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Getting smart in manufacturing

Adrian M. Reodique | June 1, 2017
Rhett Ramos of Allegro MicroSystems Philippines shares why Asian manufacturers should move towards smart manufacturing and how to do so, at the recent CIO Conference in Manila.

 

Trends in digital manufacturing

Besides augmented reality and collaborative robots (cobots), Ramos also highlighted the emergence of the digital twin and machine vision in the digital manufacturing world.

Digital twin allows companies to create a virtual factory that provides them better visualisation of the situation on the manufacturing floor.

"Some companies actually expand [the use of] digital twin to also include product design. So before [they] build a product, [they] can design it and then run it through [their] line to the digital twin to understand what bottlenecks and issues may occur, even before building a physical copy of that product," he said.

On the other hand, machine vision involves the use of computer to capture and analyse images, according to PC Magazine. It can be used to detect people in surveillance systems, inspect objects on an assembly line, or in robotic systems.

A report by market research firm, MarketsandMarkets, last January predicted that the machine vision market will grow at a compound annual growth rate of 8.15 percent from 2016 to 2022, to reach US$14.43 billion by 2022.

The report suggested that the growth will be driven by the increasing need for quality inspection and automation from companies across industry verticals, as well as the rise in demand for vision-guided robotic systems and application-specific machine vision systems. 

"What's driving machine vision? The machine vision system is more accurate than humans, and can complete highly repeatable tasks much more quickly and for longer [periods] than humans so you increase productivity. Machine vision also enables robots to complete more than one task as opposed to blind robots, which only do one thing at a time every time," said Ramos. 

Other stories from CIO Conference 2017:

 

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