Preform optimization for hot forging processes using genetic algorithms
Preform optimization for hot forging processes using genetic algorithms
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DOI：
10.1007/s00170-016-9209-9
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发表时间：
2017-03-01
影响因子：
3.4
通讯作者：
Ullmann, Georg
中科院分区：
文献类型：
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作者：
Knust, Johannes;Podszus, Florian;Ullmann, Georg
在多道次热锻造工艺中，预制件形状是影响最终锻造效果的主要参数。然而，多阶段热锻造工艺的设计仍然是一个反复试验的过程，因此非常耗时。所开发的锻造序列的质量在很大程度上取决于工程师的经验。为了克服这些障碍，本文提出了一种解决预制件设计时的多目标优化问题的算法。以楔横轧(CWR)预制件为研究对象。提出了一种综合考虑最终零件质量分布、预制件体积和形状复杂性的优化预制件形状的进化算法。以连杆为例对所提出的算法进行了验证。在有限元分析的基础上，对所实现的适应度函数进行评估，从而跟踪渐进优化。
In multi-stage hot forging processes, the preform shape is the parameter mainly influencing the final forging result. Nevertheless, the design of multi-stage hot forging processes is still a trial and error process and therefore time-consuming. The quality of developed forging sequences strongly depends on the engineer's experience. To overcome these obstacles, this paper presents an algorithm for solving the multi-objective optimization problem when designing preforms. Cross-wedge-rolled (CWR) preforms were chosen as subject of investigation. An evolutionary algorithm is introduced to optimize the preform shape taking into account the mass distribution of the final part, the preform volume, and the shape complexity. The developed algorithm is tested using a connecting rod as a demonstration part. Based on finite element analysis, the implemented fitness function is evaluated, and thus the progressive optimization can be traced.