Feed axes of CNC-grinding machine tools belong to the most mechanical stressed components of machine tools due to high process forces and the rough manufacturing environment. The resulting wear and tear depends strongly on users’ product range and the manner of machine operation. To counteract a functional deficiency of these central machine units preventive maintenance activities have to be done. Manual inspection of feed axes is complex and time-consuming. An aggravating fact is that the deterioration normally progresses very slowly and its characteristic depends on the physical location at the axis. Existing approaches for automated estimation of feed axis’ “health status” do not take this factor into account.
In this paper a procedure that closes this gap is presented. During the execution of a simple test routine drive current, axis position, and feed rate are recorded. With the help of additional machine data characteristic values are computed directly at the computer of the human machine interface (HMI). The results are then transferred to and stored on a database server at the machine manufacturer. This approach enables the service technicians to trace the progression of the axis’ “health status” over a long time. Using this approach, it will be possible to detect trends within the characteristic values at a very early point in time.
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I expect that the monitoring of the deterioration of CNC-grinding machine axes should be a topic of important interest for machine tool manufacturers. Are you aware of any developments in that direction that use artificial intelligence techniques (such as neural networks or inductive learning for example) for predicting the feed axes future health status?
Emmanuel