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  1. Article

    Open Access

    A multi-task learning-based optimization approach for finding diverse sets of microstructures with desired properties

    Optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science. In this sense, processes are supposed to manufacture workpieces with tar...

    Tarek Iraki, Lukas Morand, Johannes Dornheim in Journal of Intelligent Manufacturing (2024)

  2. Article

    Open Access

    Neural Networks for Constitutive Modeling: From Universal Function Approximators to Advanced Models and the Integration of Physics

    Analyzing and modeling the constitutive behavior of materials is a core area in materials sciences and a prerequisite for conducting numerical simulations in which the material behavior plays a central role. C...

    Johannes Dornheim, Lukas Morand in Archives of Computational Methods in Engin… (2024)

  3. Article

    Open Access

    Optimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stop** criterion

    In the field of materials engineering, the accurate prediction of material behavior under various loading conditions is crucial. Machine Learning (ML) methods have emerged as promising tools for generating con...

    Ronak Shoghi, Lukas Morand, Dirk Helm, Alexander Hartmaier in Computational Mechanics (2024)

  4. Article

    Open Access

    Deep reinforcement learning methods for structure-guided processing path optimization

    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 investigat...

    Johannes Dornheim, Lukas Morand, Samuel Zeitvogel in Journal of Intelligent Manufacturing (2022)

  5. No Access

    Chapter and Conference Paper

    CupNet – Pruning a Network for Geometric Data

    Using data from a simulated cup drawing process, we demonstrate how the inherent geometrical structure of cup meshes can be used to effectively prune an artificial neural network in a straightforward way.

    Raoul Heese, Lukas Morand, Dirk Helm in Artificial Neural Networks and Machine Lea… (2021)

  6. No Access

    Chapter and Conference Paper

    The Good, the Bad and the Ugly: Augmenting a Black-Box Model with Expert Knowledge

    We address a non-unique parameter fitting problem in the context of material science

    Raoul Heese, Michał Walczak, Lukas Morand in Artificial Neural Networks and Machine Lea… (2019)

  7. No Access

    Article

    Twinning and detwinning during compression–tension loading measured by quasi in situ electron backscatter diffraction tracing in Mg–3Al–Zn rolled sheet

    Twinning and detwinning are the important deformation modes in magnesium alloys during cyclic loading at room temperature. To analyze these two deformation mechanism, cyclic compression–tension experiments wer...

    Li Zheng, Shi-Hong Zhang, Dirk Helm, Hong-Wu Song, Guang-Sheng Song in Rare Metals (2015)

  8. No Access

    Article

    Combined crystal plasticity and phase-field method for recrystallization in a process chain of sheet metal production

    In sheet metal production, a typical process chain contains hot rolling, cold rolling and annealing as a sequence of consecutive processing steps. We investigate the grain structure evolution of body centered ...

    Alexander Vondrous, Pierre Bienger, Simone Schreijäg in Computational Mechanics (2015)

  9. No Access

    Article

    Microstructure-based description of the deformation of metals: Theory and application

    Aiming for an integrated approach to computational materials engineering in an industrial context poses big challenges in the development of suitable materials descriptions for the different steps along the pr...

    Dirk Helm, Alexander Butz, Dierk Raabe, Peter Gumbsch in JOM (2011)