Inline virtual qualification from 3D X-ray imaging for high-value 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).

This fellowship programme aims to change practices in HVM by developing a new software tool that performs production line virtual qualification with 3D X-ray imaging. By creating micro-accurate part-specific simulations of components, each part can be tested virtually, replacing costly and time-consuming experimental methods.

Variation in part manufacture and in-service loading mean that not all parts will perform equally but will be retired from service concurrently before the 'weakest link' is deemed to have reached its life-critical stage. This work will make significant savings by considering each part individually from initiation and its unique performance predicted to estimate when it should be removed. This technique is particularly applicable to advanced materials, where component variability is an issue and macroscopic performance is dominated by microstructure, such as fibre composites or additive manufacturing. Consequently, this work will benefit aerospace and energy industries in particular. Within the timeframe of this fellowship, main industrial beneficiaries will be the end-user stakeholders, namely UKAEA and Airbus representing energy generation and aerospace. A specific work package has been included to deliver a virtual qualification solution customised for application with an energy generation heat exchange component. Additionally, Airbus are providing 3D imaging data of aerospace components which the workflow will be tested against to ensure that developed tools can be used in their sector. This will be undertaken in close collaboration with engineers at these institutions for knowledge transfer on the methodology to bring about a change in practices. In the longer term, other interested parties will be sought to pursue growth in other sectors and attract further investment or exploit commercialisation opportunities.

This multidisciplinary research entails aspects of materials science, mechanical engineering, X-ray imaging, artificial intelligence and high performance computing. In addition to the end-users, this programme will include industrial collaborators who each work on different aspects of the methodology that make up virtual qualification; TWI (non-destructive testing & industrial standards), Nikon Metrology (X-ray imaging systems), Synopsys (image processing software). As such, successful realisation of virtual qualification will bring benefit to these stakeholders through added value to their products, providing more applications to offer current customers or to reach new markets. The impact on collaborators through involvement in this programme will be maximised by fostering multi-party collaborations in addition to the bilateral relationships between the research team and each stakeholder.

For the general public, one of the UK's societal challenges is how it addresses climate change. This application of part-specific modelling reduces manufacturing cost, energy consumption and CO2 emissions through reduced wastage, benefiting manufacturers, consumers and environment. Wastage is reduced at manufacturing and in-service stages:
- For low-volume manufacturing, each part has high-value therefore virtual qualification avoids prohibitively expensive or logistically impractical testing. For high-volume manufacturing, components can be rated by performance rather than pass/fail. This reduces waste and lowers costs when components that would otherwise be scrapped are sold in 'maximum allowable limit' bands, similar to material purity
- Selling components with part-specific 'digital twins' allows bespoke prediction of in-service degradation when coupled with real-time usage data. For off-normal events, viability of continued use could be simulated part-by-part. Where possible, re-scanning the component will update the model's state to recalculate performance with greater accuracy. This additional data for fitness-for-service assessments is invaluable for safety and extending lifecycle lengths.