The Machine Vision Approach As A Poka-Yoke System in Conrod Bearing Assembly Station of Engine Production Line

This paper presents a machine vision approach to detect the installation error of conrod bearing in four-cylinder engines. since there are always human errors in engine production lines that would reduce the quality of the final product, it is vital to establish the intelligent machine vision systems in order to track the process of assembling parts and prevent installation errors. the proposed method is such that by taking an engine block image, it produces a binary image that the whole image is black and only the places where conrod bearings exist are white, and also it announces which bearings are present and which ones are not installed. to this end, firstly the images of 16 different bearing installation cases were captured by camera. then, all images were analyzed with a combination of image processing methods including gaussian filtering, image thresholding and segmentation, and morphological techniques including erosion and dilation. finally, with applying if-then rules on the characteristics of the objects created in the image, its condition was decided. the results showed that the best threshold range for separating the bearing from other parts of image was between 110 and 245. also a disk-shaped structuring element with the radius of 60 provided the best morphological tool to detect the bearings. in addition, the results demonstrated that the regions belonged to conrod bearings had an area between 30,000 and about 80,000 pixels. with using if-then rule, the different bearing installation cases in all images were successfully detected with 100% accuracy. the total time for analyzing each image was about 1 s. so the results showed that the proposed machine vision system provided a non-error and fast tool for detecting the existence of conrod bearings which can be served as a poka-yoke system in the engine production line.

the machine vision approach as a poka-yoke system in conrod bearing assembly station of engine production line

this paper presents a machine vision approach to detect the installation error of conrod bearing in four-cylinder engines. since there are always human errors in engine production lines that would reduce the quality of the final product, it is vital to establish the intelligent machine vision systems in order to track the process of assembling parts and prevent installation errors. the proposed method is such that by taking an engine block image, it produces a binary image that the whole image is black and only the places where conrod bearings exist are white, and also it announces which bearings are present and which ones are not installed. to this end, firstly the images of 16 different bearing installation cases were captured by camera. then, all images were analyzed with a combination of image processing methods including gaussian filtering, image thresholding and segmentation, and morphological techniques including erosion and dilation. finally, with applying if-then rules on the characteristics of the objects created in the image, its condition was decided. the results showed that the best threshold range for separating the bearing from other parts of image was between 110 and 245. also a disk-shaped structuring element with the radius of 60 provided the best morphological tool to detect the bearings. in addition, the results demonstrated that the regions belonged to conrod bearings had an area between 30,000 and about 80,000 pixels. with using if-then rule, the different bearing installation cases in all images were successfully detected with 100% accuracy. the total time for analyzing each image was about 1 s. so the results showed that the proposed machine vision system provided a non-error and fast tool for detecting the existence of conrod bearings which can be served as a poka-yoke system in the engine production line.