Browse Publications Technical Papers 2019-01-1367

Leading Edge Assembly Real Time Process Monitoring Using Industrial Internet of Things (IIoT) 2019-01-1367

The increasing global demand for commercial aircraft creates many new challenges in manufacturing including an increased need to maximize the automation of manufacturing processes. The purpose of this research is to develop the understanding of leading edge assembly processes using robot mounted tooling and automated fixture with advanced process monitoring. Within this research real-time process monitoring data is acquired from an assembly operation and processed into an open cloud environment enabling advanced data analytics. Implementation of advanced analytics of process data could be developed for the use of machine learning algorithms which can lead to superior fault finding. The aim of this research is to improve product quality, reduce cost and increase process knowledge, enabling the potential for maximized online and offline process feedback. This paper details how an Industry 4.0 based open Industrial Internet of Things (IIoT) environment can be used to interface with a number of proprietary devices to enable real-time process monitoring in aerospace manufacturing. The implementation of the software and hardware is detailed and followed by an initial evaluation of the system architecture by performing leading edge assembly operations. In addition, a baseline of current IIoT systems is discussed, with the comparison to the specific architecture used here. The system is based on a wireless integration of proprietary devices and sensors feeding real-time data to an open cloud environment. Data is analysed and visualised in real time with online access and report generation. Data is supplied from the automated fixture and restricted access drilling end-effector prototype developed by the Manufacturing Technology Centre. Supplied process data includes hardware and system health, and environment measurements during the assembly operation. Analysed data allows to easily identify occurring abnormalities during assembly process which ultimately will allow for advancements in increased components quality and reduce manufacturing costs.


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