Effects of manufacturing direction, heat-treatment and surface operations on fatigue life in additively manufactured metals: An analysis based on statistics and artificial intelligence.

This study aimed to establish whether useful fatigue design stress-life curves could be estimated for additively manufactured metals through statistical and machine learning analysis of a large quantity of experimental fatigue data. The study focused on additively manufactured aluminium, steel and titanium. Three manufacturing parameters were considered, namely the manufacturing direction, heat-treatment and surface operations, with the results presented for 0.1 and −1 loading ratios. By gathering experimental data for all parameters, the negative inverse slopes were found to be concentrated between 3 and 6, and the mean endurance limit as a ratio to ultimate tensile strength was 0.18 and 0.21 for 0.1 and −1 loading ratios, respectively, without any statistical analysis. Surface operations were observed to have a significant effect on the fatigue strength of additively manufactured aluminium, steel and titanium regardless of other manufacturing parameters. Multiple linear regression analysis and several machine learning methods (Decision Tree, Support Vector Machines, K-Nearest Neighbour, Multi-Layer Perceptron, Partial Least Squares and Gaussian Process Regression) were used to develop predictive models. The results of these analyses highlight that the conventional approach applied to fatigue of traditional metals does not suffice for additively manufactured metals. While artificial intelligence presents a promising solution, our investigation indicates it is necessary to account for parameters in addition to those considered here such as manufacturing processes, material properties, material microstructure and defects to make reliable fatigue property estimates for additively manufactured metals using machine learning.