Intelligent assembly sequence planning method

Abstract

The invention discloses an intelligent assembly sequence planning method. The intelligent assembly sequence planning method includes the following steps of modeling an assembly relationship among sub-parts of assembly bodies by means of an interference matrix manner, evaluating an assembly sequence by means of an objective function, expressing the assembly sequence by means of a permutation matrix manner, converting the objective function into a matrix manner, subjecting an assembly sequence planning problem to iteration solving by means of a progressive non-convex and concave progress optimizing framework, converting the solved permutation matrix into a part number sequence which is a result of assembly body assembly sequence planning. Compared with traditional assembly sequence planning methods, the intelligent assembly sequence planning method has the advantages that under the condition that effectiveness of the assembly sequence planning result is guaranteed, solving speeds are greatly accelerated.

Description

technical field [0001] The invention relates to the technical field of industrial automation, and more specifically, to an intelligent assembly sequence planning method. Background technique [0002] Assembly costs account for 40% to 50% of product manufacturing costs, and assembly automation has always been a bottleneck in manufacturing automation. The assembly sequence is one of the important information describing the product assembly process, and its advantages and disadvantages directly affect the product's assemblability, assembly quality and assembly cost. Assembly sequence planning (ASP) is to find the optimal assembly sequence to guide product assembly under various constraints, so as to reduce product assembly cost and improve product assembly quality. The assembly sequence planning problem is an NP-hard combinatorial optimization problem, which is very important to the product assembly process, because it directly determines the speed, accuracy and stability of p...

AI Technical Summary

Problems solved by technology

The advantage is that the increase in the amount of calculation brought about by the increase in the number of parts is at the polynomial level, and the disadvantage is that the global optimal solution may not be obtained

[0004] The traditional assembly sequence planning algorithm has strong limitations. Although the cut set algorithm based on graph theory can obtain the global optimal solution and the optimal assembly sequence, as the number of parts increases, combination explosions will occur, and the amount of calculation will increase dramatically. increase, resulting in a situation that is difficult to solve, so this algorithm is only suitable for the case of a small number of parts

Knowledge-based expert system methods are usually more effective for specific types of products, but when the types of assemblies differ greatly, the coverage of the knowledge base is difficult to meet the requirements

The search algorithm based on intelligent optimization, although the computational complexity is reduced, but when the number of parts is very large, the amount of calculation is unbearable, and at the same time, it is difficult for the algorithm to obtain the global optimal solution

Images