| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Symposium
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2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Presentation Title |
Nondestructive Fatigue Life Prediction for Additively Manufactured Parts through a Multimodal Transfer Learning Framework |
| Author(s) |
Anyi Li, Arun Poudel, Shuai Shao, Nima Shamsaei, Jia Liu |
| On-Site Speaker (Planned) |
Jia Liu |
| Abstract Scope |
Understanding the fatigue behavior and accurately predicting the fatigue life of laser powder bed fusion (L-PBF) parts remain a pressing challenge due to complex failure mechanisms, time-consuming tests, and limited fatigue data. This study proposes a physics-informed data-driven framework, namely, a multimodal transfer learning (MMTL) framework, to understand process-defect-fatigue relationships in L-PBF by integrating various modalities of fatigue performance, including process parameters, XCT-inspected defects, and fatigue test conditions. It aims to leverage a pre-trained model with abundant process and defect data in the source task to predict fatigue life nondestructively with limited fatigue test data in the target task. MMTL employs a hierarchical graph convolutional network (HGCN) to classify defects in the source task. The synergies learned from HGCN are then transferred to fatigue life modeling in neural network layers. MMTL validation through numerical simulations and real-case studies demonstrates its effectiveness in fatigue life prediction of L-PBF parts. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |