Robotics and Manufacturing

Digital Dynamic Knowledge Platform for Welding in Manufacturing Industries” [http://www.weldgalaxy.eu/] (H2020-NMBP, no. 822106; 2018-2022): TVS is working to build a B2B online Platform that brings together global buyers and EU manufacturers, suppliers, distributors, service providers of welding equipment along with auxiliaries/consumables and services, thereby enhancing the visibility of EU’s welding products/prototypes/services to global users.. TVS will perform life Cycle Analysis and develop Parametric Life Cost Calculation model by analysing TWI’s numerous welding projects starting from 1976, considering materials, equipment, process etc. TVS will develop and incorporate Knowledge base engineering (KBE) tool that streamlines equipment selection process for end-users and allows ‘plug and produce’ digital manufacturing of the right equipment to specified customers’/end-users’ requirements and regulatory compliance. This software tool will be able to provide specification of the equipment and the expected cost and environmental benefits. Rules will be identified and written using symbolic object-oriented programming language (Parapy), which will automatically create semantic representation of the object in CAx format.

Technologies for geothermal to enhance competitiveness in smart and flexible operation” [https://www.geosmartproject.eu/] (H2020-Renewable Energy, no. 818576): TVS is developing a cloud-based platform for flexible operation of geothermal plant. The data warehouse is being populated with user, process, material, experimental and simulation data. Results from system simulator, LCA, LCC, and FEM are included. A Decision Support System (DSS) is added to the KBS to further guide optimal plant design.

Joint Industrial Data Exchange Platform. JIDEP aims to establish industry-focused collaborative spaces that are supported by a decentralised blockchain network. By leveraging this technology, JIDEP seeks to overcome obstacles related to trust, non-repudiation, and self-sovereignty of identities and data ownership. This project has received funding from Horizon Europe Research and Innovation under grant agreement number 101058732.

The project aims to provide a favourable business and technological framework for cognitive autonomous systems supporting human-robot interaction, dynamising science-driven industry approaches for the European industry. This will enable new ways of automation and flexible reconfiguration for more effective and efficient manufacturing/construction processes. TVS will develop a Shared Knowledge Base, a common cloud hub that contains the semantic information developed within the project. This knowledge base serves multiple modules within the VOJEXT ecosystem and, in turn, also implements means to synchronise information from different sources. The knowledge base will provide inference and querying services to access all relevant information of the digital engineering process for a specific application in an intuitive way.This project has received funding from the European Union’s Horizon 2020 research and innovation program under a grant agreement No 952197.

The goal of ALBATROSS is to develop advanced battery pack designs and systems that can significantly improve the energy density, weight, charging time, and driving range of electric vehicles. Specifically, the project aims to achieve a peak energy density of over 200Wh/kg, reduce the weight of the battery system by 20%, decrease charging time by 25%, increase driving range up to 480km, and improve the overall lifecycle of electric vehicles by 15-20%. In addition, ALBATROSS seeks to improve the knowledge of pack sensor and thermal management to increase battery pack lifetime, and facilitate ease of dismantling and repurposing for second life or recycling. Ultimately, the project aims to precisely determine the End of Life of electric vehicles according to the State of Health and historical driving range trips, while maintaining or improving battery capacity and useful life up to 300,000 km. This project has received funding from the European Union's H2020 research and innovation programme under grant agreement No 963580.