The objective of the activity is to develop an integrated sensor network in a spacecraft primary structure (e.g. panel,central thrust tube), by spinning-in digital factory technologies to series production of telecom satellite components. The activitywill select a structural component, and then develop both a digital model and breadboard using a sensor network for intelligent performance prediction through all stages of manufacture, assembly, integration and test.
Targeted Improvements: Enabling a fully digitalfactory for space manufacturing with real time in-situ quality monitoring.
Digital factory technologies (part ofIndustry 4.0 in a wider context) have been proven in other industry sectors, such as automotive and aeronautics, to enable process optimisation and lead time reduction. The implementation of digital factory building blocks (e.g. embedded sensor networks, networks, artificial intelligence, digital twins) into serial production of telecom satellites potentially offers around 30% reduction in MAIT(Manufacture, Assembly, Integration and Test).
There is a driving force to develop and deploy new MAIT tools, which will lead to higher performance, better manufacturing quality, high reliability and shorter time to market, particularly for producing series of parts. During MAIT operations, spacecraft structures are constantly manipulated, and sensor feedback during such operations can provide valuable data on their performance. Via machine learning algorithms, such data can complement or replace diagnostic tools for quality control. Real-time data acquisition and analysis will shorten general effort for MAIT, and provide benefits in terms of failureforecasting and process optimisation.
This activity aims to spin-in digital factory technologies to the space industry. Digital factory building blocks will be identified and demonstrated, based on typical telecoms structural components that would benefit from the application of digital factory technologies in series production. Trading off feasibility and the likely impact of applying these technologies, target components will be selected. Digital MAIT models will be developed, consisting of, but not limited to, monitoring, control, and analysis to address at least failure prediction, data correlation and statistical process control. Breadboards of the targeted structural components will be built to validate the digital models developed using a smart sensor network. The gains of using digital factory technologies in telecommunication satellites will be quantified and assessed.