Preform optimization for hot forging processes using genetic algorithms

被引：35
|
作者：
Knust, Johannes [1 ]
Podszus, Florian [1 ]
Stonis, Malte [1 ]
Behrens, Bernd-Arno [1 ]
Overmeyer, Ludger [1 ]
Ullmann, Georg [1 ]
机构：
[1] Inst Integrierte Prod Hannover, Hollerithallee 6, D-30419 Hannover, Germany
来源：
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2017年 / 89卷 / 5-8期
关键词：
Preforming optimization; Hot forging; Evolutionary algorithms; Cross-wedge rolling; DESIGN;
D O I：
10.1007/s00170-016-9209-9
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：1623 / 1634
页数：12
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