Face milling tool condition monitoring using sound signal

2017 ◽  
Vol 8 (S2) ◽  
pp. 1643-1653 ◽  
Author(s):  
C. K. Madhusudana ◽  
Hemantha Kumar ◽  
S. Narendranath
Keyword(s):  
Condition Monitoring ◽  
Face Milling ◽  
Tool Condition Monitoring ◽  
Sound Signal ◽  
Tool Condition ◽  
Milling Tool

On-line tool condition monitoring in face milling using current and power signals

2008 ◽  
Vol 46 (4) ◽  
pp. 1187-1201 ◽  
Author(s):  
P. Bhattacharyya ◽  
D. Sengupta ◽  
S. Mukhopadhyay ◽  
A. B. Chattopadhyay
Keyword(s):  
Condition Monitoring ◽  
Face Milling ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
On Line

Cutting force denoising in micro-milling tool condition monitoring

2008 ◽  
Vol 46 (16) ◽  
pp. 4391-4408 ◽  
Author(s):  
K. Zhu ◽  
G. S. Hong ◽  
Y. S. Wong ◽  
W. Wang
Keyword(s):  
Cutting Force ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Micro Milling ◽  
Tool Condition ◽  
Milling Tool

scholarly journals Tool Wear Inspection and Classification by Artificial Neural Network Based on Vision System and Cutting Force Signal.

2021 ◽  
Author(s):  
Ahmed Abdeltawab ◽  
Xi Zhang ◽  
Longjia Zhang ◽  
Chuanjun Li
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Cutting Force ◽  
Condition Monitoring ◽  
Time Domain ◽  
Vision System ◽  
Tool Condition Monitoring ◽  
Ann Model ◽  
Tool Condition ◽  
Milling Tool

Abstract The current work focuses on the cutting tool condition monitoring of end milling based on direct and indirect approach in machining AISI H13 alloy steel. Indirect process parameters such as cutting force signals are measured as responses using force sensor. In order to successfully inspect the milling tool life online for direct approaches, an automated machine vision system was used for tool condition monitoring. The image processing algorithms are developed to extract different features of rotating milling tool. A detection and compensation system for tool wear based on machine vision is designed. Feedforward Back-Propagation Neural Network applied for tool wear classification developed based on many force features. Ten time-domain features extracted and the sensitive features is determined based on Pearson’s correlation coefficient. I-kaz method which integrates between kurtosis and standard deviation is added as input feature with the ten time-domain features. A strong correlation is established between most of time-domain features and tool wear with high correlation coefficient. ANN model applied for classification tool states as normal and abnormal. Experiments with vision system have shown that area of wear at bottom and flank is suitable to inspect in-process. Actual measurements of the tool wear stages are possible to identify the abnormality in cutting using vision system. ANN model showed superior results for tool states classification. The mean squared Error (MSE) for classification model was less than 6.7E-09 and R equal to 1. The model can be used to construct fault estimation mode for tool wear online classification and inspection.

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