Li Tianyang, Yang Zheng, Cui Junyi, Chen Wenjie, Almatani Rami, Wu Yingjie
Sichuan University - Pittsburgh Institute (SCUPI), Sichuan University, Chengdu, 610207, China.
Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore.
Sci Rep. 2025 May 10;15(1):16296. doi: 10.1038/s41598-025-00786-w.
Advanced high strength steels (AHSS) exhibit diverse mechanical properties due to their complex chemical compositions and microstructures. Existing machine learning (ML) studies often focus on specific steel grades, limiting generalizability in predicting and optimizing AHSS properties. Here, an ML framework was presented to predict and optimize the stretch-flangeability of AHSS based on composition-microstructure-property correlations, using datasets from 212 steel conditions. Support vector machine, symbolic regression, and extreme gradient boosting models accurately predicted hole expansion ratio (HER), ultimate tensile strength (UTS), and total elongation (TE). Shapley additive explanations revealed the importance of bainite volume fraction (VB), carbon content (C), and chromium content (Cr) for HER, UTS, and TE, respectively. Multi-objective optimization generated 252 optimized conditions with improved comprehensive mechanical properties. The best optimized chemical compositions (0.12wt.% C-1.10Mn-0.15Si-0.47Cr) along with the carbon equivalent (CE) of 0.44 wt.%, and microstructural features (7.2% ferrite, 44.5% bainite, 40.5% martensite, and 7.8% tempered martensite) yielded HER of 119.8%, UTS of 1013.5 MPa, and TE of 22.7%. This systematic framework enables efficient prediction and optimization of material properties (especially HER), with potential applications across various fields of materials science.
先进高强度钢（AHSS）由于其复杂的化学成分和微观结构而呈现出多样的力学性能。现有的机器学习（ML）研究通常聚焦于特定的钢种，在预测和优化AHSS性能方面的通用性有限。在此，提出了一个ML框架，基于成分 - 微观结构 - 性能的相关性，利用来自212种钢条件的数据集来预测和优化AHSS的拉伸翻边性。支持向量机、符号回归和极端梯度提升模型准确地预测了扩孔率（HER）、抗拉强度（UTS）和总伸长率（TE）。夏普利加法解释分别揭示了贝氏体体积分数（VB）、碳含量（C）和铬含量（Cr）对HER、UTS和TE的重要性。多目标优化生成了252个具有改善综合力学性能的优化条件。最佳优化化学成分（0.12wt.% C - 1.10Mn - 0.15Si - 0.47Cr）以及0.44 wt.%的碳当量（CE）和微观结构特征（7.2%铁素体、44.5%贝氏体、40.5%马氏体和7.8%回火马氏体）产生了119.8%的HER、1013.5 MPa的UTS和22.7%的TE。这个系统框架能够有效地预测和优化材料性能（尤其是HER），在材料科学的各个领域具有潜在应用。
