scholarly journals Towards An Active Learning Approach To Tool Condition Monitoring With Bayesian Deep Learning

2019 ◽  
Author(s):  
Giovanna Martinez-Arellano ◽  
Svetan Ratchev
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Learning Approach ◽  
Tool Condition

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

scholarly journals Deep Learning Algorithms for Tool Condition Monitoring in Milling: A Review

2021 ◽  
Vol 1969 (1) ◽  
pp. 012039
Author(s):  
S S Patil ◽  
S S Pardeshi ◽  
A D Patange ◽  
R Jegadeeshwaran
Keyword(s):  
Deep Learning ◽  
Condition Monitoring ◽  
Learning Algorithms ◽  
Tool Condition Monitoring ◽  
Tool Condition

scholarly journals Carbide Coated Insert Health Monitoring Using Machine Learning Approach through Vibration Analysis

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Navneet Bohara ◽  
Jegadeeshwaran. R ◽  
Sakthivel G
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Vibration Analysis ◽  
Manufacturing Sector ◽  
Health Condition ◽  
Tool Condition Monitoring ◽  
Learning Approach ◽  
Tool Condition ◽  
Machine Learning Approach ◽  
Carbide Inserts

Growth in the manufacturing sector demands extensive production with precision, accuracy, tolerance, and quality. These essential factors need to be ensured for any kind of job. The listed factors stated above depend upon the condition of the tool used for manufacturing. A lot of methods have been proposed for the tool condition monitoring, based on the data acquired through acquisition techniques. Despite the continuous intensive scientific research for more than a decade, the development of tool condition monitoring is an on-going attempt. The proposed method deals with monitoring the health condition of the carbide inserts using vibration analysis. The statistical information extracted from the vibration signals was analyzed using machine learning approach in order to predict the tool condition.

scholarly journals Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing

2019 ◽  
Vol 66 (5) ◽  
pp. 3794-3803 ◽  
Author(s):  
Chengming Shi ◽  
George Panoutsos ◽  
Bo Luo ◽  
Hongqi Liu ◽  
Bin Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
Feature Spaces ◽  
Multiple Feature
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