Abstract
Microscopic imaging serves as a crucial method for assessing the quality of selective laser melting (SLM). Traditional approaches rely on manual inspection, which limits their efficiency and reproducibility. To address the demand for defect detection and analysis, this paper proposes a synergistic method for analyzing pore defects in microscopic images, integrating image segmentation with polynomial fitting. We designed a high-performance image segmentation model. Its capabilities are enhanced through an adaptive curved learning rate adjustment strategy, an attention-based feature extraction module, and a lightweight feature fusion network. Additionally, the model automatically calculates and quantifies the pixel proportion of pore defects within micrographs. Experiments conducted on a constructed SLM pore defect microscopic image dataset demonstrated excellent performance, enabling effective calibration and quantification of defect information. Chebyshev polynomials are employed to fit the nonlinear relationship between key process parameters and porosity. Based on these results, we conducted an in-depth analysis of how different process parameters influence pore defect formation, revealing the intrinsic correlation between process parameters and defects. This study provides an effective automated detection and analysis tool for SLM quality assessment and analysis.
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References
Zhou, L. F. et al. Additive manufacturing: A comprehensive review. Sensors https://doi.org/10.3390/s24092668 (2024).
Gao, B. W., Zhao, H. J., Peng, L. Q. & Sun, Z. X. A review of research progress in selective laser melting (SLM). Micromachines https://doi.org/10.3390/mi14010057 (2023).
Nandhakumar, R. & Venkatesan, K. A process parameters review on selective laser melting-based additive manufacturing of single and multi-material: Microstructure, physical properties, tribological, and surface roughness. Mater. Today Commun. https://doi.org/10.1016/j.mtcomm.2023.105538 (2023).
Uriondo, A., Esperon-Miguez, M. & Perinpanayagam, S. The present and future of additive manufacturing in the aerospace sector: A review of important aspects. Proc. Inst. Mech. Eng. G. J. Aerosp. Eng. 229(11), 2132–2147. https://doi.org/10.1177/0954410014568797 (2015).
Ahmadi, M. et al. Review of selective laser melting of magnesium alloys: Advantages, microstructure and mechanical characterizations, defects, challenges, and applications. J. Mater. Res. Technol. 19, 1537–1562. https://doi.org/10.1016/j.jmrt.2022.05.102 (2022).
Huang, S. T., Wei, H. B. & Li, D. H. Additive manufacturing technologies in the oral implant clinic: A review of current applications and progress. Front. Bioeng. Biotech. https://doi.org/10.3389/fbioe.2023.1100155 (2023).
Shi, Y. D. et al. Effect of overlap scanning strategies on micro-characteristics and mechanical properties of dual-laser selective laser melted AlSi10Mg. Mater. Today Commun. https://doi.org/10.1016/j.mtcomm.2025.113730 (2025).
He, L. C. et al. Research on the pore defects formation and mechanical properties of Ti6Al4V for selective laser melting. Int. J. Therm. Sci. https://doi.org/10.1016/j.ijthermalsci.2025.109948 (2025).
Bayat, M. et al. Keyhole-induced porosities in laser-based powder bed fusion (L-PBF) of Ti6Al4V: High-fidelity modelling and experimental validation. Addit. Manuf. https://doi.org/10.1016/j.addma.2019.100835 (2019).
Laleh, M. et al. A critical insight into lack-of-fusion pore structures in additively manufactured stainless steel. Addit. Manuf. https://doi.org/10.1016/j.addma.2020.101762 (2021).
Schwerz, C. et al. In-situ detection of redeposited spatter and its influence on the formation of internal flaws in laser powder bed fusion. Addit. Manuf. https://doi.org/10.1016/j.addma.2021.102370 (2021).
Bonato, N., Zanini, F. & Carmignato, S. Prediction of spatter-related defects in metal laser powder bed fusion by analytical and machine learning modelling applied to off-axis long-exposure monitoring. Addit. Manu. 94, 104504. https://doi.org/10.1016/j.addma.2024.104504 (2024).
Liu, F. C. et al. Micro-porosity and tensile property of high relative density Inconel 718 superalloy fabricated by selective laser melting. Rare Metal Mat. Eng. 50(10), 3684–3692. https://doi.org/10.12442/j.issn.1002-185X.20200802 (2021).
An, N. Y. et al. Application of synchrotron x-ray imaging and diffraction in additive manufacturing: A review. Acta Metallurgica Sinica (English Letters) 35(1), 25–48. https://doi.org/10.1007/s40195-021-01326-x (2022).
Sun, K., Li, F. K., Rong, C. B. & Zuo, L. Direct Energy Deposition applied to soft magnetic material additive manufacturing. J. Manuf. Process. 84, 162–173. https://doi.org/10.1016/j.jmapro.2022.10.004 (2022).
Peng, Z. X., Xu, W., Liu, Y., Zhao, K. & Hu, P. Anisotropy evaluation and defect detection on Laser Powder Bed Fusion 316L stainless steel. Micromachines https://doi.org/10.3390/mi14061206 (2023).
Na, J. K. & Oneida, E. K. Nondestructive evaluation method for standardization of fused filament fabrication based additive manufacturing. Addit. Manuf. 24, 154–165. https://doi.org/10.1016/j.addma.2018.09.024 (2018).
Sundar, V. et al. Flaw identification in additively manufactured parts using X-ray computed tomography and destructive serial sectioning. J. Mater. Eng. Perform. 30(7), 4958–4964. https://doi.org/10.1007/s11665-021-05567-w (2021).
Gallardo, D. et al. X-Ray Computed Tomography performance in metrological evaluation and characterisation of polymeric additive manufactured surfaces. Addit. Manuf. https://doi.org/10.1016/j.addma.2023.103754 (2023).
Bonato, N., Zanini, F. & Carmignato, S. Enhanced tomographic porosity measurements in laser powder bed fusion metal parts using a novel reference object. J. Manuf. Process. 155, 1049–1061. https://doi.org/10.1016/j.jmapro.2025.10.076 (2025).
Chen, Y. J. et al. Surface defect detection methods for industrial products: A review. Appl. Sci. https://doi.org/10.3390/app11167657 (2021).
Tong, K. & Wu, Y. Q. Deep learning-based detection from the perspective of small or tiny objects: A survey. Image Vis. Comput. https://doi.org/10.1016/j.imavis.2022.104471 (2022).
Wong, V. W. H., Ferguson, M., Law, K. H., Lee, Y. T. T. & Witherell, P. Segmentation of additive manufacturing defects using U-net. J. Comput. Inf. Sci. Eng. https://doi.org/10.1115/1.4053078 (2022).
Zhao, J. X. et al. Deep learning accelerated micrograph-based porosity defect quantification in additively manufactured steels for uncovering a generic process-defect-properties relation. Mater. Charact. https://doi.org/10.1016/j.matchar.2025.115094 (2025).
Surovi, N. A. & Soh, G. S. Acoustic feature based geometric defect identification in wire arc additive manufacturing. Virtual Phys. Prototyp. https://doi.org/10.1080/17452759.2023.2210553 (2023).
Acharya, P., Chu, T. P., Ahmed, K. R. & Kharel, S. A deep learning approach for defect detection and segmentation in X-Ray computed tomography slices of additively manufactured components. Int. J. Artif. Intell. Appl. 13(4), 1–15. https://doi.org/10.5121/ijaia.2022.13401 (2022).
Tang, W. Q., Luo, Z. X., Li, D. Y. & Peng, Y. H. Predicting fatigue failure induced by lack-of-fusion defects in additive manufacturing: A synergistic multiscale simulation-deep learning framework. Addit. Manuf. https://doi.org/10.1016/j.addma.2025.105047 (2026).
Su, X. et al. Enhanced defect detection in additive manufacturing via virtual polarization filtering and deep learning optimization. Photonics. https://doi.org/10.3390/photonics12060599 (2025).
Ansari, M. A., Crampton, A. & Parkinson, S. Keyhole porosity identification and localization via X-ray imaging with YOLO. IEEE Access 12, 61049–61061. https://doi.org/10.1109/ACCESS.2024.3393128 (2024).
Huang, Y. L., Zhou, X. M., Wang, G. L. & Bai, X. W. Lightweight defect detection algorithm for wire and arc additive manufacturing based on modified YOLOv8 model. J. Real-Time Image Process. https://doi.org/10.1007/s11554-025-01706-x (2025).
Sani, A. R., Zolfagharian, A. & Kouzani, A. Z. Automated defects detection in extrusion 3D printing using YOLO models. J. Intell. Manuf. https://doi.org/10.1007/s10845-024-02543-8 (2024).
Wen, Y. T., Ren, Y. X., Zhang, Y. Y. & Zhang, Z. W. An improved meta heuristic IT2 fuzzy model for nondestructive failure evaluation of metal additive manufacturing lattice structure. Expert Syst. Appl. https://doi.org/10.1016/j.eswa.2023.122838 (2023).
Hussain, M. YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection. Machines https://doi.org/10.3390/machines11070677 (2023).
Zhao, C., Shu, X., Yan, X., Zuo, X. & Zhu, F. RDD-YOLO: A modified YOLO for detection of steel surface defects. Measurement https://doi.org/10.1016/j.measurement.2023.112776 (2023).
Hua, Z. M. et al. A benchmark review of YOLO algorithm developments for object detection. IEEE Access 13, 123515–123545. https://doi.org/10.1109/ACCESS.2025.3586673 (2025).
Doherty, J., Gardiner, B., Kerr, E. & Siddique, N. BiFPN-YOLO: One-stage object detection integrating bi-directional feature pyramid networks. Pattern Recogn. https://doi.org/10.1016/j.patcog.2024.111209 (2025).
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Conceptualization, Hanning Chen and Xiaodan Liang; methodology, Maowei He; software, Rui Ni; validation, Siwen Xu; formal analysis, Xiaodan Liang.; investigation, Siwen Xu; resources, Hanning Chen; data curation, Rui Ni; writing—original draft preparation, Siwen Xu and Rui Ni; writing—review and editing, Rui Ni; visualization, Zhaodi Ge; supervision, Hanning Chen and Xiaodan Liang; project administration, Hanning Chen; funding acquisition, Hanning Chen. All authors have read and agreed to the published version of the manuscript.
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Ni, R., Xu, S., Chen, H. et al. An effective detection model based on YOLO for pore defects in additive manufacturing. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43970-2
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DOI: https://doi.org/10.1038/s41598-026-43970-2
