A Material Identification Approach Based on Wi-Fi Signal

Material identification is a technology that can help to identify the type of target material.Existing approaches depend on expensive instruments,complicated pre-treatments and professional users.It is difficult to find a substantial yet effective material identification method to meet the daily use demands.In this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level accuracy.In practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi device.After that,the amplitude ratios and phase differences can be more stably utilized to classify the materials.We implement our system and evaluate its ability to identify materials in indoor environment.The result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.

MATERIAL SURFACE THERMAL PROPERTY IDENTIFICATION USING HEAT FLUX TACTILE SENSOR

Eased on the mechanism of temperature tactile sensing of human finger,a heat flux tactile sensor com- posed of a thermostat module and a heat flux sensor is designed to identify material thermal properties. The ther- mostat module maintains the sensor temperature invariable, and the heat flux sensor（Peltier device） detects the heat flux temperature difference between the thermostat module and the object surface. Two different modes of the heat flux tactile sensor are proposed, and they are simulated and experimented for different material objects. The results indicate that the heat flux tactile sensor can effectively identify different thermal properties.

Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics

Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions.However,material identification is a challenging task,especially when the characteristic of the material is highly nonlinear in nature,as is common in biological tissue.In this work,we identify unknown material properties in continuum solid mechanics via physics-informed neural networks(PINNs).To improve the accuracy and efficiency of PINNs,we develop efficient strategies to nonuniformly sample observational data.We also investigate different approaches to enforce Dirichlet-type boundary conditions(BCs)as soft or hard constraints.Finally,we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material space.The estimated material parameters achieve relative errors of less than 1%.As such,this work is relevant to diverse applications,including optimizing structural integrity and developing novel materials.

The admittance feature representation and impact sound feature extraction in the material identification of ribbed plates

The admittance features representing the physical attributes are used as the in termediates to extract the materialattributesrelated impact sound features of ribbed plates. Firstly, the admittance feature representations of metal ribbed plates attributes are obtained and the relationship between the admittance features and the impact sound features are established via correlation analysis method. Then, materialattributesrelated impact sound features are obtained indirectly. Finally, the performances of different sound features for the material recognition of ribbedmetal plates are verified through the Support Vector Machine classifier. The results indicate that the obtained four sets of features can effectively identify the materials of the metal ribbed plates, while the accuracy of a single feature depends on the separable degree of the corresponding material attribute. And the features extracted based on admittance functions have higher average accuracy than that of timbre features. Therefore, the proposed sound feature extraction method based on admittance features is valid, and the extracted sound features can effectively reflect the physical attributes.

Accomplishment and challenge of materials database toward big data

The history and current status of materials data activities from handbook to database are reviewed, with introduction to some important products. Through an example of prediction of interfacial thermal resistance based on data and data science methods, we show the advantages and potential of material informatics to study material issues which are too complicated or time consuming for conventional theoretical and experimental methods. Materials big data is the fundamental of material informatics. The challenges and strategy to construct materials big data are discussed, and some solutions are proposed as the results of our experiences to construct National Institute for Materials Science（NIMS） materials databases.

Deep learning in two-dimensional materials:Characterization,prediction,and design

Since the isolation of graphene,two-dimensional(2D)materials have attracted increasing interest because of their excellent chemical and physical properties,as well as promising applications.Nonetheless,particular challenges persist in their further development,particularly in the effective identification of diverse 2D materials,the domains of large-scale and highprecision characterization,also intelligent function prediction and design.These issues are mainly solved by computational techniques,such as density function theory and molecular dynamic simulation,which require powerful computational resources and high time consumption.The booming deep learning methods in recent years offer innovative insights and tools to address these challenges.This review comprehensively outlines the current progress of deep learning within the realm of 2D materials.Firstly,we will briefly introduce the basic concepts of deep learning and commonly used architectures,including convolutional neural and generative adversarial networks,as well as U-net models.Then,the characterization of 2D materials by deep learning methods will be discussed,including defects and materials identification,as well as automatic thickness characterization.Thirdly,the research progress for predicting the unique properties of 2D materials,involving electronic,mechanical,and thermodynamic features,will be evaluated succinctly.Lately,the current works on the inverse design of functional 2D materials will be presented.At last,we will look forward to the application prospects and opportunities of deep learning in other aspects of 2D materials.This review may offer some guidance to boost the understanding and employing novel 2D materials.

Dynamic Characteristics Analysis and Vibration Experiment of Upper-time of Flight Counter (U-ToFC)

The dynamic characteristic parameters of Up-time of Flight Counter （U-ToFC） are important for research of structure optimization and reliability. However, the current simulation is performed based on homogenous material and simplified constraint model, the correct and reliability of results are difficult to be guaranteed. The finite element method based on identification of material parameters is proposed for this investigation on dynamic analysis, simulation and vibration experiment of the U-ToFC. The structure of the U-ToFC is complicated. Its＇ outside is made of aluminum alloy and inside contains electronic components such as capacitors, resistors, inductors, and integrated circuits. The accurate material parameters of model are identified difficultly. Hence, the parameters identification tests are performed to obtain the material parameters of this structure. On the basis of the above parameters, the experiment and FEA are conducted to the U-ToFC. In terms of the flight acceptance test level, and two kinds of joints condition between the U-ToFC and fixture are considered. The natural frequencies, vibration shapes and the response of the power spectral density of the U-ToFC are obtained. The results show simulation which is based on parameters identification is similar with vibration experiment in natural frequencies and responses. The errors are less than 10%. The vibration modes of simulation and experiment are consistent. The paper provides a more reliable computing method for the dynamic characteristic analysis of large complicated structure.

Identification of the Material Parameters of Laminated Plates

A scheme is developed to identify the material parameters of laminated plates using mathematical optimization and measured eigenfrequencies of the object. The object function of the optimization is defined as the difference between the measured frequencies and the computed frequencies of the laminated plates. The sensitivity of the structural eigenvalue with respect to the material parameters is analyzed. A numerical example is presented to show the feasibility of the scheme.

The Third-Order Viscoelastic Acoustic Model Enables an Ice-Detection System for a Smart Deicing of Wind-Turbine Blade Shells

The present work is based on the third-order partial differential equation (PDE) of acoustics of viscoelastic solids for the quasi-equilibrium (QE) component of the average normal stress. This PDE includes the stress-relaxation time (SRT) for the material and is applicable at any value of the SRT. The notion of a smart deicing system (SDS) for blade shells (BSs) of a wind turbine is specified. The work considers the stress in a BS as the one caused by the operational load on the BS. The work develops key design issues of a prospective ice-detection system (IDS) able to supply an array of the heating elements of an SDS with the element-individual spatiotemporal data and procedures for identification of the material parameters of atmospheric-ice (AI) layer accreted on the outer surfaces of the BSs. Both the SDS and IDS flexibly allow for complex, curvilinear and space-time-varying shapes of BSs. The proposed IDS presumes monitoring of the QE components of the normal stresses in BSs. The IDS is supposed to include an array of pressure-sensing resistors, also known as force-sensing resistors (FSRs), and communication hardware, as well as the parameter-identification software package (PISP), which provides the identification on the basis of the aforementioned PDE and the data measured by the FSRs. The IDS does not have hardware components located outside the outer surfaces of, or implanted in, BSs. The FSR array and communication hardware are reliable, and both cost- and energy-efficient. The present work extends methods of structural-health/operational-load monitoring (SH/OL-M) with measurements of the operational-load-caused stress in closed solid shells and, if the prospective PISP is used, endows the methods with identification of material parameters of the shells. The identification algorithms that can underlie the PISP are computationally efficient and suitable for implementation in the real-time mode. The identification model and algorithms can deal with not only the single-layer systems such as the BS layer without the AI layer or two-layer systems but also multi-layer systems. The outcomes can be applied to not only BSs of wind turbines but also non-QE closed single- or multi-layer deformable solid shells of various engineering systems (e.g., the shells of driver or passenger compartments of ships, cars, busses, airplanes, and other vehicles). The proposed monitoring of the normal-stress QE component in the mentioned shells extends the methods of SH/OL-M. The topic for the nearest research is a better adjustment of the settings for the FSR-based measurement of the mentioned components and a calibration of the parameter-identification model and algorithms, as well as the resulting improvement of the PISP.

Wear Debris Analysis: Fundamental Principle of Wear-Graphy

A new wear-graphy technology was developed, which can simultaneously identify the shape and composition of wear debris, for both metals and non-metals. The fundamental principles of the wear-graphy system and its wear-gram system are discussed here. A method was developed to distribute wear debris on a slide uniformly to reduce overlapping of wear debris while smearing. The composition identification ana-lyzes the wear debris using the scanning electron microscope (SEM) energy spectrum, infrared-thermal im-aging and X-ray imaging technology. A wear debris analysis system based on database techniques is demon-strated, and a visible digitized wear-gram is acquired based on the information of wear debris with image collection and processing of the wear debris. The method gives the morphological characteristics of the wear debris, material composition identification of the wear debris, intelligent recognition of the wear debris, and storage and management of wear debris information.