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)
A high-fidelity comprehensive framework for the additive manufacturing printability assessment
in Journal of Manufacturing Processes
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)
Understanding keyhole induced-porosities in laser powder bed fusion of aluminum and elimination strategy
in International Journal of Machine Tools and Manufacture
Huang Y
(2022)
Keyhole fluctuation and pore formation mechanisms during laser powder bed fusion additive manufacturing.
in Nature communications
Iantaffi C
(2023)
Auxetic response of additive manufactured cubic chiral lattices at large plastic strains
in Materials & Design
Leung C
(2023)
Correlative full field X-ray compton scattering imaging and X-ray computed tomography for in situ observation of Li ion batteries
in Materials Today Energy
Leung CLA
(2022)
Quantification of Interdependent Dynamics during Laser Additive Manufacturing Using X-Ray Imaging Informed Multi-Physics and Multiphase Simulation.
in Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Ma S
(2023)
Additive manufacturing enabled synergetic strengthening of bimodal reinforcing particles for aluminum matrix composites
in Additive Manufacturing
Mu J
(2023)
Application of electrochemical polishing in surface treatment of additively manufactured structures: A review
in Progress in Materials Science
Rees D
(2023)
In situ X-ray imaging of hot cracking and porosity during LPBF of Al-2139 with TiB2 additions and varied process parameters
in Materials & Design
Sinclair L
(2022)
Sinter formation during directed energy deposition of titanium alloy powders
in International Journal of Machine Tools and Manufacture
Soundarapandiyan G
(2023)
In situ monitoring the effects of Ti6Al4V powder oxidation during laser powder bed fusion additive manufacturing
in International Journal of Machine Tools and Manufacture
Sun T
(2022)
The role of in-situ nano-TiB 2 particles in improving the printability of noncastable 2024Al alloy
in Materials Research Letters
Wang A
(2023)
Blue laser directed energy deposition of aluminum with synchronously enhanced efficiency and quality
in Additive Manufacturing Letters
Zhang K
(2024)
Pore evolution mechanisms during directed energy deposition additive manufacturing.
in Nature communications