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.
