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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 OSTI2024

Design and Development of a Fixtureless, Pass-through Machine Tool for Extrusion Machining

The aerospace, construction/architecture, and transportation manufacturing industries rely heavily on the mass production of near-net shape metallic and composite extrusions. While the production of raw extrusions is a relatively fast process, adding functional features such as holes and slots require additional time, cost, and energy to produce. To compensate for the inherent flexibility of extrusions, conventional machining requires rigid purpose-built fixtures for operations such as trimming, drilling and thinning. This approach requires that the machine tools be as large or larger than the parts themselves. This results in the need for excess shop floor space, energy for auxiliary equipment and motion systems, and significant capital expenditure. Considerable engineering expense and time involved in the designing, building, and proving out of part-specific fixtures for holding the components in specific configurations while machining add to the overall manufacturing cost. The primary objective of the technical collaboration between Oak Ridge National Laboratory and Fairmount Technologies (FT) is to improve the XM-3, a fixtureless CNC milling machine designed by FT. The machine was developed to trim, drill, and thin extrusions without part specific fixturing to make the manufacturing process more efficient and flexible. Dynamic measurements of the existing structure were collected, and modeling efforts were made to evaluate optimal machining parameters for the current system. Areas of improvement to increase the system stiffness, manufacturability, and machining efficiency were evaluated and highlighted for the next generation design. The impact of this effort may enable agile manufacturing across the commercial and defense aerospace industries, and other industries where extrusions are utilized like in the construction, architecture, and transportation industries.

42 ENGINEERING↗
DOE OSTI2025

Design and Development of a Fixtureless, Pass-through Machine Tool for Extrusion Machining

The aerospace, construction/architecture, and transportation manufacturing industries rely heavily on the mass production of near-net shape metallic and composite extrusions. While the production of raw extrusions is a relatively fast process, adding functional features such as holes and slots require additional time, cost, and energy to produce. To compensate for the inherent flexibility of extrusions, conventional machining requires rigid purpose-built fixtures for operations such as trimming, drilling and thinning. This approach requires that the machine tools be as large or larger than the parts themselves. This results in the need for excess shop floor space, energy for auxiliary equipment and motion systems, and significant capital expenditure. Considerable engineering expense and time involved in the designing, building, and proving out of part-specific fixtures for holding the components in specific configurations while machining add to the overall manufacturing cost. The primary objective of the technical collaboration between Oak Ridge National Laboratory and Fairmount Technologies (FT) is to improve the XM-3, a fixtureless CNC milling machine designed by FT. The machine was developed to trim, drill, and thin extrusions without part specific fixturing to make the manufacturing process more efficient and flexible. Dynamic measurements of the existing structure were collected, and modeling efforts were made to evaluate optimal machining parameters for the current system. Areas of improvement to increase the system stiffness, manufacturability, and machining efficiency were evaluated and highlighted for the next generation design. The impact of this effort may enable agile manufacturing across the commercial and defense aerospace industries, and other industries where extrusions are utilized like in the construction, architecture, and transportation industries.

42 ENGINEERING↗
DOE OSTI2026

Machine Tool Data Analytics for Digital Twin and Machine Predictive Maintenance

The primary objective of this project is to improve machining process performance using in-process machining data from the machine tool controller and external sensors. Advances in the Industrial Internet of Things (IIoT) enable monitoring of machines using controller data. Examples of the data provided by a controller include execution status of the controller, part count, block of code being executed, door status, tool position, the spindle and axis load, etc. MTConnect and OPC-UA are the two common protocols for capturing machine information. In this collaboration, methods for retrieving the machine controller data from selected machine tool controls and making these data accessible in different subsystems (such as digital twins and machine maintenance portals, etc.) will be developed and tested. In addition, analytics to improve machining process performance (by increasing productivity and reducing downtime) will be developed.

42 ENGINEERING↗
DOE OSTI2026

Servomotor-driven leveling elements for machine tool frame-to-floor alignment

This paper presents the results of aligning the frame to concrete floor of a large 5-axis machine tool by leveling element adjustments using servomotors instead of manual operation. The machine tool leveling elements used in this study are mechanical devices that attach the machine tool frame to the concrete foundation. These leveling elements are also adjustable so that each location along the machine tool can be uniquely adjusted such that the geometric errors of the frame are minimized, and the accuracy of the machine tool is improved. Off-the-shelf servomotors and connection equipment were installed at each of 12 leveling element locations for a precision 5-axis machine tool at Oak Ridge National Laboratory. The servomotors allow a technician to align the machine tool frame remotely, instead of crawling under the machine tool to iteratively manually adjust each of the 12 leveling elements. The open-loop servomotor-leveling element system design is presented in this paper as well as machine tool frame alignment results. The servomotor system significantly reduces leveling element adjustment time, eliminates the need for under-machine access, reduces technician physical strain, and significantly improves the adjustment repeatability.

Honeycutt, Andrew [ORNL]↗
DOE OSTI2025

Rapid Commissioning of Large Machine Tools Using Finite Element-Based Correction of Geometric Errors

Large computer numerical control (CNC) machine tools derive their stiffness from monolithic cast iron bases or weldments that are sometimes integral to machine motion systems like box ways or guideways. However, the sheer size of castings and even floor flatness deviations result in dimensional errors in these systems, which manifest as machine motion errors. Typical geometric alignment processes rely on an iterative approach, where measurements are taken to assess alignment (straightness, squareness, and parallelism), followed by adjustment of the machine supports (fixators or leveling pads), which can take weeks even for an experienced operator. Conversely, a novel method is proposed to shorten the correction time by eliminating the trial-and-error process in favor of a more deterministic approach guided by a finite element (FE) method. A feasibility study is conducted on a CNC polymer hybrid machine, with a steel weldment frame, supported by six leveling pads. An FE model of the frame is utilized to obtain recommended leveling pad adjustments, based on measurement of machine errors taken using a laser tracker. After a single adjustment cycle, measurements reveal that geometric errors of the machine tool are reduced from 2.22 mm of flatness deviation to 0.32 mm, achieving an 85.6% reduction. Furthermore, the entire process including measurement, adjustment, and assessment is completed in just 6 h by two operators who are not professional service engineers. In conclusion, this methodology demonstrates feasibility for scaling up, especially to large, high-precision CNC machine tools with bases mounted by fixators, offering the capability for bidirectional adjustment.

42 ENGINEERING↗
DOE OSTI2024

Machine tool cross beam design, fabrication, and testing using metal big area additive manufacturing

This paper describes the application of metal Big Area Additive Manufacturing (mBAAM) to the fabrication of a machine tool cross beam. The replacement of a traditional box design weldment with a new design printed by wire arc additive manufacturing using the MedUSA system at Oak Ridge National Laboratory (ORNL) is detailed. This requires a new design strategy based on the unique mBAAM capabilities. The intent of the new design is to reduce mass, while maintaining the dynamic stiffness. To compare the two designs, the natural frequencies and mode shapes are measured using impact testing and predicted using finite element analysis. It is confirmed that the printed structure dynamics agreed with the numerical model predictions, which demonstrates that it is feasible to model a large-scale mBAAM part and understand its behavior prior to printing. Another notable outcome of this study is that the significant residual stress and distortion in the print indicate that knowledge gaps remain for widespread implementation of mBAAM.

42 ENGINEERING↗
DOE OSTI2026

Machine Tool Metrology in support of carbide insert production

In the context of lab modernization, careful consideration must be given to existing technologies, processes, and knowledge to support the ongoing production, maintenance, and improvement of products for the nuclear enterprise. This consideration often involves analyzing manufacturing processes both to pursue modernization or automation and to ensure the ability to maintain manufacturing capability.

42 ENGINEERING↗
DOE OSTI2026

Improved manufacturability of vacuum thermoforming molds by segmentation

Thin polymer shell components used in consumer goods, the automotive industry, and marine applications are often produced by thermoforming. Depending on the quantity of parts, molding processes can increase throughput significantly when compared to processes such as milling or additive manufacturing. One disadvantage to mold-based manufacturing methods, however, is that molds must be produced to form the required geometry. Mold production is often complex and requires a large capital investment. For large-scale molding processes, mold production is sometimes complicated by the requirement for large working volume machine tools which are capable of machining the cavities to the appropriate tolerances for the selected molding process. Here, this paper focuses on a segmented design for a large draw ratio (>3:1 molded surface area to sheet surface area) thermoforming mold and a cost-benefit analysis of segmentation versus monolithic design. Discussion of the thermoforming tests are included. The primary goals were to improve manufacturability and reduce the overall cost of the mold.

Manufacturability↗
DOE OSTI2025

FracML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage

Poster on “FRACML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage” for the CCUS 2025 conference held in Houston, Texas March 3-5, 2025. The accurate characterization of subsurface fracture networks is essential for the secure operation of carbon capture, utilization, and storage (CCUS) projects. A thorough understanding of the spatial distribution of subsurface faults and fractures is crucial for predicting CO2 plume evolution and minimizing risks such as potential leakage into overlying formations or induced seismicity. In this context, robust fracture network quantification plays a pivotal role in reservoir management, providing the data necessary to fine-tune operational parameters, and ensure the environmental and economic viability of CCUS projects. As part of the U.S. Department of Energy’s SMART (Science-informed Machine Learning for Accelerating Real-time Decisions in Subsurface Applications) initiative, we focused on the development and application of a machine learning-based tool (FRACML) designed to quantify and map fracture networks using real-world (non-synthetic) data from an active CO2 injection site. Our objective is to demonstrate the utility of this tool in improving operational efficiency and safety across CCUS sites.

artifical intelligence / machine learning (AI/ML)↗
DOE OSTI2024

A Simulated Evaluation of Powder Flowability Through a Partially Obstructed Consumable in Blown Powder Directed Energy Deposition Systems

Abstract In the interest of continued industrialization of metal additive manufacturing in modern production environments, cost is often referenced as a primary deterrent to new adopters. Conventional economic models for additive systems, processes, and supply chains often focus on specific process applications with little generalizability, or they neglect significant costs associated with production such as machine maintenance and consumable part replacement. Compounding the latter issue are substantial knowledge gaps in consumable part wear characterization for additive and other convergent manufacturing systems. In coaxial blown powder directed energy deposition systems, gas atomized metal powder is wasted during material deposition at a rate that is partly dependent on present wear phenomena in a consumable nozzle housed in the cladding head assembly. The price and lead time required to replace the nozzle incentivizes its reuse even when visibly worn. Often this initiates a process quality decline in the form of underbuilt geometry and internal defects due to losses in powder catchment efficiency. While depositing H13 steel using a hybrid manufacturing machine tool equipped with such a deposition system, a unique partial clog with a bridge-like structure formed at the consumable nozzle exit when supporting argon gas flows failed mid-process. To further understand coaxial multi-phase powder flow in the event of support gas failure, a computational fluid dynamics simulation is tailored to relevant process parameters, H13 powder material profile, and machine operator observations collected after the incident. The resulting differences in powder flow compared to control gas flow parameters is presented and discussed. The powder flowability and performance of the clogged nozzle is then assessed by using an optical profilometer to extract the profile of the clog and recreate the clog geometry within the simulation environment. In past work this simulation has been experimentally validated for a 316L steel powder material profile and used specifically for analyzing powder stream geometry and catchment efficiency. After the initial powder flow characterization, the clog is removed, and the nozzle is reprofiled. After removing the obstructing clog, the newly unobstructed nozzle geometry, the original off the shelf nozzle geometry, and additional nozzle profiles exploring different consumable refurbishment strategies are reevaluated in the simulation. Powder catchment efficiency for all variant nozzle geometries and relevant flow variables are compared and discussed, along with potential mitigation strategies for optimizing powder flowability with worn consumables. This work expands on the known morphology of blown powder obstructions and wear defects present in consumable coaxial nozzles while discussing pragmatic simulation driven responses to unanticipated subsystem failure in hybrid manufacturing machining platforms.

DeWitte, Lisa↗
DOE OSTI2025

Shear Assisted Processing and Extrusion of Magnesium Alloy Machining Chips

The Pacific Northwest National Laboratory (PNNL) utilized Shear Assisted Processing and Extrusion (ShAPE) to convert magnesium alloy machining chips directly into wrought material. PNNL fabricated 0.25” and 0.50” round rod from cold compacted briquettes pressed from dry-machined chips by Loukus Technologies. The alloy composition is proprietary and protected by a Materials Transfer Agreement (MTA) between PNNL and Loukus. Extrusions were fabricated using the ShAPE 2.0 machine, tooling, and process. PNNL is evaluating tensile properties per ASTM E8 and microstructure for grain size, texture, and porosity using Scanning Electron Microscopy (SEM) and Electron Backscatter Diffraction (EBSD). Tensile properties and microstructure results were not available at the time of writing this document. Extruded rods have also been sent to Loukus for evaluation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning visualization tool for exploring parameterized hydrodynamics

We are interested in the computational study of shock hydrodynamics, i.e. problems involving compressible solids, liquids, and gases that undergo large deformation. These problems are dynamic and nonlinear and can exhibit complex instabilities. Due to advances in high performance computing it is possible to parameterize a hydrodynamic problem and perform a computational study yielding $\mathscr{O} (TB)$ of simulation state data. We present an interactive machine learning tool that can be used to compress, browse, and interpolate these large simulation datasets. This tool allows computational scientists and researchers to quickly visualize 'what-if' situations, perform sensitivity analyses, and optimize complex hydrodynamic experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A structural equation modeling approach to leveraging the power of extant sentiment analysis tools

Machine-derived sentiment analysis has become a pervasive and useful tool to address a wide array of issues in natural language processing. Leading technology companies such as Google now provide sentiment analysis tools (SATs) as readily accessible online products. Academic researchers develop and make available SATs to support the research enterprise. One of the major challenges with SATs is the inconsistencies in results among the various SATs. Consequently, the selection of a SAT for a specific purpose may significantly impact the application. This study addresses the foregoing problem by utilizing structural equation modeling to merge the outputs of SATs to develop a combined sentiment metric without the need for a labeled training dataset. This method is applicable to a wide range of text-based problems, is data-driven, and replicable. It was tested using three publicly available datasets and compared against seven different SATs. The results indicate that as a continous measure, the proposed method outperformed other SATs in the movie reviews and SemEval datasets, and achieved a tie for first place with IBM Watson on the Sentiment 140 dataset. Also, compared to the published major alternatives, the arithmetic mean solution, this approach performed better across these three datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Active learning using hybrid surrogate tool life modeling for machining process optimization

Here, this paper describes an active learning approach for part-to-part iterative machining process optimization using a hybrid surrogate tool life model. A probabilistic interpolating tool life model is developed by combining the empirical Taylor-type tool life equation and the model fit error. The probabilistic tool life model is then used to calculate the machining cost per part distribution. The optimal machining parameters are selected using an expected improvement in machining cost per part criterion. The method is validated numerically using experimental results; the results show a median convergence error of 2.2% after three tests over 400 simulations. The method is validated experimentally on two industrial applications for Ti-6Al-4V roughing resulting in a cost per part reduction greater than 23% after two tests. The described method is a robust solution for rapid convergence to optimal machining parameters in an industrial production environment.

Active learning↗
DOE OSTI2025

Estimation of cutting tool wear using an elastomeric tactile sensor

Machining performance of cutting tools and part quality are affected by the geometric condition of the cutting edge, which is influenced by thermomechanical loads experienced during the process. Tool condition monitoring (TCM) systems provide insight for timely replacement of cutting tools. However, existing TCM systems are expensive and require specialized equipment or sensors, hindering widespread adoption. A novel TCM system is developed herein using an elastomeric tactile sensor. Sensor images of the cutting edge are used to quantify wear using two distinct algorithms. In the first algorithm, the unworn and worn edges are identified based on Canny edge detect. In the second, the unworn edge is identified using edge detection while the region of wear is identified using a relative intensity method. In both cases, the maximum wear width is calculated based on an experimentally determined pixel to-physical distance scale. The TCM system is first used to estimate flank wear on a solid carbide helical end mill before evaluating its robustness by employing it to estimate insert wear of an indexable helical end mill. Measurements are also performed manually using an optical microscope and a high-resolution focus variation microscope for verification. The novel technique estimates flank wear in the solid carbide tool with a high accuracy of 98%. Larger discrepancies are observed for the inserts, however, with overlapping uncertainties. In conclusion, the technique shows promise in adaptability, automation, and closed loop control of machine tools.

Machining↗