Hidden Markov Model-based Tool Wear Monitoring in Turning
- 11 July 2002
- journal article
- Published by ASME International in Journal of Manufacturing Science and Engineering
- Vol. 124 (3), 651-658
- https://doi.org/10.1115/1.1475320
Abstract
This paper presents a new modeling framework for tool wear monitoring in machining processes using hidden Markov models (HMMs). Feature vectors are extracted from vibration signals measured during turning. A codebook is designed and used for vector quantization to convert the feature vectors into a symbol sequence for the hidden Markov model. A series of experiments are conducted to evaluate the effectiveness of the approach for different lengths of training data and observation sequence. Experimental results show that successful tool state detection rates as high as 97% can be achieved by using this approach.Keywords
This publication has 25 references indexed in Scilit:
- Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural networkNeural Networks, 1999
- Acoustic emission for tool condition monitoring in metal cuttingWear, 1997
- A review of machine vision sensors for tool condition monitoringComputers in Industry, 1997
- Automatic supervision of blanking tool wear using pattern recognition analysisInternational Journal of Machine Tools and Manufacture, 1997
- Tool condition monitoring in drilling using vibration signature analysisInternational Journal of Machine Tools and Manufacture, 1996
- Tool-wear monitoring in machine turningJournal of the American Academy of Dermatology, 1995
- Fuzzy Pattern Recognition for Tool Wear Monitoring in Diamond TurningCIRP Annals, 1992
- Tool Wear Detection Using Time Series Analysis of Acoustic EmissionJournal of Engineering for Industry, 1989
- Acoustic emission and force sensor fusion for monitoring the cutting processInternational Journal of Mechanical Sciences, 1989
- Tool Failure Monitoring in Turning by Pattern Recognition Analysis of AE SignalsJournal of Engineering for Industry, 1988