Multiresponse Optimization of Edm Process with Nanofluids Using Topsis Method and Genetic Algorithm

Abstract

Electrical Discharge Machining (EDM) process with copper tool electrode is used to investigate the machining characteristics of AISI D2 tool steel material. The multi-wall carbon nanotube is mixed with dielectric fluids and its end characteristics like surface roughness, fractal dimension and metal removal rate (MRR) are analysed. In this EDM process, regression model is developed to predict surface roughness. The collection of experimental data is by using L9 Orthogonal Array. This study investigates the optimization of EDM machining parameters for AISI D2 Tool steel using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Analysis of variance (ANOVA) and F-test are used to check the validity of the regression model and to determine the significant parameter affecting the surface roughness. Atomic Force Microscope (AFM) is used to capture the machined image at micro size and using spectroscopy software the surface roughness and fractal dimensions are analysed. Later, the parameters are optimized using MINITAB 15 software, and regression equation is compared with the actual measurements of machining process parameters. The developed mathematical model is further coupled with Genetic Algorithm (GA) to determine the optimum conditions leading to the minimum surface roughness value of the workpiece.

Multi-objective optimization in titanium powder mixed electrical discharge machining process parameters for die steels

“…A multi-objective optimization of EDM process parameters for AISI D2 steel was performed using TOPSIS [7]. The results indicated that S/N of the ranking index determined the optimal set of process parameters, and were different from those of the index ranking.…”

“…In several studies, these combinations have been arbitrarily chosen, without considering the suitability of the Taguchi method with other methods by S/N of ranking index. Also, the weights of the criteria in many of the works are determined by empirical research, and this has led to the effectiveness of optimized results that have a scope limited to practical applications [31]. In the multi-objective optimization problem, it is essential to determine the value of the weight of quality criteria accurately and to see that it is appropriate for practical purpose.…”

“…In this study, the number of process parameters studied was 7, and the number of experiments reviewed was only 27. In traditional optimization methods, the number of experiments required is 3 7 . The number of experiments performed in the Taguchi method is 81 times less than the required number.…”

Factor Analysis Model Based on the Theory of the TOPSIS in the Application Research

“…2.1. TOPSIS Theory [1][2][3][4][5][6][7][8] TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). It is based on the normalization after the original data matrix, finding the optimal solution and the worst in this limited solution scheme (with the optimal vector and the worst vectors, resp.…”

Multi-response optimization of process parameters for powder mixed electro-discharge machining according to the surface roughness and surface micro-hardness using Taguchi-TOPSIS

“…It can be used for optimizing material removal rate, electrode wear and surface roughness responses in PMEDM (Tripathy & Tripathy, 2017). Material removal rate (MRR), surface roughness and fractal dimension are also responses to optimize when machining AISI D2 tool steel (Prabhu & Vinayagam, 2016;Zerti et al, 2018). The results showed that voltage has the strongest effect (42.42 %) and pulse on time has the smallest effect (11.13%).…”