Total QA through Autonomous Machine Vision

Total QA is seriously interesting and until last year only talked about. Now with advancement in Autonomous Machine Vision, Total QA is not just a concept – it can be realized by manufacturers themselves, and it can be implemented and realized value from very quickly.


Total QA is realization of a concept in which quality control is not limited to specific steps in the production line, but can be implemented through every step along the production line and using the same QA systems.


Pretty awesome, huh?


Advancements in Machine Learning, Computer Vision and Optics have made Autonomous Machine Vision and hence Total QA possible. Based on what I am reading, the AMV’s not only capture images of the manufacturers’ products, they also use the power of AI to self-configure themselves to the production environment they are in. That in itself is powerful – less configuration reduces complexity and lead time to set up. Moreover, in times of performance irregularities like a bad lens or bad sensor these systems are capable of performing self-diagnosis to identify the issue, significantly reducing their own mean time to repair (MTTR). This means less downtime of manufacturers’ QA systems, also avoiding costly repairs and human intervention.


I am yet to see a real one in action, but with AMV’s driving Total QA, manufacturers will finally benefit from powerful visual QA systems with very short implementation lead times and rapid diagnostics. Not to mention the impact the stream of continuously generated product quality data will have on the manufacturers’ #smartmanufacturing and #industry4.0 initiatives. 


We at #codedataio celebrate #ai powering the next wave of manufacturing and we are excited about the impact #autonomousmachinevision will have on reducing product recalls and continuously  improving product quality for manufacturers.