Physics-informed machine learning in the determination of effective thermomechanical properties

We determine the effective (macroscopic) thermoelastic properties of two-phase composites computationally. To this end, we use a physics-informed neural network (PINN)-mediated first-order two-scale periodic asymptotic homogenization framework. A diffuse interface formulation is used to remedy the lack of differentiability of property tensors at phase interfaces. Considering the reliance on the standard integral solution for the property tensors on only the gradient of the corresponding solutions, the emerging unit cell problems are solved up to a constant. In view of this and the exact imposition of the periodic boundary conditions, it is merely the corresponding differential equation that contributes to minimizing the loss. This way, the requirement of scaling individual loss contributions of different kinds is abolished. The developed framework is applied to a planar thermoelastic composite with a hexagonal unit cell with a circular inclusion by which we show that PINNs work successfully in the solution of the corresponding thermomechanical cell problems and, hence, the determination of corresponding effective properties

Computational Homogenization of Architectured Materials

Architectured materials involve geometrically engineered distributions of microstructural phases at a scale comparable to the scale of the component, thus calling for new models in order to determine the effective properties of materials. The present chapter aims at providing such models, in the case of mechanical properties. As a matter of fact, one engineering challenge is to predict the effective properties of such materials; computational homogenization using finite element analysis is a powerful tool to do so. Homogenized behavior of architectured materials can thus be used in large structural computations, hence enabling the dissemination of architectured materials in the industry. Furthermore, computational homogenization is the basis for computational topology optimization which will give rise to the next generation of architectured materials. This chapter covers the computational homogenization of periodic architectured materials in elasticity and plasticity, as well as the homogenization and representativity of random architectured materials

Prevention of Internal Cracks in Forward Extrusion by Means of Counter Pressure: A Numerical Treatise

In the context of forward bulk extrusion, where product defects are frequently observed, the effect of counter pressure on damage accumulation materializing a Continuum Damage Mechanics (CDM) approach is presented. A Lemaitre variant damage model accounting for unilateral damage evolution coupled with a multiplicative finite plasticity is utilized for this purpose. After a presentation of the crack governing mechanism, it is demonstrated that application of counter pressure introduces a marked decrease in the central damage accumulation, which in turn increases the formability of the material through keeping the tensile triaxiality in tolerable limits. It is also shown that, for a crack involving process, through systematic increase of the counter pressure, the crack sizes diminish; and at a certain level of counter pressure chevron cracks can be completely avoided

Identification of fully coupled anisotropic plasticity and damage constitutive equations using a hybrid experimental–numerical methodology with various triaxialities

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Gradient Enhanced Physically Based Plasticity: Implementation and Application to a Problem Pertaining Size Effect: Implementation and application to a problem pertaining size effect

A physically based plasticity model is implemented which describes work hardening of a material as a function of the total dislocation density. The local part of the model, which involves statistically stored dislocations (SSDs) only, is based on Bergström's original model. The nonlocal part is based on geometrically necessary dislocations (GNDs) which appear and evolve due to existence of large plastic strain gradients. The evolution of GNDs with respect to strain gradients is described based on the flow theory. The gradients are computed explicitly using the converged plastic strain field and the coupling is achieved using a staggered (weak) approach. Gradient computation is carried out using an effcient algorithm that makes use of plastic strain increments at integration points whose arrangement is not necessarily regular. The algorithm is applied on a void growth problem in which high strain gradients occur around the void due to stress concentrations

Implementation and application of a gradient enhanced crystal plasticity model

A rate-independent crystal plasticity model is implemented in which description of the hardening of the material is given as a function of the total dislocation density. The evolution of statistically stored dislocations (SSDs) is described using a saturating type evolution law. The evolution of geometrically necessary dislocations (GNDs) on the other hand is described using the gradient of the plastic strain tensor in a non-local manner. The gradient of the incremental plastic strain tensor is computed explicitly during an implicit FE simulation after each converged step. Using the plastic strain tensor stored as state variables at each integration point and an efficient numerical algorithm to find the gradients, the GND density is obtained. This results in a weak coupling of the equilibrium solution and the gradient enhancement. The algorithm is applied to an academic test problem which considers growth of a cylindrical void in a single crystal matrix