Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Computational Mechanics
Erschienen in: Computational Mechanics 1/2019

21.11.2018 | Original Paper

Datadriven HOPGD based computational vademecum for welding parameter identification

verfasst von: Y. Lu, N. Blal, A. Gravouil

Erschienen in: Computational Mechanics | Ausgabe 1/2019

Einloggen

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

search-config
loading …

Abstract

The paper presents a datadriven framework for parameter identification of welding models. Common identification procedures are based on iterative optimization algorithms which minimize the distance between experimental measures and simulations. The cost of repetitive evaluations of objective functions is prohibitive, especially in welding cases, due to the multiphysics, nonlinear and multiparametric aspects. This is why one proposes to use a novel datadriven approach to improve the efficiency of the inverse identification procedures. Based on a sparse sampling strategy, an a posteriori non-intrusive reduction method, i.e. HOPGD, is used in the offline training stage for constructing the computational vademecum. An online subspace learning method coupled with a global optimization algorithm is proposed for the online search. The efficiency of the proposed method for multiple parameters identification is demonstrated through examples based on a 3D welding model.
Vorheriger Artikel Data science for finite strain mechanical science of ductile materials
Nächster Artikel A multiscale FE-FFT framework for electro-active materials at finite strains

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
Literatur
1.
Ammar A, Mokdad B, Chinesta F, Keunings R (2006) A new family of solvers for some classes of multidimensional partial differential equations encountered in kinetic theory modeling of complex fluids. J Non-Newton Fluid Mech 139(3):153–176CrossRefMATH
2.
Amsallem D, Farhat C (2008) Interpolation method for adapting reduced-order models and application to aeroelasticity. AIAA J 46(7):1803–1813CrossRef
3.
Babkin A, Gladkov E (2016) Identification of welding parameters for quality welds in gmaw. Weld J 95(1):37S–46S
4.
Barrault M, Maday Y, Nguyen NC, Patera AT (2004) An ‘empirical interpolation’method: application to efficient reduced-basis discretization of partial differential equations. C R Math 339(9):667–672MathSciNetCrossRefMATH
5.
Bessa M, Bostanabad R, Liu Z, Hu A, Apley DW, Brinson C, Chen W, Liu WK (2017) A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality. Comput Methods Appl Mech Eng 320:633–667MathSciNetCrossRef
6.
7.
Bull AD (2011) Convergence rates of efficient global optimization algorithms. J Mach Learn Res 12(Oct):2879–2904MathSciNetMATH
8.
Canales D, Leygue A, Chinesta F, González D, Cueto E, Feulvarch E, Bergheau JM, Huerta A (2016) Vademecum-based gfem (v-gfem): optimal enrichment for transient problems. Int J Numer Methods Eng 108(9):971–989MathSciNetCrossRef
9.
Chaturantabut S, Sorensen DC (2010) Nonlinear model reduction via discrete empirical interpolation. SIAM J Sci Comput 32(5):2737–2764MathSciNetCrossRefMATH
10.
Chinesta F, Ammar A, Cueto E (2010) Recent advances and new challenges in the use of the proper generalized decomposition for solving multidimensional models. Arch Comput Methods Eng 17(4):327–350MathSciNetCrossRefMATH
11.
Chinesta F, Leygue A, Bordeu F, Aguado J, Cueto E, González D, Alfaro I, Ammar A, Huerta A (2013) Pgd-based computational vademecum for efficient design, optimization and control. Arch Comput Methods Eng 20(1):31–59MathSciNetCrossRefMATH
12.
Cognard JY, Ladevèze P (1993) A large time increment approach for cyclic viscoplasticity. Int J Plast 9(2):141–157CrossRefMATH
13.
Courard A, Néron D, Ladeveze P, Andolfatto P, Bergerot A (2013) Virtual charts for shape optimization of structures. In: 2nd ECCOMAS Young investigators conference (YIC 2013)
14.
Ghnatios C, Masson F, Huerta A, Leygue A, Cueto E, Chinesta F (2012) Proper generalized decomposition based dynamic data-driven control of thermal processes. Comput Methods Appl Mech Eng 213:29–41CrossRef
15.
Goldak J, Chakravarti A, Bibby M (1984) A new finite element model for welding heat sources. Metall Trans B 15(2):299–305CrossRef
16.
González D, Alfaro I, Quesada C, Cueto E, Chinesta F (2015) Computational vademecums for the real-time simulation of haptic collision between nonlinear solids. Comput Methods Appl Mech Eng 283:210–223CrossRefMATH
17.
González D, Masson F, Poulhaon F, Leygue A, Cueto E, Chinesta F (2012) Proper generalized decomposition based dynamic data driven inverse identification. Math Comput Simul 82(9):1677–1695MathSciNetCrossRef
18.
Holmes P, Lumley JL, Berkooz G (1998) Turbulence, coherent structures, dynamical systems and symmetry. Cambridge University Press, CambridgeMATH
19.
Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(6):417CrossRefMATH
20.
Ibañez R, Abisset-Chavanne E, Aguado JV, Gonzalez D, Cueto E, Chinesta F (2018) A manifold learning approach to data-driven computational elasticity and inelasticity. Arch Comput Methods Eng 25(1):47–57MathSciNetCrossRefMATH
21.
Ibañez R, Borzacchiello D, Aguado JV, Abisset-Chavanne E, Cueto E, Ladeveze P, Chinesta F (2017) Data-driven non-linear elasticity: constitutive manifold construction and problem discretization. Comput Mech 60(5):813–826MathSciNetCrossRefMATH
22.
Kerfriden P, Gosselet P, Adhikari S, Bordas SPA (2011) Bridging proper orthogonal decomposition methods and augmented newton-krylov algorithms: an adaptive model order reduction for highly nonlinear mechanical problems. Comput Methods Appl Mech Eng 200(5):850–866MathSciNetCrossRefMATH
23.
24.
Kirchdoerfer T, Ortiz M (2018) Data-driven computing in dynamics. Int J Numer Methods Eng 113(11):1697–1710MathSciNetCrossRef
25.
26.
Kolda TG, Lewis RM, Torczon V (2003) Optimization by direct search: new perspectives on some classical and modern methods. SIAM Rev 45(3):385–482MathSciNetCrossRefMATH
27.
Leygue A, Coret M, Réthoré J, Stainier L, Verron E (2018) Data-based derivation of material response. Comput Methods Appl Mech Eng 331:184–196MathSciNetCrossRef
28.
Lu Y, Blal N, Gravouil A (2018) Adaptive sparse grid based hopgd: toward a nonintrusive strategy for constructing space-time welding computational vademecum. Int J Numer Methods Eng 114:1438–1461MathSciNetCrossRef
29.
Lu Y, Blal N, Gravouil A (2018) Multi-parametric space-time computational vademecum for parametric studies: application to real time welding simulations. Finite Elements Anal Des 139:62–72MathSciNetCrossRef
30.
Lu Y, Blal N, Gravouil A (2018) Space-time pod based computational vademecums for parametric studies: application to thermo-mechanical problems. Adv Model Simul Eng Sci 5(1):3CrossRef
31.
Maday Y, Patera AT, Turinici G (2002) A priori convergence theory for reduced-basis approximations of single-parameter elliptic partial differential equations. J Sci Comput 17(1–4):437–446MathSciNetCrossRefMATH
32.
Martí R, Lozano JA, Mendiburu A, Hernando L (2016) Multi-start methods. In: Handbook of Heuristics. Springer, pp. 1–21
33.
Meng L, Breitkopf P, Raghavan B, Mauvoisin G, Bartier O, Hernot X (2015) Identification of material properties using indentation test and shape manifold learning approach. Comput Methods Appl Mech Eng 297:239–257MathSciNetCrossRefMATH
34.
Meng L, Raghavan B, Bartier O, Hernot X, Mauvoisin G, Breitkopf P (2017) An objective meta-modeling approach for indentation-based material characterization. Mech Mater 107:31–44CrossRefMATH
35.
Modesto D, Zlotnik S, Huerta A (2015) Proper generalized decomposition for parameterized helmholtz problems in heterogeneous and unbounded domains: application to harbor agitation. Comput Methods Appl Mech Eng 295:127–149MathSciNetCrossRefMATH
36.
Mosavi A, Rabczuk T, Varkonyi-Koczy AR (2017) Reviewing the novel machine learning tools for materials design. In: International conference on global research and education. Springer, pp 50–58
37.
Muránsky O, Smith M, Bendeich P, Holden T, Luzin V, Martins R, Edwards L (2012) Comprehensive numerical analysis of a three-pass bead-in-slot weld and its critical validation using neutron and synchrotron diffraction residual stress measurements. Int J Solids Struct 49(9):1045–1062CrossRef
38.
Niroomandi S, Alfaro I, Cueto E, Chinesta F (2010) Model order reduction for hyperelastic materials. Int J Numer Methods Eng 81(9):1180–1206MathSciNetMATH
39.
Quesada C, González D, Alfaro I, Cueto E, Chinesta F (2016) Computational vademecums for real-time simulation of surgical cutting in haptic environments. Int J Numer Methods Eng 108(10):1230–1247MathSciNetCrossRefMATH
40.
Rajan K (2015) Materials informatics: the materials “gene” and big data. Annu Rev Mater Res 45:153–169CrossRef
41.
Rozza G, Huynh DBP, Patera AT (2008) Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Arch Comput Methods Eng 15(3):229–275MathSciNetCrossRefMATH
42.
43.
Song J, Shanghvi J, Michaleris P (2004) Sensitivity analysis and optimization of thermo-elasto-plastic processes with applications to welding side heater design. Comput Methods Appl Mech Eng 193(42–44):4541–4566CrossRefMATH
44.
Vitse M, Néron D, Boucard PA (2014) Virtual charts of solutions for parametrized nonlinear equations. Comput Mech 54(6):1529–1539MathSciNetCrossRefMATH
45.
Zhang Y, Combescure A, Gravouil A (2015) Efficient hyper reduced-order model (hrom) for parametric studies of the 3d thermo-elasto-plastic calculation. Finite Elements Anal Des 102:37–51MathSciNetCrossRef
46.
Zhang Y, Combescure A, Gravouil A (2017) Efficient hyper-reduced-order model (hrom) for thermal analysis in the moving frame. Int J Numer Methods Eng 111(2):176–200MathSciNetCrossRef
Metadaten
Titel
Datadriven HOPGD based computational vademecum for welding parameter identification
verfasst von
Y. Lu
N. Blal
A. Gravouil
Publikationsdatum
21.11.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Computational Mechanics / Ausgabe 1/2019
Print ISSN: 0178-7675
Elektronische ISSN: 1432-0924
DOI
https://doi.org/10.1007/s00466-018-1656-8

Weitere Artikel der Ausgabe 1/2019

Computational Mechanics 1/2019 Zur Ausgabe

Correction

Correction to: A 3D time domain numerical model based on half-space Green’s function for soil–structure interaction analysis

Original Paper

Superposition-based coupling of peridynamics and finite element method

Original Paper

High-order mortar-based contact element using NURBS for the mapping of contact curved surfaces

Original Paper

A multiscale FE-FFT framework for electro-active materials at finite strains

Original Paper

Variable-order fractional description of compression deformation of amorphous glassy polymers

Original Paper

Isogeometric analysis for numerical plate testing of dry woven fabrics involving frictional contact at meso-scale

Neuer Inhalt

    Bildnachweise