Advancing machine learning in additive manufacturing: Perspectives on data challenges, model development, and industrial adoption

Abstract

Over the past decade, 100 of scientific studies have harnessed statistical artificial intelligence, specifically machine learning (ML), to address the existing challenges to process repeatability and part quality in additive manufacturing (AM). ML has also been applied to support structure and material design for AM. This rapidly expanding field at the intersection of two growing disciplines provides opportunities to mature AM technology in the industry. There have been numerous review articles and survey reports to summarize ML applications at design, processes, structure, and property phases. While ML models, data handling, and learning techniques for AM concerns have been summarized in these articles, no work has specifically focused on the core ML challenges of data scarcity and model complexity - two important aspects to develop ML models for the AM industry. This perspective highlights existing data and model challenges and discusses the opportunities to address them through advanced techniques in data science and ML. By enhancing the usefulness of AM datasets and by leveraging the strengths of cutting-edge ML models, the research progress at the intersection of ML and AM can be effectively translated into real-world industrial applications making the deployment of ML models in the industry much easier.

Keywords

Additive manufacturing / Artificial intelligence / Machine learning / Data scarcity / Model complexity / Perspective / Industrial integration

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