Plastic properties determination using virtual dynamic spherical indentation test and machine learning algorithms

Kashfi, Mohammad; Goodarzi, Sepehr; Rastgou, Mostafa

doi:10.1007/s12206-021-1230-8

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…The linear elastic material model was utilized for both the indenter and specimen based on their actual behavior. The mechanical properties of steel were assigned to the indenter model, with Young’s modulus of 200 GPa and Poisson’s ratio of 0.3 25 . The nodal displacements along the Y direction were fixed to define the ground plane, while displacements on the Z symmetry plane were fixed in the Z direction.…”

Section: Finite Element Analysismentioning

confidence: 99%

Experimental characterization and finite element investigation of SiO2 nanoparticles reinforced dental resin composite

Jaleh,

Kashfi,

Feizi Mohazzab

et al. 2024

Sci Rep

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In this study, a commercial dental resin was reinforced by SiO2 nanoparticles (NPs) with different concentrations to enhance its mechanical functionality. The material characterization and finite element analysis (FEA) have been performed to evaluate the mechanical properties. Wedge indentation and 3-point bending tests were conducted to assess the mechanical behavior of the prepared nanocomposites. The results revealed that the optimal content of NPs was achieved at 1% SiO2, resulting in a 35% increase in the indentation reaction force. Therefore, the sample containing 1% SiO2 NPs was considered for further tests. The morphology of selected sample was examined using field emission scanning electron microscopy (FE-SEM), revealing the homogeneous dispersion of SiO2 NPs with minimal agglomeration. X-ray diffraction (XRD) was employed to investigate the crystalline structure of the selected sample, indicating no change in the dental resin state upon adding SiO2 NPs. In the second part of the study, a novel approach called iterative FEA, supported by the experiment wedge indentation test, was used to determine the mechanical properties of the 1% SiO2-dental resin. Subsequently, the accurately determined material properties were assigned to a dental crown model to virtually investigate its behavior under oblique loading. The virtual test results demonstrated that most microcracks initiated from the top of the crown and extended through its thickness.

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Section: Finite Element Analysismentioning

confidence: 99%

Experimental characterization and finite element investigation of SiO2 nanoparticles reinforced dental resin composite

Jaleh,

Kashfi,

Feizi Mohazzab

et al. 2024

Sci Rep

Self Cite

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“…They used a dynamic indentation test system at micron level by considering loading rate on the load-displacement diagram. Kashfi et al 22 obtained mechanical properties of a thick aluminum plate using numerical dynamic spherical indentation test using finite element method and machine learning algorithms. In the current study, a new approach based on dynamic indentation test and artificial neural network is proposed to obtain the stress-strain curve of material directly from the load-depth curve measured from the indentation test.…”

Section: Introductionmentioning

confidence: 99%

Determination of the parameters of material models using dynamic indentation test and artificial neural network

Pourolajal

Majzoobi

2022

The Journal of Strain Analysis for Engineering Design

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Stress-strain curves of materials normally change with strain rate and temperature and are normally defined by material models. In this study, a new technique was developed for determining the constants of material models. This technique was based on dynamic indentation test, numerical simulation using Ls-dyna code and artificial neural network. An indenter of tapered shape was shot against the materials as the target by a gas gun. The experiments were carried out for four strain rates and four temperatures. The target was made of pure copper. The penetration depth-time and load-time histories were captured by a LVDT and a piezoelectric load-cell, respectively and the load-penetration depth curve (P-h) was obtained. This curve is characterized by five parameters which are determined for each indentation test. On the other hand, the indentation test was simulated using Ls-dyna hydrocode. From the simulations, the P-h curves were obtained using Johnson-Cook (J-C) and Zerilli-Armstrong (Z-A) material models and the characterizing parameters of the numerical P-h curves were also identified. Finally, an artificial neural network (ANN) was trained by the numerical P-h curves parameters as the input and the constants of J-C and Z-A models as the output. The trained neural network was then tested by the experimental p-h curves parameters as the input and the constants of J-C and Z-A models as the output. Moreover, a number of dynamic compression tests were performed using the well-known Split Hopkinson Bar at the same strain rates and temperatures used for indentation tests and the stress-strain curves of material were obtained. A reasonable agreement was observed between the stress-strain curves predicted by neural network and the Split Hopkinson Bar. The proposed method does not need sophisticated instrumentation and in fact, the load-time and indentation depth-time histories are directly converted to stress-strain of material using an artificial neural network.

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Hybrid numerical modeling of ballistic clay under low-speed impact using artificial neural network

Kim

Park

et al. 2023

J Mech Sci Technol

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Plastic properties determination using virtual dynamic spherical indentation test and machine learning algorithms

Cited by 5 publications

References 20 publications

Experimental characterization and finite element investigation of SiO2 nanoparticles reinforced dental resin composite

Experimental characterization and finite element investigation of SiO2 nanoparticles reinforced dental resin composite

Determination of the parameters of material models using dynamic indentation test and artificial neural network

Hybrid numerical modeling of ballistic clay under low-speed impact using artificial neural network

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