A machine learning approach for determination of coefficient of friction from ring compression test

A framework is defined for determining the coefficient of friction (COF) using numerical simulations of ring compression tests and machine learning techniques, utilizing von Mises plasticity with the three-parameter Swift law material model. A big dataset, containing 18750 rows and 20 columns, is created from 3750 numerical simulations of ring compression test in ABAQUS using MATLAB scripts. A dense feedforward neural network is trained to …