Feature Point Identification in Fillet Weld Joints Using an Improved CPDA Method

IF 2.5 4区 综合性期刊 Q2 CHEMISTRY, MULTIDISCIPLINARY Applied Sciences-Basel Pub Date : 2023-09-07 DOI:10.3390/app131810108
Yang Huang, Shaolei Xu, Xingyu Gao, Chuannen Wei, Yang Zhang, Mingfeng Li
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Abstract

An intelligent, vision-guided welding robot is highly desired in machinery manufacturing, the ship industry, and vehicle engineering. The performance of the system greatly depends on the effective identification of weld seam features and the three-dimensional (3D) reconstruction of the weld seam position in a complex industrial environment. In this paper, a 3D visual sensing system with a structured laser projector and CCD camera is developed to obtain the geometry information of fillet weld seams in robot welding. By accounting for the inclination characteristics of the laser stripe in fillet welding, a Gaussian-weighted PCA-based laser center line extraction method is proposed. Smoother laser centerlines can be obtained at large, inclined angles. Furthermore, an improved chord-to-point distance accumulation (CPDA) method with polygon approximation is proposed to identify the feature corner location in center line images. The proposed method is validated numerically with simulated piece-wise linear laser stripes and experimentally with automated robot welding. By comparing this method with the grayscale gravity method, Hessian-matrix-based method, and conventional CPDA method, the proposed improved CPDA method with PCA center extraction is shown to have high accuracy and robustness in noisy welding environments. The proposed method meets the need for vision-aided automated welding robots by achieving greater than 95% accuracy in corner feature point identification in fillet welding.
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角焊缝特征点识别的改进CPDA方法
在机械制造、船舶工业和车辆工程中,一个智能的、视觉引导的焊接机器人是非常需要的。该系统的性能在很大程度上取决于在复杂工业环境中对焊缝特征的有效识别和焊缝位置的三维重建。为获取机器人焊接中角焊缝的几何信息,研制了一种由结构化激光投影仪和CCD摄像机组成的三维视觉传感系统。针对角焊中激光条纹的倾斜特性,提出了一种基于高斯加权pca的激光中心线提取方法。更平滑的激光中心线可以在大的倾斜角度下获得。在此基础上,提出了一种改进的基于多边形逼近的弦点距离积累(CPDA)方法来识别中心线图像中的特征角点位置。通过模拟逐片线性激光条纹和自动化机器人焊接实验验证了该方法的有效性。通过与灰度重力法、基于hessian矩阵的方法和传统CPDA方法的比较,证明了该方法在噪声焊接环境下具有较高的精度和鲁棒性。该方法对角点特征点的识别精度达到95%以上,满足了视觉辅助自动化焊接机器人的需求。
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来源期刊
Applied Sciences-Basel
Applied Sciences-Basel CHEMISTRY, MULTIDISCIPLINARYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
5.30
自引率
11.10%
发文量
10882
期刊介绍: Applied Sciences (ISSN 2076-3417) provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
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