Postgraduate research opportunities

The Development of Automated Decision Making for Non Destructive Testing of Additive Manufactured Parts with BAE Systems Air

The outcome from this project will be an automated inspection and decision-making solution based around a machine learning approach which can be readily implemented at the industry partner, BAE Systems Air’s facilities.

Number of places

1

Funding

Home fee, Stipend

Opens

9 August 2019

Deadline

30 December 2019

Duration

4 years

Eligibility

Applications are welcome from all who possess or are about to obtain a first class or 2.1 BEng (Hons), MEng or MSc degree, or equivalent EU/International qualification, in a relevant discipline.

Applications from Home, Rest of UK and EU students will receive funding of the home fee and stipend.

International Students are welcome to apply but should be aware of the additional funding requirements and will have to provide evidence that they can pay the difference in fees of circa £16k per annum.

Find out more about this exciting PhD opportunity by clicking through the tabs above.

Individuals interested in this project should email Dr Dorothy Evans at dmem-pgr-recruitment@strath.ac.uk, and attach your most up-to-date cv.

Project Details

Are you looking to gain direct industrial experience whilst developing your specialist research area of interest?

 

Additive manufacturing (AM) has been receiving significant interest from industry as it offers the attraction of lighter structures, greater part flexibility and increased complexity without the need for multiple manufacturing processes. However, it’s exactly that what makes AM attractive which leads to the challenges surrounding part measurement and inspection. Part features such as internal lattice structures and channels as well as sub-surface defects are difficult if not impossible to measure with conventional contact measurement techniques currently used for component certification.

A number of non-destructive measurement solutions exist such as X-Ray Computed Tomography, ultrasound and eddy current. These techniques require significant amount of manual labour with many variables needing to be defined to capture the data in a suitable resolution leading to a high degree of variation across systems for the capture of a single component. For this technology to be widely adopted both the set-up process and the post-capture analysis must be standardised and repeatable.

This project will aim to automate the data capture process based on minimum user input to automatically determine the correct operating parameters, post capture the aim is to be able to automatically recognise internally manufactured features for measurement and internal defects for characterisation

The industrial partner; BAE Systems Air, is a leading UK aerospace company with a strong interest in adopting new technologies to increase the performance and reliability of their aircraft systems. The company is one of the earliest adopters of additive manufacturing for aerospace parts. With increased part complexity they see the need to develop better inspection capabilities. The academic partner will be the University of Strathclyde with metrological support provided by the National Physical Laboratory.

The outcome from this project will an automated inspection and decision-making solution based around a machine learning approach which can be readily implemented at the industry partner’s facilities.

Funding Details

This fully-funded industrial PhD opportunity will cover Home and EU Fees and Stipend.

We will accept applications from international students who can confirm in their email application that they are able to pay the difference between the Home and International fees (approximately £16,000 per annum). If you are unable to cover this cost the application will be rejected.

Further information

The additive manufacturing of metal parts has attracted the interest of a number of industries including aerospace. The ability to produce high value customized, complex, lightweight components offers the attraction of reduced weight and greater part integration– all critical to the performance and reliability of aircraft systems. However, a current barrier to the accelerated adoption of AM technologies is the confidence in the produced part quality. While traditional manufacturing routes are well understood and have established inspection and measurement methods, AM parts due to their complexity and internal features require alternative methods.

These methods include X-Ray Computed Tomography (XCT) and ultrasound techniques which can all generate large amounts of data relatively quickly. Currently, this data is presented visually and is interpreted manually creating a significant measurement bottleneck as well as the potential for subjectivity. As AM proliferates through the aerospace industry this reliance on manual interpretation will hinder its adoption. A challenge exists in both intelligently capturing the right data and automatically identifying features and defects thus eliminating the need for the human intervention.

According to a survey carried out by the aerospace defence sector a key challenge and thus gap exists in determining both the quality of the printed parts as well as having confidence in the certification of the parts and products.

The aims for this Doctorate programme are to address this decision-making gap as follows:

  • To develop an on-line machine learning solution for automated capture and decision-making on non-destructive measurement and inspection systems.
  • To validate this solution against an array of current approaches such as destructive measurement and operator comparison.

Due to the nature of the work this doctorate will be a close industry-academic collaboration.

The research activities for this industrial doctorate will be carried out at the Advanced Forming Research Centre (AFRC) and the Advanced Materials Research Laboratory (AMRL).

  • The AFRC is part of the University of Strathclyde and is a globally recognized centre of excellence in innovative manufacturing technologies. The AFRC is equipped with a wide variety of AM equipment as well as extensive measurement capabilities. The AFRC is at the heart of Scotland’s manufacturing research and development sector.
  • The AMRL located in Strathclyde’s main campus offers a wide range of metallurgical analytical equipment along with an XCT.
  • The University is also home to the Centre for Ultrasonic Engineering (CUE). CUE have over 30 years’ experience of ultrasonic engineering and data acquisition/analysis for Non Destructive Testing with various systems being commercially developed through the AFRC into industry.

The expected outcomes are as follows:

  • Accelerate the adoption of non-destructive measurement and inspection of aerospace parts
  • Develop a faster automated method of capturing part data
  • Provide an automated rapid decision making solution avoiding operator subjectiveness
  • Building a stronger and long term strategic links with BAE Systems

As a high value-adding category of manufacturing, additive manufacturing is envisaged to continue proliferating into many other industry sectors with interest already acknowledged in the oil & gas, shipbuilding and renewable energy sectors. Currently AM is predominantly used in industries such as medical devices, aerospace and high-end automotive applications. The benefits of this doctorate are strongly aligned with Scotland’s strategic priorities as laid out in the Government’s A Manufacturing Future of Manufacturing paper, namely smart manufacturing, innovation and international competitiveness. Furthermore, this proposal also aligns with SRPe’s strategic theme in advanced manufacturing and specifically the subthemes on digitalisation in manufacturing, light-weight manufacturing as well as advanced manufacturing for medical applications – the latter would be seen as a natural cross-over for the measurement of medical devices. There is also strong alignment with the UK Government’s Industrial Strategy and, in particular, the Made Smarter Initiative.

Contact us

How to apply

If you are interested in this brilliant opportunity and wish to apply, please send your CV in for the attention of Dr Dorothy Evans to dmem-pgr-recruitment@strath.ac.uk.