Sessions Focus on Unattended Ops, Machine Learning

How manufacturers can see exactly what happened during unattended shifts.

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Datanomix

At IMTS 2022, held September 12-17, 2022 at McCormick Place in Chicago, experts from Datanomix, a production monitoring specialist, will discuss next-generation production monitoring, including the use of machine learning.

The sessions include:

Unattended operation greatly increases the ability to produce more parts, reducing backlogs even when the lights are out. The issue with unattended operation is that it is impossible to monitor unless you are using an Automated Production Intelligence solution that requires no operator input.

This session will show how manufacturers can see exactly what happened during unattended shifts, providing a wealth of information to troubleshoot issues and improve processes.

“Unattended operation is where we’re going with the technology. There’s just not that many people looking for jobs in manufacturing these days," said Aaron Springer, Vice President, TW Springer. "The more we can automate our processes, the better. We can deliver more productivity with the same number of people, and our focus on technology helps with recruitment."

Craig Michaud, Director of Engineering, ARCH Medical Group said, “Datanomix captures our machine alarms. A lot of times, we run machines unattended. Most of the supervisors and leads have Datanomix up on their computers and they can see when an alarm happens—whether a tool broke, or a pallet changer needs more parts, or anything else that can happen while we are manufacturing. Datanomix allows us to jump on the issue and solve it right away."

With the amount of data produced by CNC machines and advanced toolpath optimization solutions, machine learning, advanced analytics and visualizations can be used to deliver the insights precision manufacturers need to fully understand what’s happening in their factory—from the tooling to the machine and all the way to overall factory performance.

For more information, visit https://datanomix.io/.

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