Abstract
Defect detection in laser powder bed fusion (LPBF) parts is a critical step for in their quality control. Ensuring the integrity of these parts is essential for a broader adoption of this manufacturing process in highly standardized industries such as aerospace. With many challenges to overcome, there is currently no standardized image analysis and segmentation process for the defect analysis of LPBF parts. This process is often manual and operator-dependent, which limits the repeatability and the reproducibility of the analytical methods applied, raising questions about the validity of the analysis. The pore segmentation step is critical for porosity analysis since the pore size and morphology metrics are calculated directly from the results of the segmentation process. In this work, Ti6Al4V specimens with purposely induced and controlled porosity were printed, scanned 5 times on two CT scan systems by two different operators, and then reconstructed as 3D volumes. The porosity in these specimens was analyzed using manual and Otsu thresholding and a convolutional neural network (CNN) deep learning segmentation algorithm. Then, a variance component estimation realized over 75 porosity analyses indicated that, independently of the operator and the CT scan system used, the CNN provided the best repeatability and reproducibility in the LPBF specimens of this study. Finally, a multimodal correlative study using higher resolution laser confocal microscopy observations was used for a multi-scale pore-to-pore comparison and as a reliability assessment of the segmentation algorithms. The validity of the CNN-based pore segmentation was thus assessed through improved repeatability, reproducibility, and reliability.
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The data supporting the results of this study are available from the corresponding author upon request.
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Acknowledgements
The authors would like to thank Professor Bernard Clément for his help with statistical tests and Etienne Moquin for his help with CT imaging
Funding
This work was supported by the Consortium for Research and Innovation in Aerospace in Quebec (CRIAQ) and the Natural Science and Engineering Research Council of Canada (NSERC).
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CD: Conceptualization, Investigation, Methodology, Software, Formal analysis, Data curation, Writing—original draft, Writing—review & editing. ML: Conceptualization, Methodology, Investigation, Data curation, Writing—original draft. FB: Investigation, Data curation, Writing—review & editing. NP: Software, Funding acquisition. BP: Software, Writing—review & editing. FC: Supervision, Funding acquisition, Writing—review & editing. FG: Conceptualization, Funding acquisition, Visualization, Supervision, Project administration, Writing—review & editing. VB: Conceptualization, Funding acquisition, Visualization, Supervision, Project administration, Writing—review & editing.
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Appendix 1: Porosity analyses boxplots for each specimen and porosity segmentation workflow
Appendix 1: Porosity analyses boxplots for each specimen and porosity segmentation workflow
See Figs.
Pore aspect ratio distribution boxplot for each scanning permutation and porosity segmentation workflow for specimens: a LED > 0.1, b LED < 0.1, c HED < 0.1, d HED > 0.1 and e OED; (Ex. for expert)
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Pore volume distributions boxplot for each scanning permutation and porosity segmentation workflow for specimens: a LED > 0.1, b LED < 0.1, c HED < 0.1, d HED > 0.1 and e OED; (Ex. for expert)
18.
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Desrosiers, C., Letenneur, M., Bernier, F. et al. Automated porosity segmentation in laser powder bed fusion part using computed tomography: a validity study. J Intell Manuf (2024). https://doi.org/10.1007/s10845-023-02296-w
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DOI: https://doi.org/10.1007/s10845-023-02296-w