Elimination of experimentation cost and time by data analysis in mechanical property prediction of aluminum alloys
Author:
Publisher
Elsevier BV
Subject
Materials Science (miscellaneous)
Reference30 articles.
1. A defect-based physics-informed machine learning framework for fatigue finite life prediction in additive manufacturing;Salvati;Mater. Des.,2022
2. Improving prediction accuracy of high-performance materials via modified machine learning strategy;Yong;Comput. Mater. Sci.,2022
3. AFLOW-ML: A RESTful API for machine-learning predictions of materials properties;Gossetta;Comput. Mater. Sci.,2018
4. Emmanuel Anuoluwa Bamidele, Ahmed Olanrewaju Ijaola, Michael Bodunrin, Oluwaniyi Ajiteru, Afure Martha Oyibo, Elizabeth Makhatha, Eylem Asmatulu,Discovery and prediction capabilities in metal-based nanomaterials: An overview of the application of machine learning techniques and some recent advances,Advanced Engineering Informatics 52 (2022) 101593.
5. Udaya Devadigaa, Rakhesha Kumar R. Poojarya, Peter Fernandesb, Artificial neural network technique to predict the properties of multiwall carbon nanotube-fly ash reinforced aluminum composite, Journal of Material research and Technology,2 0 1 9;8(5):3970–3977.
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