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An artificial neural network approach for tool path generation in incremental sheet metal free-forming

Author

Listed:
  • Christoph Hartmann

    (Technische Universität München)

  • Daniel Opritescu

    (Technische Universität München)

  • Wolfram Volk

    (Technische Universität München)

Abstract

This research considers a specific incremental sheet metal free-forming process, which allows for individualized component manufacturing. However, for a reasonable application in practice, an automation of the manual process is mandatory. Unfortunately, up to now, no general tool path generation strategies are available when free-forming processes are to be utilized. On this account, for the investigated driving process, a holistic concept for deriving tool paths for the production of sheet metal parts directly from a digital component model is presented adopting an artificial neural network architecture. Consequently, for the very first time an automated part production is possible in incremental sheet metal free-forming applications. For this, a suitable network input and output structure is designed. Balanced sample data sets are generated for appropriate training. An associated network topology is determined and undergoes a training and testing phase. The influence of different training algorithms, network configurations, as well as training sets have been studied in relation to a feedforward network structure with backpropagation. Finally, the proposed computer integrated manufacturing system is subject to validation and verification by automated sheet part production, which is followed by concluding remarks on the capabilities and limits of the concept.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-016-1279-x
    DOI: 10.1007/s10845-016-1279-x
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    Citations

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    Cited by:

    1. Ahmed A. A. Alduroobi & Alaa M. Ubaid & Maan Aabid Tawfiq & Rasha R. Elias, 2020. "Wire EDM process optimization for machining AISI 1045 steel by use of Taguchi method, artificial neural network and analysis of variances," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(6), pages 1314-1338, December.
    2. Aniket Nagargoje & Pavan Kumar Kankar & Prashant Kumar Jain & Puneet Tandon, 2023. "Application of artificial intelligence techniques in incremental forming: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 985-1002, March.
    3. Sherwan Mohammed Najm & Imre Paniti, 2023. "Investigation and machine learning-based prediction of parametric effects of single point incremental forming on pillow effect and wall profile of AlMn1Mg1 aluminum alloy sheets," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 331-367, January.
    4. Shiori Gondo & Hirohiko Arai, 2022. "Effect and control of path parameters on thickness distribution of cylindrical cups formed via multi-pass conventional spinning," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 617-635, February.
    5. Ahmed A. A. Alduroobi & Alaa M. Ubaid & Maan Aabid Tawfiq & Rasha R. Elias, 0. "Wire EDM process optimization for machining AISI 1045 steel by use of Taguchi method, artificial neural network and analysis of variances," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-25.

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