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Digital Twinning for human centric collaborative and modular scenarios S&T66

  • School: School of Science and Technology
  • Study mode(s): Full-time / Part-time
  • Starting: 2022
  • Funding: UK student / EU student (non-UK) / International student (non-EU) / Fully-funded

Overview

NTU's Fully-funded PhD Studentship Scheme 2022

Project ID: S&T66

During the last decades, there has been wide interest towards creating more agile and reconfigurable automation systems. This includes the research interest towards smart factory, human–robot collaboration (HRC) solutions, cyber physical systems (CPS), mass customization of products where semi or fully automated process could be combined with human dexterity and flexibility without complexity. Digital twinning of industrial processes lies in the centre of all this in which the exact replica of the process is simulated and linked with the actual physical process to create an effective CPS. Most of the digital twin models are used for solving complexity issues as a pre-emptive attempt in manufacturing. Due to the infancy stage of this trend, these models lack the human integration aspect in the digital twins for HRC scenarios. Human digital models are either not available or computationally too exhaustive.

AR/VR has been in use for gaming in the recent past and now gaining popularity in developing industrial validation, training, and quality control processes. AR/VR can be instrumental in developing digital twins for human centric industrial processes. The purpose of this research project is to develop AR/VR based human centric digital twins to fulfil industrial process design needs of ergonomics, validation, and safety. The research question is based on the virtual reality capabilities of designing the HRC in different industrial scenarios, thereby, enable, fast embedding into production scenarios by involving the modular nature of the industrial robots and flexibility of humans, creating a AR/VR solution for visualization, immersion and hapticity. The envisaged tool can be a very important advancement for both SME and large-scale industry to adapt to modular, collaborative robotics in the production system.

School strategic research priority

This project aligns with the Imaging, Materials and Engineering research centre and the Digital Innovation Group.

Entry qualifications

For the eligibility criteria, visit our studentship application page.

How to apply

For guidance and to make an application, please visit our studentship application page. The application deadline is Friday 14 January 2022.

Fees and funding

This is part of NTU's 2022 fully-funded PhD Studentship Scheme.

Guidance and support

Download our full applicant guidance notes for more information.

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