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Xiaohui Li
Southwest Jiaotong University

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Feature Extraction of Turing Tool Wear Based on J-EEMD Hongtao Chen; Pan Fu; Xiaohui Li
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 10: October 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

In the monitoring of cutting tool state, a large number of redundant information is contained in the sensor signal. Therefore, it is obviously not conducive to pattern recognition, and difficult to classify the tool wear state correctly from the available sensors. The test platform that had real-time information collection of the vibration and acoustic emission signals in turning was built. Observed signals were adaptively processed using the method of ensemble empirical mode decomposition introduced joint approximate diagonalization of eigenmatrices (J-EEMD). This method is based on the characteristics of the signal itself decomposed into several intrinsic mode functions (IMF), and then transforms the energy ratio between the IMF. The white noise of each IMF component has been eliminated by introducing JADE algorithm during the signal decomposition. Compared with the EEMD algorithm, the decomposition efficiency is significantly improved. The experiments showed that the method could identify the different states of tool wear, if applied to feature extraction of vibration and acoustic emission signal in the cutting process.  DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3423 
Study on the Cutting Prediction of Supercritical Material Hongtao Chen; Pan Fu; Xiaohui Li
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 9: September 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

The technology of the artificial neural network (ANN) was applied in the research of supercritical material cutting. Two-dimensional Gaussian surfaces of the three cutting elements and workpiece surface hardness had been established fitting through JMP software. Base on the orthogonal milling experiments, the rules of cutting forces variation were forecasted, as well as the effect to the hardness on workpiece surface. The cutting parameters selected according to the process were built, providing an important basis for the optimization of machining conditions. The prediction results were in good agreement with the experimental results. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3265  
Co-Authors Hongtao Chen Pan Fu