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.

Tool-Wear Monitoring and Control

2005 ◽  
Author(s):  
J. Srinivas ◽  
Rao Dukkipati ◽  
V. Sreebalaji ◽  
K. Ramakotaih
Keyword(s):  
Tool Wear ◽  
Machine Tools ◽  
Flank Wear ◽  
Cutting Parameters ◽  
Monitoring And Control ◽  
Tool Wear Monitoring ◽  
Wear Monitoring ◽  
Trained Neural Network ◽  
And Control ◽  
Control Methodology

This paper presents, a control methodology based on experimental data of the tool wear as a function of cutting variables. In automatic machine tools there is strong need to control the tool wear by adjustment of the cutting parameters. In this connection, a control system, which can adjust the cutting parameters for a desired wear rate, is necessary. A regression relation is also established between the flank-wear and the cutting parameters. An inversely trained neural network model, which supplies the modified values of the cutting parameters, is used as a controller. The results are shown in the form of tables and graphs.

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: An Automated System Based on Multiple Cutting Force Parameters and Machine Vision Technique

2009 ◽  
Vol 626-627 ◽  
pp. 5-10 ◽  
Author(s):  
Yu Teng Liang ◽  
Yih Chih Chiou
Keyword(s):  
Tool Wear ◽  
Machine Vision ◽  
Cutting Force ◽  
Monitoring System ◽  
Flank Wear ◽  
Cutting Parameters ◽  
Tool Wear Monitoring ◽  
Model Based ◽  
Wear Monitoring ◽  
Vision Technique

This study proposes a tool wear automatic monitoring system based on multiple parameters analysis of cutting force and machine vision technique. A drilling model of cutting parameters (cutting force, coating layer, spindle speed and feed rate) and tool condition (focusing on tool flank wear measurement and analysis) was developed. The experimental design methods developed in this study can be used to optimize cutting parameters efficiently and reliably. The drilling model based on cutting parameters was constructed using Taguchi method. This method enabled evaluation of wear status based on the actual force obtained from a dynamometer. The derived relation is useful for in-process wear monitoring. Tool wear dynamics are extremely complex and not yet fully understood. Therefore, vision-based tool wear monitoring techniques can help elucidate wear progression. In this study, a drilling model based on the machine vision technique was used to establish a direct relation between cutting parameters and tool wear. The object of the experiments was to measure the flank wear of cutting tools with various coatings. The experimental results show that the monitoring system clarifies the relationships between cutting force and multiple cutting parameters.

Audio signal analysis for tool wear monitoring in sheet metal stamping

2017 ◽  
Vol 85 ◽  
pp. 809-826 ◽  
Author(s):  
Indivarie Ubhayaratne ◽  
Michael P. Pereira ◽  
Yong Xiang ◽  
Bernard F. Rolfe
Keyword(s):  
Tool Wear ◽  
Sheet Metal ◽  
Signal Analysis ◽  
Audio Signal ◽  
Tool Wear Monitoring ◽  
Sheet Metal Stamping ◽  
Metal Stamping ◽  
Wear Monitoring ◽  
Audio Signal Analysis

Tool Wear Monitoring Through the Dynamics of Stable Turning

1986 ◽  
Vol 108 (3) ◽  
pp. 183-190 ◽  
Author(s):  
S. B. Rao
Keyword(s):  
Tool Wear ◽  
Flank Wear ◽  
Single Point ◽  
Forced Oscillations ◽  
Microcomputer System ◽  
Dynamic Force ◽  
Tool Wear Monitoring ◽  
Force Amplitude ◽  
On Line ◽  
Wear Monitoring

This paper describes a microcomputer-based technique for monitoring the flank wear on a single-point tool engaged in a turning operation. The technique is based on the real-time computation of a Wear Index (WI). This WI is a measure of the resistance, at the tool tip-workpiece interface along the flank, to the forced oscillations of the cantilever portion of the tool holder, during machining. Increasing flank wear results in an increasing area of contact between tool tip and workpiece. This translates to an increasing WI, proportional to flank wear-land width and independent of other cutting process variables. This WI, which can be computed on-line as a ratio of the measured dynamic force amplitude to the vibration amplitude, at the first natural frequency of the cantilever portion of the toolholder, forms the basis of the microcomputer system described in this paper for tool wear monitoring.

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