Energy efficiency state identification of milling processing based on EEMD-PCA-ICA

Abstract On-line identification of energy efficiency state in cutting process is one of the important topics in high energy efficient cutting. The energy efficiency state and other states (tool state, etc.) are mixed in various signals (force, power etc.) during manufacturing process. In this paper, an energy efficiency state identification method is proposed based on ensemble empirical mode decomposition (EEMD), principal component analysis (PCA) and independent component analysis (ICA). The energy efficiency state of milling process is classified by the energy efficiency model and experiment data. EEMD-PCA-ICA is used to separate independent components from milling force signals. The experimental results show that the EEMD-PCA-ICA identification algorithm can separate the components representing the energy efficiency state from the milling force signal. The energy efficiency state in the milling process can be identified by analyzing the proportion of ICA components, which can provide technical solutions for online monitoring of energy efficiency status.

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