Prediction of forming effects in UD-NCF by macroscopic forming simulation – Capabilities and limitations

Unidirectional non-crimp fabrics (UD-NCF) provide the highest lightweight potential among dry textile materials. Compared to multiaxial NCF, the fabric layers in UD-NCF enable a more targeted tailoring. Compared to woven fabrics, the fibres of UD-NCF are straight without weakening undulations. However, the formability of UD-NCF is more challenging compared to woven fabrics. The yarns are bonded by a stitching and the deformation behaviour highly depends on this stitching and on the slippage between the stitching and the fibre yarns. Moreover, distinct local draping effects occur, like gapping and fibre waviness, which can have a considerable impact on the mechanical performance. Such local effects are particularly challenging or even impossible to be predicted by macroscopic forming simulation. The present work applies a previously published macroscopic UD-NCF modelling approach to perform numerical forming analyses and evaluate the prediction accuracy of forming effects. In addition to fibre orientations and shear angles, as investigated in previous work, the present work also provides indication for fibre area ratios, gapping, transverse compaction and fibre waviness. Moreover, the prediction accuracy is validated by comparison with experimental tests, where full-field strains of inner plies are captured by prior application of dots onto the fibre yarns, by measuring them via radiography and applying a photogrammetry software. The modelling approach provides good prediction accuracy for fibre orientations, shear strains and fibre area ratio. Conversely, normal fibre strains, indicating fibre waviness, and transverse strains, indicating gapping, show some deviations due to the multiscale nature of UD-NCF that cannot be captured entirely on macroscopic scale

Window-opener as an example for environment measurement and combined actuation of smart hydrogels

An environment is defined by a set of field values, such as temperature, electro-magnetic field, light intensity, air humidity and air composition. Smart materials, such as hydrogels, are able to react to these kinds of stimuli. The spatial and time development of environmental values is governed by transport equations. Hence the reaction, i.e. actuation or sensing, of the smart material can be described based on the same assumptions. The displacement, here swelling and deswelling, of the material depends on the combination of the environmental parameters. Smart materials are called multi-sensitive, when more than one parameter is purposely used (i) to manipulate the material, i.e. as an actuator or (ii) to measure the quantities, i.e. as a (multi-)sensor. However, the material can also perform (iii) the objective of a logic processing unit in addition to (i) and (ii). In the current work, we present a device that realizes this concept: An automatic window opener that senses environmental parameters (light-level and air temperature) and reacts accordingly. The hydrogel material that is included in the simplistic device simultaneously acts as sensor, logic processing unit and actuator

Validation of An Energy-Based Fatigue Life Model for Fibre Reinforced Plastics Under Different Stress Ratios

The energy-based fatigue model presented in this work overcomes different shortcomings of existing model approaches, such as the need of separated assumptions for constant life diagrams. By using the range of the normalised strain energy density and a probabilistic based mode interaction approach, a failure mode dependent fatigue model for CFRP is established for directly predicting constant life diagrams and calculating the fatigue life for multiaxial loads with constant amplitude. In this contribution, the ply-based model and some of its main features, such as the consideration of residual stresses or of mode interactions at general threedimensional stress states, are shortly summarised. The stepwise model validation on different literature datasets is considered in more detail, including prediction of SN-curves with scatter band and constant life diagrams

A mixed numerical-experimental method to characterize metal-polymer interfaces for crash applications

Metallic (M) and polymer (P) materials as layered hybrid metal-polymer-metal (MPM) sandwiches offer a wide range of applications by combining the advantages of both material classes. The interfaces between the materials have a considerable impact on the resulting mechanical properties of the composite and its structural performance. Besides the fact that the experimental methods to determine the properties of the single constituents are well established, the characterization of interface failure behavior between dissimilar materials is very challenging. In this study, a mixed numerical–experimental approach for the determination of the mode I energy release rate is investigated. Using the example of an interface between a steel (St) and a thermoplastic polyolefin (PP/PE), the process of specimen development, experimental parameter determination, and numerical calibration is presented. A modified design of the Double Cantilever Beam (DCB) is utilized to characterize the interlaminar properties and a tailored experimental setup is presented. For this, an inverse calibration method is used by employing numerical studies using cohesive elements and the explicit solver of LS-DYNA based on the force-displacement and crack propagation results

Influence of reversed fatigue loading on damage evolution of cross-ply carbon fibre composites

Microcrack formation and delamination growth are the main damage mechanisms in the fatigue of composites. They lead to significant stiffness loss, introduce stress concentrations and can be the origin of subsequent damage events like buckling or fibre breakage, especially in case of shear and compression stresses during load reversal. Fatigue experiments of carbon fibre reinforced laminates were conducted at several stress ratios and analysed in terms of crack and delamination growth. These investigations were accompanied by microscopic imaging, digital image correlation and finite element modelling to take into account the effects of residual stresses and crack closure. It was found that residual stresses significantly change the local stress ratio in off-axis layers and lead to residual crack opening of inter fibre cracks. These cracks remain open and close under high compression loadings only. Furthermore, crack formation under pulsating compression loading turned out to be driven by residual stresses leading to perpendicular cracks as observed under pure tension loading. The experimental findings further confirm the severe detrimental effect of tension-compression loading on crack formation and delamination growth compared to pulsating tension-tension or compression-compression loads

Conditional diffusion-based microstructure reconstruction

Microstructure reconstruction, a major component of inverse computational materials engineering, is currently advancing at an unprecedented rate. While various training-based and training-free approaches are developed, the majority of contributions are based on generative adversarial networks. In contrast, diffusion models constitute a more stable alternative, which have recently become the new state of the art and currently attract much attention. The present work investigates the applicability of diffusion models to the reconstruction of real-world microstructure data. For this purpose, a highly diverse and morphologically complex data set is created by combining and processing databases from the literature, where the reconstruction of realistic micrographs for a given material class demonstrates the ability of the model to capture these features. Furthermore, a fiber composite data set is used to validate the applicability of diffusion models to small data set sizes that can realistically be created by a single lab. The quality and diversity of the reconstructed microstructures is quantified by means of descriptor-based error metrics as well as the Fr\'echet inception distance (FID) score. Although not present in the training data set, the generated samples are visually indistinguishable from real data to the untrained eye and various error metrics are computed. This demonstrates the utility of diffusion models in microstructure reconstruction and provides a basis for further extensions such as 2D-to-3D reconstruction or application to multiscale modeling and structure-property linkages

Influence of adhesion properties on the crash behavior of steel/polymer/steel sandwich crashboxes: an experimental study

The energy absorption behavior of crashboxes made of steel/polymer/steel (SPS) sandwich sheets can be influenced by numerous parameters, such as the materials used, their thicknesses and stacking, and the adhesion properties between their layers. Therefore, in the present study, the impact of steel/polymer adhesion quality on the occurring failure modes of the crashboxes and the resulting energy absorptions are experimentally analyzed. For this purpose, axial crushing and three-point bending tests on double-hat and top-hat crash boxes were performed, respectively. Three levels of adhesion quality are investigated: none, weak, and strong adhesion strengths. Additionally, the structural crash properties, such as energy absorption and maximal intrusion, are determined and analyzed at both of the quasi-static and highly dynamic loading rates. The results of these investigations show that the adhesion strengths chosen here significantly influence both the failure modes and the energy absorption values. In particular, the structural parameters, in the case of no adhesion, are at most half of those in the case of strong adhesion. However, it is also shown that, in the case of weak adhesion, the structural characteristics are slightly reduced. Based on these results, the possibility to adjust the adhesion strength—globally and/or locally—could be used in future activities to purposefully tailor the failure behavior of hybrid crashboxes

Experimental and Numerical Analysis of SMC Compression Molding in Confined Regions : A Comparison of Simulation Approaches

The compression molding process of sheet molding compound (SMC) is an economical manufacturing process for lightweight parts. However, molding defects, such as fiber matrix separation, and fiber re-orientation, may develop during the molding process in confined regions, such as ribs and bosses. Hence, the mechanical properties of the composite depend on the local fiber architecture. Consequently, this work compares the predictive capabilities of tensor-based and directly modeled process simulation approaches regarding compression force, fiber volume content and fiber orientation on the example of honeycomb structures molded from SMC. The results are validated by micro-computed tomography and thermal gravimetric analysis. The fiber orientation in the honeycomb varies between individual samples because a sheet molding compound is macroscopically heterogeneous and thus the fiber architecture is strongly influenced by random events. Tensor-based fiber orientation models can not reliably predict fiber volume content and fiber orientation in the part’s thickness direction if there is a lack of scale separation. Therefore, directly modeled process simulations should be preferred in cases in which fiber length and mold dimensions prohibit scale separation. The prediction of fiber volume content is a difficult task and no simulation can predict the severity of fiber matrix separation precisely in all cases

Structural integrity of aging steel bridges by 3D laser scanning and convolutional neural networks

For steel bridges, corrosion has historically led to bridge failures, resulting in fatalities and injuries. To enhance public safety and prevent such incidents, authorities mandate in-situ evaluation and reporting of corroded members. The current inspection and evaluation protocol is characterized by intense labor, traffic delays, and poor capacity predictions. Here we combine full-scale experimental testing of a decommissioned girder, 3D laser scanning, and convolutional neural networks (CNNs) to introduce a continuous inspection and evaluation framework. Classification and regression CNNs are trained on a databank of 1,421 naturally inspired corrosion scenarios, generated computationally based on point clouds of three corroded girders collected in lab conditions. Results indicate low errors of up to 2.0% and 3.3%, respectively. The methodology is validated on eight real corroded ends and implemented for the evaluation of an in-service bridge. This framework promises significant advancements in assessing aging bridge infrastructure with higher accuracy and efficiency compared to analytical or semi-analytical approaches