2017
DOI: 10.1115/1.4037553
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A Novel Generalized Approach for Real-Time Tool Condition Monitoring

Abstract: In high-speed cutting processes, late replacement of defective tools may lead to machine breakdowns and badly affect the product quality, which subsequently lead to scrap parts and high process costs. Accurate tool condition detection is essential to achieve high level of competitiveness via increasing process productivity and standardizing the quality of the produced parts. Therefore, tool condition monitoring (TCM) systems have been widely emphasized as an important principle to achieve these industrial dema… Show more

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Cited by 19 publications
(12 citation statements)
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“…Motor current sensors are considered to be more suitable for industrial manufacturing settings than cutting force sensors due to their relatively simple application and lack of installation effects on machining operations [39], [40]. Ghosh [41]. However, current sensors are less commonly employed for TCM in milling processes than the above three types of sensors.…”
Section: ) Motor Currentmentioning
confidence: 99%
“…Motor current sensors are considered to be more suitable for industrial manufacturing settings than cutting force sensors due to their relatively simple application and lack of installation effects on machining operations [39], [40]. Ghosh [41]. However, current sensors are less commonly employed for TCM in milling processes than the above three types of sensors.…”
Section: ) Motor Currentmentioning
confidence: 99%
“…One of the main drawback of the available TCM systems is the extensive experimental work needed for system learning in order to build a reliable database of features corresponding to different cutting conditions, tool geometries and tool paths. This effort has been minimized by the pre-processing approach introduced in [21]. This approach significantly reduces the required experimental system learning in intermittent cutting operations by masking the effect of the cutting conditions and magnifying the effect of the tool condition.…”
Section: Tool Post-failure Detectionmentioning
confidence: 99%
“…These extracted features can be used in a pattern recognition technique to classify the tool condition. In [21], more than 40 extracted features have been ranked according to their sensitivity to the tool condition using the results of an N-way ANOVA test. The highest ranked features of the spindle motor current signal in a milling process were the signal mean (M), maximum peak of periodogram (P p ), root mean square (rms), peak to root mean square ratio (P2rms), mean frequency (F mean ), band power (BP), median frequency (F med ), maximum peak of welch power spectral energy (P w ), kurtosis (K), minimum (min) and variance (Var).…”
Section: Tool Post-failure Detectionmentioning
confidence: 99%
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“…Since the indirect methods can implement Sensors 2020, 20, 6113 2 of 20 online monitoring, it has been widely adopted. The most commonly used indirect monitoring signals include cutting force signal [2][3][4], vibration signal [5][6][7], acoustic emission signal [8,9], machined surface image [10,11], and current signal [12][13][14][15][16][17].…”
Section: Introductionmentioning
confidence: 99%