Surface roughness monitoring method based on Bayesian network models

Aiming at the uncertainty of various factors on surface roughness, a surface roughness prediction model based on Bayesian network was proposed. The energy features of time domain and frequency domain were extracted directly from cutting force and workpiece vibration. The association relationship between surface roughness and sensor signal features were mined with Bayesian network learning process, and the monitoring result was obtained according to the probability distribution of roughness range. The effectiveness of proposed method was verified by the roughness experiment of milling process.