Real-time wear rate prediction and analysis: Gradient-weighted class activation mapping (Grad-CAM) in 1D convolutional neural network bridges experiments and neural networks

Abstract

In this study, we propose a one-dimensional convolutional neural network (1D-CNN) for predicting wear rates using friction coefficient data. The model achieves high prediction accuracy for both training and testing sets, surpassing other machine learning methods. To ensure model reliability, we employ the Grad-CAM method, calculating importance scores correlating well with wear severity assessed by surface roughness (Ra) and wear track topography. The 1D-CNN model promises a precise, quantitative assessment of wear severity compared to traditional classification-based approaches. Furthermore, the model exhibits robust generalization abilities and potential as a base model for predicting wear rates of other materials, broadening its applicability in the tribology field.