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Real-time data from machine tools and cutting tools – digital twin of the machining cycle and machine learning


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Real time - Machine tool - EDGE - Machine Learning - GROB - Hufschmied

IIoT Use Case Podcast Ralph Hufschmied (Hufschmied Zerspanungssysteme), Emil Nigl (GROB-WERKE)

In this episode, we jump into the world of machine tools and related cutting tools. For those of you who are not involved in the core business on a daily basis: Milling is a cutting (i.e. where chips are removed during the process) manufacturing process for the production of workpieces with a geometrically defined shape (example automotive – design creates the geometry – then these components are produced, for example, for the body-in-white made of steel or other materials). GROB’s core business is the manufacture of these plants (machining centers) and machine tools – Hufschmied develops and manufactures the tools that are then used in the machine tool. The two tell us more about it in this episode.

Use Case 1 | Less downtime and more transparency in production. In Use Case 1, Emil explains that when a machine breaks down on the weekend in automated operations, hours of downtime are often the result.

An IoT-based monitoring solution alerts the on-call service in a timely manner. Every reduction in downtime pays off immediately for the machine operator. Assume that one hour of machining costs 100 euros and the operating time is 6,000 to 7,000 hours per year per machine. Avoiding 10 percent downtime would save at least 60,000 euros per machine per year.

In addition, by determining machine data, companies have additional capabilities to analyze their processes, for example, to improve cycle and delivery times. A practical example: GROB was able to improve deburring in its machines thanks to the machine data. Originally, deburring was also to take place automatically inside the machine for new developments. However, data analysis showed that manual deburring outside the machine produces significantly better results.

Use Case 2 | Determine tool wear with adaptive methods. In Use Case 2, Ralph talks about tool wear in machining systems. This is usually evaluated by machine operators according to a subjective assessment – by ear. With acoustic sensors and machine learning, this assessment can be digitized and thus objectified.

Processing all data throughout the manufacturing process creates a digital twin not only of the tool or machine, but of the entire machining cycle. This makes it possible to track how the degree of wear of the tool develops minute by minute. The software can intervene at the right moment and prompt operators to change the tool. This digitizes and standardizes the process knowledge of the employees.

Interview partner

Emil Nigl
Digitization & Product Sales Manager

From Bavaria to the world: Since our founding in Munich in 1926, GROB has become a globally operating family company in the development and manufacture of systems and machine tools and continues to grow to this day. Customers include the world’s most renowned automotive manufacturers, their suppliers and renowned companies from a wide range of industries.

Ralph Hufschmied
managing director

Hufschmied Zerspanungssysteme GmbH is a developer and manufacturer of high-quality cutting tools for material processing in manufacturing. By specializing early on the machining of plastics, fiberglass materials and carbon fibers, Hufschmied has achieved a leading position in Europe in the machining of new materials.

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Ing. Madeleine Mickeleit
Digital Business Development | IIoT

Please do not hesitate to contact me if you have any questions.

Questions? Contact Madeleine Mickeleit

Ing. Madeleine Mickeleit

Host & General Manager
IoT Use Case Podcast