Papers by Author: Guang Lan Liao

Abstract: In order to enhance the application of bulk metallic glass (BMG) as engineer material, it is necessary to develop appropriate bonding technology to solve the problems of size limitation and weldability. In this work, a friction welding set-up was constructed, and the Zr₄₁Ti₁₄Cu_12.5Ni₁₀Be_22.5 BMG rods were joined. The joint interface zone was examined by X-ray diffraction, Scanning electron microscope, Vickers Micro-hardness and Transmission electron microscope. The results showed that the BMG rods were successfully joined, where no crystallization and visible defects were observed. The welding joint maintained the amorphous structure except few nanocrystallines occurred. Then the temperature field simulation was executed using ANSYS finite element software to optimize the welding parameters. It indicated that friction time cannot exceed 0.25s under the given experiment conditions, otherwise the crystallization would occurred, which is in good agreement with the experiment. It is concluded that the temperature field simulation can be used to guide the experiment and the friction welding can be used to join the BMG.

Abstract: Flip chip technology is one of the fastest growing segments of microelectronics packaging because of its ability to satisfy the increasing demands of high input/output density, package miniaturization, and reduced cost. A critical element in the successful application of flip chip technology is the reliability of solder bumps. In this paper, a nondestructive inspection method combining ultrasonic excitation with modal analysis is proposed for flip-chip solder bump defect detection. The signal generator and power amplifier are utilized to drive the capacitive air-coupled ultrasonic transducer to produce continuous ultrasonic waves for exciting the test chips. The vibration velocities of the chips are measured by the laser scanning vibrometer to extract the modal shapes and resonance frequencies. The results prove that the defective chips can be distinguished from the good chip by the modal shapes, and the resonance frequencies of the chips decrease with the increase of the open solder bumps. Therefore, this detection method may provide a new path for the improvement and innovation of flip chip on-line inspection systems.

Abstract: The influence of oxidation behavior on super-plastic microforming of bulk metallic glass Zr₆₅Cu_17.5Ni₁₀Al_7.5 in the super-cooled liquid region was investigated. Samples were heated in air from room temperature to 395°C, 410°C, and 430°C, respectively, and kept under each temperature for 40 minutes. The increased weight of samples and the thickness of oxide layer were measured. Subsequently, the sample was compressed under 410°C with a micro gear silicon mold. In result, the oxide layer of the gear cracked and could be easily removed; also, the X-ray diffractometer showed that the gear core below the oxide layer remained an amorphous structure. It can be concluded that the oxidation behavior of Zr₆₅Cu_17.5Ni₁₀Al_7.5 does not affect the super-plastic deformation, which indicates the feasibility of super-plastic microforming process in air.

Abstract: Nano-structures on the wing of Morpho butterflies generate bright blue color, and this color is sensitive to ambient gas, or more specifically, the refractive index of ambient gas. It was found that even slight change of the refractive index can lead to obvious change of the color. Such phenomenon has caught much attention and was employed as a sensing principle for detecting gas. In the present study, a typical nano-structure on the wing of Morpho butterflies is mimicked and simplified for constructing a refractive index based gas sensor. Moreover, partial derivative of the optical reflection efficiency with respect to the refractive index of ambient gas, i.e., sensitivity of the sensor, is utilized based on the rigorous coupled-wave analysis (RCWA) method. Finally, the effects of the nano-structure’s shape on the partial derivative are analyzed. The results can be applied to the design of the bioinspired refractive index based gas sensor.

Abstract: Multiphysics numerical simulation (MPNS) has certainly acquired a wide acceptance in the modeling, designing and fabrication fields and has been validated for various research applications. But it is important that the method should be thoroughly understood by students of the various fields. Keeping this aim in view, we demonstrate here the MPNS process as applied in several fields, such as: electrostatics, mechanics, chemistry, heat transfer and fluid flow. Four tasks are performed for fifteen hours in order to analyze simulation approaches such as modeling with geometrical parameters, meshing, solution and post-processing methods. Convection, buoyancy effects, microfiltration and flow analysis are investigated. This comprehensive study will enhance appreciation of the basic concepts of design and dimensioning of an object in MEMS for engineers, and help to guide workers in these fields when performing these tasks.

Abstract: Carbon-MEMS (C-MEMS) have emerged as a new category of devices for micro/nano technology with many potential applications. Dielectrophoretic manipulation of micro/nanoparticles with C-MEMS is studied in this paper. Through electric field distribution modeling in carbon electrode array, we analyze the strongest simulation effect results of electric field in three dimensional (3-D) surface plots depicting the magnitude of electric field in various cross sections at different heights above the channel floor for 2, 10, 30 and 50 μm high carbon electrodes. It is represented here that maximum intensity of electric field generates with the equality between the height above the channel floor and the height of the electrodes. Simulation parameters involved are for dielectrophoretic manipulation of micro/nano particles based on 3-D C-MEMS. The advantages of using 3-D C-MEMS electrodes over other techniques of creating high-throughput systems for dielectrophoretic manipulation environment surrounded by micro/nano horizons are: (i) complex microscale 3-D electrodes with high-aspect ratios can easily be shaped and patterned using conventional lithography (ii) carbon has a high window of stability thus allowing application of higher voltages (iii) there is no need for bulk micromachining or patterning electrodes on multiple planes (iv) the distance between electrodes can precisely be controlled through the lithography process. FEMLAB 3.4 Multiphysics Modeling software (COMSOL, Stockholm, Sweden) is used for the modeling of electric fields and one-layer C-MEMS microelectrode array was fabricated with SU-8 photoresist.

Abstract: In the fault diagnosis of a machine, frequencies of its vibration are important indicators to show conditions of the machine. There are two main categories of methods to estimate frequency. One is based on the fast Fourier transform, and the other is on the signal subspace decomposition. Using FFT directly to estimate frequency may introduce larger estimation error, several approaches are proposed to correct or decrease the error, which comprise phase difference, energy centrobaric, interpolation and search method. The signal subspace decomposition method (SSDM) consists of Pisarenko harmonic decomposition, multiple signal classification. In order to assess the performance of these methods, the Cramer-Rao bound is used to compare with the error variance of difference frequency estimation methods, and simulations are based on Monte Carlo experiments for various record sizes and signal-to-noise ratios (SNR’s). The results show that there is a turning point about 25 dB for FFT based methods, above which FFT based methods are less sensitive to the noise, and SSDM achieves higher precision estimation at higher SNR and for the short time series, but produces poor accuracy at lower SNR’s.

Abstract: Machine fault diagnosis is essentially an issue of pattern recognition, which heavily depends on suitable unsupervised learning method. The Self-Organizing Map (SOM), a popular unsupervised neural network, has been used for failure detection but with two limitations: needing predefined static architecture and lacking ability for the representation of hierarchical relations in the data. This paper presents a novel study on failure detection of gearbox using the Growing Hierarchical Self-Organizing Map (GHSOM), an artificial neural network model with hierarchical architecture composed of independent growing SOMs. The GHSOM can adapt its architecture during unsupervised training process and provide a global orientation in the individual layers of the hierarchy; hence the original data structure can be described correctly for machine faults diagnosis. Gearbox vibration signals measured under different operating conditions are analyzed using the proposed technique. The results prove that the hierarchical relations in the gearbox failure data can be intuitively represented, and inherent structure can be unfolded. Then gearbox operating conditions including normal, tooth cracked and tooth broken are classified and recognized clearly. The study confirms that GHSOM is very useful and effective for pattern recognition in mechanical fault diagnosis, and provides a good potential for application in practice.

Abstract: A general-purpose useful parameter in data analysis is the intrinsic dimension of a data set, corresponding to the minimum number of variables necessary to describe the data without significant loss of information. Feature extraction, including linear or nonlinear mapping technique, is efficient to estimate the intrinsic dimension of the data set, which is a key issue to machine fault diagnosis. This paper presents a novel application of feature extraction using the nonlinear mapping technique called curvilinear component analysis (CCA) for gear failure detection. In the approach high-dimensional data are nonlinearly projected toward an output space with dimension equal to the intrinsic dimension. Hence, enough information is remained to describe correctly the original data structure, and feature extraction based on CCA reduces dimensionality of the raw feature space for machine failure detection. Gearbox vibration signals measured under different operating conditions are analyzed using the technique. The results indicate that the intrinsic dimension of the data set is estimated and a 2-D subspace is extracted by the CCA technique, then the high-dimensional original feature data are projected into the 2-D space and form several clustering regions, each indicative of a specific gear condition, respectively. Thus, the gear operating conditions including normal, one cracked tooth, and one broken tooth are classified and detected clearly. It confirms that feature extraction based on the nonlinear mapping is very useful and effective for pattern recognition in mechanical fault diagnosis, and provides a good potential for applications in practice.

Abstract: This paper presents a study of KDA(kernel discriminant analysis) in gearbox failure feature extraction and classification. Experimental gearbox vibration signals measured from normal, gear small spall, gear severe spall and gear wear operating conditions are analyzed using either KPCA(kernel principal component analysis) or KDA as the feature extraction and fault classification methods. Experiment results indicate the effectiveness and the
superiority of KDA for gear fault classification over KPCA.