Guided Acoustic Phonon Modes in Layered Anisotropic Nanowires

Acoustic phonons play a critical role in energy transport in nanostructures. The dispersion of acoustic phonons strongly influences thermal conductivity. Recent observations show lower values of thermal conductivity in finite dimensional nanostructures than in the bulk material. In this work, we will present results for guided acoustic phonon modes in (a) a bilayered GaAs-Nb nanowire of rectangular cross section and (b) a trapezoidal Si nanowire. The former has been used for phonon counting in a nanocalorimeter for measuring thermal conductivity and the latter is commonly used in MEMS applications. A semi-analytical finite element (SAFE) analysis technique has been used to investigate the effects of layering, anisotropy, and boundaries on the dispersion of modes of propagation. Many interesting features of group velocities are found that show confinements around the corners, in the low velocity layer, and coupling of the longitudinal and flexural modes. These would strongly influence thermal conductivity and might provide means of nondestrutive evaluation of mechanical properties.

Classification of Corrosion Detected by Acoustic Emission

This paper presents an Acoustic Emission (AE) to detect pitting corrosion in stainless steel. The AE signals were analyzed to reveal the correlation between AE parameters and severity levels of pitting corrosion in austenitic stainless steel 304 (SS304). In this work, the corrosion severity is graded roughly into five levels based on the depth of corrosion. Relationships between a number of time-domain AE parameters and the corrosion severity were first studied and key parameters identified. The corrosion severity was also categorized into three stages: initial, propagation and final stages based on the source mechanisms of the AE signals. We identified these stages from the frequency-domain characteristic of the AE signal and the visual characteristic of the corroded pits in each level of corrosion severity. A number of measures were employed to quantify such characteristics and the source mechanisms hypothesized. To demonstrate the usefulness of such parameters, a feed-forward neural network was used to classify the corrosion severity. Preprocessing and verification techniques were provided to facilitate and to maintain the generalization capability of the network. The classification performance is excellent and demonstrates that the AE technique and a neural network can be efficiently used to detect and monitor the occurrence of corrosion as well as to classify the corrosion severity.

The Use of Blind-Source-Separation Algorithm for Mechanical Signal Separation and Machine Fault Diagnosis

Vibration based machine fault diagnosis is widely adopted in machine condition monitoring. Since a machine is usually composed of many mechanical components, during the machine running, each component will generate its vibration and transmit to other components thru the shaft or linkages. Hence, the vibration signal collected from a sensor is the aggregation of all generated vibrations. To enhance the accuracy in vibration based machine fault diagnosis, the vibration generated by each component must be isolated and identified. In this paper, the performance of blind-source-separation (BSS) in separating various mixed sources is discussed. The BSS based method of second order statistics (SOS) has been applied to separate the aggregated vibration signals generated from a number of mechanical components. To verify the effectiveness of the BSS based SOS, a number of experiments were conducted using both simulated data and vibration generated form the industrial machines. The results show that the BSS possesses the ability to separate both artificially and naturally mixed signals. Such ability is definitely welcome in the fields of condition monitoring and maintenance. Moreover, the paper also discusses the advantages and disadvantages of the algorithm in the applications of machine fault diagnosis and future improvements.

Modeling the Effect of Surface Roughness on the Adhesion, Contact and Friction in Micro-Electro-Mechanical Systems (MEMS) Interfaces

It has been experimentally shown that surface texturing (roughening) decreases the effect of intermolecular adhesion forces that are significant in MEMS applications. These forces can hinder normal operation of sensors and actuators as well as micro-engines where they might increase friction, which could be catastrophic. In this paper, a model that predicts the effects of roughness, asymmetry, and flatness on the adhesion, contact, and friction forces in MEMS interfaces is presented. The three key parameters used to characterize the roughness the asymmetry and the flatness of a surface topography are the root-mean-square roughness (RMS), skewness and kurtosis, respectively. It is predicted that surfaces with high RMS, high kurtosis and positive skewness exhibit lower adhesion and static friction coefficient, even at extremely low external normal forces.

Methodologies for Damage Identification in MEMS Structures

While significant research is focused on the design and fabrication of the MEMS devices, studies on in-service failure evolution and the consequences of failure/damage are limited. Long term embedding and reliable performance of MEMS devices in systems require understanding of failure evolution and recalibration/retuning of the devices. The objective of this research is to study the effects of micro-mechanical damage on MEMS device response and develop techniques for monitoring device failure. This work entails the development and validation of models for simulating device performance and emulating the response of damaged devices. A device level to system level hierarchical methodology is developed to simulate the electro-mechanical operation of MEMS devices. The methodology is illustrated using MEMS-based accelerometer as an example. The response of undamaged devices and devices with a damaged mechanical stage can be simulated using this method. The emulation of a damaged device is carried out using a special finite element developed to represent cracks in the mechanical stage of the MEMS device. An inverse damage detection methodology is formulated using the emulated device response, a local optimization technique and the response from a physical device which enables detection and localization of damage. The damage detection method can establish, while in operation, the extent and the location of damage in the MEMS device. Experimentation with a commercial MEMS accelerometer is performed to measure the characteristics of a physical device and the effectiveness of the damage detection method. Some correlations with experiments are presented. The impact of this research is on improved device reliability and robustness as well as self-calibration features of MEMS devices.

Ultrasonic Testing for Vulcanization Inspection of Natural Rubber

This paper presents a novel method to inspect the levels of vulcanization of natural rubber by ultrasonic testing. In the research, test specimens are natural rubbers of type ‘cis-1,4-polyisoprene’ vulcanized at different durations. Piezoelectric contact transducers were used to transmit and receive ultrasonic to/from each sample. Longitudinal waves at nominal frequency of 2, 2.25 and 5 MHz were used to investigate the samples. Experiments were conducted using two techniques: pulse echo and through transmission. The pulse echo technique was applied to inspect the ultrasonic attenuation and the velocity of longitudinal wave of natural rubber. The through transmission technique was implemented to identify the shape and amplitude of the frequency spectrum. The results from both time and frequency domains can be employed to classify the levels of the vulcanization. From the experiments, this ultrasonic testing provides an effective way to inspect the vulcanization of natural rubber with repeatability and allows faster inspection rate than other methods.

Built-In Inspection Using Mechanical Energy Converting Elements

The paper presents a conversion model of the mechanical energy into the electrical one and shows that autonomic diagnostic devices can be developed on that basis. Built-in inspection uses these devices working on the conversion of the mechanical energy of vibration, applying for this reason piezoelectric matrices connected with the mechanical system, which are investigated theoretically and experimentally. It is shown that converters made of piezoelectric ceramics or film can work reliably enough by being placed directly in the objects to be inspected. Relative precision and abilities of the energy conversion for monitoring and diagnostic problem can be evaluated by simulating the dynamics of a general model according to the technical state of a mechanical system. It is suggested a concrete solution for the technical state monitoring and cracks diagnostics of constructive elements in different mechanical systems.

Detecting Plate Damage by Using Wavelets

The detection of damage prior to the degradation of structural parameters of plates is investigated using an algorithm based on the continuous wavelet transform. Plates are examined because they are one of the basic building blocks for many structures, such as naval ships. A representative plate geometry is evaluated using finite element analysis. Damage to connecting joints, stiffeners, cracks, and plate inconsistencies is modeled and the ability of a wavelet based algorithm to detect and locate is evaluated. The plate is subjected to dynamic loads, such as impulse and oscillatory. From the finite element results, time histories are extracted to simulate in-plane strain sensor data. The wavelet algorithm is applied to the strain data.

Fabrication of a Micro-Opto-Mechanical Accelerometer Based on Intensity Modulation

Accelerometers are most frequently used to monitor machining states, and are therefore crucial for automated and unmanned plant operations. In such a harsh environment, micro-accelerometers based on optical methods can be effective. This paper presents a new type of micro-opto-mechanical accelerometer that was developed using a combination of new technologies, such as deep reactive ion etching (DRIE), micro-stereolithography, and intensity modulation. The advantages of intensity modulation include the simplicity of the detection principle and the lack of a requirement for a high-quality light source. This paper reports the design of two types of micro-accelerometer using the finite element method. Experiments showed that the fabricated micro-accelerometers had resonant frequencies of approximately 2 and 10 kHz, with suitable linear ranges and sensitivities. The developed micro-opto-mechanical accelerometers can thus be used for various practical purposes, including machining state monitoring in automated and unmanned plant operations.

Delamination Failure of Laminated Fabric Composites Under Various Thermal Cycles

Edge delamination test was often used to screen the material system with unindirectional fiber reinforced composites for damage tolerance structural design. No standard tests were established for fabric composites. One of the objectives of this study was to employ this method to fabric composites and to investigate feasibility of using this method for material screening. Laminates with E-glass 7781 fabric impregnated by three different matrices, namely SI-BUI, SI-ZG-5A, and RS-E9 resins were tested to investigate their delamination and ultimate strength. It was observed that the strain level at the onset of stiffness change decreases as the thermal cycles increase for the laminates with a given matrix system. No significant delamination was observed for all of the coupons except the laminates with SI-BUI matrix under no thermal cycle. However, these laminates give the highest ultimate strength among all of the test coupons. Laminates with RS-E9 resin, cured by electronic beam, have the lowest strength among all of the test coupons. All of the laminates under thermal cycles result in strength reduction. Fracture surfaces of the laminates were also investigated for their failure modes. It is suggested that for fabric composites, this edge delamination test is not a viable material screening method for determining damage tolerance property.