Optimal Design of Hot-Dip Galvanized DP Steels via Artificial Neural Networks and Multi-Objective Genetic Optimization

Reséndiz‐Flores, Edgar O.; Altamirano-Guerrero, Gerardo; Costa, Patricia Sheilla; Salas-Reyes, Antonio Enrique; Salinas‐Rodríguez, Armando; Goodwin, Frank

doi:10.3390/met11040578

Cited by 7 publications

(3 citation statements)

References 47 publications

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“…The model includes an equation of state implemented in the form of an artificial neural network (ANN) and trained according to MD simulations of uniform isothermal stretching/compression of representative volumes of copper; the equation of state is also used to calculate the shear modulus. Currently, ANNs are widely used in materials science [94][95][96]. In the present research, application of the ANN was more of a methodological issue, because there are a number of reliable equations of state for pure copper.…”

Section: Discussionmentioning

confidence: 93%

Taylor Impact Tests with Copper Cylinders: Experiments, Microstructural Analysis and 3D SPH Modeling with Dislocation Plasticity and MD-Informed Artificial Neural Network as Equation of State

Rodionov

Lupanov

Grachyova

et al. 2022

Metals

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Taylor impact tests involving the collision of a cylindrical sample with an anvil are widely used to study the dynamic properties of materials and to test numerical methods. We apply a combined experimental-numerical approach to study the dynamic plasticity of cold-rolled oxygen-free high thermal conductivity OFHC copper. In the experimental part, impact velocities up to 113.6 m/s provide a strain up to 0.3 and strain rates up to 1.7 × 104 s−1 at the edge of the sample. Microstructural analysis allows us to find out pore-like structures with a size of about 15–30 µm and significant refinement of the grain structure in the deformed parts of the sample. In terms of modeling, the dislocation plasticity model, which was previously tested for the problem of a shock wave upon impact of a plate, is implemented in the 3D case using the numerical scheme of smoothed particle hydrodynamics (SPH). The model includes an equation of state implemented in the form of an artificial neural network (ANN) and trained according to molecular dynamics (MD) simulations of uniform isothermal stretching/compression of representative volumes of copper. The dislocation friction coefficient is taken from previous MD simulations. These two efforts are aimed at building a fully MD-based material model. Comparison of the final shape of the projectile, the reduction of the sample length and increase in the diameter of the impacted edge of the sample confirm the applicability of the developed model and allow us to optimize the model parameters for the case of cold-rolled OFHC copper.

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Section: Discussionmentioning

confidence: 93%

Taylor Impact Tests with Copper Cylinders: Experiments, Microstructural Analysis and 3D SPH Modeling with Dislocation Plasticity and MD-Informed Artificial Neural Network as Equation of State

Rodionov

Lupanov

Grachyova

et al. 2022

Metals

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“…This approach greatly expands the range of alloys that can be modelled, as well as the complexity of these alloy compositions, and provides predictions that are both quick and reliable. Recent studies have had good success with using JMatPro to predict the CCT behaviour of steels [26,27]; however, direct comparisons with experimental CCTs showed some discrepancies in the study by Krbat'a et al [27], with small deviations observed between T s and T f values and critical cooling rates. These differences were concluded to be a result of variations in chemical composition, which is a reasonable assumption.…”

Section: Introductionmentioning

confidence: 99%

A Rapid, Open-Source CCT Predictor for Low-Alloy Steels, and Its Application to Compositionally Heterogeneous Material

Collins

Piemonte

Taylor

et al. 2023

Metals

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The ability to predict transformation behaviour during steel processing, such as primary heat treatments or welding, is extremely beneficial for tailoring microstructures and properties to a desired application. In this work, a model for predicting the continuous cooling transformation (CCT) behaviour of low-alloy steels is developed, using semi-empirical expressions for isothermal transformation behaviour. Coupling these expressions with Scheil’s additivity rule for converting isothermal to non-isothermal behaviour, continuous cooling behaviour can be predicted. The proposed model adds novel modifications to the Li model in order to improve CCT predictions through the addition of a carbon-partitioning model, thermodynamic boundary conditions, and a Koistinen–Marburger expression for martensitic behaviour. These modifications expanded predictions to include characteristic CCT behaviour, such as transformation suppression, and an estimation of the final constituent fractions. The proposed model has been shown to improve CCT predictions for EN3B, EN8, and SA-540 B24 steels by better reflecting experimental measurements. The proposed model was also adapted into a more complex simulation that considers the chemical heterogeneity of the examined SA-540 material, showing a further improvement to CCT predictions and demonstrating the versatility of the model. The model is rapid and open source.

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“…In [15,16], different ML models are compared to predict the nose point and the TTT diagram for pearlitic steels and galvanized dual-phase steels. To compare the proposed models, different metrics are used, e.g., the correlation coefficient (R2), the root mean square error, and the mean absolute percent error (MAPE).…”

Section: Introductionmentioning

confidence: 99%

Determination of TTT Diagrams of Ni-Al Binary Using Neural Networks

et al. 2022

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The heat treatment of a metal is a set of heating and cooling cycles that a metal undergoes to change its microstructure and, therefore, its properties. Temperature–time–transformation (TTT) diagrams are an essential tool for interpreting the resulting microstructures after heat treatments. The present work describes a novel proposal to predict TTT diagrams of the γ′ phase for the Ni-Al alloy using artificial neural networks (ANNs). The proposed methodology is composed of five stages: (1) database creation, (2) experimental design, (3) ANNs training, (4) ANNs validation, and (5) proposed models analysis. Two approaches were addressed, the first to predict only the nose point of the TTT diagrams and the second to predict the complete curve. Finally, the best models for each approach were merged to compose a more accurate hybrid model. The results show that the multilayer perceptron architecture is the most efficient and accurate compared to the simulated TTT diagrams. The prediction of the nose point and the complete curve showed an accuracy of 98.07% and 86.41%, respectively. The proposed final hybrid model achieves an accuracy of 96.59%.

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Optimal Design of Hot-Dip Galvanized DP Steels via Artificial Neural Networks and Multi-Objective Genetic Optimization

Cited by 7 publications

References 47 publications

Taylor Impact Tests with Copper Cylinders: Experiments, Microstructural Analysis and 3D SPH Modeling with Dislocation Plasticity and MD-Informed Artificial Neural Network as Equation of State

Taylor Impact Tests with Copper Cylinders: Experiments, Microstructural Analysis and 3D SPH Modeling with Dislocation Plasticity and MD-Informed Artificial Neural Network as Equation of State

A Rapid, Open-Source CCT Predictor for Low-Alloy Steels, and Its Application to Compositionally Heterogeneous Material

Determination of TTT Diagrams of Ni-Al Binary Using Neural Networks

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