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Plotkowski, Alex

Publications and source records attributed to Plotkowski, Alex.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Dynamic Beam Shape Sensing and Control in an Open Architecture Metal AM System for Microstructure Manipulation

Most commercially available metal additive manufacturing (AM) systems are closed-architecture, meaning that the system manufacturer limits the ability of the user to directly control the process conditions and can modify the specified inputs using proprietary methods. Unfortunately, this approach severely limits or confounds the ability for users to perform research on fundamental aspects of additive manufacturing. ARCTOS Technology Solutions (ARCTOS) produces an open-architecture laser powder bed fusion (PBF) additive manufacturing system that allows the user to fully control all aspects of the process control and monitoring. The purpose of this project is to help develop beam shaping capabilities and controls within the ARCTOS open-architecture system for control of microstructure evolution during metal AM.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Diffusion, atomic transport, and ordering in Al-Zr alloys: FCC and liquid phases

Additive manufacturing of materials with controlled microstructure demands knowledge of atomic scale properties near the solid-liquid transition state. Many of these properties are not affordable by experimental techniques and computer modeling is the possible solution to the problem. In this paper, we present the results of an extended atomistic study of intrinsic atomic transport due to vacancy diffusion in FCC and L12 solid phases and diffusion in the liquid phase of Al-Zr alloys. A deceleration of the overall self-diffusion was observed when Zr was added to Al. The effect was stronger in the solid and weaker in the liquid. Additionally, the effect was strongly temperature dependent in the solid phases, but not in the liquid. Atomic transport was chemically biased: transport of Zr atoms was significantly slower than that of Al atoms, and this bias effect was stronger in the solid phases. The overall diffusion and chemical ordering processes in the liquid state were five to six orders in magnitude faster than in the solid. Chemical short-range order parameters in the liquid saturated at values close to those in the ordered L12 structure of Al3Zr. Chemical and structural ordering in the solid phases was negligible over the modeled microsecond time scale. Here, the results are discussed in view of optimizing additive manufacturing parameters for the controlled formation of metastable L1 2 precipitates.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Enabling rapid X-ray CT characterisation for additive manufacturing using CAD models and deep learning-based reconstruction

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.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Validating the Use of Gaussian Process Regression for Adaptive Mapping of Residual Stress Fields

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.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microstructure and mechanical properties of hypoeutectic Al–6Ce–3Ni-0.7Fe (wt.%) alloy

Here, a hypoeutectic, Fe-modified Al–Ce–Ni alloy (Al–6Ce–3Ni-0.7Fe, wt.%) is studied in terms of microstructure, thermal stability, ambient temperature strengthening, and creep resistance. The as-cast microstructure consists of primary Al dendrites and interdendritic binary eutectic regions (Al–Al11Ce3 and/or Al–Al9(Fe,Ni)2), with micron/submicron lamellar spacing, depending on the location along the height of the ingot. The cast alloy exhibits excellent coarsening resistance at 400 °C, with mostly unchanged microstructure and microhardness after 6 weeks of aging, indicating good thermal stability of Al11Ce3 and Al9(Fe,Ni)2. Orowan strengthening and load transfer are identified as strengthening mechanisms at ambient and elevated temperature. A high volume fraction of the intermetallic phases (providing load transfer) and relatively coarse eutectic spacing (for modest Orowan strengthening) result in a moderate as-cast microhardness of 566 ± 32 MPa. Creep resistance at 300 and 350 °C is similar to a binary Al-12.5Ce eutectic alloy (with twice the Ce content) because of two countering effects: Al–6Ce–3Ni-0.7Fe shows a higher volume fraction of strengthening intermetallic phases, but it also exhibits a large fraction of primary Al dendrites which weaken the alloy. By contrast, the alloy, when laser-remelted at the surface, has a fully eutectic microstructure without primary aluminum dendrites achieved by high undercooling on solidification, with a refined network of eutectic phases that doubles the microhardness as compared to the cast alloy. Whereas coarsening is faster due to the shorter diffusion distances between the eutectic phases, hardness remains ~30% higher than the as-cast alloy after ~6 weeks aging at 400 °C.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Method for rapid development of additive manufacturing parameter set

An apparatus includes a control system that defines a test part having multiple features of multiple feature types. The control system controls an additive manufacturing (AM) machine to print multiple copies of the test part, with each copy being printed according to a respective set of values used as printing parameters. A measurement system obtains a computed tomography (CT) image of each of the copies of the test part. An analysis system, for each of the plurality of feature types, analyzes the CT images to identify a selected set of values for the printing parameters. The analysis system identifies a portion of the CT image related to a first feature and assesses its density based on an average grayscale value. The AM machine is then controlled to print production parts according to, for each feature type of the production parts, the selected set of values for the printing parameters.

Bhattad, Pradeep↗
DOE OSTI2023

3D Printed eutectic aluminum alloy has facility for site-specific properties

Additive manufacturing (AM) has the ability to print structures with site-specific properties. Existing AM approaches for site-specific properties are, however, based on complex processing-microstructure relationships in conventional alloys that were not designed for this purpose. Here, in this work, we report a straightforward approach for achieving site-specific properties that takes advantage of eutectic solidification characteristics. We demonstrate that the yield strength of a eutectic Al-Cu-Ce-Zr alloy can be tuned by varying the laser scan speed in concert with hatch spacing in laser powder bed fusion AM. A faster speed increases solidification rate resulting in finer eutectic spacing and higher strength. The hatch spacing is reduced at faster speeds to ensure overlap between melt pools which become smaller with increasing scan speed. The yield strength and its anisotropy relative to build direction are further tunable with a heat treatment. The scan speed-eutectic spacing-strength relationship is successfully applied to print a complex pattern of site-specific hardness in the alloy. The generalized principle of using AM for a eutectic alloy to create site-specific properties and anisotropy in properties is demonstrated.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Adaptive sampling for accelerating neutron diffraction-based strain mapping *

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.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Hybrid Al Casting + AM Components for Automotive Applications

Although casting Aluminum (Al) alloys is widely used for automotive application, the conventional casting process is showing limitations for modern vehicle production. A hybrid manufacturing method that is capable of fabricating automotive structures with tailored functionality and tuned attributes at a high production rate is proposed in this project, utilizing the merits of conventional casting methods and additive manufacturing (AM) methods. Ford is interested in supplementing conventional casting processes (e.g., shape casting, high pressure die casting) with local modifications to geometry and microstructure using additive manufacturing techniques. Two additive manufacturing methods, wire-arc and laser hot-wire directed energy deposition AM process, were evaluated by deposition of 4043 filler wire onto high-pressure die cast and permanent mold cast Al-Si alloys. The feasibility of this hybrid manufacturing method was determined through trial deposition and evaluation of defects, microstructure, and mechanical properties. Laser hot-wire deposition was found to be beneficial for this application with low porosity, a refined microstructure, and mechanical properties similar to those reported in the literature for 4043. The mechanical properties of the interface between the A356 casting and deposited 4043 were found to be controlled by the weaker deposit material.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Computational Requirements in Clean Energy and Manufacturing: Summary report of the virtual workshop held on June 28-29, 2021

On June 28–29, 2021, the US Department of Energy’s (DOE’s) Advanced Scientific Computing Research (ASCR) program in the Office of Science convened a workshop with the Energy Efficiency and Renew able Energy (EERE) program offices to assess the future need for advanced computing resources in the areas of clean energy and advanced manufacturing. In part, this discussion served as an update to earlier workshops and townhalls. ASCR is guided by DOE mission needs as it develops research programs, computers, and networks at the leading edge of technologies. As the exascale computing era dawns, technology changes are creating new opportunities for those who must use high-performance computing (HPC) and data systems effectively. The ASCR computing facilities are augmenting their strategy to adapt to changing science needs and emerging technologies and to leverage the utility of exascale computing across the federal government.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Additively manufactured Al-Ce-Ni-Mn alloy with improved elevated-temperature fatigue resistance

Here, the high cycle fatigue behavior of an additively manufactured (AM) Al-10.5Ce-3.1Ni-1.2Mn wt% alloy was evaluated at 350 °C. The measured fatigue strength of 50 MPa at this temperature is comparable to the most fatigue resistant wrought Al alloys (2618-T6 and 7079-T6) at 315 °C. Refinement of pore populations through advanced AM processing in the examined alloy led to oxide inclusions becoming dominant fatigue crack initiation sites. The distribution of microstructural features that determine crack initiation was measured by X-ray computed tomography and served as input to make fatigue strength model predictions with various methodologies. A Monte-Carlo based approach that was previously applied to predict the fatigue strength of cast aluminum alloys through pore size distributions yielded accurate predictions for the fatigue strength of this AM alloy. Given the tunability of defect distributions in AM alloys, the sensitivity of the Al-Ce-Ni-Mn alloy fatigue behavior to defect distributions and outstanding elevated-temperature fatigue resistance of this alloy; the results together suggest the possibility of a new generation of fatigue-resistant alloys produced by the additive manufacturing route.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Assessment of Process Modeling Tools for Determining Variability in Additively Manufactured Parts

The Advanced Materials and Manufacturing Technologies (AMMT) program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy However, the unique aspects of additive manufacturing (AM) materials in terms of their processing history, microstructure, and properties, are a major barrier for qualification and certification of nuclear components. Much of this challenge may be attributed to component scale variations in microstructure and properties that are driven by local influences of process conditions and geometry on thermal history, melt pool dynamics, and corresponding microstructure evolution. Computational modeling tools may be helpful in this regard to aid in predicting and controlling this level of variability. The purpose of this report is to review the current state-of-the-art for process modeling with regards to metal AM. For this purpose, we consider specifically the case study of laser powder bed fusion (LPBF) processing of SS316, a family of alloys that are both commonly used in nuclear energy applications and suitable for AM processing. The report first introduces the necessary components of a process modeling workflow, followed by a review of the current status of each. At the end, application of these modeling tools to understanding variability in AM process given their current state are considered, and recommendations for future development are proposed

36 MATERIALS SCIENCE↗
DOE OSTI2022

Temperature-Dependent Thermal and Mechanical Properties of a Wire Arc Additively Manufactured Low Transformation Temperature Steel

Recent research has studied the use of low transformation temperature (LTT) martensite steel as feedstock for wire arc additive manufacturing (WAAM) and low tensile residual stresses or compressive residual stresses were detected in the printed walls. These residual stress states help to improve printed product properties such as fatigue strength and corrosion resistance. However, the thermal and mechanical properties of WAAM printed LTT martensite steel walls are largely unknown. In this work, a printed LTT martensite steel was characterized for its thermal, metallurgical, and mechanical behavior at room and elevated temperatures. The temperature-dependent specific heat capacity, thermal expansion, atomic lattice spacing, and tensile properties were measured during both heating and cooling and related to observed microstructural features and computational thermodynamics predictions. These results revealed a large hysteresis in the martensitic transformation, with a martensite start temperature of 240 °C and austenite start temperature of 680 °C. Additional thermal cycles and specimen orientation did not affect the printed specimen austenite and martensite transformations. However, it was observed that the printed metal may exhibit tempering embrittlement at about 350 °C but further studies are needed to confirm that. Further, these results suggest that a temperature control of 250 °C to 350 °C during WAAM is needed to maximize the stress reduction potential of the LTT250 martensite steel. Opportunities for future implementation of LTT martensite steels and optimization of additive manufacturing process conditions are identified.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Heterogeneous phase transformation pathways in additively manufactured Al-Ce-Mn alloys

Heat treatment of additively manufactured Al-Ce based multicomponent alloys leads to complex microstructure evolution. In this research, the ability to extend the phase transformation theories involving nucleation of a product phase from a heterogeneous multi-phase microstructure typical to that of additively manufactured samples is explored. The Al-10Ce-8Mn (wt%) was used as a model alloy system. Under additive manufacturing conditions different solidification microstructures were obtained due to spatial and temporal variations of thermal gradients (G) and liquid-solid interface velocities (R) within a given melt pool. Near the melt pool boundary (high G and low R, referred as MPB region), initially, Al 20 Mn 2 Ce forms from the liquid followed by a eutectic of FCC Al and Al 11 Ce 3 . In the melt pool interiors (low G and high R referred as ES region) a eutectic structure between FCC Al and Al 20 Mn 2 Ce is observed. During subsequent heat treatments, the MPB and ES regions transform into different sets of microstructures. In the MPB region, a fine globular microstructure containing FCC Al, Al 11 Ce 3 , Al 6 Mn, and Al 12 Mn results from the decomposition of Al 20 Mn 2 Ce. In the ES region a faceted Al 51 Mn 7 Ce 4 plate phase results from the decomposition of Al 20 Mn 2 Ce. The formation of the Al 51 Mn 7 Ce 4 phase within the eutectic microstructure at the boundaries of FCC Al and Al 20 Mn 2 Ce has not been reported in the literature. Further, these two distinct phase transformation pathways are rationalized based on the role of driving force on the nucleation of (Al 6 Mn) and/or metastable intermetallic (Al 51 Mn 7 Ce 4 ) phases at the interface of aluminum (FCC) and the non-equilibrium intermetallic (Al 20 Mn 2 Ce) phases.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multi-physics modeling of grain growth during solidification in electron beam additive manufacturing of Inconel 718

While experimental work has shown promising results regarding control of additive manufacturing metal grain structure, the effects of processing parameters on the grain structure is difficult to understand and predict from experiment alone. To this end, a modeling framework is developed which sequentially couples a macro-scale, semi-analytic thermal model, and a meso-scale, cellular automata-based microstructure model. This framework is applied to electron beam additive manufacturing of Inconel 718 using a complex spot scan pattern. The model shows that, with the same scan pattern, variations in the spot time and electron-beam current produce thermal histories with significant spatial and temporal differences, which then produce complex solidification conditions from the interplay between molten pools in the same layer and subsequent layers, resulting in vastly different grain structures. Additionally, it is noted that the framework can significantly reduce the computational expenses for coupled thermal-metallurgical problems, and has the potential to be used for component level problems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Load shuffling during creep deformation of an additively manufactured AlCuMnZr alloy

Here, the high-temperature deformation behavior of an additively manufactured Al-Cu-Mn-Zr alloy is evaluated in the as-fabricated and heat-treated states using traditional ex-situ and in-situ neutron diffraction creep experiments performed at 300 °C. The dominant reinforcement phase in the alloy, θ-Al 2 Cu, despite its high volume fraction of ~10%, does not provide load transfer strengthening during creep deformation. Instead, the lattice strain evolution suggests a new mechanism we term “load shuffling” wherein the initial load is transferred away from precipitate-free zones along the grain boundaries where most of the θ-Al 2 Cu particles are located to precipitate-strengthened grain interiors. Notwithstanding the lack of load transfer strengthening, the as-fabricated AM Al-Cu-Mn-Zr alloy still possesses improved creep resistance at 300 °C relative to a cast alloy with similar composition. The proposed load shuffling mechanism explains the lack of observed L1 2 -Al 3 Zr strengthening at 300 °C and helps identify several strategies for improvement of elevated-temperature mechanical response of AM Al alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Calibrating uncertain parameters in melt pool simulations of additive manufacturing

Melt pool scale numerical modeling of additive manufacturing (AM) processes can provide predictive capabilities and theoretical insight into the process-property-structure-performance relationships for AM parts. Despite capabilities of numerical models to solve complex multi-physics problems, it is often important to consider a tradeoff between detailed physics and computational cost. Therefore, sources of uncertainty in both experimental conditions and the parameters needed for modeling require models to be validated against empirical evidence. Here, a method is proposed to calibrate uncertain parameters used in continuum-scale melt pool models for powder bed fusion (PBF) AM. Both a simplified heat transfer model and a heat transfer and fluid flow model were investigated. A surrogate model and Markov chain-based optimization algorithm calibrated melt pool geometry for models within experimental variation of the target melt pool width and depth from the NIST AM-Bench 2018-02 dataset. The melt pool temperature distributions, solidification parameters, and simulated multi-layer solidification microstructures were compared between the two models. Similar results from both models indicate that calibrated, lower fidelity numerical models may be used in place of higher fidelity models to generate melt pool solidification data. Finally, these calibrated models therefore enable lower computational cost melt pool simulations without a noticeable decrease in simulation accuracy for grain-scale microstructure simulations.

36 MATERIALS SCIENCE↗