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DOE OSTI2022

Sensor selection and tool wear prediction with data‐driven models for precision machining

Abstract Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency‐domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data‐driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time‐domain signal statistics, frequency‐domain analysis, and time‐frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least‐Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time‐frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy.

Han, Seulki↗

DOE OSTI2024

A $C^1$-Conforming Arbitrary-Order Two-Dimensional Virtual Element Method for the Fourth-Order Phase-Field Equation

We present a two-dimensional conforming virtual element method for the fourth-order phase-field equation. Our proposed numerical approach to the solution of this high-order phase-field (HOPF) equation relies on the design of an arbitrary-order accurate, virtual element space with $C^1$ global regularity. Such regularity is guaranteed by taking the values of the virtual element functions and their full gradient at the mesh vertices as degrees of freedom. Attaining high-order accuracy requires also edge polynomial moments of the trace of the virtual element functions and their normal derivatives. In this work, we detail the scheme construction, and prove its convergence by deriving error estimates in different norms. A set of representative test cases allows us to assess the behavior of the method.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Role of generic scale invariance in a Mott transition from a U(1) spin-liquid insulator to a Landau Fermi-liquid metal

Highlights: • We look at the role played by generic scale invariance (GSI) in the phase transition between a U(1) spin liquid and a Fermi liquid. • We identify dangerously irrelevant operators in the renormalization group transformation, involved with the GSI. • We highlight novel mean-field dynamics of this Mott quantum criticality, resulting from the GSI. We investigate the role of generic scale invariance in a Mott transition from a U(1) spin-liquid insulator to a Landau Fermi-liquid metal, where there exist massless degrees of freedom in addition to quantum critical fluctuations. Here, the Mott quantum criticality is described by critical charge fluctuations, and additional gapless excitations are U(1) gauge-field fluctuations coupled to a spinon Fermi surface in the spin-liquid state, which turn out to play a central role in the Mott transition. An interesting feature of this problem is that the scaling dimension of effective leading local interactions between critical charge fluctuations differs from that of the coupling constant between U(1) gauge fields and matter-field fluctuations in the presence of a Fermi surface. As a result, there appear dangerously irrelevant operators, which can cause conceptual difficulty in the implementation of renormalization group (RG) transformations. Indeed, we find that the curvature term along the angular direction of the spinon Fermi surface is dangerously irrelevant at this spin-liquid Mott quantum criticality, responsible for divergence of the self-energy correction term in U(1) gauge-field fluctuations. Performing the RG analysis in the one-loop level based on the dimensional regularization method, we reveal that such extremely overdamped dynamics of U(1) gauge-field fluctuations, which originates from the emergent one-dimensional dynamics of spinons, does not cause any renormalization effects to the effective dynamics of both critical charge fluctuations and spinon excitations. However, it turns out that the coupling between U(1) gauge-field fluctuations and both matter-field excitations still persists at this Mott transition, which results in novel mean-field dynamics to explain the nature of the spin-liquid Mott quantum criticality. We discuss physical implications of effective one-dimensional spin dynamics and extremely overdamped gauge dynamics at the Mott quantum criticality.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Sol-gel derived silicate-phosphate glass SiO 2 –P 2 O 5 –CaO–TiO 2 : The effect of titanium isopropoxide on porosity and thermomechanical stability

Despite of several decades lasting extensive research of bioactive and bioresorbable glasses the systematic parametrization and determination of the key factors affecting porosity and thermomechanical characteristics still remains challenging. Here, we present silica-phosphate glasses, with the composition 70SiO 2 - 20P 2 O 5 - (10-x)CaO-xTiO 2 (mol%; x = 0, 2.5, 5, and 7.5), prepared by sol -gel method and reinforced by titanium dioxide via titanium isopropoxide (TTIP) incorporation which demonstrated tunable variation of porosity from micro-to macro -region and superb mechanical integrity during the calcination process. The presence of 7.5 mol% TiO 2 promotes dimensional stability up to 1000°C as investigated by thermomechanical analysis. The XRD showed the dominant presence of silicon phosphate [Si(P 2 O 7 )], titanium phosphate [Ti(P 2 O 7 )] and calcium phosphates [β-Ca(P 2 O 6 ) and γ- Ca 2 (P 2 O 7 )]. The effect of TiO 2 doping on the multiscale morphology and porosity was investigated by means of SEM, MIP, μCT, N 2 adsorption and USAXS/SAXS. Increasing TiO 2 content leads to the formation of open porosity up to 70vol% and drives the formation of a refined interconnected macroporosity of 2-30 μm. In contrast, mesoporosity with a dominance of 3-6nm pores decreases in all samples with increasing TiO 2 content. USAXS/SAXS revealed an increase in primary particle size with increasing TiO 2 content which is in good agreement with the nitrogen physisorption analysis showing that microporosity decreases with increasing TiO2 content.

36 MATERIALS SCIENCE↗

DOE OSTI2026

i -Wave Symmetry Altermagnetism and Anomalous Hall Conductivity in Monolayer FeCl 3

In this work, the FeCl 3 monolayer is proposed to host a rare and exotic i -wave symmetry altermagnetism. Using first-principles calculations, we performed a detailed investigation of the structural stability and electronic, magnetic, and spintronic properties of monolayer FeCl 3 . Our results suggest the system is semiconducting in nature and exhibits i-wave symmetry nonrelativistic spin splitting (NRSS), originating from its hexagonal crystal structure with roto-inversion symmetries and anisotropic crystal field. The momentum-resolved spin-splitting further supports the presence of spin-dependent topological features. Further, the monolayer exhibits sizable conventional spin-Hall and anomalous Hall conductivities, highlighting its promise as a versatile platform for next-generation flexo-spintronic applications.

altermagnetism↗

DOE OSTI2023

Role of Cations in the Cation-Driven Assembly Process and their Effect on the Charge Storage Properties of Bilayered Vanadium Oxide and Reduced Graphene Oxide Heterostructures in Alkali Ion Systems

Exfoliated δ-Li x V 2 O 5 ·nH 2 O (ex-LVO) and reduced graphene oxide (rGO) heterostructures were constructed using different assembling cations (i.e., Li⁺, Na⁺, and K⁺ ions). The ex-LVO and rGO nanoflakes were stacked together using a concentrated chloride solution of each assembling cation and vacuum annealed at 200 °C to form three distinct two-dimensional (2D) layered architectures. X-ray diffraction and thermogravimetric analysis confirmed that the assembling ions can control the interlayer spacing of the bilayered vanadium oxide (BVO) phase as well as impact the crystallographic water content, which in turn affects the electrochemical performance. Scanning electron microscopy, scanning transmission electron microscopy (STEM), electron energy-loss spectroscopy (EELS), and X-ray photoelectron spectroscopy confirmed that a 2D heterointerface formed between LVO and rGO and that the cations used to assemble the heterostructure are trapped in the interlayer BVO region. High-resolution STEM imaging also showed the rGO dispersion throughout the LVO layers. Moreover, STEM-EELS identified a V 2 O 3 phase that forms along the rGO interface and can stabilize the materials during cycling. A charge storage mechanism analysis, combined with the galvanostatic intermittent titration technique, found that increased interlayer spacings of the BVO phase and using the assembling cations to define intercalation sites for identical charge-carrying ions lead to improved ion diffusion and increased capacities during cycling. Therefore, the Li⁺ and Na⁺ ion assembled heterostructures showed improved charge-carrying ion diffusion and charge storage capacities in each of their respective charge storage systems (i.e., Li-ion and Na-ion half-cells). In total, the cation used for heterostructure assembly can modify the final material structure and tailor the ion diffusion and charge storage capacity to tune its properties for the desired electrochemical system using a variety of 2D materials.

25 ENERGY STORAGE↗

DOE OSTI2020

Blending Ionic and Coordinate Bonds in Hybrid Semiconductor Materials: A General Approach toward Robust and Solution-Processable Covalent/Coordinate Network Structures

Inorganic semiconductor materials are best known for their superior physical properties, as well as their structural rigidity and stability. However, the poor solubility and solution-processability of these covalently bonded network structures has long been a serious drawback that limits their use in many important applications. Here, we present a unique and general approach to synthesize robust, solution-processable, and highly luminescent hybrid materials built on periodic and infinite inorganic modules. Structure analysis confirms that all compounds are composed of one-dimensional anionic chains of copper iodide (Cu m I m+2 2- ) coordinated to cationic organic ligands via Cu-N bonds. The choice of ligands plays an important role in the coordination mode (μ 1 -MC or μ 2 -DC) and Cu-N bond strength. Greatly suppressed nonradiative decay is achieved for the μ 2 -DC structures. Record high quantum yields of 85% (λ ex = 360 nm) and 76% (λ ex = 450 nm) are obtained for an orange-emitting 1D-Cu 4 I 6 (L 6 ). Temperature dependent PL measurements suggest that both phosphorescence and thermally activated delayed fluorescence contribute to the emission of these 1D-AIO compounds, and that the extent of nonradiative decay of the μ 2 -DC structures is much less than that of the μ1 -DC structures. More significantly, all compounds are remarkably soluble in polar aprotic solvents, distinctly different from previously reported CuI based hybrid materials made of charge-neutral Cu m X m (X = Cl, Br, I), which are totally insoluble in all common solvents. Finally, the greatly enhanced solubility is a result of incorporation of ionic bonds into extended covalent/coordinate network structures, making it possible to fabricate large scale thin films by solution processes

36 MATERIALS SCIENCE↗

DOE OSTI2020

Eco-friendly, solution-processable and efficient low-energy lighting phosphors: copper halide based hybrid semiconductors Cu 4 X 6 (L) 2 (X = Br, I) composed of covalent, ionic and coordinate bonds

A series of copper halide based inorganic–organic hybrid semiconductors have been synthesized. Structural analysis confirms that all compounds are composed of one-dimensional Cu 4 X 6 2- anionic chains that coordinate to cationic ligands via Cu–N dative bonds. Different coordination affinities of the ligands lead to two types of ligand arrangements with various structural distortions. All compounds are highly resistant to heat and moisture as a result of the combination of coordinate and ionic bonds. Low energy emission with high efficiency is achieved for these compounds and the emission energy (~552–615 nm) and color (yellow-orange) can be tuned by varying the ligand and halogen element. The electronic structure and luminescence mechanism are examined by both experimental and theoretical methods. More importantly, all compounds demonstrate good solubility in polar aprotic solvents, a desired property that is absent in all other CuX hybrid families of extended structures, which is attributed primarily to the ionic nature of this material class. Furthermore, the good solution-processability, and cost effective and easily scalable synthesis coupled with high quantum efficiencies and framework stability make these hybrid materials promising phosphors for general lighting applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Super resolution convolutional neural network for feature extraction in spectroscopic data

Two dimensional (2D) peak finding is a common practice in data analysis for physics experiments, which is typically achieved by computing the local derivatives. However, this method is inherently unstable when the local landscape is complicated or the signal-to-noise ratio of the data is low. In this work, we propose a new method in which the peak tracking task is formalized as an inverse problem, which thus can be solved with a convolutional neural network (CNN). In addition, we show that the underlying physics principle of the experiments can be used to generate the training data. By generalizing the trained neural network on real experimental data, we show that the CNN method can achieve comparable or better results than traditional derivative based methods. This approach can be further generalized in different physics experiments when the physical process is known.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Rare Events via Cross-Entropy Population Monte Carlo

Rare events are events that happen with very low frequency. Estimating rare event probabilities using Monte Carlo techniques is computationally expensive, often to the point of intractability, and special methods are required. Importance sampling (IS) is a well known technique that uses a proposal distribution in place of a target distribution to lower the variance of the estimator. Key to the success of IS methods is the choice of a proposal distribution, or the parameters governing the distribution. Adaptive importance sampling improves the parameters of a family or population of proposal distributions iteratively through trials. We present a novel cross-entropy population Monte Carlo algorithm, which adapts the parameters of proposals through the cross-entropy method. The proposed method stands apart from previous work in that we are not optimizing a mixture distribution. Instead, we leverage deterministic mixture weights and optimize the distributions individually through a reinterpretation of the typical derivation of the cross-entropy method. Demonstrations on rare event examples show that the algorithm can outperform existing resampling based population Monte Carlo methods, especially for higher-dimensional problems. Finally, we also demonstrate efficacy on a conjunction analysis problem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Information-Theoretic Exploration of Multivariate Time-Varying Image Databases

Modern scientific simulations produce very large datasets, making interactive exploration of such data computationally prohibitive. An increasingly common data reduction technique is to store visualizations and other data extracts in a database. The Cinema project is one such approach, storing visualizations in an image database for post hoc exploration and interactive image-based analysis. This work focuses on developing efficient algorithms that can quantify various types of multivariate dependencies existing within multi-variable datasets. It applies specific mutual information measures for the quantification of salient regions from multivariate image data. Here, using such information measures, the opacity of the images is modulated so that the salient regions are automatically highlighted and the domain scientists can interactively explore the most relevant regions for scientific discovery.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Visualizing Temporal Topic Embeddings with a Compass

—Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.

Cluster analysis↗

DOE OSTI2021

Quantitative Characterization of the Microstructure-Property Relationships in Ni and MIEC Nanocatalyst-Infiltrated Ni/YSZ Anodes

The performance of solid oxide fuel cell (SOFC) anodes can be improved by infiltration of nanoscale electrocatalysts, which increases triple phase boundary (TPB) density, but only when the added TPBs are active. Quantitative characterization of the microstructure of infiltrated nanocatalysts remains a critical challenge in understanding their role in improved performance. This paper explores the relationship between electrochemical performance and microstructure for Ni/YSZ anodes infiltrated with Ni, gadolinium-doped-ceria (GDC), and Ni/GDC nanocatalysts using a combination of electrochemical impedance spectroscopy (EIS), distribution of relaxation times (DRT) analysis, scanning electron microscopy (SEM) microstructural characterization, and three-dimensional (3-D) anode reconstruction. Estimated polarization resistance improvements as a result of nickel nanoparticle infiltration are discussed.

Rix, Jillian↗

DOE OSTI2024

Plasmonic surface lattice resonances in nanoparticle arrays

This work reviews the literature and provides a detailed computational analysis of the optical properties of one- and two-dimensional arrays of silver and gold nanoparticles, with emphasis on surface lattice resonances (SLRs) that arise when localized plasmon resonances (LSPRs) in the nanoparticles couple to diffraction resonances that are determined by the interparticle spacing to give polariton modes in which the two types of excitations are coherently coupled. The computations are based on the coupled dipole approximation, which provides a nearly quantitative description of the extinction spectra for arrays of this type where the particles are well separated and not too large. The computations are used to determine many characteristics of SLRs associated with the lower polariton mode that is mostly photonic in nature, and we also study the upper polariton that is dominated by the LSPR response, as well as Rayleigh anomalies (RAs) that correspond to purely diffractive excitation. The calculations explore the sensitivity of these excitations to the directions of the incident wave and polarization vectors relative to the array axis, the effect of array spacing and number of particles in the array, and the effect of nanoparticle radius and background refractive indices. Details of the physical mechanisms involved in determining blue- and/or redshifts as structural parameters are varied is provided, with SLR’s being sensitive to far-field coupling, while LSPR’s can also be sensitive to near- and intermediatefield interactions that in some cases are similar to effects found in dye molecule aggregates.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

Machine Learning Enabled Advanced Electron Tomography for Resolving Chemical Inhomogeneity and Materials Dynamics in Lithium-Ion Battery Electrodes

The objective of this project is to develop machine learning-assisted electron microscopy, together with three-dimensional, cryogenic, and in-situ imaging techniques, for resolving chemical inhomogeneity and materials dynamics in lithium battery electrodes and interfaces. The project aims to expand the spatial, temporal, and dimensional resolution of transmission electron microscopy and to enable quantitative analysis of beam-sensitive battery materials.

25 ENERGY STORAGE↗

DOE OSTI2022

Impurity screening and Friedel oscillations in Floquet-driven two-dimensional metals

We develop a theory for the non-equilibrium screening of a charged impurity in a two-dimensional electron system under a strong time-periodic drive. Our analysis of the time-averaged polarization function and dielectric function reveals that Floquet driving modifies the screened impurity potential in two main regimes. In the weak drive regime, the time-averaged screened potential exhibits unconventional Friedel oscillations with multiple spatial periods contributed by a principal period modulated by higher-order periods, which are due to the emergence of additional Kohn anomalies in the polarization function. In the strong drive regime, the time-averaged impurity potential becomes almost unscreened and does not exhibit Friedel oscillations. Furthermore, this tunable Friedel oscillations is a result of the dynamic gating effect of the time-dependent driving field on the two-dimensional electron system.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities

Identifying low-dimensional structure in high-dimensional probability measures is an essential pre-processing step for efficient sampling. To identify this structure, we approximate the target measure as a perturbation of an arbitrary reference measure along a few directions in $\mathbb{R}^{d}$. These directions are determined by minimizing an upper bound on the Kullback–Leibler (KL) divergence between the target and its approximation. Our contribution improves upon previous works by leveraging dimensional logarithmic Sobolev inequalities to refine the bound on the KL divergence. These inequalities lead to a uniformly tighter bound on the KL divergence, thereby enhancing the identification of the most significant perturbation directions. In particular, when the target and reference are both Gaussian, minimizing the resulting bound is equivalent to minimizing the KL divergence. We further demonstrate the applicability of this analysis to the squared Hellinger distance, where analogous reasoning shows that the dimensional Poincaré inequality offers improved bounds.

Bayesian inference↗

DOE OSTI2020

Flowthrough Pretreatment of Softwood under Water-only and Alkali Conditions

Flowthrough pretreatment provides valuable insight in the fundamentals of deconstruction of plant biomass. In this study, the potential softwood degradation pathways under water-only and alkali conditions were determined by elucidating the deconstructed biomass-derived products at 0-270 °C for 2-10 min at a flow rate of 25 mL/min with water-only or alkaline at initial pH of 8, 9, 11, and 12, respectively. Results indicated that the initial pH value was a convenient indicator along with the severity parameter to control biomass degradation through the non-oxidative (pH<9) and oxidative (pH=9) pathways. Up to 100 % of hemi-sugars, 90 % of cellulose, and 70 % of lignin were derived from softwood under non-oxidative conditions at severity parameter LogR0 around 5.5, respectively. On the contrary, at pH 12, the oxidative degradation resulted in pretreated hydrolysate rich in monomeric and oligomeric phenolic products as well as glycolic acid, acetic acid, and formic acid from carbohydrates. The two dimensional 1H-13C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) analysis revealed that non-oxidative degradation at initial pH values lower than 9 mainly cleaved ß-O-4, all of the ß-ß, and ß-5/a-O-4 linkages, whereas substantial aromatic ring structures with limited ß-O-4 structures remaining at pH 12 through oxidative degradation.

Zhang, Libing↗