Operation sequencing optimization for five-axis prismatic parts using a particle swarm optimization approach

Y.W. Guo, A.R. Mileham, G.W. Owen, P.G. Maropoulos, W.D. Li

    Research output: Contribution to journalArticle

    Abstract

    Operation sequencing is one of the crucial tasks in process planning. However, it is an intractable process to identify an optimized operation sequence with minimal machining cost in a vast search space constrained by manufacturing conditions. Also, the information represented by current process plan models for three-axis machining is not sufficient for five-axis machining owing to the two extra degrees of freedom and the difficulty of set-up planning. In this paper, a representation of process plans for five-axis machining is proposed, and the complicated operation sequencing process is modelled as a combinatorial optimization problem. A modern evolutionary algorithm, i.e. the particle swarm optimization (PSO) algorithm, has been employed and modified to solve it effectively. Initial process plan solutions are formed and encoded into particles of the PSO algorithm. The particles 'fly' intelligently in the search space to achieve the best sequence according to the optimization strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particle movements to form a modified PSO algorithm. A case study used to verify the performance of the modified PSO algorithm shows that the developed PSO can generate satisfactory results in optimizing the process planning problem. © IMechE 2009.

    Original languageEnglish
    Pages (from-to)485-497
    Number of pages13
    JournalProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
    Volume223
    Issue number5
    DOIs
    Publication statusPublished - 1 May 2009

    Fingerprint

    Particle swarm optimization (PSO)
    Machining
    Process planning
    Combinatorial optimization
    Degrees of freedom (mechanics)
    Evolutionary algorithms
    Planning
    Costs

    Keywords

    • particle swarm optimization
    • process planning
    • operation sequencing
    • five-axis machining

    Cite this

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    abstract = "Operation sequencing is one of the crucial tasks in process planning. However, it is an intractable process to identify an optimized operation sequence with minimal machining cost in a vast search space constrained by manufacturing conditions. Also, the information represented by current process plan models for three-axis machining is not sufficient for five-axis machining owing to the two extra degrees of freedom and the difficulty of set-up planning. In this paper, a representation of process plans for five-axis machining is proposed, and the complicated operation sequencing process is modelled as a combinatorial optimization problem. A modern evolutionary algorithm, i.e. the particle swarm optimization (PSO) algorithm, has been employed and modified to solve it effectively. Initial process plan solutions are formed and encoded into particles of the PSO algorithm. The particles 'fly' intelligently in the search space to achieve the best sequence according to the optimization strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particle movements to form a modified PSO algorithm. A case study used to verify the performance of the modified PSO algorithm shows that the developed PSO can generate satisfactory results in optimizing the process planning problem. {\circledC} IMechE 2009.",
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    Operation sequencing optimization for five-axis prismatic parts using a particle swarm optimization approach. / Guo, Y.W.; Mileham, A.R.; Owen, G.W.; Maropoulos, P.G.; Li, W.D.

    In: Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 223, No. 5, 01.05.2009, p. 485-497.

    Research output: Contribution to journalArticle

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