摘要
为了满足锂电池生产企业对锂电池连接片的焊接质量进行自动化检测的需求,设计了一种基于计算机视觉的锂电池连接片焊接质量检测系统.针对正、负电极材料的光学特性和检测精度要求,设计了两套光学系统分别实现锂电池正、负电极的成像,提出了多重自适应模板匹配算法提取正、负电极焊缝区域,采用自适应阈值分割法对焊缝区域实现灰度化操作,使用灰度形态学运算弱化灰度图的噪点突出焊盘特征点,利用轮廓提取算法实现焊盘特征点的提取,依据特征点数量实现极耳裁切缺陷的检测,将U^(2)-Net语义分割模型引入焊接质量检测任务,采集正、负电极焊缝和烧穿、虚焊、焊接氧化缺陷数据集,利用数据增强技术扩充数据集,解决焊接缺陷的小样本和小目标问题,融合应用U^(2)-Net语义分割模型和轮廓提取算法实现焊缝尺寸测量以及虚焊等缺陷的检测.系统试运行结果表明,缺陷检出率为99.98%,误检率仅为0.06%,满足企业的实际需求,实现了快速、无接触且准确的自动化检测.
To meet the demand for automated detection of welding quality in lithium battery tabs in production enterprises,a computer vision-based lithium battery tab welding quality detection system is designed.Considering the optical characteristics and precision requirements of the positive and negative electrode materials,two optical systems are designed to image the positive and negative electrodes of the lithium battery,respectively.A multi-adaptive template matching algorithm is proposed to extract the welding seam areas of the positive and negative electrodes.An adaptive threshold segmentation method is used to gray the welding seam areas.Gray morphological operations are employed to suppress noise in the grayscale image and highlight the weld pad feature points.The contour extraction algorithm is used to extract the weld pad feature points,and the number of feature points is used to detect lithium battery pole ear cutting defects.The U^(2)-Net semantic segmentation model is introduced into the welding quality detection task.The dataset for the positive and negative electrode welding seams,as well as the burnthrough,faulty welding,and welding oxidation defects,is augmented using data enhancement techniques to address the small sample size and small target problem in welding defect detection.The U^(2)-Net semantic segmentation model and contour extraction algorithm are used to measure welding seam dimensions and detect defects such as faulty welding defects.The system trial operation results show a defect detection rate of 99.98%and a false detection rate of only 0.06%,meeting the actual needs of enterprises and achieving rapid,non-contact,and accurate automated detection.
作者
于强
肖澜
郑大伟
彭昭鸿
宋凯宏
YU Qiang;XIAO Lan;ZHENG Dawei;PENG Zhaohong;SONG Kaihong(Qingdao Innovation and Development Base,Harbin Engineering University,Qingdao,266000;Hexagon Manufacturing Intelligence Technology(Qingdao)Co.,Ltd.,Qingdao,266000)
出处
《焊接学报》
EI
CAS
CSCD
北大核心
2024年第10期38-49,共12页
Transactions of The China Welding Institution
关键词
计算机视觉
焊接检测
语义分割
图像处理
锂电池
computer vision
welding inspection
semantic segmentation
image processing
lithium battery
