Optimization of tool path planning in 5-axis flank milling of ruled surfaces with improved PSO

Abstract

Previous studies have shown that machining error in 5-axis flank milling can be systematically reduced by optimization of tool path planning. However, the solution quality of the optimization methods adopted by those studies was not satisfactory, due to the constraint that the cutter must contact the boundary curves of the ruled surface to be machined. This work proposes an improved tool path planning method based on Particle Swarm Optimization (PSO) algorithms without this constraint. The method enlarges the feasible region in the optimization by allowing cutter location to deviate the boundary curves along the normal, tangent, and bi-normal directions. Design of experiment techniques are applied to determine better parameter setting in the PSO. A new objective function is formulated as the weighted sum of the errors induced by overcut and undercut. We can choose to minimize the total machining error, the overcut, or the undercut by properly adjusting the weight value. Compared to previous methods, the improved path planning method produces smaller machining error and offers better planning flexibility.

Optimized tool path planning in 5-axis flank machining using electromagnetism-like algorithms

“…The number of interpolation between consecutive cutter locations also relates to the precision. The values of those parameters were chosen as recommended by the previous studies [3][4][5].…”

“…Previous studies [2][3][4] have shown that meta-heuristic algorithms provide a systematic approach to controlling and reducing the geometrical deviations in 5-axis flank finishing cut of ruled surfaces through optimization of tool path planning. However, the optimization schemes based on ACS and various PSO algorithms failed to produce good search results due to high dimensionality of the solution space.…”

Improvement of Topology Algorithm's Convergence Rate Using Chaotic Map

“…Therefore, it is expected that the proposed algorithm can effectively be applied to dynamic topology optimization to improve the convergence rate. 법으로부터 자연 모방 최적화 방법인 입자 군집 최적화 (particle swarm optimization: PSO) [3] , 개미 군집 최적화(ant colony optimization: ACO) [4] 그리고 인공벌 군집 알고리즘 (artificial bee colony algorithm: ABCA) [5] 등이 있다.…”