Turning Process Parameters Optimization of Al7075 Hybrid Mmcs Composite Using Topsis Method

Abstract

Keywords: Turning; Surface Roughness; MRR; MCDM; CRITIC; TOPSIS.

1. Introduction

Turning operation is one of the most common machining operation in which rotational parts are being produced by removing material there by obtaining reduced size of required diameter. The turning operation can be performed generally in lathe machine with the use of single point cutting tool. For turning the work piece in lathe, it is required to fix the cutting tool in the fixture and the work piece made to be rotated continuously. In various kinds of industries, the turning is the most widely used machining operation to produce required shape of different components.[1– 2]. According to the wide research conducted in the area of MMC cutting process by the researchers around the globe, it has been found that the main aspects, affecting MMC turning operation predominantly depends on the type of material being used [3–4]. Usually, according to the guidelines set by standard handbooks, the experience of operators, and the knowledge, the machining parameters are selected. However, if the chosen machining parameters are not optimum, then it may eventually increase the cost of the product [5]. By choosing the best machining parameters, one can achieve high machining performance [6]. In the process of choosing the best combination of best machining parameters, the researcher makes use

Corresponding Author: e-mail: vajram22@gmail.com, tel: +251-936082152 2043

of the optimization techniques [7]. Novel materials are manufactured to meet certain industrybased requirements in the manufacturing arena. However,it may not be feasible to make use of it directly. So, an experimental study is required here [8]. AA7075/FA/SiC 10% wt. hybrid MMC gives better mechanical properties compared to base alloy and 5% wt. hybrid MMC [9]. Metal matrix composites (MMCs) find applications in areas like aircraft components, automobile, marine, structural equipment’s, etc., as they possess a combination of properties like superior hardness, enhanced strength and better wear resistance. N. Sathiya Narayanan et. al [10] has conducted experiment on polytetrafluoroethylene (PTFE) polymers to evaluate and optimize the surface roughness and material removal rate using TOPSIS optimization technique. S. Karthik et. al [11] has identified the combination of Taguchi’s and TOPSIS optimization techniques yields better result on machining parameters on WEDM of Al, MMC. Among various MCDA/MCDM strategies created to understand genuine choice problems, TOPSIS keeps on working attractively in altereduse areas.As an outstanding old style MCDA/MCDM technique, TOPSIS has gotten much enthusiasm from specialists and experts. The worldwide enthusiasm for the TOPSIS technique has exponentially developed, which we wish to record in this paper. TOPSIS algorithm was utilized by Vijay et al. to choose the best CNC machine. The TOPSIS algorithm is known as an efficient MCDM tool and is made use of, to find the best solutions for such difficult decision-making challenges in the areas of manufacturing. The studies conducted earlier [12] proved that when using TOPSIS algorithm, it is easy to compute, evaluate, and capable of choosing the best suitable machine tool from the alternatives available. Abhang et al.[13]using a combination of MADMmethods and finalized the best lubricant for turning process from the available substitute lubricants when they machined EN31 steel workpiece. In their research work, the researchers leveraged TOPSIS and AHP methods. According to their conclusion, the lubricant index assesses and positions the best lubricant when executing steel turning operation. A combination of TOPSIS and AHP methods was found to be the best approach to resolve complex MADM challenges in the domain of manufacturing. Nikunj et al. proposed a logical technique based on three well established MADM methods, for instance, AHP, TOPSIS and revised AHP in the process of tool insert assortment for conducting a turning operation on CNC turning centre. According to the conclusion, it can be inferred that there is a similarity of ranking the ‘tool insert’with that of the performance score achieved in tool insert selection index in all MADM methods [14]. TOPSIS, one of the MADM techniques, was applied by P. Senthil et al. to find a solution for multi-criteria optimization of EDMprocess parametersin Al-CuTiB2 MMCs. When compared with the alternative choices, the TOPSIS algorithm showed better assessment, and the study concluded that this is an easy, simple, computationally effective and understandable approach [15]. A novel method, TOPSIS, was recommended by arun kumar et al [16] to optimize the turning operation parameters on GFRP composites. This method has a primary advantage i.e., no requirement of computing challenging modeming formulations or process simulations. A blend of TOPSIS and AHP method was used by Balasubramaniyan et al. [17] in order to find the best blend of machining parameters in order to conduct turning operation of EN25 steel. According to the study conclusion, this combination method can be deployed in all the machining operations that work towards a high number of objectives simultaneously. TOPSIS algorithm was used in the study conducted by Dinesh et al. [18] to optimize the die-sinking EDM parameters with the working material, EN-353 grade stainless steel. In this TOPSISalgorithm based study, the researcher conducted the computational experiments using Simple Additive Weighing (SAW)-based MCDM method. The study inferred the efficiency of TOPSIS algorithm and proved the efficacy of MADM problems in EDM. N. Yuvaraj et al. executed the optimization of AWJ cutting process parameters of aluminum alloy AA5083-H32 unit that possess multi-response features, on the basis of MCDM methodology using TOPSIS approach. It was observed from the investigation that the multi-response features of AWJ cutting process could be enhanced when

using TOPSIS algorithm [19]. The TOPSIS algorithm was utilized by R. Manvinnan et al.to assess the machining parameters of micro-EDM of AISI 304 steel and the study results inferred that the TOPSIS algorithm enhanced the micro-EDM’s process parameters [20]. Subjective and objective are the two well-known methods of weight assignment. The system of subjective weight assignment is based on expert judgment, and pair-wise comparison (i.e., analytical hierarchy process (AHP) and simple multi-attribute rating technique (SMART) are the most common techniques used [21]. While the objective weight assignment methodology collects data from requirements data and calculates weights accordingly without the decision-maker's involvement [22]. Entropy, CRITIC, and the standard deviation approach are the most common techniques used [23]. The choice of the expert is not applicable, as in the present study, so an objective form of weighting is applied. Compared to entropy and standard deviation methods, CRITIC is a superior approach because it includes both conflict and comparison strength to the weights in decision-making issues and thus represents specific weight assignment conditions. So The CRITIC method, integrated with the TOPSIS method, is used in the present study to assess the response weights. With the expanding number of new materials accessible in the market each year, the producers are confronting incredible troubles in choosing the most fitting material for their items. In this manner, there is continuously a passionate need to receive a basic deliberate strategy for productive and compelling assessment of machinability of different work materials. In this paper, MCDM method i.e TOPSIS is connected to contemplate the machinability qualities of AA7075/FA/SiC 10 wt.% hybrid MMC. The TOPSIS strategy, being an effectively intelligible MCDM system and having a solid numerical foundation, is additionally very reasonable to this sort of assessment and determination issue. The TOPSIS strategy hence causes the researchers to assess the machinability charactertics of the considered metal combinations for a given machining application.

2. Experimental Setup

In the figure, the study’s experimental setup is shown. A lathe machine manufactured in Germany (S 3015, Germany; traverse length: 60 mm) was used to conduct the experiments. Ra and MRR were the output responses considered for the study. These response parameters were made use of, in the performance evaluation of turning process by altering different levels of input process parameters. Table 1 shows the process parameters along with their level values.

By utilizing the Mitutoyo’s Surftest SJ-210, the average surface roughness (Ra) of 16 specimens was calculated. The surface roughness was measured thrice at various locations from which the average value was taken. In the present research, MRR was determined by utilizing the weight loss technique i.e material removal weight over period of time in seconds.

4. Results And Discussions

This study has chosen two performance characteristics such as minimization and maximization. In order to attain the optimal machining performance, the researcher took the maximization features for MRR and minimization features for surface roughness. With the help of equation (5), the two responses were normalized at the initial stage. Since the priority given to both output responses is based on CRITIC method, the responses weight criterion was taken as 0.48 for surface roughness and 0.52 for MRR. Based on the equation (6), the weight criterion was multiplied in order to attain the normalized weighted matrix from which the best and the worst solutions were calculated. Using the equations (9) and (10), the researcher calculated the separation measures of each criterion from the best as well as the worst solutions. The relative Closeness Coefficient (CC) value was calculated at last for every combination of turning process factors with the help of equation (11) as shown in the table 4. Table 4. Normalized, weighted normalized data, Separation measures and Closeness coefficient values Exp. No.

The factor response results were taken into account by utilizing ‘higher-the-better’ expectation through MINITAB software. As per table 5, the role played by ‘f’ remains insignificant, whereas the contribution made by the parameters, ‘v’ and ‘d’, seemed to have significantly enhanced the closeness coefficient value. Fig.2 depicts the main effects plots for S/N ratio and optimal settings are shows as v4f4d4 i.e 115 m/min, 0.20 mm/rev and 0.8 mm. ANOVA can be used to calculate the amount of effect of process variables upon the performance features. Table 4 lists the ANOVA results for the preference solution by taking 95% CI as statistically significant.

Table 6. ANOVA for CC. Source DF Seq SS Adj SS F-Value v 1 0.304246 0.000014 0.00 f 1 0.031048 0.001952 0.42 d 1 0.162485 0.025701 5.52 v*v 1 0.001573 0.006839 1.47 f*f 1 0.039978 0.034987 7.52 d*d 1 0.000295 0.000333 0.07 v*f 1 0.043237 0.039622 8.52 v*d 1 0.031488 0.029057 6.25 f*d 1 0.109804 0.109804 23.60 Error 6 0.027913 0.004652 Total 15 0.752066 S= 0.0682072 R-sq = 96.29% R-sq(adj) = 90.72%

P-Value 0.958 0.541 0.057 0.271 0.034 0.798 0.027 0.047 0.003

Contribution 40.45% 4.13% 21.61% 0.21% 5.32% 0.04% 5.75% 4.19% 14.60% 3.71% 100.00%

In the table 6, the impact created by input process parameters upon turning can be observed. Among the parameters, the order of influence is as follows v (40.45%), d (21.61%), f*d (14.60%) and finally v*f (5.75%). Table 7 shows a comparison of the assessment outcomes for beginning and best choice of turning process parameters for the expected as well as the test conditions. Once the best level parameters for machining were decided, the tests for confirmation were conducted in order to ensure the improvement in the multi response feature of turning. Using optimal level of turning parameters and using the equation [12], the forecasted response value (𝛾predicted ) can be calculated. (12) In which, the 𝛾m denotes the overall mean multiresponse value and 𝛾0 denotes the mean multiresponse value at the optimum level of factors. In the equation, n denotes the number of input process parameters. From the outcomes, it can be inferred that the total 𝐶𝐶i value of the optimal parameter condition (v4f4d4) seems to be high when compared with the initial setting parameter condition (v1f1d1). In addition to that, the forecasted response value also seems to be closer to the experimental value. Table 7. Predicted and Experimental values Levels

Initial machining parameters level v=20 f=0.05 d=0.2 1.52 0.020 0.2987

Optimum machining parameters level v=115 f=0.2 d=0.8 Predicted

5. Evaluation Of Parameters By Using 3D Plots

Fig. 3 portrays the impact of different process parameters on Ra and Fig.4 portrays the impact of various process parameters on MRR. Surface roughness shows fading pattern with adevelopment of cutting speed from 20 m/min to 115 m/min. In the present investigation, when the process parameters increments from low level to high level, expanded pattern was observed for MRR.

(c) Figure 3. 3D plot for Ra (a) v Vs f (b) v Vs d (c) f Vs d.

(c) Figure 4. 3D plot for MRR (a) v Vs f (b) v Vs d (c) f Vs d.

6. Conclusions

The current study utilized the hybrid approach of CRITIC - TOPSIS method in addition to orthogonal array so as to best enhance the process parameters in the turning process of Al7075/FA/SiC hybrid MMC for multiresponse features. The researcher identified a best combination of turning parameters along with their levels when it comes to achieving the least surface roughness (Ra) value and a better Material Removal Rate (MRR). Based on the response noted from 𝐶𝐶i values, the researcher found out the optimum combination levels of input process parameters: Cutting speed 115 m/min, feed 0.2 mm/rev. and depth of cut 0.8 mm. Further, the study concluded with the proposed method (a blend of Analysis of Variance and TOPSIS) showing efficiency in finding a solution for turning multi-response problems when compared to the methods used earlier.

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