Abstract
5-axis machining technology has been widely applied in aerospace, automobile, energy, and mold industries. With two additional degrees of freedom in tool motion, this machining operation is highly suitable to manufacture of complex geometries. Compared to traditional 3-axis machining, it not only offers a good shaping capability, but also high productivity. 5-axis flank milling is normally used for producing ruled geometries. Its material removal rate is high due to the line contact of tool flank. Machining error control is highly complex and effective solutions are still lacking, though. Past research transformed the tool path planning in 5-axis flank milling into a mathematical programming problem and solved for the optimized tool path using evolutionary computation techniques. Experimental results show that this approach can effectively reduce the machining error. However, the computation efficiency is low and thus limits its practical value. This research improves optimized tool path planning in 5-axis flank milling from several aspects. First, the influence of initial solution on the quality of optimal solutions is analyzed. A PSO-based algorithm is proposed to produce local optimal solution, which is then used as the initiation solution in the later global optimization. Simulation results show that the machining error produced in this manner is lower than that of the initial solution randomly generated or the one computed by a heuristic method. Next, we propose a new concept of multi-pass tool path planning. The machined geometry at the current stage is used as the input to the next optimization stage. Greedy and non-greedy planning methods are proposed and compared. In addition, a multi-stage planning method is developed for dividing the surface to be machined into several strips. Binary search and SPSA methods are integrated and serve as a good mechanism of the surface subdivision. Finally, a novel path planning method is proposed for 5-axis flank milling based on CNC spline interpolation. The coefficients of the function that define the tool motion are alculated by SPSA. An adaptive scheme of surface subdivision is developed to guide the optimization process. In conclusion, this research proposes several new computation schemes to improve the effectiveness of the previous tool path planning methods. The practical value of the machining operation is this enhanced. Keywords: 5-Axis machining, tool path planning, particle swarm optimization, spline interpolation, Simultaneous perturbation stochastic approximation