Multi-objective optimization of parameters for milling using evolutionary algorithms and artificial neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Model-based design of manufacturing processes have been gaining popularity since the advent of machine learning algorithms such as evolutionary algorithms and artificial neural networks (ANN). The problem of selecting the best machining parameters can be cast an optimization problem given a cost function and by utilizing an input-output connectionist framework using as ANNs. In this paper, we present a comparison of various evolutionary algorithms for parameter optimization of an end-milling operation based on a well-known cost function from literature. We propose a modification to the cost function for milling and include an additional objective of minimizing surface roughness and by using NSGA-II, a multiobjective optimization algorithm. We also present comparison of several population-based evolutionary search algorithms such as variants of particle swarm optimization, differential evolution and NSGA-II.

Original languageEnglish (US)
Title of host publicationDesign, Systems, and Complexity
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791883518
DOIs
StatePublished - 2019
EventASME 2019 International Mechanical Engineering Congress and Exposition, IMECE 2019 - Salt Lake City, United States
Duration: Nov 11 2019Nov 14 2019

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume14

Conference

ConferenceASME 2019 International Mechanical Engineering Congress and Exposition, IMECE 2019
CountryUnited States
CitySalt Lake City
Period11/11/1911/14/19

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering

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    Banerjee, A., Abu-Mahfouz, I., & Esfakur Rahman, A. H. M. (2019). Multi-objective optimization of parameters for milling using evolutionary algorithms and artificial neural networks. In Design, Systems, and Complexity (ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE); Vol. 14). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/IMECE2019-11438