Online condition monitoring in micro-milling: A force waveform shape analysis approach

Abstract

The cutting force variations have long been studied for machining condition monitoring, due to their sensitivity to the tool conditions. These variations could be the statistics of the amplitude, features extracted from frequency components, wavelet coefficients, etc. Different from most current approaches, this paper introduces a novel online approach that directly correlates tool conditions with the force waveform variations and estimates the tool condition based on their corresponding singularity degree density functions. The singularities of waveforms are measured with Holder exponents (HEs), which are extracted from their wavelet transform modulus maxima. Experimental studies have shown that the HEs reflect the changes of tool conditions. The HE features are found robust to working condition variations, and their probability density functions are discriminate for tool state estimation in micromilling.

Real-time fault diagnosis and fault-tolerant control

“…The proposed algorithms are tested on a wind farm benchmark system. The paper [15], contributed by Zhu et al, develops a novel signal-based method for online condition monitoring on micro-milling. The developed online approach directly correlates tool conditions with the force waveform variations, and estimates the tool condition based on the probability densities of the force waveforms singularity measurement.…”

“…Twelve selected papers pay attention on fault diagnosis methods and applications [10][11][12][13][14][15][16][17][18][19][20][21], and the other eleven papers are concentrated on real-time fault tolerant control and applications [22][23][24][25][26][27][28][29][30][31][32]. We are going to overview the selected papers following fault diagnosis techniques and fault-tolerant control techniques sequentially.…”

“…Therefore, knowledge-based diagnosis method is also called data-driven fault diagnosis methods (e.g., see the survey paper [10,11]). The selected research papers [12][13][14][15][16][17][18][19][20][21] fall into the respective categories readily (e.g., see Table 1). …”

Reliability of Cutting Edge Radius Estimator Based on Chip Production Rate for Micro End Milling

“…by using a sensor such as an AE sensor or a dynamometer. Extensive efforts have been made to classify the amount of tool wear by applying the NN (Neural Network), GA (Genetic Algorithm), and fuzzy algorithm, etc [28][29][30][31]. But, the indirect methods cannot explain the direct physical relationship between the actual tool wear and the output signal.…”

A method for monitoring and diagnosing the circular trajectory error in micromilling

“…Os métodos utilizados na macroescala não são apropriados para a microescala, assim o desenvolvimento de novas estratégias de monitoramento estão sob demanda [8], [9].…”