Tool Failure Monitoring in Turning by Pattern Recognition Analysis of AE Signals

Emel, Erdal; Kannatey‐Asibu, Elijah

doi:10.1115/1.3187862

Cited by 93 publications

(24 citation statements)

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“…Emel and Kannatey-Asibu [22,23] applied a pattern classification methodology for sensing tool failure in turning. They developed a linear discriminate functionbased technique for detection of tool wear, tool fracture, or chip disturbance events by using the spectra of AE signals.…”

Section: Pattern Classification Methodologymentioning

confidence: 99%

A brief review: acoustic emission method for tool wear monitoring during turning

2002

International Journal of Machine Tools and Manufacture

424

167

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Section: Pattern Classification Methodologymentioning

confidence: 99%

A brief review: acoustic emission method for tool wear monitoring during turning

2002

International Journal of Machine Tools and Manufacture

424

167

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“…This study defines the between-class scatter R c and withinclass scatter R as follows [33]. First, the feature mean for each class, Y i ðkÞ is obtained from individual features Y ij (k)…”

Section: Signal Transformation and Feature Extractionmentioning

confidence: 99%

Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling

Hsieh

Chiou

2011

Int J Adv Manuf Technol

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This study develops a micro-tool condition monitoring system consisting of accelerometers on the spindle, a data acquisition and signal transformation module, and a backpropagation neural network. This study also discusses the effect of the sensor installations, selected features, and the bandwidth size of the features on the classification rate. To collect the vibration signals necessary for training the system model and verifying the system, an experiment was implemented on a micro-milling research platform along with a 700 μm diameter micro-end mill and a SK2 workpiece. A three-axis accelerometer was installed on a sensor plate attached to the spindle housing to collect vibration signals in three directions during cutting. The frequency domain features representing changes in tool wear were selected based on the class mean scatter criteria after transforming signals from the time domain to the frequency domain by fast Fourier transform. Using the appropriate vibration features, this study develops and tests a backpropagation neural network classifier. Results show that proper feature extraction for classification provides a better solution than applying all spectral features into the classifier. Selecting five features for classification provides a better classification rate than the case with four and three features along with the 30 Hz bandwidth size of the spectral feature. Moreover, combining the signals for tool condition from both direction signals provides a better classification rate than determining the tool condition using a one-direction single sensor

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“…It can be seen that, in order to produce a product that solves grinding problems such as burn and chatter vibration, the troubles must be detected using credible methods. The AE generated during a grinding process has been proven to contain information strongly related to the condition changes in the grinding zone [7,8,9,10].…”

Section: Bad Effect Of Chatter Vibration and Burning Phenomenamentioning

confidence: 99%

Intelligent diagnostic technique of machining state for grinding

Kwak

2004

The International Journal of Advanced Manufacturing Technology

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Successful grinding of a final product depends upon a large number of parameters that affect the grinding result and are strongly interlinked. It is, therefore, difficult to detect directly the generation of grinding faults such as chatter vibration and burning. In this paper, to achieve the development of an intelligent diagnostic technique for chatter vibration and burning phenomena on grinding process, acoustic emission signals were processed and signal parameters of the acoustic emission were also determined. In addition, a neural network was used as a diagnostic technique of the grinding state. A momentum coefficient, learning rate, and structure of the hidden layer were determined during the iterative learning process and the performance of the diagnostic technique was evaluated.

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Tool Failure Monitoring in Turning by Pattern Recognition Analysis of AE Signals

Cited by 93 publications

References 0 publications

A brief review: acoustic emission method for tool wear monitoring during turning

A brief review: acoustic emission method for tool wear monitoring during turning

Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling

Intelligent diagnostic technique of machining state for grinding

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