Optimal Selection Of Cutting Parameters In Blade NC Machining Based On Bp Neural Network And Genetic Algorithm

Aeroengine blade belongs to the typical thin-walled freeform surface part that is one of the key components of an aircraft engine. The blade is prone to deform in CNC milling. Therefore, it is difficult to ensure its processing accuracy and quality. Blade machining parameters optimization studies are important for improving its machining quality and precision, reducing costs, and improving the utilization of tools and equipment.In order to grasp the processing rules and mechanisms of the aeroengine blade, and seek the best cutting parameters, in the paper, computer simulation and cutting experiments are used to obtain the blade deformation rules and establish the relation between the blade deformation and cutting parameters. Taking the minimal deformation as the goal, a cutting parameters optimization model is established. Finally, a blade milling parameters optimization scheme is presented to guide rapid and reasonable cutting parameters selection.The main contents of the research are as follows:(1) Based on metal cutting theory, taking ball-end cutter spiral milling as the research object, a milling model of an aluminum blade is established using finite element analysis software ABAQUS. Based on milling simulation, blade milling deformation rules are analyzed and verified by actual milling experiments.(2) Different blade cutting parameters are used to simulate the milling process to obtain the corresponding blade deformation. An artificial neural network (ANN) is used to summarize the blade milling deformation relation between the cutting parameters and blade deformation. After the ANN is trained, the relations between the inputs and output are established, namely, the relations between the cutting parameters and blade deformation are established. Thus, a blade milling deformation prediction model is constructed.(3) After determining the design variables, objective function and constraints of cutting parameters optimization, an optimization model for aluminum blade milling is built to optimize the cutting parameters using a genetic algorithm (GA). The results of finite element analyses and milling experiments show that the optimized cutting parameters can achieve higher precision, lower costs and higher productivity.