Izvorni znanstveni članak

https://doi.org/https://doi.org/10.64486/m.65.4.5

Data-driven Prediction and Application of Steel Material Parameters

Yongshuai Xiu ; School of Mechanical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
Xiaohu Deng ; School of Mechanical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
Yixiao Sun ; School of Electronic and Information Engineering, University of Science and Technology Liao-ning, Anshan 114051, China
Yuedong Yuan ; Changshu Tiandi Coal Mining Equipment Co., Ltd., Changshu 215500, China
Gang Shen ; Zhejiang XCC Group Co., Ltd., Xinchang, Zhejiang 312500, China
Zunzhong Du ; Changshu Tiandi Coal Mining Equipment Co., Ltd., Changshu 215500, China
Xiaojun Yang ; Changshu Tiandi Coal Mining Equipment Co., Ltd., Changshu 215500, China
Dongying Ju ; Zhejiang XCC Group Co., Ltd., Xinchang, Zhejiang 312500, China

Sažetak

Accurate prediction of steel material parameters during heat treatment is essential for reliable finite element analysis (FEA) and process optimisation. Conventional experimental measurements and empirical models are often costly, time-consuming, and difficult to generalise to complex chemistries, microstruc-tures, and temperature ranges. In this work, a data-driven prediction model is es-tablished using a comprehensive dataset that integrates simulation and experi-mental data, covering 18 elemental compositions, three typical microstructures, and a wide temperature range. Six key parameters are predicted simultaneously: thermal conductivity, specific heat capacity, yield stress, coefficient of thermal ex-pansion, Young’s modulus, and density. Five machine learning models are evalu-ated, among which XGBoost shows the best performance for thermal parameters, while Gradient Boosting provides the highest accuracy for mechanical properties. After hyperparameter optimisation with grid search and cross-validation, all models achieve R² values above 0.99 and relative prediction errors within 5 %. An integrated Steel Materials Data Management System (S-MDMS) is further devel-oped to combine data storage, visualisation, and online property prediction. The proposed model provides an efficient route for rapid acquisition and application of steel parameters in FEA-based heat treatment design and process optimisation.

Ključne riječi

intelligent forecasting; machine learning; heat treatment; database; steels

Hrčak ID:

347932

URI

https://hrcak.srce.hr/347932

Datum izdavanja:

1.10.2026.

Posjeta: 10 *