Virtual testing of dual-phase steels: Effect of martensite morphology on plastic flow behavior

Pagenkopf, Jan; Butz, Alexander; Wenk, Moritz; Helm, Dirk

doi:10.1016/j.msea.2016.07.118

Cited by 21 publications

(5 citation statements)

References 52 publications

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“…as is used for example in [60,68]. The used model contains four material dependent parameters τ 0 , τ 1 , ϑ 0 , and ϑ 1 , which are calibrated to DC04 steel.…”

Section: Appendix C Taylor-type Materials Modelmentioning

confidence: 99%

“…Further implementation details can be found in [63]. In [68], it was shown that the model can reproduce experimentally measured rolling textures of a dual phase steel on the basis of a spatially resolved microstructure model. In contrast, the Taylor framework used in this study is known to typically overestimate texture evolution and produce sharper textures [6].…”

Section: Appendix C Taylor-type Materials Modelmentioning

confidence: 99%

See 1 more Smart Citation

Accurate distances measures and machine learning of the texture-property relation for crystallographic textures represented by one-point statistics

Iraki,

Morand,

Link

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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The crystallographic texture of metallic materials is a key microstructural feature that is responsible for the anisotropic behavior, e.g., important in forming operations. In materials science, crystallographic texture is commonly described by the orientation distribution function, which is defined as the probability density function of the orientations of the monocrystal grains conforming a polycrystalline material. For representing the orientation distribution function, there are several approaches such as using generalized spherical harmonics, orientation histograms, and pole figure images . Measuring distances between crystallographic textures is essential for any task that requires assessing texture similarities, e.g. to guide forming processes. Therefore, we introduce novel distance measures based on (i) the Earth Movers Distance that takes into account local distance information encoded in histogram-based texture representations and (ii) a distance measure based on pole figure images. For this purpose, we evaluate and compare existing distance measures for selected use-cases. The present study gives insights into advantages and drawbacks of using certain texture representations and distance measures with emphasis on applications in materials design and optimal process control.

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“…as is used for example in [60,68]. The used model contains four material dependent parameters τ 0 , τ 1 , ϑ 0 , and ϑ 1 , which are calibrated to DC04 steel.…”

Section: Appendix C Taylor-type Materials Modelmentioning

confidence: 99%

Section: Appendix C Taylor-type Materials Modelmentioning

confidence: 99%

Accurate distances measures and machine learning of the texture-property relation for crystallographic textures represented by one-point statistics

Iraki,

Morand,

Link

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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show abstract

“…To model the hardening behavior, a description is used which can be found for example in Baiker et al (2014) and Pagenkopf et al (2016). The hardening model is of an extended Voce-type (Tome et al 1984):…”

Section: Taylor-type Materials Modelmentioning

confidence: 99%

Deep reinforcement learning methods for structure-guided processing path optimization

et al. 2021

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A major goal of materials design is to find material structures with desired properties and in a second step to find a processing path to reach one of these structures. In this paper, we propose and investigate a deep reinforcement learning approach for the optimization of processing paths. The goal is to find optimal processing paths in the material structure space that lead to target-structures, which have been identified beforehand to result in desired material properties. There exists a target set containing one or multiple different structures, bearing the desired properties. Our proposed methods can find an optimal path from a start structure to a single target structure, or optimize the processing paths to one of the equivalent target-structures in the set. In the latter case, the algorithm learns during processing to simultaneously identify the best reachable target structure and the optimal path to it. The proposed methods belong to the family of model-free deep reinforcement learning algorithms. They are guided by structure representations as features of the process state and by a reward signal, which is formulated based on a distance function in the structure space. Model-free reinforcement learning algorithms learn through trial and error while interacting with the process. Thereby, they are not restricted to information from a priori sampled processing data and are able to adapt to the specific process. The optimization itself is model-free and does not require any prior knowledge about the process itself. We instantiate and evaluate the proposed methods by optimizing paths of a generic metal forming process. We show the ability of both methods to find processing paths leading close to target structures and the ability of the extended method to identify target-structures that can be reached effectively and efficiently and to focus on these targets for sample efficient processing path optimization.

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“…where r (η) is the slip system resistance, γ0 the reference shear rate and m the shear rate sensitivity. Here, γ0 and m are set to 0.001 sec −1 and 0.0125, respectively, [48]. Following Schmid's law, the resolved shear stress on slip system τ (η) is given by…”

Section: Materials Modelmentioning

confidence: 99%

“…The ranges inside which the parameters of the texture model vary are defined such that typical bcc rolling textures found in literature can be represented, cf. [53,54,55,41,56,48,57]. The parameter ranges are listed in Tab.…”

Section: Texture-property Data Setmentioning

confidence: 99%

A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization

Iraki¹,

Morand²,

Dornheim³

et al. 2021

Preprint

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The optimization along the chain processing-structure-propertiesperformance is one of the core objectives in data-driven materials science. In this sense, processes are supposed to manufacture workpieces with targeted material microstructures. These microstructures are defined by the material properties of interest and identifying them is a question of materials design. In the present paper, we addresse this issue and introduce a generic multi-task learning-based optimization approach. The approach enables the identification of sets of highly diverse microstructures for given desired properties and corresponding tolerances. Basically, the approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task learning with siamese neural networks. The resulting model (1) relates microstructures and properties, (2) estimates the likelihood of a microstructure of being producible, and (3) performs a distance preserving microstructure feature extraction in order to generate a lower dimensional latent feature space to enable efficient optimization. The proposed approach is applied on a crystallographic texture optimization problem for rolled steel sheets given desired properties.

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Virtual testing of dual-phase steels: Effect of martensite morphology on plastic flow behavior

Cited by 21 publications

References 52 publications

Accurate distances measures and machine learning of the texture-property relation for crystallographic textures represented by one-point statistics

Accurate distances measures and machine learning of the texture-property relation for crystallographic textures represented by one-point statistics

Deep reinforcement learning methods for structure-guided processing path optimization

A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization

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