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Thermal properties are significant for recognizing an object’s material but cannot be determined via visual and stiffness (or tactile)–based recognition techniques. Most studies have used temperature as a complementary part of multimodal sensing; however, the thermal signal is an unexplored capability that can be beneficial for recognizing target objects. Since changes in thermal responses can result from both material properties and initial temperature, realizing robust and high-accuracy recognition in different environments is a challenging issue. To tackle the issue, this paper proposes a novel strategy for material identification that can actively measure heat flow by heating and cooling a robot gripper, enabling the extraction of the thermal properties of contact materials regardless of the object’s initial temperature variation (referred to as “active heat flow sensing”). We use a robotic task as an example of one possible application of the proposed strategy. For this, we developed a gripper pad embedded in a temperature control system and heat flow sensor to monitor the thermal exchange during contact with a target object. The paper conducted some experiments divided into two scenarios. The first experimental results show that active heat flow sensing is realized within 0.4 sec from first contact for 100% classification of four heated materials. The second experimental results show that the three materials, whose thermal properties are largely different, can be classified within 0.7 sec from first contact using different initial temperatures of the training and test data. These results suggest robustness against environmental change, which has been difficult using conventional temperature-based methods.

The idea of the Kelvin transformation is extended to analyze the eddy current testing (ECT). The Kelvin transformation was originally proposed to solve the Poisson equation on the entire unbounded domain. When it was first applied to electromagnetic quasi-static problems, it was assumed that the analysis domain is surrounded only by air and nothing is inside the exterior domain. The analysis domain is truncated by a circular boundary in two dimensions and spherical in three dimensions, and another circular/spherical domain for emulating the exterior domain is connected through unknown equivalent boundary conditions. In this article, the Kelvin transformation is reformulated to derive the conductivity and permeability in the exterior domain, which conserves the conformal symmetry of Maxwell’s equations. Utilizing the derived conductivity and permeability, the materials can be both in the interior and exterior domains or even across the truncated boundaries. Some numerical examples are demonstrated to validate the proposed method. As an application of the proposed method, the electromagnetic analysis of the ECT model is performed. [ABSTRACT FROM AUTHOR]

A new methodology for considering thermo-mechanical material property variability in virtual design of experiment (vDoE) studies is presented. For the estimation of the lower and upper bounds of the properties, theoretical models and relevant correlations between material properties are presented. The focus for the bounds definition is given to composite materials, i.e., a mixture of two or more homogeneous dissimilar materials, such as metal matrix composites (MMCs), ceramic matrix composites (CMC), etc. For not composite materials, alternative approaches for defining the bounds are discussed. To exemplify the implementation of the proposed methodology, a vDoE of a 2-D finite element (FE) thermo-mechanical model of an electronic package is presented, followed by a discussion regarding the influence of material property variability on the model’s thermo-mechanical responses. [ABSTRACT FROM AUTHOR]

Deep-learning architectures were employed to simulate the self-piercing riveting process of steel and aluminum sheets and predict the cross-sectional joint shape with a zero head height. Four steels (SPRC440, SPFC590DP, GI780DP, SGAFC980Y) and three aluminum alloys (Al5052, Al5754, Al5083) were considered as the materials for the top and bottom sheets, respectively. The key objective was to consider the material properties of these metal sheets (Young’s modulus, Poisson’s ratio, and ultimate tensile strength) in a deep-learning framework. Two deep-learning models were considered: In the first model, the properties of the top and bottom sheets were adopted as the scalar inputs, and in the second model, the three properties were graphically assigned to the three channels of the input image. Both the models generated a segmentation image of the cross-section. To assess the accuracy of the predictions, the generated images were compared with ground truth images, and three key geometrical factors (interlock, bottom thickness, and effective length) were measured. The first and second models achieved prediction accuracies of 91.95% and 92.22%, respectively.

Room-temperature semiconductor radiation detectors (RTSDs) such as CdTe are becoming popular in computed tomography (CT) imaging. These detectors are often pixelated, requiring cumbersome postinteraction 3-D event reconstruction, which can benefit from detailed material characterization at the micron level. Transport properties and material defects with respect to electrons and holes are to be characterized, which is a labor-intensive process. Current state-of-the-art characterization is done either as a whole or at most pixel-by-pixel over the detector material. In this article, we propose a novel learning-based physical model to infer material properties at the microscopic level for RTSD. Our approach uses a novel physics-inspired learning model based on physical transport of charges with trapping centers for electrons and holes in the detector. The proposed model learns these material properties from known or measured input charges to the detector along with known or measured output signals and distributed charges in the bulk of the RTSD. The actual physical detector is divided into voxels in space and takes into account different material properties (such as drift, trapping, detrapping, and recombination) in each voxel as learnable model parameters. The model is based on a physics-inspired recurrent neural network model instead of traditional convolutional or fully connected networks. The advantage of our approach is the one-to-one relationship between the actual physical parameters of the voxels and learnable weights in the model, far fewer trainable parameters compared to traditional neural network approaches and less training time. The performance of our model has been evaluated on cadmium zinc telluride (CdZnTe), with voxels of three sizes, 25, 50, and $100~ \mu{\mathrm {m}}$ , for single charge input as well as multiple charge inputs at different voxel positions. Our learning-based model provides material properties with higher spatial resolution and performs well in all scenarios and matches the actual physical parameters better than state-of-the-art classical approaches. [ABSTRACT FROM AUTHOR]

Space plasmas, though tenuous, can lead to the development of large magnitude potentials on spacecraft surfaces relative to the ambient plasma in a process known as spacecraft charging. This effect poses a threat to spacecraft when surfaces charge differentially as it increases the risk of electrical arcing. Spacecraft engineers have primarily focused on the mitigation of charging and not its exploitation. The potentials and current collected by the spacecraft present an opportunity to harvest power from the space environment. Although the current density and floating potential due to incident charged species in the orbit-motion-limited model are identical for all spacecraft surfaces with similar geometries, material property differences between surfaces or charge control devices break the symmetry and allow for net current to flow. Differences in the secondary electron yield, in particular, produce strong differences in floating potential even when the spacecraft is eclipsed or far from the sun. This article presents a theory for computing the power available to harvest from spacecraft charging given the parameters of ambient space plasmas and surface material properties. Given a load connecting two differentially charged surfaces, optimal power is harvested by matching the load resistance to the input plasma impedance. While this holds for any model of the ambient plasma, the orbit-motion-limited theory yields closed-form solutions to the charging equations for spherical and planar surfaces. These solutions provide an upper bound on generated power that scales with the density and temperature of the cold electron population. An environment-specific optimal anode-to-cathode area ratio exists that minimizes the input impedance to the harvesting circuit. Deployable anodes are recommended to satisfy this condition and increase the amount of power harvested. Energy harvesting from spacecraft charging can be used in the Jovian magnetosphere to generate on the order of 10 mW/m2 of power. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) or 3-D printing of concrete allows for construction of arbitrary shape structures without a mold. Since reproducibility of 3-D printed concrete is lower than that of conventional fabrication, each 3-D printed structure should be monitored for proper curing. Conventional qualification of concrete is based on several tests, including destructive compressive strength measurements. Because a structure is 3-D printed layer-by-layer, the surfaces of AM concrete structures have significant surface roughness. This limits the applicability of conventional nondestructive testing methods. We investigated qualification of 3-D printed mortar by monitoring curing with nondestructive electrical conductivity measurements. Bulk resistance of concrete was extracted from electrochemical impedance spectroscopy (EIS) measurements made using custom flexible self-adhesive electrodes, which contour to rough surfaces. We show that bulk resistivity of concrete increases linearly with time. This allows for developing a calibration curve for compressive strength lookup from nondestructive electrical conductivity measurements. Conductivity measurements also allow for estimation of formation factor, which is an indicator of mortar permeability. [ABSTRACT FROM AUTHOR]

Haptic research has frequently equated softness with the compliance of elastic objects. However, in a recent study we have suggested that compliance is not the only perceived material dimension underlying what is commonly called softness [ ]. Here, we investigate, whether the different perceptual dimensions of softness affect how materials are haptically explored. Specifically, we tested whether also the task, i.e., the attribute that a material is being judged on, might affect how a material is explored. To this end we selected 15 adjectives and 19 materials that each associate with different softness dimensions for the study. In the experiment, while participants freely explored and rated the materials, we recorded their hand movements. These movements were subsequently categorized into distinct exploratory procedures (EPs) and analyzed in a multivariate analysis of variance (MANOVA). The results of this analysis suggest that the pattern of EPs depended not only on the material's softness dimension and the task (i.e., what attributes were rated), but also on an interaction between the two factors. Taken together, our findings support the notion of multiple perceptual dimensions of softness and suggest that participants actively adapt their EPs in a nuanced way when judging a particular softness dimensions for a given material. [ABSTRACT FROM AUTHOR]

Solar cells (SCs) based on semiconductor nanostructures with the distinctive potential of significant savings in material and effective control over light trapping and scattering processes provide a pathway to low-cost and high-efficiency next-generation SCs. To realize efficient light harvesting and reduced reflection and transmission loss of the nanostructures, the geometrical parameters and material properties must be deliberately optimized. In this article, for SCs based on vertically aligned GaAs-truncated nanopyramids, using the 3-D finite-difference time-domain (FDTD) method, we have examined the optimization study of geometrical and material parameters, including base-width, period, top-surface flatness, carrier mobility and lifetime, doping concentration, and surface recombination, to achieve absorption enhancement and in turn optimum photovoltaic parameters. The optimized structure has exhibited an efficiency of 19.16% despite considering low carrier mobility of 1000 cm2 V−1 s−1 and lifetime of 3 nanoseconds, with heavily doped core (~ 4 × 1017 cm−3) and substrate (~ 1 × 1019 cm−3) and surface recombination velocity of 105cm/s at the contacts. [ABSTRACT FROM AUTHOR]

A heat exchanger, based on a millimeter-wave absorbing ceramic composite, is under development. This article describes a 1-D finite-difference model that is used in the design of the heat exchanger. The purpose of this model is to offer a design tool that can rapidly estimate the overall performance of the heat exchanger. This fast model allows for absorber performance to be evaluated over a wide parameter space as opposed to 3-D finite-difference time-domain methods, which provide accurate results but require substantially more computational resources. The model enables quick calculations by approximating the electric field such that the simulation runs on the slower thermal dynamics time scale without having to resolve the faster electromagnetic time scale. Example calculations were performed to illustrate the performance of a realistic absorber. These calculations used experimentally measured material properties for a molybdenum-loaded aluminum nitride (AlN:Mo) ceramic composite. Simulation results show dielectric tiles reaching equilibrium temperature in around 20 s and the samples absorbing up to 70% of the power from the millimeter-wave beam. Parameter studies over Mo loading percentage and boundary condition temperatures highlight the complexity of this coupled system. An AlN:Mo composite with 3% Mo (by volume) exhibits uniform power absorption across multiple boundary condition temperatures and suggests robustness to variety of possible experimental testing conditions. [ABSTRACT FROM AUTHOR]

An accurate theoretical formulation is presented for a semi-cylindrical parallel plate capacitor, independent of the electrode dimensions. An approximate solution is also presented to relate the changes in capacitance with changing volume-ratio of the multi-phase dielectric filling of the capacitor. Two applications are considered to verify the proposed techniques. In the first application, the calculated capacitance is compared with the measured capacitance of a healthy tree trunk to identify the initial stage of pest infestation. In the second application, an approximate capacitance of a three-phase mixture, consisting of gas, oil and brine layers, are used to identify the phase-fraction of a petroleum carrying pipeline with a steady-state flow. The advantage and limitations of the proposed approximate method are studied. In addition to logging and monitoring the oil supply, this simple method can electronically control multi-phase pipes with intravenous injections to collect postoperative fluids from the human body.

In this paper, as-extruded (~1μm + ~60 nm) bimodal size SiC particle (SiCp) reinforced magnesium matrix composite was subjected to step-by-step tensile treatment. The microstructure evolution and fracture characteristic of the composites were investigated. The experimental results indicated that the dislocations were accumulated mainly around the particles. Meanwhile, dislocation density increased with the strain increasing from 0% to 2.6% and hence, improved the tensile strength of the composite. According to the observation of the fracture process for the composites, the twinning was observed (away from the ~1μm SiCp). The dominant microcrack nucleation easily occurred at the interface between ~1μm SiCp(α-SiCp) and Mg matrix due to the stress concentration (which was caused by large size particle and the polygonal morphology). However, a good interface bonding between nano SiCp (β-SiCp) and matrix was indicated, and no microcracks were initiated around β-SiCp. The fracture features of the composite show that the existence of β-SiCp is helpful to improve the tensile properties, which is explained by the mechanisms such as the crack-propagation, obstruction, and crack blunting.

Downscaling of package wiring has been the singular focus to achieve higher logic-memory interconnect density to meet next-generation needs for high-bandwidth computing. This article presents, for the first time, a systematic modeling and experimental study of sub-5- $\mu \text{m}$ -diameter microvia reliability. Geometry design considerations and build-up dielectric material properties in evaluating the microvia fatigue life are investigated. Finally, experimental thermal-cycling reliability results of sub-5- $\mu \text{m}$ -diameter microvias are correlated with the modeling results. [ABSTRACT FROM AUTHOR]

Humans implicitly rely on the properties of materials to guide our interactions. Grasping smooth materials, for example, requires more care than rough ones. We may even visually infer non-visual properties (e.g., softness is a physical material property). We refer to visually-recognizable material properties as visual material attributes. Recognizing these attributes in images can provide valuable information for scene understanding and material recognition. Unlike typical object and scene attributes, however, visual material attributes are local (i.e., “fuzziness” does not have a shape). Given full supervision, we may accurately recognize such attributes from purely local information (small image patches). Obtaining consistent full supervision at scale, however, is challenging. To solve this problem, we probe the human visual perception of materials. By asking simple yes/no questions comparing pairs of image patches, we obtain the weak supervision required to build a set of classifiers for attributes that, while unnamed, function similarly to the attributes with which we describe materials. Furthermore, we integrate this method in the end-to-end learning of a CNN that simultaneously recognizes materials and their visual attributes. Experiments show that visual material attributes serve as both a useful representation for known material categories and as a basis for transfer learning. [ABSTRACT FROM AUTHOR]

Solid diffusion bonding of the copper–zinc system is preliminarily investigated as a possible bonding solution for power modules. This article proposed a method of bonding Zn foil on substrate-plated Ag to obtain the solder joints. The formation and transformation of intermetallic compounds (IMCs) in Cu–Zn and Ag–Zn joints were investigated. The results showed that the AgZn3 phase was compressed and moved into the solder side when competed with the Cu5Zn8 phase, relieved the Kirkendall voids, and enhanced the connection of joints. Besides, the electrical resistivity could maintain at a competitive level of $9~\mu \Omega \cdot {\mathrm {cm}} $ , and the shear strength reached 43–75 MPa. These properties were also appreciated even after the aging test. The deformation behaviors of different IMCs were tested by nanoindentation. This work provides a potential alternative to high-temperature solders used for high-power-density electronic packaging applications. [ABSTRACT FROM AUTHOR]

Objective: Accurate prospective modeling of microwave ablation (MWA) procedures can provide powerful planning and navigational information to physicians. However, patient-specific tissue properties are generally unavailable and can vary based on factors such as relative perfusion and state of disease. Therefore, a need exists for modeling frameworks that account for variations in tissue properties. Methods: In this study, we establish an inverse modeling approach to reconstruct a set of tissue properties that best fit the model-predicted and observed ablation zone extents in a series of phantoms of varying fat content. We then create a model of these tissue properties as a function of fat content and perform a comprehensive leave-one-out evaluation of the predictive property model. Furthermore, we validate the inverse-model predictions in a separate series of phantoms that include co-recorded temperature data. Results: This model-based approach yielded thermal profiles in close agreement with experimental measurements in the series of validation phantoms (average root-mean-square error of 4.8 °C). The model-predicted ablation zones showed compelling overlap with observed ablations in both the series of validation phantoms (93.4 ± 2.2%) and the leave-one-out cross validation study (86.6 ± 5.3%). These results demonstrate an average improvement of 17.3% in predicted ablation zone overlap when comparing the presented property-model to properties derived from phantom component volume fractions. Conclusion: These results demonstrate accurate model-predicted ablation estimates based on image-driven determination of tissue properties. Significance: The work demonstrates, as a proof-of-concept, that physical modeling parameters can be linked with quantitative medical imaging to improve the utility of predictive procedural modeling for MWA. [ABSTRACT FROM AUTHOR]

To more actively perform fine manipulation tasks in the real world, intelligent robots should be able to understand and communicate the physical attributes of the material during interaction with an object. Tactile and vision are two important sensing modalities in robotic perception system. In this article, we propose a cross-modal material perception framework for recognizing novel objects. Concretely, it first adopts an object-agnostic method to associate information from tactile and visual modalities. It then recognizes a novel object by using its tactile signal to retrieve perceptually similar surface material images through the learned cross-modal correlation. This problem exhibits a challenge because data from visual and tactile modalities are highly heterogeneous and weakly paired. Moreover, the framework should not only consider cross-modal pairwise relevance but also be discriminative and generalized for unseen objects. To this end, we propose a weakly paired cross-modal adversarial learning (WCMAL) model for the visual–tactile cross-modal retrieval, which combines the advantages of deep learning and adversarial learning. In particular, the model fully considers the weak pairing problem between the two modalities. Finally, we conduct verification experiments on a publicly available data set. The results demonstrate the effectiveness of the proposed method. Note to Practitioners—Since cross-modal perception can improve the active operation of automation systems, it is invaluable for industrial intelligence, particularly when only one sensing modality cannot be used or suitable in some applications. In this article, we provide a framework of cross-modal material perception for object recognition using the idea of the cross-modal retrieval. Concretely, we use relevant tactile data of an unknown object to retrieve perceptually similar surface images, which are used to evaluate its material properties. Different from that previous works using tactile information as a complement or alternative to visual information to recognize specific objects, our proposed framework is able to estimate and infer material properties of both seen and unseen objects, which can enhance manipulation systems intelligence and improve the quality of the interaction. In our future works, more modality information will be incorporated to further enhance the cross-modal material perception. [ABSTRACT FROM AUTHOR]

In automotive electronics, humidity-sensitive electronics are encapsulated by protective housings that are attached to the car body. Typical housing materials are polymer composites, through which moisture transport occurs. The objective of this article is to provide a predictive capability for moisture transport through automotive housings enclosing a cavity with electronic modules. The temperature-dependent moisture properties, including moisture diffusivity, solubility, and saturated concentration of three material candidates, are characterized first. Then, the analogy between heat transfer and the mass transfer is implemented to model the moisture transport into the cavity enclosed by the materials. To cope with the transient boundary condition at the housing material and the cavity interface, the effective volume scheme is used, treating the cavity as an imaginary polymer with an extremely large diffusivity and “equivalent solubility” determined from the ideal gas law and Henry’s law. The prediction is subsequently validated through an experimental setup designed to monitor the in situ humidity condition inside the cavity sealed by the materials. The prediction and experimental results agree well with each other, which corroborates the validity of the finite-element analysis (FEA) modeling and the measured moisture properties. [ABSTRACT FROM AUTHOR]

This article is based on my plenary presentation that was associated with my Peter Haas Award, where I overview my 40 years of research and the people with whom I have had the pleasure of working, both domestically and internationally. In 1978, Sandia’s electron beam fusion program emerged from a weapons simulator community that was machine oriented and relied on design principles and J. Charlie Martin (JCM) criteria. The simulation tools were primarily used retrospectively. Fusions’ extraordinary requirements stimulated tremendous innovation in pulsed power, beams, pinches, and simulation tools. I started by developing ion beam deposition and transport models that were integrated into radiation-hydrodynamics codes; validated by experiments on Gamble II and Proto I; and helped initiate Sandia’s light-ion-beam fusion program. Strategic Defense Initiative (SDI) program research led to the development of the Integrated Tiger Series (ITS) suite of electron–photon Monte Carlo codes (1985). A research on Particle Beam Fusion Accelerator I (PBFA-I) and PBFA-II on generating, transporting, and focusing ion beams required developing transport, diagnostic simulation, and analysis tools, which were used in focusing protons to 5 TW/cm2 (1991) and lithium beams to 2 TW/cm2 and heating hohlraums to 65 eV (1996). They also helped identify anode plasma formation by electron heating as the source of diode impedance collapse leading to efforts to include electron–electrode interaction models into particle-in-cell (PIC) codes and initiating hybrid fluid-PIC development (IPROP, LSP). Electrode physics remains a power flow grand challenge for high-yield fusion. In 1999, I led rad-magnetohydrodynamic (MHD) development [arbitrary-Lagrangian–Eulerian general research applications high-energy-density physics (ALEGRA-HEDP)] and oversaw equation of state (EOS) and conductivity model development using quantum molecular dynamics/density function theory (QMD/DFT), resulting in predictive capabilities for dynamic material experiments. Improved z-pinch dynamic hohlraum modeling and experiments resulted in thermonuclear neutrons (2004). 3-D wire array dynamics were modeled and understood. We developed advanced radiographic sources and built a linear transformer driver (LTD) test bed. At the Naval Research Laboratory (NRL), I have overseen advances in modeling and experiments in beams, pinches, pulsed power, and their applications (2009-). My goal throughout has been to develop and validate predictive simulation tools, making pulsed power a Science. [ABSTRACT FROM AUTHOR]

We present a framework for the acquisition and parametrization of object material properties. The introduced acquisition device, denoted as Texplorer2, is able to extract surface material properties while a human operator is performing exploratory procedures. Using the Texplorer2, we scanned 184 material classes which we labeled according to biological, chemical, and geological naming conventions. Based on these real material recordings, we introduce a novel set of mathematical features which align with corresponding material properties defined in perceptual studies from related work and classify the materials using common machine learning techniques. Validation results of the proposed multi-modal features lead to an overall classification accuracy of 90.2% $\pm$ 1.2% and an F $_\text{\text{1}}$ score of 0.90 $\pm$ 0.01 using the random forest classifier. For the sake of comparison, a deep neural network is trained and tested on images of the material surfaces; it outperforms (90.7% $\pm$ 1.0%) the hand-crafted feature-based approach yet leads to more critical misclassifications in terms of the proposed taxonomy. [ABSTRACT FROM AUTHOR]

The efficient assessment of convolved hidden Markov models is discussed. The bottom layer is defined as an unobservable categorical first-order Markov chain, whereas the middle layer is assumed to be a Gaussian spatial variable conditional on the bottom layer. Hence, this layer appears marginally as a Gaussian mixture spatial variable. We observe the top layer as a convolution of the middle layer with Gaussian errors. The focus is on assessing the categorical and Gaussian mixture variables given the observations, and we operate in a Bayesian inversion framework. The model is defined to perform the inversion of subsurface seismic amplitude-versus-offset data into lithology/fluid classes and to assess the associated seismic material properties. Due to the spatial coupling in the likelihood functions, evaluation of the posterior normalizing constant is computationally demanding, and brute-force, single-site updating Markov chain Monte Carlo (MCMC) algorithms converge far too slowly to be useful. We construct two classes of approximate posterior models, which we assess analytically and efficiently using the recursive forward–backward algorithm. These approximate posterior densities are used as proposal densities in an independent proposal MCMC algorithm to determine the correct posterior model. A set of synthetic realistic examples is presented. The proposed approximations provide efficient proposal densities, which results in acceptance probabilities in the range 0.10–0.50 in the MCMC algorithm. A case study of lithology/fluid seismic inversion is presented. The lithology/fluid classes and the seismic material properties can be reliably predicted. [ABSTRACT FROM AUTHOR]

The magnetization behavior and thus the form of the magnetization curve of electrical steel strongly depend on the direction of the magnetic field, frequency of excitation, external mechanical stress, and cut edge effect. These factors influence the performance of electrical machines and need to be considered in advanced machine design processes or numerical modeling. Most of the aforementioned effects occur locally in the machine and, therefore, need to be described locally. It is crucial to characterize the material under realistic conditions for adequate identification and quantification of the influences. In this article, modeling and simulation of soft magnetic material are performed based on a detailed magnetic characterization, considering magnetization amplitude, angle with respect to the rolling direction of magnetization, mechanical stress, and cut edge effect. The dependent soft magnetic material characteristics are derived from the magnetic measurement data and concluded into interpolation surfaces. Subsequently, these surfaces are used to simulate a synchronous machine designed for a traction drive of an electric vehicle by finite-element simulation. One of the main challenges is the correct determination of the local material properties, depending on the operating point, which influences global quantities such as losses and torque. This article provides a methodology to consider different local influences on the magnetization behavior of electrical steel in a finite-element simulation, thus offering the potential for improving electromagnetic circuit design. [ABSTRACT FROM AUTHOR]

A novel technique of optimizing selected models of electroheat processes (laser or induction-assisted laser welding, cladding, and so on) is presented, based on the calibration of temperature-dependent material properties. These properties often exhibit uncertainties that negatively influence the quality of the results. Their calibration starts from experimental investigation and evaluation of temperature profiles of welds or tracks, and the goal is to obtain the same profiles by solving the numerical models. The technique is illustrated with an example of induction-assisted laser welding. [ABSTRACT FROM AUTHOR]

This work presents an extensive study on the temperature coefficient of frequency (TCF) for thin-film lithium niobate (LiNbO3) resonators. To capture the temperature-frequency behavior for each vibration mode, a one-dimensional (1D) multi-section TCF model is proposed and verified by both the finite-element method (FEM) and experiments. By partitioning the metallized and non-metallized sections of the interdigitated transducers (IDT) as distinct TCF blocks and properly considering the spatial energy distribution, the TCF of each mode can be accurately predicted. Two higher lateral-order resonators based on the fundamental shear-horizontal mode (SH0) are designed and fabricated on a LiNbO3-on-SiO2 wafer to provide odd- and even-order harmonics. TCFs of the first 6 overtones (from 84 to 515 MHz) are calculated using the material properties given in the literature, exhibiting a maximum TCF difference of 3 ppm/K between the theoretical prediction and measurement results. Although only SH0 resonators are selected for experimental verification, the proposed multi-section TCF model can be applied to other plate wave MEMS resonators. [2019-0112] [ABSTRACT FROM AUTHOR]

Radar systems have become more important in the automotive field, especially for autonomous driving. A crucial part of the hardware is the antenna which is connected by lines to the radar chip. These components are usually built on printed circuit boards (PCBs) that have to fulfill high automotive requests under all environmental conditions. As a consequence, material properties are a critical factor for the functionality and reliability of systems, like-wise new materials are available in the market. Fourteen interesting materials of different classes [polytetrafluorethylene (PTFE), polyphenyl ether (PPE), epoxy-based resin, and liquid crystal polymer (LCP)] were evaluated in this work at their initial state, while heating to 125 °C and after storage with 85% relative humidity (RH) at 85 °C. It is shown that there are new candidates for millimeter wave applications beyond the PTFE materials. Nevertheless, it is necessary to test each material individually for its properties and optimize the parameters in simulation because they differ from the values in the datasheet. In addition, the properties depend on the used line setup, in this work microstrip lines. For the measurements, multiline thru-reflect-line (TRL) calibration and ring resonators were used and give a good overview by loss and effective permittivity depending on the environmental condition. [ABSTRACT FROM AUTHOR]

The surface properties of an object play a vital role in the tasks of robotic manipulation or interaction with its surrounding environment. Tactile sensing can provide rich information about the surface properties of an object through physical contact. Hence, how to convey and interpret the tactile information to the user is a significant problem during the human–machine interaction. To this end, a visual–tactile cross-modal retrieval framework is proposed for perceptual estimation by associating tactile information to visual information of material surfaces. Namely, we can use tactile information of an unknown material surface to retrieve perceptually similar surfaces from an available surface visual sample set. For the proposed framework, we develop a discriminant adversarial learning method, which incorporates intramodal discriminant, cross-modal correlation, and intermodal consistency into a deep learning network for common feature representation learning. Experimental results on the publicly available data set show that the proposed framework and the method are effective. [ABSTRACT FROM AUTHOR]

This letter presents a method to realize anisotropic two-dimensional designs with wideband operation. Glide symmetry has been proven to increase the bandwidth and equivalent refractive index of periodic structures. Here, two anisotropic glide-symmetric unit cells are proposed and characterized. Our simulated results prove that these unit cells follow the general behavior of periodic structures possessing glide symmetry. Moreover, we demonstrate that higher level of anisotropy can also be achieved by glide symmetry. Combining our proposed anisotropic glide-symmetric unit cell and transformation optics, a $\text{30}\%$ compressed Luneburg lens is designed. The lens operates from 1 to 13 GHz. [ABSTRACT FROM AUTHOR]

Quasi-static elasticity imaging techniques rely on model-based mathematical inverse methods to estimate mechanical parameters from force–displacement measurements. These techniques introduce simplifying assumptions that preclude exploration of unknown mechanical properties with potential diagnostic value. We previously reported a data-driven approach to elasticity imaging using artificial neural networks (NNs) that circumvents limitations associated with model-based inverse methods. NN constitutive models can learn stress–strain behavior from force–displacement measurements using the autoprogressive (AutoP) method without prior assumptions of the underlying constitutive model. However, information about internal structure was required. We invented Cartesian NN constitutive models (CaNNCMs) that learn the spatial variations of material properties. We are presenting the first implementation of CaNNCMs trained with AutoP to develop data-driven models of 2-D linear-elastic materials. Both simulated and experimental force–displacement data were used as input to AutoP to show that CaNNCMs are able to model both continuous and discrete material property distributions with no prior information of internal object structure. Furthermore, we demonstrate that CaNNCMs are robust to measurement noise and can reconstruct reasonably accurate Young’s modulus images from a sparse sampling of measurement data. CaNNCMs are an important step toward clinical use of data-driven elasticity imaging using AutoP. [ABSTRACT FROM AUTHOR]

High-density interconnect printed-circuit boards and substrates are widely used for high-end electronics to meet the demand for greater input/output density and a smaller footprint area. Microvias as electrical interconnections between circuit layers have a much smaller scale (no larger than 150 ${\mu }\text{m}$ in diameter) compared to conventional plated-through holes, and require a different fabrication process that introduces unique reliability challenges. This paper identifies factors that affect microvia fatigue life and presents a design of experiments to quantify the effects of those factors on microvia life in terms of cycles to failure. A regression life prediction model is formulated to account for the significant design, material, and defect parameters. [ABSTRACT FROM AUTHOR]

An embedded domain decomposition method (DDM) is presented herein for solving electromagnetic problems with complex geometries. In the method, the original computational domain is decomposed into a background subdomain and multiple embedded subdomains. The subdomain problems are easier to solve than the original problem. Furthermore, the shape of the subdomains can be geometrically nonconformal, and the discretizations in the subdomains are allowed to be completely independent. Information exchange between the background and embedded subdomains are addressed in their shared regions, including four ingredients: field continuity, material difference, perfect electrical conductor (PEC), and port. Field continuity on the boundaries of the embedded subdomains is weakly enforced by employing Robin transmission condition. Modified volume sources are introduced to account for the material difference between the subdomains. For PEC and port, surface currents on them in embedded subdomains are impressed into the background subdomain in a proper way. The accuracy and flexibility of the proposed method are demonstrated through several numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate the fabrication of contour-mode resonators (CMRs) with Al0.83Sc0.17N as a piezoelectric layer. Moreover, we assess the electromechanical coupling and the maximum achieved quality factor from 150 to 500 MHz. In comparison to pure aluminum nitride (AlN) CMRs, our results show electromechanical coupling coefficients of more than a $2\times $ factor higher at around 200 MHz. The highest quality factor is measured on a CMR operating at 388 MHz and is in excess of 1600. From the characterization of devices operating at different frequencies, material parameters of the Al0.83Sc0.17N are extracted such as the stiffness constant, the relative permittivity, and the piezoelectric constant. In particular, the reported d31 piezoelectric constant is equal to −3.9 pm/V. This represents a $2.25\times $ improvement when compared to pure AlN. Finally, we report the first temperature compensation experimental results for Al0.83Sc0.17N CMRs. Our results show that about 1.5 $\mu \text{m}$ of sputtered oxide, deposited on top of a released resonator, allows near zero temperature coefficient of frequency variation for CMRs operating up to 500 MHz. [ABSTRACT FROM AUTHOR]

The objectives of this tutorial are as follows: 1) to help students and researchers develop a basic understanding of how pulsed-power systems are used to create high-energy-density (HED) matter; 2) to develop a basic understanding of a new, compact, and efficient pulsed-power technology called linear transformer drivers (LTDs); 3) to understand why LTDs are an attractive technology for driving HED physics (HEDP) experiments; 4) to contrast LTDs with the more traditional Marx-generator/pulse-forming-line approach to driving HEDP experiments; and 5) to briefly review the history of LTD technology as well as some of the LTD-driven HEDP research presently underway at universities and research laboratories across the globe. This invited tutorial is part of the Mini-Course on Charged Particle Beams and High-Powered Pulsed Sources, held in conjunction with the 44th International Conference on Plasma Science in May of 2017. [ABSTRACT FROM AUTHOR]

An often-overlooked aspect during the development process of electrical machines is the validity and accuracy of the machine material properties being used at the design stage. Designers usually consider the data provided by the material supplier, which are measured on material in an unprocessed state. However, the fact that the machining processes required to produce the finished product (e.g., the stator core) can permanently vary the material properties is very often neglected. This paper, therefore, deals with and investigates the effects that such processes can have on the overall machine performance. To do this, three sets of material data, the material suppliers’ data, material data based on conventional characterization methods, and material data based on test samples that include the manufacturing processes, are used to develop three versions of the same baseline machine. The results of these three machines are then compared, and the resulting variations of the machine's performance are presented and described. The chosen baseline machine is a high-performance and relatively high speed, aerospace, electrical machine. Special attention is focused on the efficiency maps of the machine, as this aspect is highly dependent on the material properties that are the most sensitive to manufacturing processes such as the material's anhysteretic B–H curve and its specific core loss. [ABSTRACT FROM AUTHOR]

This paper describes the implementation and performance of adiabatic absorbing layers in a fast Fourier transform (FFT)-accelerated volume integral equation (VIE) method for simulating truncated nanophotonics structures. At the truncation sites, we place absorbing regions in which the conductivity is increased gradually in order to minimize reflections. In the continuous setting, such adiabatic absorbers have been shown via coupled-mode theory to produce reflections that diminish at a rate related to the smoothness of the absorption profile function. The VIE formulation we employ relies on uniform discretizations of the geometry over which the continuously varying fields and material properties are represented by piecewise constant functions. Such a discretization enables the acceleration of the method via the FFT and, furthermore, the introduction of varying absorption can be performed in a straightforward manner without compromising this speedup. We demonstrate that, in spite of the crude discrete approximation to the smooth absorption profiles, our approach recovers the theoretically predicted reflection behavior of adiabatic absorbers. We thereby show that the FFT-accelerated VIE method is an effective and fast simulation tool for nanophotonics simulations. [ABSTRACT FROM AUTHOR]

A model-based inversion algorithm combined with the curl-conforming volume integral equation (VIE) method is presented for sensitivity analysis of electromagnetic measurement for anisotropic objects. The forward algorithm utilizes the curl-conforming VIE method. The inversion algorithm is based on the Gauss–Newton method. The approach is applied to the reconstruction of the permittivities of 3-D anisotropic objects. Moreover, sensitivity analysis of the data from different polarizations of transmitters and receivers to the anisotropic properties is performed. Numerical examples show the effectiveness of the inversion algorithm and demonstrate the sensitivities of data from different transmitter and receiver pairs to the anisotropy. [ABSTRACT FROM AUTHOR]

Vacuum vessel (VV) is the main component of the China fusion engineering test reactor. Shell forming is relatively in the earliest phase, one of the most important processes during VV manufacturing. The profile errors caused by spring back of shells at room temperature will directly influence the subsequent manufacturing and experimental studies, so a set of dies, whose profiles can be easily revised, has been designed based on the spring-back theory. The empirical formula has been used to calculate the theoretic die profile value, which is mostly determined by the mechanical properties of 316LN stainless steel. Then, choosing four relevant values to simulate the forming process in which the optimal profile errors of shell must be controlled within 1.5 mm. The optimal dimensional value of the dies has been confirmed to guide the forming experimental study. After the experiment, the actual profile errors, thickness reduction, maximum spring back, maximum deformation rate, and the surface residual stress have been measured, and those data are an accurate indicator from the finite element analysis, also verifying the reasonability of the manufacturing process in turn. [ABSTRACT FROM AUTHOR]

China Fusion Engineering Test Reactor (CFETR) central solenoid (CS) model coil (CSMC) made with cable-in-conduit conductor superconductor had been developed in the Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, China. The highest field of CSMC is 12 T when the running current is 47.65 kA, and the largest magnetic field change rate is 1.5 T/S. CSMC mainly consists of two Nb3Sn coils and three outer NbTi coils, buffer zone, feeders and joints, and preload supports. The inner diameter of the coil is 1500 mm, and the outer diameter is 3520 mm. Stress analyzes were performed using coupled solver for simultaneous structural, thermal, and electromagnetic analysis. Based on the generalized Hooke’s law, orthotropic smeared property used for the CSMC winding pancake is calculated with one so-called “unit cell” finite-element model of the jacket and insulation. A global finite-element model was created based on the design geometry data to investigate the mechanical property of CFETR CSMC. [ABSTRACT FROM AUTHOR]

CEA/Saclay has launched anR&Dprogram that aims at developing design tools and technologies for conduction-cooled MgB2 magnets. Considering that the superconducting properties of MgB2 are very sensitive to strains that develop in magnets during the winding process or as a result of thermal contraction and Lorentz forces, the electromechanical characterization of MgB2 wires is clearly paramount formagnet design calculations. For this purpose, a device for investigating the degradation of the critical current density in R&W superconductors as a function of circumferential strain has been designed. The spring geometry has been conceived for testing long length samples (>1 m) in a magnet-like configuration, where a uniformly distributed circumferential strain ([0-1]%) is applied to the superconductor through a ring-shaped spring. The paper reports on the conceptual design, analytic calculations, and numerical simulations of the spring geometry. Moreover, the working principle is validated by experimental tests on a mock-up model. [ABSTRACT FROM AUTHOR]

The effects of temperature-dependent material properties, including those of silicon steel and permanent magnet (PM) materials, on the design process of inverter-fed PM motors are investigated in this paper. The magnetization curves of these materials are measured experimentally at different temperatures and are imported into a finite-element package. Then, a statistical study of an integrated motor-drive system consisting of an interior PM motor connected to a space vector modulated inverter is performed by assigning four different temperatures and building a design space of various objectives including the integrated system efficiency, speed limits, motor startup quality, torque ripple, total harmonic distortion, and power factor as the geometrical dimensions are varied. The results reveal that incorporating the realistic material properties into the design process is essential. Moreover, different motor-drive performances have totally different variation rates as the material properties are changed. Finally, a set of optimal designs at different temperatures is proposed to show the effect of changing material properties. [ABSTRACT FROM AUTHOR]

In this letter, the performance of amorphous In–Ga–Zn-O (a-IGZO) thin-film transistors (TFTs) is investigated using the stacked Mo–Ti/Cu source (S) and drain (D) electrodes. The test samples A and B of the a-IGZO TFT have interlayers with Mo/Ti ratios of 2.29 and 1.15, respectively, for the SD electrodes, and each has a core layer of Cu stacked on the top of the interlayers. They were fabricated and measured to compare their performance in terms of the transfer characteristic and mobility. The measurement results showed that the sample B with a lower Mo/Ti ratio exhibited better performance than the sample A: higher field-effect mobility by 4.4 cm2/V-s, higher saturation mobility by 3.2 cm2/V-s, smaller sub-threshold slope by 0.91 V/decade, and lower threshold voltage by 1.4 V. In addition, the material properties of samples A and B were obtained using the line scan of the energy dispersive spectroscopy and analyzed with technology computer-aided design simulation. As a result, the performance of a-IGZO TFTs can be optimized by tuning the Mo/Ti ratios of the interlayer in the stacked SD electrodes. [ABSTRACT FROM PUBLISHER]

Microelectromechanical systems (MEMS)-based sample carriers became a breakthrough for in situ inspection techniques, especially in transmission electron microscopy where the sample carrier functions as a microsized laboratory and enables dynamic studies on samples, such as nanoparticles, nanowires, lamellas, and 2-D materials. Microheaters allow for in situ manipulation of samples by applying heat stimuli such that sample properties and interactions can be studied in real time at elevated temperatures. However, currently developed microheaters still suffer from undesired effects such as mechanical deflection and limitations in temperature range, accuracy, and homogeneity. This review discusses advancements in the technological development of microheaters. Methods and results found in the literature are categorized to provide an overview of optimization methods for thermoelectromechanical design aspects. The knowledge from various application fields, including a critical reflection on mesoscopic material properties, is combined into a series of design guidelines. These compose instructions for developing and optimizing microheater characteristics, such as mechanical and thermal stress, temperature accuracy and homogeneity, power consumption, response time, and sample drift. Although this review and guide are applicable to many application fields that require a microheater, an emphasis is laid on aspects most relevant to the microheater as a high-temperature sample carrier for in situ experiments. [2017–0149] [ABSTRACT FROM PUBLISHER]

Electrical properties of dielectric substrate are critical in designing high-speed products in terms of signal and power integrity. It is important to accurately characterize the dielectric properties to avoid overestimating or underestimating in the design. This paper proposes the improved “Root-Omega” method for extracting dielectric properties from fabricated multilayer printed circuit boards. Based on the electrical properties of fabricated transmission lines, the improved “Root-Omega” method applied to cases with smooth and rough conductors is validated using simulations. Error sensitivity analysis is performed to demonstrate the potential errors in the original “Root-Omega” procedure and the error sensitivity is significantly reduced by the proposed improvements. [ABSTRACT FROM AUTHOR]

Numerical simulations of nonlinear magnetic problems incorporating interactions of electromagnetics with material properties, thermal issues, and other physical fields, have recently attracted great interest among researchers. In this paper, the coupling strength of these interaction processes is defined. Based on this quantitative definition, we demonstrate that for strongly coupled magnetic problems a conventional indirect coupling scheme is likely to diverge. To overcome this difficulty, a hybrid indirect–direct coupling method is developed. The proposed method is a synergistic combination of a direct coupling scheme—for dealing with the nonlinearity of the coupled systems—and an indirect coupling scheme for preconditioning the heterogeneous systems. To improve the performance of the proposed method for strongly coupled systems, a parametric continuation strategy is introduced. The efficiency of the proposed method is validated through numerical tests. [ABSTRACT FROM PUBLISHER]

A tetrahedron meshing and transforming method for 3-D implementation of transformation optics is proposed and verified for a few waveguide components. The method is applicable to a wide range of electromagnetic problems. An important feature of the proposed method is that the transformed medium will be piecewise homogeneous and free of any singularities. In this method, the region under transformation is divided into a few tetrahedrons, both in the physical and virtual domains. Each tetrahedron of the physical domain transforms to a tetrahedron in the virtual domain. The material properties of each tetrahedron are then calculated using the Jacobean matrix of the transformation. Since the elements of the Jacobean matrix are constant, the $\varepsilon$ and $\mu$ tensors are finite and homogeneous in each tetrahedron. The method is verified for several rectangular waveguide coupler examples. Features of the $\varepsilon$ and $\mu$ tensors of the discussed examples are described. For instance, it is shown that the material properties may reduce to 2-D material types, or material anisotropy may reduce, under certain conditions. [ABSTRACT FROM AUTHOR]

The 2.5-D IC packaging technology is to apply a silicon interposer with Cu through silicon vias (Cu TSVs) as a platform to interconnect and integrate heterogeneous and homogeneous chips horizontally and vertically. The existing Cu TSVs might make the silicon interposers more fragile, due to their structural non-homogeneity and weak interfaces. Thus, the strength determination of silicon interposers becomes one of the important issues for ensuring reliability of 2.5-D packages. This paper aims to determine the bending strengths of silicon interposers (with back-side and front-side surface controls—corresponding to the surfaces with C4 bumps and Cu/SiO2 layers, respectively) using a point-load-on-elastic-foundation test, associated with an acoustic emission (AE) method to detect local material cracks or delamination occurring during the test before interposer breaking. The results indicate that there are a few less-than 50-dB AE signals occurring before interposer breaking, due to the micropad crush on the Cu/SiO2 layer induced by the loading-pin contact for the back-side control case, but not for the front-side control case. In addition, interposer breaking is found to be triggered by the maximum tensile stresses located at the corner of the C4 bump pad for the back-side control case, instead of the micropad crush or Cu TSVs, while at the silicon or Cu/SiO2 layer for the front-side control case. These failure behaviors have been confirmed by theory-validated finite element simulation. The bending strengths of silicon interposers have further been determined by using experimental data associated with finite element analysis by taking into account Cu material nonlinear properties and the Cu/SiO2 layer. [ABSTRACT FROM PUBLISHER]

As the geometry thickness error of composite radome impacts the electromagnetic (EM) performance of the antenna-radome system, a novel interval arithmetic analytic approach to the analysis of the effect on average power pattern of antenna-radome system with the thickness error in the composite radome-based is proposed. The thickness error of radome’s composite material is modeled as interval-valued errors. The link between the interval of thickness error and the interval power pattern along with some main EM characteristics (sidelobe level, peak power, and half-power beamwidth) expressed as intervals are efficiently constructed. Some comparisons with measured and simulated results reported in the state-of-the-art literature, experiment data and Monte Carlo (MC) method result serve as a way to validate the interval analysis (IA)-based method. Some numerical examples are reported to reveal the effect of the main geometric properties (i.e., location, size, width, and midpoint of the error interval) of the thickness error interval on power pattern. The obtained results show that the proposed IA-based approach offers tangible advantages and exhibits effectiveness versus some traditional statistical techniques (e.g., MC method). [ABSTRACT FROM PUBLISHER]

The estimation of material properties is important for scene understanding, with many applications in vision, robotics, and structural engineering. This paper connects fundamentals of vibration mechanics with computer vision techniques in order to infer material properties from small, often imperceptible motions in video. Objects tend to vibrate in a set of preferred modes. The frequencies of these modes depend on the structure and material properties of an object. We show that by extracting these frequencies from video of a vibrating object, we can often make inferences about that object's material properties. We demonstrate our approach by estimating material properties for a variety of objects by observing their motion in high-speed and regular frame rate video. [ABSTRACT FROM PUBLISHER]

Several pairs of Japanese adjective words pertaining to material's properties, such as roughness and hardness, have been used in Japanese studies to quantitatively evaluate variations in tactile sensations. This method asks observers to analyze their perceptual experiences one by one. An alternative notion is that human perceptual recognition is performed as a whole rather than by using fragmented factors. Based on this notion, we propose a system that can automatically estimate multidimensional ratings of touch from a single sound-symbolic word that has been spontaneously and intuitively expressed by a user. When a user inputs a sound-symbolic word into the system, the system refers to a database of phonemes and their auditory impressions, and calculates ratings in terms of 26 pairs of fundamental scales of touch. The estimated ratings of sound-symbolic words enable us to visualize a tactile perceptual space. Our study provides an alternative method for estimating the fine quality of tactile sensations. [ABSTRACT FROM PUBLISHER]