Abstract
The knowledge of the tool wear condition is essential for the dimensional accuracy of the workpiece. Commonly used systems for wear monitoring are usually based on the piezoelectric force measurement. However, in industry these systems are difficult to integrate. A suitable to integrate sensor type is the acoustic emission (AE) sensor. In this paper the main focus will be on the flank wear of drilling tools. For the investigation of the emitted frequency of the flank wear different analogy experiments needs to be realized. With the help of machine learning algorithms the recorded data will be classified.
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