Enhancing laser powder bed fusion processability of copper with nanocoated powder and in-situ monitoring
File(s)
Author(s)
Cheng, Kaka
Type
Thesis
Abstract
Laser powder bed fusion (LPBF) offers great potential for producing complex metal components, yet processing pure copper remains challenging due to its high reflectivity and thermal
conductivity, which limit laser energy absorption and cause process instability. This thesis addresses these challenges through an integrated framework of nanocoating surface modification,
dynamic absorptivity characterisation, and intelligent in-situ monitoring in three main research chapters.
A bioinspired polydopamine (PDA) nanocoating was developed to enhance the laser absorptivity of copper powders, optimally increasing it from below 20% to 67% with a 56 nm coating.
The improved energy coupling enabled stable melt pools, denser structures, and no detectable contamination. Synergistically, combining nanocoating with laser defocusing ensured sufficient
melting while mitigating spatter and overheating, significantly improving process stability. Dynamic absorptivity studies further revealed that focus offset strongly influenced energy coupling,
raising absorptivity from 12% to 51%. These results highlighted dynamic absorptivity as a superior predictor of LPBF performance compared to static measurement.
High-speed imaging was employed to capture the plume and spatter dynamics, and the results were analysed using advanced image processing. PDA-coated powders processed under
optimised parameters exhibited markedly reduced thermal and particle instabilities, as evidenced by a flatter plume shape and narrower spatter statistical distributions. Furthermore, convolutional neural networks were successfully implemented to predict plume orientation features with an accuracy of up to 96%. Collectively, these results demonstrate the feasibility and
promise of data-driven, closed-loop control strategies for future LPBF systems.
This work establishes a novel methodology that integrates surface engineering, process optimisation, and real-time monitoring, advancing copper LPBF and offering transferable insights
for other reflective, high-conductivity materials in laser-based additive manufacturing. Future work on topics such as bulk parts printing or conductivity properties is also discussed.
conductivity, which limit laser energy absorption and cause process instability. This thesis addresses these challenges through an integrated framework of nanocoating surface modification,
dynamic absorptivity characterisation, and intelligent in-situ monitoring in three main research chapters.
A bioinspired polydopamine (PDA) nanocoating was developed to enhance the laser absorptivity of copper powders, optimally increasing it from below 20% to 67% with a 56 nm coating.
The improved energy coupling enabled stable melt pools, denser structures, and no detectable contamination. Synergistically, combining nanocoating with laser defocusing ensured sufficient
melting while mitigating spatter and overheating, significantly improving process stability. Dynamic absorptivity studies further revealed that focus offset strongly influenced energy coupling,
raising absorptivity from 12% to 51%. These results highlighted dynamic absorptivity as a superior predictor of LPBF performance compared to static measurement.
High-speed imaging was employed to capture the plume and spatter dynamics, and the results were analysed using advanced image processing. PDA-coated powders processed under
optimised parameters exhibited markedly reduced thermal and particle instabilities, as evidenced by a flatter plume shape and narrower spatter statistical distributions. Furthermore, convolutional neural networks were successfully implemented to predict plume orientation features with an accuracy of up to 96%. Collectively, these results demonstrate the feasibility and
promise of data-driven, closed-loop control strategies for future LPBF systems.
This work establishes a novel methodology that integrates surface engineering, process optimisation, and real-time monitoring, advancing copper LPBF and offering transferable insights
for other reflective, high-conductivity materials in laser-based additive manufacturing. Future work on topics such as bulk parts printing or conductivity properties is also discussed.
Version
Open Access
Date Issued
2025-10-01
Date Awarded
01/02/2026
License URL
Advisor
Hooper, Paul
Jiang, Jun
Publisher Department
Department of Mechanical Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)