WP8 - Metrology Approach to Artificial Intelligence Control
THEME TWO
Metrology Data Analytics
WP8 Metrology Approach to Artificial Intelligence Control
WP8 focuses on building a metrology approach to bio-inspired AI control to extract maximal traceable information to optimise the control of machine-tools/assembly/measurement platforms. Without this building of rigour from the Engineering domain into the Computer Science domain, the potential for AI implementation will be hampered or precluded.
This work package focuses on the use of Artificial Intelligence/Machine Learning techniques as an enabler for advanced control. It therefore closely aligns with the “white box” technology of WP4, including the methods of incorporating “measurement uncertainty” and traceability into the control models and their implementation. In addition, the AI toolbox will be informed by results from WP5 and WP6 and can be an analytical and predictive tool with the bidirectional flow to WP7.
The outputs from the WP will be implemented as part of WP12 and platform projects “Machine-tool and Large Volume Metrology.”
Meet the team
Prof. Andrew Longstaff
Investigator
University of Huddersfield
Mr Ben Morgan
Investigator
The University of Sheffield - AMRC.
Dr Simon Fletcher
Investigator
University of Huddersfield
Dr Wencheng Pan
Research Fellow
The University of Huddersfield
Mr Andrew Bell
Applications Engineer
The University of Huddersfield
Publications
Nurudeen Alegeh, Abubaker Shagluf, Andrew Longstaff, Simon Fletcher, Accuracy in Detecting Failure in Ballscrew Assessment Towards Machine Tool Servitization, International Journal of Mechanical Engineering and Robotics Research. 8, 5, p. 667-673 2019.
Abubaker Shagluf, Simon Parkinson, Andrew Longstaff, Simon Fletcher, Adaptive Decision Support for Suggesting a Machine Tool Maintenance Strategy: From Reactive to Preventative, Journal of Quality in Maintenance Engineering. 24, 3, p. 376-399 24 2018.