Abstract: Disclosed is a method for predicting surface quality of a burnishing workpiece. The method includes the steps: using vibration sensors and signal acquisition instrument to acquire vibration signals generated on a surface of the burnishing workpiece during machining, evaluating the surface quality of the burnishing workpiece based on a coupling coordination degree model, processing signals by using an ensemble empirical mode decomposition method, identifying power spectral density, kurtosis and form factor as signal characteristics, identifying a support vector machine as a decision-making model, optimizing penalty parameters and kernel function parameters by using the Bayesian optimization method, and establishing the relationship between the signal characteristics and the surface quality.

Abstract: Disclosed is a method for predicting surface quality of a burnishing workpiece. The method includes the steps: using vibration sensors and signal acquisition instrument to acquire vibration signals generated on a surface of the burnishing workpiece during machining, evaluating the surface quality of the burnishing workpiece based on a coupling coordination degree model, processing signals by using an ensemble empirical mode decomposition method, identifying power spectral density, kurtosis and form factor as signal characteristics, identifying a support vector machine as a decision-making model, optimizing penalty parameters and kernel function parameters by using the Bayesian optimization method, and establishing the relationship between the signal characteristics and the surface quality.