Acoustic emission for tool condition monitoring in metal cutting

Wear ◽  
1997 ◽  
Vol 212 (1) ◽  
pp. 78-84 ◽  
Author(s):  
H.V. Ravindra ◽  
Y.G. Srinivasa ◽  
R. Krishnamurthy
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Metal Cutting ◽  
Tool Condition Monitoring ◽  
Tool Condition

scholarly journals Acoustic Emission Studies on Carbon/Phenolic Ablative Composites using PCD and PCBN Tools

1999 ◽  
Vol 8 (3) ◽  
pp. 096369359900800 ◽  
Author(s):  
P. S. Sreejith ◽  
R. Krishnamurthy
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Cutting Tool ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
The Status ◽  
Emission Studies ◽  
Pcbn Tools ◽  
Cutting Speeds ◽  
Phenolic Composite

During manufacturing, the performance of a cutting tool is largely dependent on the conditions prevailing over the tool-work interface. This is mostly dependent on the status of the cutting tool and work material. Acoustic emission studies have been performed on carbon/phenolic composite using PCD and PCBN tools for tool condition monitoring. The studies have enabled to understand the tool behaviour at different cutting speeds.

Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling

2014 ◽  
Vol 255 ◽  
pp. 121-134 ◽  
Author(s):  
Qun Ren ◽  
Marek Balazinski ◽  
Luc Baron ◽  
Krzysztof Jemielniak ◽  
Ruxandra Botez ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
Condition Monitoring System

Tool Condition Monitoring Based on an Adaptive Neurofuzzy Architecture

2004 ◽  
Vol 471-472 ◽  
pp. 196-200 ◽  
Author(s):  
P. Fu ◽  
A.D. Hope ◽  
G.A. King
Keyword(s):  
Condition Monitoring ◽  
Metal Cutting ◽  
Production Efficiency ◽  
Process Condition ◽  
Recognition Algorithm ◽  
Tool Condition Monitoring ◽  
Machining Process ◽  
Machining Accuracy ◽  
Knowledge Based ◽  
Tool Condition

Metal cutting operations constitute a large percentage of the manufacturing activity. One of the most important objectives of metal cutting research is to develop techniques that enable optimal utilization of machine tools, improved production efficiency, high machining accuracy and reduced machine downtime and tooling costs. Machining process condition monitoring is certainly the important monitoring requirement of unintended machining operations. A multi-purpose intelligent tool condition monitoring technique for metal cutting process will be introduced in this paper. The knowledge based intelligent pattern recognition algorithm is mainly composed of a fuzzy feature filter and algebraic neurofuzzy networks. It can carry out the fusion of multi-sensor information to enable the proposed intelligent architecture to recognize the tool condition successfully.

Acoustic Emission Condition Monitoring of the Coroning Process

2009 ◽  
Author(s):  
Juil Yum ◽  
Amir Kamouneh ◽  
Wencai Wang ◽  
Elijah Kannatey-Asibu
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Visual Observation ◽  
Tool Condition Monitoring ◽  
Selection Methods ◽  
Tool Failure ◽  
Minimum Error ◽  
Tool Condition ◽  
Frequency Components ◽  
Ae Signal

Acoustic emission (AE) is introduced for tool condition monitoring during the coroning process. The frequency components of the AE signal were used as features for classification. Two different feature selection methods were investigated, namely visual observation and the class mean scatter criterion. The minimum error rate Bayesian rule was used to distinguish between two extreme tool conditions. Although the features from visual observation could result in 100% classification, features based on the class mean scatter criterion showed excellent monitoring capability of tool failure when fewer features were used.

Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microturning of Titanium Alloy

2014 ◽  
Vol 984-985 ◽  
pp. 31-36 ◽  
Author(s):  
A. Gopikrishnan ◽  
A.K. Nizamudheen ◽  
M. Kanthababu
Keyword(s):  
Acoustic Emission ◽  
Titanium Alloy ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Dominant Frequency ◽  
Tool Condition Monitoring ◽  
Discrete Wavelet ◽  
Nose Radius ◽  
Emission Signal ◽  
Tool Condition

In this work, an online acoustic emission (AE) monitoring system is developed, to investigate the effect of tool wear during the microturning of titanium alloy with a tungsten carbide insert of nose radius 0.1 mm. The AE signal parameters were analyzed in time domain, frequency domain and discrete wavelet transformation (DWT) techniques to correlate with the tool wear status. The root mean square (AERMS) and specific AE energies are also computed for the decomposed AE signals, using the DWT. The results demonstrated that dominant frequency and DWT techniques are found to be most suitable for online tool condition monitoring, using AE sensors in the microturning of titanium alloy.

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