Characterization and assessment of composite materials via inverse finite element modeling

Authors

  • Carlos Llopis-Albert Universitat Politècnica de València https://orcid.org/0000-0002-1349-2716
  • Francisco Rubio Universitat Politècnica de València
  • Francisco Valero Universitat Politècnica de València

DOI:

https://doi.org/10.4995/muse.2019.12374

Keywords:

Inverse modeling, finite element, mechanical properties, heterogeneity characterization, biomedical, uncertainty assessment

Abstract

Characterizing mechanical properties play a major role in several fields such as biomedical and manufacturing sectors. In this study, a stochastic inverse model is combined with a finite element (FE) approach to infer full-field mechanical properties from scarce experimental data. This is achieved by means of non-linear combinations of material property realizations, with a certain spatial structure, for constraining stochastic simulations to data within a non-multiGaussian framework. This approach can be applied to the design of highly heterogenous materials, the uncertainty assessment of unknown mechanical properties or to provide accurate medical diagnosis of hard and soft tissues. The developed methodology has been successfully applied to a complex case study.

Downloads

Download data is not yet available.

Author Biographies

Carlos Llopis-Albert, Universitat Politècnica de València

Centro de Investigación en Ingeniería Mecánica (CIIM)

Francisco Rubio, Universitat Politècnica de València

Centro de Investigación en Ingeniería Mecánica (CIIM)

Francisco Valero, Universitat Politècnica de València

Centro de Investigación en Ingeniería Mecánica (CIIM)

References

Charmpis, D. C., G. I. Schueller, M. F. Pellissetti (2007). The need for linking micromechanics of materials with stochastic finite elements: A challenge for materials science. Computational Materials Science 41(1), 27-37. https://doi.org/10.1016/j.commatsci.2007.02.014

Gómez-Hernández, J.J., R.M. Srivastava, (1990). ISIM3D: an ANSI-C three-dimensional multiple indicator conditional simulation program. Computer Geoscience 16(4), 395-440. https://doi.org/10.1016/0098-3004(90)90010-Q

Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press, NY, NY, 483 pp.

Ni, Y., M. Y.M. Chiang (2007). Prediction of elastic properties of heterogeneous materials with complex microstructures. Journal of the Mechanics and Physics of Solids 55, 517-532. https://doi.org/10.1016/j.jmps.2006.09.001

Li, G., F. Xu, G. Sun, Q. Li (2014). Identification of mechanical properties of the weld line by combining 3D digital image correlation with inverse modelling procedure. International Journal of Advanced Manufacturing Technology 74, 893-905. https://doi.org/10.1007/s00170-014-6034-x

Llopis-Albert, C., Capilla, J.E. (2009). Stochastic inverse modeling of non multiGaussian transmissivity fields conditional to flow, mass transport and secondary data. 2 Demonstration on a synthetic aquifer. Journal of Hydrology, 371, 53-65. https://doi.org/10.1016/j.jhydrol.2009.03.014

Llopis-Albert, C., Capilla, J.E. (2010). Stochastic simulation of non-Gaussian 3D conductivity fields in a fractured medium with multiple statistical populations: case study. Journal of Hydrologic Engineering, 15, 554-566. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000214

Llopis-Albert, C., Capilla, J.E. (2010a). Stochastic inverse modelling of hydraulic conductivity fields taking into account independent stochastic structures: A 3D case study. Journal of Hydrology 391, 277-288. https://doi.org/10.1016/j.jhydrol.2010.07.028

Llopis-Albert, C., Palacios-Marqués, D., Merigó, J.M. (2014). A coupled stochastic inverse-management framework for dealing with nonpoint agriculture pollution under groundwater parameter uncertainty. Journal of Hydrology 511, 10-16. https://doi.org/10.1016/j.jhydrol.2014.01.021

Llopis-Albert, C., Rubio, F., Valero, F. (2015). Improving productivity using a multi-objective optimization of robotic trajectory planning. Journal of Business Research 68, 1429-1431. https://doi.org/10.1016/j.jbusres.2015.01.027

Llopis-Albert, C., Pulido-Velazquez, D. (2015). Using MODFLOW code to approach transient hydraulic head with a sharp-interface solution. Hydrological processes 29(8), 2052-2064. https://doi.org/10.1002/hyp.10354

Llopis-Albert, C., Merigó, J.M., Xu, Y.J. (2016). A coupled stochastic inverse/sharp interface seawater intrusion approach for coastal aquifers under groundwater parameter uncertainty. Journal of Hydrology 540, 774-783. https://doi.org/10.1016/j.jhydrol.2016.06.065

Llopis-Albert, C. Rubio, F., Valero, F. (2018). Optimization approaches for robot trajectory planning. Multidisciplinary Journal for Education, Social and Technological Sciences 5(1), 1-16. https://doi.org/10.4995/muse.2018.9867

Llopis-Albert, C. Rubio, F., Valero, F. (2018a). Designing Efficient Material Handling Systems Via Automated Guided Vehicles (AGVs). Multidisciplinary Journal for Education, Social and Technological Sciences, 5(2), 97-105.https://doi.org/10.4995/muse.2018.10722

Mortazavi, F., E. Ghossein, M. Lévesque, I. Villemure (2014). High resolution measurement of internal full-field displacements and strains using global spectral digital volume correlation. Optics and Lasers in Engineering 55, 44-52. https://doi.org/10.1016/j.optlaseng.2013.10.007

Rubio, F., Llopis-Albert, C., Valero, F., Suñer, J.L. (2015). Assembly line productivity assessment by comparing optimization-simulation algorithms of trajectory planning for industrial robots. Mathematical Problems in Engineering, vol. 2015, Article ID 931048, 10 pages, 2015. https://doi.org/10.1155/2015/931048

Rubio, F., Llopis-Albert, C., Valero, F., Suñer, J.L. (2016). Industrial robot efficient trajectory generation without collision through the evolution of the optimal trajectory. Robotics and Autonomous Systems 86, 106-112. https://doi.org/10.1016/j.robot.2016.09.008

Rubio, F., Valero, F., Llopis-Albert, C. (2019). A review of mobile robots: Concepts, methods, theoretical framework, and applications. International Journal of Advanced Robotic Systems, 16(2). https://doi.org/10.1177/1729881419839596

Sriramula, S., M. K. Chryssanthopoulos (2009). Quantification of uncertainty modelling in stochastic analysis of FRP composites. Composites Part A: Applied Science and Manufacturing 40(11), 1673-1684. https://doi.org/10.1016/j.compositesa.2009.08.020

Wu, X., Y. Zhu (2017). Heterogeneous materials: a new class of materials with unprecedented mechanical properties. Materials Research Letters 5:8, 527-532. https://doi.org/10.1080/21663831.2017.1343208

Zhang, Z., C. Zhan, K. Shankar, E.V. Morozov, H. Kumar, T. Ray (2017). Sensitivity analysis of inverse algorithms for damage detection in composites. Composite Structures 176, 844-859. https://doi.org/10.1016/j.compstruct.2017.06.019

Zottis, J., Theis, C.A., da Silva, A (2018). Evaluation of experimentally observed asymmetric distributions of hardness, strain and residual stress in cold drawn bars by FEM-simulation. Journal of Materials Research and Technology. https://doi.org/10.1016/j.jmrt.2018.01.004

Downloads

Published

2019-10-03

How to Cite

Llopis-Albert, C., Rubio, F., & Valero, F. (2019). Characterization and assessment of composite materials via inverse finite element modeling. Multidisciplinary Journal for Education, Social and Technological Sciences, 6(2), 1–10. https://doi.org/10.4995/muse.2019.12374

Issue

Section

Articles