Adaptive Optimization Method of Hole Group Machining Path

Abstract

The invention discloses an adaptive optimizing method for a hole group processing path. The adaptive optimizing method for the hole group processing path comprises the following steps: randomly generating an initial antibody group A when a hole group is processed, wherein each antibody in the initial antibody group A corresponds to a processing sequence of the hole group; evaluating the initial antibody group A according to an optimization condition, and retaining elite antibodies in the initial antibody group A to form an antibody group B; cloning every elite antibody in the antibody group Bto obtain an antibody group C; applying full variation operations including overturning operation and deleting operation on every elite antibody in the antibody group C, and if receptor editing fails,applying random variation to obtain an antibody group D; carrying out quantum crossover on every elite antibody in the antibody group B to obtain an antibody group E; selecting high-quality antibodies from the elite antibodies in the antibody group D and the antibody group E to form an antibody group F; and judging whether conditions are met, and if the conditions are not met, repeating the steps. By the method, the length of the processing path can be shortened, and meanwhile, the frequency of inverse conversion of a knife is reduced.

Description

technical field [0001] The invention relates to the field of numerical control machining control in mechanical engineering. More specifically, the present invention relates to an adaptive optimization method for the machining path of a hole group. Background technique [0002] Hole group machining is a typical process in CNC machining, and the rationality of machining path selection will directly affect machining efficiency and machining cost. The hole group machining path optimization problem (HMPOP) aiming at shortening the tool moving distance, reducing the number of tool replacements and the number of reverse tool transformations has become the key problem of hole group machining. In the prior art, there are simple genetic algorithms and improved genetic algorithms based on the league selection operator and the simulated annealing crossover operator to optimize the HMPOP problem. Some researchers have also applied ant colony algorithm, particle swarm algorithm and arti...

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Problems solved by technology

However, the above algorithms simplify HMPOP into a single-objective optimization problem that only considers the cost of tool movement

There are also literatures that use the artificial immune algorithm to solve the HMPOP problem. Although this algorithm performs multi-objective optimization for the two items of tool movement distance and tool reverse order transformation times, its performance in the single-objective optimization solution is still inferior to that of the improved genetic algorithm and fish swarm algorithm.

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