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Shape optimisation for aesthetics/functional requirements in the context of additive manufacturing

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Despite significant advances in techniques of structural design in the past decades, there are still significant hurdles for achieving a harmonious balance between form and function. This is often attributed to severe limitations usually imposed upon a designer’s vision by the practicalities of engineering and economics, exacerbated further by the constraints of regulatory requirements.

Judicious exploitation of modern technologies in engineering design can open up new opportunities in this context: where ostensibly conflicting requirements could lead to novel synergies; so that mathematically optimised forms of structural components could also satisfy aesthetic concerns; while other functional, engineering and economic imperatives may be addressed by optimum placement of materials through additive manufacturing.

This PhD research is intended to explore the feasibility of using a relatively quick and computationally efficient visualisation approach coupled with an efficient shape optimisation technique to achieve the aforementioned objective. The same approach could then be adapted to guide the fabrication process using a variety of modern manufacturing technologies.

Applicants should have a first or upper second class UK honours degree (or equivalent) in mechanical/structural engineering or a related discipline. A Master’s level qualification is desirable but not essential. A strong background in advanced structural modelling and numerical simulations is required.

Interested candidates should send their application including a covering letter (explaining background and motivation), CV, and publication record (if applicable) preferably as one single PDF file to Dr Payam Khazaeinejad at .


Funding Notes

No funding is available for this project

How good is research at Kingston University in General Engineering?

FTE Category A staff submitted: 14.00

Research output data provided by the Research Excellence Framework (REF)

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