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An additive manufacturing system having a heat source for melting powder particles in a desired shape and pattern to produce a product. A secondary heat source is used for heat treating the product to achieve heat treatment. The secondary heat source is used to peen or anneal residual stresses caused by the additive manufacturing process.
Additive manufacturing (AM) of metal parts can save time, energy, and produce parts that cannot otherwise be made with traditional machining methods. Near final part geometry is the goal for AM, but material microstructures are inherently different from those of wrought materials as they arise from a complex temperature history associated with the additive process. It is well known that strength and other properties of interest in engineering design follow from microstructure and temperature history. Because of complex microstructure morphologies and spatial heterogeneities, properties are heterogeneous and reflect underlying microstructure. This report describes a method for distributing properties across a finite element mesh so that effects of complex heterogeneous microstructures arising from additive manufacturing can be systematically incorporated into engineering scale calculations without the need for conducting a nearly impossible and time consuming effort of meshing material details. Furthermore, the method reflects the inherent variability in AM materials by making use of kinetic Monte Carlo calculations to model the AM process associated with a build.
Metal additive manufacturing (AM) processing consists of numerous parameters which take time to optimize for various geometries. One aspect of the metal AM process that continues to be explored is the control of thermal energy accumulation during component manufacturing due to the melting and solidification of the feedstock. Excessive energy accumulation causes thermal failure of the component while minimal energy accumulation causes lack of fusion with the build plate or previous layer. The ability to simulate the thermal response of an AM component can increase research efficiency by reducing the time to optimize thermal energy accumulation. This paper presents an effective implementation of finite element analysis to determine the thermal response of a wire arc additive manufactured component with various build plate sizes and cooling methods including, integral build plate cooling, oversized build plates with passive cooling, and non-integral build plate cooling. The use of integral build plate cooling channels was shown to decrease the interpass temperature at the conclusion of the build process by 55% and build plate temperature by 96% compared to the conventionally deposited sample with 20 second dwell time. The use of a tall build plate with passive cooling was shown to reduce the interpass temperature by 32% as compared to the conventionally deposited sample with 20 second dwell time. Each cooling strategy evaluated decreased the interpass temperature within a range of 20–55% which enables higher deposition rates and decreased dwell times during depositions. The cooling strategies are designed to be implemented in a hybrid or retrofit AM platform to mitigate concerns of the thermal input from the additive process having detrimental effects on the precision of the machining process. This paper shows that accurate simulations of all strategies can be used to accurately predict the thermal response of the various strategies discussed. These cooling strategies will allow for increased deposition rates with comparable interpass temperature and decreased dwell time, increasing deposition efficiency. This model and these simulations are verified by experimental results. It is concluded that passive strategies, such as the over-sized tall build plate, can be used when liquid coolant in the AM environment could negatively affect the deposition process. Active cooling strategies, such as the integral build plate cooling could be used if low thermal conductivity materials are deposited or higher material deposition rates are desired. This paper discusses the use of active and passive cooling used during AM and shows how a simulation model can be used to make design choices for cooling strategies. The model also enables verification of select critical process parameters such as dwell times for a desired interpass temperature.
Additive manufacturing (AM) holds great potential for improving materials efficiency, reducing life-cycle impacts, and enabling greater engineering functionality compared to conventional manufacturing (CM), and AM has been increasingly adopted by aircraft component manufacturers for lightweight, cost-effective designs. This study estimates the net changes in life-cycle primary energy and greenhouse gas emissions associated with AM technologies for lightweight metallic aircraft components through the year 2050, to shed light on the environmental benefits of a shift from CM to AM processes in the U.S. aircraft industry. A systems modeling framework is presented, with integrates engineering criteria, life-cycle environmental data, aircraft fleet stock and fuel use models under different AM adoption scenarios. Estimated fleet-wide life-cycle primary energy savings at most reach 70-173 million GJ/year in 2050, with cumulative savings of 1.2–2.8 billion GJ. Associated cumulative GHG emission reductions were estimated at 92.1–215.0 million metric tons. In addition, thousands of tons of aluminum, titanium and nickel alloys could be potentially saved per year in 2050. The results indicate a significant role of AM technologies in helping society meet its long-term energy use and GHG emissions reduction goals, and highlight barriers and opportunities for AM adoption for the aircraft industry.
In additively manufactured (AM) components, infill structure significantly affects the mechanical performance of the final printed part. However, mechanical stress induced by operation loads has been so far neglected for infill patterning. Most slicers currently available in the market provide infill patterns that are uniform in shape and size regardless of the operational loading. We develop a design approach for infill patterns that accounts for the induced stress. This approach differs from topology optimization as it focuses on the porous infill, which allows the external shape of the printed part to remain intact. The proposed approach uses a computational stress analysis to control the distribution of the local density of the infill pattern. We have applied the approach to a wind turbine blade core with infill densities optimized based on the structural loads. The blade core is fabricated in our big area additive manufacturing (BAAM) system. To ensure less warpage and better inter-layer bonding, fast layer deposition is critical in BAAM system. We have optimized the tool path sequences to minimize the deposition time via the solution to the Chinese Postman Problem (CPP). For the application of wind turbine infill, the deposition from the CPP method is twice faster than the deposition from conventional slicing.
Additive manufacturing via selective laser melting can result in variable levels of internal porosity both between build plates and within components from the same build. In this work, sample porosity levels were compared to tensile properties for 176 samples spanning eight different build plates. Sample porosity was measured both by Archimedes density, which provided an estimation of overall porosity, and by observation of voids in the fracture surface, which provided an estimation of the porosity at the failure plane. The porosity observed at the fracture surface consistently demonstrated higher porosity than that suggested by Archimedes density. The porosity values obtained from both methods were compared against the mechanical results. Sample porosity appears to have some correlation to the ultimate tensile strength, yield strength, and modulus, but the strongest relationship is observed between porosity and ductility. Three different models were used to relate the fracture surface porosity to the ductility. The first method was a simple linear regression analysis, while the other two models have been used to relate porosity to ductility in cast alloys. It is shown that all three models fit the data well over the observed porosity ranges, suggesting that the models taken from casting theory can extend to additively manufactured metals. Finally, it is proposed that the non-destructive Archimedes method could be used to estimate an approximate sample ductility through the use of correlations realized here. Such a relationship could prove useful for design and for a deeper understanding of the impact of pores on tensile behavior.
Metal additive manufacturing (AM) is a disruptive manufacturing technology that opens the design space for parts outside those possible from traditional manufacturing methods. In order to accelerate industry and R&D needs to certify AM parts, the Exascale Additive Manufacturing project (ExaAM) has developed a suite of exascale-ready computational tools to model the process-to-structure-to-properties (PSP) relationship for additively manufactured metal components. One such tool is an uncertainty quantification (UQ) pipeline to quantify the effect that uncertainty in processing conditions has on local mechanical responses. We present an overview of this pipeline and its required simulation and workflow codes. Using the Oak Ridge National Laboratory’s (ORNL) exascale computer, Frontier, we utilize this pipeline to cross multiple length and time scales to predict the local mechanical response of a location within a complex AM bridge part, AMB2018-01 produced by the National Institute of Standards and Technology (NIST) as part of their 2018 AM-Bench test series. Our results are then compared to experimental mechanical tests of parts from the NIST build to quantify the error in the ExaAM UQ workflow.
We present the current status of a large-scale computing framework to address the need of the multidisciplinary effort to study chemical dynamics. Specifically, we are enabling scientists to process and store experimental data, run large-scale computationally expensive high-fidelity physical simulation, and analyze these results using the state-of-the-art data analytics tools, machine learning, and uncertainty quantification methods using heterogeneous computing resources, such as CPU and GPU cluster. The framework can integrate or abstract out multiple domains based on roles. In order to develop this framework, we have leveraged an existing framework coupled with in-house heterogeneous computing resources. We present the results of using this framework on a single metadata triggered workflow to accelerate an additive manufacturing use case.
Additive manufacturing (AM) promises to redesign traditional manufacturing by enabling the ultimate in agility for rapid component design changes in commercial products and for fabricating complex integrated parts. Here, by significantly increasing quality and yield of metallic alloy powders, the pace for design, development, and deployment of the most promising AM approaches can be greatly accelerated, resulting in rapid commercialization of these advanced manufacturing methods. By successful completion of a critical suite of processing research tasks that are intended to greatly enhance gas atomized powder quality and the precision and efficiency of powder production, researchers can help promote continued rapid growth of AM. Finally, other powder-based or spray-based advanced manufacturing methods could also benefit from these research outcomes, promoting the next wave of sustainable manufacturing technologies for conventional and advanced materials.
Large scale additive manufacturing (AM) of fiber-reinforced polymer composites has gained traction in the mold and die community over the last decade, largely due to reduced material cost and lead time. However, limitations in mold size remain present due to printer volume constraints and costs associated with large format machines. Here, this paper describes a method for producing additively manufactured polymer molds in segments with an O-ring joint design, which can be machined and subsequently assembled for autoclave applications. The developed joining mechanism allows for maintaining the vacuum integrity during the cure cycle while accounting for the anisotropic nature of the additively manufactured polymer molds. Over a range of tested temperatures, 20–200 °C, the multi-part mold joining design was able to achieve full vacuum of ∼73.7 cmHg (29 inchHg) and maintain a vacuum integrity with vacuum losses less than 1.5 cmHg/5min (0.6 inchHg).
Abstract Defects in parts produced by additive manufacturing, instead of simply being perceived as deleterious, can act as important sources of information associated with the complex physical processes that occur during materials deposition and subsequent thermal cycles. Indeed, they act as materials-state ‘fossil’ records of the dynamic AM process. The approach of using defects as epoch-like records of prior history has been developed while studying additively manufactured Ti–6Al–4V and has given new insights into processes that may otherwise remain either obscured or unquantified. Analogous to ‘epochs,’ the evolution of these defects often is characterized by physics that span across a temporal length scale. To demonstrate this approach, a broad range of analyses including optical and electron microscopy, X-ray computed tomography, energy-dispersive spectroscopy, and electron backscatter diffraction have been used to characterize a raster-scanned electron beam Ti–6Al–4V sample. These analysis techniques provide key characteristics of defects such as their morphology, location within the part, complex compositional fields interacting with the defects, and structures on the free surfaces of defects. Observed defects have been classified as banding, spherical porosity, and lack of fusion. Banding is directly related to preferential evaporation of Al, which has an influence on mechanical properties. Lack-of-fusion defects can be used to understand columnar grain growth, fluid flow of melt pools, humping, and spattering events. Graphical abstract
The goal of this work is to study the way student designers use design for additive manufacturing (DfAM) rules or heuristics. It can be challenging for novice designers to create successful designs for additive manufacturing (AM), due to its recent surge in popularity and lack of formal education or training. A study was carried out to investigate the way novices apply DfAM heuristics when they receive them at different points in the design process. A design problem was presented to students, and three different groups of student participants were given a lecture on DfAM heuristics at three different points in the design process: before the initial design, between the initial design and redesign, and after the redesign. The novelty and quality of each of the resulting designs were evaluated. Results indicate that although the DfAM heuristics lecture had no impact on the overall quality of the designs generated, participants who were given the heuristics lecture after the initial design session produced designs that were better-suited for 3D printing in the second phase of the design activity. However, receiving this additional information appears to prevent students from creatively iterating upon their initial designs, as participants who received heuristic information between the design sessions experienced a decrease in novelty between the two sessions. Additionally, receiving the heuristics lecture increased all students’ perceptions of their ability to perform DfAM-related tasks. Furthermore, these results validate the practicality of design heuristics in lecture form as AM training tools while also emphasizing the importance of iteration in the design process.
A machine component fabricated by additive manufacturing (AM) of metal powders often possesses steep residual stress gradients with significant magnitudes due to large temperature gradients that transpire within a localized area during fabrication. These processing-induced residual stresses can cause distortion of the part, and if they are of significant magnitude, they could induce cracking of the component, degrade printability, and/or diminish subsequent mechanical performance. The ability to predict these residual stresses imparted by AM is an important step in permeating AM technology for advanced manufacturing. Calibration and validation of AM process models used for prediction are, therefore, a critical step in understanding the origin and mitigating the challenges associated with residual stresses inherent to the AM process. In the present work, the residual strain distributions in components with simple geometries fabricated by a laser powder bed fusion (LBPF) process were characterized non-destructively using energy-dispersive X-ray diffraction in support of the US Air Force Research Laboratory Additive Manufacturing Modeling Challenge Series Groeber et al. (JOM 70:441-448, 2018). The measurement setup and approach are described in detail so that the data can be used as a benchmark to calibrate and validate models for the prediction of macro-scale residual stresses due to the LPBF process.
This paper describes the measurement of convective heat transfer coefficients and friction factors for sCO 2 flowing in additively manufactured tubes with internal pin fins at the US DOE’s National Energy Technology Laboratory in Morgantown, WV. The measurement procedures were validated by conducting benchmark tests with smooth stainless-steel tube and comparing the results with published correlations for Nusselt number (Nu) and friction factor. Over Reynolds numbers (ReD) ranging from 5 × 10 4 to 2.5 × 10 5 , measured Nu was within 5% of the Dittus-Boelter correlation and measured friction factors were within 5% of the McAdams correlation for smooth tube flow. The candidate pin fin patterned pipes were additively manufactured (AM) at the Oak Ridge National Laboratory. The pins were circular or elliptical in cross-section and were printed at a 30⁰ angle relative to the inner wall (to meet AM constraints). The pin arrangement was helical to promote enhanced heat transfer due to swirl flow. Pin length to diameter aspect ratio was 1.33, 2, and 8, while the pin diameter to tube diameter ratio was 0.188, 0.125, and 0.063. Tests were performed for ReD varying from 6.9 × 10 4 to 2.2 × 10 5 and at conditions equivalent to the low pressure (LP) outlet (8.69 MPa, 361 K) and the high pressure (HP) inlet (20.7 MPa, 350 K) of the low temperature recuperator (LTR) in an indirect sCO 2 cycle. The Wilson plot technique was utilized to measure the bulk heat transfer coefficients. For the best performing design (tube A, pin length to tube diameter ratio: 1.33, pin diameter to tube diameter ratio: 0.19), the local heat transfer coefficient increased by 136% relative to the Dittus-Boelter correlation at the LTR low pressure outlet and 194% at the LTR high pressure inlet. These correspond to a 282% and a 271% increase in the product of the heat transfer coefficient and surface area (adjusted for fin efficiency) product, respectively. Large pressure drops across the test articles were observed. For Tube Design A, the average friction factor, across the range of ReD considered, was significantly larger than the McAdams correlation at both the LTR LP outlet and the LTR HP inlet. A thermal performance factor was utilized to express the ratio of material required to build a finned heat exchanger relative to a finless heat exchanger with the same heat duty and pumping power. Tube Design A was estimated to decrease the required heat exchanger material by 13%.
Supercritical CO 2 (sCO 2 ) power cycles are being developed due to their potential for high efficiency and reduced capital cost. It is necessary that these recuperators operate at high pressures and temperatures, up to 30 MPa and 900 K, with effectiveness values > 95% and pressure drops <1% to achieve high cycle efficiencies. Moreover, it is also necessary to have reasonable cost recuperators to control the capital costs of the sCO 2 power cycles. In this study, a Plate Pin-Fin (PPF) heat exchanger has been proposed as an sCO 2 recuperator. This preliminary recuperator design leverages capabilities enabled by additive manufacturing. Although the PPF design has characteristics similar to those of a plate heat exchanger, small diameter and relatively long fins are used to increase surface area, enhance heat transfer, and provide structural support for the partition plates that separate the fluid streams. Existing correlations for heat transfer and pressure drop were adapted for the PPF heat exchanger. These correlations were implemented in a 1D analytical model and used for the optimization of a 5-kW th high temperature recuperator for an indirect sCO 2 cycle by varying the design parameters to minimize the quantity of material required. A 3D conjugate heat transfer numerical simulations were conducted to validate the heat transfer and pressure loss correlations. A steepest descent method was used to minimize heat exchanger mass for a 5-kW prototype recuperator subject to a maximum specified pressure drop. Additionally, the design analysis indicated that an optimum PPF recuperator would be attained for the minimum allowable pin transverse spacing, minimum pin width, minimum pin height and near maximum cell aspect ratio. Moreover, at a low material requirement of 0.216 kg/kW and a pressure drop, which is almost five times lower than the allowable pressure drop design target, the optimized PPF heat exchanger has the high potential to be an alternative to a printed circuit heat exchanger, which is a conservative design basis for the current state-of-the-art sCO 2 recuperators.
Ultrasonic additive manufacturing (UAM) is a solid-state manufacturing technology for producing near-net shape metallic parts combining additive ultrasonic metal welding and subtractive machining. Even though UAM has been demonstrated to produce robust metal builds in Al–Al, Al–Ti, Al-steel, Cu–Cu, Al–Cu, and other material systems, UAM welding of high strength steels has proven challenging. This study investigates process and post-processing methods to improve UAM steel weld quality and demonstrates the UAM fabrication of stainless steel 410 (SS 410) builds which possess, after post-processing, mechanical properties comparable with bulk material. Unlike UAM fabrication of softer metals, this study shows that increasing the baseplate temperature from 38°C (100°F) to 204°C (400°F) improves interfacial strength and structural homogeneity of the UAM steel samples. Further improvement in strength is achieved through post-processing. The hot isostatic pressing (HIP) post treatment improves the shear strength of UAM samples to 344 MPa from 154 MPa for as-welded samples. Microstructural analyses with SEM and EBSD show no evidence of body centered cubic (BCC) ferrite to face centered cubic (FCC) austenite transformation taking place during UAM welding of SS 410. The weld quality improvement of UAM steel at higher baseplate temperatures is believed to be caused by the reduction of the yield strength of SS 410 at elevated temperature. Finaly, HIP treatment is shown to increase the overall hardness of UAM SS 410 from 204 ± 7 HV to 240 ± 16 HV due to the formation of local pockets of martensite. Nanohardness tests show that the top of layer n is harder than the bottom of layer n+1 due to grain boundary strengthening.
The production of metal parts via laser powder bed fusion additive manufacturing is growing exponentially. However, the transition of this technology from production of prototypes to production of critical parts is hindered by a lack of confidence in the quality of the part. Confidence can be established via a fundamental understanding of the physics of the process. It is generally accepted that this understanding will be increasingly achieved through modeling and simulation. However, there are significant physics, computational, and materials challenges stemming from the broad range of length and time scales and temperature ranges associated with the process. In this study, we review the current state of the art and describe the challenges that need to be met to achieve the desired fundamental understanding of the physics of the process.