Faculty of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul, 143-747, Republic of Korea.
Department of Printed Electronics, Sunchon National University, 291-19 Jungang-ro, Sunchon, Chonnam, 540-742, South Korea.
Sci Rep. 2020 Nov 24;10(1):20443. doi: 10.1038/s41598-020-77575-0.
Most data-driven machine learning (ML) approaches established in metallurgy research fields are focused on a build-up of reliable quantitative models that predict a material property from a given set of material conditions. In general, the input feature dimension (the number of material condition variables) is much higher than the output feature dimension (the number of material properties of concern). Rather than such a forward-prediction ML model, it is necessary to develop so-called inverse-design modeling, wherein required material conditions could be deduced from a set of desired material properties. Here we report a novel inverse design strategy that employs two independent approaches: a metaheuristics-assisted inverse reading of conventional forward ML models and an atypical inverse ML model based on a modified variational autoencoder. These two unprecedented approaches were successful and led to overlapped results, from which we pinpointed several novel thermo-mechanically controlled processed (TMCP) steel alloy candidates that were validated by a rule-based thermodynamic calculation tool (Thermo-Calc.). We also suggested a practical protocol to elucidate how to treat engineering data collected from industry, which is not prepared as independent and identically distributed (IID) random data.
大多数在冶金研究领域建立的数据驱动机器学习 (ML) 方法都侧重于构建可靠的定量模型，该模型可以从给定的材料条件集中预测材料性能。通常，输入特征维度（材料条件变量的数量）远高于输出特征维度（关注的材料性能数量）。与其说是这样的正向预测 ML 模型，不如说是需要开发所谓的逆向设计建模，其中可以从一组所需的材料性能中推导出所需的材料条件。在这里，我们报告了一种新颖的逆向设计策略，该策略采用了两种独立的方法：元启发式辅助常规正向 ML 模型的逆向阅读和基于改进变分自动编码器的非典型逆向 ML 模型。这两种前所未有的方法取得了成功，并得出了重叠的结果，从中我们确定了几种新型的热机械控制加工 (TMCP) 钢合金候选材料，这些候选材料通过基于规则的热力学计算工具 (Thermo-Calc.)进行了验证。我们还提出了一种实用的方案来阐明如何处理从工业中收集的工程数据，这些数据不是作为独立且同分布 (IID)随机数据准备的。
