Work

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

Year: 2024

Type: article

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.

Source: Optics Express

DOI: 10.1364/oe.540513

Authors: Xiang Shixiong, Zhang Yun, Deng Yonghong, Hu Yue, Chen Zhongjun

Institutions: Institute of Optics and Electronics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, University of Electronic Science and Technology of China

Language: English

Topic: Advanced Surface Polishing Techniques

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

OpenAlex ID: W4403774855

FWCI: 0.837

Cites: 31

Cited by: 8

Related to: 10