Microstructure Of Dual Phase Steel Research Articles

Three-Dimensional Micromechanical Modeling of Martensite Particle Size Effects on the Deformation Behavior of Dual-Phase Steels.

The objective of this study was to examine the influence of martensite particle size on the formation of stress and strain in microstructures of dual-phase steels. In order to achieve this objective, the 3D representative volume element (RVE) method was utilized. Particle size distributions were obtained from the microstructures of DP600 and DP1000 dual-phase steels as they actually exist. Virtual dual-phase steel microstructures were generated according to the above distribution and subsequent validation analyses were performed. In the subsequent phase, microstructures of varying martensite particle sizes (1 µm, 1.98 µm, 3 µm for DP600 and 1.15 µm, 2 µm, 3 µm for DP1000) were formed, and the effects of particle size on deformation behavior under tensile loads were determined. The findings indicated that an increase in martensite particle size resulted in a reduction in tensile strength, accompanied by an increase in deformation amount.

Maximization of strength–ductility balance of dual-phase steels using generative adversarial networks and Bayesian optimization

Dual-phase (DP) steels with a soft ferrite matrix and hard martensite islands exhibit an excellent combination of strength and ductility. However, further improvement of the strength–ductility balance is desirable for automotive applications, and the optimization of the DP microstructure is crucial for enhancing mechanical properties. This study aimed to maximize the strength–ductility balance of DP steel using an integrated computational framework comprised by generative adversarial networks (GAN), finite element method (FEM), and Bayesian optimization. GAN was trained on the microstructures of real DP steels to generate synthetic microstructure images randomly mapped from latent variable vectors, and the tensile properties of the generated microstructures were evaluated using FEM. Bayesian optimization was then employed to identify latent variables yielding microstructures with the highest tensile strength (TS), uniform elongation (uEL), or strength–ductility balance (TS × uEL). The optimized microstructures of DP steels were then quantitatively analyzed to elucidate the relationship between the microstructural morphology and tensile properties. The optimal synthetic DP microstructure exhibited a TS × uEL value higher by 4027 MPa% than the highest value shown by real DP steels. Thus, the proposed integrated optimization framework enables efficient computational design of various material microstructures for maximizing desired mechanical properties.

On the use of thermomechanical processing to enhance the strength-ductility-toughness balance of plain low-carbon steel

This study aimed to fabricate a dual-phase (DP) steel with a combination of high strength-ductility-toughness by thermomechanical processing for industrial applications. Accordingly, the effects of 40 % cold deformation and intercritical annealing temperature on the microstructural evolution and tensile properties of low-carbon steel were studied. The microstructure of dual-phase steels consisted of ferrite (α) and martensite (α'), however, the morphology of martensite was different. With increasing the intercritical annealing temperature, the martensite fraction increased gradually from 0.41 at 770 °C to 0.51 at 860 °C. Also, the increase of martensite fraction from 0.41 to 0.51 caused a decrease in the martensite carbon concentration from 0.174 to 0.142 wt%. All dual-phase steels had a larger hardness than the as-received steel. When the temperature of annealing increased from 770 °C to 860 °C, the yield strength enhanced from 370.4 to 496.3 MPa, the ultimate tensile strength improved from 642.0 to 809.2 MPa, the total elongation decreased slightly from 31.8 % to 29.8 %, and the energy absorption increased from 190.0 to 217.6 J/cm3. The work hardening rate of all DP samples was considerably higher than other samples. Unlike initial, quenched, and rolled samples, the DP steels exhibited three-stage strain hardening behavior with increasing the true strain. For all dual-phase steels, a perfect ductile fracture was observed, with numerous uniform deep and coarse dimples. The dominant mechanism in the fabricated dual-phase steels was interface decohesion.

Bayesian inverse inference of material properties from microstructure images

In this paper, we introduce a Bayesian framework designed for inverse inference, aiming to predict material properties/process parameters from microstructure images. The integration of Bayesian inference techniques with deep generative models establishes a robust tool for applications in materials science, particularly in material characterization and property control. This integration provides a novel approach to clarifying the reliability of predictions. The application of this framework to a sample problem involving the prediction of material properties from artificial dual-phase steel microstructures demonstrates its capability to estimate these properties while accounting for prediction uncertainties. Moreover, even in comparison to conventional regression methods in terms of point estimation, the proposed framework exhibits superior accuracy in prediction. These results clearly illustrate that the framework presented in this paper constitutes a powerful tool for achieving efficient material design.

Investigating the sensitivity of dual-phase steel microstructure to continuous annealing line parameters

The mechanical properties of dual-phase (DP) steels are influenced by ferrite grain size, martensite distribution/morphology, and strength differences between ferrite/martensite phases. A sensitivity study was conducted on a continuous annealing line cycle of DP steel manufacturing process to understand the effect of individual segments on the final microstructural features. From the study results, an optimised cycle was developed to obtain a DP steel microstructure with reduced martensite banding, fine ferrite grain size and less untempered martensite. The main modifications are austenite soaking temperature above Ac3 temperature, higher cooling rate of the slow cooling stage, increased soak time at ferrite soaking temperature, and higher over-aging temperature. The optimised cycle produced DP steel microstructure with reduced martensite banding at a similar phase fraction (35%) and decreased ferrite grain size from 8.4 to 5.1 µm. This improved the mechanical properties, increasing tensile strength from 789 ± 21 to 831 ± 14 MPa and total elongation from 15 ± 3 to 20 ± 2%.

Study on Microstructure and Tensile Behavior of X70 Dual-Phase Steel in Different Directions: Experiments and Crystal Plasticity Finite Element Method

Study on Microstructure and Tensile Behavior of X70 Dual-Phase Steel in Different Directions: Experiments and Crystal Plasticity Finite Element Method

Effect of Coiling Temperature on Microstructure and Properties of Ferritic-Bainitic Dual-Phase Steels

In this study, a 780 MPa grade ferritic-bainitic dual-phase steel with excellent matching of strength-plasticity and formability was developed using thermomechanical control processing. Optical microscopy, Scanning electron microscopy, and Electron Backscatter Diffraction techniques were used to characterize the microstructure comprehensively, and the effects of coiling temperature on the microstructure, the strength-plasticity, and hole-expansion ratio of the test steels were thoroughly investigated. The results showed that the test steel had an excellent combination of ferrite and bainite at the coiling temperature of 520 °C, 23.7 and 76.3%, respectively, with a hole expansion ratio of 58.5 ± 2.8%. The uniformity of the microstructure was the key to obtaining a high expansion ratio in ferrite-bainite dual-phase steels. The test steels formed granular bainite at low-temperature coiling, while polygonal ferrite was promoted at high-temperature coiling. The effect of coiling temperature on grain size is small. Dislocations were redistributed during high-temperature coiling, resulting in a decrease in dislocation density. The higher elongation and hole expansion rate at higher coiling temperatures were attributed to increased polygonal ferrite content, reduced grain size, and enhanced TRIP effect. When coiling at low temperatures, the agglomeration of polygonal ferrite or granular bainite tends to result in a non-uniform distribution of the soft and hard phases of the matrix. At the same time, the strong texture parallel to the rolling direction has a significant difference in plasticity in different directions, leading to non-uniform deformation, which is liable to stress concentration, causing crack nucleation and extension in the hole expanding process, thus reducing the hole expansion performance.

An effective anisotropic visco-plastic model dedicated to high contrast ductile laminated microstructures: Application to lath martensite substructure

In particular types of layer- or lamellar-like microstructures such as pearlite and lath martensite, plastic slip occurs favorably in directions parallel to inter-lamellar boundaries. This may be due to the interplay between morphology and crystallographic orientation or, more generally, due to constraints imposed on the plastic slip due to the lamellar microstructural geometry. This paper proposes a micromechanics based, computationally efficient, scale independent model for a particular type of lamellar microstructures containing softer lamellae which are sufficiently thin to be considered as discrete slip planes embedded in a matrix representing the harder lamellae. Accordingly, the model is constructed as an isotropic visco-plastic model which is enriched with an additional orientation-dependent planar plastic deformation mechanism. This additional mode is activated when the applied load, projected on the direction of the soft films, induces a significant amount of shear stress. Otherwise, the plastic deformation is governed solely by the isotropic part of the model. The response of the proposed model is assessed via a comparison to an infinite periodic two-phase laminate. It is shown that the yielding of the model follows the same behavior as the reference model. The proposed model is highly anisotropic, and the degree of anisotropy depends on the contrast between the slip resistance of the planar mode versus that of the isotropic part. The framework is applied to the substructure of lath martensite with thin films of inter-lath retained austenite, and exploited in a mesoscale simulation of a dual-phase steel microstructure. The results are compared with those of a standard isotropic model and a full crystal plasticity model which does not have the additional planar plastic mechanism. Predictions made with the proposed model show distinct differences compared with the crystal plasticity results, while keeping the computational cost comparable to that of the isotropic model — and significantly lower than that of the crystal plasticity simulation.

Correlation between microstructure, mechanical properties, and slurry erosion behavior of hot-rolled dual-phase steel

A low alloy dual-phase steel was produced via a thermo-mechanical control process to improve production efficiency and reduce costs. The study investigates the influence of martensite volume fractions on the mechanical properties and slurry erosion behavior of the hot-rolled dual-phase steel, and the correlation between the mechanical properties and slurry erosion behavior was revealed to provide insights into the material's wear resistance. Microstructure of dual-phase steel with varying martensite were characterized, and nanohardness, Vickers hardness, tensile propertiy, and slurry erosion were tested. Result indicates that martensite content significantly affects the mechanical propertiy of the hot-rolled dual-phase steel. Hardness and strength increase with increased martensite content, exhibiting a linear relationship. The main strengthening mechanisms are dislocation and solid solution strengthening. Slurry erosion result reveals that matrix hardness, martensite content/size, and work hardening ability significantly impact the slurry erosion performance of hot-rolled dual-phase steel. Martensite plays a crucial role in resisting wear failure, with higher content and larger size providing better protection to the soft ferrite and reducing erosion weight loss. Work hardening ability also leads to a decrease in periodic erosion weight loss.

Generic dual-phase classification models through deep learning semantic segmentation method and image gray-level optimization

Two generic deep learning models for automatic classification of dual-phase microstructures were constructed through the semantic segmentation method of DeepLab v3+, based on a small data including 6 SEM images of the U-2Nb alloys with dual-phase microstructure and the corresponding ground truth. One model is suitable for directly classifying images with different gray-level ranges and has a good average classifying accuracy. Although the other model is only suitable for a specific gray-level range, but it cost less for training and can realize a higher classifying accuracy than the former model combined with optimal gray-level adjustment for images. The two DL models were applied to classify dual-phase microstructures of different morphologies and materials, including the isothermal cooling and continuous cooling microstructures of U-2Nb alloys, and the dual-phase steel microstructure, the results of which manifest that both the two models have high classification accuracy and excellent general applicability.

A Novel Image Artifact Removal Scheme for Phase Percent Quantification of Dual-Phase Steel Microstructures

A Novel Image Artifact Removal Scheme for Phase Percent Quantification of Dual-Phase Steel Microstructures

Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization

The design of optimal microstructures requires first, the identification of microstructural features that influence the material’s properties and, then, a search for a combination of these features that give rise to desired properties. For microstructures with complex morphologies, where the number of features is large, deriving these structure–property relationships is a challenging task. To address this challenge, we propose a generative machine learning model that can automatically identify low-dimensional descriptors of microstructural features that can be used to establish structure–property relationships. Based on this model, we present an integrated, data-driven framework for microstructure characterization, reconstruction, and design that is applicable to heterogeneous materials with polycrystalline microstructures. The proposed method is evaluated on a case study of designing dual-phase steel microstructures created with the multi-level Voronoi tessellation method. To this end, we train a variational autoencoder to identify the descriptors from these synthetic dual-phase steel microstructures. Subsequently, we employ Bayesian optimization to search for the optimal combination of the descriptors and generate microstructures with specific yield stress and low susceptibility for damage initiation. The presented results show how microstructure descriptors, determined by the variational autoencoder model, act as design variables for an optimization algorithm that identifies microstructures with desired properties.

An integrated experimental-numerical study of martensite/ferrite interface damage initiation in dual-phase steels

Martensite/ferrite (M/F) interface damage is relevant to failure of many dual-phase (DP) steels, but the underlying microscale mechanisms remain unclear. Through an integrated experimental-numerical study, this work examines the recent hypothesis that (lath) martensite substructure boundary sliding triggers and dominates M/F interface damage initiation accompanied by apparent martensite plasticity. The mesoscale morphology and prior austenite grain reconstruction are used as modelling inputs. A multi-scale framework is adopted to predict the interface damage initiation. The M/F interface damage initiation sites predicted by the model based on a sliding-triggered interface damage mechanism adequately agree with those identified from in-situ experiments, confirming the key role of substructure boundary sliding. Moreover, the M/F interface damage initiation strongly correlates with a low M/F strain partitioning rather than the commonly accepted strong M/F strain partitioning. This fundamental understanding is instrumental for the future optimization of DP steel microstructures.

Effect of Heat Input on the Weld Thermal Cycle, Microstructure, Tensile Damage and Fracture Behavior of Pulsed Laser-Welded Dual-Phase Steel

Effect of Heat Input on the Weld Thermal Cycle, Microstructure, Tensile Damage and Fracture Behavior of Pulsed Laser-Welded Dual-Phase Steel

A Machine Learning Enabled Image‐data‐driven End‐to‐end Mechanical Field Predictor For Dual‐Phase Steel

AbstractThis contribution presents convolutional neural nets (CNN) based surrogate models for prediction of von Mises stress and equivalent plastic strain fields of commonly used Dual‐Phase (DP) steels in automotive applications. The models predict field quantities in an end‐to‐end manner, driven by segmented phase images from real experimental scanning electron micrographs as inputs and FEM calculations as outputs. Hereby, we train CNN models with the U‐net neural network structure based on around 900 elastoplastic FEM simulations of various DP steel microstructure samples under tensile test. The trained CNN models are validated and tested on 250 and 50 samples, respectively. Thereby CNN models are employed sequentially for different tasks , from the real micrographs to segmented phase maps, then from segmented phase maps to stress, strain field predictions, in an end‐to‐end manner. The field predictor model results show good agreement with the test data and convincing performance on unseen microstructural dataset. This work demonstrates the large potential of a Machine Learning model to make accumulatively use of the physics‐based simulation data of large number of boundary value problems with varying microstructure. It recaptures not only the physics, implied in each simulation training data obtained from the partial different governing equations of mechanics, but also the overarching correlation between the microstructure and the stress and strain field responses.

Effect of prior-austenite grain size on the microstructure and mechanical properties of ultrafine-grained dual-phase layer-structured steel produced by hot rolling of intercritical annealed bainite

In this study, ultrafine-grained (UFG) ferrite/martensite (F/M) layer-structured (LS) steels were fabricated by hot rolling of intercritical annealed bainite steels with a rolling reduction of 75% to ease the strength-toughness trade-off dilemma, and the effect of prior-austenite grain (PAG) size of the bainitic steels on the microstructure and mechanical properties were systematically studied. The results showed that the layer thickness of martensite and ferrite was not affected by the PAG size, but the spacing of parallel lamellae bands (PLS), which evolved from the packet of bainite, decreased with the decrease of PAG size. The PLS had negligible influence on strength and ductility, but the decrease of the PLS led to the increase of delamination and both room temperature (RT) and cryogenic toughness enhancement. By reducing the PLS to introduce more delamination in this steel, the RT and cryogenic impact energy were increased 5.7 times (301 J) and 21 times (102 J at −196 °C), but without the sacrifice of strength (over 1.5 GPa) and ductility (total elongation of about 14%), compared with the initial bainite steel with the same composition respectively. The significantly improved toughness, especially the cryogenic toughness, was considered to result from the special UFG dual-phase layered structure as strain localization could be restrained and ductile fracture could be promoted by this special microstructure even at −196 °C. The cryogenic toughness improvement with the decrease of the spacing of PLS was confirmed to originate from the ductile crack path increase induced by delamination.

Hybrid Data-Driven Deep Learning Framework for Material Mechanical Properties Prediction with the Focus on Dual-Phase Steel Microstructures

A comprehensive approach to understand the mechanical behavior of materials involves costly and time-consuming experiments. Recent advances in machine learning and in the field of computational material science could significantly reduce the need for experiments by enabling the prediction of a material's mechanical behavior. In this paper, a reliable data pipeline consisting of experimentally validated phase field simulations and finite element analysis was created to generate a dataset of dual-phase steel microstructures and mechanical behaviors under different heat treatment conditions. Afterwards, a deep learning-based method was presented, which was the hybridization of two well-known transfer-learning approaches, ResNet50 and VGG16. Hyper parameter optimization (HPO) and fine-tuning were also implemented to train and boost both methods for the hybrid network. By fusing the hybrid model and the feature extractor, the dual-phase steels' yield stress, ultimate stress, and fracture strain under new treatment conditions were predicted with an error of less than 1%.

A micromechanical study on the correlation of the microstructure and failure mechanism of dual-phase steels under tension

In this study, the influence of the volume fraction of the martensite phase as well as the size of the martensite particles on the mechanism of particle fracture in dual-phase steel were examined. A combined continuum/dislocation based approach was used in order to model the average stress in the martensite particles. It was found that the model predictions are in accordance with the experimental results. For the same volume fraction of the martensite particles, the model predicts an increase of the internal stress and the average stress in the martensite particles with increasing the particles size. Since the fracture strength of the martensite depends on its volume fraction, the particle size has no effect on the mechanism of particle fracture. Increasing the volume fraction of the martensite particles results in the enhancement of the internal stress in the martensite particles. However, it has a slight influence on the average stress in the particles. Nevertheless, because of decreasing the fracture strength of martensite with increasing its volume fraction, this parameter has a main role in the occurrence of the particle fracture mechanism.

A numerical study on the effects of DP steel microstructure on the yield locus and the stress–strain response under strain path change

The mechanical response of dual phase (DP) steel exhibits a complex dependence on the microstructure. The chemical composition and microstructure characteristics of the phases have significant effects on the contrast between the response of the phases, which affects, not only the strength and ductility, but also the anisotropic response of DP steel under strain path changes. In this work, extended dislocation-based models of the ferrite and martensite phases of DP steel are proposed and used in a finite element based representative volume element approach to account for the contrast between the local response of the phases. The flow stress of each phase is computed as a function of the amount of substitutional and interstitial solute elements and the microstructural characteristics of the phase. Particular attention is paid to the phase model of the martensite phase. The model parameter controlling the storage of dislocations is related to the carbon content, which appears to be the most important parameter affecting the strength of martensite and its contrast with the local response of the ferrite phase. The model predicts a significant effect of the contrast between the local responses of the phases and the microstructure characteristics of each phase on the yield locus after prestraining and on the stress–strain behaviour after strain path change, i.e., forward-reverse shear loading and cyclic uniaxial tension–compression loading.

Effect of Intercritical Temperature on the Microstructure and Mechanical Properties of 10CrMnMoSi Dual-phase Steel

Effect of Intercritical Temperature on the Microstructure and Mechanical Properties of 10CrMnMoSi Dual-phase Steel