Tool condition monitoring in micromilling based on hierarchical integration of signal measures

This paper presents a tool wear monitoring strategy in micromilling of cold-work tool steel, 50 HRC with a ball endmill d = 0.8 mm. The strategy is based on a large number of AE and cutting forces signal features and a hierarchical algorithm. In the first stage of the algorithm, the tool wear is estimated separately for each signal feature. In the second stage, the results obtained in the first stage, are integrated into the final tool condition evaluation. The obtained results prove that the proposed algorithm enables reliable evaluation of tool wear in spite of strongly disturbed signal features.