Core Concepts
Deep Operator Networks enable rapid and accurate prediction of multiphysics fields in materials processing and additive manufacturing.
Abstract
The content discusses the application of Deep Operator Networks (DeepONet) in predicting complete thermal and mechanical solution fields in materials processing and additive manufacturing. Two formulations, including Sequential DeepONet and Residual U-Net (ResUNet), are trained to predict complex solution fields under variable conditions. The article highlights the efficiency of DeepONets compared to traditional finite-element analysis, showcasing their potential for industrial process optimization.
Structure:
- Introduction to Steel Continuous Casting and Additive Manufacturing Processes
- Challenges in Modeling Complex Multiphysics Phenomena
- Overview of Numerical Simulation Approaches in Steel Solidification and Additive Manufacturing
- Introduction of Deep Operator Networks (DeepONet) for Field Predictions
- Training Data Generation Process for Steel Solidification Model and Additive Manufacturing Model
- Neural Network Models: S-DeepONet and ResUNet-based DeepONet
- Results and Discussion on Model Performance Evaluation
- Comparison of Inference Time between FEA Simulations and Neural Network Predictions
Stats
"Two real-world applications are demonstrated: 1- coupled thermo-mechanical analysis of steel continuous casting with multiple visco-plastic constitutive laws"
"Despite highly challenging spatially variable target stress distributions, DeepONets can infer reasonably accurate full-field temperature and stress solutions several orders of magnitude faster than traditional finite-element analysis (FEA)"
"The proposed DeepONet model’s ability to provide field predictions almost instantly for unseen input parameters opens the door for future preliminary evaluation and design optimization"