贴片元件包装纸带冲压制程中的影像系统设计
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摘要
目的为了实现0402元件包装纸带冲孔尺寸与毛边长度的检测,以便作为冲压制品的品质检验及模具观测判断和调整的依据,构建一台影像检测系统。方法采用CCD拾取纸带影像并配合电机带动平台移动,使用二值化算法及形态学内插算法处理后,测量出最长的毛边尺寸,采用LabVIEW编程控制影像的拾取、处理,并通过人机界面显示其尺寸或毛边、长度平均值、3个标准差(3σ)、最大/最小值与差值、综合制程能力指数等,以确认产品的品质。结果实验结果显示,影像检测系统每次测量10片纸带,每片测量需64 s,最大定位误差为0.005 mm,误差百分比最大为3.93%,将毛边检测中发现的纸带毛边位置及长度与国家标准相对比,可作为模具调整或更换的依据。结论此影像检测系统可替代人工测量,降低了人为测量误差,并节省了时间,提高了生产效能。
The work aims to construct an image detection system to detect the punching size and edge length of 0402 component packaging paper tape, which is regarded as the basis for quality inspection of stamping products and for observation, judgment and adjustment of mold. Firstly, CCD was used to pick up paper tape image and cooperate with motor to drive the platform to move. Secondly, the longest edge size was measured by means of binarization algorithm and morphological interpolation algorithm. Thirdly, LabVIEW programming was used to control image picking and processing. Fourthly, the dimension or edge, average length value, 3σ, maximum/minimum value and difference value, comprehensive process capability index and so on were displayed through man-machine interface to confirm the product quality. The experimental results indicated that, 10 pieces of paper tape could be measured by the image detection system each time, while the time for each piece was 64 s. The maximum positioning error was 0.005 mm, and the maximum error percentage was 3.93%. When compared with the national standards, the position and length of the edge of paper tape found in the edge detection could be used as the basis for adjusting or replacing the mold. The image detection system can replace manual measurement, reduce the error of artificial measurement, save time and improve production efficiency.
The work aims to construct an image detection system to detect the punching size and edge length of 0402 component packaging paper tape, which is regarded as the basis for quality inspection of stamping products and for observation, judgment and adjustment of mold. Firstly, CCD was used to pick up paper tape image and cooperate with motor to drive the platform to move. Secondly, the longest edge size was measured by means of binarization algorithm and morphological interpolation algorithm. Thirdly, LabVIEW programming was used to control image picking and processing. Fourthly, the dimension or edge, average length value, 3σ, maximum/minimum value and difference value, comprehensive process capability index and so on were displayed through man-machine interface to confirm the product quality. The experimental results indicated that, 10 pieces of paper tape could be measured by the image detection system each time, while the time for each piece was 64 s. The maximum positioning error was 0.005 mm, and the maximum error percentage was 3.93%. When compared with the national standards, the position and length of the edge of paper tape found in the edge detection could be used as the basis for adjusting or replacing the mold. The image detection system can replace manual measurement, reduce the error of artificial measurement, save time and improve production efficiency.
引文
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[15]VITHU P,MOSES J A.Machine Vision System for Food Grain Quality Evaluation[J].Trends in Food Science&Technology,2016,56(10):13-20.
[2]刘斌,吴盛金,邹仕放.基于机器视觉的塑胶制品表面缺陷自动检测技术[J].塑料工业,2009,37(12):5-9.LIU Bin,WU Sheng-jin,ZOU Shi-fang.Automatic Detection Technology of Surface Defects on Plastic Products Based on Machine Vision[J].China Plastics Industry,2009,37(12):5-9.
[3]LEE S,MIYOSHI T,TAKAYA Y,et al.Non-contact3D Edge Profile Measurement for Die and Mould Model Surface[J].Journal of Materials Processing Technology,2002,127(12):287-291.
[4]SORTINO M.Application of Statistical Filtering for Optical Detection of Tool Wear[J].International Journal of Machine Tool&Manufacture,2003,43(8):493-497.
[5]ISMA A,BRAHIM B,NOURA A.Motion Analysis of a Mobile Manipulator Executing Qick-up Tasks[J].Computer&Electrical Engineering,2015,43(4):257-269.
[6]杨祖彬,代小红.基于图像配准的食品包装印刷缺陷检测与实现[J].计算机科学,2015,42(8):319-322.YANG Zu-bin,DAI Xiao-hong.Printing Defects Detection and Realization Food Packaging Based on Image Registration[J].Computer Science,2015,42(8):319-322.
[7]SHRESTHA B L,KANG Y M,BAIK O D.ATwo-camera Machine Vision in Predicting Alphs-amylase Activity in Wheat[J].Journal of Cereal Science,2016,71(9):28-36.
[8]张恒,彭建飞.基于零件冲裁毛刺高度的冲压模具磨损预测[J].锻压技术,2017,42(12):123-127.ZHANG Heng,PENG Jian-fei.Prediction of Stamping Die Wear Based on Blanking Burr Height of Part[J].Forging&Stamping Technology,2017,42(12):123-127.
[9]胡方尚,郭慧,邢金鹏,等.基于印刷缺陷检测的图像配准方法研究[J].光学技术,2017,43(1):16-21.HU Fang-shang,GUO Hui,XING Jin-peng,et al.Image Registration Based on Label Printing Defect Detection[J].Optical Technique,2017,43(1):16-21.
[10]李正明,黎宏,孙俊.基于数字图像处理的印刷电路板缺陷检测[J].仪器仪表与传感器,2012(8):87-89.LI Zheng-ming,LI Hong,SUN Jun.Detection of PCBBased on Digital Image Processing[J].Instrument Technique and Sensor,2012(8):87-89.
[11]李萌,孙铁波.基于机器视觉的食品包装缺陷检测研究[J].食品研究与开发,2016,37(24):125-127.LI Meng,SUN Tie-bo.Research of Food Packaging Defects Detection Based on Machine Vision[J].Food Research and Development,2016,37(24):125-127.
[12]杨晓涵,李雨彤,王宁,等.遥感影像融合方法比较研究[J].重庆工商大学学报(自然科学版),201734(2):90-94.YANG Xiao-han,LI Yu-tong,WANG Ning,et al Comparative Research on Remote Sensing Image Fusion Method[J].Journal of Chongqing Technology and Business University(Natural Science Edition),201734(2):90-94.
[13]孙怀远,杨丽英,周夫之.基于机器视觉技术的药品瓶包装在线检测系统[J].机电信息,2011,12(2):46-50.SUN Huai-yuan,YANG Li-ying,ZHOU Fu-zhi.The Real-time Detection System of Pharmaceutical Bottles Based on Machine Vision Technology[J].Mechatronic Information,2011,12(2):46-50.
[14]胡浩.基于机器视觉的饮料瓶包装检测与抓取技术[D].天津:天津工业大学,2017.HU Hao.Beverage Bottle Packaging Detection and Grasping Technology Based on Machine Vision[D].Tianjin:Tianjin Polytechnic University,2017.
[15]VITHU P,MOSES J A.Machine Vision System for Food Grain Quality Evaluation[J].Trends in Food Science&Technology,2016,56(10):13-20.