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Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

Published online by Cambridge University Press:  03 March 2020

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

This paper proposes an intelligent model-based optimization methodology for optimizing the production cost and material removal rate subjected to surface quality constraint in turning operation of hardened AISI D2. Unlike traditional approaches, this paper deals with finding optimum cutting parameters considering the real condition of the cutting tool. Tool flank wear is predicted by the model obtained using genetic programming. On the basis of the predicted flank wear value, the surface roughness of work piece is estimated by neural networks. Applying the particle swarm optimization algorithm, the optimum machining parameters are determined. The simulation and experimental results show that machining with proposed intelligent optimization methodology has higher efficiency than conventional techniques with constant optimized cutting parameters.

Keywords

Genetic programminghard turningmodel-based optimizationneural networksparticle swarm optimization

Information

Type
Research Article
Information
AI EDAM , Volume 34 , Special Issue 3: Thematic Collection: Fablabs, Makerspaces, and Design Spaces , August 2020 , pp. 421 - 429
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Copyright
Copyright © Cambridge University Press 2020

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