A novel material removal prediction method based on acoustic sensing and ensemble XGBoost learning algorithm for robotic belt grinding of Inconel 718

Abstract

High-performance component manufacturing has increasing needs of robotic grinding process that can achieve accurate material removal. This article proposes a novel material removal model for robotic belt grinding of Inconel 718 based on acoustic sensing and machine learning. The sound signal is collected online by an audio sensor during the grinding process. A novel method to identify the idle running period and eliminate noise is developed using discrete wavelet decomposition (DWD) and fast Fourier transformation (FFT). Statistical features are extracted from each clean acoustic signal segment to better represent and quantify grinding process. A new k-fold eXtreme Gradient Boosting (k-fold-XGBoost) algorithm after training and optimization is integrated into the material removal (MR) model. The test results indicate that the values forecasted by the model are consistent with the measured values. The mean absolute percentage error (MAPE) of material removal evaluated by the model is 4.373%, which shows a better performance than the reported results which are in the range of 6.4 to 8.72%. In comparison with other prediction models, such as optimally pruned extreme learning machine and random forest and support vector regression, k-fold-XGBoost model shows superior results for the same datasets. It can be concluded that the proposed method based on acoustic signal and the ensemble learning model is effective in predicting the material removal despite the complicated grinding environment.