Neural Network Hyperparameter Optimization for the Assisted Selection of Assembly Equipment

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.