Hybrid genetic algorithm for minimizing non productive machining time during 2.5 D milling
Abstract
Minimization of non-productive time of tool during machining for 2.5 D milling significantly reduces the machining cost. The
tool gets retracted and repositioned several times in multi pocket jobs during rough machining which consumes 15 to 30% of total machining time depending on the complexity of job. The automatic CNC program commonly generates contour parallel
tool path. Optimization of tool path length during on-productive time can be modeled on Traveling Salesman Problem (TSP)
which belongs to family of non-deterministic polynomial (NP) hard problem. In the present work, a Hybrid Genetic Algorithm
(HGA) has been proposed to optimize the non-productive tool path in which the initial seed solution is generated by special
heuristic and combined with random initial solution generated by simple genetic algorithm (SGA). A defined performance index
known as Relative percentage deviation (RPD) has been used for analyzing the results by varying the size of the jobs. From the analysis, it is found that HGA shows superiority over SGA for same computation time limit as the stopping criteria.
tool gets retracted and repositioned several times in multi pocket jobs during rough machining which consumes 15 to 30% of total machining time depending on the complexity of job. The automatic CNC program commonly generates contour parallel
tool path. Optimization of tool path length during on-productive time can be modeled on Traveling Salesman Problem (TSP)
which belongs to family of non-deterministic polynomial (NP) hard problem. In the present work, a Hybrid Genetic Algorithm
(HGA) has been proposed to optimize the non-productive tool path in which the initial seed solution is generated by special
heuristic and combined with random initial solution generated by simple genetic algorithm (SGA). A defined performance index
known as Relative percentage deviation (RPD) has been used for analyzing the results by varying the size of the jobs. From the analysis, it is found that HGA shows superiority over SGA for same computation time limit as the stopping criteria.
