基于原位高温显微镜的相变预测概率深度学习框架

中文摘要 AI 辅助

结构材料的力学性能由其微观结构决定，而微观结构本身又由加工过程中施加的热历史所设定。然而，预测微观结构沿任意热轨迹如何演变仍然是一个具有挑战性的问题。例如，传统的连续冷却转变图仅提供静态描述，并且并非对所有材料都存在，尤其是新兴和新型材料。深度学习方法解决了这一问题，但它们将基于图像的表征、时间预测和微观结构预测视为独立任务。在此，我们引入了一个概率深度学习框架，将这些任务统一起来，用于原位高温共聚焦激光扫描显微镜数据。初始表面图像通过β变分自编码器压缩为低维潜在表示，该表示连同冷却速率和完整温度历史一起输入到时间融合变换器中，该变换器产生魏氏铁素体比例的量子预测。同时，高斯过程预测终态魏氏铁素体比例并修正预测的平衡水平，从而产生概率预测区间。我们在S235钢的五个冷却制度下展示了我们方法在魏氏铁素体演变上的有效性，并展示了显著的准确性。这验证了我们框架的相关性，并为在热处理下材料演变的简化和高度加速研究铺平了道路。

英文摘要

The mechanical performance of structural materials is governed by their microstructure, which is itself set by the thermal history imposed during processing. Predicting how that microstructure evolves along an arbitrary thermal trajectory, however, remains a challenging problem. Conventional continuous-cooling-transformation diagrams, for instance, provide only a static description, and they do not exist for all materials, especially emerging and novel ones. Deep-learning approaches address this issue, but they treat image-based characterization, temporal prediction, and microstructure forecasting as separate tasks. Here, we introduce a probabilistic deep-learning framework that unifies these tasks for in situ high-temperature confocal laser scanning microscopy data. The initial surface image is compressed with a beta-variational autoencoder into a low-dimensional latent representation, which, together with the cooling rate and the full temperature history, is passed to a temporal fusion transformer that produces a quantile forecast of the Widmanstatten ferrite ratio. In parallel, a Gaussian process predicts the end-state Widmanstatten ferrite ratio and corrects the forecast equilibrium level, yielding probabilistic prediction intervals. We demonstrate the efficacy of our method on Widmanstatten ferrite evolution in S235 steel across five cooling regimes and showcase significant accuracy. This verifies the relevance of our framework and paves the way for a streamlined and highly accelerated investigation of material evolution under thermal treatment.

发表机构

• Technical University of Munich（慕尼黑工业大学）

机构由 AI 辅助整理，请以论文原文为准。