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

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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.

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Acknowledgements

Financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) Collaborative Research and Development (CRD) CRDPJ 520348-17 for Ying Zhang is acknowledged with gratitude. Financial supports from the National Research Council of Canada NRC INT-015-1, McGill Engineering Doctoral Award (MEDA) grant, and Heller Family Fellowship in Engineering for Mutahar Safdar are acknowledged with gratitude. Financial supports from MITACs Advanced Manufacturing Automation, Digitization and Optimization (AMADO) grant and McGill Graduate Excellence Fellowship Award for Jiarui Xie are acknowledged with gratitude. Financial support from the NSERC CRD CRDPJ 479630-15 for Jinghao Li is acknowledged with gratitude. Jinghao Li also received partial funding from the NSERC Collaborative Research, Training Experience (CREATE) Program Grant 449343, MEDA grant, and China Scholarship Council (201706460027). Financial supports from MITACs AMADO grant and MEDA grant for Manuel Sage are acknowledged with gratitude.

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Author notes

  1. Ying Zhang and Mutahar Safdar shared co-first authorship.

Authors and Affiliations

  1. Department of Mechanical Engineering, McGill University, Montreal, QC, H3A 0G4, Canada

    Ying Zhang, Mutahar Safdar, Jiarui Xie, Jinghao Li, Manuel Sage & Yaoyao Fiona Zhao

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  1. Ying Zhang
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Zhang, Y., Safdar, M., Xie, J. et al. A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management. J Intell Manuf 34, 3305–3340 (2023). https://doi.org/10.1007/s10845-022-02017-9

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