Microstructure Of Dual Phase Steel Research Articles

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

A multi-scale framework to predict damage initiation at martensite/ferrite interface

Martensite/ferrite (M/F) interface damage largely controls failure of dual-phase (DP) steels. In order to predict the failure and assess the ductility of DP steels, accurate models for the M/F interfacial zones are needed. Several M/F interface models have been proposed in the literature, which however do not incorporate the underlying microphysics. It has been recently suggested that (lath) martensite substructure boundary sliding dominates the M/F interface damage initiation and therefore should be taken into account. Considering the computationally infeasibility of direct numerical simulations of statistically representative DP steel microstructures, while explicitly resolving the interface microstructures and the sliding activity, a novel multi-scale approach is developed in this work. Two scales are considered: the DP steel mesostructure consisting of multiple lath martensite islands embedded in a ferrite matrix, and the microscopic M/F interfacial zone unit cell resolving the martensite substructure. Based on the emerging microscopic damage initiation pattern, an effective indicator for the M/F interface damage initiation is determined from the interface microstructural unit cell response, along with the effective sliding in this unit cell. Relating these two effective quantities for different interface microstructural configurations leads to an effective mesoscale model relating the interface damage indicator to the sliding activity of the martensite island in terms of the mesoscopic kinematics. This microphysics-based M/F interface damage indicator model, which could not be envisioned a-priori, is fully identified from a set of interfacial unit cell simulations, thus enabling the efficient prediction of interface damage initiation at the mesoscale. The capability of the developed effective model to predict the mesoscopic M/F interface damage initiation is demonstrated on an example of a realistic DP steel mesostructure.

Development of Desirable Fine Ferrite Grain Size and Random Second Phase Dual-Phase Steel Microstructures Using Composition and/or Processing Modifications

Microstructural morphology is known to have a significant impact on the mechanical properties of dual-phase steels. A fine ferrite grain size and random distribution of small second phase islands are desirable to provide superior isotropic properties compared to the banded second phase distribution that is typical for this type of steel. A rapid alloy prototyping (RAP) facility has been used to investigate three different DP 800 variants by systematically varying the compositions and/or process parameters compared to the ‘standard’ DP800 composition and processing that gives a banded microstructure. For Variant 1, the heating rate during the annealing cycle after cold rolling varied between 0.65 and 30 °C/s for the 45%, 60% and 75% cold reduction samples. It was found that a cold reduction of 75% and heating rate of 15 °C/s resulted in the microstructure that can give the best combination of strength and ductility because of the fine grain size and high martensite volume fraction. For Variant 2, the effect of changing the hot rolled (HR) microstructure (ferrite–pearlite, ferrite–bainite or martensite) on the final microstructure was investigated. Both the ferrite–50% bainite and fully martensite/bainite HR materials for all cold reductions resulted in annealed microstructures with necklace martensite morphology and finer ferrite grains compared to the ferrite–pearlite HR material, which gave a typical banded ferrite–martensite microstructure with a coarser ferrite grain size. For Variant 3, the Mn content was reduced, and increased Nb was used to achieve higher pancaking during the hot rolling stage, which refined ferrite grains in the HR condition with the same hardness. After annealing with the standard parameters only the 45% cold-reduced material produced a finer ferrite grain size than the standard material, whereas the 60% and 75% cold-reduced samples required a higher heating rate to achieve finer ferrite grain sizes due to rapid recrystallisation and growth kinetics.

Insights into a dual-phase steel microstructure using EBSD and image-processing-based workflow.

Quantitative metallography to understand the morphology of different crystallographic phases in a material often rests on the segmentation and classification of electron backscatter diffraction (EBSD) maps. Image analysis offers rich toolboxes to perform such tasks based on 'scalar' images. Embracing the entire wealth of information provided by crystallography, operations such as erosion, dilation, interpolation, smoothing and segmentation require generalizations to do justice to the very nature of crystal orientations (e.g. preserving properties like frame indifference). The present study gives such extensions based on quaternion representation of crystal orientations. A dual-phase stainless steel specimen is used to illustrate the different steps of such a procedure.

Effect of the anisotropy of martensitic transformation on ferrite deformation in Dual-Phase steels

The volume increase and shape change during austenite to martensite transformation in dual-phase (DP) steels are largely accommodated in the microstructure by the deformation of the surrounding ferrite matrix. Accurate estimation of transformation-induced deformation of ferrite via experiments and modeling is essential for predicting the subsequent mechanical behavior of DP steels. This study aims to illustrate the disadvantages of simplifying the anisotropic transformation deformation of martensite to isotropic dilatation for modeling the transformation-induced deformation of ferrite. A novel methodology is developed comprising sequential experimental and numerical research on DP steels to quantify transformation-induced strains in ferrite. This methodology combines the results of prior austenite grain reconstruction, phenomenological theory of martensite crystallography and electron backscatter diffraction (EBSD) orientation data to estimate variant-specific transformation deformation. Subsequently, by comparison of full-field micromechanical calculation results on a virtual DP steel microstructure with experimental EBSD kernel average misorientation and geometrically necessary dislocation measurement results it is shown that neglecting the shear deformation associated with the martensitic transformation leads to significant underestimation in the prediction of transformation-induced strains in ferrite.

Deep Learning for automated phase segmentation in EBSD maps. A case study in Dual Phase steel microstructures

Electron Backscattering Diffraction (EBSD) provides important information to discriminate phase transformation products in steels. This task is conventionally performed by an expert, who carries a high degree of subjectivity and requires time and effort. In this paper, we question if Convolutional Neural Networks (CNNs) are able to extract meaningful features from EBSD-based data in order to automatically classify the present phases within a steel microstructure. The selected case of study is ferrite-martensite discrimination and U-Net has been selected as the network architecture to work with. Pixel-wise accuracies around ~95% have been obtained when inputting raw orientation data, while ~98% has been reached with orientation-derived parameters such as Kernel Average Misorientation (KAM) or pattern quality. Compared to other available approaches in the literature for phase discrimination, the models presented here provided higher accuracies in shorter times. These promising results open a possibility to work on more complex steel microstructures. • Deep Learning Convolutional Neural Networks were successfully applied to EBSD maps. • Beyond 98% accuracy to discriminate Ferrite from Martensite in a Dual Phase Steel • Misidentifications occurred in Widmanstätten ferrite and autotempered martensite. • Relatively few labeled EBSD maps are required for training.