Abstract
To address the low efficiency and accuracy of residual powder detection in LPBF porous structures, an automated visualization and evaluation method is proposed. Based on CT images, it develops a dual-threshold ensemble grayscale segmentation algorithm on Matlab, integrating morphological processing and U-Net for batch residual powder identification and extraction, and then analyze, compare and recommend the post-processing process based on the residual powder information in the database. Validation shows this method is 20 time more efficient than Image J (12 min vs. 240 min for 1437 images) with accuracy improved to 86.42%-89.21%. Integrated with image quality evaluation and large models, it builds a “detection-recognition-post-processing” system, offering a scalable paradigm for LPBF quality control and defect-property correlation analysis.
Data availability
All data supporting the findings of this study are included within the main manuscript. For further inquiries or requests related to the study data, please contact the corresponding author atshiwt@th.btbu.edu.cn (W.S.)
References
Shi, W. et al. Optimization of mechanical properties of Ti–6Al–4V triply periodic minimal surface porous structures prepared by laser beam powder bed fusion technology based on orientation control. Mater. Sci. Eng.: A. 894, 146183 (2024).
Ma, W. W. S. et al. Multiâ physical lattice metamaterials enabled by additive manufacturing: Design principles, interaction mechanisms, and multifunctional applications. Adv. Sci. 12 (8), 2405835 (2025).
Liao, J. et al. Research progress of laser powder bed fusion of Ti2AlNb-based alloys: Microstructure, defects and properties. J. Mater. Res. Technol. (2025).
Wahlquist, S. & Ali, A. Roles of modeling and artificial intelligence in LPBF metal print defect detection: critical review. Appl. Sci. 14 (18), 8534 (2024).
Jia, S. et al. Experimental study on the influence of abrasive flow machining on the surface quality and mechanical properties of bcc lattice structure manufactured by L-PBF. J. Alloys Compd. 985, 174077 (2024).
Cobos, S. F. et al. Cost-effective micro-CT system for non-destructive testing of titanium 3D printed medical components. Plos one. 17 (10), e0275732 (2022).
Xu, Z. et al. Laser ultrasonic detection of submillimeter artificial holes in laser powder bed fusion manufactured alloys. Opt. Laser Technol. 169, 110030 (2024).
Allam, A. et al. Ultrasonic testing of thick and thin Inconel 625 alloys manufactured by laser powder bed fusion. Ultrasonics 125, 106780 (2022).
Xiao, J. et al. Eddy current testing of artificial submillimeter internal holes in laser powder bed fusion manufactured alloys. Nondestructive Test. Eval. 2025, 1–20 (2025).
Gainov, R. R. et al. Investigation of LPBF A800H steel parts using Computed Tomography and Mössbauer spectroscopy. Additive Manuf. 32, 101035 (2020).
Zanini, F. & Carmignato, S. X-ray computed tomography for advanced geometrical measurements of metal powders and enhanced surface topography analyses of additively manufactured parts. Powder Technol. 412, 118011 (2022).
Wang, X. et al. Machine learning method for estimating the defect-related mechanical properties of additive manufactured alloys. Eng. Fract. Mech. 291, 109559 (2023).
Zhou, Y. et al. In-situ monitoring of laser powder bed fusion energy input based on a hybrid feature pyramid network and multi-sensor fusion. Meas. Sci. Technol. 36 (6), 065105 (2025).
Wu, X. et al. Laser powder bed fusion of biodegradable magnesium alloys: process, microstructure and properties. Int. J. Extreme Manuf. 7 (2), 022007 (2024).
Dong, Z. et al. Revealing anisotropic mechanisms in mechanical and degradation properties of zinc fabricated by laser powder bed fusion additive manufacturing. J. Mater. Sci. Technol. 214, 87–104 (2025).
Tawfik, A. et al. Development of an additive manufactured artifact to characterize unfused powder using computed tomography. Int. J. Autom. Technol. 14 (3), 439–446 (2020).
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 (2025).
Ye, C. et al. Effects of post-processing on the surface finish, porosity, residual stresses, and fatigue performance of additive manufactured metals: a review. J. Mater. Eng. Perform. 30 (9), 6407–6425 (2021).
Aiza, I. et al. Effects of build orientation and inclined features on physical, microstructural and mechanical properties of powder bed fusion additively manufactured metallic parts. Prog. Mater. Sci. 147, 101357 (2025).
Yuan, K. S. et al. Research advances and future perspectives of zinc-based biomaterials for additive manufacturing. Rare Met. 2025, 1–35 (2025).
Gieleciak, M. et al. Influence of magnesium addition on microstructural and mechanical stability of hydrostatically extruded biodegradable zinc alloys. Bioactive Mater. 44, 1–14 (2025).
Feng, Z. et al. Influence of scale effect on surface morphology in laser powder bed fusion technology. Virtual Phys. Prototyp. 19 (1), e2336157 (2024).
Balbaa, M. et al. A novel post-processing approach towards improving hole accuracy and surface integrity in laser powder bed fusion of IN625. Int. J. Adv. Manuf. Technol. 119 (9), 6225–6234 (2022).
Guo, M. et al. Effects of structure parameters on performances of laser powder bed fusion processed AlSi10Mg body-centered cubic lattices. J. Laser Appl. 36, 1 (2024).
Ji, H. et al. Design and testing of additive manufactured multifold rotationally symmetric lattice structures by laser powder bed fusion. Int. J. Adv. Manuf. Technol. 136 (2), 717–728 (2025).
Funding
This research was supported by National Key Research and Development Program of China (2022YFC2406000).
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Wentian Shi，Shangguo Cao ：wrote the main manuscript text Qiujin Hou , XiaoqingZhang , Jian Li , Haider Muhammad Usman： reviewed the manuscript.
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Shi, W., Cao, S., Hou, Q. et al. Research on characteristic recognition and quantification of internal powder residue in LPBF porous structure based on image processing. Sci Rep (2026). https://doi.org/10.1038/s41598-026-40479-6
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DOI: https://doi.org/10.1038/s41598-026-40479-6
