Abstract

To this day, the design of preforms for hot forging processes is still a manual trial and error process and therefore time consuming. Furthermore, its quality vastly depends on the engineer’s experience. At the same time, the preform is the most influencing stage for the final forging result. To overcome the dependency on the engineer’s experience and time-consuming optimization processes this paper presents and evaluates a preform optimization by an algorithm for cross wedge rolled preforms. This algorithm takes the mass distribution of the final part, the preform volume, the shape complexity, the appearance of folds in the final part and the occurring amount of flash into account. This forms a multi-criteria optimization problem resulting in large search spaces. Therefore, an evolutionary algorithm is introduced. The developed algorithm is tested with the help of a connecting rod to estimate the influence of the algorithm parameters. It is found that the developed algorithm is capable of creating a suitable preform for the given criteria in less than a minute. Furthermore, two of the five given algorithm parameters, the selection pressure und the population size, have significant influence on the optimization duration and quality.

中文翻译：

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用于热锻过程瓶坯优化的遗传算法设计

摘要

时至今日，用于热锻工艺的瓶坯的设计仍是一个手工试验和错误的过程，因此很费时间。此外，其质量在很大程度上取决于工程师的经验。同时，预成型坯是最终锻造结果影响最大的阶段。为了克服对工程师经验和优化过程耗时的依赖，本文介绍并通过交叉楔形轧制预成型件的算法评估预成型件的优化。该算法考虑了最终零件的质量分布，预成型坯的体积，形状复杂性，最终零件中的褶皱外观以及飞边的出现量。这形成了多准则优化问题，从而导致搜索空间很大。因此，引入了一种进化算法。借助连杆对开发的算法进行测试，以估算算法参数的影响。可以发现，开发的算法能够在不到一分钟的时间内为给定的标准创建合适的预成型件。此外，五个给定算法参数中的两个，即选择压力和总体大小，对优化持续时间和质量有重大影响。