A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures

Autor: Yuwei Mao, Hui Lin, Christina Xuan Yu, Roger Frye, Darren Beckett, Kevin Anderson, Lars Jacquemetton, Fred Carter, Zhangyuan Gao, Wei-keng Liao, Alok N. Choudhary, Kornel Ehmann, Ankit Agrawal
Rok vydání: 2022
Předmět:
Zdroj: Journal of Intelligent Manufacturing. 34:315-329
ISSN: 1572-8145
0956-5515
Popis: Part quality manufactured by the laser powder bed fusion process is significantly affected by porosity. Existing works of process–property relationships for porosity prediction require many experiments or computationally expensive simulations without considering environmental variations. While efforts that adopt real-time monitoring sensors can only detect porosity after its occurrence rather than predicting it ahead of time. In this study, a novel porosity detection-prediction framework is proposed based on deep learning that predicts porosity in the next layer based on thermal signatures of the previous layers. The proposed framework is validated in terms of its ability to accurately predict lack of fusion porosity using computerized tomography (CT) scans, which achieves a F1-score of 0.75. The framework presented in this work can be effectively applied to quality control in additive manufacturing. As a function of the predicted porosity positions, laser process parameters in the next layer can be adjusted to avoid more part porosity in the future or the existing porosity could be filled. If the predicted part porosity is not acceptable regardless of laser parameters, the building process can be stopped to minimize the loss.
Databáze: OpenAIRE
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