Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology

DOI10.3934/mbe.2020379zbMath1486.74114OpenAlexW3097544601WikidataQ104619375 ScholiaQ104619375MaRDI QIDQ1979603

Yuquan Meng, Srinivasa M. Salapaka, Chenhui Shao, Manjunath Rajagopal, Sreenath Sundar, Ho Chan Chang, Hanyang Zhao, Gowtham Kuntumalla, Nenad Miljkovic, Ricardo Toro, Sanjiv K. Sinha, Placid M. Ferreira

Publication date: 3 September 2021

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mbe.2020379

zbMATH Keywords

machine learningthermal conductivityelectrical conductivitysupport vector regressionresponse surface methodologyprocess optimizationmechanical strengthultrasonic metal welding

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Optimization of other properties in solid mechanics (74P10) Numerical and other methods in solid mechanics (74S99) Junctions (74K30)

Cites Work

• An Introduction to Statistical Learning
• The elements of statistical learning. Data mining, inference, and prediction
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