Development and deployment of a digital twin for monitoring of an adaptive clamping mechanism, used for high performance composite machining

Abstract

Abstract In this work, we present a cloud‐based digital twin for monitoring of a clamping technology for machining of composite parts. Supporting large and/or freeform composite parts is crucial to avoid bending during drilling. Bending of the part will lead to delamination and frayed edges of the drilled holes. The new active clamping technology allows to realise a stabilised fixture, localised in the area where the drilling occurs, to avoid bending. This significantly improves the quality of the drilled holes. The clamping device is equipped with an IoT edge device, with a bidirectional communication to the cloud. The cloud‐based digital twin analyses the quality of the drilled holes based on computer vision, monitors the drill wear and detects incorrect operation of the active clamping device. All data is stored in the cloud. By means of a knowledge graph, which acquires and integrates information into an ontology and provides a central information access, it will be easier for a data scientist to query this data and to gain new insights in the operation of the drill with active clamping device. The full deployment occurs on the Microsoft Azure cloud platform. This transforms the standard machine into an Industry 4.0 compliant machine.

Keywords

• data analysis
• intelligent manufacturing systems
• knowledge based systems