Extreme high accuracy prediction and design of Fe-C-Cr-Mn-Si steel using machine learning

IF 7.6 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Hao Wu, Jianyuan Zhang, Jintao Zhang, Chengjie Ge, Lu Ren, Xinkun Suo
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Abstract

Solid solution strengthening theory is essential for designing steel with high microhardness. Experimental determination is quite time consuming and costly. It is necessary to develop an alternate approach to rapidly and accurately predict new solid solution strengthening theory for steel. In this study, a data-driven model combining machine learning (ML), firefly optimization algorithm (FA) and conditional generative adversarial networks (CGANs) were proposed to predict solid solution strengthening theory of Fe-C-Cr-Mn-Si steel. Three alloys were fabricated using cladding to validate the predict accuracy of the models. The results show that the trained support vector regression (SVR) model demonstrated the highest prediction precision for microhardness. The coefficient of determination (R2) value increased from 0.85 to 0.89 and root mean square error (RMSE) decreased from 0.39 to 0.31 after introducing the modified solid solution strengthening theory. The experimental validation revealed a minimum error of 1.17% between the predicted value and the experimental value. The investigation provides a valuable method to expedite design of Fe-C-Cr-Mn-Si steel with extreme high accuracy.

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来源期刊
Materials & Design
Materials & Design Engineering-Mechanical Engineering
CiteScore
14.30
自引率
7.10%
发文量
1028
审稿时长
85 days
期刊介绍: Materials and Design is a multi-disciplinary journal that publishes original research reports, review articles, and express communications. The journal focuses on studying the structure and properties of inorganic and organic materials, advancements in synthesis, processing, characterization, and testing, the design of materials and engineering systems, and their applications in technology. It aims to bring together various aspects of materials science, engineering, physics, and chemistry. The journal explores themes ranging from materials to design and aims to reveal the connections between natural and artificial materials, as well as experiment and modeling. Manuscripts submitted to Materials and Design should contain elements of discovery and surprise, as they often contribute new insights into the architecture and function of matter.
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