Optimization Of Cutting Parameters Of Hole-making Operation Using Helical Milling Based On Dynamic Simulation

As an advanced hole-making technology,helical milling is widely used in aerospace,automobile manufacturing,rail transportation and other fields.During helical milling process,the geometrical parameters of the cutting tool,the cutting performance of workpiece material and the cutting conditions will have great effect on its cutting force,the surface quality of the machined hole and the process stability.This paper focuses on the dynamic modeling of helical milling process,the machining path optimization of hole groups and the cutting conditions optimization of single hole,with a aim to realize high performance and high quality hole-making operation using helical milling.The main research contents are as follows.First of all,based on thoroughly study on its cutting mechanism,it is found that there are two types of cutting form such as peripheral milling of side cutting edges and plunge milling of end cutting edges in helical milling process.On the basis,the prediction of dynamic cutting forces during helical milling process is implemented by discrete process of the helical cutting edges.Meanwhile,a new type of stability lobe diagram represented by the ratio of the axial feed rate per tooth to the tangential feed rate per tooth with the corresponding spindle speed is derived by calculating the average directional cutting force using numerical approach.The cutting force and cutting process stability tests result shows that the proposed cutting force model and the chatter stability model are correct.Based on the developed simulation programs,the effect of the cutting parameters on the cutting forces and the process stability is analyzed systematically,and some useful conclusions are obtained which can be used to guide helical milling operation in practical engineering applications.Secondly,it is found that the optimization problem of helical milling process can be divided into two sub optimization problem such as hole group machining path optimization and the cutting condition optimization of single hole,and the first one is naturally a typical TSP problem.After thoroughly analyzing the existing methods for solving the TSP problem,a novel improved hybrid genetic algorithm is proposed in the paper,basedon the classical genetic algorithm.The algorithm uses a fitness function with a variable penalty term to guide the genetic search,and select the excellent individuals from the initial population produced in the early stage of the ant colony optimization to construct the initial solution.As a result,the initial population of higher quality and better diversity can be obtained,and thus the efficiency and accuracy of the algorithm can be achieved.By comparing the simulation results of three algorithms and conducting the corresponding experiments,the correctness and efficiency of the proposed algorithm is validated.Lastly,in order to solve the cutting conditions optimization problem of single hole,a nonlinear optimization model is setup in which the cutting efficiency is used as the objective function,the cutting parameters are used as the design variable,the constraint conditions such as cutting forces,cutting power,surface topography as well as the process stability are taken into consideration.Furthermore,a optimization algorithm with variable search region is proposed which can obtain the required cutting parameters rapidly by altering the search scope during the solving process.The optimization results and the cutting tests have verified that the proposed optimization model and the corresponding solving algorithm are correct and valid,which can provide a feasible technical solution for high efficiency helical milling operation.