Intelligent Tools for Predictive Process Control

Abstract In complex machining processes which require long processing times, e.g. incorporating hardened or difficult to cut materials, free form surface shapes of the workpieces, such as milling of dies and molds, the avoidance of critical tool and process conditions is essential with respect to the quality of the final part and the efficiency of manufacturing. The progress of tool wear leads to changing cutting conditions, process forces and vibrations which affect the workpiece surface quality. On the other hand, tool vibrations due to varying engagement scenarios and process parameters close to the stability limits provoke an accelerated tool wear behavior. This paper first introduces comprehensive experimental studies regarding the relationship between tool wear progress and tool vibrations during milling. The investigations focus on long and slender ball nose milling cutters which are usually applied in die and mold manufacturing within semi-finishing and finishing process steps. An automated analysis setup was developed which allows recording the tool wear progress and milling behavior with a very high resolution. With respect to process simulations involving the tool wear state, a detailed database is provided by these experiments. Secondly, the paper presents a new approach for a sensor integrated long and slender ball nose milling tool which detects the process vibrations close to the cutting zone. By combination of the experimental data and the sensory tool, predictive process control strategies can be implemented in order to avoid critical wear and vibrations situations.