Adaptive Time-Frequency Distribution Based on Time-Varying Autoregressive and Its Application to Machine Fault Diagnosis

The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.     

The Prediction of Dynamic Coefficients for Tilting-Pad Journal Bearings Based on an AGA-BP Neural Network

To provide real-time dynamic coefficients of tilting-pad journal bearings( TPJBs) for the dynamic analysis of a rotor-bearing system accurately,an improved error back propagation( BP) neural network model is built in this paper.First,the samples are gained by solving the Reynolds equation with the finite differential method based on hydrodynamic lubrication theory.Secondly,the adaptive genetic algorithm( AGA) is applied to optimize the initial weights and thresholds of the BP neural network before training.Then,with a number of trial calculations,the optimum parameters for the neural network are obtained.Finally,an application case of the neural network is given as well as the results analysis.The results show that the AGA can efficiently prevent the training of the neural network from falling into a local minimum,and the AGA-BP neural network of dynamic coefficients for TPJBs built in this paper can meet the demand of engineering.     

Ultrathin flexible InGaZnO transistor for implementing multiple functions with a very small circuit footprint

There is a continuous demand to reduce the size of the devices that form a unit circuit,such as logic gates and memory,to reduce their footprint and increase device integration.In order to achieve a highly efficient circuit architecture,optimizations need to be made in terms of device processing.However,the time involved in the current reduction of device sizes according to Moore's Law has slowed down.Here,we propose a flexible transistor with ultra-thin IGZO(InGaZnO,indium-gallium-zinc-oxide)as the channel material,which not only scales down the footprints of multi-transistor logic gates but also combines the functions of the logic gates,memory,and sensors into a single cell.The transistor proposed here has an ultrathin semiconductor layer and can implement the typical functions of logic gates that conventionally have 2-6 transistors.Furthermore,it demonstrates the memory effect with a programming time as low as 5 ns.This design can also display various artificial synaptic behaviors.This new device design and structure can be adopted for the development of next-generation flexible electronics that require higher integration.     

A Modified Cellular Automaton Model for the Quantitative Prediction of Equiaxed and Columnar Dendritic Growth

Since the characteristic of dendrite is an important factor determining the performance of castings, a twodimensional cellular automaton model with decentered square algorithm is developed for quantitatively predicting the dendritic growth during solidification process. The growth kinetics of solid/liquid interface are determined by the local equilibrium composition and local actual liquid composition, and the calculation of the solid fraction increment is based on these two compositions to avoid the solution of growth velocity. In order to validate the developed model, quantitative simulations of steady-state dendritic features over a range of undercooling was performed and the results exhibited good agreement with the predictions of LGK（Liptone Glicksman-Kurz） model. Meanwhile, it is demonstrated that the proposed model can be applied to simulate multiple equiaxed dendritic growth, as well as columnar dendritic growth with or without equiaxed grain formation in directional solidification of AleC u alloys. It has been shown that the model is able to simulate the growth process of multi-dendrites with various preferential orientations and can reproduce a wide range of complex dendritic growth phenomena such as nucleation, coarsening of dendrite arms, side branching in dendritic morphologies, competitive growth as well as the interaction among surrounding dendrites.     

The GMMBS Method for Time-temperature Superposition of Viscoelastic Materials

Using the time-temperature superposition principle,the dynamic properties of viscoelastic materials can be shifted to obtain a master curve.A shifting method based on the Generalized Maxwell Model(GMMBS) ,is proposed for the time-temperature superposition process of thermo-rheological simple,linear viscoelastic materials.Experimental data points under different temperatures are all considered as a whole and expressed with one unified representation by the GMMBS,which utilizes the feature that the Generalized Maxwell Model can describe a large class of viscoelastic materials with needed accuracy.Compared with traditional overlapping window based shifting methods,the proposed constitutive model based method needn’t judge the size or existence of the overlapping window first,and computes shift factors with useful information contained in all experimental data points.The effectiveness of the proposed method is verified by simulated data,generated from published test results,with various experimental noise levels,densities of data points and sizes of overlapping windows.It has been shown that the GMMBS is robust and accurate.     

Fabrication of high-pore volume carbon nanosheets with uniform arrangement of mesopores

Carbon nanosheets with a tunable mesopore size,large pore volume,and good electronic conductivity are synthesized via a solution-chemistry approach.In this synthesis,diaminohexane and graphene oxide (GO) are used as the structural directing agents,and a silica colloid is used as a mesopores template.Diaminohexane plays a crucial role in bridging silica colloid particles and GO,as well as initiating the polymerization of benzoxazine on the surfaces of both the GO and silica,resulting in the formation of a hybrid nanosheet polymer.The carbon nanosheets have graphene embedded in them and have several spherical mesopores with a pore volume up to 3.5 cm3·g-1 on their surfaces.These nuerous accessible mesopores in the carbon layers can act as reservoirs to host a high loading of active charge-storage materials with good dispersion and a uniform particle size.Compared with active materials with wide particle-size distributions,the unique proposed configuration with confined and uniform particles exhibits superior electrochemical performance during lithiation and delithiation,especially during long cycles and at high rates.     

Design of a hexagonal air-coupled capacitive micromachined ultrasonic transducer for air parametric array

An air parametric array can generate a highly directional beam of audible sound in air,which has a wide range of applications in targeted audio delivery.Capacitive micromachined ultrasonic transducer(CMUTs)have great potential for air-coupled applications,mainly because of their low acoustic impedance.In this study,an air-coupled CMUT array is designed as an air parametric array.A hexagonal array is proposed to improve the directivity of the sound generated.A finite element model of the CMUT is established in COMSOL software to facilitate the choice of appropriate structural parameters of the CMUT cell.The CMUT array is then fabricated by a wafer bonding process with high consistency.The performances of the CMUT are tested to verify the accuracy of the finite element analysis.By optimizing the component parameters of the bias-T circuit used for driving the CMUT,DC and AC voltages can be effectively applied to the top and bottom electrodes of the CMUT to provide efficient ultrasound transmission.Finally,the prepared hexagonal array is successfully used to conduct preliminary experiments on its application as an air parametric array.     

Microstructure and Magnetic Properties of Fe-Ni Alloy Fabricated by Selective Laser Melting Fe/Ni Mixed Powders

Fe—Ni alloy,as a widely applied ferromagnetic material,is synthesized using selective laser melting(SLM).The chemical compositions and microstructure of the SLM Fe—Ni alloy are characterized by X-ray diffraction(XRD), energy dispersive X-ray spectroscopy and scanning electron microscopy.It was found that the samples exhibited fine grains with homogenous distribution when a low laser scanning velocity was used.Moreover,the magnetic properties of the samples with different laser parameters are also measured.It shows that the SLM Fe—30%Ni alloy possesses a low coercivity and high saturation magnetization.It also can be obtained that SLM is an alternative faster method to prepare soft magnetic material with complex shapes.Moreover,the magnetic properties can be influenced by the laser parameters.     

Prediction of Properties in Thermomechanically Treated Cu-Cr-Zr Alloy by an Artificial Neural Network

A supervised artificial neural network (ANN) to model the nonlinear relationship between parameters of thermomechanical treatment processes with respect to hardness and conductivity properties was proposed for Cu-Cr-Zr alloy. The improved model was developed by the Levenberg-Marquardt training algorithm. A basic repository on the domain knowledge of thermomechanical treatment processes is established via sufficient data acquisition by the network. The results showed that the ANN system is an effective way and can be successfully used to predict and analyze the properties of Cu-Cr-Zr alloy.     

Modelling a Job-Shop Plant Using Queuing Networks Techniques

This article illustrates the use of open queuing networks for the planning of discrete production systems of the job-shop type. For this purpose, it is assumed that the system can be modelled by a set of workstations. The mathematical and numerical procedures, related to the definition of the model that is used to characterize the system operations, are analyzed first. For this purpose, an ideal situation of a system with only two workstations and four products is used. Next, the application of the model to a real case study, corresponding to a metalworking company, is illustrated. In this case, the parameters that can be used to characterize the efficiency of the system are also introduced. The performance diagnostics involves the analysis of the work-in-process and the production lead-times. The optimization of the system involves the probabilistic characterization of the demand and the characterization of the probability of transference of products among workstations. Among other alternatives, a waiting-cost function, which is evaluated using the queuing parameters, can be used to compare alternative solutions.