Neural Network Hyperparameter Optimization for the Assisted Selection of Assembly Equipment

Simon Hagemann, Atakan Sünnetcioglu, T. Fahse, R. Stark
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Abstract

The design of assembly systems has been mainly a manual task including activities such as gathering and analyzing product data, deriving the production process and assigning suitable manufacturing resources. Especially in the early phases of assembly system design in automotive industry, the complexity reaches a substantial level, caused by the increasing number of product variants and the decreased time to market. In order to mitigate the arising challenges, researchers are continuously developing novel methods to support the design of assembly systems. This paper presents an artificial intelligence system for assisting production engineers in the selection of suitable equipment for highly automated assembly systems.
装配设备辅助选择的神经网络超参数优化
装配系统的设计主要是一项手工任务,包括收集和分析产品数据、推导生产过程和分配适当的制造资源等活动。特别是在汽车工业装配系统设计的早期阶段,由于产品种类的增加和上市时间的缩短,复杂性达到了一个相当大的水平。为了减轻出现的挑战,研究人员不断开发新的方法来支持装配系统的设计。本文提出了一种人工智能系统,用于帮助生产工程师选择适合高度自动化装配系统的设备。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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