Externally funded by Horizon 2020 – Technologies for Factories of the Future
Smart factories are characterised by smart processes, smart machines, smart tools and smart products as well as smart logistics operations. These generate large amounts of data, which can be used for analysis and fault prevention, as well as the optimisation of the quality of manufacturing processes and products.
DAT4.ZERO is a digitally-enhanced quality management system (DQM) that gathers and organises data from a distributed multi-sensor network, which, when combined with a DQM Toolkit and Modelling and Simulation Layer, and further integrated with existing cyber-physical systems, offers adequate levels of data accuracy and precision for effective decision support and problem-solving – utilising smart, dynamic feedback and feed-forward mechanisms to contribute towards the achievement of zero defect manufacturing (ZDM) in smart factories and their ecosystems.
The aim is to integrate smart, cost-effective sensors and actuators for process simulation, monitoring and control; develop real-time data validation and integrity strategies within actual production lines; demonstrate innovative data management strategies as an integrated approach to ZDM; and develop strategies for rapid line qualification and reconfiguration. Deployed in five distinct industrial pilot lines, we address the following primary objective: Develop and demonstrate an innovative DQM system and deployment strategy for supporting European manufacturing industry in realising ZDM in highly dynamic, high-value, high-mix, low-volume production contexts, by effective selection and integration of sensors and actuators for process monitoring and control, a DQM platform with an architecture that provides reliable and secure knowledge extraction to ensure integrity of data, and strategies for advanced real-time data analysis and modelling in multiple domains and sectors that will increase quality, reduce ramp-up times and decrease time-to-market.
The Manufacturing Metrology Team at the University of Nottingham will lead the sensor integration aspect of the project, utilising sensor fusion and machine learning algorithms to enhance process adaptability. We will bring to reality a number of outstanding metrological disciplines, including self-calibrating sensors, complex surface point-cloud uncertainty and rigorous, but efficient, transfer function modelling for optical instruments. The project will assemble the toolbox of information-rich metrology technologies, then apply the tools on the pilot lines..