Design and Implementation of Measurement System of Mechanical Parts with Multihole Based on Machine Vision
Journal of Electrical and Electronic Engineering
Volume 6, Issue 2, April 2018, Pages: 65-70
Received: Aug. 9, 2018;
Published: Aug. 13, 2018
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Chen Qiyu, Guangdong Institute of Intelligent Manufacturing, Guangzhou, China
Wang Yu, Guangdong Institute of Intelligent Manufacturing, Guangzhou, China
Wu Zhiheng, Guangdong Institute of Intelligent Manufacturing, Guangzhou, China
Tong Jigang, Guangdong Institute of Intelligent Manufacturing, Guangzhou, China
Mo Juexian, Guangdong Institute of Intelligent Manufacturing, Guangzhou, China
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The efficiency of the measurement of mechanical parts with multihole is needed to be improved in the information era, while the size measurement of mechanical parts relying mainly on manual detection currently with very low efficiency. To meet the repuiremant of equipment manufacturing automation, the efficiency of the measurement of mechanical parts should match the speed of the line. As a new technology to solve the measurement problem of the objects with irregular shape (such as multihole), machine vision provide a more effective way. To build a measurement system of mechanical parts with multihole, there are many relevant aspects to be considered, such as the choices of hardware, software development, algorithm design, ect. Image processing is one of the most important steps to build a successful visual system, which usually consists of image preprocessing, image segmentation, feature extraction and defect classification. The design and implementation of measurement system of mechanical parts with multihole based on machine vision will be discussed in this paper. The experiment results show that the improved algorithm can effectively filter the noise of surface images, which make the outline of the hole can be tested out easier. The system in this paper not only ensures the measurement precision of the pore diameter of the workpiece, but also realizes the measurement of the pore diameter of the workpiece at the same time.
Machine Vision, Mechanical Parts, Size Measurement
To cite this article
Design and Implementation of Measurement System of Mechanical Parts with Multihole Based on Machine Vision, Journal of Electrical and Electronic Engineering.
Vol. 6, No. 2,
2018, pp. 65-70.
Li Yunfeng, Han Cece, Li Shengyang. Measurement of dimension parameters of stamping parts based on machine vision [J]. Tool technology, 2015, 49 (1):95-98.
Tang Bo, Kong Jianyi Overview of machine vision surface defect detection [J]. Journal of Image and Graphics, 2017, 22 (12): 1640-1663.
Zhang Tian, Tang Chengtong, Liu Zhenhua. Measurement method of elbow space parameters based on multi-ocular vision [J]. Journal of instrumentation, 2013, 34 (2): 260-266.
Wang Zhongfei, Lin Maosong, Peng Yong, etc. Microchip thickness detection based on machine vision [J]. Computer measurement and control, 2013, 21 (1): 36-38.
Marco San Biagio, Carlos Beltrán-González, Salvatore Giunta, Alessio Del Bue, Vittorio Murino. Automatic inspection of aeronautic components [J]. Machine Vision and Applications. August 2017, Volume 28, Issue 5–6, pp 591–605.
Luo Shiguang. Machine vision based inspection method for glass bottle mouth defects [J]. Packaging engineering, 2018, 39 (3):183-187
Dan Zhongde, Zhang Fei. Research on on-line measurement technology of brinell hardness of machine vision casting [J]. Journal of mechanical engineering, 2017, 53 (1): 157-164.
Li Jifeng, Zhao Fengxia, Jin Sahobo. Design of machine vision defect detection system for glass fiber cloth [J]. Machinery Design and Manufacture, 2018, 1 (1): 163-169.
GUO Ruifeng, YUAN Chaofeng, YANG Liu, PENG Guangyu. Research on measurement of machine vision based on Open CV [J]. Computer Engineering and Applications, 2017, 53 (9):253-257.
LI Yarrfeng，LI Xuyu，YANU liu. Rapid Glass Refractive Index Measurement Method Based on Machine Vision [J]. Acta Optica Sinica, 2017, 46 (11): 1-7.