Parameter identification in elastoplastic material models by Small Punch Tests and inverse analysis with model reduction

Buljak, Vladimir; Cocchetti, Giuseppe; Cornaggia, Aram; Maier, G.

doi:10.1007/s11012-018-0914-3

Cited by 6 publications

(6 citation statements)

References 34 publications

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“…• According to the previous point, in order to ensure a robust and reliable parameter identification procedure, specific requirements are expected in the measuring stages, namely 10.672 +3.73% −4.45% +0.28% f 13 10.888 +4.45% −5.72% +0.23% f 14 12.139 +1.38% −4.10% +0.06% f 15 12.160 +4.05% −1.55% +0.01% f 16 12.490 +2.43% −1.46% +0.09% significantly reducing noise effects and providing complete structural observations, possibly complemented by local detailing measurements and estimations.…”

Section: Optimised Structural Modelling On the Case Study Of A His-to...mentioning

confidence: 99%

“…The current state of the art, in the context of aforementioned Inverse Analysis approaches, presents several contributions, particularly within Industrial Engineering applications, e.g., for material characterisation and diagnosis of metallic structural components [9,10,11,12,13], for mechanical characterisation of advanced materials [14,15], and for biomechanical identification [16]. With shared methodologies, in recent years, a growing interest has been exerted for Inverse Analysis also in Civil Engineering context, as, e.g., in material characterisation of concrete structures [17,18], in bridge and infrastructure analysis [19,20,21], and in structural identification [22,23,24].…”

Section: Introductionmentioning

confidence: 99%

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Optimised Structural Modelling for Inverse Analysis Parameter Identification Relying on Dynamic Measurements

Cornaggia,

Garbowski,

Cocchetti

et al. 2023

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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In the context of Inverse Analysis in Civil Engineering, parameter identification and model calibration, of a structural system, relying on dynamic measurements, are subjects of a growing research interest. In the present contribution, the topic is tackled with reference both to simplified structural numerical examples and to a specific case study, namely a historical road three-span reinforced concrete arched bridge, with vibrational data previously acquired by standard wired accelerometers on the deck, under operational traffic conditions. In particular, the present work aims at focussing on the identification issues, concerning the definition of a maximum allowable threshold number of sought material parameters (e.g., Young's moduli and mass densities of different structural components), with respect to the amount of available measurement data, and the investigation of the inverse analysis discrepancy function to be optimised, in order to set the intrinsic issue of multiple "realizations", in case of a plain use of modal properties, and in view of forming a well-posed optimisation problem. Structural modelling, sensitivity analysis and numerical optimisation approaches are herein combined toward a robust and efficient identification strategy, to be effectively employed in structural assessment and diagnosis, also with respect to originally available or enriched sets of experimental data. The proposed methodology, and collected results, shall outline an efficient identification procedure, in view of automated inverse analysis, practically oriented to the dynamic assessment and structural diagnosis in the Civil Engineering context, as applied e.g. to strategic bridge infrastructures. 4234COMPDYN 2023 9 th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering M. Papadrakakis, M. Fragiadakis (eds.

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Section: Optimised Structural Modelling On the Case Study Of A His-to...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimised Structural Modelling for Inverse Analysis Parameter Identification Relying on Dynamic Measurements

Cornaggia,

Garbowski,

Cocchetti

et al. 2023

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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“…w = (∆φ w /2π)•∆s w (12) where subscripts u, v and w refer to the different displacement components and ∆φ/2π is the fractional fringe order for the corresponding unwrapped phase distribution ∆φ.…”

Section: One-shot Projection Moiré Measurements and Impm Variantmentioning

confidence: 99%

“…The formulation of error functional Ω depends on the type of identification problem; for example, in indentation/nanoindentation problems, reaction forces at the indenter/body interface also may be considered, while in dynamic problems, natural frequencies and modal shapes can be utilized as target quantities; strains and stresses also may be involved in the definition of Ω. Maier and his collaborators [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] carried out pioneering work on mechanical identification of materials and structures, considering a variety of cases such as, for example, elasto-plasticity (also accounting for material anisotropy, inhomogeneity and functional grading), indentation, residual stresses, multiaxial fatigue, damage and fracture, thin foil mechanics, etc.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Characterization of Soft Membranes with One-Shot Projection Moiré and Metaheuristic Optimization

et al. 2023

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Mechanical characterization of soft materials is a complicated inverse problem that includes nonlinear constitutive behavior and large deformations. A further complication is introduced by the structural inhomogeneity of tested specimens (for example, caused by thickness variations). Optical methods are very useful in mechanical characterization of soft matter, as they provide accurate full-field information on displacements, strains and stresses regardless of the magnitude and/or gradients of those quantities. In view of this, the present study describes a novel hybrid framework for mechanical characterization of soft membranes, combining (i) inflation tests and preliminary in-plane equi-biaxial tests, (ii) a one-shot projection moiré optical setup with two symmetric projectors that project cross-gratings onto the inflated membrane, (iii) a mathematical model to extract 3D displacement information from moiré measurements, and (iv) metaheuristic optimization hybridizing harmony search and JAYA algorithms. The use of cross-gratings allows us to determine the surface curvature and precisely reconstruct the shape of the deformed object. Enriching metaheuristic optimization with gradient information and elitist strategies significantly reduces the computational cost of the identification process. The feasibility of the proposed approach wassuccessfully tested on a 100 mm diameter natural rubber membrane that had some degree of anisotropy in mechanical response because of its inhomogeneous thickness distribution. Remarkably, up to 324 hyperelastic constants and thickness parameters can be precisely identified by the proposed framework, reducing computational effort from 15% to 70% with respect to other inverse methods.

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“…The indirect method is an inverse analysis method [ 19 , 28 , 29 ] based on finite element (FE) simulation, where the appropriate TSL is determined by comparing the numerical solution with the experimental measurements. Maier [ 30 , 31 ], using an inverse analysis strategy, estimated residual stress and identified the elastic–plastic material parameters. However, this method requires continuous trial and error, is computationally inefficient, and is dependent on the initial parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning and Finite Element Analysis to Extract Traction-Separation Relations at Bonding Wire Interfaces of Insulated Gate Bipolar Transistor Modules

Zhao,

An,

Wang

et al. 2024

Materials

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For insulated gate bipolar transistor (IGBT) modules using wire bonding as the interconnection method, the main failure mechanism is cracking of the bonded interface. Studying the mechanical properties of the bonded interface is crucial for assessing the reliability of IGBT modules. In this paper, first, shear tests are conducted on the bonded interface to test the bonded interface’s strength. Then, finite element–cohesive zone modeling (FE-CZM) is established to describe the mechanical behavior of the bonded interface. A novel machine learning (ML) architecture integrating a convolutional neural network (CNN) and a long short-term memory (LSTM) network is used to identify the shape and parameters of the traction separation law (TSL) of the FE-CZM model accurately and efficiently. The CNN-LSTM architecture not only has excellent feature extraction and sequence-data-processing abilities but can also effectively address the long-term dependency problem. A total of 1800 sets of datasets are obtained based on numerical computations, and the CNN-LSTM architecture is trained with load–displacement (F–δ) curves as input parameters and TSL shapes and parameters as output parameters. The results show that the error rate of the model for TSL shape prediction is only 0.186%. The performance metric’s mean absolute percentage error (MAPE) is less than 3.5044% for all the predictions of the TSL parameters. Compared with separate CNN and LSTM architectures, the proposed CNN-LSTM-architecture approach exhibits obvious advantages in recognizing TSL shapes and parameters. A combination of the FE-CZM and ML methods in this paper provides a promising and effective solution for identifying the mechanical parameters of the bonded interfaces of IGBT modules.

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Parameter identification in elastoplastic material models by Small Punch Tests and inverse analysis with model reduction

Cited by 6 publications

References 34 publications

Optimised Structural Modelling for Inverse Analysis Parameter Identification Relying on Dynamic Measurements

Optimised Structural Modelling for Inverse Analysis Parameter Identification Relying on Dynamic Measurements

Mechanical Characterization of Soft Membranes with One-Shot Projection Moiré and Metaheuristic Optimization

Using Machine Learning and Finite Element Analysis to Extract Traction-Separation Relations at Bonding Wire Interfaces of Insulated Gate Bipolar Transistor Modules

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