Machine cutting tool condition monitoring method of aeroplane composite material processing based on artificial neural networks

2014 ◽  
Author(s):  
Xiaolin Liu ◽  
Chunyu Mu ◽  
Jianting Wang ◽  
Kun Yuan
Keyword(s):  
Neural Networks ◽  
Material Processing ◽  
Composite Material ◽  
Artificial Neural Networks ◽  
Condition Monitoring ◽  
Cutting Tool ◽  
Tool Condition Monitoring ◽  
Monitoring Method ◽  
Tool Condition ◽  
Artificial Neural

Machine Cutting Tool Condition Monitoring Method of Aeroplane Composite Material Processing

2013 ◽  
Vol 411-414 ◽  
pp. 1610-1615
Author(s):  
Xiao Lin Liu ◽  
Chun Yu Mu ◽  
Jian Ting Wang ◽  
Kun Yuan
Keyword(s):  
Neural Networks ◽  
Material Processing ◽  
Computer Vision ◽  
Composite Material ◽  
Cutting Tool ◽  
Cellular Neural Networks ◽  
Tool Condition Monitoring ◽  
Median Filtering ◽  
Monitoring Method ◽  
Tool Condition

Because that the wear of the machine cutting tool of the aeroplane composite material processing is difficult to be monitored, in this paper a monitoring method based on the cellular neural networks by the computer vision monitoring is proposed. The method uses the median filtering technology and the cellular neural networks for the image denoising and the edge detection. Then the degree of the tool wear is judged by calculating the wear characteristic value of the cutting tool. The experimental results show that the system is rational and effective.

Tool condition monitoring in drilling using artificial neural networks

2000 ◽  
Author(s):  
A. D. Baone ◽  
Kumar Eswaran ◽  
G. Venkata Rao ◽  
M. Komaraiah
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
Artificial Neural

Tool Wear Monitoring Using Microphone Signals and Recurrence Quantification Analysis when Drilling Composites

2013 ◽  
Vol 711 ◽  
pp. 239-244 ◽  
Author(s):  
Eshetu D. Eneyew ◽  
M. Ramulu
Keyword(s):  
Condition Monitoring ◽  
Cutting Tool ◽  
Flank Wear ◽  
Recurrence Quantification Analysis ◽  
Tool Condition Monitoring ◽  
Monitoring Method ◽  
Online Tool ◽  
Tool Condition ◽  
Quantification Analysis

The quality of the hole produced during the drilling of composite materials is one of the controlling factors for the resulting joint strength and integrity of the structural component. Quality of the hole depends on the condition of the cutting tool. Continuous cutting tool condition monitoring method is vital to accomplish the desired hole quality. To address this concern, an online tool condition monitoring technique using a simple audio microphone as a sensor is developed and Recurrence Quantification Analysis (RQA) methodology was used as a signal analysis tool to predict the tool condition in terms of flank wear. A series of experimental drilling operation was carried out on uni-directional carbon fiber reinforced plastic (CFRP) composite. It was found that the amplitude of the microphone signal decreases with the increase of the tool flank wear. In addition, from the selected eight RQA output variables, six of them show an increasing trend with the increase of the measured flank wear, whereas, two of them show a decreasing trend with the increase of tool wear. The same trend has been observed in both set of experiments. These results demonstrate that, this novel approach is an effective and economical online tool condition monitoring method.

scholarly journals Development of family of artificial neural networks for the prediction of cutting tool condition

2020 ◽  
Vol 15 (2) ◽  
pp. 164-178
Author(s):  
O. Spaic ◽  
Z. Krivokapic ◽  
D. Kramar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Tool ◽  
Tool Condition ◽  
Artificial Neural

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.

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