IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v30y2019i2d10.1007_s10845-016-1279-x.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-016-1279-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-016-1279-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-016-1279-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.