Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Reliability Target”

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.

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

At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

High Effectiveness, Compact, High Pressure and Low Cost Recuperator for Super-Critical CO2 Power Cycles, Paper Presented at The Fifth International Symposium – Super-Critical CO2 Power Cycles, San Antonio, Texas, March 28-31, 2016, by Dr. John Kelly, President, Altex Technologies Corporation

Closed Brayton super-critical CO2 power cycles are well suited to waste heat bottoming cycles, due to their increased efficiency and compactness, relative to Rankine steam bottoming cycles. Since waste heat applications would be retrofits, the power system compactness is important. To achieve high efficiency, these power cycles require high pressure recuperative type heat exchangers, of substantial heat duty. Current Printed Circuit Heat Exchangers (PCHE), originally developed for high pressure gas and oil applications, can be used as recuperators, but costs are higher than desired. Altex is developing a purpose-built high pressure and effectiveness recuperator to provide the reliability, compactness, performance and pressure capability of current recuperators, but at a reduced cost. The High Effectiveness Low Cost (HELC) recuperative heat exchanger design yields volume and weight metrics of .0024 m3/UA and 10.2 kg/UA, which are 4% and 77.3% below recuperator target metrics, respectively. A 50 kW test article was designed and fabricated. Performance tests on water and oil showed that the HELC design model could predict heat transfer, to within 10% of the measured value. This model was then used to project HELC performance, when operating on supercritical CO2. Besides performance tests, the test article was hydrostatically tested, for integrity at up to 4,000 psi pressure. At these conditions, some distortion of channels was encountered. To mitigate distortion, the inserts were redesigned and these results were used to project the cost of 500 kW and 5,083 kW HELC units. The cost metric for the 5,083 kW unit was determined to be $1,349/UA, which is 10% lower than the recuperator desired cost metric desired target of $1,500/UA. In addition, the 5,083 kW unit HELC cost of $61.09/kW is 33.6% lower than the $92/kW estimated cost for a PCHE. Hydrostatic pressure tests, at up to 4,000psi, showed that the unit did not leak. However, channels were distorted at this pressure differential. Design updates to minimize stress concentrations and distortion were prepared and analyzed, to show that distortion could be controlled, but to date tests have not been run to prove the design. Project results show the potential of the HELC approach, but more work is required to confirm this potential, at the larger scales of interest.

14 SOLAR ENERGY↗
DOE OSTI2022

Structure- and Interaction-Based Design of Anti-SARS-CoV-2 Aptamers

Aptamer selection against novel infections is a complicated and time-consuming approach. Synergy can be achieved by using computational methods together with experimental procedures. In this study, we aim to develop a reliable methodology for a rational aptamer in silico et vitro design. The new approach combines multiple steps: (1) Molecular design, based on screening in a DNA aptamer library and directed mutagenesis to fit the protein tertiary structure; (2) 3D molecular modeling of the target; (3) Molecular docking of an aptamer with the protein; (4) Molecular dynamics (MD) simulations of the complexes; (5) Quantum-mechanical (QM) evaluation of the interactions between aptamer and target with further analysis; (6) Experimental verification at each cycle for structure and binding affinity by using small-angle X-ray scattering, cytometry, and fluorescence polarization. By using a new iterative design procedure, structure- and interaction-based drug design (SIBDD), a highly specific aptamer to the receptor-binding domain of the SARS-CoV-2 spike protein, was developed and validated. The SIBDD approach enhances speed of the high-affinity aptamers development from scratch, using a target protein structure. The method could be used to improve existing aptamers for stronger binding. This approach brings to an advanced level the development of novel affinity probes, functional nucleic acids. It offers a blueprint for the straightforward design of targeting molecules for new pathogen agents and emerging variants.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Continuous culture of anaerobic fungi enables growth and metabolic flux tuning without use of genetic tools

Anaerobic gut fungi (AGF) have potential to valorize lignocellulosic biomass owing to their diverse repertoire of carbohydrate-active enzymes (CAZymes). However, AGF metabolism is poorly understood, and no stable genetic tools are available to manipulate growth and metabolic flux to enhance production of specific targets, e.g., cells, CAZymes, or metabolites. Herein, a cost-effective, Arduino-based, continuous-flow anaerobic bioreactor with online optical density control is presented to probe metabolism and predictably tune fluxes in Caecomyces churrovis. Varying the C. churrovis turbidostat setpoint titer reliably controlled growth rate (from 0.04 to 0.20 h −1 ), metabolic flux, and production rates of acetate, formate, lactate, and ethanol. Bioreactor setpoints to maximize production of each product were identified, and all continuous production rates significantly exceed batch rates. Formate spike-ins increased lactate flux and decreased acetate, ethanol, and formate fluxes. The bioreactor and turbidostat culture schemes demonstrated here offer tools to tailor AGF fermentations to application-specific hydrolysate product profiles.

Agriculture↗
DOE OSTI2020

Correlation approach for quality assurance of additive manufactured parts based on optical metrology

Surface topography and surface finish are two significant factors for evaluating the quality of products in additive manufacturing (AM). AM parts are fabricated layer by layer, which is quite different from traditional formative or subtractive methods. Despite rapid progress in additive manufacturing and associated optical metrology for quality control and in-situ monitoring, limited research has been conducted to investigate the reliability of 3D surface measurement data. The surface topologies scanned by multiple optical systems demonstrated significant differences due to varying sampling mechanisms, resolutions, system noises, etc. The 3D datasets should be trustworthy in order to extract parameters for quality assurance or feedback control from 3D surface measurements. In this paper, we set up new standards to evaluate the reliability of 3D surface measurement data and analyze the variation in the topographical profile. In this study, two non-contact optical methods based on Focus Variation Microscopy (FVM) and Structured Light System (SLS) were adopted to measure the surface topography of the target components. The two optical metrology systems generated two entirely different point cloud datasets. Statistical methods were applied to test the difference between the data obtained from the two systems. By using a data analytics approach for comparison, it was found that the surface roughness estimated from the point cloud data sets of FVM and SLS has no significant difference, though the point cloud data sets were completely different. This paper provides standard validation approach to evaluate the plausibility of metrology data from in-situ real-time surface analysis for process planning of AM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Testing strong-field QED with the avalanche precursor

A two-beam high-power laser facility is essential for the study of one of the most captivating phenomena predicted by strong-field quantum electrodynamics (QED) and yet unobserved experimentally: the avalanche-type cascade. In such a cascade, the energy of intense laser light can be efficiently transformed into high-energy radiation and electron–positron pairs. The future 50-petawatt-scale laser facility NSF OPAL will provide unique opportunities for studying such strong-field QED effects, as it is designed to deliver two ultra-intense, tightly focused laser pulses onto the interaction point. In this work, we investigate the potential of such a facility for studying elementary particle and plasma dynamics deeply in the quantum radiation-dominated (RD) regime, and the generation of QED avalanches. With three-dimensional particle-in-cell simulations, we demonstrate that QED avalanche precursors can be reliably triggered under realistic laser parameters and layout (namely, focusing f/2, tilted optical axes, and non-ideal co-pointing) with the anticipated capabilities of NSF OPAL. We demonstrate that seed electrons can be efficiently injected into the laser focus by using targets of three types: a gas of heavy atoms, an overcritical plasma, and a thin foil. A strong positron and high-energy photon signal is generated in all cases. The cascade properties can be identified from the final particle distributions, which have a clear directional pattern. At increasing laser field intensity, such distributions provide signatures of the transition, first, to the RD interaction regime, and then to a QED avalanche. Our findings can also be used for designing related future experiments.

Lasers↗
DOE OSTI2024

ML based control systems for nuclear physics experiments

The Experimental Physics Software and Computing Infrastructure (EPSCI) group at Jefferson Lab is leading the use of machine learning (ML) to enhance control systems in nuclear physics experiments. Collaborating closely with domain experts and data scientists, we have developed an ML-based control system that uses a Gaussian process to dynamically adjust the high voltage of the GlueX Central Drift Chamber. This results in stable detector performance by adapting to environmental changes, thereby reducing the offline calibration effort. Furthermore, we are developing ML-driven systems for optimizing the polarization of photon beams and polarized cryotargets. These systems will maintain the optimal microwave frequency in cryogenic targets and make real-time adjustments to diamond radiators for polarized photon sources, tasks traditionally handled by human operators. By automating these functions, we aim to optimize the polarization, reduce downtime, and minimize human error. This talk will highlight the development of reliable ML-based control systems and the policies to ensure they are both effective and trustworthy.

Jeske, Torri↗
DOE OSTI2024

Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context

One challenge that nuclear power plant system engineers are facing is continuous generation of an extremely large amount of equipment reliability (ER) data. These data elements come in textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) forms. They provide system engineers with valuable insights and information by discovering anomalous behaviors or degradation trends, identifying possible causes behind such behaviors and trends, and predicting their direct consequences. This paper directly targets the knowledge generation from ER data by putting “data into context.” We employ model-based system engineering (MBSE) of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by first identifying which of the developed MBSE elements they are referring to. This task is harder for textual data since the information contained in issue or maintenance reports needs to be “understood” by a computational tool. We called this process “knowledge extraction” since our methods extract knowledge from textual data. Last, once numeric and textual ER data elements have been processed and “understood,” we discover possible cause-effect relations among them. This is performed by observing whether a logical connection through the MBSE models exists, and if there is a temporal relationship among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

What, why and when to go virtual: An international analysis of early adopters of virtual building energy codes inspections

To meet greenhouse gas reduction targets, several countries are pursuing more ambitious policies in their buildings and construction sectors, such as introducing zero net energy/carbon building codes. Countries often report not having enough qualified staff for performing building energy code inspections and many are exploring faster, easier, and more reliable methods to check the compliance of buildings with their codes. Building inspections are a critical element for ensuring code compliance and they have traditionally been performed in person. However, in-person inspections can be labor and travel intensive, costly, and prone to human error. In this paper, the authors explore how virtual inspections, particularly in light of the recent COVID-19 pandemic, have impacted processes for building code compliance checks in jurisdictions and communities around the world. Here, the authors collected data on four key parameters (time and financial savings, scope of inspections, changing practices and technological innovation, and benefits to consumers) from six jurisdictions and communities in five countries (Australia, Canada, Singapore, United Arab Emirates, and the United States) to analyze the impacts of virtual inspections on code compliance checks. The analysis found the greatest value from virtual inspections in geographically dispersed regions and for cities experiencing rapid building construction. The study also explored emerging technologies that are being piloted for virtual inspections. Although many of these technologies hold promise, more resources and capacity are needed to make them viable for use in building energy code inspections.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Achieving an 80% carbon-free electricity system in China by 2035

Dramatic reductions in solar, wind, and battery storage costs create new opportunities to reduce emissions and costs in China’s electricity sector, beyond current policy goals. This study examines the cost, reliability, emissions, public health, and employment implications of increasing the share of non-fossil fuel (“carbon free”) electricity generation in China to 80% by 2035. The analysis uses state-of-the-art modeling with high resolution load, wind, and solar inputs. The study finds that achieving an 80% carbon free electricity system in China by 2035 could reduce wholesale electricity costs, relative to a current policy baseline, while maintaining high levels of reliability, reducing deaths from air pollution, and increasing employment. In our 80% scenario, wind and solar generation capacity reach 3 TW and battery storage capacity reaches 0.4 TW by 2035, implying a rapid scale up in these resources that will require changes in policy targets, markets and regulation, and land use policies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Measurement of $\nu_\mu$ CC Interactions With Two-Proton Final State in MINERvA

This dissertation presents a measurement of charged–current (CC) muon–neutrino interactions with exactly two protons and no pions in the final state (CC~$2p\,0\pi$), using data collected by the MINERvA detector in the NuMI medium–energy beam at Fermilab. Such two–proton topologies are a sensitive probe of nuclear dynamics in the few–GeV regime, including multi–nucleon correlations (npnh, notably $2p2h$) and intranuclear final–state interactions (FSI) such as pion absorption and nucleon rescattering. A precise experimental characterization of these processes is essential both for neutrino–interaction theory and for reducing systematic uncertainties in oscillation experiments that rely on accurate modeling of neutrino–nucleus interactions. Events are selected by requiring a $\nu_\mu$ CC interaction with a reconstructed $\mu^-$ and two proton tracks originating from a common vertex in MINERvA’s finely segmented scintillator tracker, with no reconstructed mesons. Muon charge and momentum are constrained by matching to the MINOS Near Detector, while proton identification exploits energy–loss profiles and stopping–proton features. Backgrounds from pion–producing channels that enter the signal region through FSI or reconstruction effects are constrained with data–driven sidebands (Michel–electron and isolated–cluster “blob” samples) and tuned via a simultaneous fit across signal and sideband regions. To correct detector resolution and acceptance effects, the analysis employs iterative Bayesian unfolding with extensive validation: statistical pseudo–experiments, and robustness checks against generator systematic “universes” and additional strong shape warps. Single–differential cross sections are reported for three observables tailored to the two–proton final state: the opening–angle cosine $\cos\!\left(\theta_{pp}\right)$, the leading–proton momentum, and the subleading–proton momentum. Systematic uncertainties include contributions from neutrino flux, interaction modeling (e.g., npnh and resonance parameters, pion FSI), and detector response (calibration, reconstruction efficiencies). The resulting distributions provide targeted constraints on the interplay of multi–nucleon dynamics and FSI that shape CC~$2p\,0\pi$ final states on hydrocarbon. Comparisons to modern GENIE–based simulations highlight kinematic regions where model components require refinement. These measurements thus inform generator tuning and improve the reliability of neutrino–energy reconstruction strategies for current and future long–baseline oscillation programs.

Syrotenko, Vladyslav S. [Tufts U.]↗
DOE OSTI2026

Energy Technology Innovation Partnership Project Strategic Energy Plan for Washington County, Maine

This Strategic Energy Plan (SEP) for Washington County, Maine was developed through the U.S. Department of Energy's Energy Technology Innovation Partnership Project (ETIPP) to address local challenges related to electricity reliability, affordability, and safety. Grounded in a "relational infrastructure" approach, the plan recognizes that improved energy outcomes depend not only on physical grid investments but also on strengthened coordination and trust between utilities and communities. Using utility-provided reliability data, public datasets, and community input, the plan identifies key risks, including vegetation-related outages, high energy burden, and vulnerability among elderly and medically dependent populations. The SEP establishes three priority areas: hazard mitigation for energy infrastructure, outage safety and emergency readiness, and energy assistance and thermal safety. It outlines community-driven actions designed to complement utility-led system improvements, including targeted outreach, municipal coordination, and expanded access to energy programs. The plan also situates local conditions within broader state policy, market dynamics, and funding opportunities. Intended as a living document, this SEP provides a scalable framework for collaborative action to improve resilience, safety, and affordability in rural energy systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Electrification FY2020 Annual Progress Report

The Electric Drive Technologies (EDT) program’s mission is to conduct early-stage research and development on transportation electrification technologies that accelerate the development of cost-effective and compact electric traction drive systems that meet or exceed performance and reliability requirements of internal combustion engine (ICE)-based vehicles, thereby enabling electrification across all light-duty vehicle types.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Ultra High-Temperature Magnetic Bearing System for s-CO 2 Turbines/Expanders. Phase II Final Report

The goal of this project is to design and construct of a prototype of ultra-high temperature permanent magnet (UHT–PM), which provides the force to pull the spinning shaft towards the target position in the magnetic bearing (MB) clearance circle. This project will develop a PM biased MB system, that is actively cooled to the 550°C maximum operating temperature limit of PMs. The MB test environment will include 700°C, 40krpm and 4,000 psi requirements to advance the supercritical carbon dioxide (sCO 2 ) and other types of Brayton power cycle systems. The proposed novel MBs combine PMs for supplying the bias field and electromagnets for supplying the control field, both in UHT conditions. The choice of material is the UHT Sm-Co PMs (US patent 6,451,132) due to their significantly better thermal stability. The 6-pole homopolar design permits uninterrupted force delivery via a redundant control capability (US patent 7,429,811) even if 3 poles were to fail. The Phase I efforts was to focus on development and demonstration of component technologies and detailed design of full scale components and a demonstrator test rig for use in Phase II. UHT–PMs were produced and used in the MB actuator. The operating environment of the EEC-T550 Sm-Co magnet material could be extended by implementing active cooling. Electromagnetic coils imbedded in the MB provide forces to stabilize the shaft position at the target via sensing and control stages. Shaft position sensors enable to identify the present position of the spinning shaft relative to its desired target position in the MB clearance circle (air gap). Auxiliary bearings will be installed to provide a backup support system in the event that power to the MB fails or there is a fault in a device providing current to the MB coils. For control, reliability and safety reasons the sensors and auxiliary bearings are typically located as close as possible to the MBs, which exposes them to the same environmental conditions. Development of only the actuator for the UHT environment is an inadequate approach to complete integration of the MB system in the turbine/expander. Therefore the UHT sensor and auxiliary bearing were designed and developed along with the actively cooled, UHT-PM actuator development. Although shaft position sensors are commercially available (Kaman) they may cost up to $10k per channel, and a full 5 axis magnetic suspension is best implemented with up to 20 sensors. Once successfully completed, this project will significantly extend the operating environment capability for turbomachinery applications such as nuclear power, concentrated solar thermal, fossil fuel, geothermal, and shipboard propulsion. The further advancement of UHT-PM-MB will also strengthen the US position in PM technology, which is presently heavily dominated by China. The UHT Sm-Co magnet material that will be utilized in the proposed work has been developed exclusively by the project proposer. Many actuators, motors, generators and other MB applications will greatly benefit from the proposed work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Modular Processing of Flare Gas for Carbon Nanoproducts

This project demonstrated the technical viability and economic promise of a modular system for converting flared natural gas into valuable carbon nanoproducts (CNPs) through catalytic chemical vapor deposition (CVD). All major project milestones were successfully completed, including reactor design and commissioning, catalyst development, process optimization, technoeconomic analysis, and application testing in concrete systems. The overarching goal was to create a scalable, field-deployable process that valorizes stranded methane by producing high-value carbon materials for use in cementitious composites. At the lab scale, the team designed and built a fluidized bed reactor optimized for use with silica fume-supported nickel catalysts synthesized via atomic layer deposition (ALD). A statistically designed sintering study enabled precise tuning of nickel nanoparticle size, identifying the influence of oxygen partial pressure, time, and temperature on catalyst morphology and performance. These insights allowed the team to target catalyst conditions that maximize carbon nanofilament growth. Subsequent CVD experiments achieved up to 31.8 wt% carbon deposition under optimized conditions, with TEM confirming the presence of nanofilament structures and sustained hydrogen evolution during reaction. Reactor upgrades and empirical fluidization studies supported the development of reliable, repeatable experimental protocols. The modular pilot-scale skid reactor was fully constructed, instrumented, and commissioned. Capable of operating at 675–800°C and pressures up to 290 psig, the system was designed for continuous operation at a carbon production rate of 1 kg/hr. Initial demonstration runs confirmed solids handling, thermal control, and system leak-tightness, although a critical reactor component (the downfeed tube) was inadvertently omitted during final assembly. This omission limited gas–solid contact and prevented meaningful carbon deposition during pilot-scale CVD runs. Nonetheless, the system operated safely under design conditions, and the root cause of performance limitations was clearly identified. Complementary work on UHPC formulations demonstrated that small additions of carbon nanoproducts, including those derived from flare gas, can significantly enhance mechanical performance while preserving workability. A comprehensive study of CNF dispersion techniques and mix design optimization led to a clear protocol for integrating these nanomaterials into concrete. Incorporation of CNPs improved flexural toughness and reduced porosity, supporting their use in high-performance infrastructure applications. A technoeconomic analysis (TEA) confirmed that this process can produce CNP-loaded catalyst material at a levelized cost below $\$$7/kg across a range of catalyst loadings and reaction yields. With estimated market values for the carbon composite product ranging from $\$$14 to over $\$$60/kg, and the ability to blend CNPs into concrete at sub-percent levels with less than 10% added cost, the system presents a compelling economic case. While additional engineering work is needed to optimize fluidization and heat transfer at scale, this project establishes a strong foundation for commercial development. The process is not only technically sound but also economically promising, representing a viable pathway for flare gas mitigation through modular carbon nanomaterial production.

03 NATURAL GAS↗
DOE OSTI2023

Modeling laser interactions with aluminum and tantalum targets using a hybrid atomistic-continuum model

A hybrid atomistic-continuum method can model the microstructure evolution of metals subjected to laser irradiation. This method combines classical molecular dynamics (MD) simulations with the two-temperature model (TTM) to account for the laser energy absorption and heat diffusion behavior. Accurate prediction of the temperature evolution in the combined MD-TTM method requires reliable accuracy in electron heat capacity, electron thermal conductivity, and electron–phonon coupling factor across the temperatures generated. This study uses the electronic density of states (DOS) obtained from first-principle calculations. The calculated electron temperature-dependent parameters are used in MD-TTM simulations to study the laser metal interactions in FCC and BCC metals and the phenomenon of laser shock loading and melting. This study uses FCC Al and BCC Ta as model systems to demonstrate this capability. When subjected to short pulsed laser shocks, the dynamic failure behavior predicted using temperature-dependent parameters is compared with the experimentally reported single-crystal and nanocrystalline Al and Ta systems. The MD-TTM simulations also investigate laser ablation and melting behavior of Ta to compare with the ablation threshold reported experimentally. In conclusion, this manuscript demonstrates that integrating the temperature-dependent parameters into MD-TTM simulations leads to the accurate modeling of the laser–metal interaction and allows the prediction of the kinetics of the solid–liquid interface.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Chapter 2: Genetics of Unstudied Thermophiles for Industry

Thermophilic organisms hold great potential for industry due to their numerous advantages in biotechnological applications such as higher reaction rate, higher substrate loading, decreased susceptibility to reaction contamination, energy savings in industrial fermentations, and ability to express thermostable proteins that can be utilized in many important industrial processes. Bioprospecting for thermophiles will continue to reveal new enzymatic and metabolic paradigms with industrial applicability. In order to translate these paradigms to production scale, routine methods for microbial genetic engineering are needed, yet remain to be developed in many newly isolated thermophiles. Major challenges and recent developments in the establishment of reliable genetic systems in thermophiles are discussed. Here, we use a hyperthermophilic, cellulolytic bacterium, Caldicellulosiruptor bescii, as a case study to demonstrate the development of a genetic system for an industrially useful thermophile, describing in detail methods for transformation, genetic tool utilization, and chromosomal modification using targeted gene deletion and insertion techniques.

93 SC - Biological and Environmental Research (BER↗
DOE OSTI2023

Quantum Dot Based Solid-State Radiation Detectors

The detection and accurate identification of nuclear material is critical to the advancement of national security, medical technology, and environmental prosperity. The gold standard solid-state detector HPGe, suffers from high manufacturing costs and the need to operate at cryogenic temperatures to ensure reliable energy resolution. The other most common solid-state detector, cadmium zinc telluride (CZT), has the ability to operate at room temperature, but the grow process results in impurities decreasing performance. In this paper we have developed a continuous flow synthesis able to mass produce targeted size cadmium selenide quantum dots. The implementation of these QDs in QD/MEH-PPV composites has the ability to rival HPGe energy resolution at room temperature.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Implementation of disruptive designs for gas turbine components using direct energy deposition additive manufacturing

This research aims to develop a framework for establishing the correlation between in-situ monitoring data, process parameters, and microstructure evolution in blown-powder laser-directed energy deposition (DED) additive manufacturing (AM). To achieve this, a comprehensive manufacturing framework has been developed, spanning from in-situ data acquisition, melt-pool simulation, microstructure modeling, and statistical microstructure quantification. A machine learning-based surrogate model is constructed to predict melt pool geometry directly from in-situ coaxial camera data. The surrogate model is trained using outputs from a high-fidelity melt pool simulation, which provides accurate melt pool dimension data under varying process conditions. The predicted melt pool geometry is then used as input to a microstructure model to predict microstructural features. To rigorously compare and analyze microstructures, the project introduces statistical metrics that quantify differences based on key features such as morphology and texture. Microstructures are represented using advanced statistical descriptors including angular chord length distribution, two-point spatial statistics, orientation distribution function, and global spherical harmonic. These representations are used to compute four distinct “dissimilarity scores” that quantitatively capture differences in texture and morphology. This framework is demonstrated to enable automated calibration of simulation parameters by minimizing discrepancies between simulated and target microstructures. The technology developed in this project enables direct correlation between in-situ monitoring data and resulting microstructure, paving the way for adaptive microstructure control in metal AM. This capability strengthens the connection between process parameters and final material properties, facilitating more precise and reliable material design.

36 MATERIALS SCIENCE↗