首页|Machine learning-based performance predictions for steels considering manufacturing process parameters:a review

Machine learning-based performance predictions for steels considering manufacturing process parameters:a review

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
Steels are widely used as structural materials,making them essential for supporting our lives and industries.However,further improving the comprehensive properties of steel through traditional trial-and-error methods becomes challenging due to the continuous development and numerous processing parameters involved in steel production.To address this challenge,the application of machine learning methods becomes crucial in establishing complex relationships between manufacturing processes and steel performance.This review begins with a general overview of machine learning methods and subsequently introduces various performance predictions in steel materials.The classification of performance pre-diction was used to assess the current application of machine learning model-assisted design.Several important issues,such as data source and characteristics,intermediate features,algorithm optimization,key feature analysis,and the role of environmental factors,were summarized and analyzed.These insights will be beneficial and enlightening to future research endeavors in this field.

SteelManufacturing processMachine learningPerformance predictionAlgorithm

Wei Fang、Jia-xin Huang、Tie-xu Peng、Yang Long、Fu-xing Yin

展开 >

Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control Technology,School of Materials Science and Engineering,Hebei University of Technology,Tianjin 300132,China

Institute of New Materials,Guangdong Academy of Science,Guangzhou 510651,Guangdong,China

National Natural Science Foundation of ChinaNatural Science Foundation of Hebei ProvinceNatural Science Foundation of Hebei ProvinceKey-Area R&D Program of Guangdong ProvinceGuangdong Academy of Science

51701061E2023202047E20212020752020B01013400042021GDASYL-20210102002

2024

10.1007/s42243-024-01179-5

钢铁研究学报(英文版)
钢铁研究总院

钢铁研究学报(英文版)

CSTPCD
影响因子：0.584
ISSN：1006-706X
年,卷(期)：2024.31(7)