Engineering-Guided Deep Learning of Melt-pool Dynamics for Additive Manufacturing Quality Monitoring

Published

August 6, 2024

Author(s)

Siqi Zhang, Hui Yang, Zhuo Yang, Yan Lu

Abstract

Additive manufacturing fabricates three-dimensional parts via layer-by-layer deposition and solidification of materials. Due to the complexity of this process, advanced sensing is increasingly employed to facilitate system visibility, leading to a large amount of high dimensional and complex-structured data. While deep learning brings attractive characteristics for data-driven process monitoring and quality prediction, it is currently limited in the ability to assimilate engineering knowledge and offer model interpretability for understanding process-quality relationships. In addition, due to spatiotemporal correlations in AM, a melt-pool anomaly observed during fabrication is not always indicative of abnormal quality characteristics. There is a pressing need to go beyond pointwise analysis of melt pools and consider spatiotemporal effects for quality analysis. In this paper, we propose a novel feature learning framework guided by engineering knowledge for AM quality monitoring. First, engineering knowledge is integrated with deep learning to delineate various sources of process variations and extract melt-pool features that reflect quality-related relationships. Second, a 3D neighborhood model is designed to characterize spatiotemporal variations of melt pools based on their domain-informed features. The resulting 3D neighborhood profiles enable us to go beyond pointwise analysis of melt pools for capturing process-quality relationships. Finally, we built a regression model to predict internal density variations using 3D neighborhood profiles. Our experiments demonstrate that the proposed framework significantly outperforms traditional hand-crafted method and blackbox learning in both the ability to provide quality-related features and predict internal density variations.

Citation

ASME Journal of Computing and Information Science in Engineering

Volume

24

Pub Type

Journals

Download Paper

https://doi.org/10.1115/1.4066026

Local Download

Keywords

Feature learning, engineering knowledge, process monitoring, quality control, additive manufacturing, melt pool

Citation

Zhang, S. , Yang, H. , Yang, Z. and Lu, Y. (2024), Engineering-Guided Deep Learning of Melt-pool Dynamics for Additive Manufacturing Quality Monitoring, ASME Journal of Computing and Information Science in Engineering, [online], https://doi.org/10.1115/1.4066026, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956741 (Accessed March 21, 2025)