###### Abstract  
The design against fatigue failures of Additively Manufactured (AM) components is a fundamental research topic for industries and universities. The fatigue response of AM parts is driven by manufacturing defects, which contribute to the experimental scatter and are strongly dependent on the process parameters, making the design process rather complex. The most effective design procedure would involve the assessment of the defect population and the defect size distribution directly from the process parameters. However, the number of process parameters is wide and the assessment of a direct relationship between them and the defect population would require an unfeasible number of expensive experimental tests. These multivariate problems can be effectively managed by Machine Learning (ML) algorithms. In this paper, two ML algorithms for assessing the most critical defect in parts produced by means of the Selective Laser Melting (SLM) process are developed. The probability of a defect with a specific size and the location and scale parameters of the statistical distribution of the defect size, assumed to follow a Largest Extreme Value Distribution, are estimated directly from the SLM process parameters. Both approaches have been validated using literature data obtained by testing the AISi10 Mg and the Ti6 Al4V alloy, proving their effectiveness and predicting capability. machine learning; supervised feed-forward neural networks (FFNNs); fatigue design; Additive Manufacturing; AISi10 Mg alloy; Ti6 Al4V alloy +
Footnote †: journal: Medical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy
tests and following analyses of the fracture surfaces. This approach is effective only if the part will be produced with the same process parameters, since the defect population is strongly dependent on them [6; 10; 11]. The correlation between the process parameters and the defect population should therefore be assessed. However, the number of process parameters for manufacturing AM parts is large, e.g., at least five for the Selective Laser Melting (SLM) process, one of the most used AM techniques. Accordingly, a correlation and, above all, the interactions between different process parameters and the critical defect cannot be experimentally detected, since an unfeasible number of experimental tests would be required. On the other hand, this multivariate problem concerning the fatigue response of AM parts can be effectively managed with Machine Learning (ML) algorithms. In the literature, ML algorithms have been increasingly used for dealing with the defect population and, in general, the fatigue response of AM components. For example, in [12], an ML methodology trained on artificial data is exploited to assess the fatigue limit of AM Ti6 Al4V and SS316L alloys. The influence of process parameters is embedded in the computational approach used to generate data. In [13], the efficiency of both shallow and deep neural networks in predicting the effect of thermal and shot peening post-treatment on the fatigue life of AM AlSi10 Mg parts is investigated. The models are trained on the experimental data of notched specimens produced with a specific set of printing parameters. In [14], a probabilistic physics-guided model for the prediction of the stress-life relationship of SLM Ti6 Al4V starting from process parameters is defined, with soft constraints on the S-N curve trend imposed and without accounting for the defect influence. In [15], an Artificial Neural Network (ANN) is developed to assess the lowest stress to be used for the design of AM components starting from the material hardness and the defect size. In [16], a Physics-Informed ML algorithm is developed for the prediction of the fatigue life of AM components. Phenomenological constraints based on Linear Elastic Fracture Mechanics are embedded in the developed algorithm, making it appropriate even when the dataset for training is limited.