A data driven computational microstructure analysis on the influence of martensite banding on damage in DP-steels

Pütz, Felix; Fehlemann, Niklas; Göksu, Volkan; Henrich, Manuel; Könemann, Markus; Münstermann, Sebastian

doi:10.1016/j.commatsci.2022.111903

Cited by 6 publications

(6 citation statements)

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“…In recent years the interest in the influence of microsegregation on the transformation behavior and resulting properties increased for many types of materials, including AHSS and UHSS [20,24,25,[28][29][30][31][32][33][34][35][36][37][38]. As concluded in the mentioned studies, microsegregation plays a significant role in not only phase transformations and the resulting phase contents [20,[38][39][40], but also in the recrystallization process and austenite formation during downstream processing [31,37,41].…”

Section: Introductionmentioning

confidence: 93%

Microsegregation Influence on Austenite Formation from Ferrite and Cementite in Fe–C–Mn–Si and Fe–C–Si Steels

Krugla,

Offerman,

Sietsma

et al. 2024

Metals

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The production reality of sheet steels from casting to the end product is such that in the cases of ultra- and advanced high-strength steels, we have to deal with the segregation of elements on macro- and microlevels. Both can have a significant impact on the microstructure formation and resulting properties. There are several production stages where it can influence the transformations, i.e., casting, hot rolling process and annealing after cold rolling. In the present work, we focus on the latter, and more specifically, the transformation from ferrite–cementite to austenite, especially the nucleation process, in cold-rolled material. We vary the levels of two substitutional elements, Mn and Si, and then look in detail at the microsegregation and nucleation processes. The classical nucleation theory is used, and both the chemical driving force and strain energy are calculated for various scenarios. In the case of a high Mn and high Si concentration, the nucleation can thus be explained. In the cases of high Mn and low Si concentrations as well as low Mn alloys, more research is needed on the nuclei shapes and strain energy.

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Section: Introductionmentioning

confidence: 93%

Microsegregation Influence on Austenite Formation from Ferrite and Cementite in Fe–C–Mn–Si and Fe–C–Si Steels

Krugla,

Offerman,

Sietsma

et al. 2024

Metals

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“…HR650 is different from three other specimens as it contains ferrite grains. These ferrite grains are always preferentially deformed in the multi-phase steels [25], which reduced the plastic strain allocated into martensite and resulted in much lower strain hardening rate at the beginning of deformation than other specimens that are composed of martensite and austenite [26]. Therefore, HR650 has the lowest YS and UTS among all the specimens due to the presence of ferrite.…”

Section: Strain Hardening Mechanismmentioning

confidence: 99%

Influence of Tempering Temperature on Microstructures and Tensile Properties of a Cr–N Alloyed Medium-Mn Martensitic Steel

Wen

et al. 2024

ISIJ Int.

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In this paper, the influence of tempering temperatures on microstructures and tensile properties of a Cr-N alloyed medium Mn martensitic steel was studied. The microstructures formed after the tempering below 400 ℃ are composed of recovered martensite as the matrix, ultrafine retained austenite (RA) and carbonitrides. The tempering at 100 ℃ led to the best combination of 2080 MPa ultrahigh ultimate tensile strength (UTS) and 15% total elongation (TE), which is attributed to the prominent strain hardening capacity caused by both the gradually release of internal stress and the pronounced austenite-to-martensite transformation. The tempering at 400 ℃ resulted in the rapid increase of yield strength (YS) by ~500 MPa due to the relief of internal tensile stress and annihilation of dislocations and the best ductility because it produced the most stable RA grains with the highest C concentration for a sustainable austenite-to-martensite transformation over the large plastic straining. The further increase of temperature to 650 ℃ caused ferrite formed, which decrease both YS and strain hardening rate, leading to the lowest UTS. Moreover, it was found that higher N content increased YS but had little influence on both UTS and TE because it mainly contributed to enhanced precipitation of carbonitrides. It is then concluded that the strength and ductility of medium Mn martensitic steel could be increased by increasing the strain hardening capacity through tailoring both the internal stress in martensite and the mechanical stability of RA via a proper tempering treatment.

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“…[18]. The general procedure of the band generation is already described in Pütz et al, [ 22 ] and we will briefly review this and explain the link to the Faster R‐CNN predictions).…”

Section: Generating Data For Representative Volume Elementsmentioning

confidence: 99%

“…Schematic overview of the generation of statistical information about the martensite bands from the Faster R‐CNN predictions. For more detailed information, see Pütz et al [ 22 ] a) Step 1: Predict the martensite band(s) in the SEM images, record the bounding box coordinates further. b) Step 2: Crop out the band, binarize the image, despeckle to remove small objects, and fill the small holes in the image.…”

Section: Generating Data For Representative Volume Elementsmentioning

confidence: 99%

“…Martensite banding, especially in DP steels, is an active research topic, since bands of a secondary hard phase lead to numerous changes in the local properties, most of which influence the local properties in a negative way. [22,23] However, to our knowledge, there is no method to describe the individual bands of a microstructure quantitatively, while taking into account the geometric features of the bands. While there are some approaches to quantify the amount of banding in a microstructure, [24,25] this description is not sufficient for the representation in S-RVE: Only if bands and islands can be described independently from each other and the rest of the microstructure, the individual effect of the bands on the microstructure and the mechanical properties can be analyzed.…”

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confidence: 99%

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Identification of Martensite Bands in Dual‐Phase Steels: A Deep Learning Object Detection Approach Using Faster Region‐Based‐Convolutional Neural Network

Fehlemann

Aguilera

Sandfeld

et al. 2023

steel research int.

Self Cite

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The drive for evermore efficient construction, design and layout of components, buildings, and vehicles also affects materials science to a large extent. For some time now, research has focused on the adjustment and tailoring of microstructure in the emerging research field of process-(micro)structureproperty-performance relations. Increasingly, machine learning methods are also being used here, for example, to accelerate material design, [1] to link grain sizes and mechanical properties, [2] or to describe dislocation networks. [3] The specific microstructure has a big influence on basic parameters such as the toughness or the yield stress but even more on local properties on the microscale like the damage initiation and accumulation [4] or the fatigue properties. [5] For well-known steels such as dual-phase (DP) steels, the relations between microstructure and damage properties have already been studied in a vast number of publications, see, for example, Pütz et al. [6] for investigations about the damage tolerance of DP800 and DP1000, Tasan et al. [7] regarding simulative experimental studies about stress and strain partioning in DP800, or Heibel et al. [8] for studies on the edge crack sensitivity of different DP and complex phase steels. Experimentally, however, it is almost impossible to quantify the specific effects of individual microstructure parameters (e.g., grain size or phase ratio), since the modification of a particular parameter normally also modifies some or all of the other parameters. [9] In order to overcome this problem, simulation-based approaches using virtual representations of the microstructure are suitable. A commonly used tool for this kind of analysis is the so called representative volume element (RVE). These are sections of the volume, whose specific properties are of a nature that is representative of the behavior of the aggregate material. For this, the following scale separation must apply: L micro ( L rve ( L macro . [10] Since the microscopic characteristic length depends strongly on the material, the required size of the RVE also depends on the properties of the material. [11] RVE are distinguished from real structure models of microstructure. These kinds of models can be generated directly from microstructure images obtained, for example, from light optical microscopy (LOM) or electron backscatter diffraction (EBSD). They are for instance used by Tasan et al. [12] . In this case, the LOM/EBSD image is directly used as the 2D model to simulate the underlying mechanical properties of the experimentally observed microstructure. In contrast to that, the different parameters of the microstructure (grain size, orientations, phase ratios) can be represented by some kinds of distribution functions

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A data driven computational microstructure analysis on the influence of martensite banding on damage in DP-steels

Cited by 6 publications

References 50 publications

Microsegregation Influence on Austenite Formation from Ferrite and Cementite in Fe–C–Mn–Si and Fe–C–Si Steels

Microsegregation Influence on Austenite Formation from Ferrite and Cementite in Fe–C–Mn–Si and Fe–C–Si Steels

Influence of Tempering Temperature on Microstructures and Tensile Properties of a Cr–N Alloyed Medium-Mn Martensitic Steel

Identification of Martensite Bands in Dual‐Phase Steels: A Deep Learning Object Detection Approach Using Faster Region‐Based‐Convolutional Neural Network

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