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Computed tomography images are of utmost importance when characterizing the heterogeneous and complex microstructure of discontinuously fiber reinforced polymers. However, the devices are expensive and the scans are time- and energy-intensive. Through recent advances in generative adversarial networks, the instantaneous generation of endless numbers of images that are representative of the input images and hold physical significance becomes possible. Hence, this work presents a deep convolutional generative adversarial network trained on approximately 30,000 input images from carbon fiber reinforced polyamide 6 computed tomography scans. The challenge lies in the low contrast between the two constituents caused by the close proximity of the density of polyamide 6 and carbon fibers as well as the small fiber diameter compared to the necessary resolution of the images. In addition, the stochastic, heterogeneous microstructure does not follow any logical or predictable rules exacerbating their generation. The quality of the images generated by the trained network of 256 pixel × 256 pixel was investigated through the Fréchet inception distance and nearest neighbor considerations based on Euclidean distance and structural similarity index measure. Additional visual qualitative assessment ensured the realistic depiction of the complex mixed single fiber and fiber bundle structure alongside flow-related physically feasible positioning of the fibers in the polymer. The authors foresee additionally huge potential in creating three-dimensional representative volume elements typically used in composites homogenization.

The objective of this paper is to investigate the debonding behavior of the interface between continuously and discontinuously fiber reinforced thermoplastics using the climbing drum peel test. The study emphasizes on the importance of considering different climatic boundary conditions on the properties of thermoplastics. Specimens with varying moisture contents, from 0m.% up to above 6m.% are prepared and tested. It is observed that an increase in moisture content from 0m.% to 2m.% results in an increase of the fracture surface energy from 1.07·103J/m2 to 2.40·103J/m2 required to separate the two materials, but a further increase in moisture to 6.35m.% conversely results in a subsequent decrease of the required energy to 1.91·103J/m2. The study presents an explanatory model of increasing plasticization of the polymer due to increased polymer chain mobility, which results in more deformation energy being required to propagate the crack, which is corroborated in SEM investigations of the fracture surface. A further increase in humidity leads to polymer degradation due to hydrolysis, which explains the subsequent reduction of the fracture energy. The experimental set up is modeled numerically for the first time with cohesive surfaces, which could successfully reproduce the effective force-displacement curve in the experiment by varying the interface parameters in the model over an influence length, allowing the conclusion of a process induced variation in the interface properties over a specific consolidation length.

The mechanical properties of plastic-based additively manufactured specimens have been widely discussed. However, there is still no standard that can be used to determine properties such as the interfacial strength of adjacent tracks and also to exclude the influence of varying manufacturing conditions. In this paper, a proposal is made to determine the interfacial strength using specimens with only one track within a layer. For this purpose, so-called single-wall specimens of polylactide were characterised under tensile load and the interfacial area between the adjacent layers was determined using three methods. It turned out that the determination of the interfacial area via the fracture surface is the most accurate method for determining the interfacial strength. The measured interfacial strengths were compared with the bulk material strength and it was found that the bulk material strength can be achieved under optimal conditions in the FFF process. It was also observed that with increasing nozzle temperature, the simultaneous printing of specimens influences the interfacial strength. To conclude, this method allows to measure the interfacial strength without superimposing the influence of voids. However, for example, the interfacial strength within a layer cannot be determined.

Technological advances have led to the increased use of plastic-based additive manufacturing processes for the production of consumer goods and spare parts. For this reason, the need for the best possible mechanical properties while maintaining geometric accuracy is becoming increasingly important. One of these additive manufacturing processes is the Arburg Plastic Freeforming process, which differs from the widely used Fused Filament Fabrication process in the way that droplets are discharged along a track instead of continuous extruded tracks. As with all other plastic-based additive manufacturing processes, due to the round shape of the tracks, voids occur between the individual tracks during manufacturing, which effects mechanical properties. In contrast to previous work, which mainly focused on how the mechanical properties change with a change in a single printing parameter, this work focused more closely on the interaction of three relevant printing parameters considered as a parameter set. Their influence on the mechanical properties was investigated by tensile tests, the influence on the residual porosity by density measurements and the influence on the geometric accuracy by surface roughness measurements. It was shown that by considering the parameters as a parameter set, states of high density and therefore high mechanical properties while reaching minimal surface roughness can be achieved for significantly more combinations than previously assumed. However, for these states the residual porosity was slightly different. This difference was explained by a parameter-dependent deformation factor of the droplets, which influences the maximal possible degree of filling during manufacturing. For the optimization of arbitrary parameter sets, an analytical model was derived.

Plastic-based additive manufacturing processes are becoming increasingly popular in the production of structural parts. Based on the idea of lightweight design and the aim of extending the functionality of additive structures, the production of additively manufactured foam structures has emerged as a new field of application. The optical characterisation of these structures is of particular importance for process adjustments and the identification of (unwanted) changes in the foam structure. The degree of foaming and the fineness of a foam structure are of interest at this point. In this context, only the part of a structure dominated by foam pores is considered a foam structure. So far, there are no sophisticated methods for such an optical characterisation. Therefore, in this work, microscope images of manufactured as well as artificially created additively manufactured foam structures were evaluated. On these images, the features porosity, pore size, pore amount and a measure for the textural change were determined in order to obtain information about changes within an additively manufactured foam structure. It is shown that additive structures show changing pore shapes depending on the orientation of the cutting plane, although there are no changes in the foaming behaviour. Therefore, caution is required when identifying changes within the foam structure. It was also found that, owing to the additive process, the total porosity is already set in the slicing process and remains constant even if the degree of foaming of individual tracks is changed. Therefore, the degree of foaming cannot be determined on the basis of the total porosity, but it can be assessed on the basis of the formation of large networks of process-related pores.

In this study, the effect of various parameters of a single screw extruder on the rheology and mechanical properties of a polylactic acid (PLA) filament with a 1.75 mm diameter was investigated. The barrel temperature, nozzle and cooling bath temperature, screw speed, nozzle diameter, water bath length, and distance to the nozzle were the process variables. A Taguchi experimental design was implemented using an L8 orthogonal matrix with seven factors and two levels, and their influence on roundness and diameter were evaluated. Among the various processing parameters, the temperature of the cooling bath affected the roundness the most. The mechanical properties and surface roughness of the PLA filament were examined using a tensile test and nanofocus optical system, respectively. Moreover, to assess the filament’s reliability and investigate its behavior further, the filament was used to print 0° plates, and then dog-bone samples were cut from them to evaluate the mechanical properties of the printed specimens. Finally, the results indicate that improved-roundness filaments of 0.004 mm can lead to enhanced mechanical properties in 3D-printed samples with 3.54 MPa.

The subject of this work is the investigation of the influence of voids on the mechanical properties of fibre-reinforced polymers (FRPs) under compression loading. To specify the damage accumulation of FRPs in the presence of voids, the complex three-dimensional structure of the composite including voids was analysed and a reduced mechanical model composite was derived. The hierarchical analysis of the model composite on a micro-scale level implies the description of the stress and strain behaviour of the matrix using the photoelasticity technique and digital image correlation technology. These studies are presented along with an analytical examination of the stability of a single fibre. As a result of the experimental and analytical studies, the stiffness of the matrix and fibre as well as their bonding, the initial fibre orientation and the fibre diameter have the highest impact on the failure initiation. All these facts lead to a premature fibre–matrix debonding with ongoing loss of stability of the fibre and followed by kink-band formation. Additional studies on the meso-scale of transparent glass FRPs including a unique void showed that the experiments carried out on the model composites could be transferred to real composites. This article is part of the themed issue ‘Multiscale modelling of the structural integrity of composite materials’.

Glass fiber-reinforced phenolic resins are well suited to substitute aluminum die-cast materials. They meet the high thermomechanical and chemical demands that are typically found in combustion engine and electric drive train applications. An injection molding process development for further improving their mechanical properties by increasing the glass fiber length in the molded part was conducted. A novel screw mixing element was developed to improve the homogenization of the long fibers in the phenolic resin. The process operation with the mixing element is a balance between the desired mixing action, an undesired preliminary curing of the phenolic resin, and the reduction of the fiber length. The highest mixing energy input leads to a reduction of the initial fiber length L0 = 5000 μm to a weighted average fiber length of Lp = 571 μm in the molded part. This is an improvement over Lp = 285 μm for a short fiber-reinforced resin under comparable processing conditions. The mechanical characterization shows that for the long fiber-reinforced materials, the benefit of the increased homogeneity outweighs the disadvantages of the reduced fiber length. This is evident from the increase in tensile strength from σm = 21 MPa to σm = 57 MPa between the lowest and the highest mixing energy input parameter settings.

Long-fiber thermoplastic (LFT) materials compounded via the direct LFT (LFT-D) process are very versatile composites in which polymers and continuous reinforcement fiber can be combined in almost any way. Polycarbonate (PC) as an amorphous thermoplastic matrix system reinforced with glass fibers (GFs) is a promising addition regarding the current development needs, for example battery enclosures for electromobility. Two approaches to the processing and compression molding of PC GF LFT-D materials with various parameter combinations of screw speed and fiber rovings are presented. The resulting fiber lengths averaged around 0.5 mm for all settings. The tensile, bending, Charpy, and impact properties were characterized and discussed in detail. Special attention to the characteristic charge and flow area formed by compression molding of LFT-D materials, as well as sample orientation was given. The tensile modulus was 10 GPa, while the strength surpassed 125 MPa. The flexural modulus can reach up to 11 GPa, and the flexural strength reached up to 216 MPa. PC GF LFT-D is a viable addition to the LFT-D process, exhibiting good mechanical properties and stable processability.

Common analytical models to predict the unidirectional compressive strength of fibre reinforced polymers are analysed in terms of their accuracy. Several tests were performed to determine parameters for the models and the compressive strength of carbon fibre reinforced polymer (CFRP) and glass fibre reinforced polymer (GFRP). The analytical models are validated for composites with glass and carbon fibres by using the same epoxy matrix system in order to examine whether different fibre types are taken into account. The variation in fibre diameter is smaller for CFRP. The experimental results show that CFRP has about 50% higher compressive strength than GFRP. The models exhibit significantly different results. In general, the analytical models are more precise for CFRP. Only one fibre kinking model’s prediction is in good agreement with the experimental results. This is in contrast to previous findings, where a combined modes model achieves the best prediction accuracy. However, in the original form, the combined modes model is not able to predict the compressive strength for GFRP and was adapted to address this issue. The fibre volume fraction is found to determine the dominating failure mechanisms under compression and thus has a high influence on the prediction accuracy of the various models.