Abrasive Wear Prediction of Three-Dimensional Printed PEEK Using Artificial Neural Network

Abstract Machine learning is a cutting-edge technology that stands out among the various artificial intelligence offerings with its exceptional ability to comprehend intricate processes in computational tools. The optimization of input parameters for polyetheretherketone (PEEK) to print samples of any geometry provides insight for fabricated samples. The samples were fabricated using the extrusion method of additive manufacturing with varying layer thickness, which was tested under abrasive wear conditions using 120-grade sandpaper. The surface properties affect the wear response and rate of the 3D-printed PEEK sample, and the wear loss is high when subjected to abrasive wear conditions. Increasing layer thickness (more than 0.2 mm) reduces the hardness, and a rougher surface causes higher wear loss. To quantify the wear loss and avoid any mishappening during the operation, when the 3D-printed PEEK part is used as a journal bearing under unfavorable conditions, the mechanical and tribological properties emerge as significant measures defining a material's worth. These properties measure a material's capacity to endure external forces and the nonuniformity of relative motion. The article aims to predict wear loss using artificial neural networks (ANNs) under such conditions to avoid system failure and timely replacement with new components. The ReLU (rectified linear unit) activation function fits the actual wear trend and predicts the wear loss with 98% accuracy.