A combined finite element and machine learning approach for the prediction of specific cutting forces and maximum tool temperatures in machining

DOI10.1553/etna_vol56s66zbMath1491.74097OpenAlexW4206188411MaRDI QIDQ2672193

Sai Manish Reddy Mekarthy, Maryam Hashemitaheri, Harish P. Cherukuri

Publication date: 8 June 2022

Published in: ETNA. Electronic Transactions on Numerical Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1553/etna_vol56s66

zbMATH Keywords

finite element methodmachine learningartificial neural networkactivation functiondeep networkorthogonal plane-strain machiningshallow network

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Contact in solid mechanics (74M15) Finite element methods applied to problems in solid mechanics (74S05) Thermal effects in solid mechanics (74F05) Numerical and other methods in solid mechanics (74S99)

Uses Software

• ABAQUS
• Python
• Adam
• SALib

Cites Work

• Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index
• Making best use of model evaluations to compute sensitivity indices
• A new stress-based model of friction behavior in machining and its significant impact on residual stresses computed by finite element method
• Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

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