29, January 2021

ESA Open Invitation to Tender AO10549
Open Date: 26/01/2021
Closing Date: 25/03/2021 13:00:00

Status: ISSUED
Reference Nr.: 20.132.06
Prog. Ref.: GSTP Element 1 Dev
Budget Ref.: E/0904-611 – GSTP Element 1 Dev
Special Prov.: DE
Tender Type: C
Price Range: 200-500 KEURO
Products: Satellites & Probes / Other / Satellites & Probes / Materials / Metallic
Technology Domains: Materials and Processes / Materials Processes / Joining / Quality, Dependability and Safety / Product and Quality Assurance / Quality Assurance Processes for Flight and Ground Systems
Establishment: ESTEC
Directorate: Directorate of Tech, Eng. & Quality
Department: Systems Department
Division: Software Systems Division
Contract Officer: Seynaeve, Christophe Rene R.
Industrial Policy Measure: N/A – Not apply
Last Update Date: 26/01/2021
Update Reason: Modified the Letter of Invitation (English version)

The objective of this activity is to develop an algorithm for manufacturing process robustness, anomaly detection and failure modesinvestigation in space applications.Description: Over the past years, the tendency to capture huge volumes of historical data describing process operations together with complex experimental datasets has become a reality. In this regard, the use of artificial intelligence (AI) and in particular machine learning (ML) for data mining addresses the question of which is the best way to use this historical data to discover regularities and to facilitate future decisions. Following the significant progress and recent success inmany science and engineering domains, the activity is aiming at exercising AI/ML technique to provide substantial benefits to the advanced manufacturing processes domain (e.g. additive manufacturing). Advanced and new manufacturing processes and technique may benefit from the integration and operational usage of AI/ML techniques. The use of these technologies might be beneficial to extract relevant information from big data generated through the different steps of the new advanced manufacturing processes. Furthermore, thefalling cost of large data storage devices and the increasing facility of collecting data over networks; the improvement of computational power, enabling the use of computationally intensive methods for data analysis in parallel to the development of robust and efficient machine learning algorithms further highlight the need and actuality of this activity. In the frame of this activity, a software prototype shall be developed, able to extract data from existing sources and categorize them in useful information in order tothe use the state of the art AI/ML algorithms for advanced manufacturing processes modelling, process anomalies detection and failure mode investigation. The main steps that shall be completed in the frame of this activity are the following:- Identification of case studies (e.g. additive manufacturing process modelling, in situ monitoring, NDI inspections, defect identifications etc.) and related data sources available;- Review of the state-of-the art, preliminary use cases selection, definition of the preliminary requirements and identification of the validation process to be applied;- Design and definition of the software preliminary architecture;- Detailed design and definition of the software architecture;- Software Implementation and integration;- Software testing and performance assessment;- Software validation and risk assessment based on the reliability of the software in the decisional process.

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