Optimization of Casting Process for Engine Stainless Steel Parts Combining PSO-BP and GA

余文洋; 何福善; 姚尧; 黄晔; 向红亮; 黄金亮

doi:10.13433/j.cnki.1003-8728.20230364

Chinese

您当前的位置：

首页 >

文章列表页 >

Optimization of Casting Process for Engine Stainless Steel Parts Combining PSO-BP and GA

更新时间：2026-04-23

- Optimization of Casting Process for Engine Stainless Steel Parts Combining PSO-BP and GA
- Mechanical Science and Technology for Aerospace Engineering Vol. 44, Issue 11, Pages: 1966-1973(2025)
- 作者机构：
  
  1. 福州大学先进制造学院,福建,泉州,362251
  2. 泉州锦煌机械有限公司,福建,泉州,362013
- 作者简介：
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20230364
  CLC：
- Published：2025
- 稿件说明：
移动端阅览
余文洋, 何福善, 姚尧, et al. Optimization of Casting Process for Engine Stainless Steel Parts Combining PSO-BP and GA[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(11): 1966-1973.
DOI：

余文洋, 何福善, 姚尧, et al. Optimization of Casting Process for Engine Stainless Steel Parts Combining PSO-BP and GA[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(11): 1966-1973. DOI： 10.13433/j.cnki.1003-8728.20230364.

摘要

针对发动机不锈钢配件熔模铸造出现缩松缩孔等铸造缺陷问题，采用粒子群优化神经网络结合遗传算法优化工艺参数的模型。借助正交试验分析法与PROCAST软件数值模拟，利用误差反向传播神经网络建立浇注温度、模壳焙烧温度、浇注速度与铸件的缩孔体积、等效应力之间的非线性映射关系。通过粒子群算法优化反向传播神经网络并结合遗传算法全局寻优，获得熔模铸造较佳工艺参数组合。结果表明，当浇注温度为1581 ℃、模壳焙烧温度为1159 ℃、浇注速度为1 kg/s时能有效改善熔模铸造铸件的缩孔体积和等效应力问题。

Abstract

Aiming at the casting defects of engine stainless steel parts such as shrinkage and porosity

the particle swarm optimization neural network combined with genetic algorithm is used to optimize the processing parameters. With the help of the orthogonal test method and numerical simulation via PROCAST software

Back propagation neural network is used to establish the nonlinear mapping relationship among the casting temperature

mold shell temperature

casting speed

and the shrinkage volume and equivalent stress of the casting. By optimizing back propagation neural network with particle swarm optimization algorithm and global optimization with genetic algorithm

the optimal processing parameter combination of investment casting is obtained. The results show that when the pouring temperature is 1581 °C

the mold shell temperature is 1159 °C and the pouring speed is 1 kg/s

the shrinkage volume and equivalent force of investment casting castings can be effectively reduced.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Study on Automotive Braking Intent Recognition Method

Particle Swarm Fuzzy PID and Deep Compensation Strategy for PMSM Position Control

Aesthetic Evaluation Method Combining Genetic Algorithm and Improved CRITIC-GRA-TOPSIS

Progress in Numerical Simulation of Plasma Arc Welding

Size Optimization Design of Solar Composite Wing Structure Using Genetic Algorithm

Related Author

郭玉凤

吴文文

王长青

孟云龙

周明岳

孟宏杰

陈峙

郑少华

Related Institution

College of Mechanical Engineering, Hebei University of Technology

CATARC Automotive Test Center (Tianjin) Co., Ltd.

Shenzhen Costan Hydraulic Equipment Co., Ltd.

School of Mechanical Engineering, Taiyuan University of Science and Technology

School of Mechanical Engineering, Guizhou University

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰