基于机器视觉的阴极铜表面质量检测系统的研究

发布时间：2019-06-04 04:21

【摘要】：由于市场对电解阴极铜表面质量要求的提高,产品需要经过筛选将不符合标准的阴极铜剔除后才能投入市场,为此云南某企业为了满足市场需求加入了人工筛选环节,但人工检测存在的一些问题:没有固定筛选标准、效率和准确率低下、工人劳动强度大。随着机器视觉的广泛普及,该技术在工业生产中的应用范围也越来越广,已成为当今工业自动化中不可或缺的重要技术之一,本文通过应用机器视觉技术解决当前人工筛选存在的问题,实现阴极铜的全自动化检测与筛选。在生产线上机器视觉是获取目标图像信息的重要手段,应用图像处理技术增加它的自主识别能力。本论文通过分析机器视觉技术的特点,结合当下生产需求,设计了基于机器视觉的阴极铜表面质量检测系统,该系统主要解决的问题有(1)提取采集到的图像信息(2)准确、快速的提取阴极铜的表面特征参数,判断阴极铜分类(3)机械手根据分类对阴极铜进行筛选。针对需要解决的问题,以图像处理为核心展开研究工作。本论文的研究内容如下:首先根据工程实际生产条件确定光源、摄像机、镜头、图像采集卡等硬件设备的参数,确保采集到能够满足处理足需求的图像;其次研究机器视觉技术,在Halcon平台上设计基于边缘检测和阈值分割的识别算法对采集到的目标图像进行分析、理解,并提取所需信息;然后开发人机交互界面,及时反馈阴极铜表面质量等信息,实现机械手与PC机间的通讯。最后完成相关实验,发现存在问题,为进一步的优化提供依据。将图像处理技术应用于阴极铜表面质量的检测,解决了人工检测无固定标准、人机工作不匹配、准确率低、工人劳动量大的问题,彻底摆脱了人为因素的干扰、实现阴极铜的全自动化生产。
[Abstract]:Due to the improvement of the surface quality requirements of electrolytic cathode copper in the market, the products need to be screened to eliminate the cathode copper which does not meet the standard before it can be put into the market. Therefore, an enterprise in Yunnan has joined the manual screening link in order to meet the market demand. However, there are some problems in manual detection: there is no fixed screening standard, the efficiency and accuracy are low, and the labor intensity of workers is high. With the wide popularization of machine vision, the application of this technology in industrial production is becoming more and more extensive, and it has become one of the indispensable and important technologies in industrial automation. In this paper, machine vision technology is applied to solve the existing problems of manual screening, and the automatic detection and screening of cathode copper is realized. Machine vision is an important means to obtain target image information on production line, and image processing technology is applied to increase its autonomous recognition ability. In this paper, by analyzing the characteristics of machine vision technology and combining with the current production requirements, a cathode copper surface quality detection system based on machine vision is designed. The main problems solved by the system are as follows: (1) extracting the collected image information (2) accurately and quickly extracting the surface characteristic parameters of cathode copper, and judging the classification of cathode copper (3) the manipulator selects the cathode copper according to the classification. Aiming at the problems that need to be solved, the research work is carried out with image processing as the core. The research contents of this paper are as follows: firstly, the parameters of light source, camera, lens, image acquisition card and other hardware equipment are determined according to the actual production conditions of the project, so as to ensure that the image which can meet the needs of processing can be collected. Secondly, the machine vision technology is studied, and the recognition algorithm based on edge detection and threshold segmentation is designed on Halcon platform to analyze, understand and extract the required information. Then the human-computer interaction interface is developed to feedback the surface quality of cathode copper in time to realize the communication between manipulator and PC. Finally, the related experiments are completed, and the existing problems are found, which provides the basis for further optimization. The image processing technology is applied to the detection of the surface quality of cathode copper, which solves the problems of no fixed standard of manual detection, mismatching of man-machine work, low accuracy and large labor volume of workers, and completely gets rid of the interference of human factors. Realize the full automation production of cathode copper.
【学位授予单位】：昆明理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TF811;TP391.41

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