Path Optimization Of NC Plasma Cutting Machine

Nowadays china's shipbuilding industry develops rapidly, and various advanced auto-machine is applied to manufacture in order to improve efficiency. Steel cutting is the first working procedure of shipbuilding, and advanced NC plasma cutting machine is used widely. The program's path programming of NC plasma cutting machine affect the cutting efficiency directly. In this paper, we research the cutting path scheduling, and suggest two different optimization algorithm for the cutting path. And we expect that they can reduce the cutting blank line, increase effective cutting time, so as to improve the cutting efficiency.Cutting path optimization problem can be return or changed into TSP(Traveling Salesman Problem). TSP is one of the typical NP-hard problems in combinatorial optimization problem which is known for its diverse applications. Because of difficulty of its solution, especially for large-scale problem, for long time researchers had been trying to find fast and efficient approximate algorithms in order to solve problem in reasonable time.Now we currently adopt traditional heuristics methods and modern heuristics algorithms to solve the TSP, which search the reasonable solution by experience and principle.We choose greedy algorithm in traditional methods. Greedy algorithm is one-step algorithm, and mainly controlled by greedy criterion. Greedy algorithm is easy to realize, it's computing time is short, and optimization result is acceptable. To solve the problem that its solution isn't compared with other viable solutions, this paper first put forward comparing the local viable solutions in local points to ask for better solution. Experiment proved that the cutting path generated by greedy algorithm is obviously better than the path generated by TRIBON software.The genetic algorithm (GA) is a highly parallel, random and self-adapting search algorithm, which refer to the biology heredity and evolution process. It is especially fit for complex and non-linear problem which traditional search algorithm can't deal with well. The various parameter of genetic algorithm all affect the final result, and the parameters such as colony size, heredity generations etc control the computing time. The choice of algorithm's parameter is still lack of powerful theoretic proof, so we must ensure the rationality of parameters via much experiment. In This paper we ameliorate the basic genetic algorithm. Firstly we use a new Chromosome coding to reduce the length of path coding,to debase the degree of complication. Secondly we apply reformative select arithmetic operators. In initial stage it minish the diversity of the individual's fitness degree to avoid"prematurity", and in end stage it increase the diversity of the individual's fitness degree to convergence rapidly, to improve the algorithm's efficiency. Experimentations validate the validity of the algorithm, which reduce the cutting blank line averagely 15%.In conclusion, greedy algorithm and genetic algorithm have shown some potential to deal with cutting path optimization problem, and gain preferable result. But they all can't soling the problem to the nines。Actually we should adopt appropriate algorithm in term of different request and different instance. At last, we expect the future of this research.