A robotized framework for real-time detection and in-situ repair of manufacturing defects in CFRP patch placement

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing Pub Date : 2024-09-24 DOI:10.1016/j.rcim.2024.102882

Yi Gong , Xiangli Li , Rui Zhou , Miao Li , Sheng Liu

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引用次数: 0

Abstract

Carbon fiber reinforced polymers (CFRP) have significant applications in aerospace and automotive manufacturing. However, due to the complexity of CFRP structures, manufacturing defects are challenging to avoid and even affect the mechanical properties. Timely detection and repair are essential to ensure product quality. In this study, we propose a robotized framework for real-time detection and in-situ repair of manufacturing defects in CFRP patch placement. First, the influence of three typical defects (delamination, wrinkle and impurity) on mechanical properties is analyzed through numerical analysis. Then, a defect detection model is improved using the channel attention mechanism and decoupling head module, which enhances detection accuracy and the ability to identify small and deep defects. Based on the identification result, a corresponding repair strategy is generated, which considers the effects of force, path, heating and repair modes. The experimental results demonstrate that the tensile stiffness and bending strength of the repaired material are improved by 12.34% and 230.92%, respectively.

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用于实时检测和原位修复 CFRP 补丁贴装制造缺陷的机器人框架

碳纤维增强聚合物（CFRP）在航空航天和汽车制造领域有着重要的应用。然而，由于 CFRP 结构的复杂性，避免制造缺陷具有挑战性，甚至会影响机械性能。及时检测和修复对确保产品质量至关重要。在本研究中，我们提出了一种机器人框架，用于实时检测和原位修复 CFRP 补丁贴装中的制造缺陷。首先，通过数值分析了三种典型缺陷（分层、皱褶和杂质）对机械性能的影响。然后，利用通道注意机制和去耦头模块改进了缺陷检测模型，提高了检测精度和识别小缺陷和深缺陷的能力。根据识别结果，生成了相应的修复策略，其中考虑了力、路径、加热和修复模式的影响。实验结果表明，修复后材料的拉伸刚度和弯曲强度分别提高了 12.34% 和 230.92%。

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来源期刊

Robotics and Computer-integrated Manufacturing 工程技术-工程：制造

CiteScore

24.10

自引率

13.50%

发文量

160

审稿时长

50 days

期刊介绍： The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.