## 5 Conclusions  
In this paper, two Machine Learning (ML) algorithms, supervised feed-forward neural networks (FFNNs), have been developed for the analysis of the critical manufacturing defects in components to be produced by Selective Laser Melting (SLM). The first one provides in output the probability associated with a specific defect starting from the main process parameters and the part risk-volume (Probability ML in the following). The second one estimates the location and the scale parameter of the defect size distribution, assumed to follow the Largest Extreme Value Distribution (LEVD), receiving in input the main process parameters and the part volume (LEVD ML in the following). The process parameters considered in input are those mainly affecting the defect population and the fatigue response, i.e., the building orientation, the input power, the scan speed, the hatch distance, the layer thickness and the average powder size. The predicting capability has been verified using literature datasets obtained with tests on AlSi10 Mg and Ti6 Al4V alloys.  
The following conclusions can be drawn:  
1. Probability ML and LEVD ML have shown a high predicting capability for both AlSi10 Mg and Ti6 Al4V datasets. A k-fold cross-validation scheme has been used for the validation, proving that both approaches can be reliably used for the analysis of defects in SLM components. The loss functions with respect to the fold considered for the validation were almost constant, thus confirming the good performances of both architectures.
2. LEVD ML has been shown to work well even for datasets with a trend significantly different from that of the other datasets considered for the training process. On the other hand, the Probability ML algorithm tends to overestimate the probability associated with each defect, being less conservative.
3. The trend in the Gumbel Plot estimated with the Probability ML algorithm can show a large scatter and, for the same process parameters, it is not ensured that larger defects are characterized by larger probabilities. This can be solved by increasing the number of training data. On the other hand, the LEVD ML \"embeds\" the LEVD statistical model based on the experimental evidence, thus overcoming this criticality.  
4. The predicting capability of both developed ML algorithms may be enhanced by adding more input factors, whose influence on the defect size population is still debated in the literature, such as heat treatment temperature, the building platform heating temperature, the powder size ranges and the SLM production system. However, the number of available datasets for the training process should be significantly increased.  
To conclude, this analysis has pointed out that the proposed ML algorithms can be reliably used for the prediction of the probability of specific defect sizes in SLM components and for the subsequent fatigue design with damage-tolerant approaches and, in general, that approaches based on ML should be used to ensure the structural integrity of AM components subjected to defect-driven fatigue failures.  
Conceptualization, A.T. and A.C.; software, A.T.; validation, A.T., writing--original draft preparation, A.T.; writing--review and editing, A.C., F.B. and D.S.P.; supervision, A.T., F.B. and D.S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data analyzed in this papers have been retrieved from literature papers.  
The authors declare no conflict of interest.