TOOL WEAR PREDICTION FROM ACOUSTIC EMISSION AND SURFACE CHARACTERISTICS VIA AN ARTIFICIAL NEURAL NETWORK
| Autor: | P. Wilkinson, Duncan Paul Hand, Steve Kidd, Robert Lewis Reuben, Julian D. C. Jones, Tom Carolan, James S. Barton |
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| Rok vydání: | 1999 |
| Předmět: |
Surface (mathematics)
Materials science Artificial neural network business.industry Mechanical Engineering Acoustics Aerospace Engineering Computer Science::Human-Computer Interaction Machine learning computer.software_genre Computer Science Applications Computer Science::Hardware Architecture Machining Acoustic emission Control and Systems Engineering Face (geometry) Signal Processing Artificial intelligence Tool wear business computer Civil and Structural Engineering |
| Zdroj: | Mechanical Systems and Signal Processing. 13:955-966 |
| ISSN: | 0888-3270 |
| DOI: | 10.1006/mssp.1999.1231 |
| Popis: | We examine the application of an artificial neural network to classification of tool wear states in face milling. The input features were derived from measurements of acoustic emission during machining and topography of the machined surfaces. Five input features were applied to the back-propagating neural network to predict a wear state of light, medium or heavy wear. We present results from milling experiments with multi- and single-point cutting and compare the neural network predictions with observed cutting insert wear states. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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