Modelling the high-temperature deformation characteristics of S355 steel using artificial neural networks

Olejarczyk-Wożeńska, I.; Mrzygłód, B.; Hojny, Marcin

doi:10.1007/s43452-022-00538-x

Cited by 3 publications

(2 citation statements)

References 15 publications

(14 reference statements)

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“…x = x ′ (x max − x min ) + x min (17) where x and x ′ are the data before and after normalization, respectively, and x max and x min are the maximum and minimum value, respectively, of the data before normalization.…”

Section: Bp Neural Network Algorithmmentioning

confidence: 99%

“…Constitutive modeling is an effective mathematical modeling method for reflecting material flow stress behavior that has been widely applied in metallic materials [15][16][17]. According to the intrinsic mechanism of the model, constitutive models can be classified into phenomenological models, empirical models, and deep learning algorithm models.…”

Section: Introductionmentioning

confidence: 99%

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Study on the Constitutive Modeling of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 Composites under Hot Compression Conditions

Qiang,

Wang,

Wang

et al. 2024

Materials

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The hot deformation behavior of titanium matrix composites plays a crucial role in determining the performance of the formed components. Therefore, it is significant to establish an accurate constitutive relationship between material deformation parameters and flow stress. In this study, hot compression experiments were conducted on a (2.5 vol%TiB + 2.5 vol%TiC)/TC4. The experiments were performed under temperatures ranging from 1013.15 to 1133.15 K and strain rates ranging from 0.001 to 0.1 s−1. Based on the stress–strain data obtained from the experiment, the constitutive models were established by using the Arrhenius model and the BP neural network algorithm, respectively. Considering the relationship between strain rate, hot working temperature, and flow stress, a comparative analysis was conducted to evaluate the prediction accuracy of two different constitutive models. The research results indicate that the flow stress of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 increases with decreasing temperature and increasing strain rate, and the stress–strain curve shows obvious work hardening and softening behaviors. Both the Arrhenius model and the BP neural network algorithm are effective in predicting the hot compression flow stress of (2.5 vol%TiB + 2.5 vol%TiC)/TC4, but the average relative error and root mean square error of the BP neural network algorithm are smaller and the correlation coefficient is higher, thus possessing higher accuracy and reliability.

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Section: Bp Neural Network Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study on the Constitutive Modeling of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 Composites under Hot Compression Conditions

Qiang,

Wang,

Wang

et al. 2024

Materials

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Application of amplified damped outrigger in seismic design of high‐rise buildings

Xue,

Zhao,

et al. 2024

Structural Design Tall Build

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SummaryThe application of the outrigger technology effectively improves the lateral stiffness of buildings, reduces harmful deformation, enhances the seismic resistance of structures, and contributes to the rapid development of high‐rise buildings. To improve the response speed and energy dissipation of a viscous damping outrigger under seismic excitation, a viscous damping outrigger with an amplifier was proposed, and its effectiveness was verified by combining it with the seismic design of a 149.2 m high‐rise building in an 8.5‐degree intensity area. To determine the additional damping ratio of the building structure, investigations were carried out using both the code method and energy ratio method to evaluate the contribution of the damping outrigger. The damped outrigger equivalent stiffness determination method was studied, and the simplified engineering calculation method was compared against the refined finite element analysis results for engineering design purposes. The displacement amplification technique incorporated into the viscously damped outrigger can enhance the damper response speed, and increase the energy dissipation of the damped outrigger system. The application of an enlarged damped outrigger in engineering can result in an additional damping ratio of approximately 1% while simplifying the calculation method based on the additional damping and equivalent damper cut‐line stiffness, which can be useful in engineering design.

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Application of Instrumented Indentation Test and Neural Networks to determine the constitutive model of in-situ austenitic stainless steel components

Ma,

Basterrech,

Halama

et al. 2024

Archiv.Civ.Mech.Eng

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Over the last few decades, Instrumented Indentation Test (IIT) has evolved into a versatile and convenient method for assessing the mechanical properties of metals. Unlike conventional hardness tests, IIT allows for incremental control of the indenter based on depth or force, enabling the measurement of not only hardness but also tensile properties, fracture toughness, and welding residual stress. Two crucial measures in IIT are the reaction force (F) exerted by the tested material on the indenter and the depth of the indenter (D). Evaluation of the mentioned properties from F–D curves typically involves complex analytical formulas that restricts the application of IIT to a limited group of materials. Moreover, for soft materials, such as austenitic stainless steel SS304L, with excessive pile-up/sink-in behaviors, conducting IIT becomes challenging due to improper evaluation of the imprint depth. In this work, we propose a systematic procedure for replacing complex analytical evaluations of IIT and expensive physical measurements. The proposed approach is based on the well-known potential of Neural Networks (NN) for data-driven modeling. We carried out physical IIT and tensile tests on samples prepared from SS304L. In addition, we generated multiple configurations of material properties and simulated the corresponding number of IITs using Finite Element Method (FEM). The information provided by the physical tests and simulated data from FEM are integrated into an NN, to produce a parametric mapping that can predict the parameters of a constitutive model based on any given F–D curve. Our physical and numerical experiments successfully demonstrate the potential of the proposed approach.

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Modelling the high-temperature deformation characteristics of S355 steel using artificial neural networks

Cited by 3 publications

References 15 publications

Study on the Constitutive Modeling of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 Composites under Hot Compression Conditions

Study on the Constitutive Modeling of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 Composites under Hot Compression Conditions

Application of amplified damped outrigger in seismic design of high‐rise buildings

Application of Instrumented Indentation Test and Neural Networks to determine the constitutive model of in-situ austenitic stainless steel components

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