TEAMING.AI | Human-AI Teaming Platform for Maintaining and Evolving AI Systems in Manufacturing
01-01-2021
-30-06-2024
01-01-2021
-30-06-2024
01-06-2020
-30-11-2024
01-06-2020
-30-11-2022
01-10-2020
-30-09-2023
01-10-2020
-31-03-2024
01-11-2020
-31-10-2023
01-10-2020
-30-09-2024
01-01-2021
-31-12-2023
01-10-2020
-30-09-2024
01-10-2020
-31-03-2024
Automotive and microelectronic industry
01-11-2020
-31-10-2023
01-01-2021
-30-06-2024
01-01-2021
-30-04-2024
01-06-2022
-31-05-2025
Waste2BioComp will develop bio-based materials for key sectors: textile, footwear and packaging. This will be done by adapting existing technologies to the new materials (faster integration of the materials in the market) or developing innovative processes. The project contemplates the whole value chain: from polymers production from waste raw materials, to the final product, and its end-of-life, with the possibility of re-introducing the recyclates in the value chain.
After the project, developments on the scale-up of the developed processes will be required.
Waste2BioComp contemplates the end-of-life of the materials developed, either by re-manufacturing approaches (colour removal to allow new printing of the substrate), chemical recycling and study of the re-introduction of the recyclates in the process, or by biodegradability and compostability.
In the end, if all goals for re-manufacturing and recycling are achieved, a circuit for product collection will need to be set-up.
Waste2BioComp will develop bio-based inks for inkjet printing, and later on, the colour removal followed by new printing will also be studied, so that the product do not need to be discarded when it is “out of fashion”.
It is expected that at the end of the project this approach will still need many developments till it is scalable.
Waste2BioComp uses bio-based raw materials to replace fossil-based ones (PHAs to replace polyester, bio-based pigments to replace fossil-based ones, etc.), and is studying how best to adapt different manufacturing processes to the new materials, in order to obtain final products with similar properties as the ones currently used.
Waste2BioComp contributes to this priority by reducing the use of fossil fuels in manufacturing (scope 1), and is developing a process for a optimal and efficient PHA production regarding energy and resource consumption. LCAs will show quantitatively this contribution.
Waste2BioComp will develop dedicated training activities to support the creation of a skilled workforce in biomaterial-based manufacturing sectors, particularly for the textile, footwear, and packaging activities. The materials will be made available on digital platforms.
01-06-2022
-31-05-2025
Disassembly of batteries
Disassembly of batteries
01-06-2022
-30-11-2025
01-06-2022
-31-05-2026
01-06-2022
-31-05-2025
01-09-2022
-31-08-2025
01-10-2022
-30-09-2025
01-06-2022
-31-05-2025
01-10-2022
-31-03-2026
01-10-2022
-30-09-2025
01-10-2022
-30-09-2026
The OPTIMAL approach involves various disciplines, which interact with each other in order to achieve the project objectives. Material research and photochemistry is needed to develop the suitable photoresists (MRT). Laser technology knowledge (JOR, ISE) is required for developing novel laser lithography methods, machines, and processes. The electronics finds its application in the optical based sensors for in-line monitoring, controlling the laser sources and patterning (ILC, DPX). The software engineering expertise (UPR) completes the required skills for the development of self-learning algorithms for generating the virtual photomasks. Mechanical and environmental engineering deal with the equipment and manufacturing processes and their life cycle assessment (JOR). Experts in training and communication science (ILC, UPR) will design workshops and training materials to explain and promote the developed technologies to broad public and stakeholders.
The OPTIMAL project will integrate for the first-time different laser lithography technologies, quality monitoring systems and processes in one platform for the development of structures with high depth, dimensions in the range from 100 nm to sub-mm, 2D&3D shape on flat surface, combining parallel & serial patterning, no need for external treatments on samples, increased speed and large area. The OPTIMAL consortium consists of three research institutes (JOR, ISE, ILC), one university (UPR), member of the Italian Photonics Association, three Small Medium Enterprises (MRT, DPX, HYP), two big industries, leaders in their corresponding market (SDA and BCD).
The OPTIMAL platform will be validated through the manufacturing of master tools for four different use cases: a) full-polymer micro lenses for industrial optics, b) hierarchical multifunctional drag reduction riblet structures for aviation, c) free-form lens arrays for high-end virtual reality displays and d) microfluidic hierarchical structures for lab on chip medical devices.The OPTIMAL consortium consists of three research institutes (JOR, ISE, ILC), one university (UPR), member of the Italian Photonics Association, three Small Medium Enterprises (MRT, DPX, HYP), two big industries, leaders in their corresponding market (SDA and BCD).
By accelerating and upscaling the structuring process, the OPTIMAL project will increase the process efficiency and yield, which will allow for “first time right” fabrication of the required structures, lower consumption of resources, waste reduction, lower CO2 emissions, increase of productivity, and cost reduction.
By accelerating and upscaling the structuring process, the OPTIMAL project will increase the process efficiency and yield, which will allow for “first time right” fabrication of the required structures, lower consumption of resources, waste reduction, lower CO2 emissions, increase of productivity, and cost reduction.
The OPTIMAL project uses self-learning algorithms to optimize the virtual photomask as well as integrates methods for an inline control of the laser patterning.
By accelerating and upscaling the structuring process, the OPTIMAL project will increase the process efficiency and yield, which will allow for “first time right” fabrication of the required structures, lower consumption of resources, waste reduction, lower CO2 emissions, increase of productivity, and cost reduction.
The OPTIMAL project uses self-learning algorithms to optimize the virtual photomask as well as integrates methods for an inline control of the laser patterning.
The OPTIMAL project uses self-learning algorithms to optimize the virtual photomask as well as integrates methods for an inline control of the laser patterning.
01-07-2022
-30-06-2025
01-05-2022
-30-04-2025
01-06-2022
-31-05-2025
01-06-2022
-31-05-2025
01-09-2022
-31-08-2026
01-06-2022
-31-05-2025
Oil&Gas, Shipbuilding, Aeronautics and Bus&Coach