scholarly journals Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

2009 ◽  
Vol 22 (4) ◽  
pp. 491-504 ◽  
Author(s):  
Sukhomay Pal ◽  
P. Stephan Heyns ◽  
Burkhard H. Freyer ◽  
Nico J. Theron ◽  
Surjya K. Pal
Keyword(s):  
Tool Wear ◽  
Cutting Conditions ◽  
Tool Wear Monitoring ◽  
Optimum Cutting ◽  
Wear Monitoring ◽  
Input Uncertainties ◽  
Selection Of

Tool wear monitoring for optimizing cutting conditions

1996 ◽  
Vol 62 (4) ◽  
pp. 374-379 ◽  
Author(s):  
T. Obikawa ◽  
C. Kaseda ◽  
T. Matsumura ◽  
W.G. Gong ◽  
T. Shirakashi
Keyword(s):  
Tool Wear ◽  
Cutting Conditions ◽  
Tool Wear Monitoring ◽  
Wear Monitoring

THE ADAPTABILITY OF A TOOL WEAR MONITORING SYSTEM UNDER CHANGING CUTTING CONDITIONS

2000 ◽  
Vol 14 (2) ◽  
pp. 287-298 ◽  
Author(s):  
R.G. SILVA ◽  
K.J. BAKER ◽  
S.J. WILCOX ◽  
R.L. REUBEN
Keyword(s):  
Tool Wear ◽  
Monitoring System ◽  
Cutting Conditions ◽  
Tool Wear Monitoring ◽  
Wear Monitoring

scholarly journals Tool Flank Wear Estimation using Emitted Sound Signal Analysis by PCA – SER Based Peak to Peak Measurements

2019 ◽  
Vol 8 (6) ◽  
pp. 5212-5216
Keyword(s):  
Tool Wear ◽  
Signal Analysis ◽  
Industry 4.0 ◽  
Flank Wear ◽  
Optimization Techniques ◽  
Tool Wear Monitoring ◽  
Tool Flank Wear ◽  
Wear Monitoring ◽  
Novel Method ◽  
Selection Of

The higher levels degrees of automation for industry 4.0 standards require optimization techniques in production activities including tool wear monitoring. The unmonitored tool may spoil the product if it is worn out more than the permitted levels or micro broken or cracked internally. A novel method suggested in this work utilizes neither extra ordinary calculation nor complex mathematical transformations in tool wear monitoring. This method follows no video capturing and image processing rather follows a simple sound wave monitoring captured at the time conversion process by a microphone. The SER a PCA variant technique with the purpose of used in selecting simply the higher velocity of principal components (PCs) in quantifying the feature extracted while separating noise from sound signals. A SER method is used for the selection of suitable PCs for consideration. The best methods of normalization suitable for the SER method is found and implemented the PCA-SER on signals after filter the signals by butter worth filter to remove noise. This proposed procedure resulted in wide differences and proper annotation in differentiating the degree of tool wear in fresh, slight and severely worn categories.

scholarly journals Tool Wear Monitoring in Turning: A Neural Network Application

2001 ◽  
Vol 34 (7) ◽  
pp. 207-222 ◽  
Author(s):  
Bernhard Sick
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Tool Wear ◽  
Process Parameters ◽  
Production Costs ◽  
Cutting Conditions ◽  
Tool Wear Monitoring ◽  
Computing Technique ◽  
Wear Monitoring ◽  
Signal Changes

Tool wear monitoring is the most difficult task in the area of tool condition monitoring for metal-cutting manufacturing processes. The main objective is to improve the process reliability, but the production costs need to be reduced as well. This article summarises a new approach for online and indirect tool wear estimation or classification in turning using neural networks. This technique uses a physical process model describing the influence of cutting conditions (such as the feed rate) on measured process parameters (here: cutting force signals) in order to separate signal changes caused by variable cutting conditions from signal changes caused by tool wear. Features extracted from the normalised process parameters are taken as inputs of a dynamic, but nonrecurrent neural network that estimates the current state of the tool. It is shown that the estimation error can be reduced significantly with this combination of a hard computing and a soft computing technique. The article represents an extended summary of the author's investigations and publications in the area of online and indirect tool wear monitoring in turning by means of artificial neural networks.

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
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