Smart coatings for PV applications, PV manufacturing line
Project: Platform-ZERO
Updated at: 09-02-2024
Project: Platform-ZERO
Updated at: 09-02-2024
Project: OPTIMAL
Updated at: 30-01-2024
Project: OPTIMAL
Updated at: 22-12-2023
Project: OPTIMAL
Updated at: 22-12-2023
Project: OPTIMAL
Updated at: 22-12-2023
Project: OPTIMAL
Updated at: 22-12-2023
Project: AUTO-TWIN
Updated at: 15-11-2023
Project: AUTO-TWIN
Updated at: 15-11-2023
Project: AUTO-TWIN
Updated at: 15-11-2023
Project: AMBIANCE
Updated at: 15-11-2023
Project: AMBIANCE
Updated at: 15-11-2023
Project: AMBIANCE
Updated at: 15-11-2023
Project: OPENZDM
Updated at: 14-11-2023
Project: DaCapo
Updated at: 14-11-2023
Project: DaCapo
Updated at: 14-11-2023
Project: DaCapo
Updated at: 14-11-2023
Project: Fluently
Updated at: 30-10-2023
Project: Fluently
Updated at: 30-10-2023
Project: BILASURF
Updated at: 27-10-2023
Additionally, the creation of functional surfaces has traditionally relied on processes such as chemical reactions and/or the complete coating of the native surfaces (e.g. aerofoils). Due to their very nature, these processes generate unwanted by-products thereby leaving a significant environmental footprint, which go against the “do no significant harm” principle of The European Green Deal.
Project: BILASURF
Updated at: 27-10-2023
Project: BIO-UPTAKE
Updated at: 25-10-2023
Project: BIO-UPTAKE
Updated at: 25-10-2023
Project: BIO-UPTAKE
Updated at: 25-10-2023
Project: SMARTHANDLE
Updated at: 26-07-2023
Project: ONE4ALL
Updated at: 26-07-2023
Project: MODUL4R
Updated at: 26-07-2023
Project: MODUL4R
Updated at: 26-07-2023
Project: MODUL4R
Updated at: 26-07-2023
Project: s-X-AIPI
Updated at: 25-07-2023
The optimization of the use of resources during the selection of the aluminium recipes will be at the core of the IDSS with the aim of optimizing the overall process, reducing waste, and reducing polluting emissions.
Project: s-X-AIPI
Updated at: 25-07-2023
To improve the efficiency of the whole value chain, AI apps will be used in predictive diagnosis of equipment efficiency to infer energy savings in production plant and maintenance optimization.
The vision of the future process with the contribution of the openZDM solutions targets an improved decision-making at the plant level to reduce defects towards a zero defects paradigm, that will facilitate defect prediction and recommendation of production recipes to adjust machine parameters according to predicted defects. In turn, improved products can be developed using the data-driven knowledge acquired, resulting in less wasted material. Towards this direction, the openZDM project performed an LCA at the beginning ot he project to compare it with the final one and also it supports integration through its platform to an LCA tool for on-demand LCA.