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A hybrid particle swarm optimization and recurrent dynamic neural network for multi-performance optimization of hard turning operation

Published online by Cambridge University Press:  19 September 2022

Vahid Pourmostaghimi Open the ORCID record for Vahid Pourmostaghimi [Opens in a new window] ,
Mohammad Zadshakoyan Open the ORCID record for Mohammad Zadshakoyan [Opens in a new window] ,
Saman Khalilpourazary  and
Mohammad Ali Badamchizadeh
Vahid Pourmostaghimi*
Affiliation:
Department of Manufacturing and Production Engineering, Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Mohammad Zadshakoyan
Affiliation:
Department of Manufacturing and Production Engineering, Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Saman Khalilpourazary
Affiliation:
Department of Renewable Energy, Faculty of Mechanical Engineering, Urmia University of Technology, Urmia, Iran
Mohammad Ali Badamchizadeh
Affiliation:
Control Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
*
Author for correspondence: Vahid Pourmostaghimi, E-mail: vahidvpm@tabrizu.ac.ir
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Abstract

In the present work, a new hybrid approach combining particle swarm optimization (PSO) algorithm with recurrent dynamic neural network (RDNN), which is described as PSO-RDNN algorithm, is proposed for multi-performance optimization of machining parameters in finish turning of hardened AISI D2. The suggested optimization problem is solved using the weighted sum technique. Process parameters including cutting speed and feed rate are optimized for minimizing operation cost, maximizing tool life, and producing parts with acceptable surface roughness. Based on experimental results, two neural network models were developed for predicting tool flank wear and surface roughness during the machining process. Based on trained neural networks and structured hybrid algorithm, optimum cutting parameters were obtained. The coefficient of determination for trained neural networks was calculated as R2 = 0.9893 and R2 = 0.9879 for predicted flank wear and surface roughness, respectively, which proves the efficiency of trained neural models in real industrial applications. Furthermore, the offered methodology returns a Pareto optimality graph, which represents optimized cutting variables for several various cutting conditions.

Keywords

Hard turninghybrid algorithmmulti-objective optimizationparticle swarm optimization algorithmrecurrent dynamic neural networktool flank wear
Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e28
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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