Prediction of the material removal rate in bonnet polishing using a Bayesian optimization deep neural network

Abstract

The material removal mechanism in robotic bonnet polishing is complex and influenced by multiple factors, necessitating an appropriate method to establish a material removal model. This study employs a Bayesian optimized deep neural network (BO-DNN) to model the intricate relationship between polishing parameters and material removal rate (MRR) using removal function spot experimental data. The tree-structured Parzen estimator (TPE) improves model convergence speed and accuracy, while particle swarm optimization (PSO) assists in inverse verification. Results show that the BO-DNN model achieves a root mean square error (RMSE) of 0.0293 and a Pearson correlation coefficient (PCC) of 99.42% for the total sample, representing approximately a 50% improvement in predictive accuracy over the unoptimized DNN model. The inverse verification results closely match the experimental data, confirming the model’s reliability. This study offers theoretical insights and practical references for advancing robotic bonnet polishing technology.