Fig 1 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Schematic diagram of the integrated casting and rolling process showing the critical process steps (C1-C5)
Source publication
In this study, artificial neural networks were used to predict the plastic flow behaviour of S355 steel in the process of high-temperature deformation. The aim of the studies was to develop a model of changes in stress as a function of strain, strain rate and temperature, necessary to build an advanced numerical model of the soft-reduction process....
Contexts in source publication
Context 1
... during strand straightening its surface or edge temperature is in the reduced range of ductility, there is an immediate risk of cracks forming in the cast strand. Figure 1 shows a schematic diagram of the integrated casting and rolling process, indicating also the critical process steps (C1-C5). The area of the cast strand leaving the mould (C1), as well as the strand bending (C2-C3) and straightening operations (C4) are classified as critical steps, while the area of high tensile stresses (C5) is critical for the installation of pressure rolls in the soft-reduction process as discussed in the manuscript [1]. ...
Citations
... In another study, it was an NN for identifying the damage stages of degraded low alloy steel with acoustic emission data [27]. In [28], the problem of plastic flow of S355 steel under high-temperature deformation was analyzed with NNs. ...
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.
... 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. ...
... In addition, due to the significant difference in data magnitude and range between temperature, strain rate, strain, and flow stress, it is necessary to use the linear normalization method. As indicated in Equations (16) and (17), normalization and denormalization of the input and output data are necessary in order to improve the computational speed, accuracy, and robustness of the BP neural network model: ...
... During the model training, the In addition, due to the significant difference in data magnitude and range between temperature, strain rate, strain, and flow stress, it is necessary to use the linear normalization method. As indicated in Equations (16) and (17), normalization and denormalization of the input and output data are necessary in order to improve the computational speed, accuracy, and robustness of the BP neural network model: ...
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.
The 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.
