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).