Investigation of a test rig based on smart vision sensors for automated inspection of press-hardened automotive body components

Defects such as cracks, overlaps and impressions are prevalent in the manufacturing of press-hardened automotive body components. The prevailing industrial practices rely on manual visual inspections, which are both costly and less effective, thereby posing a risk of undetected defects. To address these challenges, the potential of smart vision sensors for automated component inspection is being investigated. A dedicated test rig was constructed for the purpose of studying the key influencing factors on the output similarity values of the image processing system. These factors include the temperature of the component subsequent to the processing stage and the exposure to the light conditions during the inspection. The performance of the system was evaluated using confusion matrices in order to assess precision and repeatability. For the deformation and crack defect types discrepancies were not observed between the actual and predicted classifications. For the purpose of a practical acceptance of the test system, a left-tailed hypothesis test is carried out for the overlap defect type. The results of the study demonstrate the potential of inspection systems to improve accurate defect detection, thereby paving the way for their implementation in production environments.