Analysis of a Non-Traditional Micro Tube Hydroforming Process

As the demand for miniature products and components continues to increase, the need for manufacturing processes to provide these products and components has also increased. To meet this need, successful macroscale processes are being scaled down and applied at the microscale. Unfortunately, many challenges have been experienced when directly scaling down macro processes. Initially, frictional effects were believed to be the largest challenge encountered. However, in recent studies it has been found that the greatest challenge encountered has been with size effects. Size effect is a broad term that largely refers to the thickness of the material being formed and how this thickness directly affects the product dimensions and manufacturability. At the microscale, the thickness becomes critical due to the reduced number of grains. When surface contact between the forming tools and the material blanks occur at the macroscale, there is enough material (hundreds of layers of material grains) across the blank thickness to compensate for material flow and the effect of grain orientation. At the microscale, there may be under 10 grains across the blank thickness. With a decreased amount of grains across the thickness, the influence of the grain size, shape and orientation is significant. Any material defects (either natural occurring or ones that occur as a result of the material preparation) have a significant role in altering the forming potential. To date, various micro metal forming and micro materials testing equipment setups have been constructed at the Michigan Tech lab. Initially, the research focus was to create a micro deep drawing setup to potentially build micro sensor encapsulation housings. The research focus shifted to micro metal materials testing equipment setups. These include the construction and testing of the following setups: a micro mechanical bulge test, a micro sheet tension test (testing micro tensile bars), a micro strain analysis (with the use of optical lithography and chemical etching) and a micro sheet hydroforming bulge test. Recently, the focus has shifted to study a micro tube hydroforming process. The intent is to target fuel cells, medical, and sensor encapsulation applications. While the tube hydroforming process is widely understood at the macroscale, the microscale process also offers some significant challenges in terms of size effects. Current work is being conducted in applying direct current to enhance micro tube hydroforming formability. Initially, adding direct current to various metal forming operations has shown some phenomenal results. The focus of current research is to determine the validity of this process.

DEVELOPMENT OF A LOW COST IMPEDANCE TUBE TO MEASURE THE ACOUSTIC ABSORPTION AND TRANSMISSION LOSS OF MATERIALS

Traditional methods of measuring sound absorption coefficient and sound transmission loss of a material are time consuming. To overcome this limitation, normal incidence sound absorption and transmission loss measurement technique was developed. Unfortunately the equipment required for this task is equally expensive. Hence efforts are taken to develop a cost-effective equipment for measuring normal incidence sound absorption coefficient and transmission loss. An impedance tube capable of measure absorption coefficient and transmission loss is designed and built under a budget of $1500 for educational institutes. A background study is performed to gain knowledge and understanding of the normal incidence measurements technique. Based on the literature review, parameters involved such as tube material, source and microphone properties, sample holders, etc. are discussed in depth. Based on these parameters, design options are generated to meet the cost and functionality targets pre-assigned. After selection of materials and components, an impedance tube is built and tested using three fibrous absorption materials for absorption and a barrier for transmission loss performance. These measured results then compared with those obtained with the help of industry recognized Brüel & Kjær impedance tube. The results show performances are comparable, hence validation the new built tube.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

EVALUATION OF INTELLIGENT COMPACTION CONTROL IN THE M-189 RECONSTRUCTION PROJECT AT IRON RIVER, MICHIGAN

This research evaluated an Intelligent Compaction (IC) unit on the M-189 highway reconstruction project at Iron River, Michigan. The results from the IC unit were compared to several traditional compaction measurement devices including Nuclear Density Gauge (NDG), Geogauge, Light Weight Deflectometer (LWD), Dynamic Cone Penetrometer (DCP), and Modified Clegg Hammer (MCH). The research collected point measurements data on a test section in which 30 test locations on the final Class II sand base layer and the 22A gravel layer. These point measurements were compared with the IC measurements (ICMVs) on a point-to-point basis through a linear regression analysis. Poor correlations were obtained among different measurements points using simple regression analysis. When comparing the ICMV to the compaction measurements points. Factors attributing to the weak correlation include soil heterogeneity, variation in IC roller operation parameters, in-place moisture content, the narrow range of the compaction devices measurement ranges and support conditions of the support layers. After incorporating some of the affecting factors into a multiple regression analysis, the strength of correlation significantly improved, especially on the stiffer gravel layer. Measurements were also studied from an overall distribution perspective in terms of average, measurement range, standard deviation, and coefficient of variance. Based on data analysis, on-site project observation and literature review, conclusions were made on how IC performed in regards to compaction control on the M-189 reconstruction project.

Application of MFBD Algorithms to Image Reconstruction Under Anisoplanatic Conditions

All optical systems that operate in or through the atmosphere suffer from turbulence induced image blur. Both military and civilian surveillance, gun-sighting, and target identification systems are interested in terrestrial imaging over very long horizontal paths, but atmospheric turbulence can blur the resulting images beyond usefulness. My dissertation explores the performance of a multi-frame-blind-deconvolution technique applied under anisoplanatic conditions for both Gaussian and Poisson noise model assumptions. The technique is evaluated for use in reconstructing images of scenes corrupted by turbulence in long horizontal-path imaging scenarios and compared to other speckle imaging techniques. Performance is evaluated via the reconstruction of a common object from three sets of simulated turbulence degraded imagery representing low, moderate and severe turbulence conditions. Each set consisted of 1000 simulated, turbulence degraded images. The MSE performance of the estimator is evaluated as a function of the number of images, and the number of Zernike polynomial terms used to characterize the point spread function. I will compare the mean-square-error (MSE) performance of speckle imaging methods and a maximum-likelihood, multi-frame blind deconvolution (MFBD) method applied to long-path horizontal imaging scenarios. Both methods are used to reconstruct a scene from simulated imagery featuring anisoplanatic turbulence induced aberrations. This comparison is performed over three sets of 1000 simulated images each for low, moderate and severe turbulence-induced image degradation. The comparison shows that speckle-imaging techniques reduce the MSE 46 percent, 42 percent and 47 percent on average for low, moderate, and severe cases, respectively using 15 input frames under daytime conditions and moderate frame rates. Similarly, the MFBD method provides, 40 percent, 29 percent, and 36 percent improvements in MSE on average under the same conditions. The comparison is repeated under low light conditions (less than 100 photons per pixel) where improvements of 39 percent, 29 percent and 27 percent are available using speckle imaging methods and 25 input frames and 38 percent, 34 percent and 33 percent respectively for the MFBD method and 150 input frames. The MFBD estimator is applied to three sets of field data and the results presented. Finally, a combined Bispectrum-MFBD Hybrid estimator is proposed and investigated. This technique consistently provides a lower MSE and smaller variance in the estimate under all three simulated turbulence conditions.