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- ItemTemperature-dependent dynamic compressive properties and failure mechanisms of the additively manufactured CoCrFeMnNi high entropy alloy(Oxford : Elsevier Science, 2022) Chen, Hongyu; Liu, Yang; Wang, Yonggang; Li, Zhiguo; Wang, Di; Kosiba, KonradCoCrFeMnNi high entropy alloy (HEA) parts were fabricated by laser powder bed fusion (LPBF), and their dynamic compressive properties at different temperatures as well as the resulting microstructures were analyzed. The HEAs showed an unprecedented strength-ductility combination, especially at a cryogenic temperature of 77 K and a high strain rate of 3000 s−1. Under this testing condition, the yield strength (YS) of the HEAs amounted to 665 MPa. Regardless of the testing temperature, the deformation mechanism of all investigated HEAs was dominated by a synergistic effect consisting of deformation twinning and dislocation pile-up around twins. The fraction of twin boundaries and dislocation density within the deformed microstructure of the HEA correlated with the test temperature. At 77 K, the formation of nanotwins together with dislocation slip prevailed and contributed to pronounced twin-twin and twin-dislocation interactions which effectively restricted the dislocation movement and, hence, contributed to a higher YS as well as strain hardening rate in comparison to that of the HEAs at room temperature of 298 K. The LPBF-fabricated HEAs showed unpronounced thermal softening even at a high testing temperature of 1073 K. Continuous dynamic recrystallization was restricted in the HEA because of its inherent sluggish dislocation kinetics and low stacking fault energy.
- ItemDesigning the microstructural constituents of an additively manufactured near β Ti alloy for an enhanced mechanical and corrosion response(Amsterdam [u.a.] : Elsevier Science, 2022) Hariharan, Avinash; Goldberg, Phil; Gustmann, Tobias; Maawad, Emad; Pilz, Stefan; Schell, Frederic; Kunze, Tim; Zwahr, Christoph; Gebert, AnnettAdditive manufacturing of near β-type Ti-13Nb-13Zr alloys using the laser powder bed fusion process (LPBF) opens up new avenues to tailor the microstructure and subsequent macro-scale properties that aids in developing new generation patient-specific, load-bearing orthopedic implants. In this work, we investigate a wide range of LPBF parameter space to optimize the volumetric energy density, surface characteristics and melt track widths to achieve a stable process and part density of greater than 99 %. Further, optimized sample states were achieved via thermal post-processing using standard capability aging, super-transus (900 °C) and sub-transus (660 °C) heat treatment strategies with varying quenching mediums (air, water and ice). The applied heat treatment strategies induce various fractions of α, martensite (α', α'') in combination with the β phase and strongly correlated with the observed enhanced mechanical properties and a relatively low elastic modulus. In summary, our work highlights a practical strategy for optimizing the mechanical and corrosion properties of a LPBF produced near β-type Ti-13Nb-13Zr alloy via careful evaluation of processing and post-processing steps and the interrelation to the corresponding microstructures. Corrosion studies revealed excellent corrosion resistances of the heat-treated LPBF samples comparable to wrought Ti-13Nb-13Zr alloys.
- ItemTransparent model concrete with tunable rheology for investigating flow and particle-migration during transport in pipes(Amsterdam [u.a.] : Elsevier Science, 2020) Auernhammer, Günter K.; Fataei, Shirin; Haustein, Martin A.; Patel, Himanshu P.; Schwarze, Rüdiger; Secrieru, Egor; Mechtcherine, ViktorThe article describes the adaption and properties of a model concrete for detailed flow studies. To adapt the yield stress and plastic viscosity of the model concrete to the corresponding rheological properties of real concrete, the model concrete is made of a mixture of glass beads and a non-Newtonian fluid. The refractive index of the non-Newtonian fluid is adjusted to the refractive index of the glass beads by the addition of a further constituent. The rheological properties of the model concrete are characterised by measurements in concrete rheometers. Finally, the first exemplary results from experiments with the model concrete are presented, which give incipient impressions of the complex internal dynamics in flowing concrete.
- ItemThermal annealing to influence the vapor sensing behavior of co-continuous poly(lactic acid)/polystyrene/multiwalled carbon nanotube composites(Amsterdam [u.a.] : Elsevier Science, 2020) Li, Yilong; Pionteck, Jürgen; Pötschke, Petra; Voit, BrigitteWith the main purpose of being used as vapor leakage detector, the volatile organic compound (VOC) vapor sensing properties of conductive polymer blend composites were studied. Poly(lactic acid)/polystyrene/multi-walled carbon nanotube (PLA/PS/MWCNT) based conductive polymer composites (CPCs) in which the polymer components exhibit different interactions with the vapors, were prepared by melt mixing. CPCs with a blend composition of 50/50 wt% resulted in the finest co-continuous structure and selective MWCNT localization in PLA. Therefore, these composites were selected for sensor tests. Thermal annealing was applied aiming to maintain the blend structure but improving the sensing reversibility of CPC sensors towards high vapor concentrations. Different sensing protocols were applied using acetone (good solvent for PS and PLA) and cyclohexane (good solvent for PS but poor solvent for PLA) vapors. Increasing acetone vapor concentration resulted in increased relative resistance change (Rrel) of CPCs. Saturated cyclohexane vapor resulted in lower response than nearly saturated acetone vapor. The thermal annealing at 150 °C did not change the blend morphology but increased the PLA crystallinity, making the CPC sensors more resistant to vapor stimulation, resulting in lower Rrel but better reversibility after vapor exposure.
- ItemFunctional relationship of particulate matter (PM) emissions, animal species, and moisture content during manure application(Amsterdam [u.a.] : Elsevier Science, 2020) Kabelitz, Tina; Ammon, Christian; Funk, Roger; Münch, Steffen; Biniasch, Oliver; Nübel, Ulrich; Thiel, Nadine; Rösler, Uwe; Siller, Paul; Amon, Barbara; Aarnink, André J.A.; Amon, ThomasLivestock manure is recycled to agricultural land as organic fertilizer. Due to the extensive usage of antibiotics in conventional animal farming, antibiotic-resistant bacteria are highly prevalent in feces and manure. The spread of wind-driven particulate matter (PM) with potentially associated harmful bacteria through manure application may pose a threat to environmental and human health. We studied whether PM was aerosolized during the application of solid and dried livestock manure and the functional relationship between PM release, manure dry matter content (DM), treatment and animal species. In parallel, manure and resulting PM were investigated for the survival of pathogenic and antibiotic-resistant bacterial species. The results showed that from manure with a higher DM smaller particles were generated and more PM was emitted. A positive correlation between manure DM and PM aerosolization rate was observed. There was a species-dependent critical dryness level (poultry: 60% DM, pig: 80% DM) where manure began to release PM into the environment. The maximum PM emission potentials were 1 and 3 kg t−1 of applied poultry and pig manure, respectively. Dried manure and resulting PM contained strongly reduced amounts of investigated pathogenic and antibiotic-resistant microorganisms compared to fresh samples. An optimal manure DM regarding low PM emissions and reduced pathogen viability was defined from our results, which was 55–70% DM for poultry manure and 75–85% DM for pig manure. The novel findings of this study increase our detailed understanding and basic knowledge on manure PM emissions and enable optimization of manure management, aiming a manure DM that reduces PM emissions and pathogenic release into the environment.
- ItemControlling the Young’s modulus of a ß-type Ti-Nb alloy via strong texturing by LPBF(Amsterdam [u.a.] : Elsevier Science, 2022) Pilz, Stefan; Gustmann, Tobias; Günther, Fabian; Zimmermann, Martina; Kühn, Uta; Gebert, AnnettThe ß-type Ti-42Nb alloy was processed by laser powder bed fusion (LPBF) with an infrared top hat laser configuration aiming to control the Young’s modulus by creating an adapted crystallographic texture. Utilizing a top hat laser, a microstructure with a strong 〈0 0 1〉 texture parallel to the building direction and highly elongated grains was generated. This microstructure results in a strong anisotropy of the Young’s modulus that was modeled based on the single crystal elastic tensor and the experimental texture data. Tensile tests along selected loading directions were conducted to study the mechanical anisotropy and showed a good correlation with the modeled data. A Young’s modulus as low as 44 GPa was measured parallel to the building direction, which corresponds to a significant reduction of over 30% compared to the Young’s modulus of the Gaussian reference samples (67–69 GPa). At the same time a high 0.2% yield strength of 674 MPa was retained. The results reveal the high potential of LPBF processing utilizing a top hat laser configuration to fabricate patient-specific implants with an adapted low Young’s modulus along the main loading direction and a tailored mechanical biofunctionality.
- ItemPhenomenology of ultrafine particle concentrations and size distribution across urban Europe(Amsterdam [u.a.] : Elsevier Science, 2023) Trechera, Pedro; Garcia-Marlès, Meritxell; Liu, Xiansheng; Reche, Cristina; Pérez, Noemí; Savadkoohi, Marjan; Beddows, David; Salma, Imre; Vörösmarty, Máté; Casans, Andrea; Casquero-Vera, Juan Andrés; Hueglin, Christoph; Marchand, Nicolas; Chazeau, Benjamin; Gille, Grégory; Kalkavouras, Panayiotis; Mihalopoulos, Nikos; Ondracek, Jakub; Zikova, Nadia; Niemi, Jarkko V.; Manninen, Hanna E.; Green, David C.; Tremper, Anja H.; Norman, Michael; Vratolis, Stergios; Eleftheriadis, Konstantinos; Gómez-Moreno, Francisco J.; Alonso-Blanco, Elisabeth; Gerwig, Holger; Wiedensohler, Alfred; Weinhold, Kay; Merkel, Maik; Bastian, Susanne; Petit, Jean-Eudes; Favez, Olivier; Crumeyrolle, Suzanne; Ferlay, Nicolas; Martins Dos Santos, Sebastiao; Putaud, Jean-Philippe; Timonen, Hilkka; Lampilahti, Janne; Asbach, Christof; Wolf, Carmen; Kaminski, Heinz; Altug, Hicran; Hoffmann, Barbara; Rich, David Q.; Pandolfi, Marco; Harrison, Roy M.; Hopke, Philip K.; Petäjä, Tuukka; Alastuey, Andrés; Querol, XavierThe 2017–2019 hourly particle number size distributions (PNSD) from 26 sites in Europe and 1 in the US were evaluated focusing on 16 urban background (UB) and 6 traffic (TR) sites in the framework of Research Infrastructures services reinforcing air quality monitoring capacities in European URBAN & industrial areaS (RI-URBANS) project. The main objective was to describe the phenomenology of urban ultrafine particles (UFP) in Europe with a significant air quality focus. The varying lower size detection limits made it difficult to compare PN concentrations (PNC), particularly PN10-25, from different cities. PNCs follow a TR > UB > Suburban (SUB) order. PNC and Black Carbon (BC) progressively increase from Northern Europe to Southern Europe and from Western to Eastern Europe. At the UB sites, typical traffic rush hour PNC peaks are evident, many also showing midday-morning PNC peaks anti-correlated with BC. These peaks result from increased PN10-25, suggesting significant PNC contributions from nucleation, fumigation and shipping. Site types to be identified by daily and seasonal PNC and BC patterns are: (i) PNC mainly driven by traffic emissions, with marked correlations with BC on different time scales; (ii) marked midday/morning PNC peaks and a seasonal anti-correlation with PNC/BC; (iii) both traffic peaks and midday peaks without marked seasonal patterns. Groups (ii) and (iii) included cities with high insolation. PNC, especially PN25-800, was positively correlated with BC, NO2, CO and PM for several sites. The variable correlation of PNSD with different urban pollutants demonstrates that these do not reflect the variability of UFP in urban environments. Specific monitoring of PNSD is needed if nanoparticles and their associated health impacts are to be assessed. Implementation of the CEN-ACTRIS recommendations for PNSD measurements would provide comparable measurements, and measurements of <10 nm PNC are needed for full evaluation of the health effects of this size fraction.
- ItemAdditively manufactured AlSi10Mg lattices – Potential and limits of modelling as-designed structures(Amsterdam [u.a.] : Elsevier Science, 2022) Gebhardt, Ulrike; Gustmann, Tobias; Giebeler, Lars; Hirsch, Franz; Hufenbach, Julia Kristin; Kästner, MarkusAdditive manufacturing overcomes the restrictions of classical manufacturing methods and enables the production of near-net-shaped, complex geometries. In that context, lattice structures are of high interest due to their superior weight reduction potential. AlSi10Mg is a well-known alloy for additive manufacturing and well suited for such applications due to its high strength to material density ratio. It has been selected in this study for producing bulk material and complex geometries of a strut-based lattice type (rhombic dodecahedron). A detailed characterisation of as-built and heat-treated specimens has been conducted including microstructural analyses, identification of imperfections and rigorous mechanical testing under different load conditions. An isotropic elastic–plastic material model is deduced on the basis of tension test results of bulk material test specimens. Performed experiments under compression, shear, torsion and tension load are compared to their virtual equivalents. With the help of numerical modelling, the overall structural behaviour was simulated using the detailed lattice geometry and was successfully predicted by the presented numerical models. The discussion of the limits of this approach aims to evaluate the potential of the numerical assessment in the modelling of the properties for novel lightweight structures.
- ItemDirect observation of nanocrystal-induced enhancement of tensile ductility in a metallic glass composite(Amsterdam [u.a.] : Elsevier Science, 2021) Gammer, Christoph; Rentenberger, Christian; Beitelschmidt, Denise; Minor, Andrew M.; Eckert, JürgenBulk metallic glasses (BMGs) have attracted wide interest, but their successful application is hindered by their low ductility at room temperature. Therefore, the use of composites of a BMG matrix with crystalline secondary phases has been proposed to overcome this drawback. In the present work we demonstrate the fabrication of a tailored BMG nanocomposite containing a high density of monodisperse nanocrystals with a size of around 20 nm using a combination of mechanical and thermal treatment of Cu36Zr48Al8Ag8 well below the crystallization temperature. Direct observations of the interaction of the nanocrystals with a shear band during in situ deformation in a transmission electron microscope demonstrate that the achieved nanocomposite has the potential to inhibit catastrophic fracture in tension. This demonstrates that a sufficient number of nanoscale structural heterogeneities can be a route towards BMG composites with superior mechanical properties.
- ItemMachine learning for additive manufacturing: Predicting materials characteristics and their uncertainty(Amsterdam [u.a.] : Elsevier Science, 2023) Chernyavsky, Dmitry; Kononenko, Denys Y.; Han, Jun Hee; Kim, Hwi Jun; van den Brink, Jeroen; Kosiba, KonradAdditive manufacturing (AM) is known for versatile fabrication of complex parts, while also allowing the synthesis of materials with desired microstructures and resulting properties. These benefits come at a cost: process control to manufacture parts within given specifications is very challenging due to the relevance of a large number of processing parameters. Efficient predictive machine learning (ML) models trained on small datasets, can minimize this cost. They also allow to assess the quality of the dataset inclusive of uncertainty. This is important in order for additively manufactured parts to meet property specifications not only on average, but also within a given variance or uncertainty. Here, we demonstrate this strategy by developing a heteroscedastic Gaussian process (HGP) model, from a dataset based on laser powder bed fusion of a glass-forming alloy at varying processing parameters. Using amorphicity as the microstructural descriptor, we train the model on our Zr52.5Cu17.9Ni14.6Al10Ti5 (at.%) alloy dataset. The HGP model not only accurately predicts the mean value of amorphicity, but also provides the respective uncertainty. The quantification of the aleatoric and epistemic uncertainty contributions allows to assess intrinsic inaccuracies of the dataset, as well as identify underlying physical phenomena. This HGP model approach enables to systematically improve ML-driven AM processes.