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Secure Connected Intelligent Design and Manufacturing

How will we make the products of the future?

The Queen’s Doctoral Training Programme on Secure Connected Intelligent Design and Manufacturing is a Department for the Economy (DfE) investment to train 20 PhD students.

Students will explore how applying technological developments such as artificial intelligence, edge computing and robotics can address challenges associated with engineering design and manufacturing.

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Clara Gracehynes, 2nd year PhD student

"After spending a year in industry I quickly realised if I wanted to progress my career in line with peers, I would need to gain a PhD. It's an interesting topic and has the right mix between industry training and research. The training we receive allows us to gain related expertise in a breadth of topics such as data and new technologies."

The programme aims to train graduates that:

  • are cross-disciplinary, industry-conscious thinkers and leaders who will influence the roadmaps of future advanced manufacturing technologies and their applications;
  • have a balanced understanding of ICT (security, communications and data analytics) in the context of their application to Advanced Manufacturing and High Value Design;
  • are equipped with the skills to address challenges that are strongly relevant to industry;
  • can produce world leading cross-disciplinary scholarly output and impact in areas with high economic and societal value.
PhD Opportunities
Course
DTPSCIDM: Embedding intentions in robot motion patterns: capitalizing on human perceptual skills
DTPSCIDM: Finite element modelling for medium frequency power electronics design and packaging
DTPSCIDM:Software Defined Networks for Secure Connected Energy Monitoring and Real-Time Control in Manufacturing
DTPSCIDM: Advanced manufacturing of mm-wave lens antennas for future wireless technologies
DTPSCIDM: Developing a Digital-Twin of laser welding process applying Deep Learning Neural Network
DTPSCIDM: Secure Networks for Industry 4.0 and Industrial IoT
DTPSCIDM: Integration of Computational Fluid Dynamics, Artificial Intelligence and metrology to understand how uncertainty (manufacturing and modelling) can affect nozzles manufacturing and performance.
DTPSCIDM: Digital-Twin based In-Process Quality Control for Robotic Machining
DTPSCIDM: Biohaviour: Secure Connected Manufacturing – Integrating Design and Cloud based Distributed Manufacturing
DTPSCIDM: Development of an Intelligent Robotic Rotational Moulding Cell for Sustainable Manufacturing of Thermoplastics
DTPSCIDM: Innovative design capability of turbine blade using additive manufacturing
DTPSCIDM: Identifying analysis features in CAD models by machine learning
DTPSCIDM: Adaptive control of high performance machining by parallel robots
ACRG/DTPSCIDM: Multiobjective crashworthiness optimisation of additively manufactured auxetic structures under severe loading conditions
ACRG/DTPSCIDM: Development and experimental validation of an advanced-level finite element-based simulation software for the additive manufacturing of polymers
DTPSCIDM: Systems Thinking to Leverage the Amazon Effect in High Value Manufacturing
DTPSCIDM: Future factory digital design
DTPSCIDM: Manufacture for Design – A Method for Designing Performance Critical Products
DTPSCIDM: A virtual reality platform for safe testing of human-robot interactions
DTPSCIDM: Biohaviour: Generative Design Systems for Jet Engines
DTPSCIDM: Finite element modelling for medium frequency power electronics design and packaging
DTPSCIDM: Systems Thinking to Leverage the Amazon Effect in High Value Manufacturing
DTPSCIDM:Software Defined Networks for Secure Connected Energy Monitoring and Real-Time Control in Manufacturing
DTPSCIDM: Advanced manufacturing of mm-wave lens antennas for future wireless technologies
DTPSCIDM: Developing a Digital-Twin of laser welding process applying Deep Learning Neural Network
DTPSCIDM: Secure Networks for Industry 4.0 and Industrial IoT
DTPSCIDM: Integration of Computational Fluid Dynamics, Artificial Intelligence and metrology to understand how uncertainty (manufacturing and modelling) can affect nozzles manufacturing and performance.
DTPSCIDM: Digital-Twin based In-Process Quality Control for Robotic Machining
DTPSCIDM: Biohaviour: Secure Connected Manufacturing – Integrating Design and Cloud based Distributed Manufacturing
DTPSCIDM: Development of an Intelligent Robotic Rotational Moulding Cell for Sustainable Manufacturing of Thermoplastics
DTPSCIDM: Innovative design capability of turbine blade using additive manufacturing
DTPSCIDM: Identifying analysis features in CAD models by machine learning
DTPSCIDM: Adaptive control of high performance machining by parallel robots
ACRG/DTPSCIDM: Multiobjective crashworthiness optimisation of additively manufactured auxetic structures under severe loading conditions
ACRG/DTPSCIDM: Development and experimental validation of an advanced-level finite element-based simulation software for the additive manufacturing of polymers
DTPSCIDM: Future factory digital design
DTPSCIDM: A virtual reality platform for safe testing of human-robot interactions
DTPSCIDM: Manufacture for Design – A Method for Designing Performance Critical Products
DTPSCIDM: Biohaviour: Generative Design Systems for Jet Engines

Contacts

For further information or any questions, please contact the Project Coordinators:

Prof Adrian Murphy (School of Mechanical and Aeronautical Engineering): a.murphy@qub.ac.uk

Prof Hans Vandierendonck (School of Electronics, Electrical Engineering and Computer Science): h.vandierendonck@qub.ac.uk 

All Doctoral Training Programmes
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