Md Hasib Zubayer , Yi Xiong , Yafei Wang , Haque Md Imdadul
{"title":"提高增材制造精度:无缺陷连续碳纤维增强聚合物的智能检测和优化","authors":"Md Hasib Zubayer , Yi Xiong , Yafei Wang , Haque Md Imdadul","doi":"10.1016/j.jcomc.2024.100451","DOIUrl":null,"url":null,"abstract":"<div><p>Artificial intelligence (AI) has emerged as a pivotal tool in managing extensive datasets, enabling pattern recognition, and deriving solutions, particularly revolutionizing additive manufacturing (AM). This study intends to develop AI deep machine learning image processing techniques for real-time defects detection in additively manufactured continuous carbon fiber-reinforced polymer(cCFRP) specimens. Leveraging YOLOv8- a state-of-the-art, single-stage object detection algorithm, this study focuses on the relationship between printing parameters and defect occurrences, specifically misalignment errors. The research delineates a methodological advancement by correlating detected defects with parameter optimization, leading to significant quality improvements in cCFRP specimens. An impressive 94 % accuracy in detecting misalignments was achieved through fine-tuning the nozzle temperature adjustment, resulting in significant reductions in misalignment errors, while minimal impact is observed from print bed temperature, feed amount, and feed rate/<em>sec</em> on refining the proposed model for identifying optimal parameters.</p></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666682024000227/pdfft?md5=32bad56242b9e6f7f2a3faaef927359b&pid=1-s2.0-S2666682024000227-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Enhancing additive manufacturing precision: Intelligent inspection and optimization for defect-free continuous carbon fiber-reinforced polymer\",\"authors\":\"Md Hasib Zubayer , Yi Xiong , Yafei Wang , Haque Md Imdadul\",\"doi\":\"10.1016/j.jcomc.2024.100451\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Artificial intelligence (AI) has emerged as a pivotal tool in managing extensive datasets, enabling pattern recognition, and deriving solutions, particularly revolutionizing additive manufacturing (AM). This study intends to develop AI deep machine learning image processing techniques for real-time defects detection in additively manufactured continuous carbon fiber-reinforced polymer(cCFRP) specimens. Leveraging YOLOv8- a state-of-the-art, single-stage object detection algorithm, this study focuses on the relationship between printing parameters and defect occurrences, specifically misalignment errors. The research delineates a methodological advancement by correlating detected defects with parameter optimization, leading to significant quality improvements in cCFRP specimens. An impressive 94 % accuracy in detecting misalignments was achieved through fine-tuning the nozzle temperature adjustment, resulting in significant reductions in misalignment errors, while minimal impact is observed from print bed temperature, feed amount, and feed rate/<em>sec</em> on refining the proposed model for identifying optimal parameters.</p></div>\",\"PeriodicalId\":34525,\"journal\":{\"name\":\"Composites Part C Open Access\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666682024000227/pdfft?md5=32bad56242b9e6f7f2a3faaef927359b&pid=1-s2.0-S2666682024000227-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Composites Part C Open Access\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666682024000227\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, COMPOSITES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Composites Part C Open Access","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666682024000227","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, COMPOSITES","Score":null,"Total":0}
Enhancing additive manufacturing precision: Intelligent inspection and optimization for defect-free continuous carbon fiber-reinforced polymer
Artificial intelligence (AI) has emerged as a pivotal tool in managing extensive datasets, enabling pattern recognition, and deriving solutions, particularly revolutionizing additive manufacturing (AM). This study intends to develop AI deep machine learning image processing techniques for real-time defects detection in additively manufactured continuous carbon fiber-reinforced polymer(cCFRP) specimens. Leveraging YOLOv8- a state-of-the-art, single-stage object detection algorithm, this study focuses on the relationship between printing parameters and defect occurrences, specifically misalignment errors. The research delineates a methodological advancement by correlating detected defects with parameter optimization, leading to significant quality improvements in cCFRP specimens. An impressive 94 % accuracy in detecting misalignments was achieved through fine-tuning the nozzle temperature adjustment, resulting in significant reductions in misalignment errors, while minimal impact is observed from print bed temperature, feed amount, and feed rate/sec on refining the proposed model for identifying optimal parameters.