Dynamic Bayesian Network-based Surface Roughness Accuracy Grade Prediction in Turning

Abstract

A hybrid DBN model - Discrete and gaussian mixture hidden markov model with the weighted evidence fusion strategy (DGMWEFS), based on Discrete hidden Markov model (DHMM), Mixture of Gaussians hidden Markov model (MoGHMM), and DS evidence theory, is developed for surface roughness accuracy grade prediction. By analyzing the influence of tool vibration on the surface topography, the singular spectrum with wavelet analysis is proposed to extract the fusion feature (Ec). The ρ comparison is developed to overcome defect of the traditional probability comparison. For the multiple outputs of DHMM and MoGHMM, the weighted evidence fusion strategy based on the DS evidence theory is proposed for final decisions. Experiment of turning the workpiece with multi-material and hardness scale shows that, compared with the DHMM (prediction accuracy 85%) and MoGHMM(prediction accuracy 78%), the DGMWEFS proposed estimates the surface roughness with higher accuracy (prediction accuracy 93%).Therefore, the monitoring strategy proposed can be better used for supervising accuracy grade, which can be readily integrated into a computer integrated manufacturing environment.