Enhancing additive manufacturing precision: Intelligent inspection and optimization for defect-free continuous carbon fiber-reinforced polymer

IF 5.3 Q2 MATERIALS SCIENCE, COMPOSITES Composites Part C Open Access Pub Date : 2024-03-06 DOI:10.1016/j.jcomc.2024.100451
Md Hasib Zubayer , Yi Xiong , Yafei Wang , Haque Md Imdadul
{"title":"Enhancing additive manufacturing precision: Intelligent inspection and optimization for defect-free continuous carbon fiber-reinforced polymer","authors":"Md Hasib Zubayer ,&nbsp;Yi Xiong ,&nbsp;Yafei Wang ,&nbsp;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}
引用次数: 0

Abstract

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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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
人工智能(AI)已成为管理大量数据集、实现模式识别和推导解决方案的重要工具,尤其是在增材制造(AM)领域带来了革命性的变化。本研究旨在开发人工智能深度机器学习图像处理技术,用于实时检测增材制造连续碳纤维增强聚合物(cCFRP)试样中的缺陷。本研究利用 YOLOv8(一种最先进的单阶段物体检测算法),重点研究打印参数与缺陷发生(特别是不对齐误差)之间的关系。该研究将检测到的缺陷与参数优化联系起来,在方法上取得了进步,从而显著提高了 cCFRP 试样的质量。通过微调喷嘴温度,错位检测的准确率达到了令人印象深刻的 94%,从而显著降低了错位误差,同时观察到打印床温度、进料量和进料速度/秒对完善所提出的最佳参数识别模型的影响微乎其微。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Composites Part C Open Access
Composites Part C Open Access Engineering-Mechanical Engineering
CiteScore
8.60
自引率
2.40%
发文量
96
审稿时长
55 days
期刊最新文献
Hybrid lattice structure with micro graphite filler manufactured via additive manufacturing and growth foam polyurethane Cure-induced residual stresses and viscoelastic effects in repaired wind turbine blades: Analytical-numerical investigation Bioinspired surface modification of mussel shells and their application as a biogenic filler in polypropylene composites A review of repairing heat-damaged RC beams using externally bonded- and near-surface mounted-CFRP composites Comparative analysis of delamination resistance in CFRP laminates interleaved by thermoplastic nanoparticle: Evaluating toughening mechanisms in modes I and II
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1