Heating Up: the Cyber Age of Automation, Controls

Ours is a world where information is available everywhere, all the time, for our perusal. But what do we do with it? Within industrial settings, and in metalforming operations to be specific, we are in an age where controls and monitoring components gather and sift through more production data than ever before. What can be done with all of it? A lot. But just what to use and how to use it forms the cornerstone of what’s been coined as Industry 4.0.

To recap in a long-term view, Industry 1.0 refers to the use of steam power that spawned the Industrial Revolution. With the incorporation of electricity, industry was able to more efficiently harness power and operate around the clock. This development ushered in Industry 2.0. Then, Industry 3.0 described the adoption of technology and automation.

A near-term view sees Industry 1.0 as a parallel to lean manufacturing in the 1970s; Industry 2.0 as outsourcing in the ’90s; and Industry 3.0 as the acceleration of automation in the 2000s. Today we’re ushering in Industry 4.0, whose ideal definition has been suggested by analysts at McKinsey & Company, a research and consulting firm:

“…the next phase in the digitization of the manufacturing sector, driven by four disruptions: the astonishing rise in data volumes, computational power and connectivity, especially new low-power wide-area networks; the emergence of analytics and business-intelligence capabilities; new forms of human-machine interaction such as touch interfaces and augmented-reality systems; and improvements in transferring digital instructions to the physical world.”

To assess how Industry 4.0 is affecting our industry, MetalForming spoke with Mark Heitbrink, electrical engineering manager at Nidec Minster, Minster, OH. The press builder increasingly has incorporated advanced automation and control technologies into its press lines, and Heitbrink offers his take on controls evolution and its effect on automation, and where the metalforming industry likely takes that technology from here.

Big Data Supports Better-Informed Decisions

“From an operator’s standpoint, early controls used relays and minor communication devices to provide status and warning lights for operators. Then, smart digital displays with some analog meters added to the light and button display,” explains Heitbrink. “Today, computers in controls deliver much more, and in many cases a PLC does the work behind the scenes. So the screen is a PC, running an application that takes information from the processor and displays it in a nice, clean, user-friendly format.“

Press-line operators have a wealth of information at their fingertips, enabling quick shopfloor assessment of the stamping process. All of that data can be stored and analyzed to deliver robust algorithms that can better predict machine or line performance, and recommend proper courses of action. It’s Industry 4.0 in practice.

With the added muscle, more data can be fed into controls and processed for improved operator feedback, with this information then routed remotely in real-time, which enables monitoring and action away from the press line.

“Today, the buzzword with consumer-device controls is IoT, or Internet of Things,” Heitbrink says. “In our world, we refer to it as IIoT, or Industrial Internet of Things. In Industry 4.0, we get ‘cyber.’ We have a computer on every machine and now we have connectivity, which allows us to bring information to the cloud, or cyberspace, and then decide what to do with that information. A machine at the local operator level is very smart. We can get information to the cloud so that it can be processed. We then, for example, have the opportunity to perform some predictive analysis on why or when a machine will fail.”

Why is this such a tall order?

“In the metalforming industry, machines are built to last a long time,” explains Heitbrink. “To determine a likelihood of failure, processors can monitor certain data, perhaps vibration, temperature, motor load, etc., and then log that data and examine it before and after a failure. But we need to experience failures to determine how an algorithm reads that data, and understand what exactly caused the failure and what must be addressed before the failure occurs.”

That’s difficult, Heitbrink offers, because unlike home appliances, which are in the field in very high volumes and tend to have shorter lives, industrial machines such as stamping presses and line equipment exist in smaller numbers and are longer-lasting. So IoT enables plenty of consumer-appliance data to be captured. But automation and control providers, and stampers themselves, require a more-difficult-to-build database to produce reliable algorithms.