Anomaly Detection in Materials Digital Twins with Multiscale ICME for Additive Manufacturing

1. National Academies of Sciences, Engineering, and Medicine (2023) Foundational research gaps and future directions for digital twins

2. AIAA Digital Engineering Integration Committee (2020) Digital twin: Definition & value. AIAA

3. Gunasegaram DR, Murphy A, Barnard A, DebRoy T, Matthews M, Ladani L, Gu D (2021) Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing. Addit Manuf 46:102089

4. Gunasegaram DR, Murphy AB, Matthews M, DebRoy T (2021) The case for digital twins in metal additive manufacturing. J Phys Mater 4(4):04040110.1088/2515-7639/ac09fb ↗

5. Mukherjee T, DebRoy T (2019) A digital twin for rapid qualification of 3D printed metallic components. Appl Mater Today 14:59–6510.1016/j.apmt.2018.11.003 ↗

6. Kalidindi SR, Buzzy M, Boyce BL, Dingreville R (2022) Digital twins for materials. Front Mater 9:81853510.3389/fmats.2022.818535 ↗

7. Council UNR (2008) Integrated computational materials engineering: a transformational discipline for improved competitiveness and national security. National Academies Press

8. Allison J, Cowles B, DeLoach J, Pollock T, Spanos G, et al (2013) Implementing ICME in the aerospace, automotive, and maritime industries. Technical report, The Minerals Metals and Materials Society

9. US NSTC (2011) Materials genome initiative for global competitiveness. Executive Office of the President, National Science and Technology Council

10. Christodoulou J, Friedersdorf LE, Sapochak L, Warren JA (2021) The second decade of the materials genome initiative. JOM 73(12):3681–368310.1007/s11837-021-05008-y ↗

11. Boyce B (2022) Microstructural black swans. In: IOP conference series: materials science and engineering, vol 1249, p 012004. IOP Publishing10.1088/1757-899X/1249/1/012004 ↗

12. Aditya K, Kolla H, Kegelmeyer WP, Shead TM, Ling J, Davis WL IV (2019) Anomaly detection in scientific data using joint statistical moments. J Comput Phys 387:522–53810.1016/j.jcp.2019.03.003 ↗

13. Shead T, Tezaur I, Davis IV W, Carlson M, Dunlavy D, Parish E, Blonigan P, Tencer J, Rizzi F, Kolla H (2023) A novel in situ machine learning framework for intelligent data capture and event detection. In: Machine learning and its application to reacting flows: ML and combustion, Springer, pp 53–8710.1007/978-3-031-16248-0_3 ↗

14. Rodgers TM, Lim H, Brown JA (2020) Three-dimensional additively manufactured microstructures and their mechanical properties. JOM 72(1):75–8210.1007/s11837-019-03808-x ↗

15. Leung Y-S, Kwok T-H, Li X, Yang Y, Wang CC, Chen Y (2019) Challenges and status on design and computation for emerging additive manufacturing technologies. J Comput Inf Sci Eng 19(2):02101310.1115/1.4041913 ↗

16. Zhang B, Jaiswal P, Rai R, Nelaturi S (2018) Additive manufacturing of functionally graded material objects: a review. J Comput Inf Sci Eng 18(4):04100210.1115/1.4039683 ↗

17. Liu D, Wang Y (2019) Mesoscale multi-physics simulation of rapid solidification of Ti-6Al-4V alloy. Addit Manuf 25:551–562

18. Liu D, Wang Y (2019) Multi-fidelity physics-constrained neural network and its application in materials modeling. J Mech Des 141(12):12140310.1115/1.4044400 ↗

19. Steuben JC, Iliopoulos AP, Michopoulos JG (2016) Implicit slicing for functionally tailored additive manufacturing. Comput Aided Des 77:107–11910.1016/j.cad.2016.04.003 ↗

20. Steuben JC, Iliopoulos AP, Michopoulos JG (2016) Discrete element modeling of particle-based additive manufacturing processes. Comput Methods Appl Mech Eng 305:537–56110.1016/j.cma.2016.02.023 ↗

21. Feng SC, Moges T, Park H, Yakout M, Jones AT, Ko H, Witherell P (2023) Functional requirements of software tools for laser-based powder bed fusion additive manufacturing for metals. J Comput Inf Sci Eng 23(3):03100510.1115/1.4054933 ↗

22. Stopka KS, Desrosiers A, Nicodemus T, Krutz N, Andreaco A, Sangid MD (2023) Intentionally seeding pores in additively manufactured alloy 718: process parameters, microstructure, defects, and fatigue. Addit Manuf 66:103450

23. Stopka KS, Desrosiers A, Andreaco A, Sangid MD (2024) A methodology for the rapid qualification of additively manufactured materials based on pore defect structures. Integr Mater Manuf Innov 13:335–35910.1007/s40192-024-00343-9 ↗

24. Fathizadan S, Ju F, Lu Y, Yang Z (2023) Deep spatio-temporal anomaly detection in laser powder bed fusion. IEEE Trans Autom Sci Eng. https://doi.org/10.1109/TASE.2023.330992710.1109/TASE.2023.3309927 ↗

25. Feng SC, Lu Y, Jones AT, Yang Z (2022) Additive manufacturing in situ and ex situ geometric data registration. J Comput Inf Sci Eng 22(6):06100310.1115/1.4054202 ↗

26. Bishop JE, Emery JM, Field RV, Weinberger CR, Littlewood DJ (2015) Direct numerical simulations in solid mechanics for understanding the macroscale effects of microscale material variability. Comput Methods Appl Mech Eng 287:262–28910.1016/j.cma.2015.01.017 ↗

27. Bishop JE, Emery JM, Battaile CC, Littlewood DJ, Baines AJ (2016) Direct numerical simulations in solid mechanics for quantifying the macroscale effects of microstructure and material model-form error. JOM 68(5):1427–144510.1007/s11837-016-1857-6 ↗

28. Rodgers TM, Bishop JE, Madison JD (2018) Direct numerical simulation of mechanical response in synthetic additively manufactured microstructures. Model Simul Mater Sci Eng 26(5):05501010.1088/1361-651X/aac616 ↗

29. Brown JA, Bishop JE (2019) Modeling mechanical behavior of an additively manufactured metal structure with local texture variations: a study on model form error. Model Simul Mater Sci Eng 27(2):02500310.1088/1361-651X/aaf107 ↗

30. Moges T, Yang Z, Jones K, Feng S, Witherell P, Lu Y (2021) Hybrid modeling approach for melt-pool prediction in laser powder bed fusion additive manufacturing. J Comput Inf Sci Eng 21(5):05090210.1115/1.4050044 ↗

31. Dinar M, Rosen DW (2017) A design for additive manufacturing ontology. J Comput Inf Sci Eng 17(2):02101310.1115/1.4035787 ↗

32. Lu Y, Shevtshenko E, Wang Y (2021) Physics-based compressive sensing to enable digital twins of additive manufacturing processes. J Comput Inf Sci Eng 21(3):03100910.1115/1.4050377 ↗

33. Vogiatzis P, Chen S, Zhou C (2017) An open source framework for integrated additive manufacturing and level-set-based topology optimization. J Comput Inf Sci Eng 17(4):04101210.1115/1.4037738 ↗

34. Montazeri M, Rao P (2018) Sensor-based build condition monitoring in laser powder bed fusion additive manufacturing process using a spectral graph theoretic approach. J Manuf Sci Eng 140(9):09100210.1115/1.4040264 ↗

35. Sato MM, Wong VWH, Law KH, Yeung H, Yang Z, Lane B, Witherell P (2022) Anomaly detection of laser powder bed fusion melt pool images using combined unsupervised and supervised learning methods. In: International design engineering technical conferences and computers and information in engineering conference, vol 86212, American Society of Mechanical Engineers, pp 002-0207010.1115/DETC2022-88313 ↗

36. Roach RA, Argibay N, Allen K, Balch DK, Beghini LL, Bishop JE, Boyce B, Brown JA, Burchard RL, Chandross ME, et al (2018) Born qualified grand challenge LDRD final report. Technical report, Sandia National Lab.(SNL-NM), Albuquerque, NM (United States)10.2172/1481619 ↗

37. Roach AM, White BC, Garland A, Jared BH, Carroll JD, Boyce BL (2020) Size-dependent stochastic tensile properties in additively manufactured 316L stainless steel. Addit Manuf 32:101090

38. Heckman NM, Ivanoff TA, Roach AM, Jared BH, Tung DJ, Brown-Shaklee HJ, Huber T, Saiz DJ, Koepke JR, Rodelas JM (2020) Automated high-throughput tensile testing reveals stochastic process parameter sensitivity. Mater Sci Eng A 772:13863210.1016/j.msea.2019.138632 ↗

39. Jensen SC, Koepke JR, Saiz DJ, Heiden MJ, Carroll JD, Boyce BL, Jared BH (2022) Optimization of stochastic feature properties in laser powder bed fusion. Addit Manuf 56:102943

40. Salzbrenner BC, Rodelas JM, Madison JD, Jared BH, Swiler LP, Shen Y-L, Boyce BL (2017) High-throughput stochastic tensile performance of additively manufactured stainless steel. J Mater Process Technol 241:1–1210.1016/j.jmatprotec.2016.10.023 ↗

41. Plimpton S, Battaile C, Chandross M, Holm L, Thompson A, Tikare V, Wagner G, Webb E, Zhou X, Cardona CG, et al (2009) Crossing the mesoscale no-man’s land via parallel kinetic Monte Carlo. Sandia Report SAND2009-6226

42. Mitchell JA, Abdeljawad F, Battaile C, Garcia-Cardona C, Holm EA, Homer ER, Madison J, Rodgers TM, Thompson AP, Tikare V, Webb E, Plimpton SJ (2023) Parallel simulation via SPPARKS of on-lattice kinetic and Metropolis Monte Carlo models for materials processing. Model Simul Mater Sci Eng 31(5):05500110.1088/1361-651X/accc4b ↗

43. Garcia AL, Tikare V, Holm EA (2008) Three-dimensional simulation of grain growth in a thermal gradient with non-uniform grain boundary mobility. Scripta Materialia 59(6):661–66410.1016/j.scriptamat.2008.05.039 ↗

44. Rodgers TM, Madison JD, Tikare V (2017) Simulation of metal additive manufacturing microstructures using kinetic Monte Carlo. Comput Mater Sci 135:78–8910.1016/j.commatsci.2017.03.053 ↗

45. Rodgers TM, Mitchell JA, Tikare V (2017) A Monte Carlo model for 3D grain evolution during welding. Model Simul Mater Sci Eng 25(6):06400610.1088/1361-651X/aa7f20 ↗

46. Rodgers TM, Madison JD, Tikare V, Maguire MC (2016) Predicting mesoscale microstructural evolution in electron beam welding. JOM 68(5):1419–142610.1007/s11837-016-1863-8 ↗

47. Rodgers TM, Mitchell JA, Olson A, Bolintineanu DS, Vackel A, Moore NW (2021) Fast three-dimensional rules-based simulation of thermal-sprayed microstructures. Comput Mater Sci 194:11043710.1016/j.commatsci.2021.110437 ↗

48. Anderson M, Grest G, Srolovitz D (1989) Computer simulation of normal grain growth in three dimensions. Philos Magaz B 59(3):293–32910.1080/13642818908220181 ↗

49. Roters F, Eisenlohr P, Hantcherli L, Tjahjanto DD, Bieler TR, Raabe D (2010) Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: Theory, experiments, applications. Acta Mater 58(4):1152–121110.1016/j.actamat.2009.10.058 ↗

50. Maiti T, Eisenlohr P (2018) Fourier-based spectral method solution to finite strain crystal plasticity with free surfaces. Scr Mater 145:37–4010.1016/j.scriptamat.2017.09.047 ↗

51. Roters F, Diehl M, Shanthraj P, Eisenlohr P, Reuber C, Wong SL, Maiti T, Ebrahimi A, Hochrainer T, Fabritius H-O, Nikolov S, Friák M, Fujita N, Grilli N, Janssens KGF, Jia N, Kok PJJ, Ma D, Meier F, Werner E, Stricker M, Weygand D, Raabe D (2019) DAMASK-The Düsseldorf advanced material simulation kit for modeling multi-physics crystal plasticity, thermal, and damage phenomena from the single crystal up to the component scale. Comput Mater Sci 158:420–47810.1016/j.commatsci.2018.04.030 ↗

52. Hutchinson JW (1976) Bounds and self-consistent estimates for creep of polycrystalline materials. Proc R Soc London A Math Phys Sci 348(1652):101–127

53. Kalidindi SR (1998) Incorporation of deformation twinning in crystal plasticity models. J Mech Phys Solids 46(2):267–29010.1016/S0022-5096(97)00051-3 ↗

54. Peirce D, Asaro RJ, Needleman A (1983) Material rate dependence and localized deformation in crystalline solids. Acta Metall 31(12):1951–197610.1016/0001-6160(83)90014-7 ↗

55. Tran A, Lim H (2023) An asynchronous parallel high-throughput model calibration framework for crystal plasticity finite element constitutive models. Comput Mech 72:485–49810.1007/s00466-023-02308-9 ↗

56. Lu J, Becker A, Sun W, Tanner D (2014) Simulation of cyclic plastic behavior of 304L steel using the crystal plasticity finite element method. Procedia Mater Sci 3:135–14010.1016/j.mspro.2014.06.025 ↗

57. Groeber MA, Jackson MA (2014) DREAM.3D: a digital representation environment for the analysis of microstructure in 3D. Integr Mater Manuf Innov 3(1):56–7210.1186/2193-9772-3-5 ↗

58. Fritsch FN, Butland J (1984) A method for constructing local monotone piecewise cubic interpolants. SIAM J Sci Stat Comput 5(2):300–30410.1137/0905021 ↗

59. Boyce BL, Furnish TA, Padilla H, Van Campen D, Mehta A (2015) Detecting rare, abnormally large grains by x-ray diffraction. J Mater Sci 50:6719–672910.1007/s10853-015-9226-3 ↗

60. Rousseeuw PJ, Driessen KV (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics 41(3):212–22310.1080/00401706.1999.10485670 ↗

61. Rousseeuw PJ, Leroy AM (2005) Robust regression and outlier detection. John Wiley & Sons

62. Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–147110.1162/089976601750264965 ↗

63. Liu FT, Ting KM, Zhou Z-H (2008) Isolation forest. In: 2008 Eighth IEEE international conference on data mining. IEEE, pp 413–42210.1109/ICDM.2008.17 ↗

64. Breunig MM, Kriegel H-P, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data, pp 93–10410.1145/342009.335388 ↗

65. Kriegel H-P, Kröger P, Schubert E, Zimek A (2009) LoOP: local outlier probabilities. In: Proceedings of the 18th ACM conference on information and knowledge management, pp 1649–165210.1145/1645953.1646195 ↗

66. Bolintineanu D, Olson AJ, Collis HH, Pokharel R, Brown DW, Chuang AC, Vackel A, Ivanoff T, Madison JD, Moore NW (2023) Quantifying pore morphology in spray-formed tantalum using X-ray micro-computed tomography. Metall Mater Trans A 54(10):4100–412710.1007/s11661-023-07156-z ↗

67. Tran A, Wildey T, Lim H (2022) Microstructure-sensitive uncertainty quantification for crystal plasticity finite element constitutive models using stochastic collocation method. Front Mater 9:1–2010.3389/fmats.2022.915254 ↗

68. Tran A, Wildey T (2020) Solving stochastic inverse problems for property-structure linkages using data-consistent inversion and machine learning. JOM 73:72–8910.1007/s11837-020-04432-w ↗

69. Tran A, Tranchida J, Wildey T, Thompson AP (2020) Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: application to ternary random alloys. J Chem Phys 153:07470510.1063/5.0015672 ↗

70. Tran A, Wildey T, McCann S (2020) sMF-BO-2CoGP: a sequential multi-fidelity constrained Bayesian optimization for design applications. J Comput Inf Sci Eng 20(3):1–1510.1115/1.4046697 ↗

71. Tran A, Robbe P, Lim H (2023) Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods. Materialia 27:10170510.1016/j.mtla.2023.101705 ↗

72. Tran A, Robbe P, Rodgers T, Lim H (2023) Multi-fidelity uncertainty quantification for homogenization problems in structure-property relationships from crystal plasticity finite elements. JOM 76(6):1–14

73. Tran A, Eldred M, Wildey T, McCann S, Sun J, Visintainer RJ (2022) aphBO-2GP-3B: a budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture. Struct Multidiscip Optim 65(4):1–4510.1007/s00158-021-03102-y ↗

74. Bridgman W, Zhang X, Teichert G, Khalil M, Garikipati K, Jones R (2022) A heteroencoder architecture for prediction of failure locations in porous metals using variational inference. Comput Methods Appl Mech Eng 398:11523610.1016/j.cma.2022.115236 ↗

75. Yong BX, Brintrup A (2022) Bayesian autoencoders with uncertainty quantification: towards trustworthy anomaly detection. Expert Syst Appl 209:11819610.1016/j.eswa.2022.118196 ↗

76. Nemani V, Biggio L, Huan X, Hu Z, Fink O, Tran A, Wang Y, Zhang X, Hu C (2023) Uncertainty quantification in machine learning for engineering design and health prognostics: a tutorial. Mech Syst Signal Process 205:11079610.1016/j.ymssp.2023.110796 ↗

77. Killourhy KS, Maxion RA (2009) Comparing anomaly-detection algorithms for keystroke dynamics. In: 2009 IEEE/IFIP international conference on dependable systems & networks, IEEE, pp 125–13410.1109/DSN.2009.5270346 ↗