Controlling Coolant Pressure Brings Enormous Energy Savings

The Institute of Production Engineering and Machine Tools at Leibniz University in Hannover has researched whether it is possible to save energy when conducting machining work by adjusting coolant pressure. OPEN MIND provided financial support for the project and supplied the component geometry and programming for milling and analyses. During the project, a method was developed to determine the optimal level of coolant pressure concerning the degree of tool wear that occurs. The result: energy savings of up to 33%. In the future, methods based on machine learning will make it possible to control coolant pressure as needed by using an optimised NC code.

Professor Berend Denkena, Talash Malek (MS), Martin Winkler and Marcel Wichmann (MS), introduced their project in the April 2022 issue of The Association of German Engineers’ VDI-Z magazine under the title “Energy Efficient Process Planning”. The authors were searching for ways to design machining processes to be more sustainable, they began dealing with the topic of high-pressure coolant. High-pressure coolant systems can extend the service lives of tools by up to 250%, simultaneously they are responsible for up to 25% of a machine tool’s energy consumption.

Research on tool wear performance

Tools wear differently depending on which machining strategy is used and what the material removal rate is when milling. At a certain point, raising the coolant pressure no longer increases the service life. That also means that in many situations, an unnecessary amount of coolant is introduced. The researchers carried out the machining test developed by OPEN MIND, which involves roughing several pockets in a Ti-6Al-4V block using a VHM end mill. The research investigated the effects of different machining strategies and coolant pressures on tool wear.

Machine learning

Based on their findings, a simulation based on machine learning was developed that was able to use the process data to predict the amount of tool wear. The machine learning model was used to simulate the machining process with varying levels of coolant pressure. Validating their findings using real components, the tests confirmed that the same surface qualities and tool services lives were able to be achieved by using a reduced level of pressure according to the machining application. The energy savings of up to 33% were even a little higher than expected after running the simulation.

A groundbreaking advancement for the industry

“We’re happy that we were able to contribute to this project, and we’re impressed with the result,” says Dr. Josef Koch, CTO of OPEN MIND Technologies AG. “For us, the project resulted in two methods to further develop our CAD/CAM systems. Dynamic coolant pressure control might be integrated intohyperMILL’s NC code generator in the future.”

“We’re also investigating whether predictive models can be used to determine how much tool wear will occur with a given tool. That would enable users to compare the differences in how much tool wear occurs when using different milling strategies. That would be an interesting advancement for our VIRTUAL Machining Center.”