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DOE OSTI2022

Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing

Finding actionable trends in laser-based metal additive manufacturing process monitoring data is challenging owing to the diversity and complexity of the underlying physical interactions. A single monitoring solution that captures a particular process phenomenon, such as a photodiode that tracks melt pool intensity, is not alone capable of evaluating process stability or detecting flaw formation with sufficient precision for routine application in industry. In this work, to improve flaw detection performance, we adopted a data fusion approach that captures multiple process phenomena. To demonstrate this, we acquired data from laser powder bed fusion (LPBF) builds of cylindrical specimens produced with different laser spot sizes, emulating defocusing due to process faults such as thermal lensing. The resulting specimens had porosity of varying types and severity, quantified by post-build non-destructive X-ray computed tomography, Archimedes density measurements, and destructive metallographic characterization. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were subsequently used as input features in relatively simple machine learning models, such as a support vector machine, which were trained to detect laser defocusing, and in addition, predict porosity type and severity. The results show that the data fusion approach significantly enhanced system performance by reducing the overall false positive rate from ~ 0.1 to ~ 0.001 without sacrificing the true positive rate (~0.90). These results were at par with a black-box, deep machine learning approach (convolutional neural network).

36 MATERIALS SCIENCE↗

DOE OSTI2022

Enhancing Surface Finish of Additively Manufactured 316L Stainless Steel with Pulse/Pulse Reverse Electropolishing

To improve the reliable integration of additively manufactured (AM) metal components, there is an urgent need for surface finishing methods capable of processing rough, “as-printed” surfaces. Here, we present a new and scalable electropolishing (EP) technique for use on AM 316L stainless steel (SS) that is capable of achieving a surface roughness equivalent to a machined finish, with arithmetic mean heights (S a ) as low as 1.0 µm. This technique utilizes the sequential application of a new, NaCl/H 2 O pulse/pulse reverse (P/PR) EP step along with a conventional DC (direct current) EP step, where P/PR expands the capability of EP by removing roughness features > 1 µm, which has been previously unattainable with DC EP alone without substantial material removal. This is achieved by independently varying pulse parameters, such as pulse width and height, allowing more control over electrodynamic processes at the part-electrolyte interface. Additionally, P/PR EP resulted in higher smoothing efficiency, where a higher degree of smoothing was achieved with less material removal when compared with conventional DC EP alone. This minimizes unwanted change in geometry from excessive metal dissolution.

36 MATERIALS SCIENCE↗

NASA NTRS2022

Metal Additive Manufacturing for Spaceflight

NASA has been involved in the development and maturation of metal additive manufacturing (AM) for space applications since the 2000’s. Several efforts have focused on the understanding of AM processes through material characterization and testing, standards development, component fabrication, hot-fire testing, and infusion into development and flight applications. This presentation will discuss some of the various AM processes, components, testing and development efforts for spaceflight applications at NASA used on launch vehicles and propulsion systems. - AM process comparisons and selection for spaceflight applications. - Novel alloy development, characterization, and maturation for extreme environments. - Infusion and maturation of AM components into development and flight applications.

Additive manufacturing↗

DOE OSTI2025

Additive manufacturing of amorphous metal soft magnetic composites

Soft magnet alloys are used as magnetic cores for electric motors, transformers, wind turbines and other power generation systems. Soft magnetic cores are expensive and time consuming to manufacture in the complex shapes required for next-generation devices using conventional press and sinter powder metallurgy. The objective of this effort is to additively manufacture high performance soft magnets, with reduced cost and reduced material waste and 10x lower energy (core) loss at the high operating frequencies of many electric machines. Laser powder bed fusion additive manufacturing is used as the fabrication method. Electrical steel and amorphous alloys atomized powders are used as a feedstock materials. Magnetic cores are printed in topology optimized structures such as the Hilbert curve because this has been shown to reduce energy losses by minimizing the eddy currents that circulate within the magnet at high frequencies. In our project, we succeed in printing FeSi 3.5wt% and FeSi 6.5wt% electrical steels, and iron-based soft magnetic amorphous alloys in the shape of Hilbert and Peano curve topology optimized structures. We found that the Peano curve has a higher cut-off frequency than the Hilbert curve, and that amorphous alloys have high cut-off frequencies and higher mechanical hardness than electrical steels. Processing conditions such as laser power, scan speed, and hatching pattern were optimized to achieve high density prints, and optimize magnetic performance. We find that the printing of amorphous alloy soft magnetic cores may be technoeconomically feasible for large scale applications such as transformers, for which supply chain issues and the labor costs of manual fabrication of magnetic cores is prohibitive in some cases.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Additive manufacturing of highly dense anisotropic Nd–Fe–B bonded magnets

Extrusion based big area additive manufacturing process is utilized for fabrication of dense anisotropic bonded magnets. High loading fraction (≥70 vol%) of magnequench anisotropic Nd–Fe–B powder in nylon is used for preparing anisotropic bonded magnets. A higher energy product of ~143.2 kJ/m 3 is obtained for the post printed magnetic field aligned at 1 Tesla. These findings make an important step towards the fabrication of gap magnets with energy product between ferrite and Nd–Fe–B sintered magnets. Finally, printed bonded magnets exhibited better thermal stability, mechanical properties and superior magnetic properties compared to commercial injection molded magnets.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Thermo-Mechanical Modeling and Evaluation of the Cracking Response of Additively Manufactured Monolithic SiC Lattice Structures Subjected to Laser Heating

Increasing operating temperatures of solar receivers is paramount to the efficiency of concentrated solar thermal (CST) and solar power (CSP) systems. Owing to its high temperature stability combined with excellent thermal and optical properties, SiC has been the material of choice for application in high-temperature solar receivers. The state-of-the-art SiC volumetric concentrating solar air receivers such as honeycomb design have been demonstrated in field tests to achieve exit air temperatures approaching 800oC. However, successful application of CST systems for decarbonization requires significant increase in temperature capability of SiC receiver technology. Supported by an award from the Solar Technology Office (SETO), US Department of Energy (DOE), GE Research in collaboration with Heliogen Holdings Inc, is engaged in the development of ultra-High Operating Temperature SiC-matrix Solar Thermal Air Receiver (HOTSSTAR) enabled by additive manufacturing. The program objective is to design and to demonstrate a techno-economically viable SiC air receiver technology to achieve exit air temperature of 1100oC for CST applications. This report summarizes the learnings of a computational study on additively manufactured SiC lattice structures subjected to laser heating. The results from this study aim to help guide the design of a SiC receiver through a better understanding of lattice structure geometric parameters and their implications on the cracking response of the structure. In the study, a heat flux was applied to the surface of a 2”-diameter cylindrical lattice structure to simulate a 4kW CO2 laser. The resulting temperature distribution was applied to a structural model to approximate the stress distribution within the lattice structure and a Weibull analysis was performed to gain insight into the probability of failure and to evaluate the cracking response of the structure. With this approach, the influence of lattice density on temperature, stress, and probability of failure was explored for two different lattice beam spacings. A discussion on modeling assumptions, a comparison with experimental results, and an evaluation of the lattice cracking response is provided.

14 SOLAR ENERGY↗

DOE OSTI2023

Fast and accurate reduced-order modeling of a MOOSE-based additive manufacturing model with operator learning

One predominant challenge in additive manufacturing (AM) is to achieve specific material properties by manipulating manufacturing process parameters during the runtime. Such manipulation tends to increase the computational load imposed on existing simulation tools employed in AM. The goal of the present work is to construct a fast and accurate reduced-order model (ROM) for an AM model developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, ultimately reducing the time/cost of AM control and optimization processes. Our adoption of the operator learning (OL) approach enabled us to learn a family of differential equations produced by altering process variables in the laser’s Gaussian point heat source. More specifically, we used the Fourier neural operator (FNO) and deep operator network (DeepONet) to develop ROMs for time-dependent responses. Furthermore, we benchmarked the performance of these OL methods against a conventional deep neural network (DNN)-based ROM. Ultimately, we found that OL methods offer comparable performance and, in terms of accuracy and generalizability, even outperform DNN at predicting scalar model responses. The DNN-based ROM afforded the fastest training time. Furthermore, all the ROMs were faster than the original MOOSE model yet still provided accurate predictions. FNO had a smaller mean prediction error than DeepONet, with a larger variance for time-dependent responses. Unlike DNN, both FNO and DeepONet were able to simulate time series data without the need for dimensionality reduction techniques. Finally, the present work can help facilitate the AM optimization process by enabling faster execution of simulation tools while still preserving evaluation accuracy.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Refining Microstructures in Additively Manufactured Al/Cu Gradients Through TiB 2 Inclusions

Here, the additive manufacture of compositionally graded Al/Cu parts by laser engineered net shaping (LENS) is demonstrated. The use of a blue light build laser enabled deposition on a Cu substrate. The thermal gradient and rapid solidification inherent to selective laser melting enabled mass transport of Cu up to 4 mm from a Cu substrate through a pure Al deposition, providing a means of producing gradients with finer step sizes than the printed layer thicknesses. Divorcing gradient continuity from layer or particle size makes LENS a potentially enabling technology for the manufacture of graded density impactors for ramp compression experiments. Printing graded structures with pure Al, however, was prevented by the growth of Al 2 Cu 3 dendrites and acicular grains amid a matrix of Al 2 Cu. A combination of adding TiB 2 grain refining powder and actively varying print layer composition suppressed the dendritic growth mode and produced an equiaxed microstructure in a compositionally graded part. Material phase was characterized for crystal structure and nanoindentation hardness to enable a discussion of phase evolution in the rapidly solidifying melt pool of a LENS print.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Harnessing metastability for grain size control in multiprincipal element alloys during additive manufacturing

Abstract Controlling microstructure in fusion-based metal additive manufacturing (AM) remains a significant challenge due to the many parameters that directly impact solidification condition. Multiprincipal element alloys (MPEAs), also known as high entropy alloys, offer a vast compositional space to design for microstructural engineering due to their chemical complexity and exceptional properties. Here, we use the FeMnCoCr system as a model platform for exploring alloy design in MPEAs for AM. By exploiting the decreasing stability of the face-centered cubic phase with increasing Mn content, we achieve notable grain refinement and breakdown of epitaxial columnar grain growth. We employ a multifaceted approach encompassing thermodynamic modeling, operando synchrotron X-ray diffraction, multiscale microstructural characterization, and mechanical testing to gain insight into the solidification physics and its ramifications on the resulting microstructure of FeMnCoCr MPEAs. This work aims toward tailoring desirable grain sizes and morphology through targeted manipulation of phase stability, thereby advancing microstructure control in AM applications.

Wakai, Akane↗

DOE OSTI2020

Thermal Analysis of Large Area Additive Manufacturing Resistance Heating Composites for Out of Oven/Autoclave Applications

Additive Manufacturing (AM) of carbon fiber (CF) reinforced composite has received growing attention because of the design flexibility, superior mechanical properties, improved thermal properties, and weight reduction. Autoclave tooling was proven to be a successful application for large scale AM technology. The capital cost, and cost associated with heating, and cycle time in a conventional autoclave process is relatively high. Thus, an innovative design of AM mold with an efficient heating scheme is essential. This study represents an innovative method of the resistive heating of composite molds which does not require a room size oven for heating during the curing processing. Therefore, it has the potential to reduce the operating cost drastically. For the design validation and feasibility study, we performed a numerical analysis of the wire embedded and AM mold parts. The goal of this study is to determine and optimize the thermal behavior of the printed mold with embedded wire technology. It is anticipated that the larger distance between the embedded wires along the printing direction (z-direction) increase the cold spot, on the other hand, a close distance of the wire can create the unwanted localize heating, thus melting. Constant thermal properties of the 20 wt.% short CF reinforced acrylonitrile butadiene styrene (ABS) was used for the simulation purpose. Thermal characterization was set to 100°C to avoid the thermal deformation or bulging on the part surface.

billah, Kazi↗

DOE OSTI2021

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components. Third Annual Progress Report

Additive manufacturing (AM) of high-strength corrosion resistance alloys for nuclear energy applications, such as stainless steel and Inconel, is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Probability of crack initiation at the pore depends on size, shape, and orientation of the defect. Pulsed Infrared Thermography Imaging (PIT) provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PIT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PIT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PIT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PIT capabilities in detecting microscopic subsurface defects in metals, and classifying shapes and orientation of pores in thermal images. For detection of microscopic defects in PIT imaging data, we have developed Spatial Temporal Denoised Thermal Source Separation (STDTSS) unsupervised machine learning (ML) image processing algorithm. We show that flat bottom hole (FBH) defects as small as 200µm in SS316 and IN718 specimens, can be detected with STDTSS algorithm. To the best of our knowledge, these are the smallest detected defects which are reported in literature. For classification of defects shapes, we have previously developed thermal tomography (TT) algorithm to obtain depth reconstructions of material defects from data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.

36 MATERIALS SCIENCE↗

NASA NTRS2015

Additively Manufactured Metals in Oxygen Systems Project

Metals produced by additive manufacturing methods, such as Powder Bed Fusion Technology, are now mature enough to be considered for qualification in human spaceflight oxygen systems. The mechanical properties of metals produced through AM processes are being systematically studied. However, it is unknown whether AM metals in oxygen applications may present an increased risk of flammability or ignition as compared to wrought metals of the same metallurgical composition due to increased porosity. Per NASA-STD-6001B materials to be used in oxygen system applications shall be based on flammability and combustion test data, followed by a flammability assessment. Without systematic flammability and ignition testing in oxygen there is no credible method for NASA to accurately evaluate the risk of using AM metals in oxygen systems.

Tylka, Jonathan↗

NASA NTRS2016

High Temperature Thermoplastic Additive Manufacturing Using Low-Cost, Open-Source Hardware

Additive manufacturing (or 3D printing) via Fused Filament Fabrication (FFF), also known as Fused Deposition Modeling (FDM), is a process where material is placed in specific locations layer-by-layer to create a complete part. Printers designed for FFF build parts by extruding a thermoplastic filament from a nozzle in a predetermined path. Originally developed for commercial printers, 3D printing via FFF has become accessible to a much larger community of users since the introduction of Reprap printers. These low-cost, desktop machines are typically used to print prototype parts or novelty items. As the adoption of desktop sized 3D printers broadens, there is increased demand for these machines to produce functional parts that can withstand harsher conditions such as high temperature and mechanical loads. Materials meeting these requirements tend to possess better mechanical properties and higher glass transition temperatures (Tg), thus requiring printers with high temperature printing capability. This report outlines the problems and solutions, and includes a detailed description of the machine design, printing parameters, and processes specific to high temperature thermoplastic 3D printing.

Gardner, John M.↗

NASA NTRSDate not supplied

Additive Manufacture of Novel Tungsten Wicking and Heat Transfer Structures

Additive manufacturing (AM) is utilized for fabricating complex geometries from refractory metals, such as tungsten and molybdenum. In this study, plates with ultra-fine and fine lattice structures were manufactured from tungsten to demonstrate printability and the component fabrication advantages of AM. Previous work was completed on individual tungsten lattice structures; this work demonstrates the advantages of printing these structures on tubing and first-wall materials. Initial work using stainless steel demonstrated increased heat transfer properties promoted by these lattice structures. Applications for these structures include wicking structures, heat pipes, and first-wall materials in fusion devices. A major advantage of these designs is the capability to produce a monolithic component with tailored heat transfer and wicking properties. This research helps promote the ability to use liquid lithium in fusion devices since these lattice structures manufactured from tungsten could act as a high temperature first-wall or divertor material and as a wicking structure for liquid lithium. Different lattice structures with varying unit cell sizes and strut thicknesses were printed and the effects of these structures on heat transfer properties were measured. Abrasion resistance of these structures was also investigated. This presentation will discuss the results of these experiments and their implications related to heat transfer applications. This work will help inform the development and implementation of complex geometries made from tungsten for future heat pipe and fusion first-wall or divertor component designs.

Carly J. Romnes↗

DOE OSTI2020

Electromagnetic print nozzle for direct-write additive manufacturing with resistive renditions

A method and apparatus for additive manufacturing that includes a material guide for directing a supply of working material. An advancement mechanism comprising one or more pistons, pushers, plungers and/or pressure regulation systems are positioned behind at least a portion of the supply of working material for advancing the working material forward. The working material is heated using an electro-magnetic heating element and the melted or molten working material is deposited from a tip positioned at an end of the material guide.

42 ENGINEERING↗

DOE OSTI2020

Thermal Analysis of Large Area Additive Manufacturing Resistance Heating Composites for Out of Oven/Autoclave Applications

billah, Kazi↗

DOE OSTI2022

Laser pulse shaping for additive manufacturing

The present disclosure relates to an apparatus for additively manufacturing a product in a layer-by-layer sequence, wherein the product is formed using powder particles deposited on an interface layer of a substrate. A laser generates first and second beam components. The second beam component has a higher power level and a shorter duration than the first beam component. A mask creates a 2D optical pattern in which only select portions of the second beam components can irradiate the powder particles. The first beam component heats the powder particles close to a melting point, where the particles experience surface tension forces relative to the interface layer. While the particles are heated, the second beam component further heats the particles and also melts the interface layer before the surface tension forces can act on and distort the particles, enabling the particles and the interface layer are able to bond together.

DeMuth, James A.↗