A COMPARATIVE STUDY OF FEATURE SELECTION FOR HIDDEN MARKOV MODEL-BASED MICRO-MILLING TOOL WEAR MONITORING

2008 ◽  
Vol 12 (3) ◽  
pp. 348-369 ◽  
Author(s):  
K. P. Zhu ◽  
G. S. Hong ◽  
Y. S. Wong
Keyword(s):  
Feature Selection ◽  
Tool Wear ◽  
Comparative Study ◽  
Markov Model ◽  
Hidden Markov ◽  
Micro Milling ◽  
Tool Wear Monitoring ◽  
Milling Tool ◽  
Wear Monitoring ◽  
Selection For

Hidden Markov Model-based Tool Wear Monitoring in Turning

2002 ◽  
Vol 124 (3) ◽  
pp. 651-658 ◽  
Author(s):  
Litao Wang ◽  
Mostafa G. Mehrabi ◽  
Elijah Kannatey-Asibu,
Keyword(s):  
Tool Wear ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Training Data ◽  
Tool Wear Monitoring ◽  
Modeling Framework ◽  
Detection Rates ◽  
Feature Vectors ◽  
Wear Monitoring

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.

Multimodal Hidden Markov Model-Based Approach for Tool Wear Monitoring

2014 ◽  
Vol 61 (6) ◽  
pp. 2900-2911 ◽  
Author(s):  
Omid Geramifard ◽  
Jian-Xin Xu ◽  
Jun-Hong Zhou ◽  
Xiang Li
Keyword(s):  
Tool Wear ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Tool Wear Monitoring ◽  
Model Based ◽  
Wear Monitoring
Sign in / Sign up

Export Citation Format

Share Document