Getting smart in manufacturing
Rhett Ramos, Asia IT director of Allegro MicroSystems Philippines
Smart manufacturing can help factories in Asia reduce cycle times, while increasing yield and improving operations visibility, said Rhett Ramos, Asia IT director of Allegro MicroSystems Philippines, at the CIO Conference in Manila last Thursday (25 May 2017).
Ramos defined smart manufacturing as the integration of processes and equipment.
He gave an example of a multinational semiconductor manufacturing company in the Philippines that uses Industrial Internet of Things (IIoT) to speed up its production of chips.
“It typically takes about six steps to build a chip, and each step requires about one day. So there are six days [needed] just to assemble [the chips]. Then we have to test it…which may take [at least] one day. Also, each process will entail one piece of equipment and one operator. So all in all, it will take seven days, eight equipment, and eight operators at minimum to build something,” Ramos explained.
But with smart manufacturing, he said the company is able to produce the chips in just 16 hours, with the help of only two machine operators.
What made this possible? Sensors.
Ramos explained that factories need to embed sensors in their equipment to enable smart manufacturing. These sensors will allow the company to capture data about the conditions of each machine and detect the products that are running in the equipment.
These sensors must then be connected to the network and the cell controller of the factory.
“Cell controller is like the brain and the central nervous system of the manufacturing floor because it is the one that tells the equipment what to do, what products to run, and what recipes to use. It’s also connected to your business systems like ERP for sales and orders, the planning system, [and] manufacturing execution system, which is the software that is used to control the manufacturing floor. It’s also the gateway to dashboards and reports,” he explained.
He added that the role of the IT professionals in smart manufacturing is thus to maintain the network, build and maintain the cell controllers in some cases, select and maintain the business systems, and choose the tools that will be used to create the dashboards and reports.
“By digitising the manufacturing floor, you’ll increase your yields, reduce your cycle time, increase the usage of your equipment, as well as [improve product] delivery because you can analyse the best route to take and which supplier is better. The biggest [benefit] in [smart] manufacturing is reducing cost because you’ll use less material, less energy, less operators, and you’re utilising the equipment more,” he shared.
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.