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Visual measurement method of crankshaft diameter based on axis local search

Published:2023-07-18 author:ZHANG Dan, LI Chang-an, LU Hai-bin, et al. Browse: 462 Check PDF documents

Visual measurement method of crankshaft diameter

based on axis local search

ZHANG Dan1, LI Chang-an2, LU Hai-bin2, DOU Ya-ping2, SUI Wen-tao2

(1.School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China;

2.School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China)

Abstract: When the traditional machine vision measurement method is used to measure the geometric characteristics of crankshaft, the measurement accuracy is low. Therefore, a visual measurement method (measurement system) of crankshaft diameter based on local search of axis was proposed.Firstly, according to the requirements of the parts to be tested, the hardware selection and machine vision measurement platform were built.The lens distortion was corrected to improve the precision, and the acquired image was preprocessed, including morphological filtering, binarization and other operations.Then the position of the crankshaft rotation axis was obtained by pixel level coordinates of the positioning algorithm to make it coincide with the camera moving axis. The subpixel subdivision algorithm was used to extract the subpixel edge coordinates, calculate the pixel equivalent to complete the conversion between the pixel and the actual size, and achieve highprecision measurement of the shaft diameter. Finally, the accuracy and practicability of the shaft diameter measurement method based on local search of the axis were verified by comparing the measurement experiments. The research results show that the average error of the axis local search method for measuring the shaft diameter is 0.003 mm, and the stability is good, which proves the accuracy and practicability of the algorithm and its measurement system.It can be used to effectively detect the crankshaft diameter.

Key words: shaft diameter measurement; high precision measurement; machine vision measurement; subpixel subdivision algorithm; lens distortion correction; image preprocessing; pixel equivalent

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