Abstract
Additive manufacturing (AM) is a layer-by-layer material deposition process that allows for more manufacturing flexibility and design complexity than traditional manufacturing processes. However, the print quality in metal AM is hard to be predicted and controlled due to its high process variability. Numerous process parameters are correlated/intertwined and have a direct/indirect relationship with the final build quality. However, these interconnections among the process parameters and print qualities are not yet fully understood and identified. To achieve a functional part without additional post-processing to improve the surface finish/mechanical properties, the comprehensive process model, which includes the semantic and qualitative connections among the process inputs-signatures-qualities, is urgently needed. Currently, surrogate models (regression models/ physical-based models) are widely used for predicting part qualities, specifically thermal distortion. These surrogate models can provide a simulated result based on the user-defined parameter settings. However, the reverse determination of the highly correlated parameters that can manage the print qualities in the desired standard cannot be achieved through these surrogate models. Understanding this interconnectivity is the top priority in building process models that will help to make the process more predictable and controllable. An ontology-based process map that captures the complicated metal AM processes, including all input parameters, physical, thermal model, and resultant build qualities (microstructure and mechanical properties), is provided in this manuscript. The connections among these factors are used to diagnose the relevant process variables regarding the specific build qualities, which can further combine with experimental data to contribute to a “printable” zone. The ontology-based process map links variable selection to quality requirements, creating a printable zone. A printable zone indicates the allowable process parameter selections, contributing to the desired part quality. Ultimately, the selected variable process data from the ontological process map will scope the parametrical analysis. The implementations of the ontology-based process map and printable zone are presented to demonstrate this proposed method's effectiveness by demonstrating that the proposed printable zone's validity and desired build quality can be achieved.
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References
Y. Huang, M.C. Leu, J. Mazumder, and A. Donmez, Additive Manufacturing: Current State, Future Potential, Gaps and Needs, and Recommendations, Trans. ASME, Ser. B, 137(1), (2015)
Y. Zhai, D.A. Lados and J.L. LaGoy, Additive Manufacturing: Making Imagination the Major Limitation, JOM, 2014, 66(5), p 808–816.
Y. Xie, T.B. Chen, M. Lei, J. Yang, Q.J. Guo, B. Song and X.Y. Zhou, Spatial Distribution of Soil Heavy Metal Pollution Estimated by Different Interpolation Methods: Accuracy and Uncertainty Analysis, Chemosphere, 2011, 82(3), p 468–476.
C. Li, Z.Y. Liu, X.Y. Fang and Y.B. Guo, Residual Stress in Metal Additive Manufacturing, Procedia CIRP, 2018, 71, p 348–353.
B.M. Roh, S.R. Kumara, T.W Simpson, P. Michaleris, P. Witherell, and I. Assouroko, Ontology-based Laser and Thermal Metamodels for Metal-based Additive Manufacturing, ASME 2016 Int. Des. Eng. Tech. Conf. Comput. Inf. Eng. Conf., Aug. 21-24, Charlotte, NC, ASME, 50077, p V01AT02A043, (2016)
M. Mani, P. Witherell, and H. Jee, Design Rules for Additive Manufacturing: A Categorization, ASME 2017 Int. Des. Eng. Tech. Conf. Comput. Inf. Eng. Conf., Aug. 6-9, Cleveland, OH, ASME, 58110, p V001T02A035, (2017)
D.L. Bourell, J.J. Beaman, M.C. Leu, and D.W. Rosen, A Brief History of Additive Manufacturing and the 2009 Roadmap for Additive Manufacturing: Looking Back and Looking Ahead, Proceedings of RapidTech 2009: US-TURKEY Workshop on Rapid Technologies, Sept. 24–25, Istanbul, Turkey, p 1–8 (2009)
N. Guo and M.C. Leu, Additive Manufacturing: Technology, Applications and Research Needs, Front. Mech. Eng., 2013, 8(3), p 215–243.
D.L. Bourell, M.C. Leu and D.W. Rosen, Roadmap for Additive Manufacturing—Identifying the Future of Freeform Processing, University of Texas, Austin, TX, 2009.
X. Xiao and S. Joshi, Process Planning for Five-Axis Support Free Additive Manufacturing, Addit. Manuf., 2020, 36, p 101569.
L. Qian, J. Mei, J. Liang and X. Wu, Influence of Position and Laser Power on Thermal History and Microstructure of Direct Laser Fabricated Ti–6Al–4V Samples, Mater. Sci. Technol., 2005, 21(5), p 597–605.
E. Brandl, C. Leyens and F. Palm, Mechanical Properties of Additive Manufactured Ti-6Al-4V using Wire and Powder based Processes, In IOP Conf, Ser. Mater. Sci. Eng, 2011, 26(1), p 012004.
L.N. Soldatova and R.D. King, An Ontology of Scientific Experiments, J. R. Soc. Interface, 2006, 3(11), p 795–803.
R. Arp, B. Smith, and A.D. Spear, Building Ontologies with basic Formal Ontology, Mit Press, (2015)
T.W. Simpson, J. Peplinski, P.N. Koch and J.K. Allen, Metamodels for Computer-Based Engineering Design: Survey and Recommendations, Eng. Comput., 2001, 17(2), p 129–150.
J.A. Goldak and M. Akhlaghi, Computational Welding Mechanics, Springer, New York, 2005.
J.A. Slotwinski, E.J. Garboczi and K.M. Hebenstreit, Porosity Measurements and Analysis for Metal Additive Manufacturing Process Control, J. Res. Natl. Inst. Stand. Technol., 2014, 119, p 494.
A. Basak and S. Das, Epitaxy and Microstructure Evolution in Metal Additive Manufacturing, Annu. Rev. Mater. Res., 2016, 46, p 125–149.
J. Gockel and J. Beuth, Understanding Ti-6Al-4V Microstructure Control in Additive Manufacturing via Process Maps, Austin, TX, The University of Texas, Solid Freeform Fabr. Symp. Proc., 2013, p 666–674
P. Das, R. Chandran, R. Samant and S. Anand, Optimum Part Build Orientation In Additive Manufacturing For Minimizing Part Errors and Support Structures, Procedia Manuf., 2015, 1, p 343–354.
H. Gong, K. Rafi, H. Gu, T. Starr and B. Stucker, Analysis of Defect Generation in Ti–6Al–4V Parts Made using Powder Bed Fusion Additive Manufacturing Processes, Addit. Manuf., 2014, 1, p 87–98.
X. Xiao, B.M. Roh, and F. Zhu, Strength Enhancement in Fused Filament Fabrication via the Isotropy Toolpath. Appl.Sci., 2021, 11(13), p 6100
P. Witherell, S. Feng, T.W. Simpson, D.B. Saint John, P. Michaleris, Z.K. Liu, L.Q. Chen, and R. Martukanitz, Toward Metamodels for Composable and Reusable Additive Manufacturing Process Models, Trans. ASME, Ser. B, 136(6) (2014)
T.H.C. Childs, C. Hauser and M. Badrossamay, Selective Laser Sintering (Melting) of Stainless and Tool Steel Powders: Experiments and Modelling, Proc. Inst. Mech. Eng., Part B, 2005, 219(4), p 339–357.
N.J. Harrison, I. Todd and K. Mumtaz, Reduction of Micro-cracking in Nickel Superalloys Processed by Selective Laser Melting: A Fundamental Alloy Design Approach, Acta Mater., 2015, 94, p 59–68.
A. du Plessis, I. Yadroitsava and I. Yadroitsev, Effects of Defects on Mechanical Properties in Metal Additive Manufacturing: A Review Focusing on X-ray Tomography Insights, Mater. Des, 2020, 187, p 108385.
I. Yadroitsev, L. Thivillon, P. Bertrand and I. Smurov, Strategy of Manufacturing Components with Designed Internal Structure by Selective Laser Melting of Metallic Powder, Appl. Surf. Sci., 2007, 254(4), p 980–983.
M. Tang, P.C. Pistorius and J.L. Beuth, Prediction of Lack-of-Fusion Porosity for Powder Bed Fusion, Addit. Manuf., 2017, 14, p 39–48.
L.E. Criales, Y.M. Arısoy, B. Lane, S. Moylan, A. Donmez and T. Özel, Laser Powder Bed Fusion of Nickel Alloy 625: Experimental Investigations of Effects of Process Parameters on Melt Pool Size and Shape with Spatter Analysis, Int. J. Mach. Tools Manuf., 2017, 121, p 22–36.
J.P. Oliveira, A. LaLonde and J. Ma, Processing Parameters in Laser Powder bed Fusion Metal Additive Manufacturing, Mater. Des., p 108762 (2020)
C. Li, Y.B. Guo and J.B. Zhao, Interfacial Phenomena and Characteristics between the Deposited Material and Substrate in Selective Laser Melting Inconel 625, J. Mater. Process. Technol., 2017, 243, p 269–281.
T.G. Spears and S.A. Gold, In-process Sensing in Selective Laser Melting (SLM) Additive Manufacturing, Integr. Mater. Manuf. Innov., 2016, 5(1), p 16–40.
C. Chan, J. Mazumder and M. Chen, A two-dimensional Transient Model for Convection in Laser Melting Pool, Metall. Mater. Trans. A, 1984, 15(12), p 1984–2175.
J. Goldak, A. Chakravarti and M. Bibby, A New Finite Element Model for Welding Heat Sources, Metall. Trans. B, 1984, 15(2), p 299–305.
J. Alda, Laser and Gaussian Beam Propagation and Transformation, Encyclopedia of Optical Engineering, p 999-1013 (2003)
V.D. Manvatkar, A.A. Gokhale, G.J. Reddy, A. Venkataramana and A. De, Estimation of Melt Pool Dimensions, Thermal Cycle, and Hardness Distribution in the Laser-Engineered Net Shaping Process of Austenitic Stainless Steel, Metall. Mater. Trans. A, 2011, 42(13), p 4080–4087.
R.B. Patil and V. Yadava, Finite Element Analysis of Temperature Distribution in Single Metallic Powder Layer during Metal Laser Sintering, Int. J. Mach. Tools Manuf., 2007, 47(7–8), p 1069–1080.
J. Yin, H. Zhu, K. Linda, W. Lei, C. Dai and D. Zuo, Simulation of Temperature Distribution in Single Metallic Powder Layer for Laser Micro-Sintering, Comput. Mater. Sci., 2012, 53(1), p 333–339.
I.A. Roberts, C.J. Wang, R. Esterlein, M. Stanford and D.J. Mynors, A Three-dimensional Finite Element Analysis of the Temperature Field during Laser Melting of Metal Powders in Additive Layer Manufacturing, Int. J. Mach. Tools Manuf., 2009, 49(12), p 916–923.
J. Zhang, D. Li, L. Zhao and M. Zhao, Simulation of Temperature Field in Selective Laser Sintering of Copper Powder, 2010 International Conference on Mechanic Automation and Control Engineering, June 26-28, Wuhan, China, IEEE, p 3282-3285 (2010)
B. Song, S. Dong, H. Liao and C. Coddet, Process Parameter Selection for Selective Laser Melting of Ti6Al4V based on Temperature Distribution Simulation and Experimental Sintering, Int. J. Adv. Manuf. Technol., 2011, 61(9–12), p 967–974.
J. Ready, Effects of High-Power Laser Radiation, Elsevier, (2012)
K. Zeng, D. Pal, and B. Stucker, A Review of Thermal Analysis Methods in Laser Sintering and Selective Laser Melting, Solid Freeform Fabr. Symp. Proc., Austin, TX, The University of Texas, p 796-814 (2012)
A. Raghavan, H.L. Wei, T.A. Palmer and T. DebRoy, Heat Transfer and Fluid Flow in Additive Manufacturing, J. Laser Appl., 2013, 25(5), p 052006.
I. Gibson, D.W. Rosen and B. Stucker, Additive Manufacturing Technologies: Rapid Prototyping to Direct Digital Manufacturing, Springer, p 237-258 (2010)
C. Konrad, Y. Zhang and B. Xiao, Analysis of Melting and Resolidification in a Two-component Metal Powder Bed Subjected to Temporal Gaussian Heat Flux, Int. J. Heat Mass Trans., 2005, 48(19–20), p 3932–3944.
J.C. Heigel, P. Michaleris and E.W. Reutzel, Thermo-Mechanical Model Development and Validation of Directed Energy Deposition Additive Manufacturing of Ti–6Al–4V, Addit. Manuf., 2015, 5, p 9–19.
B. Schoinochoritis, D. Chantzis and K. Salonitis, Simulation of Metallic Powder Bed Additive Manufacturing Processes with the Finite Element method: A Critical Review, Proc. Inst. Mech. Eng. B, 2017, 231(1), p 96–117.
W. Zhang, G.G. Roy, J.W. Elmer and T. DebRoy, Modeling of Heat Transfer and Fluid Flow during Gas Tungsten Arc Spot Welding of Low Carbon Steel, J. Appl. Phys, 2003, 93, p 3022–3033.
Y.S. Lee, and W. Zhang, Mesoscopic Simulation of Heat Transfer and Fluid Flow in Laser Powder Bed Additive Manufacturing, Solid Freeform Fabr. Symp. Proc., Austin, TX, The University of Texas, p 1154-1165 , (2015)
W.E. King, A.T. Anderson, R.M. Ferencz, N.E. Hodge, C. Kamath, S.A. Khairallah and A.M. Rubenchik, Laser Powder Bed Fusion Additive Manufacturing of Metals; Physics, Computational, and Materials Challenges, Appl. Phys. Rev, 2015, 2(4), p 041304.
J. Trapp, A.M. Rubenchik, G. Guss and M.J. Matthews, In situ Absorptivity Measurements of Metallic Powders during Laser Powder-Bed Fusion Additive Manufacturing, Appl. Mater. Today, 2017, 9, p 341–349.
R. Pederson, “Microstructure and Phase Transformation of Ti-6Al-4V,” Ph.D. Thesis, Luleå tekniska universitet, (2002)
J. Sieniawski, R. Filip and W. Ziaja, The Effect of Microstructure on the Mechanical Properties of Two-Phase Titanium Alloys, Mater. Des., 1997, 18(4–6), p 361–363.
K. Kubiak and J. Sieniawski, Development of the Microstructure and Fatigue Strength of Two Phase Titanium Alloys in the Processes of Forging and Heat Treatment, J. Mater. Process. Technol., 1998, 78(1–3), p 117–121.
P. Nie, O.A. Ojo and Z. Li, Numerical Modeling of Microstructure Evolution during Laser Additive Manufacturing of a Nickel-Based Superalloy, Acta Mater., 2014, 77, p 85–95.
M. Megahed, H.W. Mindt, N. N’Dri, H. Duan and O. Desmaison, Metal Additive-Manufacturing Process and Residual Stress Modeling, Integr. Mater. Manuf. Innov., 2016, 5(1), p 61–93.
A.V. Gusarov, M. Pavlov and I. Smurov, Residual Stresses At Laser Surface Re-melting and Additive Manufacturing, Phys. Procedia, 2011, 12, p 248–254.
W.E. King, H.D. Barth, V.M. Castillo, G.F. Gallegos, J.W. Gibbs, D.E. Hahn, C. Kamath and A.M. Rubenchik, Observation of Keyhole-Mode Laser Melting in Laser Powder-Bed Fusion Additive Manufacturing”, J. Mater. Process. Technol., 2014, 214(12), p 2915–2925.
S. Kou, Fluid Flow and Solidification in Welding: Three Decades of Fundamental Research at the University of Wisconsin, Weld. J., 2012, 91(11), p 287S-302S.
M.C. Tsai and S. Kou, Marangoni Convection in Weld Pools with a Free Surface, Int. J. Numer. Methods Fluids, 1989, 9(12), p 1503–1516.
A. Hussan, K. Latif, A.T. Rextin, A. Hayat and M. Alam, Scalable Visualization of Semantic Nets using Power-Law Graphs, Appl. Math. Inf. Sci., 2014, 8(1), p 355–367.
M. Bastian, S. Heymann and M. Jacomy, Gephi: an Open Source Software for Exploring and Manipulating Networks, Proc. Int. AAAI Conf. Weblogs Soc. Media, 2009, 3(1), p 2009.
R. Sivakumar, and P.V. Arivoli, Ontology Visualization PROTÉGÉ Tools–A Review, Int. J. Adv. Sci. Technol., 1 (2011)
R. Diestel, Graduate Texts in Mathematics, Springer, Graph Theory, 2000.
J. Sieniawski, W. Ziaja, K. Kubiak and M. Motyka, Microstructure and Mechanical Properties of High Strength Two-Phase Titanium Alloys, Titanium Alloys-Advances in Properties Control, IntechOpen, p 69-80 (2013)
M. Letenneur, A. Kreitcberg and V. Brailovski, Optimization of Laser Powder Bed Fusion Processing using a Combination of Melt Pool Modeling and Design of Experiment Approaches: Density Control, J. Manuf. Mater. Process., 2019, 3(1), p 21.
Z. Wang, Z. Xiao, Y. Tse, C. Huang and W. Zhang, Optimization of Processing Parameters and Establishment of a Relationship between Microstructure and Mechanical Properties of SLM Titanium Alloy, Opt. Laser Technol., 2019, 112, p 159–167.
H. Gong, K. Rafi, T. Starr and B. Stucker, August. The Effects of Processing Parameters on Defect Regularity in Ti-6Al-4V Parts Fabricated by Selective Laser Melting and Electron Beam Melting, Solid Freeform Fabr. Symp. Proc., Austin, TX, The University of Texas, p 424-439 (2013)
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This work was supported by NIST Grant 2014-NIST-MSE-01 and a cooperative agreement between the National Institute of Standards and Technology (NIST) and The Pennsylvania State University.
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This invited article is part of a special topical focus in the Journal of Materials Engineering and Performance on Additive Manufacturing. The issue was organized by Dr. William Frazier, Pilgrim Consulting, LLC; Mr. Rick Russell, NASA; Dr. Yan Lu, NIST; Dr. Brandon D. Ribic, America Makes; and Caroline Vail, NSWC Carderock.
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Roh, BM., Kumara, S.R.T., Witherell, P. et al. Ontology-based Process Map for Metal Additive Manufacturing. J. of Materi Eng and Perform 30, 8784–8797 (2021). https://doi.org/10.1007/s11665-021-06274-2
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DOI: https://doi.org/10.1007/s11665-021-06274-2