Abstract
The production of high-end manufactured products requires Aesthetic Quality Control (AQC) in the form of human visual inspection. Manufactures can reduce AQC costs by incorporating semi-automated visual defect detection in units with the existing 3D metrology scans. This paper demonstrates how an image-based test method for defect detection can reduce the workload related to human visual inspection by proposing a median master comparison of batch image series. Our contribution consist of a) contrast enhancing and sorting batch image series for human visual inspection and b) providing a quality index (nQI) incorporated into statistical process control (SPC) for monitoring and controlling the AQC process. Our data shows that the median master differencing together with the nQI is great for classification of defects in batch images series. We introduce a SPC design proposal where individual batches as well as aggregated data can be inspected in synergy with the principles of Six Sigma. Based on Six Sigma control limits we have reduced the number of images in need of review by AQC assessors by a factor of 13.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
