Research Papers

A Data-Driven Approach for Process Optimization of Metallic Additive Manufacturing Under Uncertainty

[+] Author and Article Information
Zhuo Wang

Department of Mechanical Engineering,
Mississippi State University,
Mississippi, MS 39762
e-mail: zw352@msstate.edu

Pengwei Liu

Department of Mechanical Engineering,
Mississippi State University,
Mississippi, MS 39762;
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,
Hunan University,
Changsha 410082, China
e-mail: liupw789k@hnu.edu.cn

Yaohong Xiao

Department of Mechanical Engineering,
Mississippi State University,
Mississippi, MS 39762
e-mail: yx144@msstate.edu

Xiangyang Cui

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,
Hunan University,
Changsha 410082, China
e-mail: cuixy@hnu.edu.cn

Zhen Hu

Department of Industrial and Manufacturing Systems Engineering,
University of Michigan,
Dearborn, MI 48128
e-mail: zhennhu@umich.edu

Lei Chen

Department of Mechanical Engineering,
Mississippi State University,
Mississippi, MS 39762;
Department of Mechanical Engineering,
University of Michigan,
Dearborn, MI 48128
e-mail: chen@me.msstate.edu

1Corresponding authors.

Manuscript received December 19, 2018; final manuscript received May 7, 2019; published online June 10, 2019. Assoc. Editor: Qiang Huang.

J. Manuf. Sci. Eng 141(8), 081004 (Jun 10, 2019) (14 pages) Paper No: MANU-18-1876; doi: 10.1115/1.4043798 History: Received December 19, 2018; Accepted May 10, 2019

The presence of various uncertainty sources in metal-based additive manufacturing (AM) process prevents producing AM products with consistently high quality. Using electron beam melting (EBM) of Ti-6Al-4V as an example, this paper presents a data-driven framework for process parameters optimization using physics-informed computer simulation models. The goal is to identify a robust manufacturing condition that allows us to constantly obtain equiaxed materials microstructures under uncertainty. To overcome the computational challenge in the robust design optimization under uncertainty, a two-level data-driven surrogate model is constructed based on the simulation data of a validated high-fidelity multiphysics AM simulation model. The robust design result, indicating a combination of low preheating temperature, low beam power, and intermediate scanning speed, was acquired enabling the repetitive production of equiaxed structure products as demonstrated by physics-based simulations. Global sensitivity analysis at the optimal design point indicates that among the studied six noise factors, specific heat capacity and grain growth activation energy have the largest impact on the microstructure variation. Through this exemplar process optimization, the current study also demonstrates the promising potential of the presented approach in facilitating other complicate AM process optimizations, such as robust designs in terms of porosity control or direct mechanical property control.

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Brandl, E., Schoberth, A., and Leyens, C., 2012, “Morphology, Microstructure, and Hardness of Titanium (Ti-6Al-4V) Blocks Deposited by Wire-Feed Additive Layer Manufacturing (ALM),” Mater. Sci. Eng. A, 532(Suppl. C), pp. 295–307. [CrossRef]
Donoghue, J., Antonysamy, A., Martina, F., Colegrove, P., Williams, S., and Prangnell, P., 2016, “The Effectiveness of Combining Rolling Deformation With Wire–Arc Additive Manufacture on β-Grain Refinement and Texture Modification in Ti–6Al–4V,” Mater. Charact., 114, pp. 103–114. [CrossRef]
Körner, C., 2016, “Additive Manufacturing of Metallic Components by Selective Electron Beam Melting—A Review,” Int. Mater. Rev., 61(5), pp. 361–377. [CrossRef]
Laureijs, R. E., Roca, J. B., Narra, S. P., Montgomery, C., Beuth, J. L., and Fuchs, E. R., 2017, “Metal Additive Manufacturing: Cost Competitive Beyond Low Volumes,” ASME J. Manuf. Sci. Eng., 139(8), p. 081010. [CrossRef]
Nath, P., Hu, Z., and Mahadevan, S., 2017, “Multi-Level Uncertainty Quantification in Additive Manufacturing,” Solid Freeform Fabrication: Proceedings of the 28th Annual International Solid Freeform Fabrication Symposium, Austin, TX, Aug. 7–9, Univeristy of Texas at Austin, Austin, TX, pp. 7–9.
Ma, L., Fong, J., Lane, B., Moylan, S., Filliben, J., Heckert, A., and Levine, L., 2015, “Using Design of Experiments in Finite Element Modeling to Identify Critical Variables for Laser Powder Bed Fusion,” International Solid Freeform Fabrication Symposium, Austin, TX, Aug. 10–12, Univeristy of Texas at Austin, Austin, TX, pp. 219–228.
Chen, W., Allen, J. K., Tsui, K.-L., and Mistree, F., 1996, “A Procedure for Robust Design: Minimizing Variations Caused by Noise Factors and Control Factors,” ASME J. Mech. Des., 118(4), pp. 478–485. [CrossRef]
Chan, S., and Elsheikh, A. H., 2018, “A Machine Learning Approach for Efficient Uncertainty Quantification Using Multiscale Methods,” J. Comput. Phys., 354, pp. 493–511. [CrossRef]
Zhu, Y., and Zabaras, N., 2018, “Bayesian Deep Convolutional Encoder–Decoder Networks for Surrogate Modeling and Uncertainty Quantification,” J. Comput. Phys., 366, pp. 415–447. [CrossRef]
Sankararaman, S., Ling, Y., and Mahadevan, S., 2011, “Uncertainty Quantification and Model Validation of Fatigue Crack Growth Prediction,” Eng. Fract. Mech., 78(7), pp. 1487–1504. [CrossRef]
Hu, Z., and Mahadevan, S., 2017, “Uncertainty Quantification and Management in Additive Manufacturing: Current Status, Needs, and Opportunities,” Int. J. Adv. Manuf. Technol., 93(5–8), pp. 2855–2874. [CrossRef]
Kamath, C., 2016, “Data Mining and Statistical Inference in Selective Laser Melting,” Int. J. Adv. Manuf. Technol., 86(5–8), pp. 1659–1677. [CrossRef]
Lopez, F., Witherell, P., and Lane, B., 2016, “Identifying Uncertainty in Laser Powder Bed Fusion Additive Manufacturing Models,” ASME J. Mech. Des., 138(11), p. 114502. [CrossRef]
Haines, M., Plotkowski, A., Frederick, C. L., Schwalbach, E. J., and Babu, S. S., 2018, “A Sensitivity Analysis of the Columnar-to-Equiaxed Transition for Ni-Based Superalloys in Electron Beam Additive Manufacturing,” Comput. Mater. Sci., 155, pp. 340–349. [CrossRef]
Moser, D., Fish, S., Beaman, J., and Murthy, J., 2014, “Multi-Layer Computational Modeling of Selective Laser Sintering Processes,” ASME 2014 International Mechanical Engineering Congress and Exposition, Montreal, Canada, Nov. 14–20, p. V02AT02A008.
Tapia, G., King, W., Johnson, L., Arroyave, R., Karaman, I., and Elwany, A., 2018, “Uncertainty Propagation Analysis of Computational Models in Laser Powder Bed Fusion Additive Manufacturing Using Polynomial Chaos Expansions,” ASME J. Manuf. Sci. Eng., 140(12), p. 121006. [CrossRef]
Fallah, V., Amoorezaei, M., Provatas, N., Corbin, S. F., and Khajepour, A., 2012, “Phase-Field Simulation of Solidification Morphology in Laser Powder Deposition of Ti–Nb Alloys,” Acta Mater., 60(4), pp. 1633–1646. [CrossRef]
Sahoo, S., and Chou, K., 2016, “Phase-Field Simulation of Microstructure Evolution of Ti–6Al–4V in Electron Beam Additive Manufacturing Process,” Addit. Manuf., 9, pp. 14–24. [CrossRef]
Acharya, R., Sharon, J. A., and Staroselsky, A., 2017, “Prediction of Microstructure in Laser Powder Bed Fusion Process,” Acta Mater., 124, pp. 360–371. [CrossRef]
Raghavan, N., Dehoff, R., Pannala, S., Simunovic, S., Kirka, M., Turner, J., Carlson, N., and Babu, S. S., 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, pp. 303–314. [CrossRef]
Gäumann, M., Bezencon, C., Canalis, P., and Kurz, W., 2001, “Single-Crystal Laser Deposition of Superalloys: Processing–Microstructure Maps,” Acta Mater., 49(6), pp. 1051–1062. [CrossRef]
Hunt, J., 1984, “Steady State Columnar and Equiaxed Growth of Dendrites and Eutectic,” Mater. Sci. Eng., 65(1), pp. 75–83. [CrossRef]
Gockel, J., Beuth, J., and Taminger, K., 2014, “Integrated Control of Solidification Microstructure and Melt Pool Dimensions in Electron Beam Wire Feed Additive Manufacturing of Ti-6Al-4V,” Addit. Manuf., 1, pp. 119–126. [CrossRef]
Mani, M., Feng, S., Lane, B., Donmez, A., Moylan, S., and Fesperman, R., 2015, Measurement Science Needs for Real-Time Control of Additive Manufacturing Powder bed Fusion Processes, US Department of Commerce, National Institute of Standards and Technology. NISTIR 8036.
Liu, P., Ji, Y., Wang, Z., Qiu, C., Antonysamy, A., Chen, L.-Q., Cui, X., and Chen, L., 2018, “Investigation on Evolution Mechanisms of Site-Specific Grain Structures During Metal Additive Manufacturing,” J. Mater. Process. Technol., 257, pp. 191–202. [CrossRef]
Liu, P., Cui, X., Deng, J., Li, S., Li, Z., and Chen, L., 2019, “Investigation of Thermal Responses During Metallic Additive Manufacturing Using a ‘Tri-Prism’ Finite Element Method,” Int. J. Therm. Sci., 136, pp. 217–229. [CrossRef]
Liu, P., Wang, Z., Xiao, Y., Mark, H. F., Cui, X., and Chen, L., 2019, “Insight into the Mechanisms of Columnar to Equiaxed Grain Transition During Metallic Additive Manufacturing,” Addit. Manuf., 26, pp. 22–29. [CrossRef]
Donoghue, J., Gholinia, A., Fonseca, J. Q. d., and Prangnell, P., 2015, “In-Situ High Temperature EBSD Analysis of the Effect of a Deformation Step on the Alpha to Beta Transition in Additive Manufactured Ti-6Al-4V,” Proceedings of the 13th World Conference on Titanium, San Diego, CA, Aug. 16–20, The Minerals, Metals and Materials Society, Warrendale, PA, pp. 1283–1288.
Antonysamy, A. A., Meyer, J., and Prangnell, P., 2013, “Effect of Build Geometry on the β-Grain Structure and Texture in Additive Manufacture of Ti 6Al 4V by Selective Electron Beam Melting,” Mater. Charact., 84, pp. 153–168. [CrossRef]
Gockel, J., Klingbeil, N., and Bontha, S., 2016, “A Closed-Form Solution for the Effect of Free Edges on Melt Pool Geometry and Solidification Microstructure in Additive Manufacturing of Thin-Wall Geometries,” Metall. Mater. Trans. B, 47(2), pp. 1400–1408. [CrossRef]
Kundin, J., Mushongera, L., and Emmerich, H., 2015, “Phase-Field Modeling of Microstructure Formation During Rapid Solidification in Inconel 718 Superalloy,” Acta Mater., 95, pp. 343–356. [CrossRef]
Li, J., Wang, Q., and Michaleris, P. P., 2018, “An Analytical Computation of Temperature Field Evolved in Directed Energy Deposition,” ASME J. Manuf. Sci. Eng., 140(10), p. 101004. [CrossRef]
Rosenthal, D., 1946, “The Theory of Moving Sources of Heat and Its Application of Metal Treatments,” Trans. ASME, 68, pp. 849–866.
Nie, P., Ojo, O., and Li, Z., 2014, “Numerical Modeling of Microstructure Evolution During Laser Additive Manufacturing of a Nickel-Based Superalloy,” Acta Mater., 77, pp. 85–95. [CrossRef]
Cheng, B., Price, S., Lydon, J., Cooper, K., and Chou, K., 2014, “On Process Temperature in Powder-Bed Electron Beam Additive Manufacturing: Model Development and Validation,” ASME J. Manuf. Sci. Eng., 136(6), p. 061018. [CrossRef]
Wei, L. C., Ehrlich, L. E., Powell-Palm, M. J., Montgomery, C., Beuth, J., and Malen, J. A., 2018, “Thermal Conductivity of Metal Powders for Powder Bed Additive Manufacturing,” Addit. Manuf., 21, pp. 201–208. [CrossRef]
Cheng, B., Lane, B., Whiting, J., and Chou, K., 2018, “A Combined Experimental-Numerical Method to Evaluate Powder Thermal Properties in Laser Powder Bed Fusion,” ASME J. Manuf. Sci. Eng., 140(11), p. 111008. [CrossRef]
Ghosh, S., Ma, L., Ofori-Opoku, N., and Guyer, J. E., 2017, “On the Primary Spacing and Microsegregation of Cellular Dendrites in Laser Deposited Ni–Nb Alloys,” Modell. Simul. Mater. Sci. Eng., 25(6), p. 065002. [CrossRef]
abaqus version 6.10, 2010, “User Subroutines Reference Manual.” Dassault Systemes Simulia Corp.
Price, S., Lydon, J., Cooper, K., and Chou, K., 2013, “Experimental Temperature Analysis of Powder-Based Electron Beam Additive Manufacturing,” Proceedings of the Solid Freeform Fabrication Symposium, Austin, TX, Aug. 12–14, Univeristy of Texas at Austin, Austin, TX, pp. 162–173.
Wang, X., Liu, P., Ji, Y., Liu, Y., Horstemeyer, M., and Chen, L., 2019, “Investigation on Microsegregation of IN718 Alloy During Additive Manufacturing via Integrated Phase-Field and Finite-Element Modeling,” J. Mater. Eng. Perform., 28(2), pp. 657–665. [CrossRef]
Rai, A., Markl, M., and Körner, C., 2016, “A Coupled Cellular Automaton–Lattice Boltzmann Model for Grain Structure Simulation During Additive Manufacturing,” Comput. Mater. Sci., 124, pp. 37–48. [CrossRef]
Rodgers, T. M., Madison, J. D., and Tikare, V., 2017, “Simulation of Metal Additive Manufacturing Microstructures Using Kinetic Monte Carlo,” Comput. Mater. Sci., 135, pp. 78–89. [CrossRef]
Baykasoglu, C., Akyildiz, O., Candemir, D., Yang, Q., and To, A. C., 2018, “Predicting Microstructure Evolution During Directed Energy Deposition Additive Manufacturing of Ti-6Al-4V,” ASME J. Manuf. Sci. Eng., 140(5), p. 051003. [CrossRef]
Krill, C. E. III, and Chen, L.-Q., 2002, “Computer Simulation of 3-D Grain Growth Using a Phase-Field Model,” Acta Mater., 50, pp. 3057–3073.
Lee, D. N., Kim, K.-h., Lee, Y.-g., and Choi, C.-H., 1997, “Factors Determining Crystal Orientation of Dendritic Growth During Solidification,” Mater. Chem. Phys., 47(2), pp. 154–158. [CrossRef]
Ohno, M., Yamaguchi, T., Sato, D., and Matsuura, K., 2013, “Existence or Nonexistence of Thermal Pinning Effect in Grain Growth Under Temperature Gradient,” Comput. Mater. Sci., 69, pp. 7–13. [CrossRef]
Ataibis, V., and Taktak, S., 2015, “Characteristics and Growth Kinetics of Plasma Paste Borided Cp–Ti and Ti6Al4V Alloy,” Surf. Coat. Technol., 279, pp. 65–71. [CrossRef]
Al-Bermani, S., Blackmore, M., Zhang, W., and Todd, I., 2010, “The Origin of Microstructural Diversity, Texture, and Mechanical Properties in Electron Beam Melted Ti-6Al-4V,” Metall. Mater. Trans. A, 41(13), pp. 3422–3434. [CrossRef]
Schempp, P., Cross, C., Pittner, A., Oder, G., Neumann, R. S., Rooch, H., Dörfel, I., Österle, W., and Rethmeier, M., 2014, “Solidification of GTA Aluminum Weld Metal: Part 1—Grain Morphology Dependent upon Alloy Composition and Grain Refiner Content,” Weld. J., 93(2), pp. 53s–59s.
Schempp, P., Cross, C., Pittner, A., and Rethmeier, M., 2014, “Solidification of GTA Aluminum Weld Metal: Part 2—Thermal Conditions and Model for Columnar-to-Equiaxed Transition,” Weld. J., 93, pp. 69–77.
Charbon, C., and Rappaz, M., 1993, “3D Probabilistic Modelling of Equiaxed Eutectic Solidification,” Modell. Simul. Mater. Sci. Eng., 1(4), p. 455. [CrossRef]
Gockel, J., and Beuth, J., 2013, “Understanding Ti-6Al-4V Microstructure Control in Additive Manufacturing Via Process Maps,” Solid Freeform Fabrication Proceedings, Austin, TX, Aug. 12–14, University of Texas at Austin, Austin, TX, pp. 12–14.
Kobryn, P. A., and Semiatin, S., 2003, “Microstructure and Texture Evolution During Solidification Processing of Ti–6Al–4V,” J. Mater. Process. Technol., 135(2), pp. 330–339. [CrossRef]
Sahoo, S., 2014, “Microstructure Simulation of Ti-6Al-4V Biomaterial Produced by Electron Beam Additive Manufacturing Process,” Int. J. Nano Biomater., 5(4), pp. 228–235. [CrossRef]
Boivineau, M., Cagran, C., Doytier, D., Eyraud, V., Nadal, M.-H., Wilthan, B., and Pottlacher, G., 2006, “Thermophysical Properties of Solid and Liquid Ti-6Al-4V (TA6V) Alloy,” Int. J. Thermophys., 27(2), pp. 507–529. [CrossRef]
Brooks, R. F., Robinson, J. A., Chapman, L. A., and Richardson, M. J., 2004, “The Enthalpy of a Solid and Liquid Titanium-Aluminium-Vanadium Alloy,” High Temp.-High Press, 35(2), pp. 193–198. [CrossRef]
Wu, L., and Zhang, J., 2018, “Phase Field Simulation of Dendritic Solidification of Ti-6Al-4V During Additive Manufacturing Process,” JOM, 70(10), pp. 2392–2399. [CrossRef]
Yan, W., Smith, J., Ge, W., Lin, F., and Liu, W. K., 2015, “Multiscale Modeling of Electron Beam and Substrate Interaction: A New Heat Source Model,” Comput. Mech., 56(2), pp. 265–276. [CrossRef]
Klassen, A., Bauereiß, A., and Körner, C., 2014, “Modelling of Electron Beam Absorption in Complex Geometries,” J. Phys. D: Appl. Phys., 47(6), p. 065307. [CrossRef]
Körner, C., Attar, E., and Heinl, P., 2011, “Mesoscopic Simulation of Selective Beam Melting Processes,” J. Mater. Process. Technol., 211(6), pp. 978–987. [CrossRef]
Hu, Z., and Mahadevan, S., 2017, “Uncertainty Quantification in Prediction of Material Properties During Additive Manufacturing,” Scr. Mater., 135, pp. 135–140. [CrossRef]
Xiao, Y., Zhan, H., Gu, Y., and Li, Q., 2017, “Modeling Heat Transfer During Friction Stir Welding Using a Meshless Particle Method,” Int. J. Heat Mass Transf., 104, pp. 288–300. [CrossRef]
Roth, T. A., and Suppayak, P., 1978, “The Surface and Grain Boundary Free Energies of Pure Titanium and the Titanium Alloy Ti-6AI-4V,” Mater. Sci. Eng., 35(2), pp. 187–196. [CrossRef]
Roth, T. A., and Henning, W. D., 1985, “The Surface and Grain Boundary Free Energies and the Self-Diffusion Coefficient of 5Al-2.5Sn Titanium Alloy,” Mater. Sci. Eng., 76, pp. 187–194. [CrossRef]
Gil, F., and Planell, J., 2000, “Behaviour of Normal Grain Growth Kinetics in Single Phase Titanium and Titanium Alloys,” Mater. Sci. Eng. A, 283(1), pp. 17–24. [CrossRef]
Ding, R., and Guo, Z. X., 2002, “Microstructural Modelling of Dynamic Recrystallisation Using an Extended Cellular Automaton Approach,” Comput. Mater. Sci., 23(1), pp. 209–218. [CrossRef]
Mishra, S., and DebRoy, T., 2004, “Measurements and Monte Carlo Simulation of Grain Growth in the Heat-Affected Zone of Ti–6Al–4V Welds,” Acta Mater., 52(5), pp. 1183–1192. [CrossRef]
Jamshidinia, M., Kong, F., and Kovacevic, R., 2013, “Numerical Modeling of Heat Distribution in the Electron Beam Melting® of Ti-6Al-4V,” ASME J. Manuf. Sci. Eng., 135(6), p. 061010. [CrossRef]
Martin, J. H., Yahata, B. D., Hundley, J. M., Mayer, J. A., Schaedler, T. A., and Pollock, T. M., 2017, “3D Printing of High-Strength Aluminium Alloys,” Nature, 549(7672), pp. 365–369. [CrossRef] [PubMed]
Hu, Z., and Mahadevan, S., 2016, “Global Sensitivity Analysis-Enhanced Surrogate (GSAS) Modeling for Reliability Analysis,” Struct. Multidiscip. Optim., 53(3), pp. 501–521. [CrossRef]
Helton, J. C., and Davis, F. J., 2003, “Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems,” Reliab. Eng. Syst. Saf., 81(1), pp. 23–69. [CrossRef]
Hu, Z., Ao, D., and Mahadevan, S., 2017, “Calibration Experimental Design Considering Field Response and Model Uncertainty,” Comput. Methods Appl. Mech. Eng., 318, pp. 92–119. [CrossRef]
Jones, D. R., Schonlau, M., and Welch, W. J., 1998, “Efficient Global Optimization of Expensive Black-Box Functions,” J. Global Optim., 13(4), pp. 455–492. [CrossRef]
Forrester, J., Keane, A. I., and Bressloff, A. J., and W, N., 2006, “Design and Analysis of ‘Noisy’ Computer Experiments,” AIAA J., 44(10), pp. 2331–2339. [CrossRef]
Gong, X., Lydon, J., Cooper, K., and Chou, K., 2014, “Beam Speed Effects on Ti–6Al–4V Microstructures in Electron Beam Additive Manufacturing,” J. Mater. Res., 29(17), pp. 1951–1959. [CrossRef]
Narra, S. P., Cunningham, R., Beuth, J., and Rollett, A. D., 2018, “Location Specific Solidification Microstructure Control in Electron Beam Melting of Ti-6Al-4V,” Addit. Manuf., 19, pp. 160–166. [CrossRef]
Hu, Z., and Mahadevan, S., 2018, “Probability Models for Data-Driven Global Sensitivity Analysis,” Reliab. Eng. Syst. Saf., 187, pp. 40–57. [CrossRef]
Stanev, V., Oses, C., Kusne, A. G., Rodriguez, E., Paglione, J., Curtarolo, S., and Takeuchi, I., 2018, “Machine Learning Modeling of Superconducting Critical Temperature,” npj Comput. Mater., 4(1), p. 29. [CrossRef]
Jäger, M. O., Morooka, E. V., Canova, F. F., Himanen, L., and Foster, A. S., 2018, “Machine Learning Hydrogen Adsorption on Nanoclusters Through Structural Descriptors,” npj Comput. Mater, 4(1), p. 37. [CrossRef]
Rovinelli, A., Sangid, M. D., Proudhon, H., and Ludwig, W., 2018, “Using Machine Learning and a Data-Driven Approach to Identify the Small Fatigue Crack Driving Force in Polycrystalline Materials,” npj Comput. Mater., 4(1), p. 35. [CrossRef]
Ward, L., Agrawal, A., Choudhary, A., and Wolverton, C., 2016, “A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials,” npj Comput. Mater., 2, p. 16028. [CrossRef]
Medasani, B., Gamst, A., Ding, H., Chen, W., Persson, K. A., Asta, M., Canning, A., and Haranczyk, M., 2016, “Predicting Defect Behavior in B2 Intermetallics by Merging Ab Initio Modeling and Machine Learning,” npj Comput. Mater, 2(1), p. 1. [CrossRef]
Zhang, W., Mehta, A., Desai, P. S., and Fred Higgs, P. C., III, 2017, “Machine Learning Enabled Powder Spreading Process Map For Metal Additive Manufacturing (AM),” 2017 Solid Freeform Fabrication Symposium Proceedings, Austin, TX, Aug. 7–9, Univeristy of Texas at Austin, Austin, TX.
Tapia, G., Khairallah, S., Matthews, M., King, W. E., and Elwany, A., 2018, “Gaussian Process-Based Surrogate Modeling Framework for Process Planning in Laser Powder-Bed Fusion Additive Manufacturing of 316L Stainless Steel,” Int. J. Adv. Manuf. Technol., 94(9–12), pp. 3591–3603. [CrossRef]
Tang, M., Pistorius, P. C., and Beuth, J. L., 2017, “Prediction of Lack-of-Fusion Porosity for Powder Bed Fusion,” Addit. Manuf., 14, pp. 39–48. [CrossRef]
Dehoff, R., Kirka, M., Sames, W., Bilheux, H., Tremsin, A., Lowe, L., and Babu, S., 2015, “Site Specific Control of Crystallographic Grain Orientation Through Electron Beam Additive Manufacturing,” Mater. Sci. Technol., 31(8), pp. 931–938. [CrossRef]
Colegrove, P. A., Martina, F., Roy, M. J., Szost, B. A., Terzi, S., Williams, S. W., Withers, P. J., and Jarvis, D., 2014, “High Pressure Interpass Rolling of Wire+ arc Additively Manufactured Titanium Components,” Adv. Mater. Res., 996, pp. 694–700. [CrossRef]
Teng, C., Gong, H., Szabo, A., Dilip, J., Ashby, K., Zhang, S., Patil, N., Pal, D., and Stucker, B., 2017, “Simulating Melt Pool Shape and Lack of Fusion Porosity for Selective Laser Melting of Cobalt Chromium Components,” ASME J. Manuf. Sci. Eng., 139(1), p. 011009. [CrossRef]
Qiu, C., Panwisawas, C., Ward, M., Basoalto, H. C., Brooks, J. W., and Attallah, M. M., 2015, “On the Role of Melt Flow Into the Surface Structure and Porosity Development During Selective Laser Melting,” Acta Mater., 96, pp. 72–79. [CrossRef]
Lu, X., Lin, X., Chiumenti, M., Cervera, M., Hu, Y., Ji, X., Ma, L., Yang, H., and Huang, W., 2019, “Residual Stress and Distortion of Rectangular and S-Shaped Ti-6Al-4V Parts by Directed Energy Deposition: Modelling and Experimental Calibration,” Addit. Manuf. 26, 166–179. [CrossRef]
Jayanath, S., and Achuthan, A., 2018, “A Computationally Efficient Finite Element Framework to Simulate Additive Manufacturing Processes,” ASME J. Manuf. Sci. Eng., 140(4), p. 041009. [CrossRef]
Lyu, J., and Manoochehri, S., 2018, “Modeling Machine Motion and Process Parameter Errors for Improving Dimensional Accuracy of Fused Deposition Modeling Machines,” ASME J. Manuf. Sci. Eng., 140(12), p. 121012. [CrossRef]


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Fig. 1

Workflow of the proposed data-driven approach for uncertainty quantification and management

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Fig. 2

Schematic illustration of the coupled thermal model and grain growth model

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Fig. 3

Flowchart of surrogate modeling for the melt pool simulation model

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Fig. 4

Flowchart of surrogate modeling for the first-two statistical moments of the microstructure length/width ratio

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Fig. 5

Comparison of pool predictions between (a) the current FE thermal model and (b) previous experimentally validated model [49] and (c) quantitative comparison in terms of pool dimensions

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Fig. 6

Columnar-to-equiaxed structure transition Pv map. (Note that, the data scattering, e.g., the few columnar points observed in the equiaxed region, should not be mistaken as prediction errors. Instead, it just reflects the variability in the material's microstructure due to various uncertainty sources present during the AM process.) Previous experiment data [49] are also plotted. The constant pool area lines are just estimated based on Ref. [53], for a simple illustration of pool area variation in the Pv space.

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Fig. 7

Predictions of the steady temperature field developed as a function of control and noise factors [d, λ] by using the (a) finite-element-based thermal model and (b) thermal surrogate model. (c) Absolute errors of surrogate model predictions compared with finite-element simulations. (Here, each case or training point corresponds to a specific combination of control and noise factors [d, λ], see Sec. 3.4.).

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Fig. 8

Prediction of the grain length/width ratio distribution as a function of control and noise factors [d, θ], by using the physics-based AM simulation model and microstructure surrogate model: (a) mean of grain length/width ratio, μ(r) and (b) second moment of grain length/width ratio, mr2(r). (Here each case or training point corresponds to a specific combination of control and noise factors, [d, θ], see Sec. 3.4.).

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Fig. 9

Grain structure developments on the optimal manufacturing condition (robust design point) and test manufacturing conditions under uncertainty

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Fig. 10

(a) Grain length/width ratio distribution in terms of 20 different grain structures developed under the optimal manufacturing condition. They are summarized into one unconditional PDF distribution curve (the thick line), so as to better characterize the grain structure development with uncertainty. (b) Grain length/width ratio distribution (unconditional PDF) for different manufacturing conditions.

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Fig. 11

(a) Sensitivity of mean of length/width ratio to various noise factors and (b) sensitivity of the variance of length/width ratio to various noise factors



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