Integration of Machine Learning in Additive Manufacturing for Material Extrusion and Powder Bed Fusion, a Brief Literature Review

Abstract:

Today's computational capacity enables the use of advanced statistical algorithms to identify relationships between features in high-dimensional data. Additive manufacturing methods are typically complex processes with many variables in both printing parameters and material properties. Consequently, machine learning offers opportunities for process optimization, quality assurance, and innovation in both Material Extrusion and Powder Bed Fusion technologies. The paper reviews the recent findings in machine learning applications for these additive manufacturing techniques, focusing on areas like defect detection, process control, and material property prediction. Key trends reveal that, while machine learning offers promising enhancements for additive manufacturing, challenges remain in data scarcity, model generalization, real-time adaptability. Our findings underscore the potential of machine learning to improve the overall quality of additive manufacturing processes by predicting optimal manufacturing parameters.

• Periodical: Key Engineering Materials (Volume 1018)
• Pages: 61-66
• DOI: https://doi.org/10.4028/p-xAY5iO
• Online since: June 2025
• Authors: Attila Debreceni*, Sándor Bodzás
• Keywords: Additive Manufacturing, Computer Vision, Machine Learning, Material Extrusion, Optimization, Powder Bed Fusion

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