Elastic Manufacturing Analytics: Improving aerospace assembly processes in an evolvable cell through adaptive data analytics and machine learning

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Reconfigurable cells promise to deliver a new generation of manufacturing systems. These cells are designed to be adaptable in changing environments. Utilising data to make decisions, the cells will incrementally reconfigure to be better suited to production demands. However, it is not understood how these complex environments can be supported with current data analytical systems.As assets change, we may acquire new types of data as new sensors are added. Aggregating this with legacy data may be invalid as the datasets could vary in quality, quantity, or type. These datasets are used to train machine learning models. Unprocessed datasets may cause undesired outputs or overfitting. These outputs are essential to make appropriate decisions of reconfiguration.The goal of this research is twofold. Firstly, it is to identify the relevant type of data to obtain. Secondly, it is to develop data systems that make use of adaptive data analytics and machine learning methods for reconfigurable cells in complex environments.This work will deliver the foundations for future manufacturing systems ability to learn and improve reconfiguration and resilience over time. Particularly, it will become a pillar of the National Demonstrator Omnifactory, a unique reconfigurable assembly facility that aims to serve as a national test bed for future projects.