A New Approach for the Tribological and Mechanical Characterization of a Hip Prosthesis Trough a Numerical Model Based on Artificial Intelligence Algorithms and Humanoid Multibody Model

In recent years, thanks to the development of additive manufacturing techniques, pros-thetic surgery has reached increasingly cutting-edge levels, revolutionizing the clinical course of patients suffering from joint arthritis, rheumatoid arthritis, post-traumatic arthrosis, etc. This work aims to evaluate the best materials for prosthetic surgery in hip implants from a tribological and mechanical point of view by using a machine-learning algorithm coupling with multi-body modeling and Finite Element Method (FEM) simulations. The innovative aspect is represented by the use of machine learning for the creation of a humanoid model in a multibody software environment that aimed to evaluate the load and rotation condition at the hip joint. After the boundary conditions have been defined, a Finite Element (FE) model of the hip implant has been created. The material properties and the information on the tribological behavior of the material couplings under investigation have been obtained from literature studies. The wear process has been investigated through the implementation of the Archard's wear law in the FE model. The results of the FE simulation show that the best wear behavior has been obtained by CoCr alloy/UHMWPE coupling with a volume loss due to a wear of 0.004 mu m3 at the end of the simulation of ten sitting cycles. After the best pairs in terms of wear has been established, a topology optimization of the whole hip implant structure has been performed. The results show that, after the optimization process, it was possible to reduce implant mass making the implant 28.12% more lightweight with respect to the original one.