Abstract
Thanks to the development of additive manufacturing techniques, prosthetic surgery has reached increasingly advanced levels, revolutionizing the clinical course of patients with joint arthritis. 3D printing has made it possible to obtain customized prostheses based on patient needs, using high-performance materials. However, wear caused by regular gait activities such as walking, sitting, or running, leads to the deterioration of the material used in the joint. Thus, the use of traditional materials has gradually been replaced with more performing ones which have made it possible to obtain customized devices based on patient needs and, therefore, more effective. Numerical techniques have recently been adopted, such as the Finite Element Method (FEM), to support the experimentation, allowing the calculation of the useful life and the optimization of the prostheses’ functionality to accurately evaluate the distribution of the load on the prosthesis. The present work aims to develop an algorithm that optimizes hip replacement mechanically using a machine learning algorithm coupled with multi-body and finite element model simulations.
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