Predicting the sparks occurrence in electrochemical discharge machining by machine learning using convolutional neural networks

This study investigates the use of convolutional neural networks (CNNs) to define sparks from the high-speed video feed of the electrochemical discharge machining (ECDM) process. The visual data is used to monitor the spark activity in the electrolyte. The recognition of the sparks in optical data can potentially improve the prediction of material removal in ECDM since the majority of machining is caused by the sparking. The massive dataset size is a challenge to study the optical data frame by frame. The CNN model in this study generated a time series for the presence of sparks based on the image feed in sequential order. The CNN based machine learning model in this study is found to be more consistent than the manual labeling of the images. This model is used to analyze the image data and predict the presence of the sparks over 95% accuracy.