Employing knowledge transfer in machine learning for wear assessment on synthetic and biological materials

Abstract

Assessing wear is an indispensable task across almost all engineering disciplines, and automated wear assessment would be highly desirable. To determine the occurrence of wear, machine learning strategies have already been successfully applied. However, classifying different types of wear remains challenging. Additionally, data scarcity is a major bottle neck that limits the applicability of machine learning models in certain areas such as biomedical engineering. Here, we present a method to accurately classify surface topographies representing the three most common types of mechanically induced wear: abrasive, erosive, and adhesive wear. First, a random forest (RF) classifier is trained on a list of parameters determined from 3-dimensional (3D) surface scans. Then, this method is adapted to a small dataset obtained from damaged cartilage tissue by using knowledge transfer principles. In detail, two random forest models are trained separately: a base model on a large training dataset obtained on synthetic samples, and a complementary model on the scarce cartilage data. After the separate training phases, the decision trees of both models are combined for inference on the scarce cartilage data. This model architecture provides a highly adaptable framework for assessing wear on biological samples and requires only a handful of training data. A similar approach might also be useful in many other areas of materials science where training data are difficult to obtain.