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#codedataio is following #hannovermesse2019 for applications of #industry4_0 and #machinelearning

 

https://www.hannovermesse.de/home

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Automata raises $7.4M on the power of Eva – a $6,600 Industrial Robot that can perform simple tasks relieving human minds to focus on the more complex ones. Comparable equipments start at around $25,000. Interesting story (below) on how betting on a 2-person startup called Automata is paying off for ABB. Automata’s selling point?  “A prototype we could carry around in our backpack, which was insane for ABB because their robots weigh 50 kilos”.

 

Choreograph, Automata’s software is another factor that differentiates Eva from other major industrial competitors. It is cloud-based, and a company can log in, control and monitor Eva from any web-based interface.

 

A big step towards #smartmanufacturing – decreasing prices will see increased adoption of robotic devices at a time when there is a rapid expansion of #industrialautomation activities on the basis of increased efficiency, reduced human errors and improved worker safety.

 

Inspired by: https://www.cnbc.com/2019/03/19/abb-backed-automata-launches-eva-robot.html

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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.