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Machine Learning and Big Data for Additively Manufactured Medical Implants


Project Description

This PhD project will work on developing the next generation of additive technology in partnership with Stryker, a global med tech company and a world leader in productionising additive manufacturing. The focus will be on driving process innovations on both current and new additive processes and will include external supervision from Stryker’s world leading additive manufacturing team with the possibility of academic placements within one of Stryker’s Global additive facilities. These studentships are part of the EPSRC Centre for Doctoral Training (CDT) in Additive manufacturing. The CDT delivers a four-year PhD training programme. It includes specific training in Additive Manufacturing (AM) and 3D Printing methods and techniques, and the opportunity to attend international study tours. All students are part of a cohort, which encourages interdisciplinary research, innovative thinking and a supportive learning environment. Each PhD project has a specific link to an industrial partner (in this case Stryker), supported by a minimum three-month industrial internship
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Project Description
Additive Manufacturing is revolutionising UK industry, but part certification still creates a significant barrier to adoption. Uncertainties associated with AM build processes mean that thorough certification of AM builds is essential. Certification experiments are, however, expensive to conduct and these costs prevent AM technology from targeting some markets.
The fields of Machine Learning and Big Data analytics have the potential to address this issue. During the AM build process, it is now possible to generate very large sets of measurement data (relating to temperature, pressure, light intensity etc.) The current project will investigate whether AM build quality can be inferred directly from these measurements, using advanced Machine Learning approaches.
Successful applicants will work across the disciplines of Additive Manufacturing and Machine Learning. They will also have access (and will contribute) to a library of bespoke Machine Learning code and Big Data processing software that is developed and maintained by Dr. Green’s research group, at the UoL Institute for Risk and Uncertainty.

The Centre for Doctoral Training (CDT) has been successful in attracting significant funding from the Engineering and Physical Science Research Council (EPSRC) to support our innovative and comprehensive training programme. The CDT in Additive Manufacturing (AM) and 3D Printing (3DP) is therefore able to provide successful applicants that satisfy the EPSRC eligibility criteria with a generous scholarship package that includes:
• Tuition fees paid
• A tax-free annual stipend of up to £20,000 after the 1st year of study (subject to agreement)
• A dedicated training programme to enable Researchers to understand the breadth and depth of AM and 3DP technology
• An international travel budget for visits to overseas laboratories and attendance at international conferences

Applicants should have or expect to gain a first class or upper second-class honours degree (or equivalent if from overseas) in any of the following backgrounds: mechanical engineering, material science, physics, or a related discipline, or have an appropriate MSc qualification.
If English is not your first language then you must have International English Language Testing Service (IELTS) certificate with an average of 6.5 or above and at least 6.0 in each component.

Funding Notes

This is a fully funded PhD which is part of the Engineering and Physical Science Research Council (EPSRC) Centre for Doctoral Training (CDT) in Additive Manufacturing, along with industrial support from Stryker.

References

Dr Kate Black [email protected] Or Dr Peter Green [email protected]

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