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 ${\mathsf{\mu }\mathrm{m}}^3$ 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.

This work aimed to develop an automatic new methodology based on establishing if a mechanical component, designed for a conventional propulsion system, is also suitable for hybrid electric propulsion. Change in propulsion system leads to different power delivery and vehicle dynamics, which will be reflected in different load conditions acting on the mechanical components. It has been shown that a workflow based on numerical simulations and experimental tests represents a valid approach for the evaluation of the cumulative fatigue damage of a mechanical component. In this work, the front half-shaft of a road car was analyzed. Starting from the acquisition of a speed profile and the definition of a reference vehicle, in terms of geometry and transmission, a numerical model, based on longitudinal vehicle dynamics, was developed for both conventional and hybrid electric transmission. After the validation of the model, the cumulative fatigue damage of the front half-shaft was evaluated. The new design methodology is agile and light; it has been dubbed “Smart Design”. The results show that changing propulsion led to greater fatigue damage, reducing the fatigue life component by 90%. Hence, it is necessary to redesign the mechanical component to make it also suitable for hybrid electric propulsion.

The aim of this paper is the development of a 7-DOF (Degrees Of Freedom) mathematical model of an IndyCar and the implementation of an Artificial Neural Network in order to predict the optimal setup parameters of the car, reducing time and costs for race teams. The mathematical model is created by using MATLAB^TM and Simulink software starting from a telemetry acquisition at the Houston circuit and is based on Vertical Vehicle Dynamic equations. The optimal setup parameters have been predicted through an Artificial Neural Network (ANN) by using the NFTOOL Toolbox of MATLAB^TM software. ANN is implemented in a Quarter Car model, firstly, in order to train the network and predict the parameters able to reduce tire deflection and suspension travel in the time domain and the resonance peaks amplitude in the frequency domain. Then, it is implemented in the 7-DOF model in order to predict the best setup parameters able to reduce body movements and the weight transfers of the car.

The aim of this work is to design a power split transmission for an urban street sweeper in order to reduce fuel consumption. The design process starts with the comparison between a hydrostatic and a hydromechanical power split transmission. Both transmissions have been tested through an acceleration test considering 30, 50, 70 and 100 percent of the rated engine power. The results of both models developed in the Simcenter Amesim^TM environment show that the power split transmission presents a higher efficiency, which justifies the adoption of this type of transmission with respect to the hydrostatic system. Then, a pure mechanical gear is added to the base concept of the power split transmission. The mechanical gear is managed by a lockup clutch, which can be engaged during the working phase of the street sweeper, similar to an adaptive cruise control. In this case, both transmissions are tested through a regulated cycle, UNI-EN 151429-2, highlighting the advantage of using a pure mechanical branch. At the end, both transmissions are tested with a driving cycle acquired through an experimental setup consisting of a control unit, a GPS and a tablet for the monitoring of the speed profile. The results show that the adoption of a lockup clutch allows an increase in the system efficiency during the working phase, hence reducing the average fuel consumption during the mission test.