nach oben

Integrating Materials and Manufacturing Innovation

31.03.2017 | TECHNICAL ARTICLE

Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

verfasst von: Ruoqian Liu, Yuksel C. Yabansu, Zijiang Yang, Alok N. Choudhary, Surya R. Kalidindi, Ankit Agrawal

Erschienen in: Integrating Materials and Manufacturing Innovation

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The response of a composite material is the result of a complex interplay between the prevailing mechanics and the heterogenous structure at disparate spatial and temporal scales. Understanding and capturing the multiscale phenomena is critical for materials modeling and can be pursued both by physical simulation-based modeling as well as data-driven machine learning-based modeling. In this work, we build machine learning-based data models as surrogate models for approximating the microscale elastic response as a function of the material microstructure (also called the elastic localization linkage). In building these surrogate models, we particularly focus on understanding the role of contexts, as a link to the higher scale information that most evidently influences and determines the microscale response. As a result of context modeling, we find that machine learning systems with context awareness not only outperform previous best results, but also extend the parallelism of model training so as to maximize the computational efficiency.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Agrawal A, Choudhary A (2016) Perspective: materials informatics and big data: realization of the “fourth paradigm” of science in materials science. APL Mater 4(053208):1–10

Kalidindi S, Medford AJ, McDowell DL (2016) Vision for data and informatics in the future materials innovation ecosystem. JOM 68(8):2126–2137CrossRef

Panchal JH, Kalidindi S, McDowell DL (2013) Key computational modeling issues in integrated computational materials engineering. Comput-Aided Des 45(1):4–25CrossRef

Ward L, Agrawal A, Choudhary A, Wolverton C (2016) A general-purpose machine learning framework for predicting properties of inorganic materials. npj Comput Mater 2(16028)

Deshpande P, Gautham B, Cecen A, Kalidindi S, Agrawal A, Choudhary A (2013) Application of statistical and machine learning techniques for correlating properties to composition and manufacturing processes of steels. In: 2nd World Congress on Integrated Computational Materials Engineering (ICME), pp 155–160

Meredig B, Agrawal A, Kirklin S, Saal JE, Doak J, Thompson A, Zhang K, Choudhary A, Wolverton C (2014) Combinatorial screening for new materials in unconstrained composition space with machine learning. Phys Rev B 89(9):094104CrossRef

Liu R, Kumar A, Chen Z, Agrawal A, Sundararaghavan V, Choudhary A (2015) A predictive machine learning approach for microstructure optimization and materials design. Scient Rep 5

Liu R, Yabansu YC, Agrawal A, Kalidindi S, Choudhary A (2015) Machine learning approaches for elastic localization linkages in high-contrast composite materials. Integr Mater Manuf Innov 4(1):1–17CrossRef

Niezgoda SR, Yabansu YC, Kalidindi S (2011) Understanding and visualizing microstructure and microstructure variance as a stochastic process. Acta Mater 59(16):6387–6400CrossRef

10.

Niezgoda SR, Kanjarla AK, Kalidindi S (2013) Novel microstructure quantification framework for databasing, visualization, and analysis of microstructure data. Integr Mater Manuf Innov 2(1): 1–27CrossRef

11.

Gopalakrishnan K, Agrawal A, Ceylan H, Kim S, Choudhary A (2013) Knowledge discovery and data mining in pavement inverse analysis. Transport 28(1):1–10CrossRef

12.

Agrawal A, Deshpande PD, Cecen A, Basavarsu GP, Choudhary A, Kalidindi S (2014) Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters. Integr Mater Manuf Innov 3(1):1– 19CrossRef

13.

Yabansu YC, Patel DK, Kalidindi S (2014) Calibrated localization relationships for elastic response of polycrystalline aggregates. Acta Mater 81:151–160CrossRef

14.

Yabansu YC, Kalidindi S (2015) Representation and calibration of elastic localization kernels for a broad class of cubic polycrystals. Acta Mater 94:26–35CrossRef

15.

Agrawal A, Meredig B, Wolverton C, Choudhary A (2016) A formation energy predictor for crystalline materials using ensemble data mining. In: Proceedings of IEEE International Conference on Data Mining (ICDM), pp 1276–1279

16.

Agrawal A, Choudhary A (2016) A fatigue strength predictor for steels using ensemble data mining. In: Proceedings of 25th ACM International Conference on Information and Knowledge Management (CIKM), pp 2497–2500

17.

Gagorik AG, Savoie B, Jackson N, Agrawal A, Choudhary A, Ratner MA, Schatz GC, Kohlstedt KL (2017) Improved scaling of molecular network calculations: the emergence of molecular domains. J Phys Chem Lett 8(2):415–421CrossRef

18.

Fullwood DT, Niezgoda SR, Adams B, Kalidindi S (2010) Microstructure sensitive design for performance optimization. Progress Mater Sci 55(6):477–562CrossRef

19.

Liu R, Agrawal A, Chen Z, Liao W-K, Choudhary A (2015) Pruned search: a machine learning based meta-heuristic approach for constrained continuous optimization. In: Proceedings of 8th IEEE International Conference on Contemporary Computing (IC3), pp 13–18

20.

Suh C, Rajan K (2009) Invited review: data mining and informatics for crystal chemistry: establishing measurement techniques for mapping structure–property relationships. Mater Sci Technol 25(4):466–471CrossRef

21.

Rajan K (2005) Materials informatics. Mater Today 8(10):38– 45CrossRef

22.

Ward L, Liu R, Krishna A, Hegde V, Agrawal A, Choudhary A, Wolverton C (2016) Accurate models of formation enthalpy created using machine learning and voronoi tessellations. In: APS Meeting Abstracts

23.

Choudhury A, Yabansu YC, Kalidindi S, Dennstedt A (2016) Quantification and classification of microstructures in ternary eutectic alloys using 2-point spatial correlations and principal component analyses. Acta Mater 110:131–141CrossRef

24.

Furmanchuk A, Agrawal A, Choudhary A (2016) Predictive analytics for crystalline materials: Bulk modulus. RSC Adv 6(97):95246–95251CrossRef

25.

Steinmetz P, Yabansu YC, Hötzer J, Jainta M, Nestler B, Kalidindi S (2016) Analytics for microstructure datasets produced by phase-field simulations. Acta Mater 103:192–203CrossRef

26.

Liu R, Ward L, Wolverton C, Agrawal A, Liao W-K, Choudhary A (2016) Deep learning for chemical compound stability prediction. In: Proceedings of ACM SIGKDD Workshop on Large-scale Deep Learning for Data Mining (DL-KDD), pp 1–7

27.

Liu R, Agrawal A, Liao W-K, De Graef M, Choudhary A (2016) Materials discovery: understanding polycrystals from large-scale electron patterns. In: Proceedings of IEEE Big Data Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH), pp 2261–2269

28.

Yabansu YC, Steinmetz P, Hötzer J, Kalidindi S, Nestler B (2017) Extraction of reduced-order process-structure linkages from phase-field simulations. Acta Mater 124(1):182–194CrossRef

29.

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRef

30.

Gupta A, Cecen A, Goyal S, Singh AK, Kalidindi S (2015) Structure–property linkages using a data science approach: application to a non-metallic inclusion/steel composite system. Acta Mater 91:239–254CrossRef

31.

Nguyen S, Tran-Le A, Vu M, To Q, Douzane O, Langlet T (2016) Modeling thermal conductivity of hemp insulation material: a multi-scale homogenization approach. Build Environ 107:127–134CrossRef

32.

Zhou X-Y, Gosling P, Pearce C, Ullah Z (2016) Perturbation-based stochastic multi-scale computational homogenization method for the determination of the effective properties of composite materials with random properties. Comput Methods Appl Mech Eng 300:84–105CrossRef

33.

Cruzado A, Gan B, Jiménez M, Barba D, Ostolaza K, Linaza A, Molina-Aldareguia J, Llorca J, Segurado J (2015) Multiscale modeling of the mechanical behavior of in718 superalloy based on micropillar compression and computational homogenization. Acta Mater 98:242–253CrossRef

34.

Fast T, Kalidindi S (2011) Formulation and calibration of higher-order elastic localization relationships using the MKS approach. Acta Mater 59(11):4595–4605CrossRef

35.

Landi G, Niezgoda SR, Kalidindi S (2010) Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel dft-based knowledge systems. Acta Mater 58(7):2716–2725CrossRef

36.

Landi G, Kalidindi S (2010) Thermo-elastic localization relationships for multi-phase composites. Comput, Mater, Contin 16(3):273–293

37.

Guo N, Zhao J (2016) 3d multiscale modeling of strain localization in granular media. Computers and Geotechnics

38.

Seko A, Maekawa T, Tsuda K, Tanaka I (2014) Machine learning with systematic density-functional theory calculations: application to melting temperatures of single-and binary-component solids. Phys Rev B 89 (5):054303CrossRef

39.

Bhadeshia H, Dimitriu R, Forsik S, Pak J, Ryu J (2009) Performance of neural networks in materials science. Mater Sci Technol 25(4):504–510CrossRef

40.

Curtarolo S, Morgan D, Persson K, Rodgers J, Ceder G (2003) Predicting crystal structures with data mining of quantum calculations. Phys Rev Lett 91(13):135503CrossRef

41.

Fischer CC, Tibbetts KJ, Morgan D, Ceder G (2006) Predicting crystal structure by merging data mining with quantum mechanics. Nat Mater 5(8):641–646CrossRef

42.

Da Silva BC, Basso EW, Bazzan AL, Engel PM (2006) Dealing with non-stationary environments using context detection Proceedings of the 23rd International Conference on Machine Learning. ACM, pp 217–224

43.

Kalidindi S, Niezgoda SR, Landi G, Vachhani S, Fast T (2010) A novel framework for building materials knowledge systems. Comput, Mater, Contin 17(2):103–125

44.

Fast T, Niezgoda SR, Kalidindi S (2011) A new framework for computationally efficient structure–structure evolution linkages to facilitate high-fidelity scale bridging in multi-scale materials models. Acta Mater 59(2):699–707CrossRef

45.

Kröner E (1986) Statistical modelling. Springer, Netherlands, pp 229–291

46.

Kröner E (1977) Bounds for effective elastic moduli of disordered materials. J Mech Phys Solids 25(2):137–155CrossRef

47.

Fullwood DT, Niezgoda SR, Kalidindi S (2008) Microstructure reconstructions from 2-point statistics using phase-recovery algorithms. Acta Mater 56(5):942–948CrossRef

48.

Hibbitt Karlsson Sorensen (2001) ABAQUS/standard User’s Manual, vol 1. Hibbitt, Karlsson & Sorensen, Providence, RI

49.

Kalidindi S, Landi G, Fullwood DT (2008) Spectral representation of higher-order localization relationships for elastic behavior of polycrystalline cubic materials. Acta Mater 56(15):3843–3853CrossRef

50.

Al-Harbi HF, Landi G, Kalidindi S (2012) Multi-scale modeling of the elastic response of a structural component made from a composite material using the materials knowledge system. Modell Simul Mater Sci Eng 20 (5):055001CrossRef

51.

Panait L, Luke S (2005) Cooperative multi-agent learning: the state of the art. Auton Agent Multi-Agent Syst 11(3):387–434CrossRef

52.

scikit-learn: Machine Learning in Python. http://scikit-learn.github.io/. [Online; accessed August 2015]

53.

Garmestani H, Lin S, Adams B, Ahzi S (2001) Statistical continuum theory for large plastic deformation of polycrystalline materials. J Mech Phys Solids 49(3):589–607CrossRef

54.

Saheli G, Garmestani H, Adams B (2004) Microstructure design of a two phase composite using two-point correlation functions. J Comput-aided Mater Des 11(2-3):103–115CrossRef

55.

Fullwood DT, Adams B, Kalidindi S (2008) A strong contrast homogenization formulation for multi-phase anisotropic materials. J Mech Phys Solids 56(6):2287–2297CrossRef

56.

Adams B, Canova GR, Molinari A (1989) A statistical formulation of viscoplastic behavior in heterogeneous polycrystals. Textures Microstruct 11:57–71CrossRef

57.

MacQueen J (1967) Some methods for classification and analysis of multivariate observations Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol 1, CA, USA, pp 281–297

58.

Torquato S (2002) Statistical description of microstructures. Ann Rev Mater Res 32(1):77–111CrossRef

59.

Torquato S (2002) Random heterogeneous materials: microstructure and macroscopic properties, vol 16. Springer, New YorkCrossRef

60.

Liu Y, Greene MS, Chen W, Dikin DA, Liu WK (2013) Computational microstructure characterization and reconstruction for stochastic multiscale material design. Comput-Aided Des 45(1):65–76CrossRef

61.

Øren P-E, Bakke S (2002) Process based reconstruction of sandstones and prediction of transport properties. Transp Porous Media 46(2-3):311–343CrossRef

Titel: Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures
verfasst von: Ruoqian Liu
Yuksel C. Yabansu
Zijiang Yang
Alok N. Choudhary
Surya R. Kalidindi
Ankit Agrawal
Publikationsdatum: 31.03.2017
Verlag: Springer International Publishing
Erschienen in: Integrating Materials and Manufacturing Innovation
Print ISSN: 2193-9764
Elektronische ISSN: 2193-9772
DOI: https://doi.org/10.1007/s40192-017-0094-3

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht