Efficient sensitivity analysis of the thermal profile in powder bed fusion of metals using hypercomplex automatic differentiation finite element method

Rincon-Tabares, Juan-Sebastian; Aristizabal, Mauricio; Balcer, Matthew Ryan; Montoya, Arturo; Millwater, Harry; Restrepo, David

doi:10.1016/j.addma.2024.104488

Efficient sensitivity analysis of the thermal profile in powder bed fusion of metals using hypercomplex automatic differentiation finite element method

Journal Article · Tue Oct 15 00:00:00 EDT 2024 · Additive Manufacturing

DOI:https://doi.org/10.1016/j.addma.2024.104488· OSTI ID:2472616

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Univ. of Texas at San Antonio, TX (United States)
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Rapid cyclic temperature fluctuation occurring in powder bed fusion of metals using a laser beam (PBF-LB/M) influences the formation of flaws in printed parts. Consequently, there is a pressing need to enhance the quality of printed parts by developing innovative methodologies that can predict thermal histories and help uncover the intricate relationships between process parameters and thermal profiles. Sensitivity Analysis (SA) emerges as an essential tool for this, offering the potential for process optimization and enhanced quality control. Nonetheless, conventional SA methodologies often incur in excessive computational costs and potential numerical approximation errors. Here, to address this technical challenge, we present a novel method for SA that integrates the HYPercomplex-based Automatic Differentiation (HYPAD) technique with transient thermal simulations conducted via the finite element method (FEM). Leveraging this methodology, we efficiently and accurately perform SA for PBF-LB/M processes in a post-processing step. Compared to traditional methods like Finite Differences (FD), HYPAD-FEM required 96 % less computational time for obtaining sensitivities for 22 process parameters, under a comparative study conducted within the context of the 2018–02 AM benchmark of the National Institute of Standards and Technology. In summary, HYPAD-FEM offers superior efficiency and accuracy in SA over conventional methods, delivering the best sensitivity of a model without the need for step-size selection and problem or parameter-based implementations.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 2472616

Report Number(s):: LA-UR--24-24110

Journal Information:: Additive Manufacturing, Journal Name: Additive Manufacturing Vol. 94; ISSN 2214-8604

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (38)

A review of automatic differentiation and its efficient implementation Margossian, Charles C. WIREs Data Mining and Knowledge Discovery, Vol. 9, Issue 4 https://doi.org/10.1002/widm.1305	journal	March 2019
A new finite element model for welding heat sources Goldak, John; Chakravarti, Aditya; Bibby, Malcolm Metallurgical Transactions B, Vol. 15, Issue 2 https://doi.org/10.1007/BF02667333	journal	June 1984
Topology optimization of support structures in metal additive manufacturing with elastoplastic inherent strain modeling Dugast, Florian; To, Albert C. Structural and Multidisciplinary Optimization, Vol. 66, Issue 5 https://doi.org/10.1007/s00158-023-03565-1	journal	April 2023
Sensitivity analysis of material and process parameters in finite element modeling of selective laser melting of Inconel 625 Criales, Luis E.; Arısoy, Yiğit M.; Özel, Tuğrul The International Journal of Advanced Manufacturing Technology, Vol. 86, Issue 9-12 https://doi.org/10.1007/s00170-015-8329-y	journal	January 2016
Space-time hp-finite elements for heat evolution in laser powder bed fusion additive manufacturing Kopp, Philipp; Calo, Victor; Rank, Ernst Engineering with Computers, Vol. 38, Issue 6 https://doi.org/10.1007/s00366-022-01719-1	journal	September 2022
Single-Track Melt-Pool Measurements and Microstructures in Inconel 625 Ghosh, Supriyo; Ma, Li; Levine, Lyle E. JOM, Vol. 70, Issue 6 https://doi.org/10.1007/s11837-018-2771-x	journal	February 2018
Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-Informed Data-Driven Modeling Wang, Zhuo; Liu, Pengwei; Ji, Yanzhou JOM, Vol. 71, Issue 8 https://doi.org/10.1007/s11837-019-03555-z	journal	June 2019
Benchmark Study of Thermal Behavior, Surface Topography, and Dendritic Microstructure in Selective Laser Melting of Inconel 625 Gan, Zhengtao; Lian, Yanping; Lin, Stephen E. Integrating Materials and Manufacturing Innovation, Vol. 8, Issue 2 https://doi.org/10.1007/s40192-019-00130-x	journal	April 2019
Residual Strain Predictions for a Powder Bed Fusion Inconel 625 Single Cantilever Part Yang, Yangzhan; Allen, Madie; London, Tyler Integrating Materials and Manufacturing Innovation, Vol. 8, Issue 3 https://doi.org/10.1007/s40192-019-00144-5	journal	July 2019
Measurements of Melt Pool Geometry and Cooling Rates of Individual Laser Traces on IN625 Bare Plates Lane, Brandon; Heigel, Jarred; Ricker, Richard Integrating Materials and Manufacturing Innovation, Vol. 9, Issue 1 https://doi.org/10.1007/s40192-020-00169-1	journal	February 2020
Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones Khairallah, Saad A.; Anderson, Andrew T.; Rubenchik, Alexander Acta Materialia, Vol. 108 https://doi.org/10.1016/j.actamat.2016.02.014	journal	April 2016
Thermo-mechanical modeling of thermal stress in metal additive manufacturing considering elastoplastic hardening Mirkoohi, Elham; Sievers, Daniel E.; Garmestani, Hamid CIRP Journal of Manufacturing Science and Technology, Vol. 28 https://doi.org/10.1016/j.cirpj.2020.01.002	journal	January 2020
Recent developments in metal additive manufacturing Bandyopadhyay, Amit; Zhang, Yanning; Bose, Susmita Current Opinion in Chemical Engineering, Vol. 28 https://doi.org/10.1016/j.coche.2020.03.001	journal	June 2020
Modeling of additive manufacturing processes for metals: Challenges and opportunities Francois, M. M.; Sun, A.; King, W. E. Current Opinion in Solid State and Materials Science, Vol. 21, Issue 4 https://doi.org/10.1016/j.cossms.2016.12.001	journal	August 2017
Concurrent Island scanning pattern and large-scale topology optimization method for laser powder bed fusion processed parts Xu, Shuzhi; Liu, Jikai; Li, Xinming Finite Elements in Analysis and Design, Vol. 225 https://doi.org/10.1016/j.finel.2023.104018	journal	November 2023
Multipurpose ANSYS FE procedure for welding processes simulation Capriccioli, Andrea; Frosi, Paolo Fusion Engineering and Design, Vol. 84, Issue 2-6 https://doi.org/10.1016/j.fusengdes.2009.01.039	journal	June 2009
Finite element modeling discretization requirements for the laser forming process Zhang, L.; Reutzel, E. W.; Michaleris, P. International Journal of Mechanical Sciences, Vol. 46, Issue 4 https://doi.org/10.1016/j.ijmecsci.2004.04.001	journal	April 2004
Metal additive manufacturing: Technology, metallurgy and modelling Cooke, Shaun; Ahmadi, Keivan; Willerth, Stephanie Journal of Manufacturing Processes, Vol. 57 https://doi.org/10.1016/j.jmapro.2020.07.025	journal	September 2020
A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing Huang, De Jun; Li, Hua Materials & Design, Vol. 203 https://doi.org/10.1016/j.matdes.2021.109606	journal	May 2021
Part-scale thermal simulation of laser powder bed fusion using graph theory: Effect of thermal history on porosity, microstructure evolution, and recoater crash Yavari, Reza; Smoqi, Ziyad; Riensche, Alex Materials & Design, Vol. 204 https://doi.org/10.1016/j.matdes.2021.109685	journal	June 2021
Design for material properties of additively manufactured metals using topology optimization Mishra, Vibhas; Ayas, Can; Langelaar, Matthijs Materials & Design, Vol. 235 https://doi.org/10.1016/j.matdes.2023.112388	journal	November 2023
Fast surrogate modeling using dimensionality reduction in model inputs and field output: Application to additive manufacturing Vohra, Manav; Nath, Paromita; Mahadevan, Sankaran Reliability Engineering & System Safety, Vol. 201 https://doi.org/10.1016/j.ress.2020.106986	journal	September 2020
Metal additive manufacturing by laser-powder bed fusion:Guidelines for process optimisation Obeidi, Muhannad Ahmed Results in Engineering, Vol. 15 https://doi.org/10.1016/j.rineng.2022.100473	journal	September 2022
Strategies for improving the sustainability of structural metals Raabe, Dierk; Tasan, C. Cem; Olivetti, Elsa A. Nature, Vol. 575, Issue 7781 https://doi.org/10.1038/s41586-019-1702-5	journal	November 2019
Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges King, W. E.; Anderson, A. T.; Ferencz, R. M. Applied Physics Reviews, Vol. 2, Issue 4 https://doi.org/10.1063/1.4937809	journal	December 2015
Sensitivity analysis in thermoelastic problems using the complex finite element method Montoya, Arturo; Millwater, Harry Journal of Thermal Stresses, Vol. 40, Issue 3 https://doi.org/10.1080/01495739.2016.1264871	journal	January 2017
Transient thermomechanical sensitivity analysis using a complex-variable finite element method Rios, G. Aaron; Sebastián Rincón Tabares, Juan; Montoya, Arturo Journal of Thermal Stresses, Vol. 45, Issue 5 https://doi.org/10.1080/01495739.2022.2049022	journal	April 2022
Computing sensitivity coefficients by using complex differentiation: Application to heat conduction problem Jayapragasam, Puvikkarasan; Bideau, Pascal Le; Loulou, Tahar Numerical Heat Transfer, Part B: Fundamentals, Vol. 74, Issue 5 https://doi.org/10.1080/10407790.2019.1580047	journal	November 2018
Design sensitivity analysis: Overview and review Tortorelli, D. A.; Michaleris, P. Inverse Problems in Engineering, Vol. 1, Issue 1 https://doi.org/10.1080/174159794088027573	journal	October 1994
Numerical and experimental analysis of heat distribution in the laser powder bed fusion of Ti-6Al-4V Karayagiz, Kubra; Elwany, Alaa; Tapia, Gustavo IISE Transactions, Vol. 51, Issue 2 https://doi.org/10.1080/24725854.2018.1461964	journal	June 2018
A Data-Driven Approach for Process Optimization of Metallic Additive Manufacturing Under Uncertainty Wang, Zhuo; Liu, Pengwei; Xiao, Yaohong Journal of Manufacturing Science and Engineering, Vol. 141, Issue 8 https://doi.org/10.1115/1.4043798	journal	June 2019
Using Complex Variables to Estimate Derivatives of Real Functions Squire, William; Trapp, George SIAM Review, Vol. 40, Issue 1 https://doi.org/10.1137/S003614459631241X	journal	January 1998
Using Multicomplex Variables for Automatic Computation of High-Order Derivatives Lantoine, Gregory; Russell, Ryan P.; Dargent, Thierry ACM Transactions on Mathematical Software, Vol. 38, Issue 3 https://doi.org/10.1145/2168773.2168774	journal	April 2012
MultiZ: A Library for Computation of High-order Derivatives Using Multicomplex or Multidual Numbers Aguirre-Mesa, Andres M.; Garcia, Manuel J.; Millwater, Harry ACM Transactions on Mathematical Software, Vol. 46, Issue 3 https://doi.org/10.1145/3378538	journal	September 2020
Simulation of metallic powder bed additive manufacturing processes with the finite element method: A critical review Schoinochoritis, Babis; Chantzis, Dimitrios; Salonitis, Konstantinos Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 231, Issue 1 https://doi.org/10.1177/0954405414567522	journal	August 2016
Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models Martins, Joaquim R. R. A.; Hwang, John T. AIAA Journal, Vol. 51, Issue 11 https://doi.org/10.2514/1.J052184	journal	November 2013
Analytical Modeling of In-Process Temperature in Powder Bed Additive Manufacturing Considering Laser Power Absorption, Latent Heat, Scanning Strategy, and Powder Packing Ning, Jinqiang; Sievers, Daniel; Garmestani, Hamid Materials, Vol. 12, Issue 5 https://doi.org/10.3390/ma12050808	journal	March 2019
Powder Bed Fusion Additive Manufacturing Using Critical Raw Materials: A Review Popov, Vladimir V.; Grilli, Maria Luisa; Koptyug, Andrey Materials, Vol. 14, Issue 4 https://doi.org/10.3390/ma14040909	journal	February 2021

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Related Subjects

36 MATERIALS SCIENCE
Additive manufacturing
Automatic differentiation
Finite element method
HYPAD-FEM
Powder bed fusion
Selective laser melting
Sensitivity analysis

Efficient sensitivity analysis of the thermal profile in powder bed fusion of metals using hypercomplex automatic differentiation finite element method

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