Research
The global challenges and the goals of the 2030 Agenda for Sustainable Development are strong drivers for manufacturing industries to contribute and develop new solutions towards energy savings, resource optimization and lower material consumption.
To realize this, products and production processes should be designed, monitored and optimized in real-time with very high precision. Within Sentio Competence Centre we aim to make this possible through unique integration of novel developments in four research areas:
- Integrated nanosensor technology that provide real-time data and function continuously in the harsh and hard-to-access environments
- Signal analysis and communication including transfer and use of noisy data
- Manufacturing process models and digital twins
- Models that directly and dynamically link the process information to optimization and sustainable impact
Workpackage 1: Integrated Sensor Technology
Aim: To develop new sensors capable of integration in harsh and hard-to-reach environments, for the purpose of operating as close as possible to the active process without loss of function. Develop and investigate designs for retrieving sensed signals.
Workpackage Leader: Anders Mikkelsen
Project 1a: Nano/microstructures (for sensing) on non-flat surfaces that work under high temperature, stress, and vibrations.
Project 1b: Nano/microstructures (for sensing) on non-flat surfaces that work over long distances, times, as well as in corrosive environments.
Project 1c: Electronics in high temperature, noisy and moving environments.
Workpackage 2: Signal analysis and communication
Aim: To develop robust feature extraction of noisy time-varying data from sensor arrays, e.g. measurements of vibrations within high levels of noise. Development of cleaning procedures for the noisy and corrupted data, choices of optimal sets of sensors and extraction of optimal features using techniques in data mining and machine learning.
Workpackage Leader: Maria Sandsten
Project 2a: Robust Time-Frequency Analysis of Multiple Sensor Data
Workpackage 3: Manufacturing process models and Digital Twins
Aim: To Investigate and develop sensor capabilities for process monitoring and optimization. Identify a minimum required combination of sensors for data-driven process adaptation and for model-based process control. Validate and adapt discrete process models and physics-based Digital Twins.
Workpackage Leader: Volodymyr Bushlya
Project 3a: Heat transfer optimisation from sensor array
Project 3b: Multi-sensor process monitoring towards Digital Twin
Project 3c: Thermal Digital Twin for processes and products
Workpackage 4: Optimisation for environmental impact and sustainability
Aim: To develop models that find a deliberate balance between the optimisation criteria of cost, rate and environmental impact. Models need to support SDR and other mandatory reporting and need to be designed for workshop floor and higher up for strategic decisions.
Workpackage Leader: Christina Windmark
Project 4a: Integrated manufacturing optimisation model for highest overall impact on sustainability.
Project 4b: The sustainable landscape of closed-in processing
