The stress development and relaxation of magnetron sputtered copper and amorphous-silicon (a-Si) films at room temperature are studied. Samples were prepared as a function of pressure and deposition power. In-situ stress measurements with the wafer curvature method were made using a helium neon gas laser system with a 10mm beam splitter. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) were used to perform post-growth microstructural and surface analysis. SEM cross-section analysis was used to determine the final film thickness. Phase compositions were studied by X-ray diffraction. The growth rates of copper films decreased with increasing pressure. Copper film stress development followed a non-monotonic compressive, tensile then tensile relaxation curve. In order to investigate further the nature of the stress relaxation, stress curves both after deposition was stopped and after it is restarted were also measured. Correlations between growth rate and pressure were also observed in a-Si sputter deposition. In some contrast to what was observed for Cu deposition, stress measurement during a-Si deposition showed a trend of tensile development and relaxation at all pressures studied. In a new approach to understanding stress relaxation during film growth, an acoustic emission (AE) system is introduced to measure the AE energy during sputter deposition. Evidence shows a certain relation between the strain energy of films calculated using the measured stresses and AE energy recorded during the deposition. AE energy occurs immediately after deposition starts and follows the trend of stress development (increasing hits and energies) and relaxation (decreasing hits and energies). No further signal was detected after deposition, matching the results of stress curve measurements showing that stress magnitude after deposition stays at the same level as before deposition stopped. Results also show a lower AE energy magnitude with increasing deposition pressure.

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The thesis is motivated by the goal of doing initial investigation and experimentation for the development of Weigh-in-Motion (WIM) system using acoustic emission phenomenon. A great deal of research is going on for measuring the weight of moving vehicles. Weigh-in-motion of commercial vehicles is essential for management of freight traffic, highway infrastructure design and maintenance, and monitoring of heavy weight vehicles. The research work presents a methodology for correlating the weight of a moving vehicle with acoustic emission parameters (such as counts and energy). Furthermore, the correlation between the speed of vehicle with the acoustic emission parameters is developed. Preliminary analysis and experimentations were conducted for the study of propagation of acoustic signals in plate like structure and effect of dynamic loadings on Kaiser Effect. Initial testing revealed that there is a linear correlation between the impact force and the acoustic emission parameters. Also a polynomial regression of second order was found between the speed of vehicle and acoustic emission parameters. Road testing was conducted to investigate the correlation between weight of the vehicle and acoustic emission parameters. A linear relation was found between the weight of vehicle and acoustic emission parameters represented by counts, signal energy and absolute energy.

Laser powder bed fusion (L-PBF) is an additive manufacturing process where a heat source (such as a laser) consolidates material in powder form to build three-dimensional parts. For quality control purposes, this thesis uses real-time monitoring in L-PBF. Defects such as pores and cracks can be detected using Acoustic Emission (AE) during the powder bed selective laser melting process via the machine learning approach. This thesis investigates the performance of several Machine Learning (ML) techniques for online defect detection within the Laser Powder Bed Fusion (L- PBF) process. The goal is to improve the consistency in product quality and process reliability. The application of acoustic emission (AE) sensors to receive elastic waves during the printing process is a cost-effective way of meeting such a goal. For the first step, stainless steel 316L was produced via eight parameters. The acoustic emission signals received during the printing and data collection steps are analyzed using an AE sensor under various process parameters. Several time and frequency-domain features were extracted from data during the mining process from the AE signals. K-means clustering is employed during unsupervised learning, and a neural network approach was used for the supervised machine learning on the dataset. Data labelling is conducted for different laser powers, clustering results, and signal time durations. The results showed the potential of real-time quality monitoring using AE in the L-PBF process. Some process parameters within this project were intentionally adjusted to create three various levels of defects in H13 tool steel samples. First classes were printed with minimum defects, second classes with intentional cracks, and last classes with intentional cracks and porosities. AE signals were acquired during the samples' manufacturing process. Three different machine learning (ML) techniques were applied to analyze and interpret the data. First, using a hierarchical K-means clustering method, the data was labelled. This was followed by a supervised deep learning neural network (DL) to match acoustic signals with defect type. Second, a principal component analysis (PCA) was used to reduce the dimensionality of the data. A Gaussian Mixture Model (GMM) enabled the fast detection of defects, which is suitable for online monitoring. Third, a variational auto-encoder (VAE) approach was used to obtain a general feature of the signal, which could be used as an input for the classifier. Quality trends in AE signals collected from 316L samples were successfully detected using a supervised DL trained on the H13 tool steel dataset. The VAE approach shows a new method for detecting defects within the L-PBF processes, which would eliminate the need for model training in different materials. / Thesis / Master of Applied Science (MASc)

<p>Unexplained acoustic emission spikes have been reported to occur at the workpiece entry and exit in interrupted machining operations, most notably during grinding. These AE spikes have yet to be fully explained in terms of their cause, or interpreted to yield useful information regarding the grinding process. This thesis research hence focuses on investigating this phenomenon. What started as a curiosity-driven project has resulted in an experimental technique to measure the actual contact length between the workpiece and the grinding wheel, as well as a technique for detecting grinding burn, which are of remarkable practical significance.</p> / Master of Applied Science (MASc)

<p>Electrical discharge machining (EDM) is a non-conventional machining process in which material removal is accomplished through spark erosion between a workpiece and tool electrode. Process stability is of great importance to the productivity of the EDM process, especially in the wire EDM configuration where an unstable process could lead to wire breakage having a detrimental effect on productivity. This thesis investigates the application of acoustic emission (AE) in EDM as a process monitoring technique. AE techniques have been applied to almost all machining processes; however its benefit as applied to EDM has not been investigated yet. The AE signal from the EDM process is related to various EDM parameters including, electrical parameters, tool materials, flushing and some process modifications, such as dispersing metallic powder into the gap. Using this knowledge, the benefits of using an AE sensor for a real-time process monitoring technique have been proven.</p> / Master of Applied Science (MASc)

For the purpose of developing a new highly-sensitive and reliable fiber optical acoustic sensor capable of real-time on-line detection of acoustic emissions in power transformers, this dissertation presents the comprehensive research work on the theory, modeling, design, instrumentation, noise analysis, and performance evaluation of a diaphragm-based optical fiber acoustic (DOFIA) sensor system. The optical interference theory and the diaphragm dynamic vibration analysis form the two foundation stones of the diaphragm-based optical fiber interferomtric acoustic (DOFIA) sensor. Combining these two principles, the pressure sensitivity and frequency response of the acoustic sensor system is analyzed quantitatively, which provides guidance for the practical design for the DOFIA sensor probe and system. To meet all the technical requirements for partial discharge detection, semiconductor process technologies are applied, for the first time to our knowledge, in fabricating the micro-caved diaphragm (MCD) used for the DOFIA sensor probe. The novel controlled thermal bonding method was proposed, designed, and developed to fabricate high performance DOFIA sensor probes with excellent mechanical strength and temperature stability. In addition, the signal processing unit is designed and implemented with high gain, wide band response, and ultra low noise. A systematic noise analysis is also presented to provide a better understanding of the performance limitations of the DOFIA sensor system. Based on the system noise analysis results, optimization measures are proposed to improve the system performance. Extensive experiments, including the field testing in a power transformer, have also been conducted to systematically evaluate the performance of the instrumentation systems and the sensor probes. These results clearly demonstrated the feasibility of the developed DOFIA sensor for the detection of partial discharges inside electrical power transformers, with unique advantages of non-electrically conducting, high sensitivity, high frequency response, and immunity to the electro-magnetic interference (EMI). / Ph. D.

Acoustic emission activity resulting from plastic deformation of mild steel disks that were clamped and then pressurized from one side with either hydrogen or nitrogen was recorded and analyzed. It was found that during monotonic pressurization of disks in nitrogen gas, more cumulative counts were recorded than for similar disks pressurized in hydrogen gas. Possible signatures of the "births" of cracks were observed during hydrogen pressurization of disks that typically failed by leaking. The records of the nitrogen tests show very high energy and high count events occurring early in the deformation process. These events are believed to be the result of the breaking away of near-surface dislocations that had been pinned by nitrogen. The disks tested in nitrogen typically failed by bursting (ductile failure) while those tested in hydrogen typically failed by leaking ("brittle" failure). / M.S.

This document explains the design of the Acoustic Emission Detection and Characterization System (AEDCS). The AEDCS is capable of recording several Acoustic Emmision ( E) characteristics including AE event peak amplitude, duration, frequency, and time of occurrence. The analog and digital circuits designed to implement these functions are monitored and controlled by a microprocessor system. The microprocessor system processes and records the AE event information, and passes it to a host computer for mass storage and further manipulation. The AEDCS is the first low cost, self-contained device that records these AE characteristics in real time. The AEDCS is designed to be used in the AE testing of materials. It is well-suited for non-destructive applications that produce a relatively low AE count rate, such as AE monitoring of wooden structures. The AEDCS is also capable of recording AE event characteristics in destructive test applications. The results of using the AEDCS in a mechanical loading test application on several different types of wood are given here as well. / Master of Science

[Truncated abstract] Centrifugal pumps are the most prevalent, electrically powered rotating machines used today. Each pump is designed to deliver fluid of a given flow rate at a certain pressure. The point at which electrical energy is converted most efficiently into increased pressure is known as the Best Efficiency Point. For a variety of reasons, pumps often operate away from this point (intentionally or otherwise), which not only reduces efficiency, but also increases the likelihood of premature component failure. Acoustic emissions (AE) are high frequency elastic waves, in the range of 20-2000kHz, released when a material undergoes localised plastic deformation. Acoustic emission testing is the process of measuring and analysing these stress waves in an attempt to diagnose the nature and severity of the underlying fault. AE sensors mounted on the surface of a machine or structure also detect any stress waves generated within the fluid being transmitted through to the structure. Unfortunately, attempts to detect incipient component faults in centrifugal pumps using acoustic emission analysis have been complicated by the sensitivity of AE to a pump?s operating state. Therefore, the aim of this thesis was to determine how acoustic emission monitoring could be used to identify the hydraulic conditions within a pump. Data was collected during performance tests from a variety of small end-suction pumps and from one much larger double-suction pump. A system was developed to collect, process and analyse any number of AE features (be they related to discrete AE events, or due to the continuous background AE level) from continuously operating equipment. ... Unfortunately, results from smaller pumps were less conclusive, particularly at low flows, probably due to the relatively small changes in hydraulic energy across the range of flows, and consequent sensitivity to the testing process. However, even in these pumps consistent patterns in hit energies were observed resulting in the conclusion that low to medium flows in centrifugal pumps are typified by a very large number of very low energy (VLE) events. These decrease in number and increase in energy as flow approaches BEP and/or is reduced to very low flows. High flows above BEP are marked by an absence of these VLE events, with bursts having significantly higher energies and spread over a much greater range. Unfortunately, these VLE events are too small to affect averaged trends, indicating that further work on a suitable filter is required. vi