The Role of Machine Learning in Enhancing 3D-Printed Prosthetic Ankle

This research paper explores the effect of machine learning and generative design on the improvement of manufacturing 3D-printed prosthetic ankles. The generative design was used to improve performance and efficiency by removing unnecessary material from areas that were not subjected to significant loads, thereby reducing weight and addressing design challenges such as resonance and reduction of thermal stress. In addition, a machine learning model based on a dataset that contains 3D printing parameters was developed to predict optimal setting parameters for 3D printing, which improved the efficiency of printing and reduced both material usage and costs. Integration of these technologies enables more precise, efficient, and sustainable manufacturing processes.