Aufgrund von steigenden Energiekosten und einer sukzessive steigenden öffentlichen sowie politischen Forderung nach Umweltbewusstsein und Nachhaltigkeit, ist die Effizienzsteigerung von Gesamtsystemen einer der treibenden Kräfte für innovative, technologische Neuheiten geworden. Besonders bei der Entwicklung von verbrennungsmotorisch angetriebenen Fahrzeugen wurden z.B. durch die Hybridisierung von Antriebssträngen, die die Rekuperation von kinetischer Energie ermöglichen, Technologien zur Energieeinsparung etabliert. Da bei Verbrennungsmotoren ein hoher Anteil der im Kraftstoff gespeicherten Energie technologiebedingt als Abwärme im Abgas verloren geht, bietet die Wärmerekuperation ein weiteres hohes Potential für weitere Einsparungen. Diese ist z.B. mit Hilfe von thermoelektrischen Generatoren (TEG) möglich, die einen Wärmestrom direkt in elektrische Energie umwandeln. Zur effizienten TEG-Systemgestaltung ist ein hoher Temperaturgradient über dem thermoelektrisch aktivem Material notwendig, der wiederum zu kritischen thermomechanischen Spannungen im Bauteil führen kann. Diese werden zum einen durch die unterschiedlichen Temperaturausdehnungskoeffizienten der verschiedenen Materialien und zum anderen durch die mechanische Anbindung auf der heißen und kalten Seite des TEG verursacht. Somit liegt ein Zielkonflikt zwischen dem thermoelektrischen Systemwirkungsgrad und der mechanischen Festigkeit des Bauteils vor. In dieser Arbeit wird mit Hilfe einer mehrkriteriellen Parameteroptimierung eines vollparametrisierten FE-Modells des TEG in ANSYS WORKBENCH eine Methode vorgestellt, den thermoelektrischen Wirkungsgrad bei gleichzeitiger Reduktion der thermomechanischen Spannungen zu optimieren. Zur Optimierung kommt dabei ein genetischer Algorithmus der MATLAB GLOBAL OPTIMIZATION TOOLBOX zum Einsatz. Der Modellaufbau wird in ANSYS WORKBENCH mit der Makro-Programmiersprache JSCRIPT realisiert. Als Ziel- und Bewertungsfunktionen wird die mechanische Belastung jedes Bauteils im TEG ausgewertet und dessen elektrische Leistungsdichte berechnet. Die Ergebnisse zeigen, dass mit Hilfe der vorgestellten Methodik eine paretooptimale Lösung gefunden werden kann, die den gestellten Anforderungen entspricht.

<p>There is a growing need to understand the deformation mechanisms in titanium alloys due to their widespread use in the aerospace industry (especially within gas turbine engines), variation in their properties and performance based on their microstructure, and their tendency to undergo premature failure due to dwell and high cycle fatigue well below their yield strength. Crystal plasticity finite element (CPFE) modeling is a popular computational tool used to understand deformation in these polycrystalline alloys. With the advancement in experimental techniques such as electron backscatter diffraction, digital image correlation (DIC) and high-energy x-ray diffraction, more insights into the microstructure of the material and its deformation process can be attained. This research leverages data from a number of experimental techniques to develop well-informed and calibrated CPFE models for titanium alloys at multiple length-scales and use them to further understand the deformation in these alloys.</p> <p>The first part of the research utilizes experimental data from high-energy x-ray diffraction microscopy to initialize grain-level residual stresses and capture the correct grain morphology within CPFE simulations. Further, another method to incorporate the effect of grain-level residual stresses via geometrically necessary dislocations obtained from 2D material characterization is developed and implemented within the CPFE framework. Using this approach, grain level information about residual stresses obtained spatially over the region of interest, directly from the EBSD and high-energy x-ray diffraction microscopy, is utilized as an input to the model.</p> <p>The second part of this research involves calibrating the CPFE model based upon a systematic and detailed optimization routine utilizing experimental data in the form of macroscopic stress-strain curves coupled with lattice strains on different crystallographic planes for the α and β phases, obtained from high energy X-ray diffraction experiments for multiple material pedigrees with varying β volume fractions. This fully calibrated CPFE model is then used to gain a comprehensive understanding of deformation behavior of Ti-6Al-4V, specifically the effect of the relative orientation of the α and β phases within the microstructure.</p> <p>In the final part of this work, large and highly textured regions, referred to as macrozones or microtextured regions (MTRs), with sizes up to several orders of magnitude larger than that of the individual grains, found in dual phase Titanium alloys are modeled using a reduced order simulation strategy. This is done to overcome the computational challenges associated with modeling macrozones. The reduced order model is then used to investigate the strain localization within the microstructure and the effect of varying the misorientation tolerance on the localization of plastic strain within the macrozones.</p>

This thesis illustrates analysis of the influence of the non-machining processes on product quality by experimental design and statistical analysis. For the analysis objective / dishwasher production in Arcelik Dishwasher plant is examined. Sheet metal forming processes of dishwasher production constitutes the greatest portion of production cost and using the Pareto analysis technique / four pieces among twenty six pieces are determined to be investigated. These four pieces are the U Sheet, L Sheet, Inner Door and Side Panel of the dishwasher. By the help of the flow diagrams production process of the determined pieces are defined. Brainstorming technique and cause&amp / effect diagrams are used to determine which non-machining process parameters can cause pieces to be scrapped. These parameters are used as control factors in experimental design. Taguchi&amp / #8217 / s L16(215) orthogonal array, Taguchi&amp / #8217 / s L16(215) orthogonal array using S/N transformation and 28-4 fractional factorial design are used on purpose. With repetitions and confirmation experiments the effective parameters are determined and optimum level of these parameters are defined for the improvements on scrap quantity and quality of production.

<p>A recent trend in evolutionary robotics and artificial life research is to maximize self-organization in the design of robotic systems, in particular using artificial evolutionary techniques, in order to reduce the human designer bias. This dissertation presents experiments in competitive co-evolutionary robotics that integrate and extend previous work on competitive co-evolution of neural robot controllers in a predator-prey scenario with work on the ‘co-evolution’ of robot morphology and control systems. The focus here is on a systematic investigation of tradeoffs and interdependencies between morphological parameters and behavioral strategies through a series of predator-prey experiments in which increasingly many aspects are subject to self-organization through competitive co-evolution. The results show that there is a strong interdependency between morphological parameters and behavioral strategies evolved, and that the competitive co-evolutionary process was able to find a balance between and within these two aspects. It is therefore concluded that competitive co-evolution has great potential as a method for the automatic design of robotic systems.</p>

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This thesis illustrates parameter optimization of compressive strength, flexural strength and impact resistance of steel fiber reinforced high strength concrete (SFRHSC) by statistical design and analysis of experiments. Among several factors affecting the compressive strength, flexural strength and impact resistance of SFRHSC, five parameters that maximize all of the responses have been chosen as the most important ones as age of testing, binder type, binder amount, curing type and steel fiber volume fraction. Taguchi and regression analysis techniques have been used to evaluate L27(313) Taguchi&amp / #65533 / s orthogonal array and 3421 full factorial experimental design results. Signal to noise ratio transformation and ANOVA have been applied to the results of experiments in Taguchi analysis. Response surface methodology has been employed to optimize the best regression model selected for all the three responses. In this study Charpy Impact Test, which is a different kind of impact test, have been applied to SFRHSC for the first time. The mean of compressive strength, flexural strength and impact resistance have been observed as around 125 MPa, 14.5 MPa and 9.5 kgf.m respectively which are very close to the desired values. Moreover, this study is unique in the sense that the derived models enable the identification of underlying primary factors and their interactions that influence the modeled responses of steel fiber reinforced high strength concrete.

ABSTRACT PARAMETER OPTIMIZATION OF CHEMICALLY ACTIVATED MORTARS CONTAINING HIGH VOLUMES OF POZZOLAN BY STATISTICAL DESIGN AND ANALYSIS OF EXPERIMENTS Aldemir, BaSak M.S., Department of Industrial Engineering Supervisor: Prof. Dr. &Ouml / mer Saat&ccedil / ioglu Co-Supervisor: Assoc. Prof. Dr. Lutfullah Turanli January 2006, 167 pages This thesis illustrates parameter optimization of early and late compressive strengths of chemically activated mortars containing high volumes of pozzolan by statistical design and analysis of experiments. Four dominant parameters in chemical activation of natural pozzolans are chosen for the research, which are natural pozzolan replacement, amount of pozzolan passing 45 &amp / #956 / m sieve, activator dosage and activator type. Response surface methodology has been employed in statistical design and analysis of experiments. Based on various second-order response surface designs / experimental data has been collected, best regression models have been chosen and optimized. In addition to the optimization of early and late strength responses separately, simultaneous optimization of compressive strength with several other responses such as cost, and standard deviation estimate has also been performed. Research highlight is the uniqueness of the statistical optimization approach to chemical activation of natural pozzolans.

A recent trend in evolutionary robotics and artificial life research is to maximize self-organization in the design of robotic systems, in particular using artificial evolutionary techniques, in order to reduce the human designer bias. This dissertation presents experiments in competitive co-evolutionary robotics that integrate and extend previous work on competitive co-evolution of neural robot controllers in a predator-prey scenario with work on the ‘co-evolution’ of robot morphology and control systems. The focus here is on a systematic investigation of tradeoffs and interdependencies between morphological parameters and behavioral strategies through a series of predator-prey experiments in which increasingly many aspects are subject to self-organization through competitive co-evolution. The results show that there is a strong interdependency between morphological parameters and behavioral strategies evolved, and that the competitive co-evolutionary process was able to find a balance between and within these two aspects. It is therefore concluded that competitive co-evolution has great potential as a method for the automatic design of robotic systems.

Afin de poursuivre et d'améliorer la tâche de détection qui est en cours à l'INSA, nous nous sommes concentrés sur la fusion des informations visibles et infrarouges du point de vue de reconnaissance des obstacles, ainsi distinguer entre les véhicules, les piétons, les cyclistes et les obstacles de fond. Les systèmes bimodaux ont été proposées pour fusionner l'information à différents niveaux: des caractéristiques, des noyaux SVM, ou de scores SVM. Ils ont été pondérés selon l'importance relative des capteurs modalité pour assurer l'adaptation (fixe ou dynamique) du système aux conditions environnementales. Pour évaluer la pertinence des caractéristiques, différentes méthodes de sélection ont été testés par un PPV, qui fut plus tard remplacée par un SVM. Une opération de recherche de modèle, réalisée par 10 fois validation croisée, fournit le noyau optimisé pour SVM. Les résultats ont prouvé que tous les systèmes bimodaux VIS-IR sont meilleurs que leurs correspondants monomodaux. / To continue and improve the detection task which is in progress at INSA laboratory, we focused on the fusion of the information provided by visible and infrared cameras from the view point of an Obstacle Recognition module, this discriminating between vehicles, pedestrians, cyclists and background obstacles. Bimodal systems have been proposed to fuse the information at different levels:of features, SVM's kernels, or SVM’s matching-scores. These were weighted according to the relative importance of the modality sensors to ensure the adaptation (fixed or dynamic) of the system to the environmental conditions. To evaluate the pertinence of the features, different features selection methods were tested by a KNN classifier, which was later replaced by a SVM. An operation of modelsearch, performed by 10 folds cross-validation, provides the optimized kernel for the SVM. The results have proven that all bimodal VIS-IR systems are better than their corresponding monomodal ones.

No description available.