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A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management

Author

Listed:
  • Ying Zhang

    (McGill University)

  • Mutahar Safdar

    (McGill University)

  • Jiarui Xie

    (McGill University)

  • Jinghao Li

    (McGill University)

  • Manuel Sage

    (McGill University)

  • Yaoyao Fiona Zhao

    (McGill University)

Abstract

Additive manufacturing (AM) techniques are maturing and penetrating every aspect of the industry. With more and more design, process, structure, and property data collected, machine learning (ML) models are found to be useful to analyze the patterns in the data. The quality of datasets and the handling methods are important to the performance of these ML models. This work reviews recent publications on the topic, focusing on the data types along with the data handling methods and the implemented ML algorithms. The examples of ML applications in AM are then categorized based on the lifecycle stages, and research focuses. In terms of data management, the existing public database and data management methods are introduced. Finally, the limitations of the current data processing methods are discussed and suggestions on perspectives are given.

Suggested Citation

  • Ying Zhang & Mutahar Safdar & Jiarui Xie & Jinghao Li & Manuel Sage & Yaoyao Fiona Zhao, 2023. "A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3305-3340, December.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:8:d:10.1007_s10845-022-02017-9
    DOI: 10.1007/s10845-022-02017-9
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