Application of ANFIS and GRA for multi-objective optimization of optimal wire-EDM parameters while machining Ti–6Al–4V alloy

Kumar, Sandeep; Dhanabalan, Sathish Chander; Narayanan, C. Sathiya

doi:10.1007/s42452-019-0195-z

Cited by 31 publications

(16 citation statements)

References 16 publications

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“…Higher the T ON , more is the surface roughness and material removal rate. Adaptive network based fuzzy inference system (ANFIS) model was developed by Sandeep et al [17] for multi objective optimization of surface roughness and material removal rate for machining titanium alloy with wire EDM and observed that T ON and IP are the most influencing parameters affecting the responses.…”

Section: Introductionmentioning

confidence: 99%

Wear behavior of wire electric discharge machined surface of 316L stainless steel

2020

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This study presents the wear behavior of 316L stainless steel material machined with wire electric discharge machine (EDM) at different machining conditions by varying the machining parameters pulse on time (T ON), peak current (IP), servo voltage (SV) and wire tension (WT). Sliding wear tests are conducted on the cylindrical specimens using pin on disc apparatus and the worn morphology of the specimens are analyzed by SEM and XRD. The experimental investigation revealed that the wear resistance is highly influenced by the wire EDM parameters. T ON and IP are identified to be the most influencing factors effecting wear resistance. At similar values of the product of T ON and IP, the wear resistance remained constant and the wear resistance increased with decrease in the product of T ON and IP. The obtained parameters setting can be used in die making industries where wear behavior is of importance.

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Section: Introductionmentioning

confidence: 99%

Wear behavior of wire electric discharge machined surface of 316L stainless steel

2020

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“…Comparative studies reveal that soft computing models provided the more accurate prediction in comparison with mathematical models. Kumar et al [24] in their experimental studies applied ANFIS techniques to develop the predictive model during wire EDM process. They analyzed the effect of process factor on responses through the developed model.…”

Section: Introductionmentioning

confidence: 99%

Integration of GA and neuro-fuzzy approaches for the predictive analysis of gas-assisted EDM responses

Singh

Kumar

et al. 2019

SN Appl. Sci.

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This research work discusses the application of three intelligent prediction models, based on artificial neural network (ANN) with back-propagation algorithm, adaptive neuro-fuzzy inference system (ANFIS) and hybrid ANFIS and genetic algorithm (ANFIS-GA). These techniques are used for prediction and comparison of machining aspects such as material removal rate (MRR) and surface roughness during gas-assisted electrical discharge machining of D3 die steel. In the present work, helium-assisted EDM with perforated tool has been performed. In this work, parameters considered for machining are discharge current, pulse on time, duty cycle, tool rotation and discharge gas pressure. The suggested approach is based on up-gradation of ANFIS with GA. The GA algorithm is applied to improve the precision of the ANFIS model. The soft computing models were trained, tested and validated with experimental data. Mean square error (MSE), mean absolute error (MAE), root-mean-square error and correlation coefficient (R 2), were used to measure the efficacy of models predicting abilities developed through ANN, ANFIS and hybrid ANFIS-GA approaches. The experiment and anticipated measure of MRR and SR of the process, acquired by ANN, ANFIS and hybrid ANFIS-GA, was found to be in good agreement. The prediction potential of proposed models was tested using new set of data for the training and testing process. The ANFIS-GA technique provides more accurate prediction of the responses in comparison with the ANN and the ANFIS. In general, the inference of this work discloses that the hybrid algorithm like ANFIS-GA is an efficient and effective approach for precise prediction of EDM process responses.

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“…Manikandan et al (2017) investigated a multi-objective optimization using the Taguchi-gray method to enhance machining performance such as MRR and RA, overcut (OC) and perpendicularity error of electrochemical drilled Inconel 625. Kumar et al (2019) notice a significant improvement in MRR and RA using multi-optimization, namely, GRA and develop the ANFIS model for the predicted performance measure on WEDMed Ti–6Al–4V alloy.…”

Section: Introductionmentioning

confidence: 96%

Experimental investigation on WEDM performance analysis using grey-fuzzy integrated with TLBO algorithm for Inconel 625: comparison with GA and SA

Kumar

Upadhyay

Shashikant

2021

WJE

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Purpose In the present study, wire electro-discharge machining (WEDM) of Inconel 625 (In-625) is performed with the machining parameter such as spark-on time, spark-off time, wire-speed, wire tension and servo voltage. The purpose of this study is to find the most favorable machining parameter setting with respect to WEDM performance such as material removal rate (MRR) and surface roughness (RA). Design/methodology/approach Taguchi’s L27 orthogonal array has been used to design the experiments with varying machining parameters into three-level four factors. A hybrid multi-optimization technique has been purposed with grey relation analysis and fuzzy inference system integrated with teaching learning-based optimization to achieve optimum machinability (MRR and RA in present case). The obtained result has been compared with two evolutionary optimization tools via a genetic algorithm and simulated annealing. Findings It has been found that proposed hybrid technique taking minimum computational time, provide better solution and avoid priority weightage calculation by decision-makers. A confirmation test has been performed at single and multi-optimal parameter settings. The decision-makers have been chosen to select any single or multi-parameter setting as per the industry’s demand. Originality/value The proposed optimization technique provides better machinability of In-625 using zinc-coated brass wire electrode during WEDM operation.

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Application of ANFIS and GRA for multi-objective optimization of optimal wire-EDM parameters while machining Ti–6Al–4V alloy

Cited by 31 publications

References 16 publications

Wear behavior of wire electric discharge machined surface of 316L stainless steel

Wear behavior of wire electric discharge machined surface of 316L stainless steel

Integration of GA and neuro-fuzzy approaches for the predictive analysis of gas-assisted EDM responses

Experimental investigation on WEDM performance analysis using grey-fuzzy integrated with TLBO algorithm for Inconel 625: comparison with GA and SA

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