Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

2012 ◽  
Vol 67 (5-8) ◽  
pp. 1947-1954 ◽  
Author(s):  
Satyanarayana Kosaraju ◽  
Venu Gopal Anne ◽  
Bangaru Babu Popuri
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Online Tool ◽  
Tool Condition ◽  
Grade 5 ◽  
Emission Modeling ◽  
Titanium Grade

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.

scholarly journals Metacognitive learning approach for online tool condition monitoring

2017 ◽  
Vol 30 (4) ◽  
pp. 1717-1737 ◽  
Author(s):  
Mahardhika Pratama ◽  
Eric Dimla ◽  
Chow Yin Lai ◽  
Edwin Lughofer
Keyword(s):  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Learning Approach ◽  
Online Tool ◽  
Tool Condition ◽  
Metacognitive Learning

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.

scholarly journals Online Tool Condition Monitoring Based on Parsimonious Ensemble+

2020 ◽  
Vol 50 (2) ◽  
pp. 664-677 ◽  
Author(s):  
Mahardhika Pratama ◽  
Eric Dimla ◽  
Tegoeh Tjahjowidodo ◽  
Witold Pedrycz ◽  
Edwin Lughofer
Keyword(s):  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Online Tool ◽  
Tool Condition
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