Tool Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models

2014 ◽  
Vol 63 (3) ◽  
pp. 667-679 ◽  
Author(s):  
Cesar H. R. Martins ◽  
Paulo R. Aguiar ◽  
Arminio Frech ◽  
Eduardo Carlos Bianchi
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Condition Monitoring ◽  
Single Point ◽  
Tool Condition Monitoring ◽  
Tool Condition

scholarly journals Neural Networks Tool Condition Monitoring in Single-point Dressing Operations

Procedia CIRP ◽  
2016 ◽  
Vol 41 ◽  
pp. 431-436 ◽  
Author(s):  
Doriana M. D’Addona ◽  
Davide Matarazzo ◽  
Paulo R. de Aguiar ◽  
Eduardo C. Bianchi ◽  
Cesar H.R. Martins
Keyword(s):  
Neural Networks ◽  
Condition Monitoring ◽  
Single Point ◽  
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

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.

Feature Selection for Tool Condition Monitoring in Turning Processes

2006 ◽  
Vol 526 ◽  
pp. 97-102
Author(s):  
D. Rodríguez Salgado ◽  
I. Cambero ◽  
F.J. Alonso
Keyword(s):  
Neural Networks ◽  
Condition Monitoring ◽  
Industrial Applications ◽  
Tool Condition Monitoring ◽  
Turning Operations ◽  
Additional Signal ◽  
Motor Current ◽  
Tool Condition ◽  
Selection For ◽  
Condition Monitoring System

The aim of the present work is to develop a tool condition monitoring system (TCMS) using sensor fusion and artificial neural networks. Particular attention is paid to the manner in which the most correlated features with tool wear are selected. Experimental results show that the proposed system can reliably detect tool condition in turning operations and is viable for industrial applications. This study leads to the conclusion that the vibration in the feed direction and the motor current signals are best suited for the development of a TCMS than the sound signal, which should be used as an additional signal.

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