coADDVA -ADDing VAlue by Computing in Manufacturing (coADDVA )
Main funder
Funder's project number: A77973
Funds granted by main funder (€)
- 240 000,00
Funding program
Project timetable
Project start date: 01/09/2021
Project end date: 31/10/2023
Summary
The CoADDVA project develops the ability of robots to compute quickly and utilizes it from the perspective of industrial applications. Digitization is largely about being able to process the information to be collected, that is, calculating it in the most efficient way possible, and the results must serve in real-time in device decision-making. This premise requires the development of new innovative ways and algorithms, as well as knowledge of how to integrate robots to function properly, safely, and efficiently in industrial operating environments. In the project, we show how robotics can make operations more efficient with SMEs in the region in the case studies we have identified. During the project, we will develop methodological expertise in Edge-AI and Hybrid-twin technologies. The project is related to the ecosystem agreement between the City of Jyväskylä and the Ministry of Employment and the Economy, where the aim is to build an ADDVA RDI environment in the area for the needs of renewable industry. The project promotes the level of digital competence and applicability of the environment, in addition to which it builds the capacity to utilize international RDI co-operation for the benefit of actors in the Central Finland region. The project is part of the implementation of the region's survival strategy.
Principal Investigator
Other persons related to this project (JYU)
Contact person (yes/no): Yes |
Primary responsible unit
Related publications and other outputs
- A Posteriori Error Estimation for the Optimal Control of Time-Periodic Eddy Current Problems (2023) Wolfmayr, Monika; A1; OA
- Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging (2023) Wolfmayr, Monika; A1; OA
- Tiny Machine Learning for Resource-Constrained Microcontrollers (2022) Immonen, Riku; et al.; A2; OA