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DOE OSTI2020

Analyzing the co-evolution of green technology diffusion and consumers’ pro-environmental attitudes: An agent-based model

Massive diffusion of green technologies is significant for building a cleaner world. However, the process of technology diffusion is usually slow and complex. An in-depth understanding of the mechanism regarding green technology diffusion is an essential precondition for effectively stimulating this process. Green technology diffusion heavily involves both social and technological changes. Although existing studies have provided rich knowledge about identifying critical drivers and barriers affecting green technology diffusion, research that considers consumers’ pro-environmental attitudes and green technology diffusion as an evolving system is still sparse. Aiming at exploring the co-evolution of consumers’ pro-environmental attitudes and green technology diffusion, this paper builds an agent-based model that integrates the relative agreement model with technology diffusion theories to conduct a sequence of controlled numerical experiments, which progressively unveil how attitudinal and technological factors impact green technology diffusion. The main findings include that (1) improving consumers’ pro-environmental attitudes is prominently beneficial to green technology diffusion and maturation; (2) technology maturity has very limited impact on consumers’ first-time purchases but significantly affects consumers’ satisfaction, which would further impact consumers’ repeat purchases; (3) consumers that do not support green technologies frequently emerge during the evolution of attitudes (despite the high technology maturity), which corresponds to the emergence of the anti-environmental groups observed in the real world; (4) active interactions between non-adopters enable their attitudes to converge, which results in the polarization of consumers’ attitudes.

ABM↗

DOE OSTI2022

Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. In this report a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.

42 ENGINEERING↗

DOE OSTI2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

42 ENGINEERING↗

DOE OSTI2022

A machining digital twin for hybrid manufacturing

Hybrid manufacturing consisting of metal additively manufactured preforms and computer numerical control (CNC) machining has been established to be an effective method for high material use rates. However, hybrid manufacturing introduces unique challenges. Near-net shape designs are typically selected, which result in a smaller margin for part placement within the stock and stringent requirements for work coordinate system identification. Additionally, less stock material reduces the preform stiffness, which limits the material removal rates during machining. This paper demonstrates a digital twin for CNC machining of a wire arc additively manufactured preform that implements: 1) structured light scanning for stock model identification and tool path generation; 2) a fused filament fabrication apparatus to attach temporary fiducials and scan targets to the preform that enable coordinate system definition for both the CAM and CNC machine; 3) preform and tool tip frequency response function measurements to enable stable milling parameter selection; and 4) post-manufacturing measurements of geometry, surface finish, and structural dynamics to confirm designer intent. These efforts define key components of the machining digital twin for hybrid manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Hybrid manufacturing by additive friction stir deposition, metrology, CNC machining, and microstructure analysis

Aerospace flight panels must provide high strength with low mass. For aluminum panels, it is common practice to begin with a wrought plate and remove the majority of the material to attain the desired structure, comprising a thinner plate with the desired pattern of reinforcement ribs. As an alternative, this study implements hybrid manufacturing, where aluminum is first deposited on a baseplate only at the rib locations using additive friction stir deposition (AFSD). Structured light scanning is then used to measure the printed geometry. This geometry is finally used as the stock model for computer numerical control (CNC) machining. This paper details the hybrid manufacturing process that consists of: AFSD to print the preform, structured light scanning to generate the stock model and tool path, three-axis CNC machining, and post-process measurements for part geometry and microstructure.

42 ENGINEERING↗

DOE OSTI2022

Autonomous aerial flight path inspection using advanced manufacturing techniques

Robotic systems have shown capabilities to perform inspection tasks in dangerous and difficult-to-access environments, such as those found in different components of power plants. However, most of the current robotic inspection technology is designed for specific components. Aerial robots, commonly termed as Drones, have raised an option to inspect a wider range of structural components. Nevertheless, current aerial inspecting technology still relies on a human pilot with limited line of sight, field of view and a reduced perception as the drone flies away, which prevents performing close-quarter inspection in intricate, structurally complex and GPS-denied environments. This work introduces offline inspection path generation methods based on robotics-integrated to manufacturing techniques. One method uses computer-aided manufacturing (CAM) techniques and the other an additive manufacturing (AM) approach to generate the flight path. That is to say, the drone would fly along the path described by a 3D (Three Dimensional) printer’s extruder or a CNC (Computer Numerical Control) machining tool, enabling to fly very close the structure even in physical structures with a complex geometry. Once the trajectories are generated, they are introduced for its validation in the Gazebo robotics simulator. Simulation results demonstrate the proper performance of the method and confirm that this approach can be used for close inspection of structural components.

42 ENGINEERING↗

DOE OSTI2026

Autonomous elemental characterization enabled by a low cost robotic platform built upon a generalized software architecture

Despite the rapidly growing applications of robots in industry, the use of robots to automate tasks in scientific laboratories is less prolific due to the lack of generalized methodologies and the high cost of hardware. This paper focuses on the automation of characterization tasks necessary for reducing cost while maintaining generalization and proposes a software architecture for building robotic systems in scientific laboratory environments. A dual-layer (Socket.IO and ROS) action server design is the basic building block, which facilitates the implementation of a web-based front end for user-friendly operation and the use of ROS Behavior Trees for convenient task planning and execution. A robotic platform for automating mineral and material sample characterization is built upon the architecture, with an open-source, low-cost three-axis computer numerical control gantry system serving as the main robot. A handheld laser induced breakdown spectroscopy (LIBS) analyzer is integrated with a 3D printed adapter, enabling (1) automated 2D chemical mapping and (2) autonomous sample measurement (with the support of an RGB-Depth camera). We demonstrate the utility of automated chemical mapping by scanning the surface of a spodumene-bearing pegmatite core sample with a 1071-point dense hyperspectral map acquired at a rate of 1520 bits per second. Furthermore, we showcase the autonomy of the platform in terms of perception, dynamic decision-making, and execution, through a case study of LIBS measurement of multiple mineral samples. The platform enables controlled and autonomous chemical quantification in the laboratory that complements field-based measurements acquired with the same handheld device, linking resource exploration and processing steps in the supply chain for lithium-based battery materials.

Cao, Xuan [Lawrence Berkeley National Laboratory (↗

DOE OSTI2021

Design of flexible proton beam imaging energy spectrometers (PROBIES)

A simple repeating step-filtered structure has been designed and deployed for the detection of MeV-energy proton beams produced from high-intensity short-pulse laser-driven experiments. Due to the diagnostic's ability to recover energy-resolved images of proton beams, it is called the proton beam imager and energy spectrometer (or PROBIES). The flexible design has two useful applications. First, it can be fielded in a configuration with a scintillator and imaging setup in order to collect proton beam information in high-repetition rate experiments. Here we show a configuration that collects the equivalent of a nine-film radiochromic film (RCF) stack, and a technique for using a neural network (NN) to rapidly analyze the images on millisecond time-scales. Second, when using RCF stacks as the detector, the energy resolution capability is greatly increased compared to traditional RCF stacks, while simultaneously reducing the total number of films required to make each measurement. We demonstrate a configuration that provides up to 90 spatially- and energy-resolved datapoints using just ten films. Both designs presented here can be easily customized for anticipated experimental outputs and manufactured at low cost. Such designs allow for rapid prototyping using modern high-accuracy 3D printers or on-demand computer numerical control machining to reduce the reliance on time-consuming film media, analysis, or bulky electro-magnetically dispersing spectrometers. The analysis of data is discussed and a methodology for constructing a NN that is capable of rapidly analyzing data on-the-fly with greater than 95% accuracy is presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Priors on red galaxy stochasticity from hybrid effective field theory

ABSTRACT We investigate the stochastic properties of typical red galaxy samples in a controlled numerical environment. We use halo occupation distribution (HOD) modelling to create mock realizations of three separate bright red galaxy samples consistent with data sets used for clustering and lensing analyses in modern galaxy surveys. Second-order Hybrid Effective Field Theory (HEFT) is used as a field-level forward model to describe the full statistical distribution of these tracer samples, and their stochastic power spectra are directly measured and compared to the Poisson shot-noise prediction. While all of the galaxy samples we consider are hosted within haloes with sub-Poisson stochasticity, we observe that the galaxy samples themselves possess stochasticities that range from sub-Poisson to super-Poisson, in agreement with predictions from the halo model. As an application of our methodology, we place priors on the expected degree of non-Poisson stochasticity in cosmological analyses using such samples. We expect these priors will be useful in reducing the complexity of the full parameter space for future analyses using second-order Lagrangian bias models. More generally, the techniques outlined here present the first application of HEFT methods to characterize models of the galaxy–halo connection at the field level, revealing new connections between once-disparate modelling frameworks.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Time-Dependent Density Functional Theory Description of 238 U⁡(n,f), 240,242 Pu⁢(n,f), and 237 Np(n,f) Reactions

In nuclei with an odd nucleon number the nonvanishing spin number density is the source of a pseudomagnetic field, which favors the splitting of the nucleon Cooper pairs. Such a pseudomagnetic field is generated always in the dynamics of any nucleus, but its effects on Cooper pairs are significantly enhanced in the dynamic evolution of nuclei with an odd number of nucleons. We present for the first time a microscopic study of the induced fission of the odd neutron compound nuclei 239 U, 241,243 Pu, and the odd proton, odd neutron compound nucleus 238 Np, performed within the time-dependent density functional theory extended to superfluid fermion systems, without any simplifying assumptions, with controlled numerical approximations, and for a very large number of initial conditions. Because of the presence of the unpaired odd nucleon(s), the time-reversal symmetry of the fission compound nucleus is spontaneously broken, an aspect routinely neglected in the most advanced microscopic approaches of the past. The emerging fission fragment properties are quite similar to the properties of fission fragments of neighboring even-even nuclei. The time from saddle-to-scission is often significantly longer in odd-odd or odd-mass nuclei than for even-even nuclei, since systems with unpaired nucleons are easier to excite and the potential energy surfaces of these nuclei have more structure, often resembling a very complicated obstacle course, rather than a more direct evolution of the nuclear shape from the top of the outer fission barrier to the scission configuration. The Pauli blocking approximation, often invoked in the literature, expected to inhibit the fission of nuclei with unpaired nucleons, is surprisingly strongly violated during the fission dynamics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Quantum many-body simulations of the two-dimensional Fermi-Hubbard model in ultracold optical lattices

Understanding quantum many-body states of correlated electrons is one main theme in modern condensed-matter physics. Given that the Fermi-Hubbard model, the prototype of correlated electrons, was recently realized in ultracold optical lattices, it is highly desirable to have controlled numerical methodology to provide precise finite-temperature results upon doping to directly compare with experiments. Here, we demonstrate the exponential tensor renormalization group (XTRG) algorithm [Chen et al., Phys. Rev. X 8, 031082 (2018)], complemented by independent determinant quantum Monte Carlo, offers a powerful combination of tools for this purpose. XTRG provides full and accurate access to the density matrix and thus various spin and charge correlations, down to an unprecedented low temperature of a few percent of the tunneling energy. Finally, we observe excellent agreement with ultracold fermion measurements at both half filling and finite doping, including the sign-reversal behavior in spin correlations due to formation of magnetic polarons, and the attractive hole-doublon and repulsive hole-hole pairs that are responsible for the peculiar bunching and antibunching behaviors of the antimoments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Open-source electrochemical cell for in situ X-ray absorption spectroscopy in transmission and fluorescence modes

X-ray spectroscopy is a valuable technique for the study of many materials systems. Characterizing reactions in situ and operando can reveal complex reaction kinetics, which is crucial to understanding active site composition and reaction mechanisms. In this project, the design, fabrication and testing of an open-source and easy-to-fabricate electrochemical cell for in situ electrochemistry compatible with X-ray absorption spectroscopy in both transmission and fluorescence modes are accomplished via windows with large opening angles on both the upstream and downstream sides of the cell. Using a hobbyist computer numerical control machine and free 3D CAD software, anyone can make a reliable electrochemical cell using this design. Onion-like carbon nanoparticles, with a 1:3 iron-to-cobalt ratio, were drop-coated onto carbon paper for testing in situ X-ray absorption spectroscopy. Cyclic voltammetry of the carbon paper showed the expected behavior, with no increased ohmic drop, even in sandwiched cells. Chronoamperometry was used to apply 0.4 V versus reversible hydrogen electrode, with and without 15 min of oxygen purging to ensure that the electrochemical cell does not provide any artefacts due to gas purging. The XANES and EXAFS spectra showed no differences with and without oxygen, as expected at 0.4 V, without any artefacts due to gas purging. The development of this open-source electrochemical cell design allows for improved collection of in situ X-ray absorption spectroscopy data and enables researchers to perform both transmission and fluorescence simultaneously. It additionally addresses key practical considerations including gas purging, reduced ionic resistance and leak prevention.

08 HYDROGEN↗

DOE OSTI2025

Securing Smart Manufacturing: Detection of Cyber-Physical Attacks in CNC-Based Systems

As Industry 4.0 advances, the integration of computer numerical control (CNC) machines and advanced manufacturing technologies is transforming production into smart manufacturing systems that blend physical and digital processes as cyber-physical systems. However, this increased cyber-physical connectivity exposes manufacturing systems to cyber threats that can cause severe operational and financial disruptions. This paper presents a comparative study on cyber attacks and anomaly detection techniques in manufacturing, focusing on network traffic from CNC machines. The data extracted from network packets includes machine commands and control signals exchanged between the machine's interface and control system, crucial for maintaining operational integrity. We explore two types of cyber attacks, design modification and command injection, which pose substantial risks to CNC machine productivity and system integrity. Our investigation involves experiments on a real CNC system, highlighting the urgent need for effective detection mechanisms. To address these threats, we evaluate three anomaly detection methods: dynamic time warping (DTW), rolling average, and a deep learning, long short-term memory (LSTM) time-series-based autoencoder. Each is assessed for its effectiveness in identifying anomalous behaviors caused by the attacks. Our findings demonstrate the unique strengths and limitations of each detection technique, providing a deeper understanding of their applicability in realworld manufacturing environments. The comparative analysis indicates that while certain methods are highly effective against specific attack types, others offer broader applicability across different attacks. This study contributes to the accurate detection of anomalies in CNC machining processes, thereby enhancing the reliability and security of smart manufacturing systems against diverse cyber threats.

Williams, Bethanie [Tennessee Technological Univer↗

DOE OSTI2020

Design and Analysis of the Staggered Double Grating Slow Wave Circuit for 263 GHz Sheet Beam TWT

The complete design process of a high-frequency circuit for a 263 GHz staggered double grating, sheet beam traveling wave tube is presented in this article. Here, this device, for use in a pulsed electron paramagnetic resonance spectrometer instrument, requires >30 dB gain and a 20 GHz bandwidth centered at 263 GHz. In addition to the standard Pierce gain analysis and point by point particle in cell (PIC) simulation, a novel fast pulse response analysis technique was utilized to determine the accurate synchronization voltage and test the ``hot” feature of the high-frequency circuit. Since this new method can evaluate the entire bandwidth in a single simulation, it can significantly reduce the computational resources normally required for the conventional PIC analysis while also illustrating the potential instabilities near the band edge. Good agreement between the fast pulse response and PIC simulation results validate the accuracy of this novel method. According to the analysis results and given the manufacturing limits of nano/micro-computer numerical control (CNC) machining, an appropriate SWS, coupler, and sever designs were chosen to complement the simulated design. Finally, a prototype circuit was manufactured and cold tested to verify the design and inspect the machining tolerances.

42 ENGINEERING↗

DOE OSTI2021

Comparison of Rotary and Linear Cutting Methodology in Determining Specific Cutting Energy of Granite

Abstract Single polycrystalline diamond compact (PDC) cutting is a practical technique to understand the rock–tool interactions in drag-bit-type geothermal drilling operations. This paper introduces a rotary cutting method to determine specific cutting energy (SCE) and compares it with the conventional linear cutting method. In this work, granite is selected to represent hard rock formations in geothermal drilling. Cutting tests are conducted on a Computer Numerical Control (CNC) machine with a realistic cutting speed of 12.7 m/min and several chip loads ranging from 0.08 to 0.25 mm. The cutting force is measured using a dynamometer and then converted to specific cutting energy (SCE). The results show that the rotary method produces an inverse relationship between SCE and chip load, whereas the linear method shows the opposite. As a result, the produced SCE by the rotary method tends to be lower than that of the linear method at a higher chip load at and over 0.16 mm. The difference may be attributed to the cutting configuration and associated force components.

Engineering↗

DOE OSTI2022

Machine Straightness Error Measurement Based on Optical Fiber Fabry–Pérot Interferometer Monitoring Technique

Abstract In this research, we propose an Error Separation Technique (EST) based on optical fiber sensors for on-machine straightness error measurement. Two fiber optic Fabry–Pérot interferometers have been developed serving as two displacement sensors. The displacement distance is computed according to the reflected spectrum from interferometers, which can achieve a sub-micrometer resolution. The two-point method has been employed to separate the straightness error of the slides and the profile error of a fine-polished standard block. The spacing distance between two interferometers is determined by the diameter of optical fibers so that the EST's resolution has the potential to reach the sub-millimeter scale. In the experiment, the straightness error has been measured on a commercially available computer numerical control machine tool, and the measurement has been conducted on its x-axis. The spacing distance between two optical fiber sensors is 1.5 mm which equals the EST's resolution along the machine tool's x-axis. The separated profile error of the measured standard block is around 30 µm which has been verified by a high precision Coordinate Measurement Machine (CMM). The magnitude of the separated straightness error is around 40 µm. This technique is flexible and simple to be conducted, which can contribute to the micro-machine tool calibration and other straightness error applications.

Engineering↗

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 OSTI2021

Prediction of Thermal Conditions of DED With FEA Metal Additive Simulation

This paper presents the integration of wire-arc additive manufacturing (WAAM) using Gas Metal Arc Welding (GMAW) into a machine tool to create a retrofit hybrid computer numeric control (CNC) machine tool. GMAW, along with other direct energy deposition systems, has the capacity to deposit material faster than the excess thermal energy can dissipate. This results in the need to allow the part to cool between consecutive layers, which is the most time-consuming part of the additive process. Finite element analysis (FEA) was used in conjunction with monitored build plate surface temperatures during deposition samples to improve adequate dwell time prediction and to develop a cooling system. A deposition was completed where no dwell time was used and the build plate along with the machine table temperatures were monitored. A second deposition was completed where only one bead was deposited and the traverse speed was increased. The GMAW welder was mounted on a 3-axis CNC machine where two square deposition samples were completed. A FEA model was designed and verified using the monitored samples. The model will be used to determine improved depositions speeds and whether forced cooling would allow for an increased deposition rate without structural failure. It was determined the FEA software can be used to accurately model and predict the thermal response of WAAM AM components.

Heinrich, Lauren↗