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Vibration prediction of hardened steel in precision cutting based on BP neural network

ABSTRACT Cutting vibration is affected by many factors and is nonlinear, so it is difficult to accurately establish a cutting vibration model by mathematical methods. This study uses BP neural network to predict the vibration of hardened steel turning. The vibration data of cutting under five parameters, such as cutting speed, feed rate, cutting depth, corner radius, and workpiece hardness, were collected. A BP neural network model was constructed by considering the prediction accuracy and running time, the model was trained based on the experiments data, and the prediction accuracy of the model was verified. It was found that the relative error of the predicted results is less than 10%, which is basically consistent with the vibration displacement under working conditions. The prediction of vibration amplitude provides a basis for parameter selection to improve the accuracy of hardened steel turning.