Study On Contour Milling Side Roughness Modeling And Process Parameter Optimization Method Considering Curvature

Contour milling is widely used in aerospace,automotive manufacturing and mold production.Exploring the influence mechanism of the contour milling lateral quality and the relationship between lateral quality and material removal rate,which is a research hot spot on quality improvement and efficiency of contour milling.In this paper,we study the relationship between lateral roughness and material removal rate from the modeling of profile curvature and lateral roughness R_a,aiming to select reasonable process parameters to ensure the best material removal rate with the premise of optimal lateral roughness.The main work done in this paper is as follows.(1)In order to investigate the influence of contour milling machining parameters,tool structure and machining trajectory on the side roughness,the formation of workpiece side morphology during contour milling is analyzed from the geometric point of view,and the theoretical models of side roughness for linear,convex and concave arc milling are established by combining the characteristics of contour curvature.And the contour milling experiments with different radii of curvature and tool radii are constructed to verify the validity of the established theoretical model.Meanwhile,the theoretical model was further modified using polynomial regression and least squares to obtain a modified side roughness model in order to obtain a higher accuracy model.(2)A multi-objective optimization model with side roughness and material removal rate as the optimization objectives is established,and the improved multi-objective seagull optimization algorithm is used to solve the multi-objective optimization model and obtain the Pareto solution set of the multi-objective optimization model.Hierarchical analysis is then used to select the optimal machining solution,and the effectiveness of the solution is verified by milling experiments.(3)Based on the Pareto solution set obtained by the improved multi-objective seagull optimization algorithm,the data set is constructed with roughness as the input of the generalized neural network and machining parameters as the output,and the quantum genetic algorithm optimized generalized neural network algorithm(QGA-GRNN)is used to establish the inverse solution model of milling machining parameters to achieve the determination of machining parameters under the desired side roughness,which provides a methodological guide for the selection of machining parameters in the machining process.And the validity of the inverse solution model was verified by actual milling experiments.(4)In order to realize the intelligent selection of process parameters for cost reduction and efficiency enhancement in the milling process,the contour milling process system is developed based on MATLAB software,incorporating the modeling and optimization methods proposed in this paper,and the satisfactory combination of process parameters can be obtained by"click and run",which is simple to operate and effectively improves the intelligence of machining process design.