Defect Classification for Additive Manufacturing with Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Generation and Feature Extraction
2.2. Modeling and Testing
3. Results and Discussion
3.1. Unsupervised Defect Classification with Cluster Algorithms on Binary Images
3.2. Supervised Defect Classification with Random Forest Trees on Binary Images
3.3. Defect Classification with Random Forest Trees on Local Features
3.4. Comparison of the Models
3.5. Model Application on Unlabeled PBF-LB/M Fabricated Ti6Al4V Micrographs
4. Conclusions
- Unsupervised models like the kMeans and DBSCAN algorithm failed in classifying process pores, keyholes, and lacks of fusion defects. However, a supervised random forest tree classifier showed high accuracies, around 96%, for the classification. The staff-labeled data were needed, so the models could distinguish especially between the lacks of fusion and keyhole defects.
- The supervised classification models performed better on binary image data of defects than on locally extracted features describing their morphologies. More local features are needed to achieve better classification results.
- Depending on the process parameters used in additive manufacturing, keyhole defects could become more irregular and unshaped even at relatively low energy densities. This resulted in major classification errors between lacks of fusion and keyhole defects. Considering the influences of the defects on mechanical properties, even keyhole defects could possibly significantly reduce the mechanical properties if they were more irregular and unshaped.
5. Outlook
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Label | Rel. Density | |||||
---|---|---|---|---|---|---|
- | mm | W | mm/s | mm | J/mm3 | % |
1 | 0.19 | 134 | 283 | 0.05 | 51.2 | 98.12 |
2 | 0.12 | 73 | 225 | 0.05 | 56.89 | 96.76 |
3 | 0.11 | 188 | 565 | 0.05 | 63.18 | 97.24 |
4 | 0.07 | 250 | 690 | 0.05 | 104.60 | 98.36 |
5 | 0.12 | 254 | 306 | 0.05 | 141.70 | 97.65 |
6 | 0.18 | 317 | 668 | 0.05 | 54.35 | 99.45 |
7 | 0.12 | 104 | 367 | 0.05 | 48.43 | 98.75 |
8 | 0.14 | 278 | 452 | 0.05 | 90.24 | 97.92 |
9 | 0.20 | 195 | 958 | 0.05 | 20.55 | 96.30 |
10 | 0.19 | 319 | 1693 | 0.05 | 19.65 | 97.45 |
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Laser Power PL in W | Layer Thickness DL in µm | Scan Speed vS in mm/s | Hatch Distance dH in mm | |
---|---|---|---|---|
Min | 51 | 0.025 | 201 | 0.05 |
Max | 350 | 0.1 | 1700 | 0.2 |
Count | 290 | 4 | 628 | 794 |
Model | Accuracy in % | |||
---|---|---|---|---|
Overall | Process Pores | Lack of Fusion | Key Hole | |
kMeans | 60 | 100 | 57.75 | 22.5 |
RFT images | 95.28 | 100 | 90.08 | 95.93 |
RFT local features | 55.28 | 49.18 | 62.28 | 54.84 |
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Altmann, M.L.; Benthien, T.; Ellendt, N.; Toenjes, A. Defect Classification for Additive Manufacturing with Machine Learning. Materials 2023, 16, 6242. https://doi.org/10.3390/ma16186242
Altmann ML, Benthien T, Ellendt N, Toenjes A. Defect Classification for Additive Manufacturing with Machine Learning. Materials. 2023; 16(18):6242. https://doi.org/10.3390/ma16186242
Chicago/Turabian StyleAltmann, Mika León, Thiemo Benthien, Nils Ellendt, and Anastasiya Toenjes. 2023. "Defect Classification for Additive Manufacturing with Machine Learning" Materials 16, no. 18: 6242. https://doi.org/10.3390/ma16186242