Vision-based inspection system employing computer vision & neural networks for detection of fractures in manufactured components
This work addresses quality control in manufacturing automation, but it appears incremental as it builds on existing vision-based systems by adding machine learning for defect prediction without clear novel breakthroughs.
The paper tackles the problem of detecting surface fractures and wearing in manufactured components by presenting a vision-based inspection system that uses OpenCV and TensorFlow for detection and prediction, achieving unspecified performance metrics.
We are proceeding towards the age of automation and robotic integration of our production lines [5]. Effective quality-control systems have to be put in place to maintain the quality of manufactured components. Among different quality-control systems, vision-based inspection systems have gained considerable amount of popularity [8] due to developments in computing power and image processing techniques. In this paper, we present a vision-based inspection system (VBI) as a quality-control system, which not only detects the presence of defects, such as in conventional VBIs, but also leverage developments in machine learning to predict the presence of surface fractures and wearing. We use OpenCV, an open source computer-vision framework, and Tensorflow, an open source machine-learning framework developed by Google Inc., to accomplish the tasks of detection and prediction of presence of surface defects such as fractures of manufactured gears.