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Rob Ward

Project Title: Intelligent Adaptive Control of Manufacturing Machining Processes

Academic Supervisor: Dr Bryn Jones

AMRC Supervisor: Dr Erdem Ozturk

External Supervisor: Dr Burak Sencer, Oregon State University, USA

Initially Rob’s project focused on using existing sensors in CNC machines to improve performance of machining processes. This has led to the application of Iterative Learning Control to control cutting forces and surface location errors in milling. Extending into Digital Machining the research utilises real-time Digital Twins for control and optimisation of machining processes – chatter control and feedrate scheduling via measured and simulated feedback models. Rob also conducts research in feedrate prediction, trajectory generation and machining cycle time estimation supervised by Dr Burak Sencer at Oregon State University, USA. Through this work he has developed a method of accurate feedrate prediction and machining cycle time estimation that out performs modern CAM packages.

Rob supports teaching in the department of Automatic Control and Systems Engineering in Industrial Automation, State Space Control, Rapid Control and Prototyping, and Process Control alongside advising 3rd year groups projects.

Rob is a Chartered Engineer with the Institute of Engineering and Technology (IET) and a member of both the IET and IEEE.


R. Ward, O. Ozkirimli, B. Jones (2020). "Increasing Part Geometric Accuracy in High Speed Machining using Cascade Iterative Learning Control". CIRP HPC Conference 2020 [Conference Paper]

R. Ward, P. Soulantiantork, S. Finneran, R. Hughes, A. Tiwari (2020). "Real-Time Vision-Based Multiple Object Tracking of a Production Process: Industrial Digital Twin Case Study". IMechE Part B Journal of Engineering Manufacture [Journal Paper]