Determination of Mechanical Properties of Polycrystals by Using Crystal Plasticity and Numerical Homogenization Schemes

Baiker, Maria; Helm, Dirk; Butz, Alexander

doi:10.1002/srin.201300202

Cited by 12 publications

(10 citation statements)

References 32 publications

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“…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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Section: Taylor-type Materials Modelmentioning

confidence: 99%

Deep reinforcement learning methods for structure-guided processing path optimization

et al. 2021

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“…Origins of the approach can be traced back to the 1990's, where simplified microstructure features have been considered in an explicit manner . More recently, this technique found wide range of applications in modeling of complex metal forming processes, see, for example, including also prediction of texture evolution . Example of results obtained based on the DMR concept with, for example, subsequent cellular automata microstructure evolution under static recrystallization is shown in Figure .…”

Section: Full Field Models In Multiscale Analysismentioning

confidence: 99%

Perceptive Review of Ferrous Micro/Macro Material Models for Thermo-Mechanical Processing Applications

Pietrzyk

Madej

2017

steel research int.

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Perceptive review of progress in development of ferrous materials models for computational engineering applications is the subject of the present work. Development of both micro/macro mean field and full field material models during last forty years is presented and discussed. Evolution of conventional mean field models based on closed form or differential equations is described first to present the progression in thinking of materials. Then, recent progress and selected advancements in development of full field models are addressed to present the state-of-the-art, as well as directions for future developments. Finally, capabilities, as well as limitations and drawbacks of both groups of models are discussed on the basis of selected case studies.

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“…The flow curve from the tensile test is used to adjust the hardening parameters of the crystal plasticity model. Details of the procedure can be found in [4].…”

Section: The Concept Of Virtual Materials Testingmentioning

confidence: 99%

The concept of virtual material testing and its application to sheet metal forming simulations

Butz

Pagenkopf

Baiker

et al. 2016

J. Phys.: Conf. Ser.

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Abstract.A crystal plasticity based full-field microstructure simulation approach is used to virtually determine mechanical properties of sheet metals. Microstructural features like the specific grain morphology and the crystallographic texture are taken into account to predict the plastic anisotropy. A special focus is on the determination of the Lankford coefficients and on the yield surface under plane stress conditions. Compared to experimental procedures, virtual material testing allows to generate significantly more data points on the yield surface. This data is used to calibrate anisotropic elasto-plastic material models which are commonly used for sheet metal forming simulations. A numerical study is carried out to analyze the influence of the chosen points on the yield surface on the calibration procedure. IntroductionAn accurate description of the elasto-plastic material behavior is needed for a precise simulation of sheet metal forming processes. Here, the two most important aspects of the material models are the description of the hardening and the yield locus. To account for the anisotropy of sheet metals, an increasing number of anisotropic yield locus models is available, starting from the well-known Hill48 [1] yield locus description up to more modern models like Yld2000-2d [2]. It is known, that the simpler yield locus descriptions are usually not sufficient to model the plastic material behavior accurately, especially for recently developed advanced high strength steels and also for aluminum alloys. In the case of an associated flow rule, the curvature of the yield locus defines also the development of the plastic strain components which is of high importance for sheet metal forming simulations.Besides the choice of an appropriate yield locus model, the identification of the model parameters is an important issue, especially when more complex yield locus models with more parameters are considered. The determination of the parameters of more sophisticated yield locus models requires a large number of experiments which can be difficult and expensive to realize. In a standard parameter identification procedure the number of experimental results (yield stresses, r-values) usually corresponds to the number of free parameters in the yield locus model. The experimental data which were considered for the parameter identification can then be precisely reproduced by the yield locus model. However, accuracy for other stress states is unclear.Here, the concept of virtual material testing is an interesting approach. It allows to significantly extend the limited experimental data base by additional virtual experiments. These virtual data can be used as additional input for a precise parameter identification or for the assessment of yield locus models.

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Determination of Mechanical Properties of Polycrystals by Using Crystal Plasticity and Numerical Homogenization Schemes

Cited by 12 publications

References 32 publications

Deep reinforcement learning methods for structure-guided processing path optimization

Deep reinforcement learning methods for structure-guided processing path optimization

Perceptive Review of Ferrous Micro/Macro Material Models for Thermo-Mechanical Processing Applications

The concept of virtual material testing and its application to sheet metal forming simulations

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