General Materials Science; Metals and Alloys; 3d printing; DED; 2D simulation; melt pool; cooling rate
Abstract :
[en] Within the large Additive Manufacturing (AM) process family, Directed Energy Deposition (DED) can be used to create low-cost prototypes and coatings, or to repair cracks. In the case of M4 HSS (High Speed Steel), a reliable computed temperature field during DED process allows the optimization of the substrate preheating temperature value and other process parameters. Such optimization is required to avoid failure during the process, as well as high residual stresses. If 3D DED simulations provide accurate thermal fields, they also induce huge computation time, which motivates simplifications. This article uses a 2D Finite Element (FE) model that decreases the computation cost through dividing the CPU time by around 100 in our studied case, but it needs some calibrations. As described, the identification of a correct data set solely based on local temperature measurements can lead to various sets of parameters with variations of up to 100%. In this study, the melt pool depth was used as an additional experimental measurement to identify the input data set, and a sensitivity analysis was conducted to estimate the impact of each identified parameter on the cooling rate and the melt pool dimension.
Disciplines :
Materials science & engineering
Author, co-author :
Gallo, Calogero ; Université de Liège - ULiège > Urban and Environmental Engineering
Duchene, Laurent ; Université de Liège - ULiège > Département ArGEnCo > Analyse multi-échelles dans le domaine des matériaux et structures du génie civil
Quy Duc Pham, Thinh; Department ArGEnCo-MSM, University of Liège, Quartier Polytech 1, Allée de la Découverte 9, 4000 Liège, Belgium
Jardin, Ruben; Department ArGEnCo-MSM, University of Liège, Quartier Polytech 1, Allée de la Découverte 9, 4000 Liège, Belgium
Tuninetti, Víctor ; Department of Mechanical Engineering, Universidad de La Frontera, Francisco Salazar 01145, Temuco 4811230, Chile
Habraken, Anne ; Université de Liège - ULiège > Département ArGEnCo > Département Argenco : Secteur MS2F
Language :
English
Title :
Impact of Boundary Parameters Accuracy on Modeling of Directed Energy Deposition Thermal Field
Rashid R. Masood S. Ruan D. Palanisamy S. Huang X. Rahman Rashid R.A. Design Optimization and Finite Element Model Validation of LPBF-Printed Lattice-Structured Beams Metals 2023 13 184 10.3390/met13020184
ASTM F3187-16 Standard Guide for Directed Energy Deposition of Metals ANSI Washington, DC, USA 2016 Available online: https://webstore.ansi.org/standards/astm/astmf318716 (accessed on 26 January 2024)
Horgar A. Fostervoll H. Nyhus B. Ren X. Eriksson M. Akselsen O.M. Additive Manufacturing Using WAAM with AA5183 Wire J. Mater. Process. Technol. 2018 259 68 74 10.1016/j.jmatprotec.2018.04.014
Cao L. Li J. Hu J. Liu H. Wu Y. Zhou Q. Optimization of Surface Roughness and Dimensional Accuracy in LPBF Additive Manufacturing Opt. Laser Technol. 2021 142 107246 10.1016/j.optlastec.2021.107246
Jardin R.T. Tuninetti V. Tchuindjang J.T. Duchêne L. Hashemi N. Tran H.S. Carrus R. Mertens A. Habraken A.M. Optimizing Laser Power of Directed Energy Deposition Process for Homogeneous AISI M4 Steel Microstructure Opt. Laser Technol. 2023 163 109426 10.1016/j.optlastec.2023.109426
Gibson I. Rosen D. Stucker B. Directed Energy Deposition Processes Additive Manufacturing Technologies Springer New York New York, NY, USA 2015 245 268 978-1-4939-2112-6
Saboori A. Aversa A. Marchese G. Biamino S. Lombardi M. Fino P. Application of Directed Energy Deposition-Based Additive Manufacturing in Repair Appl. Sci. 2019 9 3316 10.3390/app9163316
Ahn D.-G. Direct Metal Additive Manufacturing Processes and Their Sustainable Applications for Green Technology: A Review Int. J. Precis. Eng. Manuf. Green Technol. 2016 3 381 395 10.1007/s40684-016-0048-9
Ahn D.-G. Directed Energy Deposition (DED) Process: State of the Art Int. J. Precis. Eng. Manuf. Green Technol. 2021 8 703 742 10.1007/s40684-020-00302-7
Chouhan A. Aggarwal A. Kumar A. A Computational Study of Porosity Formation Mechanism, Flow Characteristics and Solidification Microstructure in the L-DED Process Appl. Phys. A 2020 126 833 10.1007/s00339-020-04013-3
Regulin D. Barucci R. A Benchmark of Approaches for Closed Loop Control of Melt Pool Shape in DED Int. J. Adv. Manuf. Technol. 2023 126 829 843 10.1007/s00170-023-11042-8
Gerstgrasser M. Cloots M. Stirnimann J. Wegener K. Residual Stress Reduction of LPBF-Processed CM247LC Samples via Multi Laser Beam Strategies Int. J. Adv. Manuf. Technol. 2021 117 2093 2103 10.1007/s00170-021-07083-6
Lewis G.K. Schlienger E. Practical Considerations and Capabilities for Laser Assisted Direct Metal Deposition Mater. Des. 2000 21 417 423 10.1016/S0261-3069(99)00078-3
Hug E. Lelièvre M. Folton C. Ribet A. Martinez-Celis M. Keller C. Additive Manufacturing of a Ni-20 wt% Cr Binary Alloy by Laser Powder Bed Fusion: Impact of the Microstructure on the Mechanical Properties Mater. Sci. Eng. A 2022 834 142625 10.1016/j.msea.2022.142625
Heeling T. Cloots M. Wegener K. Melt Pool Simulation for the Evaluation of Process Parameters in Selective Laser Melting Addit. Manuf. 2017 14 116 125 10.1016/j.addma.2017.02.003
Yao D. Wang J. Luo H. Wu Y. An X. Thermal Behavior and Control during Multi-Track Laser Powder Bed Fusion of 316 L Stainless Steel Addit. Manuf. 2023 70 103562 10.1016/j.addma.2023.103562
Karayagiz K. Johnson L. Seede R. Attari V. Zhang B. Huang X. Ghosh S. Duong T. Karaman I. Elwany A. et al. Finite Interface Dissipation Phase Field Modeling of Ni–Nb under Additive Manufacturing Conditions Acta Mater. 2020 185 320 339 10.1016/j.actamat.2019.11.057
Li X. Zhang M. Qi J. Yang Z. Jiao Z. A Simulation Study on the Effect of Residual Stress on the Multi-Layer Selective Laser Melting Processes Considering Solid-State Phase Transformation Materials 2022 15 7175 10.3390/ma15207175 36295243
Baumard A. Ayrault D. Fandeur O. Bordreuil C. Deschaux-Beaume F. Numerical Prediction of Grain Structure Formation during Laser Powder Bed Fusion of 316 L Stainless Steel Mater. Des. 2021 199 109434 10.1016/j.matdes.2020.109434
Denlinger E.R. Jagdale V. Srinivasan G.V. El-Wardany T. Michaleris P. Thermal Modeling of Inconel 718 Processed with Powder Bed Fusion and Experimental Validation Using in Situ Measurements Addit. Manuf. 2016 11 7 15 10.1016/j.addma.2016.03.003
Kumar A. Paul C.P. Pathak A.K. Bhargava P. Kukreja L.M. A Finer Modeling Approach for Numerically Predicting Single Track Geometry in Two Dimensions during Laser Rapid Manufacturing Opt. Laser Technol. 2012 44 555 565 10.1016/j.optlastec.2011.08.026
Zhang Z. Farahmand P. Kovacevic R. Laser Cladding of 420 Stainless Steel with Molybdenum on Mild Steel A36 by a High Power Direct Diode Laser Mater. Des. 2016 109 686 699 10.1016/j.matdes.2016.07.114
Chiumenti M. Cervera M. Salmi A. Agelet De Saracibar C. Dialami N. Matsui K. Finite Element Modeling of Multi-Pass Welding and Shaped Metal Deposition Processes Comput. Methods Appl. Mech. Eng. 2010 199 2343 2359 10.1016/j.cma.2010.02.018
Buchenau T. Amkreutz M. Bruening H. Mayer B. Influence of Contour Scan Variation on Surface, Bulk and Mechanical Properties of LPBF-Processed AlSi7Mg0.6 Materials 2023 16 3169 10.3390/ma16083169
Frazier W.E. Metal Additive Manufacturing: A Review J. Mater. Eng. Perform. 2014 23 1917 1928 10.1007/s11665-014-0958-z
Hallam J.M. Kissinger T. Charrett T.O.H. Tatam R.P. In-Process Range-Resolved Interferometric (RRI) 3D Layer Height Measurements for Wire + Arc Additive Manufacturing (WAAM) Meas. Sci. Technol. 2022 33 044002 10.1088/1361-6501/ac440e
Nain V. Engel T. Carin M. Boisselier D. Seguy L. Development of an Elongated Ellipsoid Heat Source Model to Reduce Computation Time for Directed Energy Deposition Process Front. Mater. 2021 8 747389 10.3389/fmats.2021.747389
Chadha U. Selvaraj S.K. Lamsal A.S. Maddini Y. Ravinuthala A.K. Choudhary B. Mishra A. Padala D. Shashank M. Lahoti V. et al. Directed Energy Deposition via Artificial Intelligence-Enabled Approaches Complexity 2022 2022 2767371 10.1155/2022/2767371
Fetni S. Pham Q.D.T. Tran V.X. Duchêne L. Tran H.S. Habraken A.M. Thermal field prediction in DED manufacturing process using Artificial Neural Network Proceedings of the ESAFORM 2021 24th International Conference on Material Forming Virtual 14–16 April 2021 10.25518/esaform21.2812
Leroy-Dubief C. Contributions à La Définition de Règles de Fabrication Pour Le Procédé DED-LP Par Une Approche Thermique et Géométrique Ph.D. Thesis Université de Bordeaux Bordeaux, France 2023
Hashemi S.N. Study of High Speed Steel Deposits Produced by Laser Cladding, Microstructure–Wear–Thermal Model Ph.D. Thesis University of Liège Liège, Belgium 2017
Bayat M. Dong W. Thorborg J. To A.C. Hattel J.H. A Review of Multi-Scale and Multi-Physics Simulations of Metal Additive Manufacturing Processes with Focus on Modeling Strategies Addit. Manuf. 2021 47 102278 10.1016/j.addma.2021.102278
Liang X. Cheng L. Chen Q. Yang Q. To A.C. A Modified Method for Estimating Inherent Strains from Detailed Process Simulation for Fast Residual Distortion Prediction of Single-Walled Structures Fabricated by Directed Energy Deposition Addit. Manuf. 2018 23 471 486 10.1016/j.addma.2018.08.029
Keumo Tematio J. Simulation Numérique Du Procédé de Fabrication Additive DED: Résolution Thermomécanique Incrémentale Complète et Modèles Réduits de Type “Inherent Strain,” Ph.D. Thesis Université Paris Sciences et Lettres Paris, France 2022
Jardin R.T. Tuninetti V. Tchuindjang J.T. Hashemi N. Carrus R. Mertens A. Duchêne L. Tran H.S. Habraken A.M. Sensitivity Analysis in the Modelling of a High Speed Steel Thin-Wall Produced by Directed Energy Deposition Metals 2020 10 1554 10.3390/met10111554
Liu S. Zhang Y. Kovacevic R. Numerical Simulation and Experimental Study of Powder Flow Distribution in High Power Direct Diode Laser Cladding Process Lasers Manuf. Mater. Process. 2015 2 199 218 10.1007/s40516-015-0015-2
Dinovitzer M. Chen X. Laliberte J. Huang X. Frei H. Effect of Wire and Arc Additive Manufacturing (WAAM) Process Parameters on Bead Geometry and Microstructure Addit. Manuf. 2019 26 138 146 10.1016/j.addma.2018.12.013
Fetni S. Enrici T.M. Niccolini T. Tran H.S. Dedry O. Duchêne L. Mertens A. Habraken A.M. Thermal Model for the Directed Energy Deposition of Composite Coatings of 316 L Stainless Steel Enriched with Tungsten Carbides Mater. Des. 2021 204 109661 10.1016/j.matdes.2021.109661
Khan A. Jaffery S.H.I. Hussain S.Z. Anwar Z. Dilawar S. Numerical and Experimental Characterization of Melt Pool in Laser Powder Bed Fusion of SS316l Integr. Mater. Manuf. Innov. 2023 12 210 230 10.1007/s40192-023-00302-w
Simmons J.C. Chen X. Azizi A. Daeumer M.A. Zavalij P.Y. Zhou G. Schiffres S.N. Influence of Processing and Microstructure on the Local and Bulk Thermal Conductivity of Selective Laser Melted 316L Stainless Steel Addit. Manuf. 2020 32 100996 10.1016/j.addma.2019.100996
Bobach B.-J. Boman R. Celentano D. Terrapon V.E. Ponthot J.-P. Simulation of the Marangoni Effect and Phase Change Using the Particle Finite Element Method Appl. Sci. 2021 11 11893 10.3390/app112411893
Lampa C. Kaplan A.F.H. Powell J. Magnusson C. An Analytical Thermodynamic Model of Laser Welding J. Phys. Appl. Phys. 1997 30 1293 1299 10.1088/0022-3727/30/9/004
Cao J. Gharghouri M.A. Nash P. Finite-Element Analysis and Experimental Validation of Thermal Residual Stress and Distortion in Electron Beam Additive Manufactured Ti-6Al-4V Build Plates J. Mater. Process. Technol. 2016 237 409 419 10.1016/j.jmatprotec.2016.06.032
Ur Rehman A. Pitir F. Salamci M.U. Laser Powder Bed Fusion (LPBF) of In718 and the Impact of Pre-Heating at 500 and 1000 °C: Operando Study Materials 2021 14 6683 10.3390/ma14216683
Heigel J.C. Michaleris P. Reutzel E.W. Thermo-Mechanical Model Development and Validation of Directed Energy Deposition Additive Manufacturing of Ti–6Al–4V Addit. Manuf. 2015 5 9 19 10.1016/j.addma.2014.10.003
Yin H. Wang L. Felicelli S.D. Comparison of Two-Dimensional and Three-Dimensional Thermal Models of the LENS® Process J. Heat Transf. 2008 130 102101 10.1115/1.2953236
Marquardt D.W. An Algorithm for Least Square Estimation of Nonlinear Parameters J. Soc. Ind. Appl. Math. 1963 11 431 441 10.1137/0111030
Gavin H.P. The Levenberg-Marquardt Algorithm for Nonlinear Least Squares Curve-Fitting Problems Duke University Durham, NC, USA 2022
Betaieb E. Duchene L. Habraken A. Calibration of kinematic hardening parameters on sheet metal with a Computer Numerical Control machine Int. J. Mater. Form. 2022 15 69 10.1007/s12289-022-01714-3
Modest M.F. Radiative Heat Transfer 3rd ed. Academic Press New York, NY, USA 2013 978-0-12-386944-9
Pham T.Q.D. Hoang T.V. Van Tran X. Pham Q.T. Fetni S. Duchêne L. Tran H.S. Habraken A.-M. Fast and Accurate Prediction of Temperature Evolutions in Additive Manufacturing Process Using Deep Learning J. Intell. Manuf. 2023 34 1701 1719 10.1007/s10845-021-01896-8
