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A generalisable tool path planning strategy for free-form sheet metal stamping through deep reinforcement and supervised learning

Author

Listed:
  • Shiming Liu

    (Imperial College London)

  • Zhusheng Shi

    (Imperial College London)

  • Jianguo Lin

    (Imperial College London)

  • Hui Yu

    (University of Portsmouth)

Abstract

Due to the high cost of specially customised presses and dies and the advance of machine learning technology, there is some emerging research attempting free-form sheet metal stamping processes which use several common tools to produce products of various shapes. However, tool path planning strategies for the free forming process, such as reinforcement learning technique, derived from previous path planning experience are not generalisable for an arbitrary new sheet metal workpiece. Thus, in this paper, a generalisable tool path planning strategy is proposed for the first time to realise the tool path prediction for an arbitrary sheet metal part in 2-D space with no metal forming knowledge in prior, through deep reinforcement (implemented with 2 heuristics) and supervised learning technologies. Conferred by deep learning, the tool path planning process is corroborated to have self-learning characteristics. This method has been instantiated and verified by a successful application to a case study, of which the workpiece shape deformed by the predicted tool path has been compared with its target shape. The proposed method significantly improves the generalisation of tool path planning of free-form sheet metal stamping process, compared to strategies using pure reinforcement learning technologies. The successful instantiation of this method also implies the potential of the development of intelligent free-form sheet metal stamping process.

Suggested Citation

  • Shiming Liu & Zhusheng Shi & Jianguo Lin & Hui Yu, 2025. "A generalisable tool path planning strategy for free-form sheet metal stamping through deep reinforcement and supervised learning," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2601-2627, April.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02371-w
    DOI: 10.1007/s10845-024-02371-w
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    References listed on IDEAS

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    1. Christoph Hartmann & Daniel Opritescu & Wolfram Volk, 2019. "An artificial neural network approach for tool path generation in incremental sheet metal free-forming," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 757-770, February.
    2. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    3. Christian Kubik & Sebastian Michael Knauer & Peter Groche, 2022. "Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 259-282, January.
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