Skip to content
From Cutting Tool Engineering

A Machine Tool for Industry 4.0

Are visitors to a bearing manufacturing facility in Hoechstadt, Germany, getting an early glimpse of what Industry 4.0 looks like? That's the hope of Schaeffler Group, which owns the facility. The plant houses a machine tool meant to show how digitalization works—not in a laboratory, but in an actual manufacturing environment.

January 15, 2016By William Leventon

Are visitors to a bearing manufacturing facility in Hoechstadt, Germany, getting an early glimpse of what Industry 4.0 looks like?

That’s the hope of Schaeffler Group, which owns the facility. The plant houses a machine tool meant to show how digitalization works—not in a laboratory, but in an actual manufacturing environment. The machine is equipped with more than 60 extra sensors that bring data from the real world into the digital realm, where it can be used to visualize, analyze and predict machine conditions, explained Joerg-Oliver Hestermann, Schaeffler’s strategic applications engineer for production machinery.

A Machine Tool for Industry 4.0

A Machine Tool for Industry 4.0
Machine Tool 4.0 data is evaluated both locally in the machine and in the cloud. Image courtesy Schaeffler Group.

A Machine Tool for Industry 4.0

The machine was developed in conjunction with Deckel Maho Pfronten (Germany) GmbH, a subsidiary of DMG Mori, as part of a joint effort known as Machine Tool 4.0. Based on DMG Mori’s fourth-generation DMC 80 FD duoBLOCK machining center, the milling machine is used in volume production of rolling bearings.

The additional sensors collect huge amounts of data about the machine’s condition. The sensors—which measure vibrations, forces, temperatures and pressures—are mainly integrated into components that Schaeffler supplies, such as bearings and linear guidance systems. These are located where components move in the machine.

Data generated at these key points is saved and processed locally in the machine, as well as in the cloud, where evaluations that require more computing power are made using specially developed algorithms. These evaluations help users gauge the probability of various process and production faults. When necessary, measures can be taken to prevent such faults, reducing unplanned machine downtime.

Finish task to continue reading

Review the print ads from this magazine to continue

This quick advertiser review unlocks the rest of the article and keeps the full-screen reader focused on the ads instead of the page chrome.

MFGAxis MFGAxis Discussion Be part of the shop-floor conversation Like, save, or comment on this CTE story.
Be the first to engage.

MFGAxis Discussion

Be the first to engage.
Scroll for the next article