Neural network based material description of uncured rubber for use in finite element simulation

The finite element method (FEM) is widely used for structural analysis in engineering. In order to predict the behaviour of structures realistically, it is important to understand and to describe the material behaviour. Therefore, extensive material tests have to be conducted. For highly inelastic materials, such as uncured rubber, the characterisation of the behaviour requires a quite complex rheology. Rheological models are used to describe time-dependent mechanical material behaviour (stress-strain-time dependencies). The mapping of the real material behaviour by such models is only possible with restrictions. However, the evaluation of these models at each integration point within the FEM needs time consuming internal iterations in most cases. In order to describe the material behaviour without model restrictions and to reduce computational cost, the aim of this work is the development of a procedure which enables structural analyses without a specific constitutive material model. In this paper, a neural network is used in order to describe uncured rubber behaviour as a model-free approach

A large deformation and thermomechanically coupled interface approach

Interfaces are formed e.g. by the contact surface of different materials of heterogeneous solids or by crack flanks within damaged bodies. Since the combination of temperature evolution and mechanical loadings influences significantly the deformation and thermal behavior of interfacial layers, these failure layers are thermally and mechanically described in the presented approach in a fully coupled sense. Thermomechanical interface descriptions can be used for prediction of crack propagation and, as soon as a designated failure layer exists, to predict the thermomechanical behavior of the observed solid. The presented interface approach for finite deformation introduces a consistent framework derived from principle thermodynamical laws

Numerical representation of fracture patterns and post-fracture load-bearing performance of thermally prestressed glass with polymer foil

Glass can be thermally prestressed to enhance its load-bearing performance and tensile strength for civil engineering constructions. In such applications, the glass is thermally treated (internal stress state) and polymer foils/interlayers are applied to generate a laminate with a higher resistance to bending (out-of-plane loading) in case of fracture. In this contribution, a thermally prestressed glass panel with polymer foil as a backsheet is investigated as a special configuration of safety glass. In its post-fracture state, the polymer foil still provides a minimum structural integrity. Commonly, the post-fracture load-bearing performance of such polymer-glass assemblies is experimentally assessed by large scale tests related to high costs and testing time. In this research, an approach is presented to numerically assess the post-fracture load-bearing performance (bending) of such a fractured glass panel. The approach is based on A) digital image processing of the fracture pattern of three glass samples, B) the generation of a quadtree finite element (FE) mesh, C) the use of prismatic polyhedral FE to efficiently represent glass fragments in the quadtree FE mesh and D) cohesive elements with a nonlinear traction-separation law (TSL) for finite separation to represent the structural effect of the polymer foil during the post-fracture state

Exploring design space: Machine learning for multi-objective materials design optimization with enhanced evaluation strategies

Discovering optimal material designs in the design space can be significantly accelerated by leveraging machine learning (ML) models for screening candidates. However, the quality of these designs depends on the prediction accuracy of the ML models and the efficiency of the optimization algorithms used. This study comprehensively compares different ML modeling strategies, optimization algorithms and evaluation strategies. Thereby, automated ML, tree-based ML models and neural networks were compared. Various optimization algorithms were analyzed, including random search, evolutionary and swarm-based methods. In addition, different strategies for evaluating the predictive performance of the ML models were investigated, which is particularly important as these models are expected to predict design parameters that deviate significantly from the known designs in the training data throughout the optimization. Our results highlight the capability of the proposed workflow to discover material designs that significantly outperform those within the training database and approach theoretical optima. Overall, this research contributes to advancing the field of material design optimization by providing a versatile and practical workflow that introduces automated ML into material design optimization and new model error assessment strategies tailored explicitly to optimization tasks

On the behaviour of lung tissue under tension and compression

Lung injuries are common among those who suffer an impact or trauma. The relative severity of injuries up to physical tearing of tissue have been documented in clinical studies. However, the specific details of energy required to cause visible damage to the lung parenchyma are lacking. Furthermore, the limitations of lung tissue under simple mechanical loading are also not well documented. This study aimed to collect mechanical test data from freshly excised lung, obtained from both Sprague-Dawley rats and New Zealand White rabbits. Compression and tension tests were conducted at three different strain rates: 0.25, 2.5 and 25 min−1. This study aimed to characterise the quasi-static behaviour of the bulk tissue prior to extending to higher rates. A nonlinear viscoelastic analytical model was applied to the data to describe their behaviour. Results exhibited asymmetry in terms of differences between tension and compression. The rabbit tissue also appeared to exhibit stronger viscous behaviour than the rat tissue. As a narrow strain rate band is explored here, no conclusions are being drawn currently regarding the rate sensitivity of rat tissue. However, this study does highlight both the clear differences between the two tissue types and the important role that composition and microstructure can play in mechanical response

Zur Theorie und Numerik von Strukturen aus Faserverbundmaterial Schwingungen von Bauteilen aus faserverstaerktem Kunststoff und verstaerktem Elastomermaterial

SIGLEAvailable from TIB Hannover: ZA 4628(49) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman