Force-based tool wear estimation for milling process using Gaussian mixture hidden Markov models
Published 2017 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Force-based tool wear estimation for milling process using Gaussian mixture hidden Markov models
Authors
Keywords
Tool wear monitoring, Milling force signals, Correlation analysis, GMHMM, BPNN
Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume 92, Issue 5-8, Pages 2853-2865
Publisher
Springer Nature
Online
2017-04-12
DOI
10.1007/s00170-017-0367-1
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Tool wear monitoring based on kernel principal component analysis and v-support vector regression
- (2016) Dongdong Kong et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Force-based tool condition monitoring for turning process using v-support vector regression
- (2016) Ning Li et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images
- (2016) Nagaraj N. Bhat et al. MEASUREMENT
- Hidden Markov model-based approach for multimode process monitoring
- (2015) Fan Wang et al. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
- SCHMM-based modeling and prediction of random delays in networked control systems
- (2014) Yuan Ge et al. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
- Vibration sensor based tool condition monitoring using ν support vector machine and locality preserving projection
- (2014) G.F. Wang et al. SENSORS AND ACTUATORS A-PHYSICAL
- Applying the self-organization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting
- (2012) Chia-Liang Yen et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks
- (2011) Bulent Kaya et al. ADVANCES IN ENGINEERING SOFTWARE
- Detection and diagnosis of bearing and cutting tool faults using hidden Markov models
- (2011) Tony Boutros et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks
- (2011) D.A. Tobon-Mejia et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Advanced monitoring of machining operations
- (2010) R. Teti et al. CIRP ANNALS-MANUFACTURING TECHNOLOGY
- Detection process approach of tool wear in high speed milling
- (2010) M. Kious et al. MEASUREMENT
- Tool wear monitoring by machine learning techniques and singular spectrum analysis
- (2010) Bovic Kilundu et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Multi-category micro-milling tool wear monitoring with continuous hidden Markov models
- (2008) Kunpeng Zhu et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Analysis of the structure of vibration signals for tool wear detection
- (2007) F.J. Alonso et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExplorePublish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn More