Contact Samuel Court
- Tel: +44 (0) 1234 754157
- Email: s.d.court@cranfield.ac.uk
- ORCID
Areas of expertise
- Computing, Simulation & Modelling
- Manufacturing Systems
- Product and Service Design
- Throughlife Engineering Services
Background
Samuel Court is the Research Fellow in Augmented Reality and Laboratory Manager for the Centre for Digital Engineering and Manufacturing where together with his colleagues he has been instrumental in establishing our research capability. Specialising in Augmented and Virtual Reality software development, his work contributes to the Centre objectives for training, research and industrial applications.
Samuel is a Computer Scientist with experience in mobile, web and application development. His expertise includes Computer Vision, Augmented and Virtual Reality with current focus on cross-platform full-stack mobile development, software-as-a-service and the internet of things. He oversees the technology provision within the lab, supervising and supporting our MSc and PhD researchers. He has a Bachelor Honours degree from Aston University and a career that spans the Arts, Media, Humanities, Medicine and Manufacturing sectors.
Research opportunities
AR Technology for Indoor Positioning Systems PhD
This funded PhD opportunity provides the future field operative with the key contextual data overlaid in real-time to assist with indoor navigation in relation to key assets. The research will develop a real-time system able to visualize essential information in a number of example use case demonstrations – for example: emergency response, crowd control, security and asset management. Find out more
Current activities
Digital Toolkit for optimisation of operators and technology in manufacturing partnerships (DigiTOP)
EP/R032718/1
The manufacturing industry, with the drive towards 'Industrie 4.0', is experiencing a significant shift towards Digital Manufacturing. This increased digitisation and interconnectivity of manufacturing processes is inevitably going to bring substantial change to worker roles and manual tasks by introducing new digital manufacturing technologies (DMT) to shop floor processes. At the same time, the manufacturing workforce is itself also changing - globally and nationally - comprising of an older, more mobile, more culturally diverse and less specialist / skilled labour pool.
It may not be enough to simply assume that workers will adopt new roles bestowed upon them; to ensure successful worker acceptance and operational performance of a new system it is important to incorporate user requirements into Digital Manufacturing Technologies design. In the past, Human Factors has shaped the tools used in manufacturing, to make people safe, to make work easy, and to make the workforce more efficient. New approaches to capture and predict the impact of the changes that these new types of technologies, such as robotics, rapidly evolvable workspaces, and data-driven systems are required. These approaches consist of embedded sensor technologies for capture of workplace performance, machine learning and data analytics to synthesise and analyse these data, and new methods of visualisation to support decisions made, potentially in real-time, as to how digital manufacturing workplaces should function.
The DigiTOP project will develop the new fundamental knowledge required to reliably and validly capture and predict the performance of a digital manufacturing workplace, integrating the actions and decision of people and technology. It will deliver this knowledge via a Digital Toolkit, which will have three elements:
i) Specification of sensor integration and data analytics for performance capture in Digital Manufacturing
ii) Quantitative analysis of the impact of four industrial Digital Manufacturing use cases
iii) Online interactive tool(s) to support manufacturing decision making for implementation of Digital Manufacturing Technologies
The DigiTOP project brings together a team with expertise in manufacturing, human factors, robotics and human computer interaction, to develop new methods to capture and predict the impact of Digital Manufacturing on future work. This project will work closely with a range of industry partners, including Jaguar Landrover, BAE Systems, Babcock International and the High Value Manufacturing Catapult to co-create industry-specified use cases to examine. The overall goal of DigiTOP is to produce a toolkit, derived from new fundamental engineering and science knowledge, that will enable industry to increase productivity, support Digital Manufacturing Technology adoption and de-risk the implementation of future Digital Manufacturing Technologies through the consideration of human requirements and capabilities.
Clients
EPSRC
Babcock International Group Plc (UK)
BAE Systems
High Value Manufacturing (HVM) Catapult
Jaguar Land Rover Limited
University of Nottingham
NHS
Indian Institute of Science (IISc)
National Institute of Advanced Studies (NIAS)
CBCI Society for Medical Education
St John's Research Institute (SJRI)
Innovate UK
Unipart Rail
TrainFX
Loyalty Prime
WMG
Publications
Articles In Journals
- Yazdani Nezhad H, Wang X, Court SD, Thapa B & Erkoyuncu JA (2020) Development of an augmented reality equipped composites bonded assembly and repair for aerospace applications, IFAC-PapersOnLine, 53 (3) 209-215.
- Ferrati F, Erkoyuncu JA & Court S (2019) Developing an augmented reality based training demonstrator for manufacturing cherry pickers, Procedia CIRP, 81 803-808.
- Marzano A, Friel I, Erkoyuncu JA & Court S (2015) Design of a virtual reality framework for maintainability and assemblability test of complex systems, Procedia CIRP, 37 242-247.
Conference Papers
- Court S, Kirkwood L, Farnsworth M, Orlovs I, Shehab E & Tinworth N (2017) Requirements analysis of digital technology for the rail industry. In: 15th International Conference on Manufacturing Research (ICMR 2017), Greenwich, London, 5-7 September 2017.
- Peña Miñano S, Kirkwood L, Court S, Farnsworth M, Orlovs I, Shehab E & Tinworth N (2017) A review of digital wayfinding technologies in the transportation industry. In: 15th International Conference on Manufacturing Research (ICMR 2017), Greenwich, London, 5-7 September 2017.
- Farnsworth M, Kirkwood L, Court S, Shehab E & Tinworth N (2017) Optimisation strategy for efficient platform train interface activity. In: 15th International Conference on Manufacturing Research (ICMR 2017), Greenwich, London, 5-7 September 2017.
- Li W-C, Yan Z, Zhang J, Braithwaite G, Court S, Lone M & Thapa B Evaluating pilot’s perceived workload on interacting with augmented reality device in flight operations. In: 22nd International conference on Human-Computer Interaction (HCI International) 2020, Copenhagen, 19-24 July 2020.
