In addition to design geometry, surface roughness, and solid-state phase transformation, solidification microstructure plays a crucial role in controlling the performance of additively manufactured ...components. Crystallographic texture, primary dendrite arm spacing (PDAS), and grain size are directly correlated to local solidification conditions. We have developed a new melt-scan strategy for inducing site specific, on-demand control of solidification microstructure. We were able to induce variations in grain size (30 μm–150 μm) and PDAS (4 μm - 10 μm) in Inconel 718 parts produced by the electron beam additive manufacturing system (Arcam®). A conventional raster melt-scan resulted in a grain size of about 600 μm. The observed variations in grain size with different melt-scan strategies are rationalized using a numerical thermal and solidification model which accounts for the transient curvature of the melt pool and associated thermal gradients and liquid-solid interface velocities. The refinement in grain size at high cooling rates (>104 K/s) is also attributed to the potential heterogeneous nucleation of grains ahead of the epitaxially growing solidification front. The variation in PDAS is rationalized using a coupled numerical-theoretical model as a function of local solidification conditions (thermal gradient and liquid-solid interface velocity) of the melt pool.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The microstructural and strength evolution of an additively manufactured Al-8.6Cu-0.5Mn-0.9Zr alloy upon aging at 300, 350, and 400 °C is investigated. The strengthening phases of the alloy evolve ...significantly upon aging, with breakdown and spheroidization of the interconnected θ-Al2Cu network, dissolution of metastable θ'-Al2Cu precipitates, and precipitation of nanometric L12-Al3Zr from a matrix supersaturated in Zr. In the peak-aged states, the alloy displays a favorable combination of strength and ductility, with a room-temperature yield strength of 314–341 MPa and ductility of 11–13%. The measured yield strengths for microstructures with different aging treatments are compared to predictions of yield strengths from grain boundary, solid solution, and particle strengthening contributions. The observed strain hardening behavior is related to fundamental precipitate and dislocation interactions. Comparison between predicted and measured strength values indicates a continued need for strengthening models specifically developed for the heterogeneous microstructures of additively manufactured alloys.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Purpose
The purpose of this paper is to introduce the multi-solution nature of topology optimization (TO) as a design tool for additive manufacturing (AM). The sensitivity of topologically optimized ...parts and manufacturing constraints to the initial starting point of the optimization process leading to structures with equivalent performance is explored.
Design/methodology/approach
A modified bi-directional evolutionary structural optimization (BESO) code was used as the numerical approach to optimize a cantilever beam problem and reduce the mass by 50 per cent. Several optimized structures with relatively equivalent mechanical performance were generated by changing the initial starting point of the TO algorithm. These optimized structures were manufactured using fused deposition modeling (FDM). The equivalence of strain distribution in FDM parts was tested with the digital image correlation (DIC) technique and compared with that from the modified BESO code.
Findings
The results confirm that TO could lead to a wide variety of non-unique solutions based on loading and manufacturability constraints. The modified BESO code was able to reduce the support structure needed to build the simple two-dimensional cantilever beam by 15 per cent while keeping the mechanical performance at the same level.
Originality/value
The originality of this paper lies in introduction and application of the multi-solution nature of TO for AM as a design tool for optimizing structures with minimized features in the overhang condition and the need for support structures.
Laser powder bed fusion has the potential of redefining state-of-the-art processing and production methods, but defect formation and inconsistent build quality have limited the implementation of this ...process. Numerical models are widely used to study this process and predict the formation of these defects. Presently, the uncertainties of model input parameters and thermophysical properties used by these numerical simulations have not been investigated. In the present study, the uncertainty in these input parameters and material properties are quantified for laser powder bed fusion, with and without a simulated powder bed, to determine their influence on the predictive accuracy of an experimentally validated numerical model. Accounting for all possible sources of uncertainty quickly becomes computationally expensive on account of the curse of dimensionality. Uncertainty in laser absorption, solid, and liquid specific heat of the metal were found to have the largest effect on model prediction reliability with or without the use of a powder bed. Results also illustrate that accounting for these three uncertain parameters still captures the majority of model prediction uncertainty. Furthermore, the methodology of this study may be used to understand the uncertainty in as-built microstructure through propagation to microstructure prediction models, or applied under processing conditions where high Péclet numbers are observed and the thermal convection and fluid flow within the molten pool are substantial.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Probing the stress state using a high density of measurement points is time intensive and presents a limitation for what is experimentally feasible. Alternatively, individual strain fields used for ...determining stresses can be reconstructed from a subset of points using a Gaussian process regression (GPR). Results presented in this paper evidence that determining stresses from reconstructed strain fields is a viable approach for reducing the number of measurements needed to fully sample a component's stress state. The approach was demonstrated by reconstructing the stress fields in wire-arc additively manufactured walls fabricated using either a mild steel or low-temperature transition feedstock. Effects of errors in individual GP reconstructed strain maps and how these errors propagate to the final stress maps were assessed. Implications of the initial sampling approach and how localized strains affect convergence are explored to give guidance on how best to implement a dynamic sampling experiment.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. ...However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Laser powder bed fusion-based additive manufacturing (AM) is a promising method to fabricate creep-resistant Al-rare earth alloys (Al-Ce-Ni-Mn) with stable microstructures at up to 400°C. However, ...creep testing of these alloys at high temperatures shows that void coalescence and failure initiation occurs along the melt pool boundaries in the microstructure. Hence it is crucial to understand how the local mechanical behavior of the melt pool boundaries would influence the global properties of the AM produced alloy. In this study, in situ nanoindentation conducted at room temperature and 300°C revealed a reduced hardness at the melt pool boundaries. Similarly, micro-pillar compression showed a slight decline in yield strength at these boundaries, indicating that they are the weak spots in the microstructure. Such multimodal local mechanical property studies are necessary for understanding the influence of melt pool boundaries on the bulk response of fusion-based AM alloys.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Metal additive manufacturing (AM) offers flexibility and cost-effectiveness for printing complex parts but is limited to few alloys. Qualifying new alloys requires process parameter optimisation to ...produce consistent, high-quality components. High-resolution X-ray computed tomography (XCT) has not been effective for this task due to artifacts, slow scan speed, and costs. We propose a deep learning-based approach for rapid XCT acquisition and reconstruction of metal AM parts, leveraging computer-aided design models and physics-based simulations of nonlinear interactions between X-ray radiation and metals. This significantly reduces beam hardening and common XCT artifacts. We demonstrate high-throughput characterisation of over a hundred AlCe alloy components, quantifying improvements in characterisation time and quality compared to high-resolution microscopy and pycnometry. Our approach facilitates investigating the impact of process parameters and their geometry dependence in metal AM.
Abstract
Neutron diffraction is a useful technique for mapping residual strains in dense metal objects. The technique works by placing an object in the path of a neutron beam, measuring the ...diffracted signals and inferring the local lattice strain values from the measurement. In order to map the strains across the entire object, the object is stepped one position at a time in the path of the neutron beam, typically in raster order, and at each position a strain value is estimated. Typical dwell times at neutron diffraction instruments result in an overall measurement that can take several hours to map an object that is several tens of centimeters in each dimension at a resolution of a few millimeters, during which the end users do not have an estimate of the global strain features and are at risk of incomplete information in case of instruments outages. In this paper, we propose an object adaptive sampling strategy to measure the significant points first. We start with a small initial uniform set of measurement points across the object to be mapped, compute the strain in those positions and use a machine learning technique to predict the next position to measure in the object. Specifically, we use a Bayesian optimization based on a Gaussian process regression method to infer the underlying strain field from a sparse set of measurements and predict the next most informative positions to measure based on estimates of the mean and variance in the strain fields estimated from the previously measured points. We demonstrate our real-time
measure-infer-predict
workflow on additively manufactured steel parts—demonstrating that we can get an accurate strain estimate even with 30%–40% of the typical number of measurements—leading the path to faster strain mapping with useful real-time feedback. We emphasize that the proposed method is general and can be used for fast mapping of other material properties such as phase fractions from time-consuming point-wise neutron measurements.
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