Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

• Published in: Machine learning: science and technology Vol. 5; no. 1; pp. 15038 - 15059
• Main Authors: Rezasefat, Mohammad, Hogan, James D
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.03.2024
• Subjects: Accuracy; Additive manufacturing; additive manufacturing defects; Coders; convolutional neural networks; Decoders; Deep learning; Defects; Feature extraction; Finite element method; Machine learning; Manufacturing; Mechanical properties; Predictions; Stress distribution; stress field predictions; Structural integrity; temporal deep learning model; transfer learning
• ISSN: 2632-2153; 2632-2153
• DOI: 10.1088/2632-2153/ad290c

Abstract This study investigates the application of machine learning models to predict time-evolving stress fields in complex three-dimensional structures trained with full-scale finite element simulation data. Two novel architectures, the multi-decoder CNN (MUDE-CNN) and the multiple encoder–decoder model with transfer learning (MTED-TL), were introduced to address the challenge of predicting the progressive and spatial evolutional of stress distributions around defects. The MUDE-CNN leveraged a shared encoder for simultaneous feature extraction and employed multiple decoders for distinct time frame predictions, while MTED-TL progressively transferred knowledge from one encoder–decoder block to another, thereby enhancing prediction accuracy through transfer learning. These models were evaluated to assess their accuracy, with a particular focus on predicting temporal stress fields around an additive manufacturing (AM)-induced isolated pore, as understanding such defects is crucial for assessing mechanical properties and structural integrity in materials and components fabricated via AM. The temporal model evaluation demonstrated MTED-TL’s consistent superiority over MUDE-CNN, owing to transfer learning’s advantageous initialization of weights and smooth loss curves. Furthermore, an autoregressive training framework was introduced to improve temporal predictions, consistently outperforming both MUDE-CNN and MTED-TL. By accurately predicting temporal stress fields around AM-induced defects, these models can enable real-time monitoring and proactive defect mitigation during the fabrication process. This capability ensures enhanced component quality and enhances the overall reliability of additively manufactured parts.