A machine learning approach to explore tensile properties of low-temperature solders
Author:
Affiliation:
1. National Cheng Kung University,Department of Mechanical Engineering,Tainan,Taiwan,70101
2. National Cheng Kung University,Department of Materials Science and Engineering,Tainan,Taiwan,70101
Funder
National Science and Technology Council
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10129527/10129646/10129657.pdf?arnumber=10129657
Reference5 articles.
1. The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research
2. Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels
3. Scikit-learn: Machine Learning in Python;pedregosa;J Mach Learn Res,2011
4. Exploring Dielectric Constant and Dissipation Factor of LTCC Using Machine Learning
5. Exploring effective charge in electromigration using machine learning
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