摘要
自动铺丝质量检查既耗时又不充分,缺陷对比度低。对此,根据铺丝过程中的热特性,搭建丝束铺放准确性检测平台,结合形态学算法对缺陷红外图像特征进行纤维铺放准确性检测。首先进行形状相同尺度不同的结构元素的顶帽变换,然后提取每一组尺度下多尺度亮暗区域和相邻组尺度间的多尺度亮暗细节来增强图像;其次使用了多方向多尺度的结构元素和系数自调节的形态学边缘检测算法来获得丝束边缘位置。实验结果表明,该方法在减少图像噪声和增强图像的对比度之上平衡了边缘检测精度与抗噪性能之间的协调问题,有效检测了纤维铺放准确性,最大铺放误差不超过4.53%。
Automatic fibex laying quality inspection is both time consuming and inadequate,with low defect contrast.In this regard,according to the thermal characteristics of the fibex tow laying process,a platform for fibex tow placement accuracy detection is constructed,and the morphological algorithm is combined with infrared image features of defects for fibex tow placement accuracy detection.Firstly,the top-hat transform of structural elements with the same shape and different scales is carried out,and then the multi-scale bright and dark regions under each group of scales and multi-scale bright and dark details between adjacent groups of scales are extracted to enhance the image;secondly,the multi-directional and multi-scale structural elements and the morphological edge detection algorithm with coefficients self-adjustment are used to obtain the location of filament bundle edges.The experimental results show that the method balances the coordination problem between edge detection accuracy and antinoise performance on top of image noise reduction and image contrast enhancement,and effectively detects the fibex tow placement accuracy with a maximum placement error of no more than 4.53%.
作者
曹节强
李军利
刘钢
张立强
CAO Jieqiang;LI Junli;LIU Gang;ZHANG Liqiang(School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China;Key Laboratory of Intelligent Manufacturing Technology for Large Complex Thin-Walled Components of Aviation in Machinery Industry,Shanghai 201620,China;Shanghai Jiao Tong University Sichuan Research Institute,Chengdu 610041,China)
出处
《现代制造工程》
CSCD
北大核心
2024年第5期15-22,共8页
Modern Manufacturing Engineering
基金
国家自然科学基金项目(52275449)。
关键词
图像处理
自动铺丝
形态学
红外图像
缺陷检测
image processing
automated fiber placement
morphology
infrared image
defect detecting
