scholarly journals Tool Condition Monitoring in Micro-End Milling using wavelets

2018 ◽  
Vol 346 ◽  
pp. 012045
Author(s):  
N K Dubey ◽  
A Roushan ◽  
U S Rao ◽  
K Sandeep ◽  
K Patra
Keyword(s):  
Condition Monitoring ◽  
End Milling ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
Micro End Milling

Progressive tool condition monitoring of end milling from machined surface images

2016 ◽  
Vol 232 (2) ◽  
pp. 251-266 ◽  
Author(s):  
Samik Dutta ◽  
Surjya K Pal ◽  
Ranjan Sen
Keyword(s):  
Condition Monitoring ◽  
Flank Wear ◽  
End Milling ◽  
Tool Condition Monitoring ◽  
Discrete Wavelet ◽  
Tool Flank Wear ◽  
Tool Condition ◽  
Milled Surface ◽  
Occurrence Matrix ◽  
Surface Images

Indirect tool condition monitoring in end milling is inevitable to produce high-quality finished products due to the complexity of end-milling process. Among the various indirect tool condition monitoring techniques, monitoring based on image processing by analyzing the surface images of final product is gaining high importance due to its non-tactile and flexible nature. The advances in computing facilities, texture analysis techniques and learning machines make these techniques feasible for progressive tool flank wear monitoring. In this article, captured end-milled surface images are analyzed using gray level co-occurrence matrix–based and discrete wavelet transform–based texture analyses to extract features which have a good correlation with progressive tool flank wear. Contrast and second diagonal moment are extracted from gray level co-occurrence matrix and root mean square and energy are extracted from discrete wavelet decomposition of end-milled surface images as features. Finally, these four features are utilized to build support vector machine–based regression models for predicting progressive tool flank wear with 94.8% average correlation between predicted and measured tool flank wear values.

A Hybrid Model for Tool Condition Monitoring and Optimal Tool Management

2004 ◽  
Vol 471-472 ◽  
pp. 865-870 ◽  
Author(s):  
Ying Xue Yao ◽  
Y. Lu ◽  
Zhe Jun Yuan ◽  
J.Y. Hu
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Condition Monitoring ◽  
End Milling ◽  
Tool Condition Monitoring ◽  
Fuzzy Classification ◽  
Validity And Reliability ◽  
Tool Management ◽  
Tool Condition ◽  
Tool Replacement

This paper introduces a new hybrid model for tool condition monitoring (TCM) and optimal tool management (OTM) in end milling operation. The model includes a wavelet fuzzy neural network with acoustic emission (AE) and a model of fuzzy classification of tool wear state with the detected cutting parameters supported by cutting database. The results estimated by cutting conditions and detected signals are fused by artificial neural network (ANN) so as to facilitate effective tool replacement at a proper state or time. The validity and reliability of the method are verified by experimental results.

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