Sheet metal surfaces with pin-fin features have potential fluid and thermal applications. Extrusion forging process and extrusion rolling process can be used to create such surface features on sheet metals. Extrusion forging process is a metal forming process that combines extrusion and forging into one operation. In extrusion forging, the pin-fin surface feature is created by compressing the work-piece using a punch with designed cavities. Experiments and numerical analysis were conducted to investigate the effects of tooling geometries, material properties, work-piece thickness, thickness reduction ratio and friction on the deformation behavior of sheet metals. It was found that increasing fillet radius of the orifice results in decrease in compressive force and boss height. As the negative draft angle increases, the compressive force and boss height decrease. Higher yield strength and higher friction lead to higher compressive force. The boss height is not significantly affected by the friction between the tooling and the work-piece. Due to limitation in force capacity, it may not be feasible to apply extrusion forging technique to generate surface features on large surface area. As such, the extrusion rolling process is proposed. In extrusion rolling, the pin-fin feature is created by compressing the strip using a pair of rolls. The upper roll is manufactured with surface cavities. Finite element method are employed to investigate the effects of rolling speed, thickness reduction ratio, roll diameter and front tension force on the deformation behavior of metal strip. It was found that the rolling speed has little influence on the roll force and boss height. The front tension force has little effect on the average pressure and boss height. Increase the roll diameter results in in roll force increase. This research investigated the effect of parameters on the deformation behavior during the extrusion forging and extrusion rolling processes. The research generated the knowledge needed for design and manufacture of micro/meso surface features on thin metal sheet. Moreover, this thesis presents a novel bulk forming process on metal sheets, which can have significant impacts in industrial practice.

Bearing failure is one of the foremost causes of breakdown in rotating machinery. Such failure can be catastrophic and can result in costly downtime. Bearing condition monitoring has thus played an important role in machine maintenance. In condition monitoring, the observed signal at a measurement point is often corrupted by extraneous noise during the transmission process. It is important to detect incipient faults in advance before catastrophic failure occurs. In condition monitoring, the early detection of incipient bearing signal is often made difficult due to its corruption by background vibration (noise). Numerous advanced signal processing techniques have been developed to detect defective bearing signals but with varying degree of success because they require a high Signal to Noise Ratio (SNR), and the fault components need to be larger than the background noise. Vibration analyses in the time and frequency domains are commonly used to detect machinery failure, but these methods require a relatively high SNR. Hence, it is essential to minimize the noise component in the observed signal before post processing is conducted. In this research, detection of failure in rolling element bearing faults by vibration analysis is investigated. The expected time intervals between the impacts of faulty bearing components signals are analysed using the blind deconvolution technique as a feature extraction technique to recover the source signal. Blind deconvolution refers to the process of learning the inverse of an unknown channel and applying it to the observed signal to recover the source signal of a damaged bearing. The estimation time period between the impacts is improved by using the technique and consequently provides a better approach to identify a damaged bearing. The procedure to obtain the optimum inverse equalizer filter is addressed to provide the filter parameters for the blind deconvolution process. The efficiency and robustness of the proposed algorithm is assessed initially using different kinds of corrupting noises. The result show that the proposed algorithm works well with simulated corrupting periodic noises. This research also shows that blind deconvolution behaves as a notch filter to remove the noise components. This research involves the application of blind deconvolution technique with optimum equalizer design for improving the SNR for the detection of damaged rolling element bearings. The filter length of the blind equalizer needs to be adjusted continuously due to different operating conditions, size and structure of the machines. To determine the optimum filter length a simulation test was conducted with a pre-recorded bearing signal (source) and corrupted with varying magnitude noise. From the output, the modified Crest Factor (CF) and Arithmetic Mean (AM) of the recovered signal can be plotted versus the filter length. The optimum filter length can be selected by observation when the plot converges close to the pre-determined source feature value. The filter length is selected based on the CF and AM plots, and these values are stored in a data training set for optimum determination of filter length using neural network. A pre-trained neural network is designed to train the behaviour of the system to target the optimum filter length. The performance of the blind deconvolution technique was assessed based on kurtosis values. The capability of blind deconvolution with optimum filter length developed from the simulation studies was further applied in a life bearing test rig. In this research, life time testing is also conducted to gauge the performance of the blind deconvolution technique in detecting a growing potential failure of a new bearing which is eventually run to failure. Results from unseeded new bearing tests are different, because seeded defects have certain defect characteristic frequencies which can be used to track a specific damaged frequency component. In this test, the test bearing was set to operate continuously until failures occurred. The proposed technique was then applied to monitor the condition of the test bearing and a trend of the bearing life was established. The results revealed the superiority of the technique in identifying the periodic components of the bearing before final break-down of the test bearing. The results show that the proposed technique with optimum filter length does improve the SNR of the deconvolved signal and can be used for automatic feature extraction and fault classification. This technique has potential for use in machine diagnostics.

Thesis (M.S.)--University of Nevada, Reno, 2006. / "August, 2006." Includes bibliographical references (leaves 38-45). Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2006]. 1 microfilm reel ; 35 mm. Online version available on the World Wide Web.

Thesis (M.S.)--Ohio University, March, 1999. / Title from PDF t.p.

Thesis--University of Adelaide, 1961. / Typewritten.

Thesis (M.S.)--Auburn University, 2008. / Abstract. Vita. Includes bibliographical references.

Thesis (Ph. D.)--Lehigh University, 2002. / Includes bibliographical references and vita.

Thesis (Ph. D.)--Florida State University, 2005. / Advisor: Dr. Hamid Garmestani, Florida State University, College of Engineering, Dept. of Mechanical Engineering. Title and description from dissertation home page (viewed Sept. 29, 2005). Document formatted into pages; contains xiii, 101 pages. Includes bibliographical references.

Thesis (Ph. D.)--Ohio State University, 1986. / Includes vita. Includes bibliographical references (leaves 170-179). Available online via OhioLINK's ETD Center.

This thesis explores opportunities in the mathematical modelling of metal rolling processes, specifically asymmetrical sheet rolling. With the application of control systems in mind, desired mathematical models must make adequate predictions with short computational times. This renders generic numerical approaches inappropriate. Previous analytical models of symmetrical sheet rolling have relied on ad hoc assumptions about the form of the solution. The work within this thesis begins by generalising symmetric asymptotic rolling models: models that make systematic assumptions about the rolling configuration. Using assumptions that apply to cold rolling, these models are generalised to include asymmetries in roll size, roll speed and roll-workpiece friction conditions. The systematic procedure of asymptotic analysis makes this approach flexible to incorporating alternative friction and material models. A further generalisation of a clad-sheet workpiece is presented to illustrate this. Whilst this model was formulated and solved successfully, deterioration of the results for any workpiece inhomogeneity demonstrates the limitations of some of the assumptions used in these two models. Attention is then turned to curvature prediction. A review of workpiece curvature studies shows that contradictions exist in the literature; and complex non-linear relationships are seen to exist between asymmetries, roll geometry and induced curvature. The collated data from the studies reviewed were insufficient to determine these relationships empirically; and neither analytical models, including those developed thus far, nor linear regressions are able to predict these data. Another asymmetric rolling model is developed with alternative asymptotic assumptions, which shows non-linear behaviour over ranges of asymmetries and geometric parameters. While quantitative curvature predictions are not achieved, metrics of mechanisms hypothesised to drive curvature indicate these non-linear curvature trends may be captured with further refinement. Finally, coupling a curved beam model with a curvature predicting rolling model is proposed to model the ring rolling process. Both of these parts are implemented but convergence between them is not yet achieved. By analogy this could be extended with shell theory and a three-dimensional rolling model to model the wheeling process.