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Results for “Processing maps”

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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Characterization of Hot Workability of 5052 Aluminum Alloy Based on Activation Energy-Processing Map

In the design of hot forming processes for aluminum alloys, hot processing maps are usually used as a powerful tool for processing parameters optimizations. Although the stability of deformation is considered in conventional hot processing (CHP) maps, the difficulty of deformation, i.e., whether deformation is easy to occur, is not taken into account. Therefore, it is difficult for CHP maps to efficiently and accurately obtain the optimal processing parameters and achieve satisfying hot workability. Hence, in this study, thermal compression tests were performed to investigate the hot forming behavior of 5052 aluminum alloy in the deformation temperature range of 553-733 K and strain rate range of 0.001-1 s{sup −1}. We proposed an activation energy-processing (AEP) map by coupling the CHP map and the activation energy value, and thereby applied it to evaluate the hot workability of 5052 aluminum alloy. In CHP maps, the region with the highest power dissipation efficiency is generally considered to be the best processing region. However, the AEP map shows that materials with satisfying hot workability not only depends on high power dissipation efficiency, but also on low activation energy values. At the strain of 0.7, the optimal hot processing region predicted by the CHP map lies in the temperature range of 583-673 K and strain rate range of 0.001-0.1 s{sup −1}, but that predicted by AEP map locates in the temperature range of 643-733 K and strain rate range of 0.001-0.1 s{sup −1}. Microstructure characterization implies that the optimal processing region predicted by the CHP map presents necklace-like structures, which are non-uniform and unbeneficial for deformation, while that predicted by the AEP map consists of fine, uniform and equiaxed grains. Thus, it is implied that the processing regions predicted by the AEP map are more suitable for forming, i.e., the reasonability of the AEP map is verified.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. Here, the predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multi-physics melt pool modeling and process optimization for laser direct energy deposition of Nb-based refractory C103: Defect formation, geometric precision, and process mapping

Recent developments in additive manufacturing (AM) technology have reignited interest in the fabrication of the Nb-based refractory C103 alloy offering solutions to the challenges posed by traditional manufacturing methods. However, the limited numerical and experimental studies on laser direct energy deposition (DED) of C103 have hindered the understanding of the relationships between process parameters and build quality. This has made it challenging to consistently produce parts with the desired quality and microstructure suitable for critical applications. In this study, we focus on optimizing the laser DED process for C103 by employing a hybrid approach that combines experimental techniques and computational fluid dynamics (CFD). This approach facilitates the development of process maps for defect detection and geometric precision. To achieve this, multi-layer C103 samples were fabricated using laser DED under various process parameters, enabling the creation of a process map for defect detection. Additionally, a multi-physics, multiphase simulation framework was developed within a high-performance computing (HPC) environment to establish process maps for geometric precision. Using these process maps, printability windows were identified for achieving both the desired geometric accuracy and defect-free prints. It was observed that prints with a power-to-velocity (P/V) ratio close to unity resulted in defect-free outcomes. This study provides a foundation for reducing design lead time and rejected parts, ultimately optimizing the laser DED process for C103.

Defect formation and geometric precision↗
DOE OSTI2024

Investigating the Effects of Mixing Dynamics on Twin-Screw Granule Quality Attributes via the Development of a Physics-Based Process Map

Twin-screw granulation (TSG) is an emerging continuous wet granulation technique that has not been widely applied in the industry due to a poor mechanistic understanding of the process. This study focuses on improving this mechanistic understanding by analyzing the effects of the mixing dynamics on the granule quality attributes (PSD, content uniformity, and microstructure). Mixing is an important dynamic process that simultaneously occurs along with the granulation rate mechanisms during the wet granulation process. An improved mechanistic understanding was achieved by identifying and quantifying the physically relevant intermediate parameters that affect the mixing dynamics in TSG, and then their effects on the granule attributes were analyzed by investigating their effects on the granulation rate mechanisms. The fill level, granule liquid saturation, extent of nucleation, and powder wettability were found to be the key physically relevant intermediate parameters that affect the mixing inside the twin-screw granulator. An improved geometrical model for the fill level was developed and validated against existing experimental data. Finally, a process map was developed to depict the effects of mixing on the temporal and spatial evolution of the materials inside the twin-screw granulator. This process map illustrates the mechanism of nucleation and the growth of the granules based on the fundamental material properties of the primary powders (solubility and wettability), liquid binders (viscosity), and mixing dynamics present in the system. Furthermore, it was shown that the process map can be used to predict the granule product quality based on the granule growth mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Hot Deformation Characteristics and Processing Map of FV520B Martensitic Precipitation-Hardened Stainless Steel

Hot deformation characteristics of FV520B martensitic precipitation-hardened stainless steel were investigated in the temperature range of 850-1150 °C and strain rate range of 0.005-0.5 s{sup −1} with true strain 0.8 by using Gleeble-3500 thermo-mechanical simulator. Critical stress and corresponding critical strain for dynamic recrystallization initiation were calculated. The constitutive equation was developed by employing Arrhenius constitutive model to represent the nonlinear relationship of true stress and deformation parameters including temperature, strain rate and strain. The correlation coefficient and average absolute relative error are 0.9979 and 2.225%, which indicates the good predictive accuracy of the established constitutive equations. Processing maps were constructed at different plastic strains levels based on dynamic material model (DMM) and established constitutive equation. Combining the processing map with microstructure, the optimum region for good workability is designated as the temperature range of 1050-1125 °C and strain rate range of 0.027-0.23 s{sup −1} with peak efficiency of 0.32. Homogeneous fine and equiaxed dynamic recrystallization (DRX) grains can be found in this optimum according to the microstructure metallographic pictures.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Hot Deformation Behavior and Processing Maps of As-Cast Hypoeutectic Al-Si-Mg Alloy

The hot deformation behavior of as-cast hypoeutectic Al-Si-Mg alloy has been investigated through hot compression tests at temperatures between 573 and 773 K and the strain rate of 0.001-1 s{sup −1}. A modified Hansel-Spittel constitutive model is proposed, which takes the effect of strain rate on strain hardening into account. The processing maps are established based on the dynamic material model and the Murty criterion. Microstructure observations show that dynamic recovery dominates the dynamic softening behavior, and recrystallized grains are found in the sample tested at 773 K with strain rate of 0.01 and 0.001 s{sup −1}. The size of Si particles decreases by about 64.73% with the effective strain increasing from 0 to 1.2. The optimal hot processing parameters of as-cast hypoeutectic Al-Si-Mg alloy are established based on the processing maps.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Constitutive behavior and processing maps of a new wrought magnesium alloy ZE20 (Mg-2Zn-0.2Ce)

ZE20 (Mg-2Zn-0.2Ce) 2 is a new wrought magnesium alloy with improved extrudability and mechanical properties [1]. To understand the constitutive behavior and workability of this new alloy, Gleeble thermomechanical testing has been carried out in this study. The flow stress behavior of ZE20 was investigated between 250 °C–450 °C and 10 –3 s –1 –1.0 s –1 in isothermal compression. Constitutive descriptions of the flow stress are provided. A new general approach at application of the extended Ludwik equation is demonstrated and was found to be more accurate than the hyperbolic sine Arrhenius model while having a similar number of model constants. Processing maps were developed based on the experimental results and are verified with microstructural investigation. A region of safe processing with non-basal texture and high activity of dynamic recrystallization (DRX) was found between 375 °C and 450 °C, from 10 –1 s –1 to 10–2.5 s –1 . A region of potentially safe processing with annealing that is associated with shear band nucleation of non-basal grains was identified for temperatures as low as 300 °C and rates as high as 10 –1 s –1 .

36 MATERIALS SCIENCE↗
DOE OSTI2019

Workability of High-Strength Automobile Steel at High Temperatures Using Hot Processing Maps and Electron Backscatter Diffraction

Hot deformation behavior of a high-strength steel Docol 1500 Bor was investigated by compression tests on a thermal–mechanical simulator Gleeble 3500. The workability of tested steel was analyzed by building processing maps at three strains based on the test results of flow stresses. The essential deformation mechanism was further studied based on the deformation texture analysis on a scanning electronic microscope with a TSL-OIM™EBSD system. It was found that that strain can affect the workability of the tested steel remarkably. At the engineering strain of 0.6, obvious textures in martensite mainly include recrystallization textures <110>//ND and other texture components <110>//RD and {001} <100>, etc. Dynamic recrystallization and recrystallization texture component <111>//ND in deformed austenite can contribute to increasing the workability of a steel Docol 1500 Bor. Finally, the optimized deformation conditions of the tested steel were obtained.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Hot Deformation Behavior and Processing Map of a Fe-11Mn-10Al-0.9C Duplex Low-Density Steel Susceptible to κ-Carbides

The hot deformation behavior of a Fe-11Mn-10Al-0.9C duplex low-density steel has been investigated, based on a series of isothermal compression tests at temperatures of 800-1100 °C and strain rates of 0.001-10 s{sup −1}. At relatively lower temperatures, with austenite decomposing, continuous dynamic recrystallization of ferrite and the formation of inter-granular κ-carbide were responsible for significant softening. At high deformation temperature where austenite was a dominant phase, dynamic recrystallization (DRX) is the restoration mechanism. Processing maps were developed at different plastic strains employing dynamic materials model and further verified through microstructural characterization. The optimal hot deformation condition at a large strain (0.7) was identified as deformation temperature of 950-1100 °C and strain rate of 0.01-1.0 s{sup −1}. In this domain, the original coarse grains could be replaced by the fine and uniform recrystallized grains, indicating that the high efficiency was dissipated by DRX. Meanwhile, two instable regions resulted from the formation of inter-granular κ-carbides and necklace structure should be avoided during hot processing.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Hot Deformation and Processing Maps of B{sub 4}C/6061Al Nanocomposites Fabricated by Spark Plasma Sintering

We developed 3 vol.% B{sub 4}C/6061Al nanocomposites by spark plasma sintering. We then characterized their flow and hot deformation behaviors using isothermal compression tests at deformation temperatures of 673-823 K and strain rates of 0.001-1 s{sup −1}. Results indicated that their steady flow characteristic can be described by the Zener–Hollomon parameter (Z) as a hyperbolic-sine function of flow stress. Based on pinning effect from B{sub 4}C nanoparticles, the estimated activation energy was calculated to be 274.80 kJ/mol. At the unstable deformation condition (723 K/1 s{sup −1}) with high Z, flow localization appeared; the main softening mechanisms involved are dynamic recovery and dynamic recrystallization (DRX). At the stable deformation condition (823 K/0.001 s{sup −1}) with low Z, adequate DRX occurred. Based on the processing map and microstructure evolution, the optimum hot working conditions for B{sub 4}C/6061Al nanocomposites were determined to be a deformation temperature of 773-823 K and a strain rate of 0.001-0.003 s{sup −1}. These working conditions were verified by a hot extrusion test.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Domain knowledge-informed, process-mapping AI graph for designing Fe-based alloys

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Continuous improvement in efficiency of a power plant relies on designing materials for use at increasingly higher temperature and/or pressure, for 100,000s hours of operation. Due to complexity, non-linearity and high-dimensionality of the problem, traditional Machine Learning (ML) approaches require unreasonably large datasets for the data-driven model development. Science-based material and process engineering complements hard data with, sometimes soft and intuitive, empirical domain knowledge. Artificial Intelligence (AI) was used in this study to incorporate such knowledge into computational graph architecture (process-mimicking artificial neuron design, causal layer and graph structures, ensemble modeling of latent states) and learning procedures (variable transformation, fuzzy physics pre-training and freezing of deep layers, virtual microstructure representation, and adversarial multi-objective optimization). The first alloys design pathways suggested by the AI tool (pyroMind) passed a preliminary engineering review on soundness and transparency.</span>

Romanov, Vyacheslav↗
DOE OSTI2021

Designing alloys with process-mapping AI pre-trained on empirical knowledge

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Accelerated materials design should match the recent trends in the product development cycles. Materials data analytics can be used to significantly shorten development time of specialized alloys needed for next generation energy applications. However, it faces a challenge of scarce data available for training ML models. Incorporation of the domain knowledge into deep-learning graph structure via fuzzy pre-training and causal process imitation presents a viable approach to developing accurate data-driven models and reliable alloy design tools, with limited datasets. Artificial Intelligence (AI) was used in this study to incorporate such knowledge in the domain-specific computational tool, pyroMind. The tool provides not only novel design ideas but also their interpretation via physics and engineering concepts.</span>

Romanov, Vyacheslav↗
DOE OSTI2025

A high-throughput approach for statistical process optimization in Laser Powder Bed Fusion

Process variability is inherent in metal additive manufacturing (AM). However, it is often overlooked in process optimization frameworks, constraining the understanding of process uncertainties and their influence on parameter selection. To address this, we present an integrated framework that combines high-throughput single-track experiments, GAN-based melt pool geometry extraction, robust statistical and machine learning modeling, and uncertainty-quantified process mapping. Process variability is characterized through single-track melt pool behaviors, and its influence on defect formation is systematically quantified to enable statistically guided process parameter optimization. This approach is demonstrated on Laser Powder Bed Fusion (L-PBF) of stainless steel 316L, effectively capturing the interplay between process parameters, melt pool variability, and defect probability. By integrating uncertainty quantification into process optimization, this study provides a structured methodology for addressing variability challenges in AM quality control, ultimately contributing to enhanced manufacturing reliability.

Laser Powder Bed Fusion↗