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

Ying Zhang (), Mutahar Safdar (), Jiarui Xie (), Jinghao Li (), Manuel Sage () and Yaoyao Fiona Zhao ()
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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

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 8, No 4, 3305-3340

Abstract: 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.

Keywords: Additive manufacturing data; Machine learning applications; Data types; Data handling; Data management (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-022-02017-9

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