Recent research discussed the development of image processing tools as a part of the quality control framework in manufacturing environments. This research could be divided into two image-based fault detection approaches: 1) MVS; and 2) MVS and control charts. Despite the intensive research in both groups, there is a disconnect between research and the actual needs on the shop-floor. This disconnect is mainly attributed to the following:
• The literature for the first category has mainly focused on improving fault detection accuracy through the use of special setups without considering its impact on the manufacturing process. Therefore, many of these methods have not been utilized by industry, and these tools lack the capability of using images already present on the shop floor.
• The studies presented on the second category have been mainly developed in isolation. In addition, most of these studies have focused more on introducing the concept of utilizing control charts on image data rather than tackling specific industry problems.
In this thesis, these limitations are investigated and are disseminated to the research community through two different journal papers. In the first paper, it was shown that a face-recognition tool could be successfully used to detect faults in real-time in stamped processes, where the changes in image lighting conditions and part location were allowed to emulate actual manufacturing environments. On the other hand, the second paper reviewed the literature on image-based control charts and suggested recommendations for future research.
