Recent developments in material joining, specifically arc-welding, have increased in scope and extended into the aerospace, nuclear, and underwater industries where complex geometry and hazardous environments necessitate fully automated systems. Even traditional applications of arc welding such as off-highway and automotive manufacturing have increased their demand in quality, accuracy, and volume to stay competitive. These requirements often exceed both skill and endurance capacities of human welders. As a result, improvements in process parameter feedback and sensing are necessary to successfully achieve a closed-loop control of such processes. <br ><br /> One such feedback parameter in gas-metal arc welding (GMAW) is acoustic emissions. Although there have been relatively few studies performed in this area, it is agreed amongst professional welders that the sound from an arc is critical to their ability to control the process. Investigations that have been performed however, have been met with mixed success due to extraneous background noises or inadequate evaluation of the signal spectral content. However, if it were possible to identify the salient or characterizing aspects of the signal, these drawbacks may be overcome. <br ><br /> The goal of this thesis is to develop methods which characterize the arc-acoustic signal such that a relationship can be drawn between welding parameters and acoustic spectral characteristics. Three methods were attempted including: Taguchi experiments to reveal trends between weld process parameters and the acoustic signal; psycho-acoustic experiments that investigate expert welder reliance on arc-sounds, and implementation of an artificial neural network (ANN) for mapping arc-acoustic spectral characteristics to process parameters. <br ><br /> Together, these investigations revealed strong correlation between welding voltage and arc-acoustics. The psycho-acoustic experiments confirm the suspicion of welder reliance on arc-acoustics as well as potential spectral candidates necessary to spray-transfer control during GMA welding. ANN performance shows promise in the approach and confirmation of the ANN?s ability to learn. Further experimentation and data gathering to enrich the learning data-base will be necessary to apply artificial intelligence such as artificial neural networks to such a stochastic and non-linear relationship between arc-sound and GMA parameters.

Recent developments in material joining, specifically arc-welding, have increased in scope and extended into the aerospace, nuclear, and underwater industries where complex geometry and hazardous environments necessitate fully automated systems. Even traditional applications of arc welding such as off-highway and automotive manufacturing have increased their demand in quality, accuracy, and volume to stay competitive. These requirements often exceed both skill and endurance capacities of human welders. As a result, improvements in process parameter feedback and sensing are necessary to successfully achieve a closed-loop control of such processes. <br ><br /> One such feedback parameter in gas-metal arc welding (GMAW) is acoustic emissions. Although there have been relatively few studies performed in this area, it is agreed amongst professional welders that the sound from an arc is critical to their ability to control the process. Investigations that have been performed however, have been met with mixed success due to extraneous background noises or inadequate evaluation of the signal spectral content. However, if it were possible to identify the salient or characterizing aspects of the signal, these drawbacks may be overcome. <br ><br /> The goal of this thesis is to develop methods which characterize the arc-acoustic signal such that a relationship can be drawn between welding parameters and acoustic spectral characteristics. Three methods were attempted including: Taguchi experiments to reveal trends between weld process parameters and the acoustic signal; psycho-acoustic experiments that investigate expert welder reliance on arc-sounds, and implementation of an artificial neural network (ANN) for mapping arc-acoustic spectral characteristics to process parameters. <br ><br /> Together, these investigations revealed strong correlation between welding voltage and arc-acoustics. The psycho-acoustic experiments confirm the suspicion of welder reliance on arc-acoustics as well as potential spectral candidates necessary to spray-transfer control during GMA welding. ANN performance shows promise in the approach and confirmation of the ANN?s ability to learn. Further experimentation and data gathering to enrich the learning data-base will be necessary to apply artificial intelligence such as artificial neural networks to such a stochastic and non-linear relationship between arc-sound and GMA parameters.

Diagnostic ultrasound (US) is safer, quicker and cheaper than other diagnostic imaging modalities. Over the past two decades, the applications of US imaging has been widened due to the development of injectable, compressible and encapsulated microbubbles (MBs) that provide an opportunity to improve conventional echocardiographic imaging, blood flow assessment and molecular imaging. The encapsulating material is manufactured by different biocompatible materials such as proteins, lipids or polymers. In current research, researchers modify the encapsulated shell with the help of advanced molecular chemistry techniques to load them with dyes (for fluorescent imaging), nanoparticles and radioisotopes (for multimodal imaging) or functional ligands or therapeutic gases (for local drug delivery). The echogenicity and the radial oscillation of MBs is the result of their compressibility, which undoubtedly varies with the encapsulated shell characteristics such as rigidity or elasticity. In this thesis, we present acoustic properties of novel type of polyvinyl alcohol (PVA)-shelled microbubble (PVA-MB) that was further modified with superparamagnetic iron oxide nanoparticles (SPIONs) to work as a dual-modal contrast agent for magnetic resonance (MR) imaging along with US imaging. Apparently, the shell modification changes their mechanical characteristics, which affects their acoustic properties. The overall objective of the thesis is to investigate the acoustic properties of modified and unmodified PVA-MBs at different ultrasound parameters. The acoustic and mechanical characterization of SPIONs modified PVA-MBs revealed that the acoustical response depends on the SPION inclusion strategy. However they retain the same structural characteristics after the modification. The modified MBs with SPIONs included on the surface of the PVA shell exhibit a soft-shelled behavior and produce a higher echogenicity than the MBs with the SPIONs inside the PVA shell. The fracturing mechanism of the unmodified PVA-MBs was identified to be different from the other fracturing mechanisms of conventional MBs. With the interaction of high-pressure bursts, the air gas core is squeezed out through small punctures in the PVA shell. During the fracturing, the PVA-MBs exhibit asymmetric (other modes) oscillations, resulting in sub- and ultra-harmonic generation. Exploiting the US imaging at the other modes of the oscillation of the PVA-MBs would provide an opportunity to visualize very low concentrations of (down to single) PVA-MBs. We further introduced the PVA-MBs along with particles mimicking red blood cells in an acoustic standing-wave field to observe the acoustic radiation force effect. We observed that the compressible PVA-MBs drawn toward pressure antinode while the solid blood phantoms moved toward the pressure node. This acoustic separation method (acoustophoresis) could be an efficient tool for studying the bioclearance of the PVA-MBs in the body, either by collecting blood samples (in-vitro) or by using the extracorporeal medical procedure (ex-vivo) at different organs. Overall, this work contributes significant feedback for chemists (to optimize the nanoparticle inclusion) and imaging groups (to develop new imaging sequences), and the positive findings pave new paths and provide triggers to engage in further research. / <p>QC 20150827</p> / 3MiCRON

Hand transmitted vibration (HTV) is one of the most common hazards faced by workers in the construction industry. A major source of HTV is hand held percussion tools, such as pneumatically driven chipping hammers and rock drills. This thesis presents a new approach to measuring the vibration from these tools using an experimental hand arm model to which the tools are attached. The experimental hand-arm model has been designed to have similar dynamic characteristics to that of a human hand-arm system. This approach addresses the issue of repeatability as HTV measurements suffer from variability between cases. The measured acceleration of the hand-arm system is in range or close to range of the measured accelerations of the test subjects with superior repeatability. Further, the thesis presents a nonlinear numerical model of a pneumatic impact hammer. Fundamentally, the numerical model was made up of two different sub-models, 1) a fluid flow model and 2) a structural dynamic model. The fluid flow model was based on the equations for mass flow rate of air though a bleed orifice assuming an isentropic process. The second sub-model deals with modeling the structural components of the impact hammer consisting of the major hammer like the center body, handle, piston and chisel as well as the human hand and the ground. Time domain simulations of the hammer were carried out by using a state space formulation to get displacements, velocities and accelerations of the each component as well as the exhaust jet velocities. Experiments were carried out to measure the handle response and exhaust jet velocities as well as pressure profiles. The results obtained from the numerical model were then validated using these experimental results. Finally, a parametric study using the numerical model was carried out to explore different vibration control techniques. / Master of Science

Pneumatic percussion tools are extensively used in the construction industry. They are one of the noisiest machines in the construction industry generating noise levels above 110 dBA which are well beyond the permissible exposure limit (PEL) of 85 dBA. This work presents a comprehensive methodology for the acoustic characterization and noise source identification of these percussion tools. The methodology is applied to a representative pneumatic tool and the characterization results are described in detail. A mechanical analysis was performed on a chipping hammer finding mode shapes and natural frequencies of individual components. The mechanical analysis included modal hammer measurements and creating FE models. Fluid measurements were performed on the chipping hammer to find the velocity of the exhaust and pressure in the upper and lower chambers. The fluid tests found that the velocity of the exhaust is approximately Mach 1.0 or greater. Noise measurements were carried out on the chipping hammer to determine the spectral characteristics, overall sound power level, and spatial source strength maps of the tool. A spherical array of microphones was used to obtain an accurate estimate of the overall sound power levels and the directivity. The overall sound power radiation was found to be in the range of 110-115dBA. An advanced 63 microphone phased array was used to successfully locate and identify the major sources of noise from this tool via the use of beam-forming maps. This thesis also presents a preliminary noise control method employing commercial-off-the-shelf pneumatic silencers. The outcome of the tests is illustrated in detail in this thesis. / Master of Science

The use of ultrasound contrast agents increases the information available for reconstruction during ultrasound imaging. Previously studied microbubbles, consisting of a gas core, are subject to limitations such as a short lifetime and a large size. Droplets with a liquid perfluorocarbon core that is stabilized by cellulose nanofibers eliminate these drawbacks, but require further investigation. By studying the frequency-dependent attenuation profile of the cellulose nanofiber coated perfluorocarbon droplets within an ultrasound field, information about the droplets as oscillators can be retrieved, enabling characterization of their physical properties. In this study, the frequency-dependent attenuation profile was experimentally acquired and compared between two concentrations, using flat transducers covering the frequency range of 1-15 MHz. The data collected in the time domain was processed and transformed into the frequency domain and the attenuation was calculated across the entire frequency range. Among the frequencies studied, the attenuation increases with frequency and covers the range of approximately 0.25-8.3 dB/cm and 0.01-3.3 dB/cm at the concentrations of 50 million droplets/ml and 10 million droplets/ml respectively. The attenuation reaches a minimum at 3 MHz within the highest concentration, compared to 4.43 MHz within the lowest. The increase of the attenuation with frequency is explained by the droplets not exhibiting large oscillations within the range covered. It is probable that the droplets do exhibit oscillations, due to a viscosity lower than that of water, but a resonance frequency is not found within the spectrum studied. This could be explained by a shell elasticity or a small droplet radius placing the resonance frequency outside of the spectrum studied, or high levels of damping broadening the resonance peak. Localizing the resonance frequency would enable characterization of these physical properties of the cellulose nanofiber shell as well as the perfluorocarbon liquid core of the droplets. The increase of the attenuation with frequency demonstrates that the droplets do not produce a maximized amount of scattering at a specific frequency within the range studied, which is observed among other oscillating particles implemented as ultrasound contrast agents. The attenuation is, however, larger than that of blood across all frequencies except for those among which the attenuation reaches its minimum. Potential errors that are affecting the results include droplet vaporization, the formation of flocs after the mechanical agitation has ceased, the experimental setup, the settings on the pulse generator, the sensitivity of the transducers and the processing code.

Today, microbubbles are one of the most commonly used ultrasound contrast agents, since their high compressibility results in a strongly scattered signal. Despite this advantage, microbubbles experience limitations by the decreased stability and large diameter. The cellulose nanofiber (CNF) stabilized perfluoropentane (PFC5) droplets have the possibility of eliminating these drawbacks. In order to examine the droplet behavior and scattering ability when exposed to ultrasound, the acoustic response of the droplets is studied and compared with that of microbubbles (MBs). Therefore, this thesis aims to design an experimental set-up and a processing method to determine the phase velocity, bulk modulus and compressibility of the CNF-coated PFC5 droplets. The experimental study of the acoustic characterization uses pulse-echo spectroscopy with an aluminum reflector and seven flat transducers covering the frequency range 0.7 to 14.1 MHz. By using fast Fourier transform, while accounting for the 2πn ambiguity, the phase velocity profiles are obtained. The dispersions within this frequency spectrum are 1391-1487 m/s and 1387-1488 m/s for the concentrations 10 ∙ 106 and 50 ∙ 106 droplets/ml, respectively. These profiles display an increasing phase velocity with frequency and a slight increase in dispersion with concentration. These results agree with theory and studies examining the phase velocity of MBs. The bulk modulus presents values between 3-4 GPa, while the compressibility is 2.7 − 3.2 ∙ 10-10 𝑃𝑎-1 within the frequency range studied. Compared to water and certain MBs, both possessing a lower bulk modulus, the droplets are less compressible. To conclude, the droplets have similar phase velocity profiles with the same dependencies on frequency and concentration as MBs, resulting in similar behavior of these droplets when exposed to ultrasound. Hence, affecting the wave similarly to MBs in terms of spreading. The droplet are, however, not as compressible. This most likely affects their oscillation and they, hence, might not have equally beneficial scattering ability. This could reduce their utilization as contrast agents. Some of the potential error sources present during the laboratory work and the development of the post-processing code were not achieving perfect optimization of the transducer alignment, vaporization of the droplets resulting in reduced concentration, possible diffraction, not optimal processing of data and inadequate correction for 2πn ambiguity.

A road can be considered as a noise source based on its traffic density. Intuition says, a road, with closely spaced vehicles, can be classified as a line source and for sparsely spaced vehicles, a collection of point sources. This study deals with the classification of a stretch of highway into either a line source or a collection of point sources based on the sound pressure measured by 7 microphones placed at certain distances from the highway and on the law of attenuation applicable. The results indicate a strong dependence of source classification with the traffic density. / En väg kan betraktas som en bullerkälla baserat på dess trafikintensitet. Intuitivt bör en väg med hög trafikintensitet klassificeras som en linjekälla och för få fordon en samling punktkällor. Denna studie handlar om klassificeringen av en motorvägssträcka till antingen en linjekälla eller en samling punktkällor baserat på ljudtrycket uppmätt med sju mikrofoner placerade på varierande avstånd från motorvägen. Resultaten visar att källklassificeringen är beroende av trafiktätheten och av avståndet till vägen.