Edge Computing Enables Smart CNC Machine Digitalization

Edge devices enhance smart CNC machines without impacting controller operation ensuring machine performance and productivity.

A smart CNC machine edge computing device is a ruggedized industrial PC designed to run in industrial/manufacturing environments. The edge device software consumes data from machines on the shop floor, processes that data, and provides valuable insights or information directly back to the machine, upper-level systems, servers or data lakes.“Because edge computing devices are separate from the actual machine controller, these devices can be managed and updated on the fly without necessarily having to take the machine out of production, which would impact productivity. It allows users to address new use cases without impacting machine performance,” said Gus Gremillion, solutions consultant at Siemens.

The primary connections on an edge device use standard industrial Ethernet to communicate with what Siemens calls the southbound and northbound connections (Figure 1). The southbound connection ties the edge device to the machine through a switch. The northbound connection—also via a switch—allows users to collect data from and interact with the edge computing device.

Running advanced applications on the edge computing device greatly reduces the computing burden on the CNC controller. For example, collision avoidance requires a considerable amount of computational overhead. “When our edge devices are used, it results in very little additional load on the CNC from a computational perspective,” explained Gremillion. “That makes [edge devices] well suited for existing machines on the shop floor.”“An edge device puts less than five percent load on the CPU of the machine,” Gremillion continued. He said that some users are concerned that putting edge devices on their existing machines would affect machine performance or behavior. “We can dispel that [concern], because we don’t put that load on the CNC; all of that computation is handled on the edge device.”

A running CNC produces “look ahead” values of the machine’s near-future path, which the edge device can capture. Look ahead values facilitate collision avoidance by comparing real-time operation to a 3-D model of the machine space—including the fixture and the workpiece—loaded onto the edge device.“The edge device is always checking where the machine will be in 800 milliseconds while running, or even jogging, the machine,” explained Gremillion. “If the machine is going to crash into the workpiece, the fixture or the table, the edge device will trigger an E-stop [emergency stop] at the machine before the machine actually crashes.”“Because collision avoidance is running on the edge device,” continued Gremillion, “we are able to model the machine, the fixture, the part and even the material removed from the part. Users get 3-D simulation and collision avoidance because they are able to see the exact SINUMERIK machine space setup on the fly.”The digital twin can be imported into the edge device. This helps to expedite both modeling and collision avoidance. Typically, machine builders build the digital twin because they know their machines; they have the 3-D CAD files of their machines. They frequently use the digital twin to help commission and engineer their machines. Then they can export the model file to the edge device.

Edge devices can optimize the machining process. For example, Siemens has an application that monitors and provides feedback to the machine that indicates if the process is nominal based on historical data. If an abnormality like a spike in spindle load or hard spot occurs during the machining process (Figure 2), the feedback to the control can trigger a feed hold at the machine, send a message or alarm to an operator, or trigger a subroutine.

Edge devices have a part to play in analyzing data from CNC machines. “In facilities, some machines behave differently, they have different ‘personalities,’” Gremillion said. “By collecting the data [from each machine], users can compare machine to machine to identify variances. Based on that data, they can retune a particular machine or replace mechanical components to improve its performance.”Gremillion said that sometimes a user may report a “problem child” machine. “Collecting data allows users to dive into why a particular machine is a problem child. It allows them to make comparisons to understand their processes better and then optimize them,” he said.

“With edge computing, users can do fleet monitoring of all their machines,” Gremillion explained. “During each run, the edge device collects data that provides a mechanical fingerprint of the machine, which yields parameters like static and dynamic friction values, backlash, drive train stiffness and so on. It allows users to track individual machines over time to understand their mechanical health and perhaps initiate predictive maintenance.”

Jack Smith is senior contributing editor for Automation.com and ISA’s InTech magazine. He spent more than 20 years working in industry—from electrical power generation to instrumentation and control, to automation, and from electronic communications to computers—and has been a trade journalist for more than 25 years.

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