A digital twin hierarchy for metal additive manufacturing

A Phua, CHJ Davies, GW Delaney - Computers in Industry, 2022 - Elsevier
Digital twins present a conceptual framework for product life-cycle monitoring and control
using a simulated replica of the physical system. Since their emergence, they have garnered …

Applications of machine learning in process monitoring and controls of L-PBF additive manufacturing: A review

D Mahmoud, M Magolon, J Boer, MA Elbestawi… - Applied Sciences, 2021 - mdpi.com
One of the main issues hindering the adoption of parts produced using laser powder bed
fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels …

Part geometry and conduction-based laser power control for powder bed fusion additive manufacturing

H Yeung, B Lane, J Fox - Additive manufacturing, 2019 - Elsevier
Laser powder bed fusion (LPBF) uses a focused, high power laser to repeatedly scan
geometric patterns on thin layers of metal powder, which build up to a final, solid three …

Deep learning-based data registration of melt-pool-monitoring images for laser powder bed fusion additive manufacturing

J Kim, Z Yang, H Ko, H Cho, Y Lu - Journal of Manufacturing Systems, 2023 - Elsevier
Melt-pool monitoring (MPM) has been widely used in the laser powder bed fusion (LPBF)
additive manufacturing process for process control and part quality prediction. Achieving this …

Thermal control of laser powder bed fusion using deep reinforcement learning

F Ogoke, AB Farimani - Additive Manufacturing, 2021 - Elsevier
Powder-based additive manufacturing techniques provide tools to construct intricate
structures that are difficult to manufacture using conventional methods. In Laser Powder Bed …

Keyhole pores reduction in laser powder bed fusion additive manufacturing of nickel alloy 625

H Yeung, FH Kim, MA Donmez, J Neira - International Journal of Machine …, 2022 - Elsevier
Keyhole pores are common in additively manufactured parts and can badly deteriorate the
part's performance. In this study, we demonstrated that the keyhole pores formation in the …

Investigation of deep learning for real-time melt pool classification in additive manufacturing

Z Yang, Y Lu, H Yeung… - 2019 IEEE 15th …, 2019 - ieeexplore.ieee.org
Consistent melt pool geometry is an indicator of a stable laser powder bed fusion (L-PBF)
additive manufacturing process. Melt pool size and shape reflect the impact of process …

A meltpool prediction based scan strategy for powder bed fusion additive manufacturing

H Yeung, Z Yang, L Yan - Additive Manufacturing, 2020 - Elsevier
In this study a feedforward control method for laser powder bed fusion additive
manufacturing is demonstrated. It minimizes the meltpool variation by updating the laser …

Real-time process monitoring and closed-loop control on laser power via a customized laser powder bed fusion platform

R Wang, B Standfield, C Dou, AC Law, ZJ Kong - Additive Manufacturing, 2023 - Elsevier
Additive manufacturing (AM) is one of the most effective ways to fabricate parts with complex
geometries using various materials. However, AM also suffers from printing quality issues …

A residual heat compensation based scan strategy for powder bed fusion additive manufacturing

H Yeung, B Lane - Manufacturing letters, 2020 - Elsevier
Typical scan strategies for laser powder bed fusion (LPBF) additive manufacturing systems
apply a constant laser power and scan speed. Localized preheating from adjacent scan …