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Experimental and numerical characterization of imperfect additively manufactured lattices based on triply periodic minimal surfaces
Lattices based on triply periodic minimal surfaces (TPMS) are attracting increasing interest in seminal industries such as bone tissue engineering due to their excellent structure-property relationships. However, the potential can only be exploited if their structural integrity is ensured. This requires a fundamental understanding of the impact of imperfections that arise during additive manufacturing. Therefore, in the present study, the structure-property relationships of eight TPMS lattices, including their imperfections, are investigated experimentally and numerically. In particular, the focus is on biomimetic network TPMS lattices of the type Schoen I-WP and Gyroid, which are fabricated by laser powder bed fusion from the biocompatible alloy Ti-42Nb. The experimental studies include computed tomography measurements and compression tests. The results highlight the importance of process-related imperfections on the mechanical performance of TPMS lattices. In the numerical work, firstly the as-built morphology is artificially reconstructed before finite element analyses are performed. Here, the reconstruction procedure previously developed by the same authors is used and validated on a larger experimental matrix before more advanced calculations are conducted. Specifically, the reconstruction reduces the numerical overestimation of stiffness from up to 341% to a maximum of 26% and that of yield strength from 66% to 12%. Given a high simulation accuracy and flexibility, the presented procedure can become a key factor in the future design process of TPMS lattices.
Temperature-dependent dynamic compressive properties and failure mechanisms of the additively manufactured CoCrFeMnNi high entropy alloy
CoCrFeMnNi 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.
Designing the microstructural constituents of an additively manufactured near β Ti alloy for an enhanced mechanical and corrosion response
Additive 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.
Direct observation of nanocrystal-induced enhancement of tensile ductility in a metallic glass composite
Bulk 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.
Machine learning for additive manufacturing: Predicting materials characteristics and their uncertainty
Additive 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.
Controlling the Young’s modulus of a ß-type Ti-Nb alloy via strong texturing by LPBF
The 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.
Additively manufactured AlSi10Mg lattices – Potential and limits of modelling as-designed structures
Additive 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.
Laser powder bed fusion of a superelastic Cu-Al-Mn shape memory alloy
Dense and crack-free specimens of the shape memory alloy Cu71.6Al17Mn11.4 (at.%) were produced via laser powder bed fusion across a wide range of process parameters. The microstructure, viz. grain size, can be directly tailored within the process and with it the transformation temperatures (TTs) shifted to higher values by raising the energy input. The microstructure, and the superelastic behavior of additively manufactured samples were assessed by a detailed comparison with induction melted material. The precipitation of the α phase, which inhibit the martensitic transformation, were not observed in the additively manufactured samples owing to the high intrinsic cooling rates during the fabrication process. Fine columnar grains with a strong [001]-texture along the building direction lead to an enhanced yield strength compared to the coarse-grained cast samples. A maximum recoverable strain of 2.86% was observed after 5% compressive loading. The first results of our approach imply that laser powder bed fusion is a promising technique to directly produce individually designed Cu-Al-Mn shape memory parts with a pronounced superelasticity at room temperature.
Constrained incipient phase transformation in Ni-Mn-Ga films: A small-scale design challenge
Ni-Mn-Ga shape-memory alloys are promising candidates for large strain actuation and magnetocaloric cooling devices. In view of potential small-scale applications, we probe here nanomechanically the stress-induced austenite–martensite transition in single crystalline austenitic thin films as a function of temperature. In 0.5 µm thin films, a marked incipient phase transformation to martensite is observed during nanoindentation, leaving behind pockets of residual martensite after unloading. These nanomechanical instabilities occur irrespective of deformation rate and temperature, are Weibull distributed, and reveal large spatial variations in transformation stress. In contrast, at a larger film thickness of 2 μm fully reversible transformations occur, and mechanical loading remains entirely smooth. Ab-initio simulations demonstrate how an in-plane constraint can considerably increase the martensitic transformation stress, explaining the thickness-dependent nanomechanical behavior. These findings for a shape-memory Heusler alloy give insights into how reduced dimensions and constraints can lead to unexpectedly large transformation stresses that need to be considered in small-scale actuation design.
Structure-property relationships of imperfect additively manufactured lattices based on triply periodic minimal surfaces
Lattices based on triply periodic minimal surfaces (TPMS) have recently attracted increasing interest, but their additive manufacturing (AM) is fraught with imperfections that compromise their structural integrity. Initial research has addressed the influence of process-induced imperfections in lattices, but so far numerical work for TPMS lattices is insufficient. Therefore, in the present study, the structure–property relationships of TPMS lattices, including their imperfections, are investigated experimentally and numerically. The main focus is on a biomimetic Schoen I-WP network lattice made of laser powder bed fusion (LPBF) processed Ti-42Nb designed for bone tissue engineering (BTE). The lattice is scanned by computed tomography (CT) and its as-built morphology is examined before a modeling procedure for artificial reconstruction is developed. The structure–property relationships are analyzed by experimental and numerical compression tests. An anisotropic elastoplastic material model is parameterized for finite element analyses (FEA). The numerical results indicates that the reconstruction of the as-built morphology decisively improves the prediction accuracy compared to the ideal design. This work highlights the central importance of process-related imperfections for the structure–property relationships of TPMS lattices and proposes a modeling procedure to capture their implications.