A study of tool wear using statistical analysis of metal-cutting acoustic emission

Wear ◽  
1982 ◽  
Vol 76 (2) ◽  
pp. 247-261 ◽  
Author(s):  
E. Kannatey-Asibu ◽  
D.A. Dornfeld
Keyword(s):  
Acoustic Emission ◽  
Statistical Analysis ◽  
Tool Wear ◽  
Metal Cutting

On-Line Monitoring the Hard Turning Using Distribution Parameters of Acoustic Emission Signal

2015 ◽  
Vol 787 ◽  
pp. 907-911
Author(s):  
J. Bhaskaran
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Hard Turning ◽  
Metal Cutting ◽  
Cubic Boron Nitride ◽  
Emission Signal ◽  
Distribution Parameters ◽  
On Line ◽  
Ae Signal ◽  
Wear Monitoring

In hard turning, tool wear of cutting tool crossing the limit is highly undesirable because it adversely affects the surface finish. Hence continuous, online tool wear monitoring during the process is essential. The analysis of Acoustic Emission (AE) signal generated during conventional machining has been studied by many investigators for understanding the process of metal cutting and tool wear phenomena. In this experimental study on hard turning, the skew and kurtosis parameters of root mean square values of AE signal (AERMS) have been used for online monitoring of a Cubic Boron Nitride (CBN) tool wear.

Integrated System for Monitoring the Tool State Using Temperature Measuring by Natural Thermocouple Method

2014 ◽  
Vol 1036 ◽  
pp. 274-279 ◽  
Author(s):  
Marinela Inţă ◽  
Achim Muntean
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Manufacturing Systems ◽  
Metal Cutting ◽  
Block Diagram ◽  
Cutting Temperature ◽  
Integrated System ◽  
Work Piece ◽  
Tool Temperature ◽  
On Line

The intensive developments of intelligent manufacturing systems in the last decades open the large possibilities of more accurate monitoring of the metal cutting process. One of the most important factors of the process is the tool state given by the rate of the tool wear, which is the result of a lot of influences of almost all cutting parameters. The modern tool monitoring systems relieved that the accuracy of the results increases when using a combination of surveyed signals such as: vibrations, power consumption, acoustic emission, forces or tool temperature. Combining the output signals in a monitoring function using the neural network method gives the best results when using on-line monitoring. Considering the tool temperature as an important factor in the tool wear process and adding it to the acoustic emission and force measuring the accuracy of the results seems to improve significantly. The present paper describes an integrated monitoring system with integration of the cutting temperature, the calibration device for work piece-tool thermocouple, and the block diagram for on-line survey measuring using LabView platform.

Wavelet analysis of acoustic emission signals in boring

2000 ◽  
Vol 214 (5) ◽  
pp. 421-424 ◽  
Author(s):  
X Li ◽  
J Wu
Keyword(s):  
Acoustic Emission ◽  
Wavelet Analysis ◽  
Tool Wear ◽  
Metal Cutting ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Experimental Results ◽  
Cutting Processes ◽  
Ae Signals

Using acoustic emission (AE) signals to monitor tool wear states is one of the most effective methods used in metal cutting processes. As AE signals contain information on cutting processes, the problem of how to extract the features related to tool wear states from these signals needs to be solved. In this paper, a wavelet packet transform (WPT) method is used to decompose continuous AE signals during cutting; then the features related to tool wear states are extracted from decomposed AE signals. Experimental results verified the feasibility of using the WPT method to extract features related to tool wear states in boring.

Tool wear monitoring using acoustic emission in the existence of chatter

1990 ◽  
Vol 28 (10) ◽  
pp. 1861-1869 ◽  
Author(s):  
YOICHI MATSUMOTO ◽  
NGUN TJIANG ◽  
BOBBIE FOOTE ◽  
YNGVE NAERHEIMH
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Tool Wear Monitoring ◽  
Wear Monitoring

Tool Wear Detection Using Time Series Analysis of Acoustic Emission

1989 ◽  
Vol 111 (3) ◽  
pp. 199-205 ◽  
Author(s):  
S. Y. Liang ◽  
D. A. Dornfeld
Keyword(s):  
Time Series ◽  
Acoustic Emission ◽  
Tool Wear ◽  
Time Series Analysis ◽  
Cutting Tool ◽  
Shear Zones ◽  
Parameter Vector ◽  
Model Parameter ◽  
Cutting Tool Wear ◽  
Series Analysis

This paper discusses the monitoring of cutting tool wear based on time series analysis of acoustic emission signals. In cutting operations, acoustic emission provides useful information concerning the tool wear condition because of the fundamental differences between its source mechanisms in the rubbing friction on the wear land and the dislocation action in the shear zones. In this study, a signal processing scheme is developed which uses an autoregressive time-series to model the acoustic emission generated during cutting. The modeling scheme is implemented with a stochastic gradient algorithm to update the model parameters adoptively and is thus a suitable candidate for in-process sensing applications. This technique encodes the acoustic emission signal features into a time varying model parameter vector. Experiments indicate that the parameter vector ignores the change of cutting parameters, but shows a strong sensitivity to the progress of cutting tool wear. This result suggests that tool wear detection can be achieved by monitoring the evolution of the model parameter vector during machining processes.

In-Process Monitoring and Estimation of Tool Wear on CNC Turning by Applying Multi-Sensor with Back Propagation Technique

2011 ◽  
Vol 291-294 ◽  
pp. 3036-3043 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Channarong Rungruang
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Monitoring System ◽  
Process Monitoring ◽  
Back Propagation ◽  
Force Sensor ◽  
Accelerometer Sensor ◽  
Cnc Turning ◽  
Propagation Technique ◽  
Cnc Turning Process

The aim of this research is to propose and develop the in-process monitoring system of the tool wear for the carbon steel (S45C) in CNC turning process by utilizing the multi-sensor which are the force sensor, the sound sensor, the accelerometer sensor and the acoustic emission sensor. The progress of the tool wear results in the larger cutting force, the higher amplitude of the acceleration signal, and the higher power spectrum densities of sound and acoustic emission signals. Hence, their signals have been integrated via the neural network with the back propagation technique to monitor the tool wear. The experimentally obtained results showed that the in-process monitoring system proposed and developed in this research can be effectively used to estimate the tool wear level with the higher accuracy and reliability.

In-process tool wear and chip-form monitoring in face milling operation using acoustic emission.

1994 ◽  
Vol 44 (3-4) ◽  
pp. 207-214 ◽  
Author(s):  
Sunilkumar Kakade ◽  
L. Vijayaraghavan ◽  
R. Krishnamurthy
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Face Milling ◽  
Milling Operation
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