Search NASA⌕ Search

SEARCH · Search NASA

Results for “MILLING MACHINE”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Plasma Focused Ion Beam Milling of High-Aspect-Ratio Holes in Diamond

Plasma focused ion beam (PFIB) technology has a great potential for applications requiring rapid micro-machining of high-aspect-ratio features. Here, we use 30-keV Ar, Xe, and O 2 ion beams to study PFIB milling of high-aspect-ratio circular holes in polycrystalline diamond. Our results show that Xe and O 2 ion beams have the highest milling efficiency for low-aspect-ratio and high-aspect-ratio hole milling, respectively. Each ion beam species produces holes with its characteristic shape and wall surface morphology. In conclusion, based on these results, we demonstrate a “hybrid” hole milled in a two-step process with Xe and O 2 ion beams with the geometry and wall morphology attractive for the attachment of a fuel fill tube to the inertial confinement fusion capsule.

ICF targets↗
DOE OSTI2020

Chatter Stability of Machining Operations

Here, this paper reviews the dynamics of machining and chatter stability research since the first stability laws were introduced by Tlusty and Tobias in the 1950s. The paper aims to introduce the fundamentals of dynamic machining and chatter stability, as well as the state of the art and research challenges, to readers who are new to the area. First, the unified dynamic models of mode coupling and regenerative chatter are introduced. The chatter stability laws in both the frequency and time domains are presented. The dynamic models of intermittent cutting, such as milling, are presented and their stability solutions are derived by considering the time-periodic behavior. The complexities contributed by highly intermittent cutting, which leads to additional stability pockets, and the contribution of the tool's flank face to process damping are explained. The stability of parallel machining operations is explained. The design of variable pitch and serrated cutting tools to suppress chatter is presented. The paper concludes with current challenges in chatter stability of machining which remains to be the main obstacle in increasing the productivity and quality of manufactured parts.

42 ENGINEERING↗
DOE OSTI2022

Multi‐objective optimization for cost‐efficient and resilient machining under tool wear

Abstract With the onset and rapid growth of smart manufacturing, there is a constant increase in the demand for automation technologies to enhance productivity while providing uninterrupted, cost‐efficient, and resilient machining. Traditional manufacturing systems, however, suffer from several losses due to machine faults and degradation. Specifically, tool wear directly impacts the precision and quality of the milled parts, which causes an increase in the scrap production. Hence, more attempts are required to meet the desired quota of successful parts, which in turn results in wasted material, longer delays, further tool degradation, and higher energy, machining, and labor costs. As such, this paper develops a multi‐objective optimization framework to generate the optimal control set points (e.g., feed rate and width of cut) that minimize the total cost of machining operations resulting from multiple contradictory cost functions (e.g., material, energy, tardiness, machining, labor, and tool) in the presence of tool wear. Notably, we estimate the total expected cost in dollars, which provides automatic and intuitive weighting in this multi‐objective formulation. The optimization framework is tested on a high‐fidelity face milling model that has been validated on real data from industry. Results show significant dollar savings of up to as compared to the default control scheme.

Wilson, James P.↗
DOE OSTI2021

Computationally Tractable High-Fidelity Representation of Global Hydrology in ESMs via Machine Learning Approaches to Scale-Bridging

Focal Areas: This paper responds primarily to Focal Area 2, focusing on AI techniques to improve model fidelity. Science Challenge: “Hyperresolution” [1, 2] land surface models (LSMs) running at far higher resolution than typically employed in global Earth system models (ESMs) can help answer critical questions about the water cycle and associated ecosystem and biogeochemical feedbacks. Even with all foreseeable advances in computing power and efficient solver algorithms, however, employing hyperresolution LSMs inside ESMs for studies of long-term global climate is not computationally feasible. Instead, we argue for incorporating the fidelity of hyperresolution LSMs only where and when it is needed by using machine learning approaches to scale-bridging.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Automation of Laser Plasma Focused Ion Beam Microscopy for Next-Gen Energy Materials

Automation can revolutionize the use of ultrafast laser ablation and plasma-focused ion beam (PFIB) techniques for high-throughput, reproducible cross-sectioning and various sample preparation in materials characterization. As these methods become essential for analyzing complex energy materials and next-generation devices, efficient, standardized workflows are needed to minimize variability and enhance precision. This work highlights our advancements in developing automated processes for sample preparation that integrates machine learning, workflow optimization, and large-scale data acquisition to improve efficiency and scalability in applications such as electrolyzers, photovoltaic cells, and microelectronics. To streamline cross-sectioning and lamella fabrication, we have implemented fully automated workflows that standardize laser ablation and PFIB milling sequences. These workflows incorporate pre-programmed protocols for material removal, alignment, and thinning, reducing user intervention and ensuring consistency across different sample types. Machine learning algorithms further enhance automation by predicting optimal milling strategies and adapting parameters based on material properties and sectioning requirements. This approach significantly improves throughput while maintaining the structural integrity of prepared samples for high-resolution imaging and analysis, including transmission electron microscopy. Beyond sample preparation, our automation platform enables the acquisition of large, high-resolution datasets through serial sectioning, image alignment, and 3D reconstruction. These automated routines facilitate multi-scale characterization, capturing structural and compositional details from the nanoscale to the device level. By reducing variability and increasing efficiency, our automated approach enhances defect analysis, failure diagnostics, and process optimization, accelerating advancements in materials research and device engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Detection of Control Injection Attacks using Energy Data Anomalies in CNC Machining

The widespread adoption of networked devices, sophisticated automation, and data-driven processes in the industry - also known as Industry 4.0 - has boosted the quantity and quality of manufacturing products. With these benefits, however, comes a substantial increase in the attack surface of these systems. In addition to affecting the readiness and the quality of critical products, the attacks against manufacturing processes and systems carry the potential to have severe physical consequences, including human injury and death. In this paper we present the results of a remote network-based control injection attack on a CNC mill. Specifically, we focus on the impact of this type of the attack on the movement of CNC mill during operation. Evaluating the physical effect of these attacks on a workpiece, we provide machine agnostic, affordable, and scalable solution for their monitoring. We then demonstrate a simple threshold-based method for the detection of these attacks and evaluate the effectiveness of detection.

Taylor, Curtis↗
DOE OSTI2020

Oxide dispersion strengthened 304 L stainless steel produced by ink jetting and laser powder bed fusion

This paper discusses the fundamentals of a novel hybrid method to synthesize oxide dispersion strengthened (ODS) 304 L stainless steel (SS) alloy using a modified laser powder bed fusion (LPBF) machine. Previously, ODS metal matrix composites have been produced by LPBF via ball-milling, which is expensive to scale. Here, we selectively dope yttria nanoparticles into a SS matrix by jetting a precursor chemistry onto the SS substrate prior to laser conversion and consolidation. Here, the new alloy shows good room temperature mechanical properties. Microstructures are studied using electron microscopy, energy dispersive spectroscopy and electron backscatter diffraction.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Installation and Demonstration of CNC Machining for Mechanical Test Specimens in the IFEL Hot Cell

This report documents the installation and demonstration of computer numerical control (CNC) machining capabilities in the Irradiated Fuels Examination Laboratory hot cell facility at Oak Ridge National Laboratory (ORNL). A modified Tormach PCNC 440 mill was integrated into the hot cell with custom fixturing, fines management, and manipulator-compatible interfaces to enable the fabrication of axial tension test (ATT) and ring tension test (RTT) specimens from irradiated cladding. The first irradiated specimens machined included ATT and RTT geometries harvested from the high-burnup 6XV fuel rod. Dimensional inspections confirmed that machined specimens met the ±0.025 mm tolerance envelope established in prior development; deviations were consistent with expected measurement scatter and inherent specimen variability, such as wall thickness gradients and eccentricity. Comparisons with out-of-cell metrology confirmed that in-cell machining performance aligns with baseline scatter observed under ideal conditions. This work establishes reproducible, end-to-end specimen preparation at ORNL, directly supporting the US Department of Energy’s Accident-Tolerant Fuel program by enabling reliable, traceable mechanical testing of irradiated cladding.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A six degrees of freedom femtosecond laser system for fabrication of small-scale mechanical property specimens

Here, we present the details of a novel ultra-short pulsed laser machining workstation that has been employed for high-throughput laser machining of small-scale mechanical property specimens. This system employs a six degrees of freedom hexapod positioning stage capable of macroscopic movements at high positional accuracy. We developed a methodology that uses quantitative image analysis to measure key parameters required to minimize the hexapod positioning and rotational error. Application of this system to laser machining of small-scale 316L stainless steel tensile specimens and ultra-high molecular weight polyethylene compressive specimens using eucentric tilt and rotation about the specimen axis will be shown, where serial laser milling at a specimen tilt angle of 10° was used to effectively eliminate any taper in the sample cross section that is typically found in laser machining.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Production of mechanically-generated 316L stainless steel feedstock and its performance in directed energy deposition processing as compared to gas-atomized powder

The objective of this work is to study the feasibility of mechanically-generated feedstock for use in directed energy deposition (DED) processing. Mechanically-generated powder was created by machining 316L stainless steel bar stock followed by comminution of the resulting chips through oscillation ball milling. This methodology's production yield and processing time for the specifications of a commercially available DED system are presented along with resulting powder morphology. Performance of the mechanically-generated feedstock was compared to gas-atomized powder and evaluated based on the following figures of merit: flowability, printed part height, printed part density, and chemical compositional stability throughout processing. Mechanically-generated feedstock was created to meet deposition system requirements. Compared with gas-atomized powder, mechanically-generated powder did not flow as well through the powder-delivery system. Parts printed from mechanically-generated feedstock were generally taller than their counterparts from gas-atomized feedstock, but their densities were less predictable. As a result, chemical composition of prints using both feedstocks was within standard nominal compositions for 316L stainless steel.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Smart Preprocessing & Robust Integration Emulator

To achieve the desired particle size of biomass feedstocks during preprocessing for trouble-free handling and conversion to produce biofuels and bioproducts, the raw materials must undergo a crucial milling process. The particle size of biomass plays a critical role in subsequent biofuel manufacturing, where a larger area-to-volume ratio facilitates efficient synthesis while balancing the impact of moisture on biomass storage. To optimize biofuel production efficiency and overcome these challenges, it is imperative to accurately predict the particle size distribution (PSD) of the biomass in the design of efficient preprocessing systems. The population balance model (PBM), upon empirical calibration and validation, can provide rapid prediction of post-milling PSD of granular biomass. However, PSD has limitations related to mass conservation and the absence of moisture considerations. To overcome these drawbacks, a deep learning model called the enhanced deep neural operator (DNO+) is implemented in the code. This model not only retains the capabilities of the PBM in handling complex mapping functions but also incorporates additional factors influencing the system. By considering various experimental conditions such as sieve size and moisture content, the trained DNO+ model can effectively predict the PSD after milling for any given feed PSD. To further reduce the reliance on experimental data, the PBM is integrated into the DNO+ model, resulting in a physics-informed DNO+ (PIDNO+). The PIDNO+ model addresses the non-conservation of quality exhibited by the PBM while inheriting the advantages of the DNO+ model in considering multiple influencing factors. Moreover, the PIDNO+ model significantly reduces the amount of data required for model training. Both deep learning models, i.e., DNO+ and PIDNO+, are excellent in predictive performance, offering swift and accurate machine learning-based predictions. The use of this code that contains these models will assist in guiding the proper milling equipment selection and operational conditions to achieve the desired biomass particle sizes, ensuring the efficiency of subsequent biofuel and bioproduct production processes.

Xia, Yidong [Idaho National Laboratory (INL), Idah↗
DOE OSTI2023

LCA-based environmental sustainability assessment of hybrid additive manufacturing of a turbine blade

In this paper, a cradle-to-gate life cycle assessment (LCA) method is used to compare the overall energy consumption and environmental impact of hybrid additive manufacturing (HAM) and the traditional CNC milling process with a case study of turbine blade manufacturing. Six environmental impacts are assessed in this study: acidification potential (AP), eutrophication potential (EP), global warming potential (GWP), photochemical ozone creation potential (POCP), ozone depletion potential (ODP), and abiotic depletion potential (ADP). The results suggest that HAM can not only reduce energy consumption and material waste but also reduce the environmental impact by 53% from a life cycle perspective. Specifically, the results of GWP, AP, EP, ODP, POCP and ADP of the HAM are only 32.2%, 34.6 %, 44.7%, 27.2%, 25.6%, and 24.7 % of that in traditional CNC machining.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Physics-guided logistic classification for tool life modeling and process parameter optimization in machining

This paper describes a physics-guided logistic classification method for tool life modeling and process parameter optimization in machining. Tool life is modeled using a classification method since the exact tool life cannot be measured in a typical production environment where tool wear can only be directly measured when the tool is replaced. Here, in this study, laboratory tool wear experiments are used to simulate tool wear data normally collected during part production. Two states are defined: tool not worn (class 0) and tool worn (class 1). The non-linear reduction in tool life with cutting speed is modeled by applying a logarithmic transformation to the inputs for the logistic classification model. A method for interpretability of the logistic model coefficients is provided by comparison with the empirical Taylor tool life model. The method is validated using tool wear experiments for milling. Results show that the physics-guided logistic classification method can predict tool life using limited datasets. A method for pre-process optimization of machining parameters using a probabilistic machining cost model is presented. The proposed method offers a robust and practical approach to tool life modeling and process parameter optimization in a production environment.

Machine learning↗
DOE OSTI2024

Near-Net-Shape Hot Isostatic Press Manufacturing Modality for sCO2 CSP Capital Cost Reduction

Through this DOE funded 3.5-year research project, feasibility of fabricating supercritical carbon dioxide (sCO2) turbine components by powder metallurgy (PM) based near-net-shape (NNS) hot isostatic pressing (HIP) was demonstrated in a turbine nozzle ring, a turbine casing with Haynes 282 powder, and a bimetallic pipe with Haynes 282 and SS415 powder. As-HIP microstructure of various powders and HIP processing conditions was studied to downselect a condition for the prototype components. Tensile strength and low cycle fatigue (LCF) capability of PM HIP 282 was found to be superior to cast 282, despite a debit in creep stress capability that could be mitigated by component design modification. Near-Net-shape with minimal post machining was achieved by modeling the non-uniform shrinkage during HIP cycle and designing the HIP tooling to meet dimensional targets. The prototypes of turbine nozzle ring and bimetallic pipe were successful in achieving overall dimension, microstructure, and properties, despite dimensional tolerance affected by chemical milling rate. The prototype of a 1700lbs turbine casing was partially successful in achieving dimension, microstructure in as-HIP state, and providing consistent mechanical properties, however, cracking issue during post heat treatment required further investigation. The estimated manufacturing cost using NNS HIP was a ~50% reduction compared to forging with extensive machining, which translated into ~$100/kWe CAPEX cost reduction for concentrated solar power (CSP) power block.

14 SOLAR ENERGY↗
DOE OSTI2023

Process planning for hybrid manufacturing using additive friction stir deposition

Additive friction stir deposition (AFSD) provides a solid-state approach to metal deposition that does not rely on local melting and solidification, but rather on kinetic energy and plastic flow. Here, in this study, AFSD is combined with structured light scanning, turning, and milling to produce metal components while considering the unique requirements imposed by the hybrid manufacturing process sequences. Two demonstrations are presented which include: 1) a cylindrical build plate selection to enable coordinate system transfer between deposition and turning of a hollow cone; and 2) intermittent deposition-machining operations with structured light scanning to fabricate a two-sided hexagon-cylinder geometry.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-Informed and Data-Driven Prediction of Residual Stress in Three-Dimensional Machining

Efficient and reliable prediction of machining-induced residual stress (RS) is a key requirement for truly integrated computational materials engineering (ICME). Currently available process modeling approaches, including empirical, analytical, and numerical methodologies lack predictive power and require substantial calibration and validation data. Moreover, most model-based approaches consider only two-dimensional (2D) (i.e., orthogonal), cutting processes. Meanwhile, industrial processes such as milling, turning, and drilling are inherently three-dimensional (3D). The present work attempts to bridge the gap between 2D and 3D through careful consideration of the process physics, including geometric, kinematic, and size-effect constraints to realize robust prediction of how RS develops in 3D machining. Using a novel in-situ experimental technique and digital image correlation (DIC) to determine equivalent Hertzian contact widths, contact pressures, and friction coefficients, the proposed methodology leverages a discretized conversion algorithm that includes multi-pass shakedown effects. This paper presents a semi-analytical model to predict machining-induced RS in 3D turning operations, which are used representatively for 3D processes more generally. Rather than follow a ‘brute force’ 3D FEM approach or conduct countless experiments to train a purely data-driven machine learning algorithm, the proposed approach builds on previous 2D modeling work. Through careful consideration of the process physics, including complex geometry/kinematic considerations of 3D turning, the authors demonstrated an experimentally calibrated approach, as well as validation based on published RS data. Model predictions and previously published measurement data of RS depth profiles for turning of Inconel 718 were compared for a range of process parameters. Correlation between the proposed 3D model and validation data was found to be within the margin of experimental error for most conditions. The proposed model appears to capture the overall behavior of 3D RS depth profiles with acceptable accuracy, particularly the key metrics of near-surface stress, peak stress magnitude and location, as well as overall stress profile depth. This report presents a physics-informed, data-driven approach for efficient calibration of a 2D model for machining-induced RS through DIC analysis of in-situ characterized subsurface displacement fields.

42 ENGINEERING↗
DOE OSTI2021

Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning

Here using a model heat engine, we show that neural-network-based reinforcement learning can identify thermodynamic trajectories of maximal efficiency. We consider both gradient and gradient-free reinforcement learning. We use an evolutionary learning algorithm to evolve a population of neural networks, subject to a directive to maximize the efficiency of a trajectory composed of a set of elementary thermodynamic processes; the resulting networks learn to carry out the maximally efficient Carnot, Stirling, or Otto cycles. When given an additional irreversible process, this evolutionary scheme learns a previously unknown thermodynamic cycle. Gradient-based reinforcement learning is able to learn the Stirling cycle, whereas an evolutionary approach achieves the optimal Carnot cycle. Our results show how the reinforcement learning strategies developed for game playing can be applied to solve physical problems conditioned upon path-extensive order parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Using GANs to predict milling stability from limited data

Milling is a key manufacturing process that requires the selection of operating parameters that provide efficient performance. However, the presence of chatter, a self-excited vibration causing poor surface finish and potential damage to the machine and cutting tool, makes it challenging to select the appropriate parameters. To predict chatter, stability maps are commonly used, but their generation requires expensive data, making it difficult to employ these maps in industry. Therefore, there is a pressing need for an approach that can accurately predict stability maps using limited experimental data. This study introduces the new Encoder GAN (EGAN) approach based on Generative Adversarial Networks (GANs) that predicts stability maps using limited experimental data. The approach consists of the encoder, generator, and discriminator subnetworks and uses the trained encoder and generator to predict the target stability map. This versatile method can be applied to various tool setups and can accurately predict stability maps with limited experimental data (five to 10 cutting tests) even when there is little information available for unknown parameters. In conclusion, the study evaluates the proposed approach using both numerical data and experiments and demonstrates its superior performance compared to state-of-the-art benchmarks.

42 ENGINEERING↗