Manufacturing Quality Prediction Based on Deep Learning in Conjunction with Gramian Angular and Markov Transition Fields

A method to predict the surface roughness of wire electrical discharge machining (WEDM) workpieces is proposed in this paper. It first utilizes the Gramian angular field (GAF) and the Markov transition field (MTF) to encode the time series data into 2D images, which are then fed into the CBR long short-term memory (CBR-LSTM) neural network to predict the WEDM workpiece surface roughness, where CBR represents the combination of the convolution, batch normalization, and rectified linear unit (ReLU) layers. The Keras hyperband tuner is used to optimize CBR-LSTM network hyperparameters. The proposed method is shown to have the best prediction when compared with related ones.