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A framework for simulation-based multi-objective optimization and knowledge discovery of machining process

  • Kaveh AmouzgarEmail author
  • Sunith Bandaru
  • Tobias Andersson
  • Amos H. C. Ng
Open Access
ORIGINAL ARTICLE
  • 202 Downloads

Abstract

The current study presents an effective framework for automated multi-objective optimization (MOO) of machining processes by using finite element (FE) simulations. The framework is demonstrated by optimizing a metal cutting process in turning AISI-1045, using an uncoated K10 tungsten carbide tool. The aim of the MOO is to minimize tool-chip interface temperature and tool wear depth, that are extracted from FE simulations, while maximizing the material removal rate. The effect of tool geometry parameters, i.e., clearance angle, rake angle, and cutting edge radius, and process parameters, i.e., cutting speed and feed rate on the objective functions are explored. Strength Pareto Evolutionary Algorithm (SPEA2) is adopted for the study. The framework integrates and connects several modules to completely automate the entire MOO process. The capability of performing the MOO in parallel is also enabled by adopting the framework. Basically, automation and parallel computing, accounts for the practicality of MOO by using FE simulations. The trade-off solutions obtained by MOO are presented. A knowledge discovery study is carried out on the trade-off solutions. The non-dominated solutions are analyzed using a recently proposed data mining technique to gain a deeper understanding of the turning process.

Keywords

Machining Turning simulation Multi-objective optimization Cutting parameters Tool geometry 

Notes

Acknowledgements

This work was partially financed by the Knowledge Foundation (KKS), Sweden, through the Synergy project, Knowledge-Driven Decision Support via Optimization (KDDS). The authors gratefully acknowledge their provision of research funding, and thank the industrial partners, Volvo Car Corporation and AB Volvo, for their collaborative supports during the project. We also we would like to thank Dr. Mirza Cenanovic for developing the initial architecture of the automation system.

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© The Author(s) 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.School of Engineering ScienceUniversity of SkövdeSkövdeSweden

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