Research Of Cutting Stock And Tool Path Algorithm Based On DXF Format

Nowadays,the development trend of computer science and technology is rapid,which brings pressure and opportunities to the surrounding traditional enterprises.Traditional enterprises try their best effort to improve production equipment,improve the utilization of materials and make profits by using the power of science and technology.Two important problems have existed in the field of industrial manufacturing for many years.One is the nesting problem,the other is the cutting path problem.Effective solution of nesting problem can improve material utilization and production efficiency greatly.The solution of cutting path problem is an important foundation of the automatic of NC machining and it is of great significance to realize the automation of machining.In this paper,DXF file's parse is the starting point of the study,and focus on the nest and cutting path problem,and present relevant solutions and implementation of algorithm.Through the test and analysis of the algorithm,the feasibility of the algorithm is demonstrated.For DXF files parsing,a table-driven model is adopted.Because of inconsistency storage of primitives in DXF files,an optimized method is proposed and the data conversion is achieved,which provides support for nest and cutting path problem.In the problem of nest problem,some basic algorithms of computational geometry are introduced.Then,various nesting algorithms based on rectangular parts,such as BL algorithm and walk down algorithm,are described.Finally,a new nesting method based on NFP and genetic algorithm is proposed.The construction of NFP is used for non-overlapping placement of different components,while genetic algorithm is used for global optimization.When talking about cutting path problem,some algorithms are introduced.Such as nearest neighbor algorithm and optimal nearest neighbor algorithm based on binary tree,whose advantages and disadvantages are analyzed.Next,we transform the cutting path problem into TSP problem,and use an improved ant colony algorithm to solve the TSP problem.