Closed-Loop Control of Surface Preparation for Metallizing Fiber-Reinforced Polymer Composites

Pub Date : 2024-07-12 DOI:10.1139/tcsme-2024-0035
Shiva Shokri, Pooria Sedigh, Mehdi Hojjati, Tsz Ho Kwok
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Abstract

This study introduces a novel approach to enhance the surface properties of fiber-reinforced polymer composites through thermal spray coatings, utilizing a metal mesh as an anchor to improve coating adhesion. A critical step in this process is achieving optimal exposure of the metal mesh by sandblasting prior to coating. To address this challenge, we propose a closed-loop control system designed to inspect and blast parts effectively. Our method leverages top-view microscope images as inputs, employing a convolutional neural network (CNN) to correlate these images with the corresponding exposure levels of the metal mesh, measured via a destructive method. Upon training, the CNN model accurately estimates the exposure level solely from the top-view images, facilitating real-time feedback to guide subsequent sandblasting operations. Unlike traditional manual inspection methods, which demand expertise and experience, our automated approach streamlines the inspection process using a cost-effective portable digital microscope. Experimental findings validate the efficacy of our method in successfully discerning surface preparation status with an accuracy rate of 95% and demonstrate its practical utility in closed-loop control. Our study not only offers a robust methodology for quantifying surface preparation data but also presents a significant advancement in automating the inspection process. Moreover, the broader implications of our approach extend to various manufacturing sectors, where defect detection and closed-loop control are crucial for optimizing production efficiency and product quality.
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金属化纤维增强聚合物复合材料表面制备的闭环控制
本研究介绍了一种通过热喷涂涂层提高纤维增强聚合物复合材料表面性能的新方法,利用金属网作为锚来提高涂层附着力。这一过程中的一个关键步骤是在喷涂前通过喷砂使金属网达到最佳暴露效果。为了应对这一挑战,我们提出了一种闭环控制系统,旨在对部件进行有效的检测和喷砂。我们的方法利用顶视显微镜图像作为输入,采用卷积神经网络(CNN)将这些图像与通过破坏性方法测量的金属网的相应曝光水平相关联。经过训练后,卷积神经网络模型就能仅通过顶视图像准确估算出暴露水平,从而为指导后续喷砂作业提供实时反馈。与需要专业知识和经验的传统人工检测方法不同,我们的自动化方法利用经济高效的便携式数码显微镜简化了检测流程。实验结果验证了我们的方法在成功辨别表面处理状态方面的功效,准确率高达 95%,并证明了其在闭环控制中的实用性。我们的研究不仅为量化表面处理数据提供了一种可靠的方法,还在检测过程自动化方面取得了重大进展。此外,我们的方法还可延伸到各种制造领域,因为缺陷检测和闭环控制对于优化生产效率和产品质量至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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