High Precision Edge Detection Algorithm for Mechanical Parts

Abstract

High precision and high efficiency measurement is becoming an imperative requirement for a lot of mechanical parts. So in this study, a subpixel-level edge detection algorithm based on the Gaussian integral model is proposed. For this purpose, the step edge normal section line Gaussian integral model of the backlight image is constructed, combined with the point spread function and the single step model. Then gray value of discrete points on the normal section line of pixel edge is calculated by surface interpolation, and the coordinate as well as gray information affected by noise is fitted in accordance with the Gaussian integral model. Therefore, a precise location of a subpixel edge was determined by searching the mean point. Finally, a gear tooth was measured by M&M3525 gear measurement center to verify the proposed algorithm. The theoretical analysis and experimental results show that the local edge fluctuation is reduced effectively by the proposed method in comparison with the existing subpixel edge detection algorithms. The subpixel edge location accuracy and computation speed are improved. And the maximum error of gear tooth profile total deviation is 1.9 μm compared with measurement result with gear measurement center. It indicates that the method has high reliability to meet the requirement of high precision measurement.

Polynomial edge reconstruction sensitivity, subpixel Sobel gradient kernel analysis

“…A precision analysis of the effect of kernel sizes and sampling interval has initially quantified the limits of operation for the method in real optical systems. The demonstrated accuracies for the investigations in References 23‐26 and32,33 have reinforced the potential application, of this initial study, in digital correlation and deformation measurement driven by planar features at the millimeter and micrometer scale. In comparison to the popular subpixel edge detection method, based on spatial moments, 5 the authors show a consistent measurement sensitivity of order 10 −6 perturbed by speckle and Gaussian noise with a runtime of 0.004 second for a 11 × 11 region.…”

“…Applications that we envisage the proposed method may be useful for are the accurate registration between two images, nonrigid/rigid body measurements and defect detection. [23][24][25][26] In Reference 25, the authors demonstrated the use of digital correlation to investigate localized thermal deformation within semiconductor devices. It was shown that minimizing alignment errors between images was a crucial preprocessing step before applying correlation.…”

“…Measurement precision is analyzed based on typical noise sources encountered in optical imaging systems. Applications that we envisage the proposed method may be useful for are the accurate registration between two images, nonrigid/rigid body measurements and defect detection 23‐26 . In Reference 25, the authors demonstrated the use of digital correlation to investigate localized thermal deformation within semiconductor devices.…”

Profile Design Method of Twin-Screw Compressor Rotors Based on the Pixel Solution

“…In the Bresenham algorithm, the active pixel moves one unit with every frequency change along the principal direction. e secondary directional movement is determined by the midpoint deviation discrimination [13,14]. e starting point of the ideal line is defined as P 0 (x 0 , y 0 ), and the ending point is defined as P n (x n , y n ).…”

Vibration Detection and Experiment of PMSM High Speed Grinding Motorized Spindle Based on Frequency Domain Technology

“…The bearings support the spindle during rotation and sustain the forces exerted by the spindle and external loads. The interaction effect between the electromagnetic field and mechanical structure may cause undesired subcritical resonance vibration of the spindle, and high precision and high efficiency measurement is becoming an imperative requirement for the vibration of the spindle [3].…”