A Novel High Recognition Rate Defect Inspection Method for Carbon Fiber Plain-Woven Prepreg Based on Image Texture Feature Compression.

Lun Li, Yiqi Wang, Jialiang Qi, Shenglei Xiao, Hang Gao

Abstract read

In one paragraph

Article in Polymers, 2022. 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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5 · Who and what money

Authors and funding

5 authors.

Lun LiKey Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.

Yiqi WangKey Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.ORCID 0000-0001-8505-0530

Jialiang QiKey Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.

Shenglei XiaoKey Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.

Hang GaoKey Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.

Funding

Fundamental Research Funds for the Central Universities DUT21LAB108National Natural Science Foundation of China U1708256

6 · The paper itself

Abstract

Carbon fiber plain-woven prepreg is one of the basic materials in the field of composite material design and manufacturing, in which defect identification is an important and easily neglected part of testing. Here, a novel high recognition rate inspection method for carbon fiber plain-woven prepregs is proposed for inspecting bubble and wrinkle defects based on image texture feature compression. The proposed method attempts to divide the image into non-overlapping block lattices as texture primitives and compress them into a binary feature matrix. Texture features are extracted using a gray level co-occurrence matrix. The defect types are further defined according to texture features by k-means clustering. The performance is evaluated in some existing computer vision and machine learning methods based on fiber recognition. By comparing the result, an overall recognition rate of 0.944 is achieved, which is competitive with the state-of-the-arts.

Indexed as

defect inspectionk-means algorithmplain-woven prepregtexture feature compression

Identifiers

PMID35567024

DOI10.3390/polym14091855

PMCPMC9103920

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LicenceCC BY

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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.