MEMS Technology for Biomedical Imaging Applications

Biomedical imaging is the key technique and process to create informative images of the human body or other organic structures for clinical purposes or medical science. Micro-electro-mechanical systems (MEMS) technology has demonstrated enormous potential in biomedical imaging applications due to its outstanding advantages of, for instance, miniaturization, high speed, higher resolution, and convenience of batch fabrication. There are many advancements and breakthroughs developing in the academic community, and there are a few challenges raised accordingly upon the designs, structures, fabrication, integration, and applications of MEMS for all kinds of biomedical imaging. This Special Issue aims to collate and showcase research papers, short commutations, perspectives, and insightful review articles from esteemed colleagues that demonstrate: (1) original works on the topic of MEMS components or devices based on various kinds of mechanisms for biomedical imaging; and (2) new developments and potentials of applying MEMS technology of any kind in biomedical imaging. The objective of this special session is to provide insightful information regarding the technological advancements for the researchers in the community

Out-of-plane fiber waviness in composite materials: origins, detection and mechanical evaluation

Out-of-plane fiber waviness, also referred to as wrinkling, is considered one of the most significant effects that occur in composite materials. It significantly affects mechanical properties, such as stiffness, strength and fatigue and, therefore, dramatically reduces the load carrying capacity of the material. Fiber waviness is inherent to various manufacturing processes of fiber-reinforced composite parts. They cannot be completely avoided and thus have to be tolerated and considered as an integral part of the structure. Because of this influenceable but in many cases unavoidable nature of fiber waviness, it might be more appropriate to consider fiber waviness as effects or features rather than defects. Hence, it is important to understand the impact of different process parameters on the formation of fiber waviness in order to reduce or, in the best case, completely avoid them as early as possible in the product and process development phases. Mostly depending on the chosen geometry of the part and the specific manufacturing process used, different types of fiber waviness result. Fiber-reinforced composite materials allow for a significant mass reduction due to the comparably low density (c.f. 4-5 times less than steel) and, in addition, fibers can be aligned in accordance with the load paths. This possibility of alignment allows the fibers to be placed at the exact position where they are needed to provide the component with the required stiffness and strength. However, this can lead to a load path-optimized composite structure, which is not necessarily easy to produce and free of defects. The placement of the fibers or semi-finished textile products is still often carried out by hand-lay-up, especially in the aviation industry. This allows a diverse draping of the unidirectional (UD) layers, woven textiles or non-crimped fabrics (NCF) onto the production tool. However, manufacturing effects such as fiber waviness, porosity, delamination and distortion cannot be completely avoided. The increased demand for composite components and their production process stability for the aviation and automotive industries requires a transition to at least partially automated manufacturing processes. Those systems come with a higher deposition rate and ensure reproducible quality, but also imply production effects, e.g. fiber waviness. This necessitates a sophisticated understanding of those implicit effects on the mechanical properties of the manufactured structure. The decision as to whether these unwanted irregularities are considered as manufacturing features (effects), or as defects, depends on the size, number and location in the component. Those allowance limits depend on the strength and stiffness reserve at the location of the feature, as well as on functional requirements, e.g. water tightness. The assessment of manufacturing effects further depends on the industry. In the aviation industry, the allowance limits for defects are very restricted, while in the automotive industry the need for short cycle times leads to a trade-off between robust processes and tolerated manufacturing imperfections. To this point, there is still no generally accepted approach to quantitatively support accept/reject/repair-decisions and make a consistent assessment of wavy layers in composites. If the effect is termed to be a defect, typically a deviation from design must be requested in the aviation industry and an individual decision must be made on "use as is", repair or reject entirely. In some cases, experiments on representative test samples are performed at the subcomponent-level on a statistical basis. However, this is both time consuming and cost intensive. It is necessary to strive for a fiber-oriented and in particular a manufacturing-oriented design and construction of composite components. Towards this goal, design and production engineers aim to expand the permissible margin of safety by assessing the effect on stiffness and strength of those production effects, i.e. fiber waviness, porosity, delamination etc. Additionally, they aim to reduce or, in the best case, avoid them on the process side, increasingly with the help of finite element based process simulations. In this thesis, numerous mechanisms of wrinkling were analyzed, leading to several recommendations to prevent wrinkle formation not only during composite processing, but also at an earlier design stage, where generally several influencing factors are defined. Based on that, an overview of typically occurring wave shapes is presented and a classification scheme based on ten characteristic features is suggested for categorization purposes. The assessment of out-of-plane fiber waviness in composite materials is strongly dependent on the accuracy of detection and quantification of the wave parameters such as amplitude, wavelength and position in the laminate. In the aviation industry, ultrasonic testing (UT) is the preferred method for the evaluation of composite materials. The evaluation of the ultrasound signal from different manufacturing effects is difficult and it often cannot be clearly determined whether there are actually wavy regions in the laminate or not. In this thesis, different non-destructive testing (NDT) methods, such as infrared thermography (IRT), digital shearography, eddy current testing (ET) and X-ray computed tomography (CT) have been used to assess their potential for the detection and characterization of embedded out-of-plane fiber waviness in composite materials. These methods were applied on test plates with artificially embedded waviness with varying amplitudes, wavelengths and positions in the laminate and evaluated with respect to their ability of detecting the wrinkle morphology. The experimental non-destructive procedures of infrared thermography and digital shearography were simulated using the Finite Element Method (FEM) to gain a deeper understanding on the influence of fiber waviness on the measured results. To understand the complex failure behaviour of composite materials containing out-of-plane fiber waviness under compressive and tensile loading, numerous experimental tests have been carried out. Digital image correlation (DIC), passive thermography (IRT) and acoustic emission (AE) test methods have been used to investigate damage initiation and propagation on specimen level. In addition to that, an extensive material characterization on planar specimens was also performed. Composite materials exposed to harsh environmental conditions, i.e. hot-wet, show considerably reduced mechanical properties, governed by a degrading matrix. To investigate the effect of fiber waviness on the mechanical properties at both room temperature and after 12 months hot-wet conditioning at 70°C and 85% relative humidity, mechanical tests (compressive and tensile loading) were conducted. The basic strategies for the assessment of fiber waviness are briefly described. In engineering practice several approaches are used, i.e. empirical, generic, and semi-empirical. These include experimentally obtained knockdown factors, simplified simulations or extensive testing on subcomponent level, both experimentally and numerically. A developed micromechanical model is implemented in a MATLAB GUI to determine the effective elastic properties as well as the resulting complex stress state of uniform and graded fiber waviness. The well-established Puck failure criterion was implemented and applied on the calculated stresses to predict local ply failure and determine the strength of wavy plies. The mechanical behavior of out-of-plane fiber waviness is investigated for both unidirectional and quasi-isotropic laminates by numerically simulating damage initiation and propagation. A nonlinear material model was implemented in ABAQUS/Explicit as a material user-subroutine, which is able to capture the material behavior including shear nonlinearities, failure initiation and propagation in unidirectional laminates reasonably accurate

Intelligent e-monitoring of plastic injection molding machines.

Lau Hau Yu.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 79-83).Abstracts in English and Chinese.Abstract --- p.iAcknowledgements --- p.ivTable of Contents --- p.viChapter Chapter 1: --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Objective --- p.4Chapter Chapter 2: --- Literature Survey --- p.6Chapter 2.1 --- Plastic Injection Molding Process --- p.6Chapter 2.2 --- Monitoring and Diagnosis Methods --- p.10Chapter 2.3 --- Remote Monitoring --- p.12Chapter Chapter 3: --- Monitoring Methods --- p.15Chapter 3.1 --- Predict nozzle pressure and part weight using the Radial Basis Function Neural Network --- p.15Chapter 3.1.1 --- Motivation --- p.15Chapter 3.1.2 --- Background --- p.15Chapter 3.1.3 --- Hybrid RBF neural network --- p.17Chapter 3.1.4 --- Estimation of nozzle pressure --- p.21Chapter 3.1.5 --- Estimation of part weight: The two steps and one step methods --- p.22Chapter 3.2 --- Short shot Monitoring using Similarity --- p.25Chapter 3.2.1 --- Background --- p.25Chapter 3.2.2 --- The Dissimilarity Approach --- p.26Chapter 3.3 --- Parameter Resetting using Support Vector Machine (SVM) and Virtual Search Method (VSM) --- p.27Chapter 3.3.1 --- Background --- p.27Chapter 3.3.2 --- Support Vector Regression --- p.27Chapter 3.3.3 --- SVM Parameters Resetting using Virtual Search Method (VSM) --- p.31Chapter 3.4 --- Experiments and Results --- p.33Chapter 3.4.1 --- Introduction to Design of Experiment (DOE) --- p.33Chapter 3.4.2 --- Set-points selection based on Design of Experiment (DOE) --- p.34Chapter 3.4.3 --- Nozzle pressure estimation --- p.40Chapter 3.4.4 --- Part weight prediction using the One Step Method --- p.47Chapter 3.4.5 --- Similarity Monitoring using estimated nozzle pressure --- p.49Chapter 3.4.6 --- Similarity Monitoring using ram position --- p.54Chapter 3.4.7 --- Parameter Resetting using SVM and VSM --- p.61Chapter Chapter 4: --- The Remote Monitoring and Diagnosis System (RMDS) --- p.63Chapter 4.1 --- Introduction to the Remote Monitoring and Diagnosis System --- p.63Chapter 4.2 --- Starting Use of the Software --- p.65Chapter 4.3 --- Properties and Channel Settings --- p.66Chapter 4.3.1 --- Statistic Process Control (SPC) --- p.69Chapter 4.3.2 --- Settings --- p.71Chapter 4.3.3 --- Viewing the signals --- p.72Chapter 4.3.4 --- Short shot monitoring --- p.73Chapter 4.3.5 --- Data management --- p.73Chapter Chapter 5: --- Coeclusions and Future Works --- p.76References --- p.79Appendix A: Machine settings in the experiment --- p.84Appendix B: Measured part weight in the part weight prediction experiment --- p.86Appendix C: Measured part weight in the similarity monitoring experiment --- p.87Appendix D: Results of Parameters Resetting Experiment --- p.88Appendix E: List of figures --- p.89Appendix F: List of tables --- p.9

University of Nevada, Las Vegas Transmutation Research Program Annual Progress Report Academic Year 2007-2008

It is my pleasure to present the UNLV Transmutation Research Program’s seventh annual report that highlights the academic year 2007-2008. Supporting this document are the many technical reports and theses that have been generated over the past seven years. In the seventh year of our program, we continued to see growth in the Radiochemistry Ph.D. program with a total of 20 students in the fourth year of the program (we anticipated twelve in the program proposal). Since our inception, the program has sponsored to their conclusion 48 M.S. and 6 Ph.D. degrees. The program supported 53 graduate students, 11 undergraduates, and eight post-doctoral scholars in eight academic departments across the UNLV scientific and engineering communities in the academic year 2007-2008. Our research tasks span the range of technology areas for transmutation, including separation of actinides from spent nuclear fuel, methods of fuel fabrication, reactoraccelerator coupled experiments, corrosion of materials exposed to lead-bismuth eutectic, and special nuclear materials protection and accountability. We continued our emphasis on molten metal technology and actinide chemistry in our enhancements to UNLV this year to build a foundation in areas that are in line with UNLV’s strategic growth and our ability to address student-appropriate research in the transmutation program