Shiva Shokri, Pooria Sedigh, Mehdi Hojjati, Tsz Ho Kwok
{"title":"Closed-Loop Control of Surface Preparation for Metallizing Fiber-Reinforced Polymer Composites","authors":"Shiva Shokri, Pooria Sedigh, Mehdi Hojjati, Tsz Ho Kwok","doi":"10.1139/tcsme-2024-0035","DOIUrl":null,"url":null,"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.","PeriodicalId":0,"journal":{"name":"","volume":"11 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1139/tcsme-2024-0035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.