Product Quality Prediction for Wire Electrical Discharge Machining with Markov Transition Fields and Convolutional Long Short-Term Memory Neural Networks

This paper proposes a wire electrical discharge machining (WEDM) product quality prediction method, called MTF-CLSTM, to integrate the Markov transition field (MTF) and convolutional long short-term memory (CLSTM) neural network. The proposed MTF-CLSTM method can accurately predict WEDM workpiece surface roughness right after manufacturing by collecting analyzing static parameters dynamic conditions. highly accurate is due following two reasons. First, MTF transform data into images extract temporal information state probability information. Second, CLSTM network image spacial features relationship of that are separated far apart. In short, predicts with model using compared 10 related research studies in many aspects. There only one existing like Experiments conducted evaluate performance show significantly outperforms terms mean absolute percentage error.