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Tool sequence optimisation using preferential multi-objective search

Published: 06 July 2013 Publication History

Abstract

This work presents a new multi-objective approach to tool sequence optimisation in end milling applications. In this way, the process planner is presented with a selection of solutions offering a good trade-off between total machining time and total tooling costs. The majority of previous research has concentrated either on optimising tool selection or machining parameters. In the presented approach, each tool in a sequence has its most important parameter, cutting speed, simultaneously optimised creating a problem with both discrete and continuous properties. The major constraint, excess material, is included as an additional objective. The problem is solved using NSGA-II with preferential search modifications to guide solutions towards the feasible region.

References

[1]
Churchill, A. W., Husbands, P. and Philippides, A. 2012. Metaheuristic approaches to tool selection optimisation. In Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference (GECCO '12). ACM, New York, 1079--1086.
[2]
Churchill, A. W., Husbands, P., and Philippides, A. 2013. Multi-objectivization of the Tool Selection Problem on a Budget of Evaluations. In Proceedings of the 14th international conference on Evolutionary multi-criterion optimization. Springer, Berlin Heidelberg. 600--614.
[3]
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation, 6(2), 182--197.
[4]
Deb, K. and Sundar, J. 2006. Reference point based multi-objective optimization using evolutionary algorithms. In Proc. 8th Annual Conference on Genetic and Evolutionary Computation Conference (GECCO '06), 635--642.
[5]
Krimpenis, A. and Vosniakos, G.-C. 2008. Rough milling optimisation for parts with sculptured surfaces using genetic algorithms in a Stackelberg game. Journal of Intelligent Manufacturing. 20(4), 447--461.
[6]
López-Ibáñez, M., Stützle, T., and Paquete, L. 2010. Graphical tools for the analysis of bi-objective optimization algorithms. In Proc. 12th annual conference companion on Genetic and evolutionary computation.1959--1962.
[7]
Spanoudakis, P., Tsourveloudis, N. and Nikolos, I. 2008. Optimal Selection of Tools for Rough Machining of Sculptured Surfaces. Proc. Int. MultiConference of Engineers and Computer Scientists, 1697--1702.

Cited By

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  • (2018)Reference point based evolutionary multi-objective optimization with dynamic resampling for production systems improvementJournal of Systems and Information Technology10.1108/JSIT-10-2017-008420:4(489-512)Online publication date: 12-Nov-2018

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  1. Tool sequence optimisation using preferential multi-objective search

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    Published In

    cover image ACM Conferences
    GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
    July 2013
    1798 pages
    ISBN:9781450319645
    DOI:10.1145/2464576
    • Editor:
    • Christian Blum,
    • General Chair:
    • Enrique Alba
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 06 July 2013

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    Author Tags

    1. cam
    2. emo
    3. end milling
    4. nsga-ii
    5. roughing

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    GECCO '13
    Sponsor:
    GECCO '13: Genetic and Evolutionary Computation Conference
    July 6 - 10, 2013
    Amsterdam, The Netherlands

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    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    • (2018)Reference point based evolutionary multi-objective optimization with dynamic resampling for production systems improvementJournal of Systems and Information Technology10.1108/JSIT-10-2017-008420:4(489-512)Online publication date: 12-Nov-2018

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