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  Digital Twin Control of Manufacturing Operations


   Department of Automatic Control and Systems Engineering

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The future of machining lies in the fully autonomous machine tool. New technologies must be developed that predict, sense and action intelligent decisions autonomously. Digital twins are one component on this journey and are already having significant impact in the manufacturing industries. Despite this, the implementation of machining Digital Twins has been slow due to the computational burden of simulating cutting forces online resulting in no commercially available Digital Twin that can automatically control the machining process in real time.

Addressing this problem, our vision aspires to equip machining with the intelligence and autonomy needed to sense its performance, adapt to cope with uncertainty and predict the quality of its outcomes in real-time.

This research will develop new sensing, monitoring, and Digital Twin control loop architectures. Focusing on Industry 4.0 solutions, the design of a data processing platform at the edge of the machining process will be critical to achieving real-time decision making and control in manufacturing. The research will be validated through experimental validation and verification trials using commercial machining centres and industrial case studies at the Factory of the Future, Advanced Manufacturing Research Centre.

The candidate will work closely with experts at the Advanced Manufacturing Research Centre (the University of Sheffield) and wider industrial engagement to ensure the project is complemented by case study driven industrial challenges in this space. Please note due to industry collaboration I am looking to recruit Home students only for this project.

Computer Science (8) Engineering (12)

Funding Notes

This is a self-funded research project. We require applicants to have either an undergraduate honours degree (1st) or MSc (Merit or Distinction) in a relevant science or engineering subject. Prospective candidates for this project should have a background in manufacturing, robotics, engineering or embedded systems and good programming skills, ideally in MATLAB and/or C/C++ . Full details of how to apply can be found at the following link: View Website
Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive: View Website

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