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About this Abstract
Meeting	2025 TMS Annual Meeting & Exhibition
Symposium	Artificial Intelligence Applications in Integrated Computational Materials Engineering
Presentation Title	Generative Adversarial Network (GAN)-Based Microstructure Mapping from Surface Profile For Laser Powder Bed Fusion (LPBF)
Author(s)	Jingwen Gao, Chenyang Zhu, Shubo Gao, Ming Xue, Kun Zhou
On-Site Speaker (Planned)	Jingwen Gao
Abstract Scope	The promise of laser powder bed fusion (LPBF) rests in its potential of tailoring microstructures via optimizing process parameters. However, conventional approaches often rely on trial-and-error experiments, during which characterizing microstructures is time-consuming and destructive to built specimens. By investigating the correlation between process parameters, surface profile, and internal orientation mapping, we propose a novel framework for generating statistically equivalent microstructures of LPBF samples from corresponding as-printed surface inputs. Integrating an image-to-image translation Generative Adversarial Network (GAN)-based model, this workflow captures characteristic crystallographic and morphological features of LPBF-fabricated nickel and learns the intricate relationships between them. The results demonstrate that our model can generate orientation mappings effectively and efficiently across a range of LPBF process parameters, both within and beyond the training dataset. The developed model is material-agnostic and can be fine-tuned for other LPBF materials via transfer learning, providing potential for process optimization and quality control in LPBF manufacturing.
Proceedings Inclusion?	Planned:
Keywords	Additive Manufacturing, Machine Learning, Characterization

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