Real-time prediction of grinding surface roughness based on multi-sensor signal fusion

Abstract

Abstract In this study, aiming at problems that the existing surface roughness prediction models fail to consider the time-varying characteristics of the rough grinding process and the difficulty in feature selection, a real-time surface roughness prediction model based on multi-sensor signal fusion is developed. Firstly, features are extracted from the time domain, frequency domain, and time-frequency domain of force and vibration signal, and then features that do not reflect the time-varying characteristics of the machining process are eliminated. Finally, a multi-sensor signal fusion method is proposed based on the principal component analysis (PCA). Results show that fused features are capable of retaining the physical meaning of original features, and achieving stable and high-precision prediction of surface roughness when they are input into the BP neural network (BPNN). In addition, it is found that the surface roughness at different stages of grinding processing is dominated by different factors. Specifically, during the first stage, the fourth principal component PC4 is the redundant information for the model. However, this feature significantly affects the performance of the model during the second stage. Overall, this paper lays the foundation for understanding mechanisms of time-varying surface roughness in an actual grinding process and realizing accurate monitoring.