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Assembly tolerance optimization designing method based on genetic algorithm

A technology of assembly tolerance and genetic algorithm, applied in calculation, gene model, special data processing application, etc.

Inactive Publication Date: 2014-07-02
XIJING UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

There are many factors that affect the relationship between processing cost and tolerance, and it is difficult to use a unified mathematical model to accurately describe the relationship between processing cost and tolerance of all features

Method used

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  • Assembly tolerance optimization designing method based on genetic algorithm
  • Assembly tolerance optimization designing method based on genetic algorithm
  • Assembly tolerance optimization designing method based on genetic algorithm

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Embodiment Construction

[0076] The above and other technical features and advantages of the present invention will be described in more detail below in conjunction with the accompanying drawings.

[0077] The invention takes the minimum processing cost as the optimization goal, comprehensively considers the size tolerance and shape tolerance, and establishes a new cost-tolerance model. The total tooling cost of an assembly consists of the tooling costs of all attached parts, and the tooling cost of a part consists of the tooling costs of all its feature faces. Not every feature face participates in the assembly of the part. Since the present invention mainly considers the optimization design of assembly size tolerance and shape tolerance size, only the processing cost of the assembly feature surface is considered.

[0078] The process of the assembly tolerance optimization design method based on cost target optimization of the present invention is as follows:

[0079] Step a, determine the minimum ...

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Abstract

The invention relates to an assembly tolerance optimization designing method based on the genetic algorithm. The method comprises the steps that a target function for assembly tolerance optimization is established with the minimum processing cost of an assembly body as the optimization target; the constraint conditions of assembly tolerance are determined; tolerance type information is added to a VGC network to obtain a tolerance network of the assembly body and the value range of dimensional tolerance of various assembly features and the value range of geometric tolerance of the assembly features are selected and determined; genetic coding is conducted by the adoption of a multi-parameter cascade coding method; a fitness function is determined; an operator function is selected and determined; operating parameters of the genetic algorithm are determined. According to the assembly tolerance optimization designing method based on the genetic algorithm, the genetic algorithm shows great application potential through the global searching capacity, the high robustness and the calculation parallelism.

Description

technical field [0001] The invention relates to a method for optimizing design of mechanical assembly tolerance, in particular to a method for optimizing design of assembly tolerance based on genetic algorithm. Background technique [0002] The assembly size tolerance and geometric tolerance of parts in the prior art are generally determined according to the product’s performance requirements, assembly function requirements, quality assurance, processing materials, production conditions, manufacturing costs, and corresponding national, industry, or enterprise standards. of. [0003] In the design stage, how to correctly and reasonably select the assembly tolerance value is a design problem that must be considered comprehensively. It is of great significance to ensure the assembly and performance of the product, improve product quality, and reduce manufacturing costs. Among the factors that affect the cost of machining a part, tolerances play a very important role. The smal...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/12
Inventor 张毅张梦旖曾祥福
Owner XIJING UNIV
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