Manufacturing the goods people and society need in intelligent ways is a key to improving sustainability. In fact, smart manufacturing can help us to tackle many societal challenges, from climate change, resource scarcity and social welfare to global competition and profitability issues.
In the search for intelligent manufacturing solutions, the Faculty of Engineering Technology fuses knowledge of future-oriented processes with the unique ability to design, test, and integrate solutions across a variety of product, process, and system-level applications. We do this in close collaboration with many hands-on industrial partners. By seizing digital opportunities, we strive toward solutions that impact productivity, flexibility, and sustainability. In combination with tailored data acquisition, we´re establishing digital twins that enable advanced planning and operations in manufacturing, for example through model-based inline control of processes and systems. Our work contributes to several United Nations’ Sustainable Development Goals (SDGs), including SDG 9, ‘Industry, innovation, and infrastructure’, and SDG 12, ‘Responsible consumption and production’.
Numerous researchers contribute to this theme, with research topics including:
- Laser processing
- Additive manufacturing
- Industrial robotics
- Product development
- Manufacturing systems and factories
- Forming technology
- Composite production
- Particle processing & simulation
- Operations and design
Each field has dedicated labs available on campus or with partners, such as the ThermoPlastic Composites Research Centre or the Advanced Manufacturing Centre.
1. Developing the workplace of the future
The NWO’s Smart Industry project ‘Human Centered Smart Factories: design for wellbeing for future manufacturing’ utilizes innovative digital approaches to develop smart workstations that adapt to the unique physical and cognitive needs of a worker. The workstations are responsive in real-time and promote dynamic activities by understanding employees’ work context.
2. Finding the optimum control points in manufacturing
In Digital Twin’s project, researchers are developing a methodology to update physical models based on data from a multi-stage metal forming process. The resulting meta-models will be used to directly optimise and find the optimum control points in the multi-stage manufacturing process.
Theme leader:
prof.Dr.-Ing. S. Thiede (Sebastian)
Full Professor, Chair of Manufacturing Systems
