Fastener size metrology with machine vision

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Sekar, Nirmal Kumar
Boldsaikhan, Enkhsaikhan
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This study aims to establish a new manufacturing systems integration method that enhances the fastener size metrology using a machine vision sensor that is mounted on the wrist of an industrial robot. This metrology method is applicable to inspection of any parts with varying sizes in advanced manufacturing applications, particularly in automotive and aircraft manufacturing. The proposed method offers a new way of integrating algorithmic principles into existing manufacturing systems. It consists of data processing and analysis steps that involve image acquisition via machine vision followed by image processing for feature extraction and metrology. Firstly, a machine vision camera is mounted on the end of a robotic arm and then calibrated. The robot arm is used to automatically move the camera to different perspective view poses for capturing images. Images from two different perspective views are used for disparity mapping that produces a depth map generated from two stereo images. Secondly, the disparity map edges are identified by using edge detection and metrology tools for fastener size metrology. The experimentation used ideal simulation images instead of actual camera images for analysis and validation. The results with simulation images indicate that the proposed methodology can detect ±0.005 cm variations in the fastener length. The accuracy of fastener size metrology depends on the accuracy of edge detection as the edge detection tool may make mistakes due to sporadic variations in the image quality. The hit/miss data of edge detection with the intensity difference threshold of 64 is statistically evaluated by the Probability of detection (POD) analysis. According to the POD analysis, an intensity difference greater than 192 can guarantee the 1.0 (100%) mean probability of detection with the 95% lower confidence interval curve that is greater than 0.8 (80%). Keywords: Stereo Images, Disparity Map, Probability of Detection, Machine Vision, Metrology.

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Thesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Industrial, Systems, and Manufacturing Engineering
Wichita State University
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