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Neural Network (NN)-Based RSM-PSO Multiresponse Parametric Optimization of the Electro Chemical Discharge Micromachining Process During Microchannel Cutting on Silica Glass

Visvesvaraya Technological University, Belagavi, Karnataka, India

Faculty of Mechanical Engineering Department, Alva’s Institute of Engineering and Technology, Moodabidri, Mangaluru, Karnataka 574225, India

E-mail Address: bellubbisadashiv@gmail.com

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Bijan Mallick

Faculty of Mechanical Engineering Department, Global Institute of Management and Technology, Krishnanagar, W.B., India

E-mail Address: bijan.ju@gmail.com

Corresponding author.

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Azzam Sabah Hameed

Faculty Mechanical Engineering Department College of Engineering, Wasit University, Iraq

E-mail Address: azzam@uowasit.edu.iq

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Pijush Dutta

Faculty of ECE Department, Global Institute of Management and Technology, Krishnanagar, MAKAUT, W.B., India

E-mail Address: pijushdutta009@gmail.com

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Manoj Kumar Sarkar

Department of Production Engineering, Tufanganj Government Polytechnic, W.B., India

E-mail Address: manoj.cobpoly@gmail.com

Lecturer of Department of Production Engineering.

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, and

Sathisha Nanjundaswamy

Faculty of Department of Mechanical Engineering, Yenepoya Institute of Technology, Moodbidri, Mangaluru, Karnataka, India

E-mail Address: satn20@gmail.com

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https://doi.org/10.1142/S0219686722500330Cited by:4 (Source: Crossref)

Abstract

The production of miniature parts by the electrochemical discharge micromachining process ( $μ$ -ECDM) draws the most of attractions into the industrial field. Parametric influences on machining depth (MD), material removal rate (MRR), and overcut (OC) have been propounded using a mixed electrolyte (NaOH:KOH- 1:1) varying concentrations (wt.%), applied voltage ( $V$ ), pulse on time ( $μ$ s), and stand-off distance (SOD) during microchannel cutting on silica glass (SiO $_{2} + {NaSiO}_{3}$ ). Analysis of variances has been analyzed to test the adequacy of the developed mathematical model and multiresponse optimization has been performed to find out maximum MD with higher material removal at lower OC using desirability function analysis as well as neural network (NN)-based Particle Swarm Optimization (PSO). The SEM analysis has been done to find unexpected debris. MD has been improved with better surface quality using a mixed electrolyte at straight polarity using a tungsten carbide (WC) cylindrical tool along with $X$ , $Y$ , and $Z$ axis movement by computer-aided subsystem and combining with the automated spring feed mechanism. PSO-ANN provides better parametric optimization results for micromachining by the ECDM process.

Keywords:

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