Prediction of ball-on-plate friction and wear by ANN with data-driven optimization

Abstract For training artificial neural network (ANN), big data either generated by machine or measured from experiments are used as input to “learn” the unspecified functions defining the ANN. The experimental data are fed directly into the optimizer allowing training to be performed according to a predefined loss function. To predict sliding friction and wear at mixed lubrication conditions, in this study a specific ANN structure was so designed that deep learning algorithms and data-driven optimization models can be used. Experimental ball-on-plate friction and wear data were analyzed using the specific training procedure to optimize the weights and biases incorporated into the neural layers of the ANN, and only two independent experimental data sets were used during the ANN optimization procedure. After the training procedure, the ANN is capable to predict the contact and hydrodynamic pressure by adapting the output data according to the tribological condition implemented in the optimization algorithm.