Efficient machining Research project: Control of coolant pressure leads to tremendous energy savings
Leibniz University Hannover’s Institute of Production Engineering and Machine Tools has researched whether it is possible to save energy when conducting machining work by adjusting coolant lubricant pressure (CL pressure). Open Mind provided financial support for the project and supplied the component geometry and programming for milling and analyses.
During a research project conducted at Leibniz University Hannover’s Institute of Production Engineering and Machine Tools, a method was developed that makes it possible to determine the optimal level of CL pressure concerning the degree of tool wear that occurs. The result: energy savings of up to 33 percent. In the future, methods based on machine learning will make it possible to control CL pressure as needed by using an optimized NC code.
Professor Berend Denkena, Talash Malek (MS), Martin Winkler (qualified engineer), 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”. As the authors were searching for ways to design machining processes to be more sustainable, they began dealing with the topic of high-pressure cooling. High-pressure CL systems can extend the service lives of tools by up to 250 percent, simultaneously they are responsible for up to 25 percent 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 CL 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 CL pressures on tool wear.
Based on their findings, a simulation based on machine learning (ML) 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 CL 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 percent were even a little higher than expected after running the simulation.
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. “For us, the project resulted in two methods to further develop our CAD/CAM systems. Dynamic CL pressure control might be integrated into Hypermill’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.”