×

Acceleration strategies for explicit finite element analysis of metal powder-based additive manufacturing processes using graphical processing units. (English) Zbl 1465.74165

Summary: Metal powder-based Additive Manufacturing (AM) processes are increasingly used in industry and science due to their unique capability of building complex geometries. However, the immense computational cost associated with AM predictive models hinders the further industrial adoption of these technologies for time-sensitive applications, process design with uncertainties or real-time process control. In this work, a novel approach to accelerate the explicit finite element analysis of the transient heat transfer of AM processes is proposed using Graphical Processing Units. The challenges associated with this approach are enumerated and multiple strategies to overcome each challenge are discussed. The performance of the proposed algorithms is evaluated on multiple test cases. Speed-ups of about \(100 \times -150 \times\) compared to an optimized single CPU core implementation for the best strategy were achieved.

MSC:

74S05 Finite element methods applied to problems in solid mechanics
74F05 Thermal effects in solid mechanics
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] Yang, L.; Harrysson, O.; West, H.; Cormier, D., Compressive properties of Ti-6Al-4V auxetic mesh structures made by electron beam melting, Acta Mater, 60, 3370-3379, (2012)
[2] Guo, C.; Ge, W.; Lin, F., Dual-material electron beam selective melting: hardware development and validation studies, Engineering, 1, 124-130, (2015)
[3] Wenjun, G.; Chao, G.; Feng, L., Microstructures of components synthesized via electron beam selective melting using blended pre-alloyed powders of Ti6Al4V and Ti45Al7Nb, Rare Metal Mater Eng, 44, 2623-2627, (2015)
[4] Tan, X.; Kok, Y.; Tan, YJ; Descoins, M.; Mangelinck, D.; Tor, SB; Leong, KF; Chua, CK, Graded microstructure and mechanical properties of additive manufactured Ti-6Al-4V via electron beam melting, Acta Mater, 97, 1-16, (2015)
[5] Dehoff, R.; Kirka, M.; Sames, W.; Bilheux, H.; Tremsin, A.; Lowe, L.; Babu, S., Site specific control of crystallographic grain orientation through electron beam additive manufacturing, Mater Sci Technol, 31, 931-938, (2015)
[6] Gibson I, Rosen DW, Stucker B (2010) Sheet lamination processes. In: Additive manufacturing technologies. Springer, pp 223-252
[7] Gu, D.; Meiners, W.; Wissenbach, K.; Poprawe, R., Laser additive manufacturing of metallic components: materials, processes and mechanisms, Int Mater Rev, 57, 133-164, (2012)
[8] King, W.; Anderson, A.; Ferencz, R.; Hodge, N.; Kamath, C.; Khairallah, S.; Rubenchik, A., Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges, Appl Phys Rev, 2, 041304, (2015)
[9] Parry, L.; Ashcroft, I.; Wildman, RD, Understanding the effect of laser scan strategy on residual stress in selective laser melting through thermo-mechanical simulation, Addit Manuf, 12, 1-15, (2016)
[10] Schoinochoritis, B.; Chantzis, D.; Salonitis, K., Simulation of metallic powder bed additive manufacturing processes with the finite element method: a critical review, Proc Inst Mech Eng Part B: J Eng Manuf, 231, 96-117, (2017)
[11] Khairallah, SA; Anderson, AT; Rubenchik, A.; King, WE, 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)
[12] Rai, A.; Markl, M.; Körner, C., A coupled cellular automaton-lattice Boltzmann model for grain structure simulation during additive manufacturing, Comput Mater Sci, 124, 37-48, (2016)
[13] Yan, W.; Lin, S.; Kafka, OL; Lian, Y.; Yu, C.; Liu, Z.; Yan, J.; Wolff, S.; Wu, H.; Ndip-Agbor, E., Data-driven multi-scale multi-physics models to derive process – structure – property relationships for additive manufacturing, Comput Mech, 61, 1-21, (2018) · Zbl 1471.74081
[14] Wolff, SJ; Lin, S.; Faierson, EJ; Liu, WK; Wagner, GJ; Cao, J., A framework to link localized cooling and properties of directed energy deposition (DED)-processed Ti-6Al-4V, Acta Mater, 132, 106-117, (2017)
[15] Francois MM, Sun A, King WE, Henson NJ, Tourret D, Bronkhorst CA, Carlson NN, Newman CK, Haut T, Bakosi J (2017) Modeling of additive manufacturing processes for metals: challenges and opportunities. Curr Opin Solid State Materials Sci 21(LA-UR-16-24513)
[16] NVIDIA (2016) NVIDIA GPU accelerated applications catalog
[17] Tajdari M, Tai BL (2016) Modeling of brittle and ductile materials drilling using smoothed-particle hydrodynamics. In: ASME 2016 11th international manufacturing science and engineering conference, 2016. American Society of Mechanical Engineers
[18] Bell N, Hoberock J (2011) Thrust: a productivity-oriented library for CUDA. In: GPU computing gems Jade edition. Elsevier, pp 359-371
[19] Bolz J, Farmer I, Grinspun E, Schröoder P (2003) Sparse matrix solvers on the GPU: conjugate gradients and multigrid. In: ACM transactions on graphics (TOG). ACM
[20] Nvidia C (2014) Cusparse library. NVIDIA Corporation, Santa Clara
[21] Price AD (2013) Multi-GPU Computing with Abaqus: benchmarking and scaling for multiphysics applications in mechatronics
[22] Lukarski D (2015) Paralution-library for iterative sparse methods
[23] Pichler, F.; Haase, G., Finite element method completely implemented for graphic processor units using parallel algorithm libraries, Int J High Perf Comput Appl, 33, 53-66, (2019)
[24] Cecka, C.; Lew, AJ; Darve, E., Assembly of finite element methods on graphics processors, Int J Numer Meth Eng, 85, 640-669, (2011) · Zbl 1217.80146
[25] Markall, G.; Slemmer, A.; Ham, D.; Kelly, P.; Cantwell, C.; Sherwin, S., Finite element assembly strategies on multi-core and many-core architectures, Int J Numer Meth Fluids, 71, 80-97, (2013)
[26] Markall, GR; Ham, DA; Kelly, PH, Towards generating optimised finite element solvers for GPUs from high-level specifications, Proc Comput Sci, 1, 1815-1823, (2010)
[27] Dziekonski, A.; Lamecki, A.; Mrozowski, M., A memory efficient and fast sparse matrix vector product on a GPU, Prog Electromagn Res, 116, 49-63, (2011)
[28] Dziekonski A, Lamecki A, Mrozowski M (2016) GPU-accelerated finite element method. In: 2016 IEEE MTT-S international conference on numerical electromagnetic and multiphysics modeling and optimization (NEMO). IEEE · Zbl 1352.65494
[29] Dziekonski, A.; Sypek, P.; Lamecki, A.; Mrozowski, M., Finite element matrix generation on a GPU, Prog Electromagn Res, 128, 249-265, (2012) · Zbl 1352.65494
[30] Saad Y (2003) Iterative methods for sparse linear systems, vol 82. SIAM · Zbl 1031.65046
[31] Knepley MG, Rupp K, Terrel AR (2016) Finite element integration with quadrature on the GPU. arXiv preprint arXiv:1607.04245
[32] Knepley, MG; Terrel, AR, Finite element integration on GPUs, ACM Trans Math Softw (TOMS), 39, 10, (2013) · Zbl 1298.65176
[33] Georgescu, S.; Chow, P.; Okuda, H., GPU acceleration for FEM-based structural analysis, Arch Comput Methods Eng, 20, 111-121, (2013) · Zbl 1354.65246
[34] Van Belle L, Vansteenkiste G, Boyer JC (2012) Comparisons of numerical modelling of the selective laser melting. In: Key engineering materials. Trans Tech Publ
[35] Zaeh, MF; Branner, G., Investigations on residual stresses and deformations in selective laser melting, Prod Eng Res Dev, 4, 35-45, (2010)
[36] Wang, Z.; Beese, AM, Effect of chemistry on martensitic phase transformation kinetics and resulting properties of additively manufactured stainless steel, Acta Mater, 131, 410-422, (2017)
[37] Belytschko T, Liu WK, Moran B, Elkhodary K (2013) Nonlinear finite elements for continua and structures. Wiley, Hoboken · Zbl 1279.74002
[38] Fish J, Belytschko T (2007) A first course in finite elements. Wiley, Hoboken · Zbl 1135.74001
[39] Smith, J.; Xiong, W.; Cao, J.; Liu, WK, Thermodynamically consistent microstructure prediction of additively manufactured materials, Comput Mech, 57, 359-370, (2016) · Zbl 1382.74126
[40] Golub, GH; Welsch, JH, Calculation of Gauss quadrature rules, Math Comput, 23, 221-230, (1969) · Zbl 0179.21901
[41] Zhu J (2013) The finite element method: its basis and fundamentals. Elsevier, Amsterdam · Zbl 1307.74005
[42] Yan, W.; Lin, S.; Kafka, OL; Lian, Y.; Yu, C.; Liu, Z.; Yan, J.; Wolff, S.; Wu, H.; Ndip-Agbor, EJCM, Data-driven multi-scale multi-physics models to derive process – structure – property relationships for additive manufacturing, Comput Mech, 61, 521-541, (2018) · Zbl 1471.74081
[43] Mozaffar, M.; Paul, A.; Al-Bahrani, R.; Wolff, S.; Choudhary, A.; Agrawal, A.; Ehmann, K.; Cao, JJMI, Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks, Manuf Lett, 18, 35-39, (2018)
[44] Cheng J, Grossman M, McKercher T (2014) Professional Cuda C programming. Wiley, Hoboken
[45] NVIDIA (2008) NVIDIA CUDA C programming guide, pp. 1-261
[46] Lee, C-C; Lee, D-T, A simple on-line bin-packing algorithm, J ACM (JACM), 32, 562-572, (1985) · Zbl 0629.68045
[47] Graham, RL, Bounds on multiprocessing timing anomalies, SIAM J Appl Math, 17, 416-429, (1969) · Zbl 0188.23101
[48] NVIDIA (2018) Features and technical specifications. https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capabilities
[49] Meng, H-T; Nie, B-L; Wong, S.; Macon, C.; Jin, J-MJIA; Magazine, P., GPU accelerated finite-element computation for electromagnetic analysis, IEEE Antennas Propag Mag, 56, 39-62, (2014)
[50] Wang, H.; Zeng, Y.; Li, E.; Huang, G.; Gao, G.; Li, GJCMIAM, “Seen Is Solution” a CAD/CAE integrated parallel reanalysis design system, Comput Methods Appl Mech Eng, 299, 187-214, (2016) · Zbl 1425.74495
[51] Zhang, R.; Wen, L.; Naboulsi, S.; Eason, T.; Vasudevan, VK; Qian, DJCM, Accelerated multiscale space – time finite element simulation and application to high cycle fatigue life prediction, Comput Mech, 58, 329-349, (2016)
[52] Yamaguchi, T.; Fujita, K.; Ichimura, T.; Hori, T.; Hori, M.; Wijerathne, LJPCS, Fast finite element analysis method using multiple gpus for crustal deformation and its application to stochastic inversion analysis with geometry uncertainty, Proc Comput Sci, 108, 765-775, (2017)
[53] Bennett, JL; Wolff, SJ; Hyatt, G.; Ehmann, K.; Cao, J., Thermal effect on clad dimension for laser deposited Inconel 718, J Manuf Process, 28, 550-557, (2017)
[54] Commons W (2015) File: selective laser melting system schematic.jpg—Wikimedia Commons{,} the free media repository. https://commons.wikimedia.org/w/index.php?title=File:Selective_laser_melting_system_schematic.jpg&oldid=154088078. Accessed 15 Oct 2018
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.