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: 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: 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: 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
In the demonstrator of SONAE Arauco ES, the openZDM digital tools and platform will be used to achieve the following:- improvement of data collection and in-situ monitoring, namely in what concerns time-related annotation
- improvement of inspection of quality issues through artificial intelligence and digital-twin technology.
- further advances in predictive quality based on data-driven and model-driven approaches for defect prediction and quality assessment.
- improvement in manufacturing decision making for ZDM strategies and process adaptation. This will also include recommendation models for new products, an explainable AI approach for a better understanding of Machine Learning decisions and a graphical user interface through UX/UI strategies.
thus effectively support productivity through the following aspects:
1. Improved decision-making at the plant level to reduce defects towards a zero defects paradigm
2. Improved product development (faster and with less waste generated)
3. Improved evaluation of machinery components