Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process

2018 ◽  
Vol 98 (9-12) ◽  
pp. 3217-3227 ◽  
Author(s):  
Fatemeh Aghazadeh ◽  
Antoine Tahan ◽  
Marc Thomas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Condition Monitoring ◽  
Milling Process ◽  
Tool Condition Monitoring ◽  
Spectral Subtraction ◽  
Tool Condition

Tool condition monitoring in the milling process with vegetable based cutting fluids using vibration signatures

2019 ◽  
Vol 61 (3) ◽  
pp. 282-288 ◽  
Author(s):  
Thangamuthu Mohanraj ◽  
Subramaniam Shankar ◽  
Rathanasamy Rajasekar ◽  
Ramasamy Deivasigamani ◽  
Pallakkattur Muthusamy Arunkumar
Keyword(s):  
Condition Monitoring ◽  
Milling Process ◽  
Tool Condition Monitoring ◽  
Cutting Fluids ◽  
Tool Condition ◽  
Vibration Signatures

Feature Selection for Tool Condition Monitoring in Turning Processes

2006 ◽  
Vol 526 ◽  
pp. 97-102
Author(s):  
D. Rodríguez Salgado ◽  
I. Cambero ◽  
F.J. Alonso
Keyword(s):  
Neural Networks ◽  
Condition Monitoring ◽  
Industrial Applications ◽  
Tool Condition Monitoring ◽  
Turning Operations ◽  
Additional Signal ◽  
Motor Current ◽  
Tool Condition ◽  
Selection For ◽  
Condition Monitoring System

The aim of the present work is to develop a tool condition monitoring system (TCMS) using sensor fusion and artificial neural networks. Particular attention is paid to the manner in which the most correlated features with tool wear are selected. Experimental results show that the proposed system can reliably detect tool condition in turning operations and is viable for industrial applications. This study leads to the conclusion that the vibration in the feed direction and the motor current signals are best suited for the development of a TCMS than the sound signal, which should be used as an additional signal.

Sign in / Sign up

Export Citation Format

Share Document