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How to apply artificial intelligence in the additive value chain: A systematic literature review

In: Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 31

Author

Listed:
  • Brylowski, Martin
  • Schwieger, Lea-Sophie
  • Nagi, Ayman
  • Kersten, Wolfgang

Abstract

Purpose: Additive manufacturing (AM) enables the manufacturing of metal parts and is therefore increasingly important for industry. Unfortunately, the manufactured parts exhibit many imperfections, such as faults or other quality defects. The use of artificial intelligence (AI) allows for the steady optimization of processes, making its potential implementation in AM interesting as it could help to improve processes for industrialization and serial production. Methodology: A systematic review was conducted of the literature on applications of AI in AM. A total of 741 articles published between 2008 and 2020 were scanned to determine whether they described an explicit application of AI in a metalworking process. A detailed analysis yielded 87 relevant sources. Findings: The articles were scanned for existing application areas of AI in AM, including application in the associated value chain phases of AM planning and AM execution. In AM planning, AI is frequently used to support the design process, while in AM execution, AI is mostly used for process monitoring and defect detection. Originality: The applications of AI in AM were investigated by means of a systematic literature review. The resultant findings should provide insights into existing and potential application areas for AI in AM.

Suggested Citation

  • Brylowski, Martin & Schwieger, Lea-Sophie & Nagi, Ayman & Kersten, Wolfgang, 2021. "How to apply artificial intelligence in the additive value chain: A systematic literature review," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 65-100, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    • Handle: RePEc:zbw:hiclch:249612
    DOI: 10.15480/882.3960
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    References listed on IDEAS

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    1. Sheng Yang & Thomas Page & Ying Zhang & Yaoyao Fiona Zhao, 2020. "Towards an automated decision support system for the identification of additive manufacturing part candidates," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1917-1933, December.
    2. William Mycroft & Mordechai Katzman & Samuel Tammas-Williams & Everth Hernandez-Nava & George Panoutsos & Iain Todd & Visakan Kadirkamanathan, 2020. "A data-driven approach for predicting printability in metal additive manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1769-1781, October.
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    Artificial Intelligence; Blockchain;

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