SEARCH · Search NASA

Results for “PROCESSING”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,603 records · Page 89

All sources NASA NTRS DOE OSTI

DOE OSTI2024

A novel digital lifecycle for Material‐Process‐Microstructure‐Performance relationships of thermoplastic olefins foams manufactured via supercritical fluid assisted foam injection molding

Abstract This research significantly enhances the applicability of thermoplastic olefins (TPOs) in the automotive industry using supercritical N 2 as a physical foaming agent, effectively addressing the limitations of traditional chemical agents. It merges experimental results with simulations to establish detailed material‐process‐microstructure‐performance (MP2) relationships, targeting 5–20% weight reductions. This innovative approach labeled digital lifecycle (DLC) helps accurately predict tensile, flexural, and impact properties based on the foam microstructure, along with experimentally demonstrating improved paintability. The study combines process simulations with finite element models to develop a comprehensive digital model for accurately predicting mechanical properties. Our findings demonstrate a strong correlation between simulated and experimental data, with about a 5% error across various weight reduction targets, marking significant improvements over existing analytical models. This research highlights the efficacy of physical foaming agents in TPO enhancement and emphasizes the importance of integrating experimental and simulation methods to capture the underlying foaming mechanism to establish material‐process‐microstructure‐performance (MP2) relationships. Highlights Establishes a material‐process‐microstructure‐performance (MP2) for TPO foams Sustainably produces TPO foams using supercritical (ScF) N 2 with 20% lightweighting Shows enhanced paintability for TPO foam improved surface aesthetics Digital lifecycle (DLC) that predicts both foam microstructure and properties DLC maps process effects & microstructure onto FEA mesh for precise prediction

Engineering↗

DOE OSTI2024

In‐mold rheology and automated process control for injection molding of recycled polypropylene

Abstract Manufacturing plastic parts with secondary feedstocks has risen to the forefront of importance in recent years. However, the variation in molecular weight and rheology of secondary feedstock can lead to inconsistent part quality. This work evaluates the effectiveness of a novel closed‐loop adaptive process control system that adjusts nozzle pressure in response to in‐mold pressure data. Five different recycled polypropylene blends, with a broad distribution of flow properties, were evaluated to determine the effectiveness of the control system at reducing processing variation. The experimental results show that the process control strategy reduced the variation within the mold, as seen by in‐mold pressure curves and calculated in‐mold viscosity values. Additionally, the parameters that control the automated process adjustments were investigated, showing the importance of optimization. The analysis of the correlation between in‐mold rheology and mechanical properties showed a slight variation in the mechanical properties and parts weight with a coefficient of variation of under 5%. Overall, the results demonstrate the ability of pressure‐controlled molding and automated viscosity adjustment to reduce the variability when molding a secondary feedstock. Highlights Pressure‐controlled injection molding of recycled polypropylene. Automated closed‐loop adaptive process control methodology. Methodology resulted in a reduction in pressure variation during molding. Changes in mechanical properties and in‐mold viscosity were investigated. Results show the potential of pressure‐controlled molding at reducing variation.

Krantz, Joshua↗

DOE OSTI2025

Correlating processing variables to material properties in recycled polypropylene: A data‐driven approach

Abstract Polypropylene (PP) is one of the most widely used plastics, yet its recycling remains limited, with less than 1% of solid waste PP being reprocessed. Mechanical recycling through extrusion is the most practical method, but inconsistent reprocessing conditions introduce variability in material properties. While temperature, screw speed, and residence time influence the thermomechanical stress applied during reprocessing, there are no standardized guidelines for optimizing these parameters. This study examines how these factors shape the properties of recycled PP, using conditions designed to mimic post‐industrial recycled (PIR) scrap. Residence time was measured using colorimetric tracking and correlated with molecular weight, viscosity, and mechanical properties over multiple extrusion cycles. Data‐driven modeling, including response surface methodology, support vector machines, and artificial neural networks, identified processing temperature as the dominant factor in material degradation, followed by residence time. Mechanical properties remained stable, while viscosity decreased predictably with increasing residence time. By linking reprocessing conditions to property evolution, this study provides a method to optimize processing parameters and reduce variability in recycled PP. These findings help manufacturers improve process control, making recycled PP more predictable for reuse in manufacturing. Highlights Study of PIR‐quality PP without additives or compatibilizers. Residence time analysis shows processing temperature drives PP property changes. Mark‐Houwink enables quick molecular weight checks for quality control. Models predict mechanical and rheological shifts in reprocessing. Optimized processing parameters minimize property degradation in recycling.

Estela‐García, John E. [Polymer Engineering Center↗

DOE OSTI2022

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

Abstract In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.

Gaikwad, Aniruddha (ORCID:0000000285642621)↗

DOE OSTI2024

A Gaussian process autoregressive model capturing microstructure evolution paths in a Ni–Mo–Nb alloy

Additive manufacturing is increasingly being employed to produce components of complex geometries in structural alloys because of the expected energy savings associated with the near-net-shape capability and the ability to build in novel internal features that are not possible with many conventional manufacturing approaches. However, because of the extreme thermal conditions encountered, the non-equilibrium microstructures produced during powder bed-based additive manufacturing processes must be subjected to custom post-heat treatment processes to recover the target mechanical properties. Phase-field models and simulation techniques have matured to a state where the microstructure evolution paths, and the morphologies of the resulting precipitate phases can be predicted reasonably accurately, considering alloy-specific thermodynamic and kinetic aspects of the nucleation and growth processes. However, phase-field simulations are computationally intensive, which precludes the ability to apply the simulations directly to the length scale of the entire component. Therefore, it is highly desirable to develop low-computational-cost surrogate models that effectively capture the physics at the microstructural length scale, while facilitating the design of optimized processing conditions resulting in location-specific targeted microstructures at the component scale. The work presented here demonstrates the application of the materials knowledge system framework to develop a surrogate model that effectively captures the microstructural path during annealing of a Ni–Mo–Nb alloy containing different Mo and Nb compositions known to segregate during solidification under additive manufacturing conditions. Specifically, the surrogate model built in this work is based on a Gaussian process autoregressive model informed by statistical representation of simulated microstructures using two-point correlations and dimensionality reduction through principal component analysis. In conclusion, this surrogate model is shown to capture the bifurcation of the microstructural path during precipitation, which yields a microstructure dominated by the $\gamma^{\prime\prime}$ phase at high Nb concentrations and the $\delta$ phase at low Nb concentrations.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Improvement of the Processes of Liquid-Phase Epitaxial Growth of Nanoheteroepitaxial Structures

We have revealed the shortcomings of equipment and technological approaches in growing nanoheteroepitaxial structures with quantum dots by liquid-phase epitaxy. We have developed and fabricated a new vertical barreltype cassette for growing quantum dots and epitaxial layers of various thicknesses in one technological process. A physico-mathematical simulation has been carried out of the processes of liquid-phase epitaxial growth of quantumdimensional structures with the use of the program product SolidWorks (FlowSimulation program). Analysis has revealed the presence of negative factors influencing the growth process of the above structures. The mathematical model has been optimized, and the equipment has been modernized without additional experiments and measurements. The flow dynamics of the process gas in the reactor at various flow rates has been investigated. A method for tuning the thermal equipment has been developed. The calculated and experimental temperature distributions in the process of growing structures with high reproducibility are in good agreement, which confirms the validity of the modernization made.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2018

Process-Defect-Structure-Property Correlations During Laser Powder Bed Fusion of Alloy 718: Role of In Situ and Ex Situ Characterizations

Components made by laser powder bed fusion (L-PBF) additive processes require extensive trial and error optimization to minimize defects and arrive at targeted microstructure and properties. In this paper, in situ infrared thermography and ex situ surface roughness measurements were explored as methodologies to ensure Inconel ® 718-part quality. For a given laser energy of 200 Watts, prismatic samples were produced with different exposure times (80 to 110 µs) and point spacings (80 to 110 µm). The infrared intensities from laser–material interaction zones were measured spatially and temporally. The conditions leading to higher IR intensity and lowest surface roughness values correlated well with less porosity and coarse solidification grain structure. The transition from highly columnar to misoriented growth is attributed to changes in thermal gradients and liquid–solid interface velocities. Hardness measurements and electron microscopy of the as-processed and post-processed heat-treated samples show complex transitions in microstructural states including the heavily dislocated FCC matrix, reduction of dislocation density, and copious precipitation, respectively. These results show that the geometry-process-structure-property correlations are dynamic, and they cascade depending on the transitions of phase states from powder to liquid to solid, as well as phase decompositions and deformations within the solid FCC phase. Finally, validity of using analytical weld process models to describe the above phenomena is also highlighted.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Kinetic Study on Alloying Element Transfer During an Electroslag Remelting Process

Experimental and theoretical studies have been carried out to investigate the effects of slag on the alloying elements in ingots during the electroslag remelting (ESR) process with a focus on developing a mass-transfer model to understand the mechanism of slag-metal reaction. Stainless steel 1Cr21Ni5Ti was used as the electrode and remelted with two different kinds of slags using a 50-kg ESR furnace. The contents of sulfur, aluminum, titanium and silicon along the axial direction of the produced ingots were analyzed. On the basis of the penetration and film theories, the theoretical model developed in this work elucidates the kinetics of the slag-metal reaction revealing the mechanism of alloying element transfer during the ESR process. The calculation results obtained from the model agree well with the experimental results. The model indicates that the resultant [O] coming from the desulfurization reaction of (O{sup 2−}) + [S] = (S{sup 2−}) + [O] causes the oxidation of alloying elements in steel by [M] + [O] = (MO). The distribution ratio of sulfur L{sub S} decreases with the increase of slag temperature in the first slag-temperature-rising period, and the concentration of sulfur in the ingot at the beginning of the ESR process is lower than in the rest of the process because of the combination of the large distribution ratio of sulfur L{sub S} and excellent kinetic conditions. The concentration of aluminum along the height of the ingot has an increasing trend in the first slag-temperature-rising period, while it has a decreasing trend in the rest of the process. Two methods can solve this problem: one is starting up the ESR furnace by high temperature molten slag technology and the other is continually adding extra titania into the molten slag in the first slag-temperature-rising period.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Process Evaluation of AISI 4340 Steel Manufactured by Laser Powder Bed Fusion

Laser powder bed fusion (L-PBF) involves the consolidation of metal powder, layer by layer, through laser melting and solidification. In this study, process parameters are optimized for AISI 4340 steel to produce dense and homogeneous structures. The optimized process parameters produce mechanical properties at the center of the build plate that are comparable to wrought in the vertical and horizontal orientations after heat treatment and machining. Four subsequent builds are filled with specimens to evaluate the mechanical behavior as a function of location and orientation. Variations in the mechanical properties are likely due to recoater blade interactions with the powder and uneven gas flow. The results obtained in this study are analyzed to assess the reliability and reproducibility of the process. A different build evaluates the performance of near-net-shaped tensile specimens angled 35°-90° from the build plate surface (horizontal). Ductility measurements and surface roughness vary significantly as a function of the build angle. In the stress-relieved and as-built conditions, the mechanical behavior of vertically oriented specimens exhibits somewhat lower and more variable ductility than horizontally oriented specimens. Therefore, several process variables affect the mechanical properties of parts produced by the L-PBF process.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Correlation Between Structural Parameters and Mechanical Properties of Al5083 Sheets Processed by ECAR

In the paper, structural parameters and mechanical properties of Al5083 alloy sheet during equal channel angular rolling (ECAR) process and their relationship are studied. In order to evaluate the effect of ECAR process, Al5083 strips were subjected to the ECAR process for 1, 2, and 3 passes through the die channel angles 110°, 120°, and 130° at two routes. The effect of the process on the microstructural evolution of samples was investigated by means of x-ray diffraction and EBSD techniques. X-ray pattern has been analyzed using Rietveld method to compute structural parameters, including microstrain, average crystallite size, and dislocation density. The results showed that the dislocation density and microstrain were increased, and crystallite size was decreased during ECAR process. It was found that the behavior of variations in mechanical properties was in accordance with the dislocation density changes. This paper revealed that the increase in the strength of ECARed samples could be attributed to reduction in high-angle grain boundaries and the increase in dislocation density. Moreover, the yield strength estimated from Taylor equation was found in accordance very well with the experimentally measured yield strength of the samples.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Optimization of Process Parameters for the Axial Distortion and Distortion Range During Die Quenching of a Spiral Bevel Gear

The strength of high-duty gears can be improved by carburizing and quenching processes. Quenching distortion is inevitable during the process, and it has direct effects on the precision of the gear. For spiral bevel gear with big diameter and small thickness, die quenching process is applied in the manufacturing to control the distortion in axial direction. Die quenching parameters must be optimized to minimize the distortion while considering non-uniformity of chemical compositions and loads. In this paper, material models of gear material (22CrMoH) built based on the phase transformation kinetics, static mechanical properties and phase transformation plasticity equations. Finite element analysis (FEA) models are conducted to study the whole die quenching process based on the production process. Die quenching experiments and testing of microstructures and distortions are conducted to verify the accuracy of the FEA models. The effects of loads and bottom die obliquity on the final distortion are studied to set the appropriate range and control distortion. The distortion was very sensitive to the bottom die obliquity and some part of load ranges. The distortion changes little when the load of inner die was in the range of 1.38-2.07 MPa and the load of outer die was in the range of 2.07-2.76 MPa. The distortion range is fitted the production requirements by setting the inclination angle of bottom die between 0.12° and 0.29°.

36 MATERIALS SCIENCE↗

DOE OSTI2019

An Investigation of Process Parameter Modifications on Additively Manufactured Inconel 718 Parts

Additive manufacturing (AM) allows for the fabrication of complex parts via layer-by-layer melting of metal powder. Laser powder-bed AM processes use a variety of process parameters including beam power, beam velocity, and hatch spacing to control melting. Alterations to these parameters have often been attempted to reduce porosity, for example, but less work has been done to on comprehensive effects of process parameter modifications. This study looks at the effects of altering these parameters on microstructure, porosity, and mechanical performance of Inconel 718. The results showed that process parameter modifications that result in porosity formation can significantly reduce fatigue life, while microstructure changes were minimal and had little effect on tensile properties. The precipitate structure was not found to be changed significantly. These results can inform future process parameter modifications, as well as heat treatments to optimize mechanical properties.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Workability Characteristics and Deformation Mechanisms of Die-Cast AM60 and AZ91 Magnesium Alloys: Correlation with Processing Maps

Thermo-mechanical deformation behavior of the die-cast AM60 and AZ91 alloys was assessed by performing uniaxial compression tests on Gleeble 3800 simulator under different temperatures and strain rate conditions. Low-temperature deformation behavior of both alloys was accessed at 25, 100, and 150 °C, at the strain rates of 0.1 and 1 s{sup −1}. Processing maps at a true strain of 0.5 were generated to study the intrinsic hot workability of AM60 and AZ91 alloys. Optimum processing windows were identified, and the evolution of microstructures and textures was correlated with the imposed deformation conditions. The processing map for the AM60 alloy exhibited three dynamic recrystallization (DRX) domains in the ranges: (1) 320-400 °C and 0.2-1.5 s{sup −1}, (1A) 300-325 °C and 0.1-0.2 s{sup −1}, and (2) 360-400 °C and 3-5 s{sup −1}. In Domain 1, DRX occurred predominantly by pyramidal slip and recovery mechanisms by cross-slip as confirmed by the randomized textures. Deformation in Domain 1A is by the combination of basal and prismatic slip. In Domain 2, dynamic recovery was a softening mechanism controlled by the climb. The processing map for AZ91 alloy exhibited two DRX domains in the ranges: (1) 260-310 °C and 0.1-0.5 s{sup −1} and (2) 325-350 °C and 0.8-3 s{sup −1}. The rate-controlling mechanism was climb by lattice self-diffusion in Domain 1 and by grain boundary self-diffusion in Domain 2. Both alloys exhibited flow instability at 200 and 250 °C, and therefore, they were unsuitable for processing at these temperatures.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Hybrid SSF/SHF Processing of SO 2 Pretreated Wheat Straw—Tuning Co-fermentation by Yeast Inoculum Size and Hydrolysis Time

Wheat straw is one of the main agricultural residues of interest for bioethanol production. This work examines conversion of steam-pretreated wheat straw (using SO 2 as a catalyst) in a hybrid process consisting of a short enzymatic prehydrolysis step and a subsequent simultaneous saccharification and fermentation (SSF) step with a xylose-fermenting strain of Saccharomyces cerevisiae. A successful process requires a balanced design of reaction time and temperature in the prehydrolysis step and yeast inoculum size and temperature in the SSF step. The pretreated material obtained after steam pretreatment at 210 °C for 5 min using 2.5 % SO 2 (based on moisture content) showed a very good enzymatic digestibility at 45 °C but clearly lower at 30 °C. Furthermore, the pretreatment liquid was found to be rather inhibitory to the yeast, partly due to a furfural content of more than 3 g/L. The effect of varying the yeast inoculum size in this medium was assessed, and at a yeast inoculum size of 4 g/L, a complete conversion of glucose and a 90 % conversion of xylose were obtained within 50 h. An ethanol yield (based on the glucan and xylan in the pretreated material) of 0.39 g/g was achieved for a process with this yeast inoculum size in a hybrid process (10 % water-insoluble solid (WIS)) with 4 h prehydrolysis time and a total process time of 96 h. The obtained xylose conversion was 95 %. A longer prehydrolysis time or a lower yeast inoculum size resulted in incomplete xylose conversion.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Tailoring the Properties of a Ni-Based Superalloy via Modification of the Forging Process: an ICME Approach to Fatigue Performance

Traditionally, material design and property modifications are usually associated with compositional changes. However, subtle changes in the manufacturing process parameters can also have a dramatic effect on the resulting material properties. In this work, an integrated computational materials engineering (ICME) framework is adopted to tailor the fatigue performance of a Ni-based superalloy, RR1000. An existing fatigue model is used to identify microstructural features that promote enhanced fatigue life, namely a uniform, fine grain size distribution, random orientation, a distinct grain boundary distribution (specifically high twin boundary density and limited low-angle grain boundaries). A deformation mechanism map and process models for grain boundary engineering of RR1000 are used to identify the optimal thermo-mechanical processing parameters to realize these desirable microstructural features. For validation, small-scale forgings of RR1000 were produced and heat-treated to attain fine grain and coarse grain microstructures that represent the conventionally processed and grain boundary engineered (GBE) conditions, respectively. For each of the four microstructural variants of RR1000, the twin density and grain size were characterized and were in agreement with the desired microstructural attributes. In order to validate the deformation mechanisms and fatigue behavior of the material, high-resolution digital image correlation was performed to generate strain maps relative to the microstructural features. The high density of twin boundaries was confirmed to inhibit the length of slip bands, which is directly attributed to extended fatigue life. Thus, this study demonstrated the successful role of models, both process and performance, in the design and manufacture of Ni-based superalloy disk forgings.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Investigation of Remanufacturing Methods Utilizing Mechanically-Generated and Gas-Atomized Feedstock Production for Directed Energy Deposition Processing Through Specific Energy Consumption Modeling

The goal of this work was to assess the energy consumption of a novel remanufacturing method that utilizes mechanically-generated feedstock for directed energy deposition. Gas-atomization is the state-of-the-art production process for directed energy deposition feedstock, and for this work was incorporated into a remanufacturing process path to provide a comparison method. Specific energy consumption models of remanufacturing utilizing both of these proposed process paths were developed and applied to a case study to investigate energy saving opportunities for future manufacturing paradigms. The energy modelling was done in three stages. First, the mechanically-generated feedstock production energy consumption model was generated from experimental observation. Second, the gas-atomized feedstock production energy consumption model was generated from a combination of experimental observation, reported estimates from the manufacturer, and data found in the literature. Lastly, the energy consumption model of directed energy deposition was derived from experimental observation and compares favorably with reported estimates in the literature. With the models, the specific energy consumption in the two process paths were compared and their application was demonstrated by estimating the energy consumption to remanufacture a bracket. The two feedstock production methods had similar specific energy consumptions. The specific energy consumption of the directed energy deposition process was the greatest component in the respective remanufacturing paths by an order of magnitude; increasing deposition rate is the most important factor for lowering the overall specific energy consumption. The analyzed remanufacturing technologies were estimated to consume less energy than replacement when repairing up to approximately 15% of the original part’s mass.

42 ENGINEERING↗

DOE OSTI2024

A Conceptual Design and Economic Assessment of a Chitin Biorefinery Based on Shrimp Processing Wastes

Due to the extensive production of shrimp in captivity, waste generation has increased significantly and has become an environmental problem. The recovery of biomolecules can be an important way to mitigate the environmental problems associated with processing in this sector. In this sense, the present work aimed to evaluate a biorefinery approach for valuing shrimp farming waste to obtain astaxanthin, chitin and chitosan. Stoichiometric segmentation was used as a tool to identify the process steps, whose information on process variables (fresh water consumption, flow rate and reaction conditions) was adapted from the literature. A biorefinery coupled to a shrimp processing plant was proposed for the immediate use of highly perishable biomass, to guarantee the quality of the extracted products and reduce storage and transportation costs. In practice, the biomass treatment sequence adopted (demineralization followed by deproteinization) eliminates the depigmentation step, as the chitin obtained has a lighter tone. The proposed route was evaluated for four scenarios based on two indicators: gross economic potential (EGP) and metrics for inspection of sales and reagents (MISR). The results indicate that economic viability is achieved only for the production of chitosan, resulting in a gross revenue of US$832.50/cycle and a MISR value > 1. The sale of astaxanthin promotes an increase of US$3.73/cycle in the EGP, considered too low for the inclusion of another stage in the process.

astaxanthin↗

DOE OSTI2025

A detailed study of pre-heating effects in electron beam melting powder bed fusion process

Metal-based additive manufacturing processes, such as powder bed fusion with electron beam (PBF-EB) process, also referred to as electron beam melting (EBM), can produce high-density parts with minimal residual stresses due to the uniform and coherent preheating of the powder bed. However, understanding and controlling the multiple stages of preheating is required to enable the production of high-quality, consistent parts of various materials. This work presents a large-scale, multi-layer, three-dimensional numerical analysis focused on studying the preheating stages for predicting thermal history during the PBF-EB process. The model follows a continuous multi-stage cyclic process, that incorporates all the main stages of the PBF-EB process for 316 L stainless steel. This includes the gradual deposition of a new powder layer, the first and second preheating levels of the powder bed, and the energy deposition during melting (excluding the actual melt-pool behavior simulation). The model employs an adaptive time-scaling approach that automatically adjusts the energy deposition for each solution time-increment. This allows for localized changes in time-resolution over an otherwise computationally expensive multi-layer procedure. The material property variations are also taken into account, with an emphasis on the subtle irreversible changes in powder effective thermal conductivity after the two requisite preheating stages of the powder bed. This effect is studied using simplified conductivity models from the literature for partially sintered powder, validated by a dedicated experiment and numerical simulation. The large-scale model is then used to estimate the actual temperatures during first and second preheating levels for 316 L steel, which is not yet fully supported commercially for PBF-EB. Model predictions are corroborated by experiments, using and analyzing IR images, taken at the completion of each layer by the machine’s built-in infrared camera. The current model also incorporates a qualitative assessment for the effects of conductivity change during pre-heating, as well as evaluates the applicability of the time-scaling approach.

36 MATERIALS SCIENCE↗