Research on characteristic recognition and quantification of internal powder residue in LPBF porous structure based on image processing.

Wentian Shi, Shangguo Cao, Qiujin Hou, Xiaoqing Zhang, Jian Li, Haider Muhammad Usman

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Wentian ShiDepartment of Mechanical Engineering, Beijing Technology and Business University, Beijing, 100048, China. shiwt@th.btbu.edu.cn.

Shangguo CaoDepartment of Mechanical Engineering, Beijing Technology and Business University, Beijing, 100048, China.

Qiujin HouDepartment of Mechanical Engineering, Beijing Technology and Business University, Beijing, 100048, China.

Xiaoqing ZhangDepartment of Mechanical Engineering, Beijing Technology and Business University, Beijing, 100048, China.

Jian LiDepartment of Mechanical Engineering, Beijing Technology and Business University, Beijing, 100048, China.

Haider Muhammad UsmanDepartment of Mechanical Engineering, Beijing Technology and Business University, Beijing, 100048, China.

Funding

National Key Research andDevelopment Program of China 2022YFC2406004

6 · The paper itself

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.

Indexed as

CT imageImage processingLPBFPorous structureResidual powder detection

Identifiers

PMID41820447

DOI10.1038/s41598-026-40479-6

PMCPMC13102960

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.