Detection of Wear Condition of Micro Milling Cutters Based on Length Fractal Dimension

Abstract:

In this paper, a new method to realize online wear detection of micro-milling cutters based on length fractal dimension is proposed. On the basis of expression derivation of length fractal dimension, experiments are conducted. First, several cutters with different wear condition are chosen as reference samples. Their multi-section vibration signals in time-domain are collected and the clustering domain δ of each sample are obtained based on length fractal dimensions. Then, the vibration signals of tested cutters are monitored and analysed in time domain, thus their length fractal dimension are abstracted. Comparing the length fractal dimension of tested cutters with the clustering domain δ of reference samples, the wear condition of tested cutters are detected. The experimental results show that the length fractal dimension of each tested cutter falls in the clustering domain corresponding to the actual wear condition.

• Periodical: Applied Mechanics and Materials (Volume 577)
• Pages: 697-700
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.577.697
• Online since: July 2014
• Authors: Zhi Qiang Wang*, Gui Ying Zhang, Bin Liu
• Keywords: Length Fractal Dimension, Micro-Milling Cutters, Reference Sample, Wear Detection

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