Abstract
5-axis flank machining has received much attention in various industries since the late 90s. This advanced machining operation is particularly suitable in manufacturing complex components such as turbine blades, compressors, molds, and automobile as well as aerospace structure parts. With two rotational degrees of freedom in the cutter motion, 5-axis flank machining offers superior shaping capability and reduces part handling tasks compared to traditional 3-axis machining. However, tool path planning becomes complicated, as the cutter is likely to collide with objects in the machining environment. Past studies tried to use meta-heuristic methods to solve this problem, such as particle swarm optimization, genetic algorithm, and ant colony optimization, but the search process is lack of efficiency and the solution quality is sometimes not acceptable. Thus, this research develops advanced tool path planning methods based on statistical techniques and completes the following tasks: “Electromagnetism-Like Algorithms for Optimized Tool Path Planning,” “Iterative Optimization of Tool Path Planning by Integrating Sampling Techniques,” “Multilevel Simplification of Solution Space in 5-Axis Flank Machining,” and “ Tool Path Planning based on Distribution of Tolerances.” Simulation results of representative test surfaces validate the effectiveness of the proposed methods. This work provides a feasible approach to controlling machining errors, which enhances the practical values of 5-axis CNC machining technologies.