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At least 127 records · Page 7

Processing of Alnico Magnets by Additive Manufacturing

Permanent magnets without rare earth (RE) elements, such as alnico, will improve supply stability and potentially decrease permanent magnet cost, especially for traction drive motors and other increased temperature applications. Commercial alnico magnets with the highest energy product are produced by directional solidification (DS) to achieve a < 001 > columnar grain orientation followed by significant final machining, adding to the high cost. Additive manufacturing (AM) is an effective method to process near net-shape parts with minimal final machining of complex geometries. AM also, has potential for texture/grain orientation control and compositionally graded structures. This report describes fabrication of alnico magnets by AM using both laser engineered net shaping (LENS)/directed energy deposition (DED) and electron beam melting powder bed fusion (EBM/PBF). High pressure gas atomized (HPGA) pre-alloyed alnico powders, with high purity and sphericity, were built into cylindrical and rectangular samples, followed by magnetic annealing (MA) and a full heat treatment (FHT). The magnetic properties of these AM processed specimens were different from their cast and sintered counterparts of the same composition and show a great sensitivity to heat treatment. The AM process parameters used in this developmental study did not yet result in any preferred texture within the alnico AM builds. These findings demonstrate feasibility for near net-shape processing of alnico permanent magnets for use in next generation traction drive motors and other applications requiring increased operating temperatures and/or complex engineered part geometries, especially with further AM process development for texture control.

36 MATERIALS SCIENCE↗

Development of Additively Manufactured Complex Tools for Autoclave Cure Composites

IACMI Project 4.9, Tooling for Composites with Washout Features Produced by Additive Manufacturing, assembled a team including the industry lead, Ability Composites, NREL and Colorado State University (CSU). Ability Composites had originally expressed interest in alternate methods of producing tooling for composite parts. In follow-up discussions, it became clear that one of the bigger tooling challenges revolved around small production volume composite parts that were tooled on washout material due to the complex geometry. To build an understanding of the potential, both from a technology and a cost perspective, for replacing conventional washout tooling with 3D printed thermoplastic tooling, a number of commercially available dissolvable FDM printing materials were evaluated, leading to tooling representative of commercial articles of interest to Ability Composites. Ultimately, Ability Composites was able to directly compare autoclave processed prepreg composite parts produced on conventional washout tooling to composite parts molded on 3D printed dissolvable tooling produced at CSU. Small, laboratory test specimens were developed to investigate the structural performance of the candidate materials under autoclave processing conditions, which were nominally 121 °C (250 °F) and 345 kPa (50 psi). In addition, several internal structural configurations (infills) were evaluated under autoclave conditions using model materials. The results of these tests indicated that two materials, Stratasys ST 130 and Infinite Materials Solutions Aquasys 180 (AQ 180), were the best candidates, given the specified autoclave processing conditions. ST-130 was slightly more robust than AQ-180; however, the AQ-180 was carried forward as it was dissolvable in water, not requiring the basic solution needed to dissolve ST-130. Based on the preliminary material and 3D printed structures evaluations, larger tools with a truncated square pyramid geometry were created to produce prepreg composite test articles for 3D printed dissolvable tool evaluation under standard autoclave fabrication conditions. Two tools were manufactured using ST-130 and one tool using traditional ceramic washout tooling media. The tools were evaluated for geometric fidelity and surface roughness changes before and after carbon fiber/epoxy prepreg composites were manufactured on the tooling. The autoclave processing did not impact the geometry significantly and was completed at 121 °C and 345 kPa, indicating satisfactory tool performance. The results from surface roughness testing of both the resulting composite and the associated tooling indicated that an adequate surface resulted without the need for a surface sealing step, as was required for the conventional washout tooling. Based on results of the truncated pyramid tests as a basis, ST-130, AQ-120 and AQ-180 materials were carried forward to the tool geometry of interest to Ability Composites. These hollow rectangular bent ducts, which were complex in nature and not extractable after cure, were used to understand the impacts of tool material and thickness. One ST-130 tool was produced as a partially solid part, with an enclosed 40% dense infill region to reduce weight and material use. This was the same approach evaluated in the truncated pyramid portion of the study. This tool was to be envelope vacuum bagged and directly compared to a monolithic tool of conventional washout material. The traditional monolithic ceramic tool was manufactured by Ability composites using CNC-based subtractive methods. An additional five dissolvable polymer tools, manufactured from ST-130, AQ-120, and AQ-180, using a hollow design were 3D printed and used to produce carbon fiber/epoxy composite evaluation articles. These hollow dissolvable tools were expected to be less influenced by the autoclave conditions as the wall was solid and vacuum bagging was inside and outside the tool. This alternative geometry was also evaluated as an option in techno-economic modeling. Print times were reduced from in excess of 3 days to under 30 hours, while surface quality and and tool integrity were substantially improved in the transition from the partially solid tool to the hollow tooling concept. Ability Composites produced autoclave-cured prepreg ducts on each of these tools. The autoclave conditions utilized were more severe than those of the initial trials, reaching temperatures of 160 °C and a pressure of 414 kPa. Under these conditions, the partially solid 3D printed tool with skin and 40% dense infill crushed significantly; however, the thicker ST-130 hollow tool showed good promise, deforming only slightly. The thinner hollow tool walls were unsuccessful as were the other materials. Overall, the hollow tool manufacturing process saved significant amounts of time and material in manufacturing as compared to the solid ducts and produced composite surface quality improvements compared to the traditional washout tooling. The TEM was developed to allow direct comparisons between conventional washout tool manufacturing processes and those developed at CSU. It also allowed for two separate 3D printed tool geometries to be analyzed and compared. In this case, the square bent duct tool geometry was determined to be representative of common washout tools. This geometry was compared with a scaled-up version of it to assess differences in the two manufacturing processes based on tool size. The model was developed to make use of user input in the form of geometry details, process steps, manufacturing parameters, bulk material costs, capital equipment costs, and general costs to calculate overall labor, material, capital equipment, and energy costs per manufactured tool for the conventional and additive manufacturing processes for the two representative geometries. It was also able to estimate step-by-step process times for the manufacturing process and geometries. Based on significant input from Ability Composites and CSU from their knowledge gained from hands-on manufacturing of the 3D printed bent duct tool geometry, costs and process times were calculated for the two manufacturing processes. Results showed that the additive manufacturing techniques developed at CSU can substantially reduce the costs of tool manufacturing by reducing labor times and material usage. This is because additive manufacturing is a relatively hands-off process and allows for the tool design to be optimized to reduce material usage. The disadvantage, however, is that process times for additive manufacturing are significantly longer. The three-dimensional (3D) printing process is slow if tight tolerances are required, but the analysis did show that print times could be reduced with the hollow tool geometry. Also, further advances in additive manufacturing could expedite the process. Costs and process times for the tool washout process were calculated separately. They showed that costs are relatively insignificant when compared to the overall tool manufacturing processes, but with increases in tool size, costs for the conventional manufacturing approach are larger than for additive manufacturing. Again, the washout process for conventional tools is very hands-on, whereas for additively manufactured tools the print medium is dissolved in an automated detergent bath at the sacrifice of process time. The analysis showed that optimizing the additively manufactured tools may also reduce washout times. Overall, Project 4.9 demonstrated that commercially available dissolvable 3D printing materials exist that can be used to produce dissolvable tooling capable of surviving prepreg composites fabrication under autoclave conditions of 121 °C (250 °F) and 345 kPa (50 psi). An alternative hollow dissolvable tool design was developed which was structurally superior to the initial concept and was cost and time effective versus conventional washout tooling. The 3D printed sacrificial tool required no added surface sealing steps prior to composite part layup and cure, offering a significant advantage over the porous conventional washout tooling.

36 MATERIALS SCIENCE↗

Variance Estimation in Monte Carlo Eigenvalue Simulations Using Spectral Analysis Method

The Monte Carlo (MC) method is the 'gold standard' for performing nuclear reactor calculations and criticality safety analyses because of its ability to handle complex geometries and physics. Reactor and criticality safety analyses require solving eigenvalue problems in complex nuclear systems. Once a stationary fission source distribution is obtained in MC simulations, the sample mean of many stationary cycles is calculated. The MC simulation sample mean is typically referred to as a tally. Variance, or standard deviation, of the sample mean/tally is needed to indicate the level of statistical uncertainty in the simulation and to understand the convergence of the sample mean. Current MC codes typically use sample variance to estimate the statistical uncertainty of the simulation and assume that the MC stationary cycles are independent. However, it is known that cycles are correlated, and estimators of the variance that ignore these correlations systematically underestimate the variance. In this paper, we propose a novel method in the Fourier frequency domain to estimate the variance of the sample mean in MC eigenvalue calculations. Results indicate that the proposed estimator can be unbiased compared to the real variance. The proposed estimator requires minimal additional computing effort.

42 ENGINEERING↗

Experimental investigation of the crush performance of prepreg platelet molding compound tubes

This work presents an experimental study characterizing the crush performance of hollow cylindrical tubes made with prepreg platelet molding compound (PPMC). PPMC is a composite material system that uses platelets from chopped and slit unidirectional prepreg as the basis for a molding compound. This material system has a higher fiber volume fraction and better mechanical properties than traditional short fiber systems and can be molded into complex geometries unlike continuous fiber systems. As such, this material system shows promise for use with complex structural members in vehicles. The failure morphology and specific energy absorption of the material are evaluated with different thickness-to-diameter geometries and test speeds. In addition, this work investigates how PPMC components compare to traditional continuous fiber components and finds that PPMC performs as well as continuous fiber layups with similar effective laminate stiffnesses.

Materials Science↗

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-effective data-driven approach. Furthermore, it can be coupled with quantum computing to enhance optimization processes, especially when paired with a special form of surrogate model (i.e., quadratic unconstrained binary optimization), formulated by factorization machine (FM). However, current practices often overlook the variability in design space sizes when determining the initial data size for optimization. In this work, we investigate the optimal initial data sizes required for efficient convergence across various design space sizes. By employing averaged piecewise linear regression, we identify initiation points where convergence begins, highlighting the crucial role of employing adequate initial data in achieving efficient optimization. These results contribute to the efficient optimization of functional materials by ensuring faster convergence and reducing computational costs in FM-based active learning.

active learning↗

The trends and challenges of fiber reinforced additive manufacturing

In the last few years, utilizing fiber reinforced additive manufacturing (FRAM)-based components in several industries has become quite popular. Compared to conventional AM technologies, FRAM offered complementary solutions to their needs. In general, fibers have been traditionally used in many manufacturing processes for various reasons. However, using conventional methods, there are obstacles in obtaining the desired complex geometries and low setup costs. AM offers possible avoidance of these limitations. Shape complexity, infill density, and manufacturing lead times are no longer barriers. Bridging AM with fiber reinforced materials offers a vast opportunity for lightweight and strong parts. Depending on the affinity, fibers with different structures can be mixed with different matrix materials and, thus, create stronger parts with improved mechanical properties. Process parameters like raster angle, infill speed, layer thickness, and nozzle temperature also strongly impact physical properties of FRAM products and are considered carefully. FRAM-based components are used in many industries such as aerospace, motorsports, and biomedicine, where the weight, strength, and complexity of parts are critical. Hence, numerous industrial companies and research facilities are investigating the implementation and adaptation of FRAM to their requirements. Studies are generally conducted on new materials, new FRAM technologies, the effect of fiber orientations and fraction on the performance of parts, improving the printing parameters, and other subjects. Furthermore, this study reports the current trends and challenges that FRAM is bringing to AM ecosystem.

36 MATERIALS SCIENCE↗

Generating MCNP Input Files for Unstructured Mesh Geometries

Los Alamos National Laboratory's (LANL) Monte Carlo N-Particle (MCNP) transport code version 6 has the capability for tracking particles on unstructured mesh (UM) geometry models. The MCNP UM feature has been developed for performing calculations of complex geometry models. This capability tracks particles on hybrid geometries where finite element meshes are embedded into constructive solid geometry (CSG) cells. The MCNP UM feature was originally designed to read UM models created by Abaqus/CAE software suite. MCNP versions 6.2.0 and later can process UM models read from Abaqus input les or MCNPUM les converted from Abaqus input les. Sandia National Laboratory's Cubit Toolkit and other finite element analysis software packages may generate UM models and then convert these models into Abaqus input formats.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Counter cross-flow evaporator geometries for supercritical organic Rankine cycles

With recent advancements in advanced manufacturing techniques, it is now possible to fabricate complex geometries that take advantage of well known principles of heat transfer. Therefore, unconventional configurations to enhance effectiveness beyond conventional designs can now be considered for practical application. Thermal performance and overall cost of a new design of heat exchangers in counter cross-flow configurations are studied using a simplified but accurate computational method. The new heat exchanger design was introduced and studied previously for a cross-flow configuration by Sabau et al. (2016, 2018). This new design concept uses multi-scale configurations with successive plenums for the working fluid. At the smallest scale the tubes are sized to be equal to the hydraulic entrance length of the inside fluid, in accord with constructal design. With this being said, results indicate that compared to the earlier cross-flow configuration, the counter cross-flow arrangement improves the thermal performance of the heat exchanger by as much as 17% and lowers the total cost by as much as 14%.

42 ENGINEERING↗

Environmental Cracking and Irradiation Resistant Stainless Steels by Additive Manufacturing

Metal additive manufacturing (AM), or metal 3D printing is an emergent advanced manufacturing method that can create near net shape geometries directly from computer models. This technology can provide the capability to rapidly fabricate complex parts that may be required to enhance the integrity of reactor internals components. Such opportunities may be observed during a plant refueling outage and AM parts can be rapidly custom designed, manufactured and deployed within the outage interval. Additive manufacturing of stainless steel (SS) components can add business benefits on fast delivery on repair hardware, installation tooling, new design prototypes tests, etc. For the nuclear industry, the supply chain is always an issue for reactor service. AM can provide through-life supply chain (40-60 years) for high-value low-volume components. In the meantime, the capability of generating complex geometries and functional gradient materials will improve the performance, reduce the overall component cost, plant asset management cost and increase the plant reliability by the improvement in materials performance in nuclear environments. While extensive work has been conducted regarding additively manufacturing of austenitic SS parts, most efforts focused only on basic attributes such as porosity, residual stress, basic tensile properties, along with components yield and process monitoring. Little work has been done to define and evaluate the material requirements for nuclear applications. Technical gaps exist, which limit this technology adoption in the nuclear industry, which includes high manufacturing cost, unknown risks, limited nuclear related data, lack of specification and qualification methods, and no prior business experience. The main objective of this program was to generate research data to address all these technical gaps and establish a commercial practice to use AM technology in the nuclear power industry. The detailed objectives are listed as follows: (1) Evaluate nuclear related properties of AM 316L SS, including microstructure, tensile properties, impact toughness, stress corrosion cracking (SCC), corrosion fatigue (CF), irradiation effects, and irradiation assisted stress corrosion cracking (IASCC). (2) Understand the correlations among laser processing, heat treatment, microstructure and SCC/irradiation properties; (3) Optimize and improve the manufacturing process to achieve enhanced nuclear application properties; (4) Fabricate, evaluate, qualify and test a prototype reactor component to demonstrate the commercial viability and cost benefit; (5) Create regulatory approval path and commercialization plans for the production of a commercial reactor component.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Freeform Hybrid Manufacturing: Binderjet, Structured Light Scanning, Confocal Microscopy, and CNC Machining

This paper describes a hybrid manufacturing approach for silicon carbide (SiC) freeform surfaces using binder jet additive manufacturing (BJAM) to print the preform and machining to obtain the design geometry. Although additive manufacturing (AM) techniques such as BJAM allow for the fabrication of complex geometries, additional machining or grinding is often required to achieve the desired surface finish and shape. Hybrid manufacturing has been shown to provide an effective solution. However, hybrid manufacturing also has its own challenges, depending on the combination of processes. For example, when the subtractive and additive manufacturing steps are performed sequentially on separate systems, it is necessary to define a common coordinate system for part transfer. This can be difficult because AM preforms do not inherently contain features that can serve as datums. Additionally, it is important to confirm that the intended final geometry is contained within the AM preform. The approach described here addresses these challenges by using structured light scanning to create a stock model for machining. Results show that a freeform surface was machined with approximately 70 µm of maximum deviation from that which was planned.

Dvorak, Jake (ORCID:0000000260989944)↗

Extending the excluded volume for percolation threshold estimates in polydisperse systems: The binary disk system

For dispersions containing a single type of particle, it has been observed that the onset of percolation coincides with a critical value of volume fraction. When the volume fraction is calculated based on excluded volume, this critical percolation threshold is nearly invariant to particle shape. The critical threshold has been calculated to high precision for simple geometries using Monte Carlo simulations, but this method is slow at best, and infeasible for complex geometries. This article explores an analytical approach to the prediction of percolation threshold in polydisperse mixtures. Specifically, this paper suggests an extension of the concept of excluded volume, and applies that extension to the 2D binary disk system. The simple analytical expression obtained is compared to Monte Carlo results from the literature. In conclusion, the result may be computed extremely rapidly and matches key parameters closely enough to be useful for composite material design.

97 MATHEMATICS AND COMPUTING↗

Learning interpretable surface elasticity properties from bulk properties via neural network equation learners

Surface elasticity is central to understanding the mechanics and stability of surfaces and interfaces. It is characterized by quantities such as surface tension, residual surface stress, and surface stiffness. However their analytical expressions are typically difficult to derive from atomistic data, and depend strongly on modeling choices. This work presents a neural network-based equation learner which combines customized activation functions and connection-based pruning to discover parsimonious, closed-form equations for surface elasticity from atomistic simulations. Applying the method to seven face-centered cubic (FCC) metals, our equation learner uncovers interpretable equations that describe both low-Miller index and high-Miller index surface properties, capturing long-tail property distributions accurately. The discovered expressions are decoupled into two components: a universal, geometry-driven orientation function, and material-specific baseline coefficients. We find that lower-order properties such as surface tension are fundamentally geometry dependent, while higher-order properties such as surface stress and elasticity show more complex geometry and material dependence. We also relate material dependent coefficients to bulk properties, forming a clear map from bulk material properties to surface elasticity. Overall, this approach demonstrates that interpretable neurosymbolic machine learning can bridge the gap between atomistic simulations and physical laws, enabling the discovery of generalizable structure–property relationships for materials science phenomena such as surface elasticity.

Equation learning↗

Optimal design of acoustic metamaterial cloaks under uncertainty

In this work, we consider the problem of optimal design of an acoustic cloak under uncertainty and develop scalable approximation and optimization methods to solve this problem. The design variable is taken as an infinite-dimensional spatially-varying field that represents the material property, while an additive infinite-dimensional random field represents the variability of the material property or the manufacturing error. Discretization of this optimal design problem results in high-dimensional design variables and uncertain parameters. To solve this problem, we develop a computational approach based on a Taylor approximation and an approximate Newton method for optimization, which is based on a Hessian derived at the mean of the random field. We show our approach is scalable with respect to the dimension of both the design variables and uncertain parameters, in the sense that the necessary number of acoustic wave propagations is essentially independent of these dimensions, for numerical experiments with up to one million design variables and half a million uncertain parameters. Additionally, we demonstrate that, using our computational approach, an optimal design of the acoustic cloak that is robust to material uncertainty is achieved in a tractable manner. The optimal design under uncertainty problem is posed and solved for the classical circular obstacle surrounded by a ring-shaped cloaking region, subjected to both a single-direction single-frequency incident wave and multiple-direction multiple-frequency incident waves. Finally, we apply the method to a deterministic large-scale optimal cloaking problem with complex geometry, to demonstrate that the approximate Newton method’s Hessian computation is viable for large, complex problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Modular fabrication and characterization of complex silicon carbide composite structures Advanced Reactor Technologies (ART) Research Final Report (Feb 2015 – May 2017)

Advanced ceramic materials exhibit properties that enable safety and fuel cycle efficiency improvements in advanced nuclear reactors. In order to fully exploit these desirable properties, new processing techniques are required to produce the complex geometries inherent to nuclear fuel assemblies and support structures. Through this project, the state of complex SiC-SiC composite fabrication for nuclear components has advanced significantly. New methods to produce complex SiC-SiC composite structures have been demonstrated in the form factors needed for in-core structural components in advanced high temperature nuclear reactors. Advanced characterization techniques have been employed to demonstrate that these complex SiC-SiC composite structures provide the strength, toughness and hermeticity required for service in harsh reactor conditions. The complex structures produced in this project represent a significant step forward in leveraging the excellent high temperature strength, resistance to neutron induced damage, and low neutron cross section of silicon carbide in nuclear applications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

The Application of MCNP6.2 Unstructured Mesh Geometry Capabilities to the Study of Heavy Ion Single Event Effect on Microelectronic Devices and the Reactor Physics Analysis - 25691

Unstructured meshes allow irregular or complex geometries to be modeled precisely in Monte Carlo radiation transport codes. Through this capability, it is possible to share an unstructured mesh model between multiple codes that use unstructured mesh geometry and thus improving coupled multi-physics simulations. In this work, unstructured meshes are used to model energy deposition from heavy ion strikes for single event effects on microelectronic devices as well as neutron flux mapping of nuclear reactor configurations. In each case, MCNP6.2's unstructured mesh functionality and applicability to the problem is demonstrated and analyzed. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Evaluating geothermal and hydrogeologic controls on regional groundwater temperature distribution

Described is a one-dimensional (1-D) analytic solution is developed for heat transport through an aquifer system where the vertical temperature profile in the aquifer is nearly uniform. The general anisotropic form of the viscous heat generation term is developed for use in groundwater flow simulations. The 1-D solution is extended to more complex geometries by solving the equation for piece-wise linear or uniform properties and boundary conditions. A moderately complex example, the Eastern Snake River Plain (ESRP), is analyzed to demonstrate the use of the analytic solution for identifying important physical processes. For example, it is shown that viscous heating is variably important and that heat conduction to the land surface is a primary control on the distribution of aquifer and spring temperatures. Use of published values for all aquifer and thermal properties results in a reasonable match between simulated and measured groundwater temperatures over most of the 300 km length of the ESRP, except for geothermal heat flow into the base of the aquifer within 20 km of the Yellowstone hotspot. Previous basal heat flow measurements (110 mW/m 2 ) made beneath the ESRP aquifer were collected at distances of > 50 km from the Yellowstone Plateau, but a higher basal heat flow of 150 mW/m 2 is required to match groundwater temperatures near the Plateau. As a result, the ESRP example demonstrates how the new tool can be used during preliminary analysis of a groundwater system, allowing efficient identification of the important physical processes that must be represented during more-complex 2-D and 3-D simulations of combined groundwater and heat flow.

58 GEOSCIENCES↗

pCubit

SAND2025-01894O pCubit is a software tool that can easily place particles or pores into a given geometry using desired statistics and the Poisson point process. It reduces the need for users to manually create complex geometry creation and placement. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Buche, Michael↗

Local prediction of Laser Powder Bed Fusion porosity by short-wave infrared imaging thermal feature porosity probability maps

We report that local thermal history can significantly vary in parts during metal Additive Manufacturing (AM), leading to local defects. However, the sequential layer-by-layer nature of AM facilitates in-situ part voxelmetric observations that can be used to detect and correct these defects for part qualification and quality control. The challenge is to relate this local radiometric data with local defect information to estimate process error likelihood in future builds. This paper uses a Short-Wave Infrared (SWIR) camera to record the temperature history for parts manufactured with Laser Powder Bed Fusion (LPBF) processes. The porosity from a cylindrical specimen is measured by ex-situ micro-computed tomography (μCT). Specimen data from the SWIR camera, combined with the μCT data, are used to generate thermal feature-based porosity probability maps. The porosity predictions made by various SWIR thermal feature-porosity probability maps of a specimen with a complex geometry are scored against the true porosity obtained via μCT. The receiver operating characteristic curves constructed from the predictions for the complex sample demonstrate the porosity probability mapping methodology’s potential for in-situ based porosity detection.

36 MATERIALS SCIENCE↗