|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Coping with Materials Variance Using Transfer Learning
||Ali Riza Durmaz, Aurčle Goetz, Martin Müller, Akhil Thomas, Pierre Kerfrieden
|On-Site Speaker (Planned)
||Ali Riza Durmaz
Materials' microstructures exist in pronounced variety as they are signatures of alloying composition and processing route. As materials become increasingly intricate and their development is accelerated, deep learning (DL) becomes relevant for the automated and objective microstructure constituent quantification. While DL frequently outperforms classical techniques by a large margin, shortcomings are poor data-efficiency and inter-domain generalizability, which inherently opposes expensive data annotation and materials diversity.
To alleviate this issue, we propose to apply a sub-class of transfer learning methods called unsupervised domain adaptation (UDA). This class of learning algorithms addresses the task of finding domain-invariant features when supplied with annotated source data and unannotated target data, such that performance on latter distribution is optimized despite the absence of annotations. This study addresses different surface etchings and imaging modalities in a complex phase steel segmentation task. The UDA approach surpasses the generalizability of supervised trained models by a large extent.
||Machine Learning, Iron and Steel, Characterization