Analytical Investigation of the Effect of Tool Wear on the Temperature Variations in a Metal Cutting Tool

1976 ◽  
Vol 98 (1) ◽  
pp. 251-257 ◽  
Author(s):  
E. K. Levy ◽  
C. L. Tsai ◽  
M. P. Groover
Keyword(s):  
Wear Rate ◽  
Tool Wear ◽  
Metal Cutting ◽  
Analytical Study ◽  
Analytical Investigation ◽  
Transient Temperature ◽  
Temperature Variations ◽  
Line Measurement ◽  
On Line ◽  
Metal Cutting Tool

An analytical study of the effect of crater wear on the response of a remote thermocouple sensor is described. The remote thermocouple sensor is at present being developed as a device for the on-line measurement of tool wear. This technique depends for its operation on the strong influence of wear on the transient temperature variations in the tool. The two-dimensional transient temperature variations in the chip and tool regions are determined using a numerical finite-difference technique. Results are obtained under idealized cutting conditions with a zero wear rate, a normal wear rate, and an accelerated wear rate. Comparisons are made between the three cases to develop relationships for the effect of wear on the temperature at the remote thermocouple location.

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.

Intelligent Detection of Boring Tool Conditions

Manufacturing ◽  
2003 ◽  
Author(s):  
Tien-I. Liu ◽  
Akihiko Kumagai ◽  
Shin-Da Song ◽  
Zhenwen Fu ◽  
Yann-Chiu Wang ◽  
...  
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Estimation Error ◽  
Longitudinal Force ◽  
Line Measurement ◽  
Boring Tool ◽  
On Line ◽  
Sequential Forward Search ◽  
Line Classification

Adaptive neuro-fuzzy inference systems (ANFIS) were used for on-line classification and measurement of tool wear for the boring of titanium parts. The input vectors consist of extracted features from cutting force data. A total of fourteen features were extracted by processing cutting force signals using virtual instrumentation. Feature selection was carried out using a Sequential Forward Search (SFS) algorithm to select the best combination of features. For the on-line classification, the outputs are boring tool conditions, which are either usable or worn out. For the on-line measurement, the outputs are estimated values of the tool wear. Using ANFIS, three features were selected for the on-line classification of boring tools. They are the average longitudinal force, average of the ratio between the tangential and radial forces, and kurtosis of the longitudinal force. Only one feature, kurtosis of the longitudinal force, was needed for the on-line measurement of tool wear using ANFIS. A 3×5 ANFIS can achieve a 100% success rate for the on-line classification of boring tool conditions. Using a 1×5 ANFIS, the average flank wear estimation error is below 5% for on-line measurement of tool wear.

Intelligent Monitoring and Measurement of Tool Wear for the Turning of Stainless Steel Parts

2004 ◽  
Author(s):  
Tien-I Liu ◽  
Shin-Da Song
Keyword(s):  
Stainless Steel ◽  
Tool Wear ◽  
Fuzzy Inference ◽  
Estimation Error ◽  
Tangential Force ◽  
Radial Force ◽  
Line Measurement ◽  
On Line ◽  
Sequential Forward Search ◽  
Monitoring And Measurement

Cutting forces were used as indices in this research for the monitoring and measurement of tool wear during the turning of stainless steel parts. Virtual instrumentation was applied to extract the fourteen features from cutting force signals. The best combination of features, which would be used as input vectors for on-line monitoring and measurement, was selected by using a Sequential Forward Search (SFS) algorithm. Adaptive neuro-fuzzy inference systems (ANFIS) were used for the recognition of tool wear. The tool conditions, which are either usable or worn out, are the outputs for on-line monitoring. The outputs for on-line measurement are estimated values of tool wear. When ANFIS was applied, three features were needed for the monitoring of tool wear. They are the average of radial force, the average of tangential force, and the skewness of tangential force. For on-line measurement, four features were used as inputs. The input vector includes the average of radial force, the average of tangential force, the skewness of tangential force, and the kurtosis of longitudinal force. For the on-line monitoring of turning tool conditions, a 7 × 2 ANFIS can achieve a success rate of higher than 96% to distinguish usable tools from worn-out tools. For the on-line measurement of tool wear, the average flank wear estimation error is below 8.9% using a 3 × 3 ANFIS.

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