Numerical control (NC) drilling path optimization method and system and NC drilling equipment

Abstract

The invention relates to a numerical control (NC) drilling path optimization method and a system and NC drilling equipment. The NC drilling path optimization method includes the following steps: analyzing steps: analyzing reading drilling files, and obtaining drilling boot files and N groups of hole site information files, wherein N is the number of drilling tools of different apertures; optimizing steps: optimizing processing is conducted to the N groups of hole site information files by adopting a cataclysm genetic algorithm, and obtaining N groups of post-optimized hole site information files. According to the NC drilling path optimization method and the system and the NC drilling equipment, the cataclysm genetic algorithm is adopted to optimize machining paths. Therefore, the NC drilling path optimization method and the system and the NC drilling equipment have the advantages of fast searching optimal solutions and greatly reducing computations of a traditional genetic algorithm, and meanwhile the problem of stucking in local extremum is avoided.

Description

technical field [0001] The present application relates to the technical field of printed circuit board (PCB) numerical control, in particular to a numerical control drilling path optimization method and system, and a numerical control drilling device. Background technique [0002] PCB board is one of the most important components in electronic equipment. The PCB drilling process is the most important link in the PCB manufacturing process. There are usually many holes of different diameters on the PCB. The tool path of PCB drilling can be described as: start from the tool change point, process all the holes of the same diameter without repetition and return to the tool change point for tool change operation. Another diameter of hole is machined until all holes to be machined are completed. The existing tool path of PCB CNC drilling is usually based on the automatic generation of the tool path by the automatic PCB programming system, such as figure 1 As shown, it is only ne...

AI Technical Summary

Problems solved by technology

The TSP problem is a complete polynomial non-deterministic problem, referred to as NP (Non-Deterministic Polynomial) complete problem, which is easy to define but difficult to handle

However, although the advantage of the greedy algorithm is that it chooses the optimal solution at each step of solving the problem, so that the algorithm is easy to implement and easy to understand, and it also improves efficiency and saves time, but the greedy algorithm obtains the optimal solution step by step. The method of the optimal solution does not consider the overall optimality, and what it makes is only a local optimal solution in a certain sense

Therefore, the greedy algorithm cannot obtain the overall optimal solution for all problems.

However, the relative convergence speed of the simulated annealing algorithm is too slow. If the cooling process is too fast, the global optimal solution may not be obtained.

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