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We are proceeding towards the age of automation and robotic integration ofour production lines [5]. Effective quality-control systems have to be put inplace to maintain the quality of manufactured components. Among differentquality-control systems, vision-based inspection systems have gainedconsiderable amount of popularity [8] due to developments in computing powerand image processing techniques. In this paper, we present a vision-basedinspection system (VBI) as a quality-control system, which not only detects thepresence of defects, such as in conventional VBIs, but also leveragedevelopments in machine learning to predict the presence of surface fracturesand wearing. We use OpenCV, an open source computer-vision framework, andTensorflow, an open source machine-learning framework developed by Google Inc.,to accomplish the tasks of detection and prediction of presence of surfacedefects such as fractures of manufactured gears.

Vision-based inspection system employing computer vision & neural networks for detection of fractures in manufactured components