Collaborative and Data-centric Engineering Research Cluster

The collaborative and data-centric engineering research cluster members work with our industrial partners to develop solutions to improve their productivity and processes using Artificial Intelligence (AI) and Data Science. We have developed digital twins to support product design and assembly in the manufacturing sector.

Digital twins, together with virtual reality and augmented reality, are used to support collaborative engineering. Machine learning is used during the various manufacturing phases and for material sciences. An example of an application is data-driven maintenance which can reduce maintenance costs and improve productivity. 

Our aims

Derby and Derbyshire have a long history with engineering going back to the industrial revolution in the 18th century, when the world's first commercially successful water-powered cotton spinning mill was built. It is therefore no surprise that the engineering discipline is well anchored within the University of Derby where research and development constitute one of its fundamental and core subjects and disciplines.

Research in Engineering at the University of Derby is supported by the Institute for Innovation and Sustainable Engineering (IISE) and colleagues from the School of Computing and Engineering (SCoE). Researchers from IISE and SCoE use Artificial Intelligence, data analytics and machine learning in their research in collaboration with colleagues from the Data Science Research Centre. It is on the basis of existing research and development projects and current collaborations that this cluster is being developed. Furthermore, there are many small and medium-sized enterprises (SMEs) and large engineering companies in Derbyshire and nationally that are research partners with the University of Derby.

Collaborative Engineering

Collaborative engineering allows organisations to design, build, test and service anywhere. It allows bringing in decision-makers early in the engineering process. It uses Product Lifecycle Management and visualisation platforms to support geographically dispersed teams. Artificial Intelligence is used in all the phases of manufacturing from design to maintenance.

Data-Centric Engineering

Like many other disciplines, engineering has benefited from the data revolution with the availability of large volumes and better-quality data. Data-centric engineering is seen as the interface between engineering and data science. It combines computer simulation, mathematical modelling and statistics developed for engineering systems with techniques used for big data and artificial intelligence. The result is the development of data-driven engineering models.

AI for Digital Twin and Additive Manufacturing

Additive manufacturing is the industrial production of 3D printing to create three-dimensional objects. AI in additive manufacturing is used mainly in process control to predict defects. Algorithms and parameters can then be tuned with the aim of achieving zero defect production. AI is also used for formulating new 3D printing materials.

On the other side, digital twin is the virtual representation of a real-world physical system or product. People try to recreate what occurs in the material world in digital space. It requires and uses the full spectrum of data science in terms of data acquisition, data modelling and data applications. AI is at the heart of such applications as many digital twin systems will try to imitate human intelligence, hence they use techniques such as computer vision, natural language generation and decision-making. It has attracted applications in the fields of engineering construction and intelligent manufacturing. 

Join us

If you are interested in joining this research centre, want to find out more or are interested in applying for a PhD in this area, please contact Dr Lee Barnby.