Deep learning-assisted real-time defect detection and closed-loop adjustment for additive manufacturing of continuous fiber-reinforced polymer composites
参考中译:深度学习辅助的连续纤维增强聚合物复合材料添加剂制造的实时缺陷检测和闭环调整


          

刊名：Robotics and Computer-Integrated Manufacturing
作者：Lu Lu（Unmanned System Research Institute, Northwestern Polytechnical University）
Jie Hou（Unmanned System Research Institute, Northwestern Polytechnical University）
Shangqin Yuan（Unmanned System Research Institute, Northwestern Polytechnical University）
Xiling Yao（Singapore Institute of Manufacturing Technology）
Yamin Li（State IJR Center of Aerospace Design and Additive Manufacturing, Northwestern Polytechnical University）
Jihong Zhu（State IJR Center of Aerospace Design and Additive Manufacturing, Northwestern Polytechnical University）
刊号：737C0069
ISSN：0736-5845
出版年：2023
年卷期：2023, vol.79
页码：102431-1--102431-12
总页数：12
分类号：TP24
关键词：Deep learning；Continuous fiber-reinforced composites；Defect detection；Additive manufacturing
参考中译：深度学习；连续纤维增强复合材料；缺陷检测；添加剂制造
语种：eng
文摘：Real-time defect detection and closed-loop adjustment of additive manufacturing (AM) are essential to ensure the quality of as-fabricated products, especially for carbon fiber reinforced polymer (CFRP) composites via AM. Machine learning is typically limited to the application of online monitoring of AM systems due to a lack of accurate and accessible databases. In this work, a system is developed for real-time identification of defective regions, and closed-loop adjustment of process parameters for robot-based CFRP AM is validated. The main novelty is the development of a deep learning model for defect detection, classification, and evaluation in realtime with high accuracy. The proposed method is able to identify two types of CFRP defects (i.e., misalignment and abrasion). The combined deep learning with geometric analysis of the level of misalignment is applied to quantify the severity of individual defects. A deep learning approach is successfully developed for the online detection of defects, and the defects are effectively controlled by closed-loop adjustment of process parameters, which is never achievable in any conventional methods of composite fabrication.
参考中译：添加剂制造(AM)的实时缺陷检测和闭环调整是保证成品质量的关键，特别是碳纤维增强复合材料(CFRP)由于缺乏准确和可访问的数据库，机器学习通常仅限于AM系统的在线监控应用。本文开发了一套缺陷区域实时识别系统，并验证了机器人CFRP AM工艺参数的闭环调整。主要的新颖性是开发了一个深度学习模型，用于实时、高精度地检测、分类和评估缺陷。所提出的方法能够识别两种类型的CFRP缺陷(即错位和磨损)。将深度学习与错位程度的几何分析相结合，用于量化单个缺陷的严重程度。成功地开发了一种深度学习方法用于缺陷的在线检测，并通过对工艺参数的闭环调整来有效地控制缺陷，这在任何传统的复合材料制造方法中都是无法实现的。



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