Copper-alloy pipe-material casting-milling technology parameter designing and optimizing method

Abstract

The invention discloses a method for designing and optimizing cast-rolling process parameters of copper alloy tubing, using database as design basic, using nerve network as design method of process parameters and indexes, and using genetic algorithm as process parameter optimizing means, integrating nerve network, genetic algorithm, finite element simulation, experiment design, CAD parameterized design and database technique into the process design and parameter optimization, designing and optimizing the cast-rolling process parameters of the copper alloy tubing. And the invention has high automation degree, and can be applied to machining deformation of copper alloy tubing, and make personnel short of special knowledge able to make accurate and standard machining process.

Description

technical field [0001] The present invention relates to copper alloy pipe processing technology, specifically a method of applying neural network, genetic algorithm, finite element simulation, test design, CAD parametric design and database technology to process parameter design and optimization to determine the copper alloy pipe material. Method for optimal process parameters of casting and rolling production. technical background [0002] The casting and rolling process, also known as horizontal continuous casting-planetary rolling tube feeding method, is a method for the production of precision copper tubes developed and invented by the Finnish OUTOKUMPU company in the mid-1980s. This process has significant advantages such as short process, high yield, low cost, and less investment in equipment. It is the current advanced ARC copper tube production technology. It cancels ingot heating, extrusion, etc., and the hollow billet is directly produced by the horizontal continu...

AI Technical Summary

Problems solved by technology

However, metal forming is a very complex deformation process, which has both material nonlinearity and geometric nonlinearity, coupled with the influence of complex external constraints, resulting in a very complicated forming process

The interaction of various process parameters makes it difficult to use traditional reasoning machines to find the optimal combination of process parameters

Moreover, because the acquisition of empirical knowledge is indirect, it is difficult to acquire the knowledge of the reasoning machine, and the knowledge combination explosion is often caused by the poor structure of the knowledge base.

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