Machine Learning Methods to predict the Fatigue Life of Selectively Laser Melted Ti6Al4V Components

The aim of the following work is to search a relation between the different process parameters and the fatigue life of Ti6Al4V components, produced via Selective Laser Melting technologies, using Machine Learning techniques and their several types of Neural Networks, as an attempt to reduce the cost of further fatigue testing, as well as to have an idea of the life of an hypothetical component, in relation to the inputted process parameters, thermal treatments and surface treatments.