Readying robots and the workforce for Industrie 4.0

Industrie 4.0 is not a distant vision for the factory of the future. Already networks of robots are connecting to the cloud and contributing massive amounts of insightful data to simplify asset management and maintenance, maximize equipment and process efficiency, and improve product quality.

08/02/2017


Industrie 4.0 may seem more conceptual than real. For many manufacturers, the Industrial Internet of Things (IIoT), cyber-physical systems, cloud robotics, fog computing, and big data, can be intimidating. Visions of a smart factory can make us feel pretty dumb.

The smart factory connects the digital world of information technology with the physical world of operational technology, what many call information technology/operations technology (IT/OT) convergence. But Industrie 4.0 is not a distant vision for the factory of the future. It is here and it is now. Networks of robots are connecting to the cloud and contributing massive amounts of insightful data. Today, manufacturers are using these information pipelines to simplify asset management and maintenance, maximize equipment and process efficiency, and improve product quality. 

Stop downtime before it occurs

General Motors (GM) is putting IoT and the building blocks of Industrie 4.0 to work-today. The automaker's robot supplier and strategic partner, Fanuc America Corporation, is helping GM build a strong foundation for smart manufacturing. GM, Fanuc, and Cisco together developed the zero down time (ZDT) solution, which uses a cloud-based software platform to analyze data collected from robots across GM's factories in order to detect potential problems that could lead to production downtime.

In automobile manufacturing where a new car body comes down the assembly line every 60 or 90 seconds, downtime can cost original equipment manufacturers (OEMs) over $20,000 a minute. A single downtime incident easily could rack up millions in losses. When lines screech to a halt, those backups can impact the entire supply chain, further compounding the losses. The delays also trickle down to customers, automotive dealers, fleet users, and the car-buying public.

"We've had initiatives ongoing for some time now trying to better predict and maintain the health of our manufacturing equipment," said Marty Linn, manager of Advanced Automation Technologies and principal engineer of robotics at GM in Detroit, Mich. "We got together with Fanuc and talked about what we could do to avoid issues while we're doing productive manufacturing. This wasn't some great vision for Industrie 4.0. It was about what we can do to eliminate downtime in our plants from unpredicted maintenance."

A ZDT pilot program was launched at GM in 2014. The strategic partnership between GM and FANUC was a key enabler for the successful launch. The history between the two companies dates back to the early 1980s, when GM entered into a joint venture with the Japanese robot manufacturer to form GMFanuc Robotics Corporation to develop and market robots in the U.S. The venture would later be divested, but the strong relationship continued. "We're integrating robots as we speak. Every day we have robots and systems from our integrators that get shipped to the plants as we're introducing new products and new programs," Linn said. 

Cloud-connected welding robots on this automotive assembly line help realize the vision of Industrie 4.0 by monitoring themselves for potential downtime issues and facilitating predictive maintenance. Courtesy: Robotic Industries Association (RIA)/Fanuc AReturn on investment (ROI)

ZDT continues to make a difference on the plant floor for the company. Linn said GM has been able to avoid over 100 significant unscheduled downtimes since the program's inception.

"That avoids on the order of six to eight hours of unscheduled downtime, depending on what was going to fail. You can do the math. It's a lot. It's a big deal to us for any of our facilities, but especially in our high-volume truck and SUV plants, where each downtime event is significant."

With thousands of robots connected and communicating with the cloud, it wasn't long before GM began realizing their return on investment (ROI).

"This is not 'Jetsons' technology," Linn said. "This is using Big Data, the internet of things, new algorithms, computer capacity, all those things that have evolved over the past years, and using them in the most efficient way. Preventing downtime, and anticipating or even forgoing maintenance until it's needed, is huge." 

Maintenance only when needed

GM started slowly, connecting a couple thousand robots over the first year or two. By 2017, though, over 8,500 Fanuc robots were connected.

"We started out slow with the deployment," Linn said. "When we saw a problem, we went in and replaced parts. Then we studied those parts. Sure enough, we've been able to validate and verify that those parts were going to fail. They were going to cause us downtime. It was at that point when we were able to reduce unscheduled maintenance events that everybody got really excited and started saying, this is great, what else can we do with it?"

GM is also using ZDT to schedule maintenance only as needed, rather than beholden to routine maintenance schedules.

"For example, a robot might be designed to have routine scheduled maintenance at 1,000 hours. So we would plan to maintain it at that time," says Linn. "But it might actually last 1,250 hours before it needs maintenance. So we're working on getting away from fixed maintenance schedules to instead schedule as needed. This is one of the major ways you can find significant savings going forward." 

Cloud-connected software platform collects and analyzes massive amounts of data from thousands of robots to prevent downtime, predict maintenance, and optimize process efficiency. Courtesy: Robotic Industries Association (RIA)/Fanuc America Corporation

Machine learning

ZDT doesn't only apply to robots. It's also applicable to process equipment. Processes that are directly controlled by the robot, such as welding, painting, and some dispensing applications. Linn cited GM's automotive paint shop as an example.

"Looking at air pressures, looking at downdraft pressures, the speeds of the actuators that are dispensing the paints, looking at a lot of the paint processes and the parameters that go into that, we're able to monitor the health of the equipment and therefore the quality of the job," Linn said.

Finish quality is crucial in the automotive paint shop. All of Fanuc's paint robots are ZDT-ready, which means they can monitor a variety of functions including paint canisters, spray applicators, regulators, and drive health.

"If you count the total number of moving parts that relate to automotive painting, there are over 200 moving parts per robot," Tsai said. "A significant number of those moving parts are related to the process-specific devices controlling the gun, regulator, and pressure. If any one of those devices have any kind of premature failure, it can cause a quality issue and/or production downtime."

Right now, GM is using ZDT more as a predictive maintenance tool as opposed to an in-process adaptive tool. But as the technology evolves, and more data is collected and analyzed, and the algorithms get more sophisticated, you can see how with machine learning it could become more of an adaptive tool for real-time process improvement.

"In the case of the paint shop, by recognizing and understanding that there are very subtle process changes going on and correcting for those, we're able to improve our processes," Linn said. "We want to expand this strategy of having the equipment be smart, able to diagnose itself, and notify us of changes to its operational performance, so that we can go in during the opportunities that suit us to make adjustments or repairs as needed." 

Automotive and beyond

Fanuc's analytics solution is monitoring over 10,000 cloud-connected robots at customers' facilities around the world, and growing every day. While it is currently used in the automotive industry, Fanuc plans to release software and hardware support for general industry, non-automotive customers in late 2017.

"Our solution needs to be scalable for small general industry manufacturers with two to three robots," Tsai said. "The way you install the software and set up the hardware needs to be plug and play for the smaller manufacturer, because they don't have the IT department to support it."

Eventually, Fanuc and Cisco intend to use this data communication highway developed for ZDT to connect other equipment beyond robots. ZDT is part of the Fanuc Intelligent Edge Link and Drive (FIELD) system, which provides an open software platform that allows for advanced analytics and deep learning capabilities for CNCs, robots, peripheral devices, and sensors used in automation systems. FIELD is based on edge computing technology where a large amount of data is processed within the manufacturing site at the edge of the network, thereby minimizing the volume and cost of sharing data.

"With the ZDT Cloud solution the data is flowed from the devices on the production floor all the way to the cloud, where you will have latency or delays," Tsai said. "The benefit of receiving the data on the floor using the FIELD platform is then you can respond to the event in real time, and that's what the FIELD does. It's a piece of open platform software that can be loaded into computing hardware, which then allows you to access data from the robot, the programmable logic controller (PLC), or the machine tool device, and apply analytics in real time. It can even change your production based on how you behave. That's where real-time machine learning can provide good value.

"Industrie 4.0 is not just a dream. It's real," Tsai said. "It's an exciting time for automation."

Exciting indeed, as more robot manufacturers introduce their own IIoT solutions for embracing the level of connectivity heralded by Industrie 4.0. 


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