Dr Robert Ward
School of Electrical and Electronic Engineering
Industrial Research Fellow
Full contact details
School of Electrical and Electronic Engineering
Amy Johnson Building
Portobello Street
Sheffield
S1 3JD
- Profile
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Dr Rob Ward holds a joint academic position between the department of Automatic Control and Systems Engineering (ACSE) and the University of Sheffield Advanced Manufacturing Research Centre (AMRC). As an Industrial Research Fellow, he develops, leads and supervises research programmes in self-optimising autonomous manufacturing. Rob is a Chartered Engineer and he brings a wealth of project and operational management experience to support the undergraduate teaching and postgraduate supervision in the dept.
Rob has an MEng(Hons) degree in Avionics and Aerospace Systems from the University of Manchester and a Engineering Doctorate in Machining Science and Control Engineering from the University of Sheffield. Prior to joining the University of Sheffield and the AMRC in 2017, Rob served as an Engineering Officer in the UK Military.
At the AMRC Rob sits within Digital Machining Research Team and heads up the control of subtractive manufacturing operations theme. Rob collaborates and consults on both higher technology readiness level (TRL) industrial projects and lower TRL HVMC and EPSRC funded projects. He is particularly interested in the domains of CNC machine tool control, machining digital twins, robotic machining and CNC interpolation.
At ACSE, Rob is the module leader on 3rd and 4th year undergraduate courses in Rapid Control Prototyping. He supervises UG and PG students and is passionate about preparing them for professional engineering careers. Rob is the impact and innovation lead for the Department and the UG admissions tutor.
Rob welcomes applications from potential undergraduate, masters and PhD students to join the team and undertake industry focused projects.
As part of the joint role, Rob is keen to narrow the gap between the Faculty and the Advanced Manufacturing Group, promote opportunities for academic staff collaboration, increase the number of UG/PGT/PGR projects and promote summer placements, industrial placements and SURE schemes.
- Research interests
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- Digital Machining
- Machine Tool Control
- CNC Trajectory Generation & Interpolation
- Precision Motion Control Systems
- Robotic Machining
- Digital Twins
- Industry 4.0 Technologies
- Adaptive CNC Control
- Publications
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Journal articles
- Accurate prediction of five-axis machining cycle times with deep neural networks using Bi-LSTM. CIRP Journal of Manufacturing Science and Technology, 48, 28-41.
- Accurate prediction of machining cycle times and feedrates with deep neural networks using BiLSTM. Journal of Manufacturing Systems.
- Accurate TCP Position and Orientation Trajectory Generation in 6DOF Robotic Manipulators and CNC Machine Tools using FIR Filtering and Haversine Synchronisation. Procedia CIRP, 120, 27-32.
- Machining cycle time prediction: Data-driven modelling of machine tool feedrate behavior with neural networks. Robotics and Computer-Integrated Manufacturing, 75, 102293-102293.
- Five-Axis Trajectory Generation Considering Synchronization and Nonlinear Interpolation Errors. Journal of Manufacturing Science and Engineering, 144(8).
- Machining Digital Twin using real-time model-based simulations and lookahead function for closed loop machining control. The International Journal of Advanced Manufacturing Technology. View this article in WRRO
- Accurate prediction of machining feedrate and cycle times considering interpolator dynamics. The International Journal of Advanced Manufacturing Technology, 116(1-2), 417-438.
- Real-time vision-based multiple object tracking of a production process : industrial digital twin case study. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. View this article in WRRO
Conference proceedings papers
- On-The-Fly CNC interpolation using frequency-domain FFT-based filtering. Procedia CIRP, Vol. 107 (pp 1571-1576). Lugano, Switzerland, 29 June 2022 - 1 July 2022.
- Increasing part geometric accuracy in high speed machining using cascade iterative learning control. Procedia CIRP, Vol. 101 (pp 298-301). Online conference, 24 May 2021 - 26 May 2021.
Preprints
- Machining Cycle Time Prediction: Data-driven Modelling of Machine Tool
Feedrate Behavior with Neural Networks, arXiv.
- Accurate Prediction Of Machining Feedrate And Cycle Time Prediction
Considering Interpolator Dynamics, arXiv.
- Accurate prediction of five-axis machining cycle times with deep neural networks using Bi-LSTM. CIRP Journal of Manufacturing Science and Technology, 48, 28-41.
- Teaching interests
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ACS336/ACS61015 Rapid Control Prototyping
- Professional activities and memberships
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Professional Accreditation
- Chartered Engineer (CEng)
- Member of Institute of Engineering and Technology (MIET)
- Member of Institute of Electrical and Electronic Engineers (MIEEE)
Journal Reviewer
- International Journal of Machine Tools and Manufacture
- ASME Journal of Manufacturing Science and Engineering
- International Journal of Advanced Manufacturing Technology
- Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Conference Reviewer
- CIRP Conference of Manufacturing Systems
- CIRP Conference on High Performance Cutting
- CIRP Conference of Modelling of Machining Operations