Abstract

The advanced manufacturing and machining techniques are adopting a population-based metaheuristic algorithm for production, predicting and decision-making. Using the same approach, this paper deals with the application of bees algorithm and differential evolution to forecast the optimal parametric values aiming to obtain maximum material removal rate during electrochemical discharge machining of silicon carbide particle/glass fiber–reinforced polymer matrix composite. The bees algorithm follows swarm-based approach, while differential evolution works on a population-based approach. The experimental design was prepared on the basis of Taguchi’s methodology using an L16 orthogonal array. For the experimental analysis, the main variables in the process, that is, electrolyte concentration (g/L), inter-electrode gap (mm), duty factor and voltage (volts), were selected as main input parameters, and material removal rate (mg/min) was adjudged as output quality characteristic. A comparative investigation reveals that the maximum material removal rate was obtained by the parametric value proposed by differential evolution that follows the bees algorithm and Taguchi’s methodology. Furthermore, the results prove that the differential evolution algorithm has better collective assessment capability with a rapid converging rate.

Highlights

  • The usage of polymer matrix fibrous composites has increased within industrial applications because of their improved mechanical properties.[1]

  • This research work focuses on metaheuristic optimization approach aiming to improve the process parameters of electrochemical discharge machining (ECDM) for producing an enhanced material removal rate (MRR) by coupling Taguchi’s methodology (TM) with the evolutionary algorithms, that is, bees algorithm (BA) and differential evolution (DE)

  • Some possibilities to produce a robust improvement of machining rate of silicon carbide particle (SiCp)/ glass fiber–reinforced polymer matrix composites (PMCs) was analyzed during the ECDM process using the evolutionary algorithm

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Summary

Introduction

The usage of polymer matrix fibrous composites has increased within industrial applications because of their improved mechanical properties.[1]. PMC: polymer matrix composite; ECDM: electrochemical discharge machining; GRA: grey relational analysis; MRR: material removal rate; MMC: metal matrix composite; RSM: response surface methodology; ABCA: artificial bee colony algorithm; TLBO: teaching–learning-based optimization algorithm; DE: differential evolution; GA: genetic algorithm; ECM: electrochemical machining. This research work focuses on metaheuristic optimization approach aiming to improve the process parameters of ECDM for producing an enhanced material removal rate (MRR) by coupling TM with the evolutionary algorithms, that is, BA and DE.

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