Inverse identification of heat source model parameters for laser-powder directed energy deposition of AISI H13 deposit on AISI H11 substrate
BERTRAND Johanna, ABBES Fazilay, BONNEFOY Hervé, FLAN Bruno, ABBES Boussad
download PDFAbstract. Numerical modeling and simulation are very useful tools for assessing the impact of process parameters and predicting optimized conditions in Laser Directed Energy Deposition (L-DED) processes. Heat source parameters have a great influence on the accuracy of numerical modeling for predicting temperature fields and residual stresses. This paper presents a coupled experimental-numerical procedure to determine the Goldak’s heat source model parameters. Graded single-clad tracks were printed with the laser beam power increased continuously at a constant powder feeding rate and scanning speed. Bead widths, heights, and penetration depths were measured at different locations along the deposited track and then used as experimental data for an inverse identification process using the FEA code ABAQUS and the optimizer iSIGHT. The obtained results show that the accuracy of the numerical model is increased with optimized parameters.
Keywords
Directed Energy Deposition, Numerical Modeling, Goldak Heat Source, Parameters Identification
Published online 4/24/2024, 10 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: BERTRAND Johanna, ABBES Fazilay, BONNEFOY Hervé, FLAN Bruno, ABBES Boussad, Inverse identification of heat source model parameters for laser-powder directed energy deposition of AISI H13 deposit on AISI H11 substrate, Materials Research Proceedings, Vol. 41, pp 32-39, 2024
DOI: https://doi.org/10.21741/9781644903131-4
The article was published as article 4 of the book Material Forming
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] H. Paydas, A. Mertens, R. Carrus, J. Lecomte-Beckers, J. Tchoufang Tchuindjang, Laser cladding as repair technology for Ti–6Al–4V alloy: Influence of building strategy on microstructure and hardness, Materials and Design 85 (2015) 497–510, https://dx.doi.org/10.1016/j.matdes.2015.07.035
[2] M. Liu, A. Kumar, S. Bukkapatnam, M. Kuttolamadom, A Review of the Anomalies in Directed Energy Deposition (DED) Processes & Potential Solutions – Part Quality & Defects, Procedia Manufacturing 53 (2021) 507–518, https://doi.org/10.1016/j.promfg.2021.06.093
[3] S. M. Thompson, L. Bianc, N. Shamsaei, A. Yadollahi, An overview of Direct Laser Deposition for additive manufacturing; Part I: Transport phenomena, modeling and diagnostics, Additive Manufacturing 8 (2015) 36–62, https://doi.org/10.1016/j.addma.2015.07.001
[4] M. S. Sumanlal, N. S. Sivasubramaniyan, V. M. Joy Varghese, M. Shafeek & D. T. Ananthan, Estimation of heat source model parameters for partial penetration of TIG welding using numerical optimization method, Welding International 37(7) (2023) 400-416, https://doi.org/10.1080/09507116.2023.2242777
[5] N. Kang, X. Lin, M. El Mansori, Q.Z. Wang, J.L. Lu, C. Coddet, W.D. Huang, On the effect of the thermal cycle during the directed energy deposition application to the in-situ production of a Ti-Mo alloy functionally graded structure, Additive Manufacturing 31 (2020) 100911, https://doi.org/10.1016/j.addma.2019.100911
[6] X. Lu, M. Chiumenti, M. Cervera, G. Zhang, X. Lin, Mitigation of residual stresses and microstructure homogenization in directed energy deposition processes, Engineering with Computers 38 (2022) 4771–4790, https://doi.org/10.1007/s00366-021-01563-9
[7] A. Kiran, Y. Li, J. Hodek, M. Brázda, M. Urbánek, and J. Džugan, Heat Source Modeling and Residual Stress Analysis for Metal Directed Energy Deposition Additive Manufacturing, Materials 15(7) (2022) 2545, https://doi.org/10.3390/ma15072545
[8] X. Jia, J. Xu, Z. Liu, et al., A new method to estimate heat source parameters in gas metal arc welding simulation process, Fusion Eng. Des. 89(1) (2014) 40–48, https://doi.org/10.1016/j.fusengdes.2013.11.006
[9] Dassault Systemes, Abaqus Analysis User’s Guide 2019, Simulia Inc., Providence, RI, USA (2019).
[10] A. Kiran, J. Hodek, J. Vavřík, M. Urbánek and J. Džugan, Numerical Simulation Development and Computational Optimization for Directed Energy Deposition Additive Manufacturing Process, Materials 13(11) (2020) 2666, https://doi.org/10.3390/ma13112666
[11] “Iron” Breaking Atom. https://www.breakingatom.com/elements/iron (Accessed Jan. 02, 2024).
[12] I. Jeon, L Yang, K. Ryu, & H. Sohn, Online melt pool depth estimation during directed energy deposition using coaxial infrared camera, laser line scanner, and artificial neural network, Additive Manufacturing 47 (2021), 102295, https://doi.org/10.1016/j.addma.2021.102295
[13] “H13 Tool Steel – Chromium Hot-Work Steels” AZO Materials. https://www.azom.com/article.aspx?ArticleID=9107 (Accessed Jan. 02, 2024).
[14] V. Nain, T. Engel, M. Carin, D. Boisselier & L. Seguy, Development of an elongated ellipsoid heat Source model to reduce computation time for directed energy deposition process, Frontiers in Materials 8 (2021), 747389, https://doi.org/10.3389/fmats.2021.747389
