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Natural and predictable changes in consumer needs often require the development of new products. Providing solutions that anticipate, account for, and allow for these changes over time is a significant challenge to manufacturers and design engineers. Products that adapt to these changes through the addition of modules reduce production costs through product commonality and provide a set of products that cater to customization and adaptation. In this thesis, a multiobjective optimization design method using s-Pareto frontiers – sets of non-dominated designs from disparate design models - is developed and used to identify a set of optimal adaptive product designs that satisfy changing consumer needs. The novel intent of the method is to design a product that adapts to changing consumer needs by moving from one location on the s-Pareto frontier to another through the addition of a module and/or reconfiguration. The six-step method is described as follows: (A) Characterize the multiobjective design space. (B) Identify the anticipated regions of interest within the search space based on predicted future needs. (C) Identify the platform design variables that minimize the performance losses due to commonality across the anticipated regions of interest. (D) Assemble the s-Pareto frontier within each region of interest. (E) Determine the values of all design variables for the optimal product design in each region of interest by multiobjective optimization. (F) Identify the module design variables, and identify the platform and module designs by constrained module design. An example of the design of a simple unmanned air vehicle is used to demonstrate application of the method for a single Pareto frontier case. The design of a manual irrigation pump is used to demonstrate application of the method for a s-Pareto frontier case. In addition, these examples show the ability of the method to design a product that adapts to changing consumer needs by traversing the s-Pareto frontier.

One of the most important decisions in the product development process is the selection of a promising design concept because of the large influence it has on the final product. A thorough search for the best design is a significant challenge to designers, who are trying to balance the objective and subjective performance of the designs they create. In this thesis, a computationally-assisted design methodology is developed and used in the early stages of design to more thoroughly search for designs that perform well according to objective physics-based models and subjective designer-specific preference-based models. The method presented herein uses an initial pool of user-created designs that is parameterized and used in a numerical search that recombines design features to form new designs in a semi-automated way. Designs are then evaluated quantitatively by objective performance calculations and evaluated qualitatively by human designers. Designer preference is interactively gathered when visual representations of new computer-created designs are presented to the designer for subjective evaluation. A mathematical model is then formed using statistical probability methods to approximate the designer's preference and incrementally updated after the designer subjectively evaluates a new set of designs at each iteration of the automated search process. The methodology uses a multiobjective approach to search for optimally performing designs, treating both the physics-based models and the preference-based models as objectives. The methodology couples the speed of computational searches with the ability of human designers to subjectively evaluate unmodeled objectives. The method is demonstrated with two product examples to find optimal designs that designers may not have otherwise discovered among the vast number of possible combinations of features. The proposed methodology brings the ability to search for and find numerous, optimal solutions across a wide solution space, in an efficient and human-centered way, and does so in the early stages of design.

The use of multiobjective optimization in identifying systems that account for changes in customer needs, operating environments, system design concepts, and analysis models over time is generally not explored. Providing solutions that anticipate, account for, and allow for these changes over time is a significant challenge to manufacturers and design engineers. Products that adapt to these changes through the addition and/or subtraction of modules can reduce production costs through product commonality, and cater to customization and adaptation. In terms of identifying sets of non-dominated designs, these changes result in the concept of dynamic Pareto frontiers, or dynamic s-Pareto frontiers when sets of system concepts are simultaneously evaluated over time. In this dissertation, a five-step optimization-based design method identifying a set of optimal adaptive product designs that satisfy the predicted changes by moving from one location on the dynamic s-Pareto frontier to another through the addition of a module and/or through reconfiguration is developed. Development of this five-step method was separated into four phases. The first two phases of developments respectively focus on Pareto and s-Pareto cases, where changes in concepts, models, and environments that would effect the Pareto/s-Pareto frontier are ignored due to limitations in traditional optimization problem formulations. To overcome these limitations, and allow for these changes, the third phase of developments presents a generic optimization formulation capable of identifying a dynamic s-Pareto frontier, while the fourth phase adapts the phase three method to incorporate this new dynamic optimization formulation. Example implementations of the four phases of developments were respectively provided through the design of a modular UAV, a hurricane and flood resistant modular residential structure, a simple aircraft design example inspired by the Lockheed C-130 Hercules, and a modular truss system. Noting that modular products only represent one approach for dealing with changes in preferences, environments, models, and concepts, the final research contribution connects the presented method with parallel research developments in collaborative product design and design principles identification, followed by two case study implementations of this unifying design approach in the development of a modular irrigation pump and a modular plywood cart for developing countries.

Collaborative products are created by combining components from two or more products to result in a new product that performs previously unattainable tasks. The resulting reduction in cost, weight, and size of a set of products needed to perform a set of functions makes collaborative products useful in the developing world. In this thesis, multiobjective optimization is used to design a set of products for optimal individual and collaborative performance. This is introduced through a nine step method which simultaneously optimizes multiple products both individually and collaboratively. The method searches through multiple complex design spaces while dealing with various trade-offs between products in order to optimize their collaborative performance. An example is provided to illustrate this method and demonstrate its usefulness in designing collaborative products for both the developed and developing world. We conclude that the presented method is a novel, useful approach for designing collaborative products while balancing the inherent trade-offs between the performance of collaborative products and the product sets used to create them.

In the design of mechanical components, numerical simulations and experimental methods are commonly used for design creation (or modification) and design optimization. However, a major challenge of using simulation and experimental methods is that they are timeconsuming and often cost-prohibitive for the designer. In addition, the simultaneous interactions between aerodynamic, thermodynamic and mechanical integrity objectives for a particular component or set of components are difficult to accurately characterize, even with the existing simulation tools and experimental methods. The current research and practice of using numerical simulations and experimental methods do little to address the simultaneous “satisficing” of multiple and often conflicting design objectives that influence the performance and geometry of a component. This is particularly the case for gas turbine systems that involve a large number of complex components with complicated geometries. Numerous experimental and numerical studies have demonstrated success in generating effective designs for mechanical components; however, their focus has been primarily on optimizing a single design objective based on a limited set of design variables and associated values. In this research, a multiobjective design optimization framework to solve a set of userspecified design objective functions for mechanical components is proposed. The framework integrates a numerical simulation and a nature-inspired optimization procedure that iteratively perturbs a set of design variables eventually converging to a set of tradeoff design solutions. In this research, a gas turbine engine system is used as the test application for the proposed framework. More specifically, the optimization of the gas turbine blade internal cooling channel configuration is performed. This test application is quite relevant as gas turbine engines serve a iv critical role in the design of the next-generation power generation facilities around the world. Furthermore, turbine blades require better cooling techniques to increase their cooling effectiveness to cope with the increase in engine operating temperatures extending the useful life of the blades. The performance of the proposed framework is evaluated via a computational study, where a set of common, real-world design objectives and a set of design variables that directly influence the set of objectives are considered. Specifically, three objectives are considered in this study: (1) cooling channel heat transfer coefficient, which measures the rate of heat transfer and the goal is to maximize this value; (2) cooling channel air pressure drop, where the goal is to minimize this value; and (3) cooling channel geometry, specifically the cooling channel cavity area, where the goal is to maximize this value. These objectives, which are conflicting, directly influence the cooling effectiveness of a gas turbine blade and the material usage in its design. The computational results show the proposed optimization framework is able to generate, evaluate and identify thousands of competitive tradeoff designs in a fraction of the time that it would take designers using the traditional simulation tools and experimental methods commonly used for mechanical component design generation. This is a significant step beyond the current research and applications of design optimization to gas turbine blades, specifically, and to mechanical components, in general.

No description available.

No description available.

No description available.

Na área de inteligência artificial existem algoritmos de aprendizado, notavelmente aqueles pertencentes à área de aprendizado de máquina AM , capazes de automatizar a extração do conhecimento implícito de um conjunto de dados. Dentre estes, os algoritmos de AM simbólico são aqueles que extraem um modelo de conhecimento inteligível, isto é, que pode ser facilmente interpretado pelo usuário. A utilização de AM simbólico é comum no contexto de classificação, no qual o modelo de conhecimento extraído é tal que descreve uma correlação entre um conjunto de atributos denominados premissas e um atributo particular denominado classe. Uma característica dos algoritmos de classificação é que, em geral, estes são utilizados visando principalmente a maximização das medidas de cobertura e precisão, focando a construção de um classificador genérico e preciso. Embora essa seja uma boa abordagem para automatizar processos de tomada de decisão, pode deixar a desejar quando o usuário tem o desejo de extrair um modelo de conhecimento que possa ser estudado e que possa ser útil para uma melhor compreensão do domínio. Tendo-se em vista esse cenário, o principal objetivo deste trabalho é pesquisar métodos de computação evolutiva multiobjetivo para a construção de regras de conhecimento individuais com base em critérios definidos pelo usuário. Para isso utiliza-se a biblioteca de classes e ambiente de construção de regras de conhecimento ECLE, cujo desenvolvimento remete a projetos anteriores. Outro objetivo deste trabalho consiste comparar os métodos de computação evolutiva pesquisados com métodos baseado em composição de rankings previamente existentes na ECLE. É mostrado que os métodos de computação evolutiva multiobjetivo apresentam melhores resultados que os métodos baseados em composição de rankings, tanto em termos de dominância e proximidade das soluções construídas com aquelas da fronteira Pareto-ótima quanto em termos de diversidade na fronteira de Pareto. Em otimização multiobjetivo, ambos os critérios são importantes, uma vez que o propósito da otimização multiobjetivo é fornecer não apenas uma, mas uma gama de soluções eficientes para o problema, das quais o usuário pode escolher uma ou mais soluções que apresentem os melhores compromissos entre os objetivos / Machine Learning algorithms are notable examples of Artificial Intelligence algorithms capable of automating the extraction of implicit knowledge from datasets. In particular, Symbolic Learning algorithms are those which yield an intelligible knowledge model, i.e., one which a user may easily read. The usage of Symbolic Learning is particularly common within the context of classification, which involves the extraction of knowledge such that the associated model describes correelation among a set of attributes named the premises and one specific attribute named the class. Classification algorithms usually target into creating knowledge models which maximize the measures of coverage and precision, leading to classifiers that tend to be generic and precise. Althought this constitutes a good approach to creating models that automate the decision making process, it may not yield equally good results when the user wishes to extract a knowledge model which could assist them into getting a better understanding of the domain. Having that in mind, it has been established as the main goal of this Masters thesis the research of multi-objective evolutionary computing methods to create individual knowledge rules maximizing sets of arbitrary user-defined criteria. This is achieved by employing the class library and knowledge rule construction environment ECLE, which had been developed during previous research work. A second goal of this Masters thesis is the comparison of the researched evolutionary computing methods against previously existing ranking composition methods in ECLE. It is shown in this Masters thesis that the employment of multi-objective evolutionary computing methods produces better results than those produced by the employment of ranking composition-based methods. This improvement is verified both in terms of solution dominance and proximity of the solution set to the Pareto-optimal front and in terms of Pareto-front diversity. Both criteria are important for evaluating the efficiency of multi-objective optimization algorithms, for the goal of multi-objective optimization is to provide a broad range of efficient solutions, so the user may pick one or more solutions which present the best trade-off among all objectives