RESUMEN
A neural network (NN)-based method is presented in this paper which allows the identification of parameters for material cards used in Finite Element simulations. Contrary to the conventionally used computationally intensive material parameter identification (MPI) by numerical optimization with internal or commercial software, a machine learning (ML)-based method is time saving when used repeatedly. Within this article, a self-developed ML-based Python framework is presented, which offers advantages, especially in the development of structural components in early development phases. In this procedure, different machine learning methods are used and adapted to the specific MPI problem considered herein. Using the developed NN-based and the common optimization-based method with LS-OPT, the material parameters of the LS-DYNA material card MAT_187_SAMP-1 and the failure model GISSMO were exemplarily calibrated for a virtually generated test dataset. Parameters for the description of elasticity, plasticity, tension-compression asymmetry, variable plastic Poisson's ratio (VPPR), strain rate dependency and failure were taken into account. The focus of this paper is on performing a comparative study of the two different MPI methods with varying settings (algorithms, hyperparameters, etc.). Furthermore, the applicability of the NN-based procedure for the specific usage of both material cards was investigated. The studies reveal the general applicability for the calibration of a complex material card by the example of the used MAT_187_SAMP-1.
RESUMEN
To be able to use finite element (FE) simulations in structural component development, experimental investigations for the characterization of the material properties are required to subsequently calibrate suitable material cards. In contrast to the commonly used computational and time-consuming method of parameter identification (PI) by using analytical and numerical optimizations with internal or commercial software, a more time-efficient method based on machine learning (ML) is presented. This method is applied to simulate the material behavior of additively manufactured specimens made of acrylonitrile butadiene styrene (ABS) under uniaxial stress in a structural simulation. By using feedforward artificial neural networks (FFANN) for the ML-based direct inverse PI process, various investigations were carried out on the influence of sampling strategies, data quantity and data preparation on the prediction accuracy of the NN. Furthermore, the results of hyperparameter (HP) search methods are presented and discussed and their influence on the prediction quality of the FFANN are critically evaluated. The investigations show that the NN-based method is applicable to the present use case and results in material parameters that lead to a lower error between experimental and calculated force-displacement curves than the commonly used optimization-based method.
RESUMEN
The electric field response of the lead-free solid solution (1-x)Bi0.53Na0.47TiO3-xBaTiO3 (BNT-BT) in the higher BT composition range with x = 0.12 was investigated using in situ synchrotron X-ray powder diffraction. An introduced Bi-excess non-stoichiometry caused an extended morphotropic phase boundary, leading to an unexpected fully reversible relaxor to ferroelectric (R-FE) phase transformation behavior. By varying the field frequency in a broad range from 10-4 up to 102 Hz, BNT-12BT showed a frequency-dependent gradual suppression of the field induced ferroelectric phase transformation in favor of the relaxor state. A frequency triggered self-heating within the sample was found and the temperature increase exponentially correlated with the field frequency. The effects of a lowered phase transformation temperature TR-FE, caused by the non-stoichiometric composition, were observed in the experimental setup of the freestanding sample. This frequency-dependent investigation of an R-FE phase transformation is unlike previous macroscopic studies, in which heat dissipating metal contacts are used.
RESUMEN
All hitherto identified aromatic compounds accumulating in leaves of Arabidopsis thaliana (L.) Heynh. upon infection with virulent or avirulent strains of Pseudomonas syringae pathovar tomato ( Pst) were indolic metabolites. We now report the strong accumulation of a novel type of natural product, 3'-O-beta-D-ribofuranosyl adenosine (3'RA), exclusively during compatible interactions. In contrast to the various indolic metabolites, 3'RA was undetectable in incompatible interactions of A. thaliana leaves with an avirulent Pst strain, as well as in uninfected control leaves. A similar, strong induction of 3'RA was observed in compatible but, again, not in incompatible interactions of Pst with its natural host, Lycopersicon esculentum. The strength of the effect and its confinement to compatible interactions suggests that it may be applicable as a diagnostic tool.