A new BRTCN model for predicting discharge status of WEDM based on acoustic emission

Abstract

Machine status detection is very critical to ensure the smoothness of processing workpieces in terms of efficiency and quality. In light of the significance of acoustic emission (AE) signals for the monitoring of wire electrical discharge machining (WEDM) processes and the strong feature extraction ability of deep learning, this paper proposes a physical propagation mechanism of AE and develops an effective deep learning dual-input model called batch relevance temporal convolution neural network (BRTCN) with a new labeling method after analyzing the collected signals. BRTCN, mainly composed of noise extractor, encoder, batch relevance and decoder, is applied to build the AE model that can accurately predict discharge status. Through comparative experiments, the encoder of BRTCN is capable of extracting the AE local sequence features, grabbing long dependencies and reducing computational costs. It is found that the noise extractor in BRTCN is essential for the AE model to converge stably. This paper innovatively detects the discharge status of WEDM based on dual channel AE signals and the proposed BRTCN model examines the relationship between AE and pulse time series with low computation and high accuracy.