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Automation For Surface Finish Of Additively Manufactured Knee Implant S&T12

  • 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&T12

Additive manufacturing (AM) is an exciting technology that has been adopted in many industrial sectors for the manufacture of parts with lightweight design and greater functional performance. Within healthcare, metal additive manufacturing is capable of unlocking design constraints enabling the manufacture of complex geometries. This is especially relevant for the manufacture of medical implants as it enables medical implants to be produced matching unique personal anatomical needs which can improve patients’ outcomes. Despite its advantages, a common problem of AM is that it produces rough surface which reduces the fatigue durability of functional AM parts in general. In the context of medical implants, this can cause them to fail. Furthermore, localised osteolysis can be caused when AM medical implants with unmachined surfaces are used because semi-melted particles are left on surfaces after AM. Therefore, a precision surface finish of AM parts (using post-processing technology) is required for medical implants, as well as other high specification applications.

Improving the surface finish of AM parts with complex surfaces (e.g. a knee implant with personal anatomical geometry) is a resource intensive challenge. Adding a secondary process such as machining to improve the surface always adds extra cost to the part, and the more complex or non-uniform the part is, the greater the cost. By optimising the machining process to reduce processing times – i.e. by optimising material removal rates, significant cost savings and improved quality can be achieved.

This project will develop and validate an automated digital twin to optimise the machining of additively manufactured knee implant.  A digital twin can be described as an organised collection of physics-based methods and advanced analytics that is used to model the state of every asset in a process and thereby make optimal decisions in real time.

The project will aid avoiding excessive vibrations, known as chatter, while maintaining high productivity and quality by implementing a real time data-driven chatter model applied to a real-world application where 5-axis machining is needed. Through this, the measured cutting forces will be used to calculate optimal cutting tool dynamics and stability thus enabling the digital twin to determine how conservative or aggressive the process parameters should be.

Supervisory Team

Dr Shukri Afazov – Shukri authored 20+ journal papers in advanced manufacturing and digital engineering/manufacturing. He worked and led multimillion research projects in academia and industry. He has 7 years of industrial experience including leading a team of 17 engineers at the manufacturing Technology Centre. He supported Mike Carr for the submission of the Ashfield Towns Fund bid for the proposed Automated Distribution Manufacturing Centre.

Professor Neil Mansfield – experienced in supervision of PhD students to completion.

School strategic research priority

The project aligns with the Imaging, Materials and Engineering Centre.

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.

Still need help?

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