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DOE OSTI2020

Effect of heat treatment on microstructural evolution and hardness homogeneity in laser powder bed fusion of alloy 718

The thermal history developed in laser powder bed fusion (LPBF) processes has been shown to be complex resulting in equally complex microstructures and mechanical properties. Microscopic observations and Vickers micro-hardness mapping measurements were carried out on diff ;erent section planes of LPBF alloy 718 cuboids. Three-dimensional finite element analysis was used to simulate thermal history and to predict the residual stress distribution in the as-built material. Computational thermodynamics was used to predict the micro-segregation and nucleation driving force of various phases in the bulk and in segregated regions. Varied heat-treatments such as simulated hot isostatic pressing, and double aging were applied. Their influence on the microstructure, microsegregation, precipitate formation, and micro-hardness variations of LPBF alloy 718 were investigated. Hardness map results showed heterogeneous micro-hardness on the xy- and xz-planes of the as-built parts where the bottom plane and center regions had larger hardness of ~315 HV 0.5 while the top plane and contours showed hardness of ~300 HV 0.5 . It was found that the aging treatment increased the overall hardness of the as-built condition from ~310 HV 0.5 to 470 HV 0.5 but also increased the hardness gradient throughout the coupon. After simulated hot isostatic pressing process (i.e., without applied pressure) at 1020 °C for 4 h followed by water quench (HIPWQ), the hardness gradient and hardness was minimized (~210 HV 0.5 ) as the microstructure transitioned from heterogeneous columnar grains in the as-built condition to more uniform recrystallized grains. A double aging treatment was applied to enhance hardness from ~210 HV 0.5 to ~440 HV 0.5 . Finally, HIPWQ followed by double aging produced a homogeneous microstructure and more uniform hardness map with enhanced mechanical properties in LPBF alloy 718 coupons.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Additively manufactured novel Al-Cu-Sc-Zr alloy: Microstructure and mechanical properties

An in-depth understanding of microstructure and resultant properties is paramount in the design of a novel alloy system, especially for additive manufacturing (AM). The present investigation aims to characterize a prototypical AM Al alloy with great potential for structural applications. An Al-1.5Cu-0.8Sc-0.4Zr alloy designed using integrated computational material engineering was printed using the laser powder bed fusion AM process. This novel alloy shows promising combination of strength and ductility in as-built and peak-aged conditions. This improvement in the tensile properties is attributed to the presence of both coherent L1 2 Al 3 Sc/Al 3 (Sc,Zr) precipitates and Cu-rich regions. The microstructures were studied via extensive microscopy at different length scales using X-ray microscopy, scanning electron microscopy, and transmission electron microscopy. Fractography revealed that the columnar grain boundaries in as-built condition allow easy slip transfer as compared to the equiaxed grains, with the apex of the melt pool acting as the crack nucleation site. Furthermore, the peak aged condition resulted in improved strength while marginally sacrificing ductility due to precipitates decorating dislocations, grain boundaries and melt pool boundaries thus acting as obstacles to slip transfer.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Parametrically designed surface topography on CAD models of additively manufactured lattice structures for improved design validation

Additively Manufactured (AM) lattice structures of the laser powder bed fusion (LPBF) process generally have different surface geometries depending on the overhang angle and location. This means that the design validation stage is often challenging, considering it is based on an ideal Computer Aided Design (CAD) model that is not truly representative, since the AM part will be different in terms of dimensional accuracy and surface finish. Previous studies have relied on the design of surface textures that are independent of the overhang angle, or techniques based on directly using X-ray Computed Tomography (XCT) data. In this paper, a new technique for designing surface texture on the CAD of lattices has been investigated and correlated with areal surface roughness parameters. After extracting areal surface parameters from the XCT data of the lattice, the method allowed for the design of this surface texture on the part CAD taking in consideration if its up skin or down skin, without using XCT data as the main input. By applying this method, it is possible to obtain a lattice CAD model with a designed surface texture and geometry that is more representative of the actual AM lattice. The mean deviation between the CAD model with the designed surface and the XCT was a third of the one between the XCT and the initial CAD. The proposed method allows for designing and replicating LPBF AM surfaces on the CAD of a lattice, taking into consideration the dimensional deviation caused by AM surfaces, especially on overhangs. Assuming that an LPBF AM process is stable and produces approximately the same AM surface, this method can be used to predict the geometry of a lattice, providing a cost-effective and efficient methodology that minimizes the time needed for design validation.

42 ENGINEERING↗

DOE OSTI2025

Quantification and prediction of solidification textures under additive manufacturing conditions

Crystallographic textures are a major determinant of the macroscale anisotropic properties of polycrystalline metallic alloys produced in a wide range of additive manufacturing (AM) processes. Here, we introduce a statistical method that can accurately quantify the degree of orientational order of textures despite the large random fluctuations in the orientation of individual grains inherent in AM processes. The method, demonstrated for laser and resolidification of AlSi thin films, extends Z-scoring to a dynamical regime to assess the statistical significance of observed textures compared to randomly generated ones at different stages of solidification. We further show that, combined with phase-field modeling, this method can be used to infer fundamental anisotropic properties of the solid-liquid interface that are essential for texture prediction, and are compared here to the results of atomistic simulations. In addition, phase-field modeling reveals that, even at rapid AM solidification rates, the observed 〈110〉-dominated textures in the AlSi thin films are controlled predominantly by the anisotropy of the interface free-energy and sheds light on the physical mechanism of grain competition. These results significantly enhance both the existing tools for the quantification and prediction of AM crystallographic textures and our basic understanding of their formation.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Evidence for Trapping-Induced Nonlinear Frequency Shifts in Langmuir Waves Driven via Stimulated Raman Scattering

Thomson scattering is used to detect the spectra of Langmuir waves driven through the backward stimulated Raman scattering process in a diffraction limited laser focal spot. Measured Langmuir wave spectral frequencies are found to vary in time and have broadened spectral power, consistent with a nonlinear frequency shift of the driven Langmuir wave due to electron-trapping. Broadening of the Langmuir wave spectral power is observed to decrease in time, consistent with measured variations in the frequency shift of the driven Langmuir waves. Furthermore, the observed spectral broadening is consistent with the temporally short (ps), bursty nature of backward stimulated Raman scattered light observed in simulations that cannot be resolved by the Thomson scattering diagnostic. Comparison of the broadened spectrum with time integrated spectra from two-dimensional particle-in-cell simulations shows favorable comparison in the broadened spectral widths, supporting the supposition of electron-trapping induced, nonlinear shifting of daughter Langmuir wave frequencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Explosive fragmentation of additively manufactured stainless steel

Properties of fragmentation from an explosively driven 316L stainless steel spherical shell section fabricated by a laser powder bed additive manufacturing process with minimal surface finishing are investigated. This shell is driven by an insensitive high explosive, resulting in high strain rate deformation (>8 × 10 3 s –1 ) and failure of the stainless steel. Photonic Doppler velocimetry measures the expansion rate; dynamic radiography and high-speed imaging capture the fracture behavior of the stainless steel. The fracture response of the additively manufactured stainless steel shell is compared to published experimental results on additively manufactured 316L stainless steel and conventionally manufactured wrought 316L and 304 stainless steel shell fragmentation. Despite preferred crack orientation, suggesting the influence of surface grooves on fracture time, fragment size is identical to that measured in a similar experiment on wrought 304 stainless steel. Further analysis indicates that the 316L additively manufactured stainless steel shell exhibits comparable spall strength and fragmentation toughness to conventionally manufactured stainless steel yet lower failure strain due to surface stress concentrations.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Impact of Additive Manufacturing on Internal Cooling Channels With Varying Diameters and Build Directions

Abstract The use of additive manufacturing (AM) processes, such as direct metal laser sintering, provides the design freedom required to incorporate complex cooling schemes in gas turbine components. Additively manufactured turbine components have a range of cooling feature sizes and, because of the inherent three-dimensionality, a wide range of build angles. Previous studies have shown that AM built directions influence internal channel surface roughness that, in turn, augment heat transfer and pressure loss. This study investigates the impact of AM on channel feature size and builds direction relative to tolerance, surface roughness, pressure losses, and convective cooling. Multiple AM coupons were built from Inconel 718 consisting of channels with different diameters and a variety of build directions. An experimental rig was used to measure pressure drop to calculate friction factor and was used to impose a constant surface temperature boundary condition to collect Nusselt number over a range of Reynolds numbers. Significant variations in surface roughness and geometric deviations from the design intent were observed for distinct build directions and channel sizes. These differences led to notable impacts in friction factor and Nusselt number augmentations, which were a strong function of build angle.

Engineering↗

DOE OSTI2021

Convection in Scaled Turbine Internal Cooling Passages With Additive Manufacturing Roughness

Abstract Additive manufacturing processes, such as direct metal laser sintering (DMLS), enable the creation of novel turbine cooling internal passages and systems. However, the DMLS method produces a significant and unique surface roughness. Previous work in scaled passages analyzed pressure losses and friction factors associated with the rough surfaces, as well as investigated the velocity profiles and turbulent flow characteristics within the passage. In this study, the heat transfer characteristics of scaled additively manufactured surfaces were measured using infrared (IR) thermography. Roughness panels were CNC machined from plates of aluminum 6061 to create near isothermal roughness elements when heated. Fluid resistance differences between the aluminum roughness panels and roughness panels constructed from ABS plastic using the same roughness patterns from McClain et al. (2020, “Flow in a Simulated Turbine Blade Cooling Channel With Spatially Varying Roughness Caused by Additive Manufacturing Orientation,” ASME Turbo Expo 2020, Turbomachinery Technical Conference and Exposition, Virtual Conference, Sept. 21–25, GT2020-16069) were investigated. Finally, the overall thermal performance enhancements and friction losses were assessed through the calculation of surface averaged “global thermal performance” ratios. The global thermal performance characterizations indicate results in-line with those found for traditional commercial roughness and slightly below traditional internal passage convection enhancement methods such as swirl chambers, dimples, and ribs. The passages investigated in this study do not include compressibility effects or the long-wavelength artifacts and channel geometric deviations observed by Wildgoose et al. (2020, “Impact of Additive Manufacturing on Internal Cooling Channels with Varying Diameters and Build Directions,” ASME Turbo Expo 2020, Turbomachinery Technical Conference and Exposition, Virtual Conference, Sept. 21–25, GT2020-15049). However, the results of this study indicate that, based on the roughness augmentation alone, artificial convective cooling enhancers such as turbulators or dimples may still be required for additively manufactured turbine component cooling.

Engineering↗

DOE OSTI2020

Development of Thermal Tomography Imaging System for In-Service Nondestructive Evaluation of Additively Manufactured Components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size structures. We investigate TT of AM stainless steel 316L specimen with imprinted internal porosity defects using relatively low-cost, small form factor infrared (IR) camera based on uncooled micro bolometer detector. Sparse coding related K-means singular value decomposition (SVD) machine learning, image processing algorithms are developed to improve quality of TT images through removal of Additive white Gaussian noise without blurring the images. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact TT can also be used for in-service nondestructive evaluation (NDE). With capability to perform in-service NDE of the AM component lifecycle, TT data can be used for development of a component digital twin. MIT research reactor (MITR) has been identified as a representative environment to perform investigation of feasibility of in-service NDE during shutdown. Preliminary study of radiation level in the MITR viewport, with geometric parameters potentially compatible with compact TT system, has been conducted.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Detection of Defects in Additively Manufactured Metallic Materials with Machine Learning of Pulsed Thermography Images

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Quality control (QC) requires nondestructive evaluation (NDE) of actual AM structures. Pulsed thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, detection sensitivity of this method is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. The ML methods with higher accuracy require longer run time. However, this runtime is sufficiently short to perform QC within a realistic time frame.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Performance of Compact Pulsed Thermal Imaging System for In-Service Applications. Pulsed thermal tomography nondestructive examination of additively manufactured reactor materials and components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which has the capability of melting metallic powder and net shaping the structures with relatively high precision. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to creep in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures nondestructively. Pulsed Thermography (PT) imaging provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PT 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. The data cube of PT measurements consists of surface temperature taken at sequential time intervals T(x,y,t). Material defects can be detected either by analyzing the thermograms T(x,y,t) data cube, or by using thermal tomography (TT) algorithm to obtain 3D spatial reconstruction of thermal effusivity e(x,y,z). To reduce the cost and enable in-service NDE in spatially constrained environment, it is highly desirable to develop PT with compact and inexpensive IR camera. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact PT system can also be used for in-service nondestructive evaluation (NDE) applications. However, data cube obtained with PT based on compact IR camera suffers from strong thermal noises and loss of features due to relatively low sampling rate. In this report we describe two unsupervised machine learning (ML) algorithms for enhancement of PT images obtained with compact IR camera. In one approach, we introduce Sparse Coding Discrete Cosine Transform (SC/DCT) algorithm to remove additive white Gaussian noise (AWGN) from spatial thermal effusivity reconstructions. In another approach we introduce a Spatial Temporal Denoised Thermal Source Separation (STDTSS) ML algorithm to process thermograms. The STDTSS algorithm consists of spatial and temporal denoising using Gaussian and Savitzky–Golay filtering, followed by the matrix decomposition using Principal Component Analysis (PCA), and Independent Component Analysis (ICA) to automatically detect flaws. In the work described in this report, we constructed a compact PT system using a relatively small and low-cost FLIR A65 camera, consisting on uncooled microbolometer detector. Performance of SC/DCT algorithm was demonstrated on enhancing TT images of Inconel 718 AM plate. Performance of the STDTSS methods was investigated using thermography data obtained from imaging stainless steel 316L specimens produced with LPBF method with imprinted calibrated porosity defects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Performance Validation of Pulsed Thermal Imaging System for In-Service Applications

42 ENGINEERING↗

DOE OSTI2021

Performance of Pulsed Thermal Tomography Imaging with Machine Learning-Based Classification of Defects in Additively Manufactured Structures

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications 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. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures non-destructively. Pulsed Thermal Tomography Imaging (PTT) provides a capability for non-destructive evaluation (NDE) of subsurface defects in arbitrary size structures. The PTT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PTT 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 PTT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PTT capabilities in detecting and visualizing microscopic defects in metallic specimens. The thermal tomography (TT) algorithm obtains depth reconstructions of spatial effusivity from the data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring of images 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. CNN is trained on a database of TT images created for a set of simulated metallic structures with elliptical subsurface voids. Test of CNN performance demonstrate the ability to classify radii and angular orientation of subsurface defects in TT images. In addition, we have shown that CNN trained on elliptical defects is capable of classifying irregular-shaped defects obtained from scanning electron microscopy (SEM) of stainless steel sections printed with LPBF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

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 OSTI2023

Mechanical Properties of Additively Manufactured 316L Stainless Steel Before and After Neutron Irradiation–FY23

This report presents the observed mechanical behavior of the additively manufactured (AM) 316L stainless steel (SS) before and after neutron irradiation. In the Advanced Materials and Manufacturing Technologies (AMMT) program, a variety of mechanical and physical property data are generated and accumulated to assess the AM austenitic alloy for nuclear reactor applications. The testing and evaluation task in the FY 2023 focused on elucidating the effects of sampling location and build size on the mechanical properties of AM 316L SS (in stress-relieved condition) before and after neutron irradiation. The laser powder bed fusion (LPBF) process produced 316L plates of three distinct sizes from which SS-J3 miniature tensile specimens were machined from six different locations. The tensile specimens were irradiated in the High Flux Isotope Reactor (HFIR) normally to 2 and 10 dpa at the target temperatures of 300 °C and 600 °C. Post-irradiation tensile testing was performed at room temperature, 300 °C, and 600 °C. The mechanical properties of AM 316L SS were significantly influenced by the characteristic microstructures of printed materials, which include fine grains and high-density dislocations. Compared with the traditional 316L SS, AM 316L showed higher initial strength and lower ductility. Regardless of sampling location, the AM 316L steel retained relatively high strength and ductility to the highest irradiation dose. A prompt necking at yield (with little uniform ductility) was observed after irradiation at 300 °C but no embrittlement was observed up to 10 dpa. Ductilization by irradiation–the radiation-induced increase of ductility–was observed for the 600 °C irradiation only and it occurred in low dose range only. The neutron irradiation increased the data variation in many tensile property datasets, particularly after 600 °C irradiation, and no clear dependence of tensile properties on build thickness or sampling location was observed.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Excited-State Dynamics of All the Mono-cis and the Major Di-cis Isomers of β-Apo-8′-Carotenal as Revealed by Femtosecond Time-Resolved Transient Absorption Spectroscopy

Cis isomers of carotenoids play important roles in light harvesting and photoprotection in photosynthetic bacteria, such as the reaction center in purple bacteria and the photosynthetic apparatus in cyanobacteria. Carotenoids containing carbonyl groups are involved in efficient energy transfer to chlorophyll in light-harvesting complexes, and their intramolecular charge–transfer (ICT) excited states are known to be important for this process. Previous studies, using ultrafast laser spectroscopy, have focused on the central-cis isomer of carbonyl-containing carotenoids, revealing that the ICT excited state is stabilized in polar environments. However, the relationship between the cis isomer structure and the ICT excited state has remained unresolved. In this study, we performed steady-state absorption and femtosecond time-resolved absorption spectroscopy on nine geometric isomers (7-cis, 9-cis, 13-cis, 15-cis, 13′-cis, 9,13′-cis, 9,13-cis, 13,13′-cis, and all-trans) of β-apo-8′-carotenal, whose structures are well-defined, and discovered correlations between the decay rate constant of the S1 excited state and the S0−S1 energy gap, as well as between the position of the cis-bend and the degree of stabilization of the ICT excited state. Our results demonstrate that the ICT excited state is stabilized in polar environments in cis isomers of carbonyl-containing carotenoids and suggest that the position of the cis-bend plays an important role in the stabilization of the excited state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Support of Adhesion Mechanisms in Al 2 O 3 Aerosol Deposition Through Laser-Induced Particle Impact Testing

Aerosol deposition (AD) is a kinetic spray process capable of depositing ceramic coatings at room temperature, but AD process development is generally a laborious exploration of a large process parameter space. Here, this paper presents a case study investigating whether laser-induced particle impact testing (LIPIT) could be applied to expedite development of an alumina (Al 2 O 3 ) coating on nickel (Ni): Specifically, whether LIPIT measurements could predict critical velocities of adhesion on Ni and Al 2 O 3 , and the effect of ball milling the Al 2 O 3 powder. Because LIPIT has a diffraction-limited lower bound on imageable particle size, the usefulness of Al 2 O 3 powder agglomerates as a proxy for single particles was additionally studied. Overall, LIPIT measurements and AD sprays agreed that ball milling dramatically improves adhesion. Additionally, LIPIT measurements of critical velocity of adhesion of Al 2 O 3 powder agglomerates on Ni and Al 2 O 3 substrates (150 meters per second [m/s] and 250 m/s, respectively) quantitatively agreed with predictions from a previously published model based on picoindentation and molecular dynamics simulations. Together, these findings support the established hypothesis that Al 2 O 3 adheres via a dislocation-mediated mechanism in AD, that Al 2 O 3 powder agglomerates adhere as individual constituent particles rather than collectively, and that, for this case study, LIPIT measurements were predictive of AD process parameters.

Al2O3↗

DOE OSTI2022

Manufacturing-induced contamination in common multilayerdielectric gratings

Contamination of pulse compression gratings during the manufacturing process is known to give rise to reduced laser damage performance and represents an issue that has not yet been adequately resolved. The present work demonstrates that the currently used etching methods introduce carbon contamination inside the etched region extending to a 50- to 80-nm layer below the surface. This study was executed using custom samples prepared in both, a laboratory setting and by established commercial vendors, showing results that are very similar. The laser-induced-damage performance of the etched and unetched regions in the grating-like samples suggest that contaminants introduced by etching process are contributing to the reduction of the laser-induced damage threshold.

42 ENGINEERING↗