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.

Detection of Roller Bearing Conditions Using Mechatronics Approach

1999 ◽  
Author(s):  
T. I. Liu ◽  
F. Ordukhani
Keyword(s):  
Neural Networks ◽  
Roller Bearing ◽  
Diagnostic System ◽  
Feature Selection Technique ◽  
Vibration Signals ◽  
Selection Technique ◽  
On Line ◽  
Line Classification ◽  
Nonlinear Pattern

Abstract An on-line monitoring and diagnostic system is needed to detect faulty bearings. In this work, by applying the feature selection technique to the data obtained from vibration signals, six indices were selected. Artificial neural networks were used for nonlinear pattern recognition. An attempt was made to distinguish between normal and defective bearings. Counterpropagation neural networks with various network sizes were trained for these tasks. The counterpropagation neural networks were able to recognize a normal from a defective bearing with the success rate between 88.3% to 100%. The best results were obtained when all the six indices were used for the on-line classification of roller bearings.

Automatic Classification of 2D Partial Discharge from Generator On-Line Measurement

2021 ◽  
Author(s):  
Claude Hudon ◽  
Melanie Levesque ◽  
Olivier Kokoko ◽  
Normand Amyot ◽  
Ryad Zemouri
Keyword(s):  
Partial Discharge ◽  
Automatic Classification ◽  
Line Measurement ◽  
On Line

On-line classification of pollutants in water using wireless portable electronic noses

Chemosphere ◽  
2016 ◽  
Vol 152 ◽  
pp. 107-116 ◽  
Author(s):  
José Luis Herrero ◽  
Jesús Lozano ◽  
José Pedro Santos ◽  
José Ignacio Suárez
Keyword(s):  
Electronic Noses ◽  
On Line ◽  
Line Classification

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.

Sign in / Sign up

Export Citation Format

Share Document