Profile prediction in ECM using machine learning

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ECM (Electrochemical Machining) with a micro-second pulsed power supply can decrease stray current corrosion and thus provide better surface quality on top of higher machining accuracy. However, the use of a pulsed power supply makes it challenging to predict the final workpiece profile based on the typical multi-physical-based FEM(Finite Element method) models. First, it is impractical to introduce high-frequency pulsed electric current (ns - µs) into a multi-physical model because of time step limitations. Second, there arise problems of compatibility of the time-steps within each physical module, as e.g. hydrogen bubbles have a lifetime of ms, the power supply would use μs, and thermal phenomena take ms. Multi-scale models have been proposed, but the prediction accuracies are rather low and computation times are very long. In this article, based on machine learning approaches, we present 3 data-driven ECM models for predicting the final workpiece profile when using pulsed-ECM: the linear regression (LR) model, the neural network (NN) model, and the convolutional neural network (CNN) model. After training this data-driven ECM model with different levels of pulse voltage and electrolyte flow conditions, predictions and experimental validation are conducted. Experiments with parameters outside the training parameter window are also carried out to show the performance and general applicability of our data-driven ECM model. The machine learning model shows a good generalizing ability, the CNN model presents a prediction MSE of 7.60. The present results also demonstrate that more accurate predictions will be achieved when using in-processing data. Hence, the prediction accuracy of data-driven models can be further improved on top of advancing in-processing monitoring systems.