document: Hidden Markov model-based tool wear monitoring in turning This paper presents a new modeling framework for tool wear monitoring in machining processes using hidden Markov models -LRB- HMMs -RRB- . 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
doc_bio_tags: O O O B I I O O O O O O O O O O B I I O B I O B I I O O O O O O O O O B I O O O O O B O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O
extractive_keyphrases: tool wear monitoring machining processes hidden markov models vibration signals codebook vector quantization tool state detection
abstractive_keyphrases: feature extraction turning process hmm training discrete wavelet transform