Data-driven wear prediction method for complex engineering structures

Predicting the evolution of wear in metallic structural components is vital for accurately estimating the lifetime of engineering equipment. However, this remains a significant challenge due to the prohibitively large number of cycles required for traditional experiments or simulations. To address this, we established a data-driven approach to predict metal wear evolution during dynamic mechanical interactions. Our methodology involves two main steps: developing a high-fidelity finite element (FE) model to accurately simulate the wear, and then training a deep learning model that uses applied loads and historical wear data to predict future wear evolution. We selected contact wire clips in a high-speed railway system as a practical example, where the accuracy of our numerical model was successfully validated by experimental results in calculating wear distribution. The subsequent deep learning model demonstrated high accuracy (R2>0.95) in predicting future wear depth at distinct positions against ground truth data. This presented approach offers a wide range of applications for predicting the wear evolution of equipment in various engineering fields.