Assessment of the Critical Defect in Additive Manufacturing Components through Machine Learning Algorithms
Abstract
:1. Introduction
2. Machine Learning and Defect Size: Algorithms
2.1. Defects in AM Components
2.2. Process Parameters and Defects in AM Components
- Building orientation: several experimental results have proved the influence of the building orientation on the defect size and, accordingly, on the fatigue strength. In the following, with 0° and 90° the authors refer to a building orientation with the specimen axis parallel and perpendicular to the building platform (horizontal and vertical building orientation), respectively [23,24].
- Power and scan speed: these two parameters are strongly correlated, since the energy per unit length, dependent on both input power and scan speed, controls the formation of pores or lack of fusion defects [25].
- Powder size: the powder size affects the defect formation. For example, in [28,29], it has been shown that defects tend to be larger in parts produced with smaller powder, thus affecting the fatigue response. In the following analysis, the average powder size has been considered as the input parameter for the developed ML algorithms.
2.3. Neural Networks Architecture
2.3.1. NN Architecture: Probability of a Specific Defect Size (Probability ML)
2.3.2. NN Architecture: LEVD Distribution Parameters (LEVD ML)
2.4. k-Fold Cross Validation
3. Experimental Validation
3.1. AlSi10Mg Validation
3.1.1. Probability ML Validation
3.1.2. LEVD ML Algorithm: Validation
3.2. Ti6Al4V Validation
3.2.1. Probability ML Validation
3.2.2. LEVD ML Algorithm Validation
4. Discussion
5. Conclusions
- Probability ML and LEVD ML have shown a high predicting capability for both AlSi10Mg and Ti6Al4V 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.
- 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.
- 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.
- 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.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Orientation | Power | Speed | Hatch Distance | Layer Thickness | Average Powder Size | Risk Volume |
---|---|---|---|---|---|---|
[W] | [mm/s] | [ | [ | [] | [mm3] | |
[0, 90] | [220, 380] | [600, 1650] | [130, 190] | [30, 60] | [30, 41.5] | [250, 2300] |
Orientation | Power | Speed | Hatch Distance | Layer Thickness | Average Powder Size | Risk Volume |
---|---|---|---|---|---|---|
[W] | [mm/s] | [ | [ | [] | [mm3] | |
[0, 90] | [175, 400] | [150, 1400] | [120, 140] | [30, 60] | [34, 45] | [84, 1204] |
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Tridello, A.; Ciampaglia, A.; Berto, F.; Paolino, D.S. Assessment of the Critical Defect in Additive Manufacturing Components through Machine Learning Algorithms. Appl. Sci. 2023, 13, 4294. https://doi.org/10.3390/app13074294
Tridello A, Ciampaglia A, Berto F, Paolino DS. Assessment of the Critical Defect in Additive Manufacturing Components through Machine Learning Algorithms. Applied Sciences. 2023; 13(7):4294. https://doi.org/10.3390/app13074294
Chicago/Turabian StyleTridello, Andrea, Alberto Ciampaglia, Filippo Berto, and Davide Salvatore Paolino. 2023. "Assessment of the Critical Defect in Additive Manufacturing Components through Machine Learning Algorithms" Applied Sciences 13, no. 7: 4294. https://doi.org/10.3390/app13074294
APA StyleTridello, A., Ciampaglia, A., Berto, F., & Paolino, D. S. (2023). Assessment of the Critical Defect in Additive Manufacturing Components through Machine Learning Algorithms. Applied Sciences, 13(7), 4294. https://doi.org/10.3390/app13074294