| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Process-aware Topology Optimization using Deep Learning Surrogate Model-based Constraint Functions |
| Author(s) |
Praveen Vulimiri, Albert To |
| On-Site Speaker (Planned) |
Albert To |
| Abstract Scope |
In this work, a gradient-based discrete variable topology optimization method is used to include additive manufacturing (AM) processing effects in topology optimization using a data-driven surrogate model. The data-driven surrogate model is developed to predict the residual stress of a laser powder bed fusion AM part based on the inherent strain method in seconds of computational time, compared to days using finite element analysis. The model is trained for a specific material and process parameter set for a variety of geometries, making the model geometry agnostic. As this model is trained on discrete 0/1 elements, a gradient-based discrete optimizer is used to perform the topology optimization, rather than the computationally intensive genetic algorithm. This novel approach requires minimal additional cost to the overall optimization strategy. The example parts shown have similar end-use performance as those without the process constraint and can be printed without failure. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |