Landing Gear Cnc Machining Milling Optimization Study

Selection of optimal machining parameters for numerical control milling operations of undercarriage is related to productivity, cost and the quality of product. And it is unsolved important problem in the manufacture of aviation. There are many ways for selection of optimal machining parameters. With the mathematical model for cutting undercarriage and optimizaiton algorithm, the optimized cutting parameters will be found. And this is an impactful measure.The model of cutting parameter optimization about milling operations on undercarriage is established based on the restraints from cutting conditions such as work piece, cutter and machining center in use. Complex shape of undercarriage and particularity of material are analyzed in this dissertation. In this model, actual situations of opterations on undercarriage are considered. The objective function is determined not only by single objection (the least cutting time and the most material removal rate) but also multi-objections which is unified by multiply-division method. It can balance the two objectives according to the actual situations. Because of non-linear and non-protruding characteristics of the established model, a modern optimization algorithm---- genetic simulated annealing algorithm (GSA) is employed. In this algorithm, the constrained optimization function can be turned into an unconstrained optimization function by a sequential unconstrained minimization technique. This algorithm does not involve any complicated calculation such as derivative etc, so the global optimum that satisfies the retrained functions can be sought in a short time. Project application is achieved from the model to the result.In order to verify the practicability of the model and the algorithm, the experiments are carried out on cutting undercarriage of some types in Shannxi Liaoyuan Aero-mech Corporation. The result of the test shows that using the optimized parameters to machine work-piece can effectively shorten machining time and increase productivity. The experiments prove that the models for the optimization of machining parameters on the undercarriage and genetic simulated annealing algorithm to seek the optimum are correct and practicability, and offer the theoretical foundation for rational choice of cutting parameters.