Automated end-of-line quality assurance with visual inspection and convolutional neural networks

Abstract End-of-line (EOL) quality assurance of finished components has so far required additional manual inspections and burdened manufacturers with high labor costs. To automate the EOL process, in this paper a fully AI-based quality classification system is introduced. The components are automatically placed under the optical inspection system employing a robot. A Convolutional Neural Network (CNN) is used for the quality classification of the recorded images. After quality control, the component is sorted automatically in different bins depending on the quality control result. The trained CNN models achieve up to 98.7% accuracy on the test data. The classification performance of the CNN is compared with that of a rule-based approach. Additionally, the trained classification model is interpreted by an explainable AI method to make it comprehensible for humans and reassure them about its trustworthiness. This work originated from an actual industrial use case from Witzenmann GmbH. Together with the company, a demonstrator was realized.