2016
DOI: 10.1016/j.measurement.2015.09.010
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An automatic system based on vibratory analysis for cutting tool wear monitoring

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Cited by 102 publications
(32 citation statements)
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References 18 publications
(18 reference statements)
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“…Many researchers studied tool Condition Monitoring (TCM) in turning operation. Efficient mathematical models were developed to estimate tool wear based on the variation of speed, 4 force signals, 5 sound amplitude, 6 wear coefficient-rate of rise of load-index of diffusion, 7 vibration-cutting forces-acoustic emission, 8 mean power of vibration signatures, 9 chatter vibration, 10 image segmentation and texture 11 and acoustic energy. 12,13 Feed rate is a dominant factor in contributing surface roughness of workpiece, whereas speed and depth of cut are dominant factors in influencing tool wear.…”
Section: Introductionmentioning
confidence: 99%
“…Many researchers studied tool Condition Monitoring (TCM) in turning operation. Efficient mathematical models were developed to estimate tool wear based on the variation of speed, 4 force signals, 5 sound amplitude, 6 wear coefficient-rate of rise of load-index of diffusion, 7 vibration-cutting forces-acoustic emission, 8 mean power of vibration signatures, 9 chatter vibration, 10 image segmentation and texture 11 and acoustic energy. 12,13 Feed rate is a dominant factor in contributing surface roughness of workpiece, whereas speed and depth of cut are dominant factors in influencing tool wear.…”
Section: Introductionmentioning
confidence: 99%
“…A wide variety of wear indicators have also been associated with tool wear [27][28][29], therefore opening perspectives for online Tool Condition Monitoring [11,30]. Current approaches attempt to demonstrate the feasibility of prognosis regarding the Tool Lifetime based on few explanatory variables.…”
Section: Introductionmentioning
confidence: 99%
“…Pislaru et al (8) used wavelet transformation to identify the resonance frequencies in machine tools and the machine status. Rmili et al (9) used an acceleration gauge to obtain wear vibration characteristics. This was done to determine whether average power signal processing analysis can be used to develop an automatic detection system for the analysis of tool wear.…”
Section: Introductionmentioning
confidence: 99%