PSI - Issue 77

Pawel Madejski et al. / Procedia Structural Integrity 77 (2026) 357–364 Author name / Structural Integrity Procedia 00 (2026) 000–000

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Table 5. Estimated density by segmentation method for Triangles pattern Segmentation Method Experimental Density [g/cm 3 ] Estimated density [g/cm 3 ] Est. Deviation Otsu 1.188 1.254 +6.3% Adaptive-threshold 1.188 1.248 -5.8% Watershed 1.188 1.356 +14.9% Region-growing 1.188 1.264 +7.1%

4. Conclusion

The presented study demonstrates the effective application of micro-computed tomography (micro-CT) combined with advanced image segmentation techniques to characterize internal porosity and defects in FDM 3D-printed PLA samples with different infill patterns. The dual porosity classification approach successfully isolates true manufacturing defects from intentional infill voids, enabling precise quantification of structural and defect porosity. A comparative analysis of Line and Triangle infill patterns revealed distinct differences in porosity characteristics, with the Triangle pattern exhibiting higher structural porosity but significantly lower defect porosity. The computed densities derived from porosity estimates for Triangle and Lines patterns generally align well with experimental densities, with deviations typically within ±7% except for the watershed method, which occasionally overestimated density notably (up to +14.9% for Triangles). This result validates the imaging and analysis workflow as a reliable non-destructive quality assessment tool. In the near future, our work will be extended to investigate porosity for other geometrical infill patterns and their correlation with mechanical properties, particularly in terms of strength and failure modes. 5. Acknowledgment The research project is supported by the program “Excellence Initiative – Research University” for AGH University. References [1] Ngo, T. D., Kashani, A., Imbalzano, G., Nguyen, K. T. Q., & Hui, D. (2018). Additive manufacturing (3D printing): A review of materials, methods, applications and challenges. Composites Part B: Engineering, 143, 172–196. https://doi.org/10.1016/j.compositesb.2018.02.012 [2] Alafaghani, A., Qattawi, A., Alrawi, B., & Guzman, A. (2017). Experimental optimization of fused deposition modelling processing parameters: A design-for-manufacturing approach. Procedia Manufacturing, 10, 791–803. https://doi.org/10.1016/j.promfg.2017.07.079 [3] Yang, L., et al. (2020). Non-destructive evaluation of 3D printed polymers using X-ray computed tomography. Additive Manufacturing, 31, 100933. https://doi.org/10.1016/j.addma.2019.100933 [4] Madejski, P., Krakowska, P., Habrat M., Pusakrczyk, E., J ędrychowski, M. Comprehensive approach for porous materials analysis using a dedicated preprocessing tool for mass and heat transfer modeling. Journal of Thermal Science, 2018, vol. 27 (5), pp. 479–486. [5] Mieloszyk, M., Madejski, P., Wroński, S., & Muna, I. I. (2025). Structural analyses of additively manufactured carbon fiber reinforced polymer with embedded fiber optic using THz spectroscopy and micro-computed tomography. Optics and Lasers in Engineering, 193, 109086. https://doi.org/10.1016/j.optlaseng.2024.109086 [6] Bui, Q. C., Lin, W., Huang, Q., & Byun, G. S. (2025). Automated Internal Defect Identification and Localization Based on a Near-Field SAR Millimeter-Wave Imaging System. IEEE Access. [7] Revol, C., & Jourlin, M. (1997). A new minimum variance region growing algorithm for image segmentation. Pattern Recognition Letters, 18(3), 249-258. [8] Otsu, N. (1975). A threshold selection method from gray-level histograms. Automatica, 11(285-296), 23-27. [9] Levner, I., & Zhang, H. (2007). Classification-driven watershed segmentation. IEEE Transactions on Image Processing, 16(5), 1437-1445. [10] Feng, Q., Gao, B., Lu, P., Woo, W. L., Yang, Y., Fan, Y., ... & Gu, L. (2018). Automatic seeded region growing for thermography debonding detection of CFRP. Ndt & E International, 99, 36-49. [11] Chawla, K., Talabi, S. I., Rodriguez, B., Kumar, V., Kim, S., Kunc, V., & Hassen, A. A. (2025). Benchmarking Image Processing Techniques for Automatic Porosity Measurement in Polymer Additive Manufacturing: Review and Experimental Analysis. Composites Part B: Engineering, 112857.