基于机器视觉零件轴线直线度误差测量的研究

Straightness of shaft parts is an important criterion to judge whether a part passes quality control. To solve the problems of low efficiency and insufficient accuracy of traditional methods for measuring straightness, a platform for measuring the straightness of short shaft parts has been developed. A sharpness function based on eight neighborhood hollow gradient weighting is proposed for achieving autofocus. Using image preprocessing, morphological operations, and sub-pixel edge coordinate extraction, the central axis of each part is obtained using the radial local area search method. Next, a large-variation double-tangent cross genetic algorithm based on the minimum region method is proposed for measuring the straightness of the central axis. Four algorithms are integrated by the graphical user interface. Evaluation error using this algorithm is less than that obtained using the least squares method, the segmentation approximation method, or the minimum area method, consistent with the literature reports of results obtained using this algorithm. Finally more than 94% of the results are within 10 μm of the results obtained using a 3-axis measuring machine. This system can thus be used to measure the straightness error of short shaft parts.