De- and Re- manufacturing of consumer WEEE
Project: Circular TwAIn
Updated at: 14-11-2024
Project: Circular TwAIn
Updated at: 14-11-2024
Project: MASTERLY
Updated at: 07-11-2024
Project: MASTERLY
Updated at: 07-11-2024
Project: MASTERLY
Updated at: 07-11-2024
Project: Platform-ZERO
Updated at: 09-02-2024
Project: Platform-ZERO
Updated at: 09-02-2024
Project: QU4LITY
Updated at: 01-02-2024
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
OPENZDM solutions for AI quality assessment and decision support aim for data driven defect prediction and estimation of machine components degradation through drifts in data distribution. The main goal is to predict defects such as broken paper, dust or glued paper so that changes in machinery/parameters can be performed and the defect avoided and also to estimate degradation based on slight and smooth changes in the process start occurring due to component wear-out / degradation and, if data is representative enough, it may be different data distributions in time.
OPENZDM is expected to contribute to the SONAE Arauco use case in the aspects of proactive quality control towards zero-defect manufacturing through the digital twin enabled machine learning approaches for data analytics and quality assessment.
Project: OPENZDM
Updated at: 14-11-2023
Project: OPENZDM
Updated at: 14-11-2023
Project: OPENZDM
Updated at: 14-11-2023
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: 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.
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.