Abstract
New study results on artificial intelligence have been published. According to news reporting originating from Beijing, People's Republic of China, by NewsRx correspondents, research stated, "Aimed to achieve quantitative control of workpiece surface after robotic polishing and improve polishing efficiency, a two-step processing optimization method involves artificial intelligence algorithms is investigated." Funders for this research include Scientific And Technological Project of Quanzhou. The news editors obtained a quote from the research from Beijing University of Technology: "Firstly, based on XGBoost algorithm, a prediction model for polished workpiece surface depending on key parameters is proposed, and the accuracy of the model is verified by experiments. After that, by using the above model, the influence of each parameter on the roughness was evaluated quantitatively. Subsequently, target roughness-driven optimization of processing parameters was presented by combining the roughness prediction model with NSGA II-TOPSIS algorithm based on the influence of each parameter on the roughness. To verify the proposed processing optimization method, polishing experiments of mold steel samples were conducted. The experimental results show that the maximum absolute error between the predicted and experimental roughness is 0.035 mm, and the maximum relative error is ＜9%. At the same time, when the minimum is set as the optimization objective."
NETL
NSTL
Sage