Aluminum alloy engine cylinder block casting process design optimization method based on BP neural network and fish swarm algorithm

Abstract

The invention discloses an aluminum alloy engine cylinder block casting process design optimization method based on a BP neural network and a fish swarm algorithm. The method is characterized by comprising the following steps: designing a process and establishing a numerical simulation analysis model; improving the process design; determining optimization variables and test design; establishing a BP neural network model; and performing fish swarm algorithm process parameter optimization and production inspection. The invention is high in reliability and applicability, the excellent casting process design and the optimal process parameter scheme of the aluminum alloy engine cylinder block can be effectively obtained, the casting forming quality can be improved, the development cost is saved, the development period is shortened, and reference is provided for development of the casting process of the automobile aluminum alloy engine cylinder block.

Description

technical field [0001] The invention belongs to the field of casting technology, in particular to the field of low-pressure casting technology, in particular to a casting process design optimization method for an aluminum alloy engine cylinder block based on BP neural network and fish swarm algorithm. Background technique [0002] As the core component of the automobile power system, the engine block supports and ensures the accurate position of moving parts such as pistons, connecting rods, and crankshafts when they work, so it needs to have sufficient strength and rigidity. The aluminum alloy engine block is generally box-shaped, with a relatively complex structure and uneven wall thickness. In the actual casting production process, due to the unreasonable design of the process structure or the inappropriate selection of process parameters, shrinkage and shrinkage defects are prone to occur at the wall thickness, such as the parts between the cylinders, which reduces the s...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a casting process design optimization method for aluminum alloy engine cylinder block based on BP neural network and fish swarm algorithm in order to solve the problems of low process design efficiency and difficult to guarantee the molding quality in the current engine casting process. The design method can Improve the molding quality of aluminum alloy engine block, reduce casting defects, greatly shorten the trial production time of new products, reduce trial production costs, and improve production efficiency

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