{"title":"利用点云数据进行三维织物平滑度评估,加强质量控制","authors":"Zhijie Yuan, Binjie Xin, Jing Zhang, Yingqi Xu","doi":"10.1007/s10845-024-02367-6","DOIUrl":null,"url":null,"abstract":"<p>Assessing the smoothness appearance of fabrics, especially in three-dimensional forms, is vital for quality control. Existing methods often lack objectivity or fail to consider the full 3D structure of the fabric. In this study, we introduce an innovative system that harnesses point cloud data to overcome these limitations. We use a 3D scanning system to capture a multi-directional point cloud representation of the textile surface. The data undergoes stitching and filtering to obtain an optimized point cloud model for feature extraction. We propose the 3D and 2D alpha-shape area ratio as a novel feature parameter for determining surface smoothness. Validation was conducted with 730 point clouds from 146 fabric samples, achieving an impressive 95.81%, recognition accuracy, which aligns with expert subjective evaluations. This research not only presents a dependable method for 3D textile smoothness grading but also indicates its applicability in other industries where surface evaluation is pivotal.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"38 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Three-dimensional fabric smoothness evaluation using point cloud data for enhanced quality control\",\"authors\":\"Zhijie Yuan, Binjie Xin, Jing Zhang, Yingqi Xu\",\"doi\":\"10.1007/s10845-024-02367-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Assessing the smoothness appearance of fabrics, especially in three-dimensional forms, is vital for quality control. Existing methods often lack objectivity or fail to consider the full 3D structure of the fabric. In this study, we introduce an innovative system that harnesses point cloud data to overcome these limitations. We use a 3D scanning system to capture a multi-directional point cloud representation of the textile surface. The data undergoes stitching and filtering to obtain an optimized point cloud model for feature extraction. We propose the 3D and 2D alpha-shape area ratio as a novel feature parameter for determining surface smoothness. Validation was conducted with 730 point clouds from 146 fabric samples, achieving an impressive 95.81%, recognition accuracy, which aligns with expert subjective evaluations. This research not only presents a dependable method for 3D textile smoothness grading but also indicates its applicability in other industries where surface evaluation is pivotal.</p>\",\"PeriodicalId\":16193,\"journal\":{\"name\":\"Journal of Intelligent Manufacturing\",\"volume\":\"38 1\",\"pages\":\"\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2024-05-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Intelligent Manufacturing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s10845-024-02367-6\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s10845-024-02367-6","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Three-dimensional fabric smoothness evaluation using point cloud data for enhanced quality control
Assessing the smoothness appearance of fabrics, especially in three-dimensional forms, is vital for quality control. Existing methods often lack objectivity or fail to consider the full 3D structure of the fabric. In this study, we introduce an innovative system that harnesses point cloud data to overcome these limitations. We use a 3D scanning system to capture a multi-directional point cloud representation of the textile surface. The data undergoes stitching and filtering to obtain an optimized point cloud model for feature extraction. We propose the 3D and 2D alpha-shape area ratio as a novel feature parameter for determining surface smoothness. Validation was conducted with 730 point clouds from 146 fabric samples, achieving an impressive 95.81%, recognition accuracy, which aligns with expert subjective evaluations. This research not only presents a dependable method for 3D textile smoothness grading but also indicates its applicability in other industries where surface evaluation is pivotal.
期刊介绍:
The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.