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Current testing methods are capable of measuring strain near the surface on structural parts, for example by using strain gauges. However, stress peaks often occur within the material and can only be approximated. An alternative strain measurement incorporates fibre-optical strain sensors (Fiber Bragg Gratings, FBG) which are able to determine strains within the material. The principle has already been verified by using embedded FBGs in tensile specimens. The transition area between fibre and aluminium, however, is not yet properly investigated. Therefore, strains in tensile specimens containing FBGs were measured by neutron diffraction in gauge volumes of two different sizes around the Bragg grating. As a result, it is possible to identify and decouple elastic and plastic strains affecting the FBGs and to transfer the findings into a fully descriptive FE-model of the strain transition area.We thus accomplished closing the gap between the external load and internal straining obtained from cast-in FBG and generating valuable information about the mechanisms within the strain transition area.It was found that the porosity within the casting has a significant impact on the stiffness of the tensile specimen, the generation of excess microscopic tensions and thus the formation of permanent plastic strains, which are well recognized by the FBG. The knowledge that FBG as internal strain sensors function just as well as common external strain sensors will now allow for the application of FBG in actual structural parts and measurements under real load conditions. In the future, applications for long-term monitoring of cast parts will also be enabled and are currently under development.

In this study, the response of fiber Bragg gratings (FBGs) embedded in cast aluminum parts under thermal and mechanical load were investigated. Several types of FBGs in different types of fibers were used in order to verify general applicability. To monitor a temperature-induced strain, an embedded regenerated FBG (RFBG) in a cast part was placed in a climatic chamber and heated up to 120 ∘C within several cycles. The results show good agreement with a theoretical model, which consists of a shrink-fit model and temperature-dependent material parameters. Several cast parts with different types of FBGs were machined into tensile test specimens and tensile tests were executed. For the tensile tests, a cyclic procedure was chosen, which allowed us to distinguish between the elastic and plastic deformation of the specimen. An analytical model, which described the elastic part of the tensile test, was introduced and showed good agreement with the measurements. Embedded FBGs - integrated during the casting process - showed under all mechanical and thermal load conditions no hysteresis, a reproducible sensor response, and a high reliable operation, which is very important to create metallic smart structures and packaged fiber optic sensors for harsh environments.

Evaluating casting’s mechanical stress is of significant interest from a safety inspection’s point of view. Residual stress, in particular, leads to an early or even immediate failure of some parts. Therefore, several methods exist to determine casting’s external and internal strain leading to stress. By determining the state of stress and residual stress, it is possible to design casting parts that are much safer. The present concept of a test bench shows a new way of inspecting parts for safety reasons using an optical fiber confocal chromatic sensor to measure strain. The method is based on the deep-drilling method, where a minimal invasive hole is placed at an area of interest in the first step. In a second step, this hole is then precisely measured to be able to map the borehole. From this information, conclusions can be drawn of any forces acting on the inspected part. In this case, the concept of the test bench uses gun drills to place boreholes measuring 6.0 mm in diameter with up to 500.0 mm in depth, and for the inspection, a confocal sensor with a precision of ± 100.0 nm in dissolving distances is used. This work focuses on evaluating the test bench’s precision in conducting such measurements and on how an external thermal load and mechanical load influence the results when conducting differential measurements. The experiments are performed on typical cast alloys such as iron cast and Zamac.Article HighlightsTest bench prototype using confocal chromatic spectroscopy.Hybrid system combining different technologies for placing and mapping boreholes in cast parts.Method for mobile inspection of defects in metal parts with non-contact measurement technologies.

The clogging of piezoelectric nozzles is a typical problem in various additive binder jetting processes, such as the manufacturing of casting molds. This work aims at print head monitoring in these binder jetting processes. The structure-born noise of piezoelectric print modules is analyzed with an Artificial Neural Network to classify whether the nozzles are functional or clogged. The acoustic data are studied in the frequency domain and utilized as input for an Artificial Neural Network. We found that it is possible to successfully classify individual nozzles well enough to implement a print head monitoring, which automatically determines whether the print head needs maintenance.

In the search of an adequate real time strain measurement method in aluminum casting, the use of Fiber-Bragg-Grating ( FBG ) is being investigated with great interest. In order to do so, the behaviour of glass fiber sensors in a liquid aluminium alloy at temperatures up to 750°C is experimentally analysed in a laboratory environment. For better process understanding a simulation of the fiber alloy composite is conducted. FBG is an optical measurement method, which uses engraved Bragg reflectors in a 125 µm in diameter thick glass fiber. This reflector transmits most of the wavelengths but only reflects one specific wavelength. This specific wavelength can be measured and changes due to the axial strain on the grating by the fluid alloy reaction and by the changes in temperature. Using a so-called mirror furnace, several experiments with the fiber alloy composite are evaluated. These measurements are also the basis for the further understanding of hot tearing. The data gathered during the measurement campaign - both numerical and experimental - is used to parameterize a simulation. As a result, the understanding of the fiber alloy composite behaviour is expanded and a digital twin is modeled with MATLAB’s partial differential equation toolbox.

Since years, fiber reinforced polymer composite (FRPC) parts made by Liquid composite molding (LCM) make up a significant share of the composite market. In LCM dry reinforcing structure gets impregnated with a resin system. Permeability, a material parameter with key influence on all LCM processes, quantifies the conductance of technical textiles for resin flow. Today, when a numerical filling simulation is applied for process design, large experimental test programs are required for characterization of permeability, as the permeability has to be measured for all textiles processed and also the dependency e.g. on the fiber volume content has to be considered. Together with Math2Market GmbH (M2M), the IVW currently develops a novel simulative-experimental approach (SEA), using experimental tests to calibrate a simulation model for replacing a significant amount of the experimental tests through “virtual” measurements. In a first step the functionality of the simulation and the most appropriate methods for textile modelling were investigated. For this, three routes were followed: At first a micro-computer tomograph (μCT)-scan of a glass fiber non-crimp fabric was fed into GeoDict, the material simulation software developed by M2M. Second, a digital model (DM) of the textile was created by computer modelling of basic structure and subsequent virtual compaction. μCT-model and DM were then used for computational fluid flow simulation which gives the direction-dependent permeability as an output. The DM calibrated by experiments represents the SEA and results in the digital twin. Third route was the experimental permeability measurement to generate reference values. Comparing the results of all three routes allows statements about the functionality of the simulation and accuracy of modelling. The rather deficient correlation between the results of experiments and μCT-model based simulations revealed that segmentation is a remarkable source of error despite the use of recognized methods. Different modeling approaches were followed to build up the digital twin. The best results were achieved with models undergoing a virtual compaction step, which takes various imperfections such as yarn deformation and varying nesting behavior into account. With this 2.25 - 6.75% deviation from the experimental results at an average standard deviation of 21.9 - 61.2% were achieved. Hence, the digital twin shows a better correlation than the μCT-model and high potential for substitution of experiments. Even better results are expected when in a next step a local anisotropic permeability will be allocated to the yarns.

Rising weight and cost requirements in the automotive industry have led to an increasing substitution of metals by short-or endless-fiber reinforced thermoplastics. The use of thermoplastic matrices is necessary to meet the cycle time challenges which arise from large production quantities. The substituted components are often applied in the chassis or motor compartment, which means an exposition to environmental influences, e.g. moisture or thawing salts, during the entire operating lifetime. The degradation of the material properties of PA6GF30 due to a longtime exposition in DI (deionized) water, sodium-and calcium-chloride solutions is investigated and the fracture behavior examined by scanning electron microscopy analysis. Also, the fatigue properties were determined on a special test rig, which allows the spraying of the specimen with the different fluids during the mechanical cyclic testing.

Castings are predestined for the application of structural optimization, but to date, the integration of process simulation into structural optimization is limited due to high computational cost and is therefore often neglected at the beginning of the design process. This leads to the need for surrogate models, which allow a fast and simplified evaluation of design proposals during the optimization in order to improve the integration. This article introduces a novel approach that estimates the solidification time of randomly created geometries solely based on the casting geometry. The approach uses ray-tracing methods to calculate the distance function along preset directions. The estimated solidification time is calculated using a Spherical Convolutional Neural Network (CNN). The training data is obtained by several thousand solidification simulations using the optimization toolkit of a commercial casting simulation software combined with further data augmentation. The model is experimentally validated for five different geometries in the sand casting process.