MPTP-Net: melt pool temperature profile network for thermal field modeling in beam shaping of laser powder bed fusion

Shengli Xu (), Rahul Rai (), Robert D. Moore (), Giovanni Orlandi () and Fadi Abdeljawad ()
Additional contact information
Shengli Xu: Clemson University
Rahul Rai: Clemson University
Robert D. Moore: Lehigh University
Giovanni Orlandi: Clemson University
Fadi Abdeljawad: Lehigh University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 6, No 26, 4233-4249

Abstract: Abstract To thoroughly investigate the impact of beam shaping on melt pool behavior and accurately predict the microstructure and mechanical properties of the final product in laser powder bed fusion (LPBF) for metal additive manufacturing (AM), it is crucial to efficiently model the temperature profiles of melt pools subjected to different laser beam shapes. Numerical methods necessitate significant computational resources and time. Machine learning (ML) based surrogate models, on the other hand, are incapable of precisely predicting three-dimensional temperature profiles and lack generalizability in modeling distinct beam shapes beyond the Gaussian beam. To address these limitations, this paper introduces the Melt Pool Temperature Profile Network (MPTP-Net), a novel model developed to efficiently predict the three-dimensional temperature profile of the melt pool based on laser beam parameters, including power, scan velocity, standard deviation of power distribution, and ring radius (applicable to ring beams). By incorporating an auxiliary geometry branch alongside the temperature profile head, our constructed multi-task learning framework is capable of learning the underlying connection between the laser beam parameters and melt pool morphology in the latent space. Hence, the proposed model improves accuracy and generalizability in predicting the 8-layer temperature profile across a wide range of melt pool dimensions. Additionally, the progressively upsampling module of MPTP-Net contributes in predicting the high-fidelity temperature profile with accurate boundaries and smooth temperature gradients of the melt pool. Through extensive validation using datasets derived from both Gaussian and ring beams, our model consistently demonstrates a superior degree of concordance between the predicted and actual melt pool temperature profiles than the state-of-the-art methods.

Keywords: Metal additive manufacturing; Laser powder bed fusion; Melt pool modeling; Temperature profile prediction; Deep learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-024-02449-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:6:d:10.1007_s10845-024-02449-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-024-02449-5

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().