Since the beginning of humanity our societies have been based on commerce, i.e. we make things and we sell them to other people. Relatively simple beginnings led to the Industrial Revolution and now to the technological age. Over-generalising, the Far East are currently the masters of mass manufacture and the West are (or wish to be) the masters of advanced manufacture - the production of high-value goods, often involving a significant degree of innovation. To be able to manufacture goods in a cost-effective, environmentally-sustainable manner, quality control procedures are required. And quality control in turn requires an appropriate measurement infrastructure to be in place. It is a sub-set of this measurement infrastructure that is the subject of this fellowship. The UK government has been investing heavily in advanced manufacturing. In the academic arena, there are the sixteen EPSRC Centres of Innovative Manufacturing. To ease the pain of transferring academic research to the manufacturing sector, there are the seven High-Value Manufacturing Catapults (the Manufacturing Technology Centre being the main one of note here). For industry, there are a number of funding initiatives and tax breaks. To support this burgeoning UK advanced manufacturing infrastructure, there are a small number of academic centres for metrology - those based at Huddersfield and Bath are the main players. And, at the top of the measurement tree, there is the world-class National Physical Laboratory - a centre of excellence in metrology. But, there are still some gaps in the manufacturing metrology research jigsaw, and the aim of this fellowship is to plug those gaps. Coordinate metrology has been used for decades in the manufacturing industry as the most dominant form of process control, usually employing tactile coordinate measuring machines (CMMs). However, due to the slow speed of tactile systems and the fact that they can only take a limited amount of points, optical CMMs are starting to flourish. On the smaller scale, there are many optical surface measuring devices that tend to be used off-line in industry. When making small, high-precision, complex components, with difficult to access geometries, it is a combination of the surface measurement systems and the CMMs that is required. This requirement is one of the main aims of the fellowship - to develop a suite of fast, high-accuracy, non-contact measurement systems, which can be employed in industry. These principles will also be applied to the field of additive manufacturing - a new paradigm in manufacturing which is seeing significant government support and, in some cases, media hype. As with high-precision components, a coordinate metrology infrastructure for additive manufacturing is required, in many cases in-line to allow direct feedback to the manufacturing process. This is the second field of metrology that the fellowship will address. The outputs of the fellowship will be in the form of academic publications; new measurement instruments, along with new ways to use existing instruments; methods to allow manufacturers to verify the performance of their instruments; and the necessary pre-normative work that will lead to specification standards in the two fields (currently lacking). The academic world will benefit from the fundamental nature of elements of the research, and the industrial manufacturing world will benefit from the techniques developed and routes to standardisation. But, ultimately, it will be the UK citizens that will reap the greatest benefit in terms of new and enhanced products, and the wealth creation potential from precision and additive manufacturing.

This fellowship programme will apply state-of-the-art 3D image processing and machine learning methods, developing them further where necessary, to deliver a new software tool that performs industrial production line 'virtual qualification' using part-specific simulations from 3D X-ray imaging in high-value manufacturing (HVM). Qualification is when manufactured parts are verified fit for purpose, often achieved by performing experimental tests representative of in-service conditions. Virtual qualification will verify by modelling micro-accurate digital replicas of the final part (flaws included) replacing costly and time-consuming experimental methods. Additionally, this will assess defects for performance impact (rather than expensive but unspecific pass/fail testing). The challenge is that image-based modelling currently requires significant human interaction over a timescale of weeks. Applying this to many parts takes significant time to complete unless methodology can be changed. The novelty of this proposal is to use machine learning with foreknowledge, due to production line parts being similar, to automate conversion of microresolution 3D images into part-specific models that simulate in-service conditions. This automation is required for the technique to scale for deployment in industrial manufacturing. Additionally, because much of the decision making entailed is subjective, and therefore prone to human error, a consequential benefit of automation is consistent outputs by removing this variability. This proposal focuses on image-based finite element methods (IBFEM), which merge real and virtual worlds to account for deviations caused by manufacturing processes not considered by design-based finite element methods (FEM), e.g. due to tolerancing or micro-defects. This implementation of part-specific modelling has applications in advanced manufacturing wherever there is variability from one component to another e.g. additive manufacturing or composites. A case study will be undertaken with the UK Atomic Energy Authority (UKAEA) for a heat exchange component. This will showcase the capabilities of the technique to automatically produce a report that estimates the impact of deviations from design on performance. Unlike FEM, which have undergone extensive certification and are industry-wide trusted methods, there has not been a systematic approach which can be used to benchmark image-based modelling workflows against verified experimental data. This work will produce benchmarks based on standards for experimental measurements of thermomechanical material properties to give confidence in the technique for industrial adoption. The database of benchmarks will be useful for those wishing to use image-based modelling to validate workflows and could contribute towards establishing new standards in the field. Central to this proposal is the use of FEM, the de-facto tool for predicting thermomechanical performance in engineering. Prof Zienkiewicz's research at Swansea University established it as a birthplace for FEM, and is now recognised as a leading research centre in the field. The team undertaking this fellowship, led by Dr Llion Evans, will be based at the Zienkiewicz Centre for Computational Engineering, Swansea University and will work in collaboration with the centre's head, Prof Nithiarasu, an expert in image-based modelling for biomechanics. Access to the equipment required for all aspects of this highly multidisciplinary work i.e. thermomechanical characterisation, 3D imaging and computing is available through complementary centres at the College of Engineering, Swansea University. To support this extremely multidisciplinary work, key industrial organisations will be collaborating on this project. Nikon Metrology Ltd. (X-ray imaging systems), Synopsys Inc. (image processing software), TWI (non-destructive testing and industrial standards), UKAEA (energy generation end-user) and Airbus (aerospace end-user).