Iterative optimization of tool path planning in five-axis flank milling by sampling techniques

Optimization of tool path planning using meta-heuristic algorithms provides a feasible approach to reducing geometrical machining errors in 5-axis flank machining of ruled surfaces. However, these algorithms experienced unsatisfactory quality of optimal solutions and lengthy search time in high-dimensional search space. To solve this problem, we propose an iterative optimization framework by integrating sampling techniques. First, significant factors are identified by Akaike Information Criterion (AIC) from sample points generated by various sampling techniques. Simplified solution space is constructed only with those significant factors. Electromagnetism-like Mechanism (EM) is then applied to search through the simplified solution space constructed using those significant factors. Final optimal solutions are obtained after several iterations of the previous three steps. The test results of representative surfaces validate the effectiveness of the proposed framework. The solutions are similar to those of previous work while the number of calculations was significantly reduced.