Natural gas leak location with K–L divergence-based adaptive selection of Ensemble Local Mean Decomposition components and high-order ambiguity function

2015 ◽  
Vol 347 ◽  
pp. 232-245 ◽  
Author(s):  
Jiedi Sun ◽  
Qiyang Xiao ◽  
Jiangtao Wen ◽  
Ying Zhang
Keyword(s):  
Natural Gas ◽  
Ambiguity Function ◽  
High Order ◽  
Adaptive Selection ◽  
Local Mean Decomposition ◽  
Leak Location ◽  
Gas Leak ◽  
Local Mean ◽  
Selection Of

Ensembled local mean decomposition and genetic algorithm approach to investigate tool chatter features at higher metal removal rate

2020 ◽  
pp. 107754632097115
Author(s):  
Pankaj Gupta ◽  
Bhagat Singh
Keyword(s):  
Genetic Algorithm ◽  
Prediction Models ◽  
Metal Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Metal Removal Rate ◽  
Local Mean Decomposition ◽  
Local Mean ◽  
Chatter Index ◽  
Selection Of

Improper selection of cutting parameters leads to regenerative chatter and loss in productivity. In the present work, a methodology has been proposed to select a proper combination of input cutting parameters for stable turning with improved metal removal rate. Chatter signals generated during the turning of Al6061-T6 have been acquired using a microphone. Stability lobes diagram has been plotted to access the stability regime. Further, to study the effect of feed rate on stability, the recorded signals have been processed using local mean decomposition signal processing technique, followed by the selection of dominating product functions using Fourier transform. The decomposed signals have been used to evaluate the new output parameter, that is, chatter index. Prediction models of chatter index and metal removal rate have been developed. Moreover, these prediction models have been optimized using multi-objective genetic algorithm for ascertaining the optimal range of cutting parameters for stable turning with higher metal removal rate. Finally, obtained stable range has been validated by performing more experiments.

Analyzing chatter vibration during turning on computer numerical control lathe using ensemble local mean decomposition and probabilistic approach

2021 ◽  
Vol 52 (6) ◽  
pp. 168-180
Author(s):  
Pankaj Gupta ◽  
Bhagat Singh
Keyword(s):  
Probability Distribution ◽  
Probabilistic Approach ◽  
Numerical Control ◽  
Chatter Vibration ◽  
Signal Processing Technique ◽  
Local Mean Decomposition ◽  
Incipient Stage ◽  
Local Mean ◽  
Selection Of ◽  
Tool Chatter

Chatter vibration is an undesired and indispensable phenomenon in turning operation, which cannot be completely avoided. However, it can be suppressed by early identification and with the proper choice of input turning parameters. The key issue of chatter detection is to process the acquired signals and extract the features pertaining to it. In the present work, a methodology has been proposed for exploring tool chatter features in the incipient stage during turning on lathe. Chatter signals generated during the turning of Al 6061-T6 have been acquired using a microphone. A stability lobe diagram has been plotted to access the stability regime. Further, in order to study the effect of feed rate on stability, the recorded signals have been processed using a local mean decomposition signal processing technique, followed by the selection of dominating product functions using the Fourier transform. The decomposed signals have been used to evaluate the new output parameter, that is, chatter index. Further, the Nakagami probability distribution has been used to ascertain stability region (threshold). From the experimental validation, it has been inferred that cutting combinations obtained from the Nakagami probability distribution are significant and capable of limiting chatter vibrations. The present methodology will serve as guidelines to the researchers and machinist for the identification of tool chatter in the incipient stage, explore its severity, and finally suppress it with the proper selection of input turning parameters.

Adaptive Selection of Sampling Interval in Frequency Domain for Estimating the Poles of Scatterers

PIERS Online ◽  
2007 ◽  
Vol 3 (1) ◽  
pp. 106-110
Author(s):  
Shaogang Wang ◽  
Xinpu Guan ◽  
Dangwei Wang ◽  
Xingyi Ma ◽  
Yi Su
Keyword(s):  
Frequency Domain ◽  
Sampling Interval ◽  
Adaptive Selection ◽  
Selection Of
Sign in / Sign up

Export Citation Format

Share Document