A novel sound-based belt condition monitoring method for robotic grinding using optimally pruned extreme learning machine

A novel method based on sound signals is proposed to realize realtime quantitative monitoring of abrasive belt conditions in robotic grinding system. Robotic belt grinding experiments are carried out on Inconel 718 alloy workpieces. The belt wear styles are observed under scanning electron microscope (SEM). The belt wear conditions are quantified by the grinding ability factor. During grinding process, sound signals are obtained concurrently by an omnidirectional capacitance microphone. Fast Fourier Transform (FFT) and Discrete Wavelet Decomposition (DWD) are conducted to separate the belt-condition-related sound signals from the raw signals. Then sound features are extracted to establish a novel data-driven model using Optimally Pruned Extreme Learning Machine (OP-ELM). The model is developed to predict the belt grinding ability factor. Experimental datasets are used to train and validate the established model. The results show that the grinding sound in high frequency region from 10 to 15 kHz is sensitive to the belt wear conditions, and that the OP-ELM model speedily and accurately predicts the belt grinding ability factor after optimizing the hidden layer numbers and kernel function type. Compared with experimental results, the mean absolute percentage errors (MAPE) of the predicted grinding ability factor are less than 0.4% and the maximum absolute percentage errors (MAXE) are within 14%. It is thus concluded that the proposed sound-based monitoring approach using the OP-ELM model is robust in accurately predicting the grinding belt condition despite grinding dynamics involving contact pressures.