Concept for an augmented intelligence-based quality assurance of assembly tasks in global value networks

Abstract The aim of this paper is to present a conceptual approach to an augmented intelligence-based worker assistance system in manual assembly. This approach is designed to address current challenges in global value networks. We propose a self-learning multi-camera system that (1) provides augmented reality-based assembly instructions and (2) enables automated real-time in-process testing of complex manual assembly operations by using visual camera and CAD data, operational experiences and expert knowledge. As the proposed solution is targeted at enabling SMEs, cost-effectiveness is a main goal of the conceptual approach. Consequently, weak artificial intelligence is applied to realise the algorithmic chain subject to performance restricted hardware. The approach states a novelty in research and development and contributes to practical application in the field of augmented intelligence.