机械工程学报 ›› 2022, Vol. 58 ›› Issue (10): 31-50.doi: 10.3901/JME.2022.10.031
刘源1,2, 魏世忠1,3
收稿日期:
2021-06-08
修回日期:
2021-10-29
出版日期:
2022-05-20
发布日期:
2022-07-07
通讯作者:
魏世忠(通信作者),男,1966年出生,博士,教授,博士研究生导师。主要研究方向为先进耐磨材料、耐磨材料的寿命延长与控制技术、耐磨件的表面强化与复合技术。E-mail:hnwsz@126.com
作者简介:
刘源,女,1985年出生,博士研究生。主要研究方向为耐磨材料性能预测、先进耐磨材料。E-mail:lewyuan@zua.edu.cn
基金资助:
LIU Yuan1,2, WEI Shizhong1,3
Received:
2021-06-08
Revised:
2021-10-29
Online:
2022-05-20
Published:
2022-07-07
摘要: 数据驱动方法利用机器学习算法挖掘数据中隐藏的规则,是一种符合"第四范式"的研究方法。该研究方法的开展基于大量材料基础数据。通过对比国内外材料基础数据平台,分析利用现有数据平台已开展的研究,指出钢铁耐磨材料基础数据存在数据匮乏和缺乏统一采集标准两个问题。针对此,介绍符合材料基因组计划的数据采集标准,并给出钢铁耐磨材料专用数据平台的框架以及数据来源。分析钢铁耐磨材料性能的影响因素,讨论各种特征选择技术的特点。回顾在材料科学研究中成功应用的几种机器学习算法,分析每种算法的应用场景,讨论它们的优缺点,并对算法性能进行了比较。最后总结一些建议为特征提取和机器学习算法选择提供指导,并指出数据驱动方法在性能预测、发现新材料和自动化自主试验等方面具有良好的应用前景。
中图分类号:
刘源, 魏世忠. 数据驱动的钢铁耐磨材料性能预测研究综述[J]. 机械工程学报, 2022, 58(10): 31-50.
LIU Yuan, WEI Shizhong. Review on Data-driven Method for Property Prediction of Iron and Steel Wear-resistant Materials[J]. Journal of Mechanical Engineering, 2022, 58(10): 31-50.
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