Data-driven, Reliable, and Effective Additive Manufacturing using multi-BEAM technologies (DREAM BEAM)
Lead Research Organisation:
University College London
Department Name: Mechanical Engineering
Abstract
Laser powder bed fusion (LPBF) additive manufacturing (AM) transforms digital designs into functional products by joining materials together, layer upon layer. It offers flexible, sustainable manufacturability and short product development time to produce high-value components with complex geometries for business across the globe, including aerospace, automotive, and biomedical sectors. The global market for AM is expected to grow from $6b (2016) to $26b (2022), resulting in major initiatives launched across the globe to grow AM technologies, including "UK Industrial strategy", "Fraunhofer Additive Manufacturing Alliance", "Made in China 2025", and "America Makes".
Despite the key advantages of AM, industries are facing technical challenges to use AM technology for safety-critical products, e.g. propellers and turbine blades, etc. These products may exhibit poor mechanical performance due to the presence of processing defects. To produce high-performance AM products, the stakeholders must understand the process and defect dynamics during AM, however, they are difficult to characterise due to the fast, complex laser-matter and multi-phase (solid-liquid-gas-plasma) interactions which occur in milliseconds.
This project involves UCL and world-leading industrial partners in AM (Renishaw plc.), laser technologies (STFC - Central laser facility), machine learning (STFC - Scientific Machine-learning group), ultra-fast imaging (European Synchrotron Radiation Facility) and process simulations (European Space Agency) to co-develop engineering solutions to understand, evaluate, and control the process-structure-property-performance relationships in AM. This project is expected to collect a wide range of digital data that can be used to develop a data-driven, reliable and efficient AM process.
Firstly, a unique chemical imaging tool will be developed and deployed to monitor and evaluate the metal vapourisation process during LPBF with a temporal resolution of 200 kHz. These results will be cross-validated by flagship ultra-fast X-ray imaging experiments which enable users to see inside the melt pool and defect dynamics during LPBF at micron resolution and a time resolution of up to 1 MHz. Correlative chemical and X-ray imaging of AM will be a game-changer characterisation technique to study the dynamic behaviour and multiphase interaction in AM. It will bring new understanding by which defects are introduced during AM and suggest ways to improve the overall process.
Secondly, we will make advancement of novel beam shaping technologies to control the heat input to the fusion process, minimising metal vapourisation and defect formation during LPBF. The performance of the beam-shaping technologies will be assessed and verified by correlative imaging.
Thirdly, all the digital data collected through this project will be used to build, train and deploy machine learning (ML) model(s) for process control, i.e. ML-guided process control. They will also be used to verify, validate, and advance an open-source high fidelity process simulation model that analyses multi-phase and multi-physics interactions in AM, which can be extended to other advanced manufacturing processes.
Besides the development of new technologies, this project will also provide opportunities for early-career researchers to disseminate their research to the public, industries, and scientific communities, promote knowledge exchange and technology transfer activities.
Despite the key advantages of AM, industries are facing technical challenges to use AM technology for safety-critical products, e.g. propellers and turbine blades, etc. These products may exhibit poor mechanical performance due to the presence of processing defects. To produce high-performance AM products, the stakeholders must understand the process and defect dynamics during AM, however, they are difficult to characterise due to the fast, complex laser-matter and multi-phase (solid-liquid-gas-plasma) interactions which occur in milliseconds.
This project involves UCL and world-leading industrial partners in AM (Renishaw plc.), laser technologies (STFC - Central laser facility), machine learning (STFC - Scientific Machine-learning group), ultra-fast imaging (European Synchrotron Radiation Facility) and process simulations (European Space Agency) to co-develop engineering solutions to understand, evaluate, and control the process-structure-property-performance relationships in AM. This project is expected to collect a wide range of digital data that can be used to develop a data-driven, reliable and efficient AM process.
Firstly, a unique chemical imaging tool will be developed and deployed to monitor and evaluate the metal vapourisation process during LPBF with a temporal resolution of 200 kHz. These results will be cross-validated by flagship ultra-fast X-ray imaging experiments which enable users to see inside the melt pool and defect dynamics during LPBF at micron resolution and a time resolution of up to 1 MHz. Correlative chemical and X-ray imaging of AM will be a game-changer characterisation technique to study the dynamic behaviour and multiphase interaction in AM. It will bring new understanding by which defects are introduced during AM and suggest ways to improve the overall process.
Secondly, we will make advancement of novel beam shaping technologies to control the heat input to the fusion process, minimising metal vapourisation and defect formation during LPBF. The performance of the beam-shaping technologies will be assessed and verified by correlative imaging.
Thirdly, all the digital data collected through this project will be used to build, train and deploy machine learning (ML) model(s) for process control, i.e. ML-guided process control. They will also be used to verify, validate, and advance an open-source high fidelity process simulation model that analyses multi-phase and multi-physics interactions in AM, which can be extended to other advanced manufacturing processes.
Besides the development of new technologies, this project will also provide opportunities for early-career researchers to disseminate their research to the public, industries, and scientific communities, promote knowledge exchange and technology transfer activities.
Publications
Bhatt A
(2023)
In situ characterisation of surface roughness and its amplification during multilayer single-track laser powder bed fusion additive manufacturing
in Additive Manufacturing
Chen R
(2024)
Exploring the Properties of Disordered Rocksalt Battery Cathode Materials by Advanced Characterization
in Advanced Functional Materials
Fan X
(2023)
Thermoelectric magnetohydrodynamic control of melt pool flow during laser directed energy deposition additive manufacturing
in Additive Manufacturing
Fleming T
(2023)
Synchrotron validation of inline coherent imaging for tracking laser keyhole depth
in Additive Manufacturing
Fleming T
(2023)
In situ correlative observation of humping-induced cracking in directed energy deposition of nickel-based superalloys
in Additive Manufacturing
Gao Z
(2023)
Data-driven design of biometric composite metamaterials with extremely recoverable and ultrahigh specific energy absorption
in Composites Part B: Engineering
Gao Z
(2022)
Additively manufactured high-energy-absorption metamaterials with artificially engineered distribution of bio-inspired hierarchical microstructures
in Composites Part B: Engineering
Guo L
(2023)
Understanding keyhole induced-porosities in laser powder bed fusion of aluminum and elimination strategy
in International Journal of Machine Tools and Manufacture
Guo L
(2023)
Quantifying the effects of gap on the molten pool and porosity formation in laser butt welding
in International Journal of Heat and Mass Transfer
Guo L
(2023)
A high-fidelity comprehensive framework for the additive manufacturing printability assessment
in Journal of Manufacturing Processes
Description | UCL and Anna Paradowska, University of Sydney/ANSTO collaboration |
Organisation | Australian Nuclear Science and Technology Organisation |
Country | Australia |
Sector | Public |
PI Contribution | Joint project - Solid-State Additive Manufacturing For Recycled Aluminium Alloys |
Collaborator Contribution | Joint project - Solid-State Additive Manufacturing For Recycled Aluminium Alloys |
Impact | Paper submitted |
Start Year | 2023 |
Description | UCL and Michael Mallon, Gian Lorenzo Casini and Miguel Yagues Palazon from European Space Agency collaboration |
Organisation | European Space Agency |
Department | Harwell Centre |
Country | United Kingdom |
Sector | Public |
PI Contribution | Joint project - Enable active control of surface roughness during advanced manufacturing using laser pulse shaping technologies |
Collaborator Contribution | Joint project - Enable active control of surface roughness during advanced manufacturing using laser pulse shaping technologies |
Impact | Joint PDRA Anastassia Milleret hired. |
Start Year | 2024 |
Description | UCL and Wentao Yan, National University of Singapore collaboration |
Organisation | National University of Singapore |
Country | Singapore |
Sector | Academic/University |
PI Contribution | Provide expertise on AM experiment and data analytics. |
Collaborator Contribution | Provide expertise on AM modelling. |
Impact | No output yet. |
Start Year | 2023 |
Description | UCL and Xiao Shang, University of Toronto collaboration |
Organisation | University of Toronto |
Country | Canada |
Sector | Academic/University |
PI Contribution | Provision of beamtime experiment, expertise in AM machines, data analysis. |
Collaborator Contribution | Participation in beamtime experiment, expertise in data analysis and AM |
Impact | No output yet. |
Start Year | 2023 |
Description | Anna Getley talk at TMS2023 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Anna Getley talk at TMS2023, Characterising the Vapour Plume and Preferential Vaporisation of Alloy Elements during Laser Powder Bed Fusion Additive Manufacturing, California |
Year(s) Of Engagement Activity | 2023 |
Description | C.L.A. Leung and K. Kim were invited to give a talk at the Carpenter Additive, Widnes |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Industry/Business |
Results and Impact | 3 - 4 researchers attended to discuss and share the latest work related on the impact of powder oxidation on Additive Manufacturing, which sparked questions and discussion afterwards. |
Year(s) Of Engagement Activity | 2023 |
Description | C.L.A. Leung hosted an academic visit/knowledge exchange workshop with Dr. Wentao Yan from National University of Singapore, Singapore, Research Complex at Harwell, Harwell Campus |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Postgraduate students |
Results and Impact | Organised a knowledge exchange workshop on multiscale modelling of AM with Dr. Wentao Yan from National University of Singapore, Singapore. >20 early career researchers attended the workshop, which sparked questions and discussion afterwards in related subject areas. |
Year(s) Of Engagement Activity | 2023 |
Description | C.L.A. Leung hosted the 'IOM3 Road to Professional Membership and Chartership event' with UKRI RCaH, IOM3, and Renishaw plc. |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | I co-host the upcoming event: 'Road to professional membership and chartership' with the Research Complex at Harwell, IOM3 (Institute of Materials, Minerals & Mining), UCL, and Renishaw. If you want to find answers to any of the following questions, please come along to our in person event at Harwell campus (29th Nov 2023). We have got an excellent line-up of speakers, e.g. Ms Sarah Boad, Dr. Sarah Glanvill, and Prof. Mark Miodownik. The goal is to showcase professional practitioners to learn more about the following areas: ? Are you interested in joining a professional body or the benefits of being a member of one? ? Do you want to join the Institute of Materials, Minerals, and Mining (IOM3)? ? Do you know how being chartered could benefit your career? ? Do you know the process for becoming chartered? ? Do you even know what chartership is? |
Year(s) Of Engagement Activity | 2023 |
URL | https://www.rc-harwell.ac.uk/events/road-professional-membership-and-chartership |
Description | CLA Leung and PD Lee, Invited Talk at virtual seminar for academic and industry in Ireland |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | CLA Leung and PD Lee, Invited Talk at virtual seminar for academic and industry in Ireland, "Quantify melt pool and defect dynamics during additive manufacturing using physical twin coupled with X-ray imaging and digital twins", Ireland, January 2023 |
Year(s) Of Engagement Activity | 2023 |
Description | CLA Leung talk at AMRC |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | C L A Leung talk at AMRC (University of Sheffield Advanced Manufacturing Research Centre) "Progress towards intelligent manufacturing", UK |
Year(s) Of Engagement Activity | 2023 |
Description | CLA Leung talk at Centre for Artificial Intelligence and Robotics (CAIR) Hong Kong Institute of Science & Innovation |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | C L A Leung talk at Centre for Artificial Intelligence and Robotics (CAIR) Hong Kong Institute of Science & Innovation (Chinese Academy of Sciences), Hong Kong |
Year(s) Of Engagement Activity | 2023 |
Description | CLA Leung talk for Additive Manufacturing Defects |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | CLA Leung invited talk at Ulster University "Quantify melt pool and defect dynamics during additive manufacturing using physical twin coupled with X-ray imaging and digital twins" , Ulster |
Year(s) Of Engagement Activity | 2023 |
Description | CLA Leung, Invited Talk at virtual seminar series, Wuhan University of Technology |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | CLA Leung, Invited Talk at virtual seminar series, Wuhan University of Technology, "A journey to data-driven reliable efficient additive manufacturing (DREAM)", Wuhan, China, July 2022 |
Year(s) Of Engagement Activity | 2022 |
Description | PD Lee, CLA Leung, AM hub Meeting |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | PD Lee and CLA Leung attended an meeting to form a hub for dynamic and large scale synchrotron imaging of additive manufacturing at ESRF. The proposal will be submitted in Jan 2024 and the Hub would start in Autumn 2024 if successful, bringing together groups from across Europe. |
Year(s) Of Engagement Activity | 2023 |