Microstructure, deformation and fracture mechanisms in Al-4043 alloy produced by laser hot-wire additive manufacturing
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36 MATERIALS SCIENCE↗
Engineering topics
Plotkowski, Alex
Publications and source records attributed to Plotkowski, Alex.
Simurgh: A Framework for Cad-Driven Deep Learning Based X-Ray CT Reconstruction
High-resolution X-ray computed tomography (XCT) is an important technique for the inspection of additively manufactured (AM) parts. While XCT is typically used off-line to inspect a subset of manufactured parts, significantly accelerating measurement speed while retaining accuracy would enable use of XCT for in-line inspection to rapidly identify defects in each part as it is manufactured. Here, we propose a deep learning (DL) based approach that uses computer aided design (CAD) models of the AM parts and physics-based information to rapidly produce high-quality reconstructions from sparse XCT measurements without high quality ground truth data. Our approach uses a generative adversarial neural network (GAN) to produced realistic training data from the CAD-based simulations and a deep neural network that is trained using data from the first stage to produce accurate 3D reconstructions. Using experimental XCT data of metal parts, we demonstrate enhanced defect detection capabilities while dramatically reducing the scan time.
Ziabari, Amir↗
Influence of microstructural heterogeneities on small-scale mechanical properties of an additively manufactured Al-Ce-Ni-Mn alloy
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.
36 MATERIALS SCIENCE↗
Phase stability in cast and additively manufactured Al-rich Al-Cu-Ce alloys
Additively manufactured (AM) eutectic Al alloy systems have been studied extensively for advantageous thermal stability and mechanical properties due to their refined microstructures. Al-Cu-Ce alloys are one subset of these AM eutectic alloys. Here we studied phase stability in AM Al-Cu-Ce alloys and compared it to that of conventionally cast ones. A new phase, Al 8 Cu 3 Ce, was identified in the microstructures of both AM and cast Al-Cu-Ce alloys. This Al 8 Cu 3 Ce phase was not previously included on experimental or thermodynamically calculated phase diagrams of the Al-Cu-Ce system. Therefore, we performed additional thermodynamic modeling of the system. These models were experimentally validated with cast and subsequently heat-treated Al-Cu-Ce alloys. We found that despite the refined microstructure of the AM alloys, the phases formed were consistent with the cast alloys, suggesting that AM processing did not significantly alter the formation and stability of phases from that in the conventional alloys. In conclusion, this work has resolved inconsistent previous descriptions of the Al-Cu-Ce ternary phase diagram in the Al-rich region and resulted in the addition of the Al 8 Cu 3 Ce as an equilibrium phase above 500 °C.
36 MATERIALS SCIENCE↗
ExaCA: A performance portable exascale cellular automata application for alloy solidification modeling
Modeling the as-solidified grain structures that form during alloy processing is a critical component in understanding process-property relationships, particularly for additive manufacturing (AM) where grain structure is very sensitive to processing conditions. While cellular automata (CA)-based models have proven able to predict aspects of microstructure for several alloys and AM process conditions, long run times and large resource sets required limit the utility and the problem size to which existing CA models can be applied. As part of the ExaAM project, an initiative within the Exascale Computing Project (ECP) to develop, test, and optimize an exascale-capable coupled and self-consistent model of AM parts, we developed ExaCA (https://github.com/LLNL/ExaCA) for the liquid–solid phase transformation in the wake of AM melt pools. The CA-based code is parallelized using MPI and the Kokkos programming model, the latter enabling simulation on both CPUs and GPUs within a single-source implementation. Here, we detail the steps taken to transform a baseline, MPI-based CA code into one that is performant on CPUs and GPUs. Performance testing of ExaCA on Summit (a pre-exascale machine at Oak Ridge National Laboratory) was used to quantify CPU–GPU speedup comparing with equal numbers of nodes. Testing showed comparable CPU performance to the MPI-only CA code and a 5-20x speedup when running AM-based test problems using GPUs. The improved performance of CA through GPU utilization and the performance portable nature of ExaCA will enable accurate part-scale modeling by harnessing the power of current and future generations of high performance computing resources. Future work will include improving the strong scaling of ExaCA on GPUs by reducing load imbalance associated with the locality of the problem, and continuing performance optimization across exascale hardware.
36 MATERIALS SCIENCE↗
On the high-temperature stability of the Al 8 Cu 3 Ce intermetallic in an additively manufactured Al-Cu-Ce-Zr alloy
High-temperature resistant eutectic Al alloys are crucial materials for lightweight and energy efficient design in the automotive and aviation industries. Additive manufacturing offers a pathway to refine eutectic microstructures and develop novel alloys with superior high-temperature strength. High-volume fraction intermetallic Al-Cu-Ce alloys have been developed to deliver high-temperature strength in combination with reduced hot-tearing susceptibility. Zr is added to provide additional strengthening via nanoscale Al 3 Zr precipitation, and to stabilize and avoid coarsening of the Al 8 Cu 3 Ce phase. However, the detailed interaction between Zr and Al 8 Cu 3 Ce remains unexplored. In this work, we show with synchrotron X-ray diffraction that laser powder bed fusion fabricated Al-Cu-Ce and Al-Cu-Ce-Zr alloys contain predominantly the Al 8 Cu 3 Ce intermetallic in the as-fabricated condition. Heat treatment of the Al-Cu-Ce alloy results in the Al 8 Cu 3 Ce → Al 8 Cu 4 Ce phase transformation. In the Al-Cu-Ce-Zr alloy, minor fractions of (Al,Cu,Si) 4 Ce and Al 2 Cu-θ are found in the as-fabricated condition, while Al 8 Cu 3 Ce remains stable during heat treatment. Atom probe microscopy quantifies intermetallic stoichiometries and reveals how Zr is enriched at the Al-matrix/Al 8 Cu 3 Ce interface acting as a diffusion barrier against solute exchange. Calibrated thermodynamic modeling underpins this as a kinetic effect. Here, a qualitative microstructural model summarizes, how Zr stabilizes Al 8 Cu 3 Ce against phase transformations and coarsening.
36 MATERIALS SCIENCE↗
High Throughput Deep Learning-Based X-ray CT Characterization for Process Optimization in Metal Additive Manufacturing
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Ziabari, Amir↗
A creep-resistant additively manufactured Al-Ce-Ni-Mn alloy
Existing additively manufactured aluminum alloys exhibit poor creep resistance due to coarsening of their strengthening phases and refined grain structures. In this paper, we report on a novel additively manufactured Al-10.5Ce-3.1Ni-1.2Mn wt.% alloy which displays excellent creep resistance relative to cast high-temperature aluminum alloys at 300–400 °C. In this work, the creep resistance of this alloy is attributed to a high volume fraction (~35%) of submicron intermetallic strengthening phases which are coarsening-resistant for hundreds of hours at 350 °C. The results herein demonstrate that additive manufacturing provides opportunities for development of creep-resistant aluminum alloys that may be used in bulk form in the 250–400 °C temperature range. Pathways for further development of such alloys are identified.
36 MATERIALS SCIENCE↗
Data Mining and Visualization of High-Dimensional ICME Data for Additive Manufacturing
Integrated computational materials engineering (ICME) methods combining CALPHAD with process-based simulations can produce rich, high-dimensional data for alloy and process design. In ICME methods for metallurgical applications, the visualization and interpretation of such high-dimensional data has previously been through heat maps represented in 2 or 3 dimensions. While such an approach is ideal when one variable is varied at a time, in the case of high-dimensional data with multiple variables varied simultaneously, as is the case in additive manufacturing, interpreting the trends through two- or three-dimensional heat maps becomes challenging. Here, we propose a strategy of mixed visual data mining and quantitative analysis for high-dimensional metallurgical and process data using high-throughput thermodynamic calculations. Two case studies show the application of the proposed approach. The first case study investigated the effects of feedstock chemistry on the δ ferrite formation in 316L stainless steel powders used for binder jet additive manufacturing. The second case study linked Scheil–Gulliver calculations to a process model for dissimilar joining of aluminum alloys 5356 and 6111 during laser hot-wire additive manufacturing. Both cases contained thousands of calculated data points, showcasing the utility of visual data analysis through parallel coordinate plotting, Pearson correlation coefficient matrices, and scatter matrices compared to traditional process maps. These visualization techniques can be extended to many additive manufacturing problems to capture process–structure–property relationships for additively manufactured components.
36 MATERIALS SCIENCE↗
ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure
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.
3D printing↗
Microstructure and creep properties of cast near-eutectic Al–Ce–Ni alloys
This study investigates the as-cast and aged microstructures, thermal stability, ambient temperature strengthening, and creep resistance of three ternary Al–Ce–Ni alloys (wt%): near-eutectic Al–10Ce–5Ni (with both eutectic and hypoeutectic regions), hypoeutectic Al-7.5Ce-3.75Ni (with numerous primary Al dendrites), and hypereutectic Al-12.5Ce-6.25Ni (with coarse, blocky primary Al3Ni and Al11Ce3 precipitates and some primary Al dendrites). Depending on the alloy composition and local solidification conditions, the following eutectic morphologies are found: (i) coarse Al–Ce eutectic colonies where Al 11 Ce 3 is in the form of “Chinese script”, (ii) intermingled regions of binary Al–Ce and Al–Ni eutectic colonies, with finer Al 3 Ni and Al 11 Ce 3 fibers, (iii) large ternary eutectic colonies, where the binary Al 3 Ni and Al 11 Ce 3 phases are alternating or intertwining within the individual, fine fibers (diameters of ~60–170 nm, depending on solidification rates), and (iv) ternary eutectic zones (between primary Al dendrites), where fine Al 3 Ni and Al 11 Ce 3 build up a 3D-interconnected network. The high volume fraction of intermetallic phases and extremely fine eutectic spacing/fiber diameter both contribute to high ambient strengthening (higher as-cast microhardness than binary Al–Ce or Al–Ni), and also provide enhanced creep resistance at 300 and 350 °C. Additionally, the alloys are coarsening-resistant up to 425 °C for extended periods, with a gradual decrease in microhardness. The alloys aged at 400 °C to 1050 h show fiber fragmentation and coarsening of the resulting particles, with the faster-diffusing Ni driving more rapid coarsening of the Al 3 Ni particles which engulf finer, more stable Al 11 Ce 3 particles. Severe overaging (performed at 590 °C for 24 h) leads to Al 3 Ni and Al 11 Ce 3 spheroids which remain submicron-sized in eutectic colonies, but micron-sized at colony boundary and at Al dendrite-eutectic interface. Creep resistance at 300 °C of overaged Al–10Ce–5Ni remains substantial, consistent with load-transfer based composite strengthening being an important strengthening mechanism in these alloys, making them excellent candidates for replacement of heavier steel or titanium parts operating under stress up to 300 °C.
36 MATERIALS SCIENCE↗
Role of scan strategies and heat treatment on grain structure evolution in Fe-Si soft magnetic alloys made by laser-powder bed fusion
A major goal in printing soft magnetic Fe-Si steels using additive manufacturing is to take advantage of the potential for complex geometric designs and site-specific grain control. One major step in the processing of these alloys is understanding how processing parameters might impact how the as-built microstructure responds to annealing (i.e. the annealing response). The impact of scan strategy on the annealing response for thin wall geometries is specifically explored. Two scan strategies were explored for a thin wall geometry that produced a strongly columnar grain structure and equiaxed grain structure. Additionally, samples from both scan strategies annealed at 1200 °C showed a marked difference in annealing response with the more equiaxed sample seeing full recrystallization and grain growth, while the more columnar grain structure saw little change in microstructure. After analysis through characterization techniques and thermal-mechanical simulations Differences in internal energy within the grains were ruled out because calculated GND density values were similar for both samples. The formation of secondary particles was ruled out as a contributing factor due to the type of oxide formations and their size. It was concluded that the contributing factor to the difference in the annealing response were a difference in the resulting grain size and the density of high angle grain boundaries. These two differences were largely attributed to differences in the thermal gradient conditions due to grains preferentially growing in the direction of the steepest thermal gradient.
36 MATERIALS SCIENCE↗
Al-Cu-Ce(-Zr) alloys with an exceptional combination of additive processability and mechanical prope
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Bahl, Sumit↗
Self-Supervised Anomaly Detection via Neural Autoregressive Flows with Active Learning
Many self-supervised methods have been proposed with the target of image anomaly detection. These methods often rely on the paradigm of data augmentation with predefined transformations such as flipping, cropping, and rotations. However, it is not straightforward to apply these techniques for non-image data, such as time series or tabular data, while the performance of the existing deep approaches has been under our expectation on tasks beyond images. In this work, we propose a novel active learning (AL) scheme that relied on neural autoregressive flows (NAF) for self-supervised anomaly detection, specifically on small-scale data. Unlike other generative models such as GANs or VAEs, flow-based models allow to explicitly learn the probability density and thus can assign accurate likelihoods to normal data which makes it usable to detect anomalies. The proposed NAF-AL method is achieved by efficiently generating random samples from latent space and transforming them into feature space along with likelihoods via invertible mapping. The samples with lower likelihoods are selected and further checked by outlier detection using Mahalanobis distance. The augmented samples incorporating with normal samples are used for training a better detector so as to approach decision boundaries. Compared with random transformations, NAF-AL can be interpreted as a likelihood-oriented data augmentation that is more efficient and robust. Extensive experiments show that our approach outperforms existing baselines on multiple time series and tabular datasets, and a real-world application in advanced manufacturing, with significant improvement on anomaly detection accuracy and robustness over the state-of-the-art.
Zhang, Jiaxin↗
Elevated temperature ductility dip in an additively manufactured Al-Cu-Ce alloy
The deformation and failure mechanisms of Al-9Cu-6Ce (wt%) based alloys fabricated with laser powder bed fusion were investigated from room temperature to 400°C. The yield and ultimate tensile strengths decreased monotonically with increase in temperature, but the tensile elongation dipped unexpectedly at elevated temperatures and exhibited a minimum at 300°C. The dip in tensile elongation occurred with a concomitant dip in strain-rate sensitivity (SRS) of deformation. The as-fabricated alloy microstructure was heterogeneous, and the heat affected zone (HAZ) underneath the melt pool boundary was prone to strain localization. At 300 °C, the reduced SRS promoted the progression of strain localization in the HAZ leading to failure initiation and the dip in tensile elongation. Furthermore, a higher SRS or strain-hardening rate at other temperatures improved the tensile elongation by slowing the progression of strain localization in the HAZ such that failure initiated by other mechanisms elsewhere in the microstructure. Notably, the tensile elongation was limited by the defect structure only in a narrow temperature range (150 - 200 °C) while at other temperatures it was limited by the inherent microstructural features. This investigation exemplifies unexpected deformation and failure mechanisms possible in heterogeneous microstructures that result from additive manufacturing.
36 MATERIALS SCIENCE↗
Towards high-temperature applications of aluminium alloys enabled by additive manufacturing
Not Available
36 MATERIALS SCIENCE↗
Propagation of Input Uncertainties in Numerical Simulations of Laser Powder Bed Fusion
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.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Sensitivity of cellular automata grain structure predictions for high solidification rates
Understanding as-solidified grain structure development during constrained (G 0) alloy solidification conditions is a necessary component of understanding the larger process-microstructure relationships during additive manufacturing (AM) processing. This study applies a cellular automata (CA) model, for static solidification velocities and thermal gradients on the order of those expected in AM melt pools, to investigate grain shape development across ranges of input parameters governing temperature field evolution, nucleation, and growth. Variation in nucleated grain shape from columnar to equiaxed as a function of thermal gradient and nucleation density is modeled with CA and verified against the established transition model of Hunt and Gaumann. Furthermore, while thermal conditions and nucleation density are shown to have the largest roles on microstructure development, the sensitivity of grain structure was very unevenly distributed across parameter space. In particular, it was found that microstructure is very sensitive to mean nucleation undercooling and the interfacial response function in process space regimes near grain structure transitions, but entirely controlled by a combination of thermal gradient, solidification velocity, and nucleation density away from such transitions. The understanding of grain shape sensitivity obtained through CA modeling, particularly at large thermal gradient and nucleation density, will be necessary for accurate microstructure prediction during the extreme processing conditions and large input parameter uncertainty common to AM.
36 MATERIALS SCIENCE↗