Intelligent analysis of tool wear state using stacked denoising autoencoder with online sequential-extreme learning machine

Abstract Impeller is a critical component of much large equipment, such as hydropower, nuclear pump, and so on. Its processing quality seriously affects the operation states and service life span of the equipment, while the milling cutter wear determines the processing accuracy of the impeller. Therefore, it is of great important to investigate an intelligent recognition method of tool wear state during the processing. In this research, a new method named stacked denoising autoencoder (SDAE) with online sequential extreme learning machine (OS-ELM) is put forward for intelligent recognition of tool wear states. Firstly, three current signals of the spindle of CNC machine tool are collected during the cutting process and the effective values are synthesized. Then, a new SDAE neural network is trained to acquire the low dimensional features using raw current signals. Finally, OS-ELM is used to realize recognition and classification of the milling cutter, and compared the accuracy with other methods. The method proposed in this paper was verified by both the laboratory signals and the actual engineering signals. The results for spindle current signals show that the developed model has achieved effective performance on tool wear recognition.

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