The production line for the assembly of automotive products requires 100 % control of the presence of components that are manually installed in the designated places. It is necessary to ensure the presence and correct orientation of components to avoid producing defective pieces or damaging production line elements during operation. The diploma thesis analyses the current state of visual control of the presence of the housing and washer in assembly workplaces. The performance is improved by changing the gripper at the mounting location of the washer. As a result of the change, a realignment and machine learning of the Keyence smart camera was carried out. A comparable machine vision system was developed at the workplace. The most optimal placement of cameras was selected, as was the illumination of the components. Image processing was done using the RoboRealm software. A comparison of the improved current system and the custom system is shown with the results of the image processing. The housing is detected by both systems with similar reliability and could be used on the automated line. The washer is detected with greater reliability by the current system; although even in its current state, it is still not 100 % reliable. The final results emphasize the importance of the correct design and setup of the machine vision system to achieve high reliability and efficiency on the production line.
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