Path Optimization Algorithm Of NC Spraying

With the rapid world economic development, the competition of ship industry is becoming increasingly intense which urgently requests for manufacturing technology improvement. The Improving the processing efficiency and quality, shortening the shipbuilding cycle, reducing the cost are the eternal pursuit of shipbuilding industry. Mark assembly symbols and construction information for cutting parts is essential. NC spraying has gradually become a new digital processing method. This article studies the optimization of the NC spraying path to reduce its consuming time and thus improve spraying efficiency.In this article, according to the spraying characteristics, firstly the characters on steel plates are geometrically abstracted to directed line segment. Path optimization problem is simplified to the problem that is calculating the shortest path by determining the characters of each word segment.Such path optimization problem can be transformed into TSP problem in math. we're going to look for some kind of algorithm which can fast and efficiently solve problems within a reasonable amount of time. There are many algorithm used in solving TSP problem.Next, the greedy algorithm and genetic algorithm are adopted to optimize spraying path in this paper. Greedy algorithm is mainly controlled by the law of greed, so the solution is not the most optimal one. But it needs less time and can get better results of optimization. In order to obtain better solution, this paper puts forward the improved greedy algorithm. The parameters of genetic algorithm is the key effect to the calculation results of genetic algorithm. The computational complexity of genetic algorithm is directly influenced by the population size and genetic algebra. This article uses their experience for reference to determine the parameters of the genetic algorithm, and then the objective function of optimal plasma cutting, applying the genetic algorithm MATLAB toolbox to solve the optimal problem.At last, by comparing greedy algorithm and genetic algorithm, the conclusion is drawn: in solving cutting path optimization, greedy algorithm and genetic algorithm has their own advantages and disadvantages, either algorithm is likely to be perfect. In view of the different engineering problems, suitable algorithm should be choosed. Finally the prospects for future research are put forward.