Wavelet Based Cutting State Identification

Berger, B. S.; Minis, Ioannis; Harley, Joel B.; Rokni, M.; Papadopoulos, Michael

doi:10.1006/jsvi.1997.1495

Cited by 43 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Berger et al [164] apply wavelet decomposition of cutting force signals to discriminate between chatter and non chatter states. Govekar et al [105] use entropy rate of resultant cutting force signals to detect broken chip formation and chatter onset in turning.…”

Section: Chatter Detectionmentioning

confidence: 99%

Advanced monitoring of machining operations

et al. 2010

View full text Add to dashboard Cite

Section: Chatter Detectionmentioning

confidence: 99%

Advanced monitoring of machining operations

et al. 2010

View full text Add to dashboard Cite

“…Wu and Du (1996) used the wavelet packet transform to extract the features for tool condition monitoring in turning and drilling processes. Berger et al (1998), as well as Velayudham et al (2005) used the wavelet transform as a tool to study the signal characteristics of cutting processes. However, these studies lack a detailed investigation of the selection of wavelet basis functions and decomposition levels.…”

Section: Introductionmentioning

confidence: 99%

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

et al. 2009

View full text Add to dashboard Cite

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.

show abstract

“…Researches on chatter detection generally adopt three types of signal processing methods. First, frequency domain analysis such as the wavelet transform (WT) [5], [6] transform [7], Hilbert-Huang transform (HHT) [8] and coherence function [9]. As Heisenberg-Gabor inequality indicates, it is impossible for the WT to obtain fine resolutions in both time and frequency at the same window [10].…”

Section: Introductionmentioning

confidence: 99%

Real-time chatter detection using the weighted wavelet packet entropy

Sun

Zhuang

Xiong

2014

2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

View full text Add to dashboard Cite

The weighted wavelet packet entropy (WWPE) is presented for real-time detection of chatter before it has fully developed in turning processes. This method is based on low dimensionality of process dynamics shown by chatter. The energy of frequency band that contains chatter information is weighted. Compared with the WPE, the WWPE is more sensitive to energy variation caused by the onset of chatter. Therefore, chatter can be detected in an earlier stage. From computational point of view, the wavelet packet matrix is employed instead of the Mallat algorithm, which is computationally expensive due to its complex convolution operations. A moving average filter combined with a threshold algorithm is also introduced to improve the performance of the WWPE. Finally, numerical simulations are conducted to demonstrate the efficiency of the proposed method. Moreover, the WWPE with different weights is experimentally explored and the proper value is obtained.

show abstract

Wavelet Based Cutting State Identification

Cited by 43 publications

References 0 publications

Advanced monitoring of machining operations

Advanced monitoring of machining operations

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

Real-time chatter detection using the weighted wavelet packet entropy

Contact Info

Product

Resources

About