Blackbox optimization for approximating high-fidelity heat transfer calculations in metal additive manufacturing
Tài liệu tham khảo
Carlson
Stump, 2019, An adaptive integration scheme for heat conduction in additive manufacturing, Appl. Math. Model., 75, 787, 10.1016/j.apm.2019.07.008
Brackett, 2011, Topology optimization for additive manufacturing, vol. 1, 348
Zegard, 2016, Bridging topology optimization and additive manufacturing, Struct. Multidiscip. Optim., 53, 175, 10.1007/s00158-015-1274-4
Sirui Bi, Jiaxin Zhang, and Guannan Zhang. Scalable deep-learning-accelerated topology optimization for additively manufactured materials. In NeurIPS 2020 Workshop on Machine Learning for Engineering Modeling, Simulation and Design.
Bi, 2020
Mohammadpour, 2020, Revisiting solidification microstructure selection maps in the frame of additive manufacturing, Addit. Manufact., 31, 100936, 10.1016/j.addma.2019.100936
Aakash, 2019, On the high-temperature crushing of metal foams, Int. J. Solid Struct., 174, 18, 10.1016/j.ijsolstr.2019.06.007
Fu, 2021, Comparison of the microstructure, mechanical properties and distortion of stainless steel 316l fabricated by micro and conventional laser powder bed fusion, Addit. Manufact., 102067, 10.1016/j.addma.2021.102067
Gäumann, 2001, Single-crystal laser deposition of superalloys: processing–microstructure maps, Acta Mater., 49, 1051, 10.1016/S1359-6454(00)00367-0
Bi, 2020, Additive manufacturing and characterization of brittle foams, Mech. Mater., 145, 103368, 10.1016/j.mechmat.2020.103368
Stump, 2020, Spatiotemporal parallelization of an analytical heat conduction model for additive manufacturing via a hybrid openmp+ mpi approach, Comput. Mater. Sci., 184, 109861, 10.1016/j.commatsci.2020.109861
Coleman, 2020, Sensitivity of thermal predictions to uncertain surface tension data in laser additive manufacturing, J. Heat Tran., 142, 10.1115/1.4047916
Khairallah, 2016, Laser powder-bed fusion additive manufacturing: physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones, Acta Mater., 108, 36, 10.1016/j.actamat.2016.02.014
2017, Multi-physics modeling of single/multiple-track defect mechanisms in electron beam selective melting, Acta Mater., 134, 324, 10.1016/j.actamat.2017.05.061
Matthews, 2016, Denudation of metal powder layers in laser powder bed fusion processes, Acta Mater., 114, 33, 10.1016/j.actamat.2016.05.017
Sabau, 2018
Stump, 2021, Solidification dynamics in metal additive manufacturing: analysis of model assumptions, Model. Simulat. Mater. Sci. Eng., 29, 10.1088/1361-651X/abca19
Rosenthal, 1946, The theory of moving sources of heat and its application of metal treatments, Trans. ASME, 68, 849
Forslund, 2019, Analytical solution for heat conduction due to a moving Gaussian heat flux with piecewise constant parameters, Appl. Math. Model., 66, 227, 10.1016/j.apm.2018.09.018
Schwalbach, 2019, A discrete source model of powder bed fusion additive manufacturing thermal history, Addit. Manufact., 25, 485, 10.1016/j.addma.2018.12.004
Wolfer, 2019, Fast solution strategy for transient heat conduction for arbitrary scan paths in additive manufacturing, Addit. Manufact., 30, 100898, 10.1016/j.addma.2019.100898
Stump, 2019, A forward time stepping heat conduction model for spot melt additive manufacturing, J. Heat Tran., 141, 10.1115/1.4044595
Matsuoka K Suzuki N Maeda Y. Nguyen NT, Ohta A. 78:265–s, 1999.
Halsey, 2020, Geometry-independent microstructure optimization for electron beam powder bed fusion additive manufacturing, Addit. Manufact., 35, 101354, 10.1016/j.addma.2020.101354
Ogoke, 2021
Snoek, 2012, Practical bayesian optimization of machine learning algorithms, 2951
Wierstra, 2014, Natural evolution strategies, J. Mach. Learn. Res., 15, 949
Hansen, 2003, Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es), Evol. Comput., 11, 1, 10.1162/106365603321828970
Whitley, 1994, A genetic algorithm tutorial, Stat. Comput., 4, 65, 10.1007/BF00175354
Mirjalili, 2019, Genetic algorithm, 43
Lagarias, 1998, Convergence properties of the Nelder–Mead simplex method in low dimensions, SIAM J. Optim., 9, 112, 10.1137/S1052623496303470
Kennedy, 1995, Particle Swarm Optimization, vol. 4, 1942
Van Laarhoven, 1987, Simulated annealing, 7
Li, 2016, High dimensional bayesian optimization via restricted projection pursuit models, 884
Mutny, 2018, Efficient high dimensional bayesian optimization with additivity and quadrature fourier features, 9005
Rana, 2017, High dimensional bayesian optimization with elastic Gaussian process, vol. 70, 2883
Wang, 2017, Batched high-dimensional bayesian optimization via structural kernel learning, vol. 70, 3656
Eriksson, 2019, Scalable global optimization via local bayesian optimization, 5497
Zhang, 2021, A scalable gradient free method for bayesian experimental design with implicit models, 3745
Wang, 2018
Zhang, 2021, Enabling long-range exploration in minimization of multimodal functions
DebRoy, 2018, Additive manufacturing of metallic components–process, structure and properties, Prog. Mater. Sci., 92, 112, 10.1016/j.pmatsci.2017.10.001
Khairallah, 2016, Laser powder-bed fusion additive manufacturing: physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones, Acta Mater., 108, 36, 10.1016/j.actamat.2016.02.014
Yan, 2017, Multi-physics modeling of single/multiple-track defect mechanisms in electron beam selective melting, Acta Mater., 134, 324, 10.1016/j.actamat.2017.05.061
Körner, 2011, Mesoscopic simulation of selective beam melting processes, J. Mater. Process. Technol., 211, 978, 10.1016/j.jmatprotec.2010.12.016
Matthews, 2016, Denudation of metal powder layers in laser powder bed fusion processes, Acta Mater., 114, 33, 10.1016/j.actamat.2016.05.017
Zhao, 2019, Bulk-explosion-induced metal spattering during laser processing, Phys. Rev. X, 9
Francois, 2017, Modeling of additive manufacturing processes for metals: challenges and opportunities, Curr. Opin. Solid State Mater. Sci., 21, 10.1016/j.cossms.2016.12.001
Qiu, 2015, On the role of melt flow into the surface structure and porosity development during selective laser melting, Acta Mater., 96, 72, 10.1016/j.actamat.2015.06.004
Panwisawas, 2018, Modelling of thermal fluid dynamics for fusion welding, J. Mater. Process. Technol., 252, 176, 10.1016/j.jmatprotec.2017.09.019
Plotkowski, 2017, Verification and validation of a rapid heat transfer calculation methodology for transient melt pool solidification conditions in powder bed metal additive manufacturing, Addit. Manufact., 18, 256, 10.1016/j.addma.2017.10.017
Forslund, 2019, Analytical solution for heat conduction due to a moving Gaussian heat flux with piecewise constant parameters, Appl. Math. Model., 66, 227, 10.1016/j.apm.2018.09.018
Schwalbach, 2019, A discrete source model of powder bed fusion additive manufacturing thermal history, Addit. Manufact., 25, 485, 10.1016/j.addma.2018.12.004
Plotkowski, 2020, Geometry-dependent solidification regimes in metal additive manufacturing, Weld. J., 99
Donegan, 2020, Zoning additive manufacturing process histories using unsupervised machine learning, Mater. Char., 161, 110123, 10.1016/j.matchar.2020.110123
Dias, 2015, A method of recursive images to solve transient heat diffusion in multilayer materials, Int. J. Heat Mass Tran., 85, 1075, 10.1016/j.ijheatmasstransfer.2015.01.138
Zhang, 2020, Robust data-driven approach for predicting the configurational energy of high entropy alloys, Mater. Des., 185, 108247, 10.1016/j.matdes.2019.108247
Liu, 2021, Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: a data-driven approach, Comput. Mater. Sci., 187, 110135, 10.1016/j.commatsci.2020.110135
Quinonero-Candela, 2005, A unifying view of sparse approximate Gaussian process regression, J. Mach. Learn. Res., 6, 1939
Rasmussen, 2003, Gaussian processes in machine learning, 63
Shields, 2016, The generalization of Latin hypercube sampling, Reliab. Eng. Syst. Saf., 148, 96, 10.1016/j.ress.2015.12.002
Zhang, 2019, Efficient Monte Carlo resampling for probability measure changes from bayesian updating, Probabilist. Eng. Mech., 55, 54, 10.1016/j.probengmech.2018.10.002
Zhang, 2021, Modern Monte Carlo methods for efficient uncertainty quantification and propagation: a survey, Wiley Interdiscip. Rev. Comput. Stat., 13, 10.1002/wics.1539
Fodor, 2002, A survey of dimension reduction techniques. Technical report, Lawrence Livermore National Lab, CA (US)
Zhang, 2019, Learning nonlinear level sets for dimensionality reduction in function approximation, Adv. Neural Inf. Process. Syst., 32, 13220
Li, 2018
Zhang, 2020
Tran, 2020
Nesterov, 2017, Random gradient-free minimization of convex functions, Found. Comput. Math., 17, 527, 10.1007/s10208-015-9296-2
Quarteroni, 2007, vol. 332
1972
Zhang, 2021, Accelerating reinforcement learning with a directional-Gaussian-smoothing evolution strategy, Elec. Res. Archive, 29, 4119, 10.3934/era.2021075
Raghavan, 2016, Numerical modeling of heat-transfer and the influence of process parameters on tailoring the grain morphology of in718 in electron beam additive manufacturing, Acta Mater., 112, 303, 10.1016/j.actamat.2016.03.063
Prabhakar, 2015, Computational modeling of residual stress formation during the electron beam melting process for inconel 718, Addit. Manufact., 7, 83, 10.1016/j.addma.2015.03.003
Stump, 2021, Solidification dynamics in metal additive manufacturing: analysis of model assumptions, Model. Simulat. Mater. Sci. Eng., 29, 10.1088/1361-651X/abca19
Quarteroni, 2010, vol. 37
Kingma, 2014
Ahrens, 2005, An end-user tool for large data visualization, Visualizat. Handbook, 717, 10.1016/B978-012387582-2/50038-1