Experimental investigation of tool wear using vibration signals: An ANN approach

2020 ◽  
Vol 24 ◽  
pp. 1365-1375
Author(s):  
Ritesh Upase ◽  
Nitin Ambhore
Keyword(s):  
Experimental Investigation ◽  
Tool Wear ◽  
Vibration Signals

scholarly journals Deep Learning Approach for Vibration Signals Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3929
Author(s):  
Han-Yun Chen ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Parameters Optimization ◽  
Frequency Spectra ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Time Frequency ◽  
And Performance ◽  
The One ◽  
Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

Experimental Investigation of Tool Wear in Machining of SiC Based Al-MMC

2021 ◽  
pp. 1-20
Author(s):  
Arvind M Sankhla ◽  
Kaushik M Patel ◽  
Mayur A Makhesana ◽  
Kuldeep K Saxena ◽  
Nakul Gupta
Keyword(s):  
Experimental Investigation ◽  
Tool Wear
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