Machine learning of mechanical properties of steels
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Engineering,General Materials Science
Link
https://link.springer.com/content/pdf/10.1007/s11431-020-1599-5.pdf
Reference20 articles.
1. Ramprasad R, Batra R, Pilania G, et al. Machine learning in materials informatics: Recent applications and prospects, npj Comput Mater, 2017, 3: 54
2. Xue D, Xue D, Yuan R, et al. An informatics approach to transformation temperatures of NiTi-based shape memory alloys. Acta Mater, 2017, 125: 532–541
3. Ward L, Agrawal A, Choudhary A, et al. A general-purpose machine learning framework for predicting properties of inorganic materials, npj Comput Mater, 2016, 2: 1–7
4. Yosipof A, Nahum O E, Anderson A Y, et al. Data mining and machine learning tools for combinatorial material science of all-oxide photovoltaic cells. Mol Inf, 2015, 34: 367–379
5. Agrawal A, Choudhary A. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Mater, 2016, 4: 053208
Cited by 86 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Predicting the compression properties of Phyllostachys edulis based on the volume of its fiber sheaths;Industrial Crops and Products;2024-12
2. Development of anti-corrosive layer on steel alloy using novel synthesized 8-hydroxyquinoline-based inhibitors: Experimental and theoretical approach;Inorganic Chemistry Communications;2024-09
3. Green buildings: requirements, features, life cycle, and relevant intelligent technologies;Internet of Things and Cyber-Physical Systems;2024-09
4. Machine learning and experimental study on a novel Cr–Mo–V–Ti high manganese steel: Microstructure, mechanical properties and abrasive wear behavior;Journal of Materials Research and Technology;2024-07
5. Reservoir Computing neural networks for estimating mechanical properties of hot steel strips;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3