Research On Optimization Of Laser Cutting Process Path Based On Ant Colony Algorithm

In today's society,energy conservation and waste of resources are increasingly valued by enterprises.In the pursuit of lower costs and high efficiency,laser cutting,with high efficiency,high-precision and other advantages,is more and more popular in the sheet metal processing enterprises.The design of processing path is an important part of laser cutting,which influences efficiency and quality of sheet metal processed,so the research on this problem is of significant social and economic value.Firstly,in the beginning of this paper,the problem of laser cutting path optimization is modeled,the laser cutting path optimization problem is defined and described.Through the analysis of the laser cutting path optimization problem,the parts to be cut and outline of the graphic elements such as mathematical definition and expression,the objective and constraint of the optimization problem in this paper are described mathematically.Then,an algorithm is proposed to solve the problem of laser cutting process path optimization,based on ant colony algorithm(ant Colony Algorithm,AC)to solve TSP(Travel salesman problem,TSP).The algorithm adopts candidate city set strategy,in order to deal with the constraint of the problem,the adjacent parts set strategy and the deformation-resistant sequential cutting method are established to meet the requirements of collision-proof,nonspan and cut smoothly.Through the example verification,this algorithm compared with the traditional heuristic algorithm figures out a better optimization result.Lastly,this paper puts forward a reduced-dimension strategy for the path optimization of complex outline problem.In this paper,large-scale,multiple nesting and batch repetition are researched,area-segmentation method,tree structure layering method and path replication strategy are proposed,and the complex path optimization problem is simplified.The example verifies that the dimensionality reduction strategy can effectively reduce the problem scale and make the algorithm widely used availably.