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Heuristic techniques for modelling machine spinning processes

Author

Listed:
  • Roman Stryczek

    (University of Bielsko-Biala)

  • Kamil Wyrobek

    (University of Bielsko-Biala)

Abstract

In spite of many efforts made a complete model of machine spinning processes, due to its complexity, multidimensionality of the decision space and the present state of knowledge, is unachievable. The paper addresses the issues of constructing a local process model to enable the search for a locally optimal course of the process, within a short time and with the cost as low as possible. Comparison was made between the theoretically well-grounded response surface designs method with a few approaches to the model construction based on intuitively understood heuristic bases justified by their successful practical applications. In order to determine a set of Pareto-optimal solutions for a discrete decision space, the durations of process execution were generated through a virtual simulation. In order to outline and justify the adopted solutions a comprehensive example of the practical construction of the machine spinning process model was presented, including its various versions. The results obtained were validated and evaluated. The main utilitarian conclusion is the indication whereby basing on a partial experiment plan it is possible, thanks to simple heuristic methods, to obtain Pareto-optimal solutions which are close to those obtained when the full experiment plan is carried out.

Suggested Citation

  • Roman Stryczek & Kamil Wyrobek, 2021. "Heuristic techniques for modelling machine spinning processes," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1189-1206, April.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:4:d:10.1007_s10845-020-01683-x
    DOI: 10.1007/s10845-020-01683-x
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    References listed on IDEAS

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    1. Hengyuan Ma & Wei Liu & Xionghui Zhou & Qiang Niu & Chuipin Kong, 2020. "An effective and automatic approach for parameters optimization of complex end milling process based on virtual machining," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 967-984, April.
    2. Yingxin Ye & Tianliang Hu & Yan Yang & Wendan Zhu & Chengrui Zhang, 2020. "A knowledge based intelligent process planning method for controller of computer numerical control machine tools," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1751-1767, October.
    3. Mohammad Reza Khosravani & Sara Nasiri, 2020. "Injection molding manufacturing process: review of case-based reasoning applications," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 847-864, April.
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