A comparative study of spark assisted bending process using teaching–learning based optimization, desirability approach and genetic algorithm

• Published in: Applied soft computing 2022-11, Vol.130, p.109712, Article 109712
• Main Authors: Tiwari, Tanmay, Nag, Akash, Pramanik, Alokesh, Dixit, Amit Rai
• Format: Article
• Language: English
• Subjects: Bending; EDM; Genetic algorithm; Optimization; TLBO
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2022.109712

The present work deals with the application and comparison of advanced meta-heuristic-based optimization techniques on the micron-thin sheet bending process. Nature-inspired Teaching–Learning Based Optimization Algorithm (TLBO), Genetic Algorithm (GA), and desirability function-based optimization techniques have been used to predict the optimal parametric levels for obtaining desired bend angles. Spark discharges were applied to bend sheets using electro-discharge machining. Process parameters, namely, peak current (Pc), duty factor (Df), and gap voltage (Gv), were varied to obtain the response, i.e., bend angle (θb). Box–Behnken design in Response Surface Methodology (RSM) was used to obtain a regression model. Statistical analysis of the developed model was done using analysis of variance (ANOVA), which showed that θb was statistically affected by variation in Pc, Df, and Gv at a 95% confidence level. Minimum (θbmin) and maximum (θbmax) bend angles obtained from the experiments were reported to be θbmin=8.57° and θbmax=26.48° at Pc=6A, Df=30% and Gv=40V and Pc=10A, Df=50% and Gv=50V, respectively. Further, developed model adequacy was inspected using standard error design plots and analysis of residuals. The developed quadratic regression model was used to optimize the desired response (θb). The results revealed that the genetic algorithm provided the desired output corresponding to the requirement of bend angle. The values obtained after the optimization of bend angles by performing a confirmatory test were θbmin=8.454° and θbmax=28.015°. Hence the values obtained were better concerning the initial practical experimental data set. •The aim is to control the bend angle of micron-thin sheets obtained by confined spark discharges.•Controlling of bend angle is performed by using the desirability approach along with TLBO, and GA.•Statistical analysis and optimization using metaheuristic algorithms were performed.•Comparative study to analyze the application of different optimization techniques to the process.