Assembly Process Digitization Through Self-learning Assistance Systems in Production

Abstract

AbstractAs product specifications change, manufacturing processes have to adapt. In manual production tasks, the human worker is forced to adapt at the same pace. Fast-changing work tasks lead to high stress and therefore increase failures. Digital assistance systems aim to support the human workforce by providing assembly instructions at the right time and in the right place to reduce the cognitive load. The latest digital assistance systems provide multimodal human-machine interfaces, such as augmented reality, haptic feedback, and voice control to provide information or react to the user's input. However, those digital assistance systems require the manufacturing information themselves, which are mostly provided through text-based or graphical programming. Both manufacturing experts and programmers are needed to create a digital assistance system workflow or adapt it to changes. This process is costly, time-consuming, and inflexible. This work presents a gesture recognition based approach for a self-learning digital assistance system. Therefore, assembly gestures are classified based on anatomical grip descriptions. Assembly sequences are recognized and learned by the digital assistance system using machine learning techniques. The learned procedures are used to automatically generate work instructions and guide the worker through the assembly task.KeywordsDigital assistance systemHuman-machine interactionDigitization