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.

scholarly journals Implementation of an Algorithm to Prevent Chatter Vibration in a CNC System

Materials ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3193 ◽  
Author(s):  
Marcin Jasiewicz ◽  
Karol Miądlicki
Keyword(s):  
Experimental Validation ◽  
Final Section ◽  
Dynamic Properties ◽  
Numerical Control ◽  
Chatter Vibration ◽  
Technological Parameters ◽  
Proper Selection ◽  
Receptance Coupling ◽  
Chatter Vibrations ◽  
Selection Of

Machining of shafts characterized by a high compliance is difficult due to the occurrence of self-excited chatter vibrations. It is possible to limit their occurrence through the appropriate selection of technological parameters. For a proper selection of these parameters it is necessary to know the dynamic properties of the machine–tool–workpiece. This study proposes an approach through which these properties can be determined as a result of the synthesis of the dynamic properties of the system, using the receptance coupling method. Knowledge of these properties allows us to select the technological parameters of the lathe using the assistance system integrated into the CNC (Computerized Numerical Control). The final section of this work presents the experimental validation of the assistant and proposed procedures.

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.

Improved Local Mean Decomposition and its Application to Fault Diagnosis of Train Bearing

2013 ◽  
Vol 819 ◽  
pp. 155-159
Author(s):  
Peng Wang ◽  
Huai Xiang Ma
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Processing Method ◽  
Nonstationary Signal ◽  
Practical Application ◽  
Local Mean Decomposition ◽  
Signal Processing Method ◽  
Signal Demodulation ◽  
Improvement Method ◽  
Local Mean

Fault diagnosis of train bearing is an important method to ensure the security of railway. The key to the fault diagnosis is the method of vibration signal demodulation. The local mean decomposition (LMD) is a self-adapted signal processing method which has a good performance in nonlinear nonstationary signal demodulation. The improved LMD method based on kurtosis criterion can prevent errors in the process of calculating the product functions. With the verification of simulation and wheel set experiment, the improvement method has been certified usefully in practical application.

The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis

2014 ◽  
Vol 1014 ◽  
pp. 510-515 ◽  
Author(s):  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
Min Gou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Predictive Model ◽  
Feature Vector ◽  
Local Mean Decomposition ◽  
Outer Race ◽  
Model Based ◽  
Gear Fault ◽  
Local Mean ◽  
Gear Fault Diagnosis ◽  
Non Stationary Signal

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

Partial discharge signal denoising method based on frequency spectrum clustering and local mean decomposition

2020 ◽  
Vol 14 (10) ◽  
pp. 853-861
Author(s):  
Shanjun Li ◽  
Sashuang Sun ◽  
Qin Shu ◽  
Minwei Chen ◽  
Dakun Zhang ◽  
...  
Keyword(s):  
Frequency Spectrum ◽  
Partial Discharge ◽  
Signal Denoising ◽  
Denoising Method ◽  
Local Mean Decomposition ◽  
Local Mean
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