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All issues Volume 417 (2025) MATEC Web Conf., 417 (2025) 05001 References
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MATEC Web Conf.
Volume 417, 2025
2025 RAPDASA-RobMech-PRASA-AMI Conference: Bridging the Gap between Industry & Academia - The 26th Annual International RAPDASA Conference, joined by RobMech, PRASA and AMI, co-hosted by CSIR and Tshwane University of Technology, Pretoria
Article Number 05001
Number of page(s) 11
Section Process Development
DOI https://doi.org/10.1051/matecconf/202541705001
Published online 25 November 2025
  1. W.E. Frazier, Metal additive manufacturing: a review. J. Mater. Eng. Perform. 23, 1917–1928 (2014). https://doi.org/10.1007/s11665-014-0958-z [CrossRef] [Google Scholar]
  2. W.L. Ng, G.L. Goh, G.D. Goh, J.S.J. Ten, W.Y. Yeong, Progress and opportunities for machine learning in materials and processes of additive manufacturing. Adv. Mater. 36, 2310006 (2024). https://doi.org/10.1002/adma.202310006 [Google Scholar]
  3. S. Deshpande, V. Venugopal, M. Kumar, S. Anand, Deep learning-based image segmentation for defect detection in additive manufacturing: an overview. Int. J. Adv. Manuf. Technol. 134, 2081–2105 (2024). https://doi.org/10.1007/s00170-024-14191-6 [Google Scholar]
  4. Y. Fu, A.R.J. Downey, L. Yuan, T. Zhang, A. Pratt, Y. Balogun, Machine-learning algorithms for defect detection in metal laser-based additive manufacturing: a review. J. Manuf. Process. 75, 693–710 (2022). https://doi.org/10.1016/j.jmapro.2021.12.061 [Google Scholar]
  5. H. Zhang, C.K.P. Vallabh, X. Zhao, Influence of spattering on in-process layer surface roughness during laser powder bed fusion. J. Manuf. Process. 104, 289–306 (2023). https://doi.org/10.1016/j.jmapro.2023.08.058 [Google Scholar]
  6. D. Mahmoud, M. Magolon, J. Boer, M.A. Elbestawi, M.G. Mohammadi, Applications of machine learning in process monitoring and controls of L-PBF additive manufacturing: a review. Appl. Sci. 11, 11910 (2021). https://doi.org/10.3390/app112411910 [Google Scholar]
  7. L. Scime, C. Joslin, D. Collins, M. Sprayberry, A. Singh, W. Halsey, R. Duncan, Z. Snow, R. Dehoff, V. Paquit, A data-driven framework for direct local tensile-property prediction of laser powder bed fusion parts. Materials 16, 7293 (2023). https://doi.org/10.3390/ma16237293 [Google Scholar]
  8. Oak Ridge National Laboratory, Laser Powder Bed Fusion Dataset (2025). https://doi.ccs.ornl.gov/dataset/c0247625-951c-5616-a2e3-03803e848896 (accessed 25 February 2025). [Google Scholar]
  9. M. Baehr, T. Klecker, S. Pielmeier, T. Ammann, M.F. Zaeh, Experimental and analytical investigations of the removal of spatters by various process gases during the powder bed fusion of metals using a laser beam. Prog. Addit. Manuf. 9, 905–917 (2024). https://doi.org/10.1007/s40964-023-00491-y [Google Scholar]
  10. O.H. Aremu, F.S. Alneif, M. Salah, H. Abualrahi, U. Ali, Spatter transport in a laser powder-bed fusion build chamber. Addit. Manuf. 94, 104439 (2024). https://doi.org/10.1016/j.addma.2024.104439 [Google Scholar]
  11. M. Kim, S.J. Han, H.-S. Kang, G.B. Bang, T.W. Lee et al., Optimization of hatch spacing for improved build rate and high-density preservation in laser powder bed fusion of pure titanium. J. Mater. Res. Technol. 33, 9853–9861 (2024). https://doi.org/10.1016/j.jmrt.2024.11.265 [Google Scholar]
  12. J. Xia, P. Berglund, R. Kutty et al., Atmosphere effects in laser powder bed fusion: a review. Materials 17, 5549 (2024). https://doi.org/10.3390/ma17225549 [Google Scholar]
  13. C.F. Montero, A.H. Alvarez, F.M. Calderón, Monitoring of the powder-bed quality in metal AM using a recoater-based line sensor. Wear 496–497, 204594 (2022). https://doi.org/10.1016/j.wear.2022.204594 [Google Scholar]
  14. A. Farrag, Y. Yang, N. Cao, D. Won, Y. Jin, Physics-informed machine learning for metal additive manufacturing. Prog. Addit. Manuf. 10, 171–185 (2025). https://doi.org/10.1007/s40964-024-00612-1 [Google Scholar]
  15. T.L. Bergman, A.S. Lavine, F.P. Incropera, D.P. DeWitt, Fundamentals of Heat and Mass Transfer, 6th ed. (John Wiley & Sons, 2011). [Google Scholar]
  16. S.A. Khairallah, A.T. Anderson, A. Meshold, Ö. Gülsoy, Laser powder-bed fusion additive manufacturing: physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones. Acta Mater. 108, 36–45 (2016). https://doi.org/10.1016/j.actamat.2016.02.014 [Google Scholar]
  17. A. Simcoe, J. Risse, J. Gockel, S. Daniewicz, Beam shaping in laser powder bed fusion: Peclet number and dynamic simulation. In: Proc. Solid Freeform Fabrication Symposium (2022), pp. 1–6. [Google Scholar]
  18. S.V. Patankar, Numerical Heat Transfer and Fluid Flow (McGraw-Hill, 1980), p. 102. [Google Scholar]
  19. A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd ed. (O’Reilly, 2019). [Google Scholar]

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