Manufacturing and Process Industry
Research and Development under Contract
Software Engineering and Data Intelligence
Computer Vision, Interaction and Graphics
Artificial intelligence, machine learning, and decision support systems
Ontologies and interoperability
Computer graphics, virtual, augmented and mixed reality
FRAMEWORK
The ADM.IN project's primary objective is to develop intelligent tools to support decision-making, which have a direct contribution to increasing production availability and the quality and value of the Sonae Arauco product, towards Industry 4.0.
It advocates the creation of a system based on advanced artificial intelligence, digital twin and augmented and mixed reality technologies, creating a collaborative network within Sonae Arauco, which allows for excellence in its production.
PROPOSED SOLUTION
Enable intelligent management of Sonae Arauco's production process, as well as preventive maintenance of the company's machinery.
This project includes 3 different development lines:
1. Digital Twin - Data-driven decision making
2. Asset maintenance and management
3. Product traceability
CCG/ZGDV CONTRIBUTION
The CCG/ZGDV, through its R&I, EPMQ and CVIG Departments, contributes to the ADM.IN project in the following aspects:
- Development of an integrated solution for analytical, predictive, and stochastic simulation modules for effective defect and stoppage forecasting. The objective is to create relationships that allow for extracting information and value from large volumes of stored data and predicting downtime and defects based on this information.
- Development of a knowledge management architecture and framework for the integration of Augmented Reality and Mixed Reality, oriented towards knowledge of the maintenance and monitoring process through indicators applied in some critical maintenance activities
- A digital twin proposal was created for the wood industry in order to materialize the junction of digital reality with the physical system. By removing information and value from large volumes of data, it is possible to create alternative production scenarios, forecasting, and simulations specific to this domain, with the possibility of generalizing to companies in the same sector