5-Axis Milling Machining Learns to Optimise Itself
When milling, the entire energy of the process is often concentrated on a small area of the tool cutting edge, which leads to rapid wear of the tool. If the energy of the milling process were spread over the entire cutting edge of the tool, the service life of the entire milling tool would also be extended. It would also be useful to have information on the degree of tool wear at any time, for example in the CAM system. In this way, ball milling heads would only have to be replaced when they were completely worn out. Together with an industrial consortium, the Fraunhofer Institute for Production Technology IPT is now launching the research project "OptiWear" in order to achieve longer tool life without sacrificing quality. To achieve this, the partners are developing both the technology and simulation software for 5-axis milling further. With the aid of an artificial neural network, the software identifies precisely those sections of the cutting edge for which particularly high tool wear is to be expected. The network therefore learns how to accurately predict tool wear during milling and adjust the tool paths so that wear is spread over a large area of the cutting edge.
Easy to integrate: interfaces to existing CAM systems
The researchers have combined the information from the neural network with a simulation platform for 5-axis milling processes, SimCutPro, which has been developed by Fraunhofer IPT. The software module is integrated into the CAM system to ensure an automated and consistent production planning process. Since SimCutPro already has interfaces to CAM systems, companies can easily integrate the new module into their production processes if they already use the simulation systems.
Due to the reduced wear of the tool cutting head, workpieces can be milled more precisely. Longer tool life not only improves product quality, but also reduces overall manufacturing costs. The new software module is based on a similar module that Fraunhofer IPT has already developed for turning processes. The scientists are therefore convinced that the functional principle can also be applied to other processing technologies. The project is funded by the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF) in the small and medium-sized enterprise innovative funding initiative, which runs from July 2017 to June 2019.