Dirk Mohr
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View article: Quantifying damage mechanisms through FE-based void tracking: Application to shear and tension in-situ laminography experiments on AA2198-T851
Quantifying damage mechanisms through FE-based void tracking: Application to shear and tension in-situ laminography experiments on AA2198-T851 Open
International audience
View article: Punch in a Punch: Validating FLC and fracture models for severe strain path changes
Punch in a Punch: Validating FLC and fracture models for severe strain path changes Open
While generating experimental linear loading strain paths is still required for the identification of Forming and Fracture Limit Curves, non-linear loading paths are necessary to validate models for industrial applications. Commonly non-li…
View article: An Engineering Approximation on the Transformation of Plastic Work into Heat at Various Strain Rates and Stress States
An Engineering Approximation on the Transformation of Plastic Work into Heat at Various Strain Rates and Stress States Open
Accurate estimation of plastic work conversion into heat is crucial for analyzing metals under dynamic deformation. This study investigates DP800 sheet metal specimens across nine strain rates (0.001/s to 150/s) using notched tension (NT) …
View article: Non-symmetric plate-lattices: Recurrent neural network-based design of optimal metamaterials
Non-symmetric plate-lattices: Recurrent neural network-based design of optimal metamaterials Open
The elastic response of plate-lattices with cubic symmetry can reach the upper, isotropic Hashin-Shtrikman bound. Here, our primary objective is the design of stiff lattices with tailored properties beyond any symmetry or isotropy. We prop…
View article: Recurrent neural network plasticity models: Unveiling their common core through multi-task learning
Recurrent neural network plasticity models: Unveiling their common core through multi-task learning Open
Recurrent neural network models are known to be particularly suitable for data-driven constitutive modeling due to their built-in memory variables. The main challenge preventing their widespread application to engineering materials lies in…
View article: Transfer learning of recurrent neural network‐based plasticity models
Transfer learning of recurrent neural network‐based plasticity models Open
Mechanics‐specific recurrent neural network (RNN) models are known for their ability to describe the complex three‐dimensional stress–strain response of elasto‐plastic solids for arbitrary loading paths. To apply RNN models to real materia…
View article: Using surround DIC to extract true stress–strain curve from uniaxial tension experiments
Using surround DIC to extract true stress–strain curve from uniaxial tension experiments Open
International audience
View article: From CP-FFT to CP-RNN: Recurrent neural network surrogate model of crystal plasticity
From CP-FFT to CP-RNN: Recurrent neural network surrogate model of crystal plasticity Open
Recurrent Neural Network (RNN) based surrogate models constitute an emerging class of reduced order models of history-dependent material behavior. Recently, the authors have proposed an alternative RNN formulation that provides stress-resp…
View article: Axisymmetric V-bending: A single experiment to determine the fracture strain and weakest sheet material direction for plane strain tension
Axisymmetric V-bending: A single experiment to determine the fracture strain and weakest sheet material direction for plane strain tension Open
International audience
View article: Plasticity and fracture of AA7075 at elevated strain rates and temperatures
Plasticity and fracture of AA7075 at elevated strain rates and temperatures Open
The accurate description of the strain rate and temperature dependent response of Aluminium alloys is a perpetual quest in the hot forming industry. In the present study, uniaxial tension, and notched tension experiments are conducted for …
View article: Axisymmetric V-Bending of Sheet Metal: Determining the Fracture Strain and the Weakest Material Direction for Plane Strain Tension in One Test
Axisymmetric V-Bending of Sheet Metal: Determining the Fracture Strain and the Weakest Material Direction for Plane Strain Tension in One Test Open
Plane strain tension is one of the most critical loading conditions leading to ductile failure in forming and crash applications. Hence knowing the fracture strain and weakest orientation for this stress state is crucial for safe design. A…
View article: Strength and Failure of Self-Piercing Riveted Aluminum and Steel Sheet Joints: Multi-axial Experiments and Modeling
Strength and Failure of Self-Piercing Riveted Aluminum and Steel Sheet Joints: Multi-axial Experiments and Modeling Open
The mechanical failure of self-piercing rivet (SPR) joints connecting seven series aluminum and high strength steel sheets is investigated, both numerically and experimentally. The joint strength and failure mechanisms are characterized fo…
View article: Counterexample-trained neural network model of rate and temperature dependent hardening with dynamic strain aging
Counterexample-trained neural network model of rate and temperature dependent hardening with dynamic strain aging Open
Constitutive models dealing with the thermal and visco-plasticity of metals have seen wide applications in the automotive industry. A basic plasticity and fracture characterization of a 1.5 mm thick DP780 dual phase steel sheet based on un…
View article: On the importance of self-consistency in recurrent neural network models representing elasto-plastic solids
On the importance of self-consistency in recurrent neural network models representing elasto-plastic solids Open
Recurrent neural networks could serve as surrogate material models, removing the gap between component-level finite element simulations and numerically costly microscale models. Recent efforts relied on gated recurrent neural networks. We …