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.

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 Research on On-line Tool Wear Monitoring Technology Based on GPR

2021 ◽  
Vol 252 ◽  
pp. 01046
Author(s):  
Shan Fan ◽  
Yi Huang ◽  
Haixia Zeng
Keyword(s):  
Tool Wear ◽  
Online Monitoring ◽  
Gaussian Process Regression ◽  
Force Sensor ◽  
Cutting Process ◽  
Tool Wear Monitoring ◽  
Monitoring Technology ◽  
On Line ◽  
Wear Monitoring ◽  
The Impact

At present, many kinds of sensors are used for on-line monitoring of cutting process, tool identification and timely replacement. However, most of the original monitoring signals extracted from the cutting process are time series signals, which contain too much process noise. As the signal noise is relatively low, it is difficult to establish a direct relationship with the tool wear. Therefore, how to obtain the effective information from the online monitoring signal and extract the characteristics that can directly reflect the tool wear from the complex original signal, so as to establish an effective and reliable tool wear monitoring system, is the key and difficult problem in the research of the online monitoring technology of tool wear. Firstly, an experimental platform based on the force sensor for on-line monitoring of tool wear was built, and the signal obtained by the force sensor was used to monitor the tool wear, and the feature information was extracted and fused. The innovation of the project lies in the use of Gaussian process regression (GPR) method to predict the tool wear, the use of feature dimensional rise technology, to reduce the impact of noise, on the premise of ensuring the prediction accuracy, improve the confidence interval of GPR prediction results, improve the stability and reliability of the monitoring process.

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