Aluminum alloy speed reducer shell casting parameter design method based on extreme learning machine

Abstract

The invention discloses an aluminum alloy speed reducer shell casting parameter design method based on an extreme learning machine. The method comprises the steps of designing a preliminary pouring process scheme for a main reducer shell model, preliminarily selecting process parameters having great influence on quality indexes as training sample points of an extreme learning machine, designing a test to obtain a training set and a test set, creating a training extreme learning machine and carrying out simulation test, and solving optimal process parameters through a fish swarm algorithm, and finally carrying out production verification. The casting method of the aluminum alloy main speed reducer shell is programmed, the extreme learning machine is combined with the fish swarm algorithm, the casting defects of the aluminum alloy main speed reducer shell are scientifically predicted and reduced, the forming quality is improved, and the manufacturing cost is reduced.

Description

technical field [0001] The invention relates to a method for designing casting parameters of an aluminum alloy reducer housing, which belongs to the technical field of casting molding, in particular to a method for designing casting parameters of an aluminum alloy reducer housing based on an extreme learning machine. Background technique [0002] As an important component, the automobile reducer housing provides support and protection for the main reducer, bears the load transmitted by the drive shaft, frame and road, and directly affects the reliability and handling stability of the vehicle. The traditional reducer housing material is nodular cast iron, but with the development of automobile lightweight, lightweight materials such as aluminum alloy have the characteristics of light weight and good comprehensive performance, and are gradually used in the manufacture of reducer housing components. [0003] At present, the main manufacturing method of the aluminum alloy reduce...

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that casting defects are difficult to eliminate in the casting process of the aluminum alloy reducer housing, and to provide a method for designing the casting parameters of the aluminum alloy reducer housing based on the extreme learning machine, which can scientifically and efficiently determine the optimum The combination of optimal process parameters improves the molding quality of the reducer housing, reduces casting defects, reduces manufacturing costs, and improves production efficiency

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