锻压技术

汽车尾气后处理系统铝泵体的精锻工艺智能优化

英文标题：Intelligence optimization on precision forging process for aluminum pump body in automobile exhaust aftertreatment system

作者：李曙辉邵毅明

单位：北京交通运输职业学院重庆交通大学

关键词：铝泵体人工蜂群算法中心复合实验二阶响应面模型决定系数

分类号：TG319

出版年,卷(期):页码：2021,46(2):1-8

摘要：

为了提高铝泵体精锻过程中的材料利用率和模具使用寿命，建立了目标参数与工艺参数的响应面模型，提出了基于人工蜂群算法的参数优化方法。介绍了汽车尾气后处理系统中铝泵体的中间体和坯料，并设计了开式模锻模具。使用中心复合实验法设计了4因素5水平的30组实验，基于Deform-3D有限元软件得到了实验结果。使用二阶响应面模型建立了目标参数与工艺参数之间的回归关系，利用模型的决定系数验证了模型的拟合精度。设计了蜜蜂位置和蜜源适应度函数，成功地将最优参数优化问题转化为人工蜂群搜索最优蜜源问题。经实验验证，优化后的铝泵体锻件填充饱满、力学性能参数满足标准要求。且优化后的材料利用率提高了4.41%，模具使用寿命也得到了极大提高。

To improve material utilization rate and mold service life in precision forging process of aluminum pump body, the response surface model between object parameters and process parameters was built, and the optimization method of parameters was proposed based on artificial bee colony algorithm. Then, the forged center body and blank of aluminum pump body in automobile exhaust aftertreatment system were introduced, and the mold of open die forging was designed. Furthermore, thirty sets of experiments with four factors and five levels were designed by central composite experiment method, and the experimental results were obtained by finite element software Deform-3D. Finally, the regression relationship between object parameters and process parameters was constructed by two-order response surface model, and the model fitting accuracy was clarified by the determination coefficient of model. In addition, the bee location and bee source fitting function were designed, and the optimization problem of optimal parameters was successfully transformed into a problem of artificial bee colony searching for the optimal nectar source. The experimental verification shows the optimized aluminum pump body forgings are fully filled, and the mechanical property parameters meet the standard requirement. Besides, the optimized material utilization rate increases by 4.41%, and the service life of mold is improved greatly.

基金项目：

中国交通教育研究会教育科学研究课题 (交教研1802-129)

李曙辉（1972-），男，硕士，副教授，E-mail：lishuhui72727@163.com

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