Smart European Space Access through Modern Exploitation of data science
Project funded by European Union
This project has received funding from the European Union’s Horizon 2020 research and innovation framework programme under grant agreement No 821875. Click here for more info
Demonstrate the ability of european space companies to work together and to reduce the costs using data science for predictive quality, predictive maintenance and supply chain agility.
Develop a complete data management framework to proactively manage risks in new automated production and operations.
Develop new predictive maintenance and quality components to implement new automated launcher production and operations maintaining quality and reliability. The global project technical progress objective is to pass from TRL 3 to TRL 7.
Implement new logistic processes (adaptative operations) that allow an optimal management of resources in an environment where resources are shared among different organizations and products.
Accompany the consequent new logistic processes (adaptative operations) that allows an optimal management of resources in an environment where resources are shared among different organizations and products.
Evaluate other possible sectors (for which the proposed predictive framework could be applied to create a large ecosystem with tools for predictive maintenance and quality.
Increase flexibility of the production of Ariane 6 and future programs manufacturing facilities (in France and Germany).
Create an ecosystem involving the overall European launcher industry and possibly other sectors to share knowledge and implement new more effective industrial processes with less risks and costs.
Improve competitiveness of European launch service from the Guiana Space Center (CSG) spaceport by improving service availability and reducing overhead cost.
(Deliverables, Roadmap, Achievement, …)
Friction Stir Welding predictive quality & maintenance
ArianeGroup uses FSW or Friction Stir Welding technology – a solid-state welding process – to join Ariane 6 parts together. This is a key process within the Ariane 6 tanks assembly operations, that has to be mastered both in terms of quality, time and cost. So how can ArianeGroup reduce the time it takes to weld a tank by also improving its quality ?
Within the framework of the SESAME project, multiple sensors have been placed on the FSW. Those sensors collect a large amounts of data coming from its Computer Numerical Control while in operation. Every 100 millisecond 1500 variables such as temperature, speed, or position are gathered. They are then sorted and analyzed by Data Scientists (helped by algorithms) to formulate a “health status” both of the tank being welded and the FSW machinery. This way, operators can be made aware of a non-quality or malfunction and intervene before it even occurs, guaranteeing improved quality and safety at lowered costs.
Guiana Space Center optimization of resources usage
Since several decades, the Guiana Space Center has seen hundreds of launch campaigns. This vast area also hosts a tremendous amount of multiple space activities that share the same resources (i. g. tools or on-site transportation). As a consequence, employees don’t always immediately have access to the resources they need to perform a specific task. So how can ArianeGroup maximize its resources potential and increase its supply chain agility ?
Within the framework of the SESAME project, trackers are deployed to be placed on the different resources and collect geolocation data. They are then crossed with other data, such as scheduling or resources needs for a specific task or project, before being processed and analyzed by algorithms. As a result resources availability and utilization is maximized while the schedules are easier to manage.
ArianeGroup, SESAME project special guest at a one-day COMET event
How – and why – does the SESAME project integrate the human factor into its development process? This is the subject that Fabrice Blondeau, SESAME programme manager at ArianeGroup, tackled during a workshop organised by the CNES-supported COMET network.
COMET, a community of experts
Since its launch in 1998 by CNES, the COMET group of expert communities has regularly brought aerospace specialists together to share their knowledge, feedback and expertise, for everyone’s mutual benefit and to prepare for the future. With a focus on new technologies and processes, these encounters help to advance professions and improve working practices. They are also an opportunity to promote exchange and cooperation between the space sector and other sectors of activity.
Today, COMET comprises 20 communities totalling more than 3,000 experts from the academic, industrial and institutional fields. Around 60 days of exchange forums of many kinds are organised every year – think tanks, workshops, colloquiums, visits, good practice guides, discussion with experts, etc.
Everything you want to know about SESAME.
Co-financed by the European Commission and under the leadership of ArianeGroup, SESAME officially started on the Les Mureaux site. By combining European cooperation and Data Science, its goal is to improve Ariane 6 competitiveness.
In more concrete terms, Smart European Space Access thru Modern Exploitation of Data Science is a project which aims to improve manufacturing and supply chain operations of European launchers thanks to digital technologies. This consortium combining ArianeGroup, CNES, Capgemini, Predict (France), Eurecat (Spain), Vitrociset, Crat (Italy) and SNSPA (Romania) will work for the next 3 years to:
- Establish a data management framework to anticipate and prevent risks related to the automation of Ariane 6 operations and production;
- Develop predictive maintenance and predictive quality algorithms that will be integrated into newly automated operations;
- Set up new logistic procedures intended to optimize the available resources as much as possible;
- Measure the benefits of its new practices in realistic operational scenarios;
- Support the transformation of skills, in particular by creating new worker profiles;