A5000892994- Additive Manufacturing Materials and Processes
- Additive Manufacturing and 3D Printing Technologies
- Mechanical Behavior of Composites
- Structural Load-Bearing Analysis
- Manufacturing Process and Optimization
- 3D Printing in Biomedical Research
- Innovations in Concrete and Construction Materials
- Engineering Structural Analysis Methods
- Composite Structure Analysis and Optimization
- Model Reduction and Neural Networks
- Machine Learning in Materials Science
- Advanced Multi-Objective Optimization Algorithms
- 3D Shape Modeling and Analysis
- Computer Graphics and Visualization Techniques
- Vibration and Dynamic Analysis
- Structural Behavior of Reinforced Concrete
- Welding Techniques and Residual Stresses
- Composite Material Mechanics
- Material Properties and Processing
Singapore University of Technology and Design
2022-2023
ABSTRACTIn order to enable the industrialization of additive manufacturing, it is necessary develop process simulation models that can rapidly predict part quality. Although multi-physics simulations have shown success at predicting residual stress, distortion, microstructure and mechanical properties additively manufactured parts, they are generally too computationally expensive be directly used in applications, such as optimization, controls, or digital twinning. In this study, a critical...
Computational Intelligence (CI) techniques have shown great potential as a surrogate model of expensive physics simulation, with demonstrated ability to make fast predictions, albeit at the expense accuracy in some cases. For many scientific and engineering problems involving geometrical design, it is desirable for models precisely describe change geometry predict consequences. In that context, we develop graph neural networks (GNNs) which allow us directly train on 2/3D designs are...
Abstract A multi-physics high-fidelity computational model is required to study the melting and grain growth phenomena in a laser powder-bed fusion (LPBF) additive manufacturing process. The major challenge with long time, which makes it unsuited for any feasible process parameter optimization high dimensional design space. To address this challenge, surrogate models are good option replace model, resulting significant shortening of time at expense an acceptable drop accuracy. In study,...