Esta dissertação apresenta um estudo da modelagem de experimentos aplicados a um processo industrial de tratamento térmico. A motivação deste trabalho surgiu diante das dificuldades associadas aos processos de recozimento industrial de aços do tipo baixa liga, na tentativa de encontrar temperaturas nas quais as durezas superficiais dos aços atingissem valores suficientemente baixos, adequados para etapas posteriores de fabricação, em especial a usinagem. Inicialmente forem realizados diversos experimentos com diferentes aços, onde a dureza superficial é obtida em função da temperatura de recozimento e dos teores de carbono e silício das amostras utilizadas. Em seguida propôs-se um modelo quadrático para modelar a dureza superficial como função dessas três variáveis. A estimação de parâmetros do modelo proposto foi realizada com o emprego do algoritmo Simulated Annealing, uma meta-heurística para otimização global que procura imitar o processo de recozimento de um material sólido. Finalmente, usando-se o modelo proposto, foi resolvido o chamado problema inverso, o qual consiste na estimação da temperatura de recozimento em função dos teores de carbono e silício e da dureza desejada. / This dissertation presents a study for experiments modeling applied for a heat treatment industrial process. The driving force to this work have raised from usual difficulties while low-alloy steels annealing industrial processes get on, trying to find out temperatures which the superficial hardness measurements reaches low values enough and suitable for the following stages, in special focus, the machining. At the first time, some experiments have been done with several steel grades, where superficial hardness is achieved as a function by annealing temperature, percent carbon content and percent silicon content of the samples. There was purposed a quadratic model for modeling the hardness as a function of that three parameters. The parameter estimation of the purposed model was done by the simulated annealing algorithm, a metaheuristic for global optimization which can be as the same as annealing of a solid material. Starting at the purposed model, the inverse problem was solved, where the estimated annealing temperature was achieved by the carbon and silicon percent contents and the suitable hardness.

Esta dissertação apresenta um estudo da modelagem de experimentos aplicados a um processo industrial de tratamento térmico. A motivação deste trabalho surgiu diante das dificuldades associadas aos processos de recozimento industrial de aços do tipo baixa liga, na tentativa de encontrar temperaturas nas quais as durezas superficiais dos aços atingissem valores suficientemente baixos, adequados para etapas posteriores de fabricação, em especial a usinagem. Inicialmente forem realizados diversos experimentos com diferentes aços, onde a dureza superficial é obtida em função da temperatura de recozimento e dos teores de carbono e silício das amostras utilizadas. Em seguida propôs-se um modelo quadrático para modelar a dureza superficial como função dessas três variáveis. A estimação de parâmetros do modelo proposto foi realizada com o emprego do algoritmo Simulated Annealing, uma meta-heurística para otimização global que procura imitar o processo de recozimento de um material sólido. Finalmente, usando-se o modelo proposto, foi resolvido o chamado problema inverso, o qual consiste na estimação da temperatura de recozimento em função dos teores de carbono e silício e da dureza desejada. / This dissertation presents a study for experiments modeling applied for a heat treatment industrial process. The driving force to this work have raised from usual difficulties while low-alloy steels annealing industrial processes get on, trying to find out temperatures which the superficial hardness measurements reaches low values enough and suitable for the following stages, in special focus, the machining. At the first time, some experiments have been done with several steel grades, where superficial hardness is achieved as a function by annealing temperature, percent carbon content and percent silicon content of the samples. There was purposed a quadratic model for modeling the hardness as a function of that three parameters. The parameter estimation of the purposed model was done by the simulated annealing algorithm, a metaheuristic for global optimization which can be as the same as annealing of a solid material. Starting at the purposed model, the inverse problem was solved, where the estimated annealing temperature was achieved by the carbon and silicon percent contents and the suitable hardness.

In this thesis a simulated annealing algorithm is employed as an optimization tool for a large scale optimization problem in wireless communication. In this application, we have 100 places for transition antennas and 100 places for receivers, and also a channel between each position in both areas. Our aim is to nd, say the best 3 positions there, in a way that the channel capacity is maximized. The number of possible combinations is huge. Hence, nding the best channel will take a very long time using an exhaustive search. To solve this problem, we use a simulated annealing algorithm and estimate the best answer. The simulated annealing algorithm chooses a random element, and then from the local search algorithm, compares the selected element with its neighbourhood. If the selected element is the maximum among its neighbours, it is a local maximum. The strength of the simulated annealing algorithm is its ability to escape from local maximum by using a random mechanism that mimics the Boltzmann statistic.

This thesis reports the work of using simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is used within this work as a method for learning the best configurations of type-1 and type-2 fuzzy logic systems to maximise their modelling ability. Therefore, it presents the combination of simulated annealing with three models, type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and general type-2 fuzzy logic systems to model four bench-mark problems including real-world problems. These problems are: noise-free Mackey-Glass time series forecasting, noisy Mackey-Glass time series forecasting and two real world problems which are: the estimation of the low voltage electrical line length in rural towns and the estimation of the medium voltage electrical line maintenance cost. The type-1 and type-2 fuzzy logic systems models are compared in their abilities to model uncertainties associated with these problems. Also, issues related to this combination between simulated annealing and fuzzy logic systems including type-2 fuzzy logic systems are discussed. The thesis contributes to knowledge by presenting novel contributions. The first is a novel approach to design interval type-2 fuzzy logic systems using the simulated annealing algorithm. Another novelty is related to the first automatic design of general type-2 fuzzy logic system using the vertical slice representation and a novel method to overcome some parametrisation difficulties when learning general type-2 fuzzy logic systems. The work shows that interval type-2 fuzzy logic systems added more abilities to modelling information and handling uncertainties than type-1 fuzzy logic systems but with a cost of more computations and time. For general type-2 fuzzy logic systems, the clear conclusion that learning the third dimension can add more abilities to modelling is an important advance in type-2 fuzzy logic systems research and should open the doors for more promising research and practical works on using general type-2 fuzzy logic systems to modelling applications despite the more computations associated with it.

In this thesis, a class of combinatorial optimization methods rooted in statistical mechanics and their use in signal processing applications will be discussed. The thesis consists of two separate parts. The first part deals with the rationale for my work and also covers the background information necessary to put the second part, which consists of a number of papers, in context. There are (at least) two sides to an optimization problem---the problem statement arising from an application or a design and the selection of an algorithm to solve the problem. In this work the problem statements are practical problems, of combinatorial nature, frequently encountered in signal processing and the algorithms of choice are annealing based algorithms, founded in statistical mechanics. From my work, it is my experience that solving a particular problem often leads to new developments on the part of the algorithm which, in turn, open up possibilities to apply the modified algorithm to a new set of problems, leading to a continuously improving algorithm and a growing field of applications. The included papers deal with the application of annealing optimization methods to the problems of configuring active noise and vibration control systems, digital filter design and adaptive filtering. They also describe the successive development of a highly efficient entropy-directed deterministic annealing (EDDA) optimization algorithm detailed in the final paper.

published_or_final_version / Mechanical Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / toc / Physics / Master / Master of Philosophy

published_or_final_version / abstract / Physics / Doctoral / Doctor of Philosophy

Self-assembly is a process of non-intrusive transformation of a system from a disordered to an ordered state. For engineering purposes, self-assembly of microscopic objects can benefit significantly from macroscopic guidance and control. This dissertation is concerned with controlling self-assembly in binary monolayers of electrically charged particles that follow basic laws of statistical mechanics. First, a simple macroscopic model is used to determine an optimal thermal control for self-assembly. The model assumes that a single rate-controlling mechanism is responsible for the formation of spatially ordered structures and that its rate follows an Arrhenius form. The model parameters are obtained using molecular dynamics simulations. The optimal control is derived in an analytical form using classical optimization methods. Two major lessons were learned from that work: (i) isothermal control was almost as effective as optimal time-dependent thermal control, and (ii) neither electrostatic interactions nor thermal control were particularly effective in eliminating voids formed during self-assembly. Accordingly, at the next stage, the focus is on temperature-pressure control under isothermal-isobaric conditions. In identifying optimal temperature and pressure conditions, several assumptions, that allow one to relate the optimal conditions to the phase diagram, are proposed. Instead of verifying the individual assumptions, the entire approach is verified using molecular dynamics simulations. It is estimated that under optimal isothermal-isobaric conditions the rate of self-assembly is about five time faster than that under optimal temperature control conditions. It is argued that the proposed approach of relating optimal conditions to the phase diagram is applicable to other systems. Further, the work reveals numerous and useful parallels between self-assembly and crystal physics, which are important to exploit for developing robust engineering self-assembly processes. / text

No description available.