A physics-driven deep learning model for process-porosity causal relationship and porosity prediction with interpretability in laser metal deposition. Issue 1 (2020)

• Record Type: Journal Article
• Authors: Guo, Weihong "Grace"; Tian, Qi; Guo, Shenghan; Guo, Yuebin
• Is Part Of: CIRP annals. Volume 69:Issue 1(2020)
• Page Start: 205
• Page End: 208
• Publication Date: 2020
• Subjects: Machine learning -- Additive manufacturing -- Porosity; Production engineering -- Research -- Periodicals; 670.5
• DOI: 10.1016/j.cirp.2020.04.049
• Languages: English
• ISSNs: 0007-8506

Abstract

Abstract: Porosity produced in laser metal deposition hampers its application due to the absence of an effective prediction method. Measured thermal images of the melt pool provide a unique opportunity for porosity analytics. Furthermore, a physical model may provide complementary rich data that cannot be measured otherwise. How to leverage both types of data to predict porosity is very challenging. This paper presents a physics-driven deep learning model to predict porosity by integrating both measured and predicted data of the melt pool. The model fidelity is validated with the predicted pore occurrence and size with enhanced interpretability of Ti–6Al–4V thin-wall structures.