industrial machinery (Photo: Colourbox)

One step closer to self-healing industrial machines in Industry 4.0

Tuesday 30 May 17

Contact

Dimitrios Papageorgiou
Postdoc
DTU Electrical Engineering
+45 45 25 35 72

Contact

Mogens Blanke
Professor
DTU Electrical Engineering
+45 45 25 35 65

Contact

Dr. Jan Richter
Siemens
+49 911 895-4712
janrichter@siemens.com

Facts

DTU is one of the top foreign universities outside Germany, with which Siemens collaborates.
DTU researchers have developed robust methods for automatic compensation of wear in industrial machine tools. This essential step toward self-healing machinery is being developed in collaboration with Siemens in Germany. 

Present computer control of machine tools (CNC) obtain high precision in conditions when the machine is new. Position control algorithms ensure that any single piece of a production, e.g. 10,000 die-cut metal components, has precisely the same dimensions and finish as the rest.

However, precision is no longer the only necessary feature for an industrial machine tool. Today’s expectations also include high reliability to foresee equipment degradation and prevent unplanned production stops. 

“In the past couple of years our research has led to new methods in this field. On that basis, we have formulated algorithms that make the machine tool itself capable of pointing out when maintenance of various mechanical parts is needed”, says Dimitrios Papageorgiou, PhD student at DTU Electrical Engineering.

“Our research also makes the machine tool able to perceive changes in the workspace and adjust its operating parameters to adapt to new conditions. This means that the production need not come to a total stop for re-tuning its parameters, but can continue to provide the required accuracy even under equipment degradation. Our great vision is to develop the automatic control for the machine tool to a stage where it is both able to compensate for gradual mechanical wear of the machine and also to advise on suitable time for maintenance”, says Dimitrios Papageorgiou.

Operational reliability 
The new research results from DTU will have a big impact on Industry 4.0, the current big step in industrial automatization and digitalization. 

“Ever increasing productivity of production requires that the reliability of the machine tools increases and requires smarter and higher degrees of automation of the production. Operational reliability is crucial, so when we can remove the concern of unexpected production stops, also smaller industries will look to the highly automated solutions that are expected with the digital industry 4.0 disruption, which is already in progress. The perspectives are enormous when it comes to self-healing machine tools, for the time being our new method and algorithms make it possible to ensure precision in normal conditions and when wear would otherwise degrade production quality”, says professor Mogens Blanke, DTU Electrical Engineering. 

The research project is being conducted in collaboration with Siemens, that has contributed with know-how about the machine tools domain and are also sponsoring the research. 

“DTU is one of the global innovating leaders in the field of automation. We are very pleased with the first results of Dimitrios Papageorgiou’s research and have great expectations for the final findings. The research presents an overview of our possibilities to address the technical challenges of fault-tolerant operation and is valuable input to the development of next-generation motion controls firmware”, says Dr. Jan Richter, Siemens.

(Photo: DTU Elektro)

Photo: The experimental setup is a single-axis drive train system with a drive-motor (left), a load motor (right) and a friction adjustment component (middle). The motors are controlled by a Siemens Sinamics S120 drive controller (background) enhanced with custom nonlinear position control algorithms.

Circular test Siemens

Illustration: Circular track test from an industrial machine. Comparison of state-of-the-art position control with a friction-compensating algorithm in normal mode (left) and in wear condition (right).

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