rs.3.rs-2731847/v1

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Turning Parameters Optimizing for Surface Quality of Al- MMCs: A Comparative Study of Cutting Environments

https://doi.org/10.21203/rs.3.rs-2731847/v1

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The machining of Aluminum-based Metal Matrix Composites (Al-MMCs) is challenging due to their inhomogeneity, anisotropic nature, and dynamic cutting forces. In this paper, the effect of machining parameters, including cutting speed, feed rate, and depth of cut, on surface quality (Ra) and cutting forces (Fc) during turning of Al-MMCs under different cutting conditions (DRY, WET, and MQL) was investigated. Statistical analysis tools were used to analyze the experimental results, and ANOVA and RSM techniques were used to model the relationships between machining parameters and responses. The results showed that feed rate had the most significant effect on both Ra and Fc for all machining conditions. The optimum feed rate of 0.03 mm/rev was found to produce the best surface finish and lower cutting forces in all conditions. The DRY mode of machining was found to be optimal for surface finish, and the MQL mode was found to be effective in reducing cutting forces due to its cooling and lubrication properties. Future research should focus on investigating the effect of different cutting tool materials and geometries on the machinability of Al-MMCs and developing more effective and sustainable machining strategies.

Optimization

process parameters

machining

metal matrix composite

design of experiments

response surface methodology

Despite significant advances in machining technology, machining of Aluminum-based Metal Matrix Composites (Al-MMCs) is still a challenging task. To improve the surface quality of machined components and reduce tool wear, it is necessary to optimize the machining parameters such as cutting speed, feed rate, and depth of cut. The use of the proper machining environment mode is also critical to achieving the desired surface quality.

The increasing demand for Al-MMCs in various industries such as automobile, marine, aircraft, and aerospace calls for further research to address the challenges associated with their machining. This is due to the AI-MMCs properties like high strength-to-weight ratio, excellent fatigue resistance, low coefficients of thermal expansion, high stiffness, good workability, and better wear resistance. However, machining of MMCs is difficult due to their inhomogeneity, anisotropic nature, and dynamic cutting forces. Industrial applications of MMCs are often hampered by rapid tool wear, high cutting energy, and poor surface quality during machining [1]. In today’s scenario of metal cutting industries, the input parameters such as cutting velocity, depth of cut, feed or any other vital parameters are required to be optimized. Optimization will result into economic benefits for any manufacturing industries by reducing the total manufacturing cost per component. The latest techniques which are used for optimization are response surface methodology(RSM), Particle Swarm Optimization (PSO), Genetic algorithm(GA), Artificial Bee Colony(ABC), Grey relational theory, fuzzy logic, scatter search technique, Simulated Annealing(SA), and Taguchi technique, etc. An optimization method is a significant tool broadly adopted in the machining process to find the appropriate range of input process parameters so that the requirements of the product can be achieved. In other words, the optimization of machining variables is an important area where efforts are made to obtain the optimum values of variables for certain desired output responses. Therefore, various studies have been conducted on machining MMCs to optimize the machining parameters for desirable responses such as surface roughness and cutting forces [2–9]. Optimizing machining parameters helps in determining suitable input parameter values in advance for machining MMCs to achieve high-quality components. S.P. Palaniappan et al, investigates the machining of Aluminium 6082 using Taguchi and ANOVA. They found that the most significant parameter for MRR was speed similarly feed was the most significant parameter for Surface roughness. For MRR the optimum parameters are 1600 rpm speed, 0.25 mm/rev feed and 1.0 mm depth of cut and for Surface roughness the optimum parameters are 1200 rpm speed, 0.15 mm/rev feed and 1.5 mm depth of cut [10]. K. Soorya Prakash et al also studied Multi-objective optimization using Taguchi based grey relational analysis on reinforced Aluminum MMC [11]. A mathematical model to foresee responses, namely surface finish, vibration intensity, and temperature at work-tool interface was developed by M. Natraj and K. Balasubramanian when assessing machinability of hybride MMCs on CNC lathe [12]. According to Das, et al [13] the input parameters were optimized by making use of the Grey based Taguchi approach. They projected surface roughness values from their developed regression-based models. They have obtained the optimum value for depth of cut and feed is 0.4mm and 0.04mm/rev respectively. Among the parameters influencing surface roughness (Ra &Rz)., the feed was the most influential. Mohammed et al [14] concluded that RSM is a preferable tool for optimization and can predict the effects of variables on response. For nonlinear modeling applications, artificial neural networks (ANN) and fuzzy logic are additionally applied. However, ANN uses greater volume of experimental data. The rigidity of fuzzy logic is based on uncertainty, so rules must be developed properly in order to prevent errors. In this multi-objective optimization approach, Warsi et al. [15]. found that cutting speed and feed were the most effective parameters. Numerous works in machinability of MMCs was conducted by using Taguchi method. Using the Taguchi technique [16], the parameter values of its process were also optimized. The characteristics of the reinforcing materials affected the surface roughness and other machinability indices of AMCs significantly. significant impact on the surface roughness of AMCs as well as other machinability indices. This is because the reinforced material's overall ductility has decreased, and its hardness has increased. [17–19].

A methodology called Multivariate Robust Parameter Design (MRPD), utilised by Paiva and colleagues [20], is utilized to optimize the cutting parameters of AISI 52100 hardened steel when used in conjunction with wiper mixed ceramic inserts. Cutting speed of 200 m/min, feed of 0.20mm/rev and depth of cut of 0.2 mm were attained to be the optimum input parameters. Even Principal Component Analysis (PCA) was also employed to find out the association between the responses. A response surface methodology (RSM) was used by Meddour et.al [21] to model cutting forces and surface roughness. Based on the results of an ANOVA, they concluded that cutting force is primarily determined by the depth of cut. Ozel et al [22] used wiper ceramic inserts to turn on AISI D2 steels. Multiple linear regression models and neural network models were created in order to make predictions for surface roughness. In their study, Senthilkumar et al. [23] utilized the GRA-PCA technique to optimize the input process parameters. The experimental results indicated that a cutting speed of 250 m/min, a feed of 0.2 mm/rev, and a depth of cut of 0.4 mm led to optimal tool wear, workpiece surface roughness, and material removal. According to the ANOVA analysis, the most significant input parameter was the tool coating material, accounting for 72.87% of the output response variability, followed by the feed (17.15%), depth of cut (4.89%), and cutting speed (2.78%). The machinability behavior of hardened steel was examined by Gopalsamy et al. [24], who also utilized grey relational analysis to evaluate the optimal parameters for both rough and finish turning processes.

The appropriate range of machining parameters was selected and a design of experiment was planned accordingly. The Al-MMCs were subjected to the turning process, while the cutting was executed with TiAlN coated tungsten carbide inserts in three varying cutting environments, namely DRY, WET, and MQL. To measure the average surface roughness (Ra), Taylor Hobson Talysurf was used, which took into account the results of all runs in each environment. The cutting force (Fc) was recorded during turning of Al-MMCs using Kistler 9129 three-axis dynamometer. RSM was employed to build regression models for prediction, which were further supported by confirmatory experiments. The prominent parameters for each cutting environment were determined using ANOVA. Finally, the created models were used in the optimization process to achieve the optimum values of parameters under the appropriate cutting environments for higher quality surface finish and lower Fc.

2.1 Design of Experiments and RSM (Development of Mathematical models)

For the trials, a design matrix with four input variables i.e. cutting speed, feed rate, depth of cut, and nose radius each with five levels was created using a Central Composite Design (CCD). As indicated in Table 1, the design matrix included 31 experimental runs with 16 factorial points, 7 centre points, and 8 axial points. The ANOVA was carried out using Minitab 19 software to analyze the data.

The Response Surface Methodology (RSM), created by George E. P. Box and K. B. Wilson in 1951, was used to examine how input variables such as surface roughness (Ra) and cutting force affected the output responses (Fc). Multi-input/multi-output systems can be modelled and optimized with the help of RSM, which also makes it possible to determine the actual relationship between the input and output variables. This study's quadratic model was based on a second-order polynomial regression equation with 15 terms and 4 design variables.

Ra or Fc

=C₀ + C₁×V + C₂×f + C₃×D + C₄×R + C₅×V² + C₆×f² + C₇×D² + C8×R² + C₉×V×f + C₁₀×V×D + C₁₁×V×R + C₁₂×f×D + C₁₃f×R + C₁₄×D×R

(1)

In this equation, C0 to C14 represent the regression coefficients, and the model includes not only constant and linear terms, but also squared and interaction terms. The quadratic model was chosen based on its ability to capture the nonlinear effects of the input variables on the output responses.

2.2 Optimization Design and Procedure

The multi-criteria optimization problem can be formulated using the output variable's function, we find the value of V, f, D, and R considering the following formulation:

Minimize: Ra and Fc
s.t.
V_L-V ≤ 0, V-V_U,		(2)
f_L-f ≤ 0, f-f_U		(3)
D_L-D ≤ 0, D-D_U		(4)
R_L-R ≤ 0, R-R_U		(5)

Here, V, f, D, and R are the input variables, Ra and Fc are the objective functions, and VL, fL, DL, RL, VU, fU, DU, and RU represent the lower and upper bounds of the design variables.

The optimization problem can also be expressed as follows:

Find X to
Minimize 𝑓(𝑋) + qΣ𝑔_𝑗(𝑋) _𝑗		(6)
Maximize 𝑓(Y) + qΣ𝑔𝑗(𝑋) 𝑗		(7)

where 𝑋 is a design variables vector, 𝑓(𝑋) and f(Y) are the responses to be minimized, 𝑔𝑗(𝑋) signify constraints of positive values (for j = 1 to z, where z signifies number of constraints), and q is a penalty factor that starts at 1 and increases up to 100. The optimum point is obtained through convergence, which is achieved when the difference between two successive events or the ratio shift between two functions becomes less than10⁻⁶.

The turning of Al-MMCs on a workpiece was performed using a CNC lathe machine and TiAlN-coated tungsten carbide (KC5526) inserts. The workpiece was a cylinder measuring 100 mm in diameter and 300 mm in length. The inserts used were of ISO designation CNMGXXXXXX and were paired with a left-hand tool holder specification, specifically PCLNL2020K12 (KENNAMETAL). To prevent the influence of edge or nose wear on responses, a new insert was used for every run of 10 mm length. For both the Wet and Minimum Quantity Lubrication (MQL) conditions, castor oil was used. An indigenous MQL set-up was employed, with an air pressure of 1.4 bar and a 1:10 mixture of castor oil and water, as shown in Fig. 1

Surface roughness measurements were taken using a Talysurf, specifically the Talor Hobson Surtronic 3 + roughness tester. A sampling length of 0.8 mm and an evaluation length of 4 mm were set up, along with a Gaussian filter to measure surface roughness. Three readings of surface roughness were taken at three different locations (i.e., machined segments) that were 120 degrees apart from each other on all the machined segments. To record the cutting forces during the experiment, a Kistler 9129 Dynamometer integrated with a PC-based data-acquisition system was used.

4.1 ANOVA and Regression Analysis

The significance of turning parameters and their interactions was evaluated using ANOVA for all cutting conditions. The results of the regression analysis were obtained using RSM for each mode, and the regression coefficients, as well as ANOVA findings, are presented in Tables 2, Table 3, and Table 4 for DRY, WET, and MQL cutting conditions.

Based on the variance analysis at a 95% confidence level, if the P-values are less than 0.05, the corresponding regression terms of predictive models were identified as significant parameters. Moreover, the R-squared values were found to be higher than 95% and 97% for Ra and Fc, respectively, while R-sq (adj) values were higher than 91% and 96% for Ra and Fc, respectively. Based on these findings, the models for Ra and Fc are statistically fit and can be used to estimate Ra and Fc with a high level of confidence.

From ANOVA Tables 2, Table 3 and Table 4, it can be observed that the feed value has a significant influence on both Ra and Fc for all turning conditions of Al-MMCs.

4.2 Turning parameters influence on Ra and Fc

The feed rate was found to have a greater impact on the responses compared to cutting speed, depth of cut, and nose radius. To achieve a favourable Fc, increasing the cutting speed was found to be suitable in the MQL condition, but poor results were obtained in the WET and DRY modes. The sharp nose radius of the insert wore out quickly, causing the SiC particles present in the MMCs to be pulled out, resulting in poor surface finish. Increasing the nose radius leads to lower surface roughness and improves the surface quality of the turned component. The MQL technique's cooling and lubrication properties were found to be superior to other modes of turning Al-MMCs, resulting in lower cutting forces. Wet turning was found to be suitable for achieving good surface finish due to the effective cooling and cleaning of the cutting zone, with a positive impact on cutting force during cutting

Table 1

Central composite design of experiment
S. No.	V (m/min)	f (mm/rev.)	D (mm)	R (mm)	S. No.	V (m/min)	f (mm/rev.)	D (mm)	R (mm)
1	70	0.03	0.7	0.4	17	50	0.10	1.0	0.8
2	110	0.03	0.7	0.4	18	130	0.10	1.0	0.8
3	70	0.18	0.7	0.4	19	90	0.04	1.0	0.8
4	110	0.18	0.7	0.4	20	90	0.22	1.0	0.8
5	70	0.03	1.3	0.4	21	90	0.10	0.4	0.8
6	110	0.03	1.3	0.4	22	90	0.10	1.6	0.8
7	70	0.18	1.3	0.4	23	90	0.10	1.0	0.2
8	110	0.18	1.3	0.4	24	90	0.10	1.0	1.6
9	70	0.03	0.7	1.2	25	90	0.10	1.0	0.8
10	110	0.03	0.7	1.2	26	90	0.10	1.0	0.8
11	70	0.18	0.7	1.2	27	90	0.10	1.0	0.8
12	110	0.18	0.7	1.2	28	90	0.10	1.0	0.8
13	70	0.03	1.3	1.2	29	90	0.10	1.0	0.8
14	110	0.03	1.3	1.2	30	90	0.10	1.0	0.8
15	70	0.18	1.3	1.2	31	90	0.10	1.0	0.8
16	110	0.18	1.3	1.2

Table 2

ANOVA for Ra and Fc in DRY condition
Predictor	Ra (µm)		Fc (N)
Predictor	Coef.	_P-value	Coef.	_P-value
Constant	-1.9314		-205.978
V	0.0217	0.001	2.291	0.008
f	5.2883	˂ 0.0001	724.244	˂ 0.0001
D	2.6554	0.0140	188.219	0.015
R	0.5731	0.033	108.747	0.005
V*V	-0.0002	0	-0.007	0.061
f*f	-31.4109	0	318.853	0.572
D*D	-1.31	0	-40.171	0.019
R*R	-0.5613	0	-82.899	0
V*f	0.0429	0.006	-8.756	0
V*D	0.0006	0.795	-0.116	0.713
V*R	0.0016	0.361	0.912	0.001
f*D	-0.2792	0.76	732.917	0
f*R	0.1844	0.788	497.812	0
D*R	-0.012	0.917	-88.802	0
Model		˂ 0.0001		˂ 0.0001
R-sq(adj)		91.4		98.72
R-sq		95.41		99.32

Table 3

ANOVA for Ra and Fc in WET condition
Predictor	Ra (µm)		Fc (N)
Predictor	Coef.	_P-value	Coef.	_P-value
Constant	-0.0777		-276.69
V	0.0088	0.02	5.2	0.001
f	-2.3154	˂ 0.0001	213.95	˂ 0.0001
D	0.2097	0.326	102.5	0.216
R	0.5775	0.001	157.42	0.015
V*V	-0.0001	0	-0.03	0.001
f*f	-11.4327	0	3551.65	0.002
D*D	-0.3648	0	-76.07	0.011
R*R	-0.4935	0	-60.03	0.004
V*f	0.0551	0	-8.54	0.016
V*D	0.0023	0.11	0.94	0.094
V*R	0.0003	0.744	-0.09	0.819
f*D	1.4417	0.019	820	0.001
f*R	0.1937	0.646	-159.38	0.331
D*R	0.0771	0.28	-14.17	0.6
Model		˂ 0.0001		˂ 0.0001
R-sq(adj)		93.05		96.04
R-sq		96.29		97.89

Table 4

ANOVA for Ra and Fc in MQL condition
Predictor	Ra (µm)		Fc (N)
Predictor	Coef.	_P-value	Coef.	_P-value
Constant	-0.0074		-194.34
V	0.0033	0.214	3.76	0.001
f	2.3497	˂ 0.0001	-182.3	˂ 0.0001
D	0.8986	0	121.87	0.056
R	0.0229	0.844	131.32	0.007
V*V	-0.0001	0	-0.02	0.001
f*f	-25.1857	0	4296.73	0
D*D	-0.7024	0	-36.9	0.077
R*R	-0.4233	0	-30.62	0.033
V*f	0.0349	0	-3.69	0.137
V*D	0.0039	0.002	0.37	0.356
V*R	0.0031	0.001	-0.04	0.886
f*D	0.9875	0.032	594.58	0.002
f*R	1.0781	0.003	-57.19	0.634
D*R	0.1234	0.032	-58.39	0.009
Model		˂ 0.0001		˂ 0.0001
R-sq(adj)		96.01		98.81
R-sq		97.82		97.76

4.2 Validation Tests

The accuracy of the Ra and Fc predictive models developed for each mode was confirmed through validation tests. Table 6 displays the excellent agreement between experimental and predicted values of Ra and Fc for all cutting environments. Contour plots for Ra and Fc were generated for the WET and MQL cutting conditions, as shown in Figs. 2 and 3, respectively.

These plots demonstrate that the roughness value may vary between 0.2 and 0.9 µm, while Fc may range between 118 N to 480 N. The findings suggest that the predictive models can be reliably used to estimate Ra and Fc under different cutting conditions, thereby enabling manufacturers to optimize their machining processes and achieve high-quality machined components.

Table 5

Validation tests
S. No.	V (m/min)	f (mm/rev.)	D (mm)	R (mm)
16	110	0.18	1.3	1.2
23	90	0.13	1	0.2

Table 6

Validation tests outcome for distinct cutting environments
		Ra (µm)		Fc (N)
	Run	Predicted	Measured	Predicted	Measured
DRY	16	0.2045	0.2169	318.37	322.58
	23	0.5466	0.5395	203.52	213.67
WET	16	0.4281	0.4311	242.20	251.54
	23	0.4169	0.4285	174.24	186.25
MQL	16	0.4523	0.4599	257.45	269.36
	23	0.5394	0.5448	194.52	201.93

4.3 Optimization of Surface Roughness and Cutting Force

Table 7 displays the suitable values of Ra and Fc obtained for the optimal levels of cutting parameters V, f, D, and R for the DRY, WET, and MQL conditions

Table 7

The optimum solutions for surface roughness
S. No.	V (m/min)	f(mm/rev.)	D (mm)	R (mm)	Ra (µm)	Fc (N)
DRY	70	0.03	0.4	1.2	0.2097	196.15
WET	110	0.03	1.6	0.2	0.3168	245.41
MQL	130	0.03	0.4	1.6	0.2164	162.8

As shown in Figs. 3 and 4, Ra and Fc increased with the feed rate, but the cutting speed had the most significant impact. A feed rate of 0.03 mm/rev was found to be the most favorable for all cutting environments as it resulted in lower Ra and Fc values. Turning in the WET condition produced lower Ra due to effective flushing of chips and cleaning of the cutting zone. In contrast, turning in the MQL condition reduced cutting forces due to the effective cooling and lubrication properties of the fluid.

This paper presents a comprehensive investigation of the effect of various cutting conditions on the surface roughness (Ra) and cutting forces (Fc) during turning of Al-MMCs. The results showed that the feed rate had the greatest influence on Ra and Fc for each cutting condition. The optimization study demonstrated that the lowest feed rate of 0.03 mm/rev was found to be the most favourable for achieving better quality of surface finish and lower cutting forces in all cutting conditions. The dry condition of machining was found to be most suitable for achieving better surface finish, while MQL condition was effective in reducing cutting forces due to its efficient cooling and lubrication properties. These findings can be useful for improving the quality of machined parts and reducing manufacturing costs in various industries that utilize Al-MMCs. Further studies can be performed to investigate the effect of other cutting parameters and their interactions on the machinability of Al-MMCs.

Future research should focus on developing new cutting tools with improved wear resistance, toughness, and edge sharpness, suitable for machining MMCs. Additionally, advanced cooling and lubrication systems should be developed to enhance the machining environment and reduce the high cutting energy associated with machining MMCs.

Moreover, exploring new techniques such as cryogenic machining, laser-assisted machining, and electro-discharge machining can potentially improve the machining process of Al-MMCs. These techniques have shown promise in achieving high-quality components with minimal tool wear and reduced cutting forces.

Furthermore, it is necessary to investigate the impact of MMC microstructure on machining performance. The microstructure of MMCs significantly affects the machining response, and hence, understanding its influence is crucial to achieving optimal machining parameters. Therefore, further research is required to develop a comprehensive understanding of the relationship between the microstructure of MMCs and their machinability.

Data Availability Statement

The currently provided statement has been changed as per the journal policy as follows: The datasets are collected from the experiments and is truly original in its form and the same is included in manuscript after conclusion section. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Pul, M., & Yağmur, S. (2022). Examination of the effect of B4C and GNP reinforcements on machinability in the machining of Al 6061 matrix B4C/GNP reinforced hybrid composites. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 44(10), 1-14.
Gururaja, S., Ramulu, M., & Pedersen, W. (2013). Machining of MMCs: a review. Machining Science and Technology, 17(1), 41-73.
Karabulut, Ş., Karakoç, H., & Çıtak, R. (2016). Influence of B4C particle reinforcement on mechanical and machining properties of Al6061/B4C composites. Composites Part B: Engineering, 101, 87-98.
Nicholls, C. J., Boswell, B., Davies, I. J., & Islam, M. N. (2017). Review of machining metal matrix composites. The International Journal of Advanced Manufacturing Technology, 90(9), 2429-2441.
Kumar, N. S., Shankar, G. S., Basavarajappa, S., & Suresh, R. (2017). Some studies on mechanical and machining characteristics of Al2219/n-B4C/MoS2 nano-hybrid metal matrix composites. Measurement, 107, 1-11.
Ajithkumar, J. P., & Xavior, M. A. (2019). Cutting force and surface roughness analysis during turning of Al 7075 based hybrid composites. Procedia Manufacturing, 30, 180-187.
Venkatesan K, Ramanujam R, Joel J, Jeyapandiarajan P, Vignesh M, Tolia DJ, Venkata Krishna R (2014) Study of cutting force and surface roughness in machining of Al alloy hybrid composite and optimized using response surface methodology. Procedia Engineering, 97:677–686.
Memiş F, Turgut Y (2020) Experimental ınvestigation of surface roughness and cutting forces in machining of AISI 2205 duplex stainless steel on CNC lathe. Manufacturing Technology Applications 1(1):22–33
Akgün M, Demir H (2021) Experimental and numerical analysis of the effect of cutting parameters on cutting force and chip formation in turning process. El-Cezeri 8(2):897–908.
Palaniappan, S. P., Muthukumar, K., Sabariraj, R. V., Kumar, S. D., & Sathish, T. (2020). CNC Turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA. Materials Today: Proceedings, 21, 1013-1021.
Prakash, K. S., Gopal, P. M., & Karthik, S. (2020). Multi-objective optimization using Taguchi based grey relational analysis in turning of Rock dust reinforced Aluminum MMC. Measurement, 157, 107664.
Nataraj, M., & Balasubramanian, K. (2017). Parametric optimization of CNC turning process for hybrid metal matrix composite. The International Journal of Advanced Manufacturing Technology, 93, 215-224.
Das, D. K., Sahoo, A. K., Das, R., & Routara, B. C. (2014). Investigations on hard turning using coated carbide insert: Grey based Taguchi and regression methodology. Procedia Materials Science, 6, 1351-1358.
Iqbal, U. Mohammed, VS Senthil Kumar, and S. Gopalakannan. "Application of Response Surface Methodology in optimizing the process parameters of Twist Extrusion process for AA6061-T6 aluminum alloy." Measurement 94 (2016): 126-138.
Warsi, S. S., Agha, M. H., Ahmad, R., Jaffery, S. H. I., & Khan, M. (2019). Sustainable turning using multi-objective optimization: a study of Al 6061 T6 at high cutting speeds. The International Journal of Advanced Manufacturing Technology, 100, 843-855.
D.S.C. Kishore, K.P. Rao, A. Ramesh, Optimization of machining parameters for improving cutting force and surface roughness in turning of Al6061-TiC in-situ metal matrix composites by using Taguchi method, Mater. Today:. Proc. 2 (4- 5) (2015) 3075–3083, https://doi.org/10.1016/j.matpr.2015.0 7.249.
S. Bose, A. Pandey, A. Mondal, Comparative analysis on aluminum-silicon carbide hybrid green metal matrix composite materials using waste egg shells and snail shell ash as reinforcements, Mater. Today:. Proc. 5 (14) (2018) 27757–27766, https://doi.org/10.1016/j.matpr.2018.10.011.
V. Mahesh Kumar, C.V. Venkatesh, Effect of ceramic reinforcement on mechanical properties of aluminum matrix composites produced by stir casting process, Mater. Today . Proc. 5 (1) (2018) 2466–2473, https://doi.org/ 10.1016/j.matpr.2017.11.027.
W. Bai, A. Roy, R. Sun, V.V. Silberschmidt, Enhanced machinability of SiCreinforced metal-matrix composite with hybrid turning, J. Mater. Process. Technol. 268 (2019) 149–161, https://doi.org/10.1016/j.jmatprotec.2019.01.017
A. P. Paiva, P. H. Campos, J. R. Ferreira, L. G. D. Lopes, E. J. Paiva, and P. P. Balestrassi, “A multivariate robust parameter design approach for optimization of AISI 52100 hardened steel turning with wiper mixed ceramic tool,” Int. J. Refract. Met. Hard Mater., vol. 30, no. 1, pp. 152–163, Jan. 2012, doi: 10.1016/j.ijrmhm.2011.08.001
I. Meddour, M. A. Yallese, R. Khattabi, M. Elbah, and L. Boulanouar, “Investigation and modeling of cutting forces and surface roughness when hard turning of AISI 52100 steel with mixed ceramic tool: cutting conditions optimization,” Int. J. Adv. Manuf. Technol., vol. 77, no. 5, pp. 1387–1399, Mar. 2015, doi: 10.1007/s00170-014-6559-z.
T. Özel, Y. Karpat, L. Figueira, and J. P. Davim, “Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts,” J. Mater. Process. Technol., vol. 189, no. 1, pp. 192–198, Jul. 2007, doi: 10.1016/j.jmatprotec.2007.01.021.
N. Senthilkumar, V. Selvakumar, and T. Tamizharasan, “Optimization and Performance Analysis of Uncoated and Coated Carbide Inserts during Hard Turning AISI D2 Steel Using Hybrid GRA-PCA Technique,” Applied Mechanics and Materials, 2016.
B. M. Gopalsamy, B. Mondal, and S. Ghosh, “Optimisation of machining parameters for hard machining: grey relational theory approach and ANOVA,” Int. J. Adv. Manuf. Technol., vol. 45, no. 11, p. 1068, Apr. 2009, doi: 10.1007/s00170-009-2054-3.

No competing interests reported.

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Turning Parameters Optimizing for Surface Quality of Al- MMCs: A Comparative Study of Cutting Environments

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Abstract

Figures

1. Introduction

2. Methodology

2.1 Design of Experiments and RSM (Development of Mathematical models)

2.2 Optimization Design and Procedure

3. Experimental Methods And Measurements

4. Results And Conversation

4.1 ANOVA and Regression Analysis

4.2 Turning parameters influence on Ra and Fc

4.2 Validation Tests

4.3 Optimization of Surface Roughness and Cutting Force

5. Conclusion

Declarations

References

Additional Declarations

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