Fabrication of large antenna substrates of monolithic spatially variable ceramics and an optimization framework for nano-antennas

The aim of this thesis is driven by two main challenges in the antenna and propagation community: The possibility to manufacture exact replicas of spatially variable dielectric substrates for low frequency Radiofrequency (RF) applications and to achieve performance enhancements via formal optimization techniques for high frequency applications such as nano-antennas. In the RF and optics community, metamaterials have gained significant interest due to their extraordinary properties which are not accessible in nature. Textured composites with novel properties allow for the realization of state-of-the-art devices which are functionalized through spatially variable properties of dielectrics, magnetics and polymers. The possibility of spatially controlling permittivity and permeability at the preferred frequency and the capability of realizing multi-material volumetric variations is an ancient vision in the RF community. One such technique has been proposed and adopted to produce spatially variable ceramic substrates of small size (2" square) and assembled to construct a UHF SATCOM antenna substrate. In the first part of the thesis, the objective is to use earlier proposed Dry Powder Deposition (DPD) technique for producing large monolithic substrates with spatial variation of ceramic constituents that will allow for impressive performance enhancements as dictated by design results. Commercially available LTCC powders namely Calcium Magnesium Titanates (MCT) of dielectric permittivities 15, 20, 70 and loss tangent < 0.0015 are used as the ceramic constituents. Thermogravimetric analysis of each constituent powder is used to analyze efficient removal of 1-3 % binder content of spray dried MCT powders and achieve complete sintering of their textured composites. Also, a detailed analysis of the process parameters such as compaction pressure and cosintering temperature within the DPD method is carried out. As a result, smooth and large substrates with sizes up to 82mm x 82mm of monolithic dielectric textured composites were obtained by cosintering at optimal conditions. Cracks and unwanted defects such as porosities in textured composites were eliminated. Density measurements and SEM stressed that final substrates obtained were over %98 dense ceramic constituents. Microstructure characterizations of pellets made of sintered constituent material were carried out by SEM and dielectric permittivity measurements were performed. In the second part of the thesis, the objective is to develop a basic framework to optimize a nano antenna's intensity enhancement and absorbed power according to variables such as length, thickness, width and wavelength using gradient and heuristic based methods (sequential quadratic programming and genetic algorithms). This framework will allow for more effective assessment of high-frequency antenna applications subject to multiple competing performance criteria and complex design variables in the future including the effect of material substrates, hence enable novel designs with superior performance for emerging plasmonic applications as was the case for the SATCOM antenna design in the first part of the thesis

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Bewertung des Rekuperatorpotenzials eines 300-kW-Turbowellen-Hubschraubermotors

In this thesis, an integrated rotorcraft multidisciplinary simulation framework has been developed to comprehensively evaluate the potential of the rotorcraft adopting a recuperator under various flight conditions as well as at mission levels. Through the execution of multi-objective genetic algorithm (GA) optimization, the Pareto front model is derived qualifying the associated interdependency and trade-off between the fuel saving potential and recuperator weight penalty. The proposed methodology proves to be a computationally efficient tool for the multidisciplinary design and optimization of the recuperated rotorcraft powerplant system.In dieser Arbeit wurde ein integriertes multidisziplinäres Simulationssystem für Drehflügler entwickelt, um das Potenzial des Drehflüglers für den Einsatz eines Rekuperators unter verschiedenen Flugbedingungen sowie auf Missionsebene umfassend zu bewerten. Durch die Ausführung einer Optimierung mit einem genetischen Algorithmus mit mehreren Zielsetzungen (GA) wird das Pareto-Frontmodell abgeleitet, das die damit verbundene Abhängigkeit und den Kompromiss zwischen dem Kraftstoffeinsparpotenzial und der Gewichtsstrafe für den Rekuperator qualifiziert. Die vorgeschlagene Methodik erweist sich als rechnerisch effizientes Werkzeug für die multidisziplinäre Auslegung und Optimierung eines derart modifizierten Triebwerkssystems für Drehflügler

Quality control and improvement of the aluminum alloy castings for the next generation of engine block cast components.

This research focuses on the quality control and improvement of the W319 aluminum alloy engine blocks produced at the NEMAK Windsor Aluminum Plant (WAP). The present WAP Quality Control (QC) system was critically evaluated using the cause and effect diagram and therefore, a novel Plant Wide Quality Control (PWQC) system is proposed. This new QC system presents novel tools for off line as well as on line quality control. The off line tool uses heating curve analysis for the grading of the ingot suppliers. The on line tool utilizes Tukey control charts of the Thermal Analysis (TA) parameters for statistical process control. An Artificial Neural Network (ANN) model has also been developed for the on-line prediction and control of the Silicon Modification Level (SiML). The student t-statistical analysis has shown that even small scale variations in the Fe and Mn levels significantly affect the shrink porosity level of the 3.0L V6 engine block bulkhead. When the Fe and Mn levels are closer to their upper specification limits (0.4 wt.% and 0.3wt.%, respectively), the probability of low bulkhead shrink porosity is as high as 0.73. Elevated levels of Sn (∼0.04 wt.%) and Pb (∼0.03 wt.%) were found to lower the Brinell Hardness (HB) of the V6 bulkhead after the Thermal Sand Removal (TSR) and Artificial Aging (AA) processes. Therefore, Sn and Pb levels must be kept below 0.0050 wt.% and 0.02 wt.%, respectively, to satisfy the bulkhead HB requirements. The Cosworth electromagnetic pump reliability studies have indicated that the life of the pump has increased from 19,505 castings to 43,904 castings (225% increase) after the implementation of preventive maintenance. The optimum preventive maintenance period of the pump was calculated to be 43,000 castings. The solution treatment parameters (temperature and time) of the Novel Solution Treatment during the Solidification (NSTS) Process were optimized using ANN and the Simulated Annealing (SA) algorithm. The optimal NSTS process (516°C and 66 minutes) would significantly reduce the present Thermal Sand Removal (TSR) time (4 hours) and would avoid the problem of incipient melting without sacrificing the mechanical properties. In order to improve the cast component characteristics and to lower the alloy price, a new alloy, Al 332, (Si=10.5 wt.% & Cu=2 wt.%) was developed by optimizing the Si and Cu levels of 3XX Al alloys as a replacement for the W319 alloy. The predicted as cast characteristics of the new alloy were found to satisfy the requirements of Ford engineering specification WSE-M2A-151-A2/A4.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .F735. Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6201. Thesis (Ph.D.)--University of Windsor (Canada), 2005

The discovery of new functional oxides using combinatorial techniques and advanced data mining algorithms

Electroceramic materials research is a wide ranging field driven by device applications. For many years, the demand for new materials was addressed largely through serial processing and analysis of samples often similar in composition to those already characterised. The Functional Oxide Discovery project (FOXD) is a combinatorial materials discovery project combining high-throughput synthesis and characterisation with advanced data mining to develop novel materials. Dielectric ceramics are of interest for use in telecommunications equipment; oxygen ion conductors are examined for use in fuel cell cathodes. Both applications are subject to ever increasing industry demands and materials designs capable of meeting the stringent requirements are urgently required. The London University Search Instrument (LUSI) is a combinatorial robot employed for materials synthesis. Ceramic samples are produced automatically using an ink-jet printer which mixes and prints inks onto alumina slides. The slides are transferred to a furnace for sintering and transported to other locations for analysis. Production and analysis data are stored in the project database. The database forms a valuable resource detailing the progress of the project and forming a basis for data mining. Materials design is a two stage process. The first stage, forward prediction, is accomplished using an artificial neural network, a Baconian, inductive technique. In a second stage, the artificial neural network is inverted using a genetic algorithm. The artificial neural network prediction, stoichiometry and prediction reliability form objectives for the genetic algorithm which results in a selection of materials designs. The full potential of this approach is realised through the manufacture and characterisation of the materials. The resulting data improves the prediction algorithms, permitting iterative improvement to the designs and the discovery of completely new materials

Statistical investigation on effect of Electroless coating Parameter on Coating Morphology of Short Basalt Fiber.

The Objective of the present paper is to investigate the effect of electroless coating parameters, such as Sensitization time (A), Activation time (B) and Metallization time (C), on the coating morphology of the basalt short fiber and the optimization of the coating process parameters based on L27 Taguchi orthogonal design. Coated and non-coated basalt short fiber, typically used with 7075 Aluminium alloy as einforcement, is studied. The effect of coating the short basalt fiber with copper has proved beneficial to interfacial bonding (wettability) between the reinforcement and the matrix. The interface between the matrix and the reinforcement plays a crucial role in determining the properties of metal matrix composites (MMCs). An L27 array was used to accommodate the three levels of factors as well as their interaction effects. From the Taguchi methodology, the optimal combinations for coating parameters were found to be A1B3C3 (i.e., 5 min. sensitization time, 15 min. activation time and 3 min. for metallization time). In addition, the interaction between pH value and the coating time and that between the coating time and the temperature, influence the coating parameters significantly. Furthermore, a statistical analysis of variance reveals that the metallization time has the highest influence followed by the activation time and the sensitization time. Finally, confirmation tests were carried out to verify the experimental results, Scanning Electron Microscopic (SEM) & Energy Dispersive Spectroscope (EDS) studies were carried out on basalt fiber

Computational Dynamics of Anti-Corrosion Performance of Laser Alloyed Metallic Materials

Laser surface alloying (LSA) is a material processing technique that utilizes the high power density available from defocused laser beam to melt both reinforcement powders and a part of the underlying substrate. Because melting occurs solitary at the surface, large temperature gradients exist across the boundary between the underlying solid substrate and the melted surface region, which results in rapid self-quenching and resolidifications. Reinforcement powders are deposited in the molten pool of the substrate to produce corrosion-resistant coatings. These processes influence the structure and properties of the alloyed region. A 3D mathematical model is developed to obtain insights on the behavior of laser melted pools subjected to various process parameters. It is expected that the melt pool flow, thermal and solidification characteristics will have a profound effect on the microstructure of the solidified region