基于GRA-DBN的机器人打磨工艺多目标优化方法OA

Robot polishing has become an important machining method for improving the surface quality of civil aircraft fuselages due to its flexibility,extensive workspace,and high operability.In the polishing process of civil aircraft fuselages,there exists a mutual constraint relationship between surface roughness and material removal rate.There-fore,achieving optimal balance between surface roughness and material removal rate is a significant challenge in current research.To address this issue,this paper proposes a multi-objective optimization method for robot polis-hing processes based on GRA(grey relational analysis)-DBN(deep belief networks).Initially,principal component analysis(PCA)is employed to optimize GRA and establish the mapping relationship between multi-objectives and process parameters.Subsequently,the multi-objective optimization is transformed into a single-objective optimiza-tion problem.Furthermore,integrating DBN facilitates the construction of an optimization model for robot polishing process parameters.Experimental validation demonstrates that the proposed method not only effectively balances surface roughness and material removal rate but also enhances the stability and reliability of the polishing process.