  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.
