基于形态学的自动铺丝纤维铺放准确性检测*

doi:10.16731/j.cnki.1671-3133.2024.05.003

现代制造工程 ›› 2024, Vol. 524 ›› Issue (5): 15-22.doi: 10.16731/j.cnki.1671-3133.2024.05.003

基于形态学的自动铺丝纤维铺放准确性检测^*

曹节强¹, 李军利^1,2, 刘钢^1,3, 张立强¹

1 上海工程技术大学机械与汽车工程学院,上海 201620;
2 机械工业航空大型复杂薄壁构件智能制造技术重点实验室,上海 201620;
3 上海交通大学四川研究院,成都 610041

收稿日期:2023-08-17 出版日期:2024-05-18 发布日期:2024-05-30
通讯作者: 张立强,博士,教授,主要研究方向智能制造与数控技术、复材结构健康诊断、航空装备数字孪生。E-mail:zhanglq@sues.edu.cn
作者简介:曹节强,硕士研究生,主要研究方向为自动铺丝缺陷检测与机器视觉。E-mail:m310121118@sues.edu.cn;李军利,博士,高级工程师,主要研究方向为计算机辅助设计与制造与软体机器人。E-mail:junl@sues.edu.cn;刘钢,博士,教授,主要研究方向为航空航天高端自动化装备和工艺核心技术。E-mail:liugang@sues.edu.cn
基金资助:
^*国家自然科学基金项目(52275449)

Automatic fiber placement accuracy detection based on morphology

CAO Jieqiang¹, LI Junli^1,2, LIU Gang^1,3, ZHANG Liqiang¹

1 School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science, Shanghai 201620,China;
2 Key Laboratory of Intelligent Manufacturing Technology for Large Complex Thin-Walled Components of Aviation in Machinery Industry,Shanghai 201620,China;
3 Shanghai Jiao Tong University Sichuan Research Institute,Chengdu 610041,China

Received:2023-08-17 Online:2024-05-18 Published:2024-05-30

摘要/Abstract

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 %。

关键词: 图像处理, 自动铺丝, 形态学, 红外图像, 缺陷检测

Abstract: 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 %.

Key words: image processing, automated fiber placement, morphology, infrared image, defect detecting

中图分类号:

曹节强, 李军利, 刘钢, 张立强. 基于形态学的自动铺丝纤维铺放准确性检测^*[J]. 现代制造工程, 2024, 524(5): 15-22.

CAO Jieqiang, LI Junli, LIU Gang, ZHANG Liqiang. Automatic fiber placement accuracy detection based on morphology[J]. Modern Manufacturing Engineering, 2024, 524(5): 15-22.

参考文献

[1] LI J L,HUANG Z R,LIU G,et al.Topology optimization design and research of lightweight biomimetic three-dimensional lattice structures based on laser powder bed fusion[J].Journal of Manufacturing Processes,2021,74(74): 220-232.
[2] RUDBERG T,CEMENSKA J,SHERRARD E.A process for delivering extreme AFP head reliability[J].SAE International Journal of Advances and Current Practices in Mobility,2019,1(2): 333-342.
[3] ENGELBART R W,HANNEBAUM R,POLLOCK T.In-process vision detection of flaws and FOD by back field illumination: US7678214[P].2010-3-16.
[4] CHEN M J,JIANG M,LIU X L,et al.Intelligent inspection system based on infrared vision for automated fiber placement[C]//2018 IEEE International Conference on Mechatronics and Automation (ICMA). Changchun:IEEE,2018:918-923.
[5] JUAREZ P D,GREGORY E D.In situ thermal inspection of automated fiber placement for manufacturing induced defects[J].Composites Part B: Engineering,2021,220:109002.
[6] DENKENA B,SCHMIDT C,VOELTZER K,et al.Thermographic online monitoring system for automated fiber placement processes[J].Composites Part B Engineering,2016,97(7):239-243.
[7] JUAREZ P D,GREGORY E D.In situ thermal inspection of automated fiber placement manufacturing[J].AIP Conference Proceedings,2019,2102:120005.
[8] 蔡志强,肖军,文立伟,等.基于预浸纱自动铺放缺陷的分割算法[J].航空材料学报,2017,37(2):21-27.
[9] 张珂,廖育荣,罗亚伦,等.基于改进同态滤波的红外图像增强算法[J].激光与光电子学进展,2023,60(10): 1010003.
[10] 李昌兴,雷柳,张晓璐.基于形态学图像增强和PCNN的脑部CT与MRI图像融合[J].计算机科学,2020,47(10):194-199.
[11] SCHMIDT C,DENKENA B,VOELTZER K,et al.Thermal image-based monitoring for the automated fiber placement process[J].Procedia Cirp,2017,62(62): 27-32.
[12] 倪金辉,肖军,文立伟.一种基于直线拟合的预浸料边缘直线在线视觉检测方法[J].计算机科学,2015,42(S1):16-19,23.
[13] 唐守锋,翟少奇,仝光明,等.改进Canny算子与形态学融合的边缘检测[J].计算机工程与设计,2023,44(1):224-231.
[14] 郑恩壮,钟宝江.各向异性的多尺度边缘检测算法[J].激光与光电子学进展,2022,59(4):0412002.
[15] 王睿男,武穆清,陈铁英,等.基于形态学的红外图像边缘检测[J].北京邮电大学学报,2021,44(1):66-71.
[16] ROMAN J C M,AYALA H L,NOGUERA J L V.Top-Hat transform for enhancement of aerial thermal images[C]//2017 30th SIBGRAPI Conference on Graphics,Patterns and Images (SIBGRAPI).Niteroi,Brazil:IEEE,2017: 277-284.
[17] ENGELBART R W,CHAPMAN M R,JOHNSON A,et al.Systems and methods enabling automated retumn to and/or repair of defects with a material placement machine: US039485[P].2006-05-02.

基于形态学的自动铺丝纤维铺放准确性检测^*

Automatic fiber placement accuracy detection based on morphology

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