Redes neurais aplicadas à predição de propriedades de cerâmicas multicomponentes

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência e Engenharia de Materiais.Aplicou-se a metodologia de delineamento de misturas combinado com redes neurais em dois sistemas cerâmicos multicomponentes: porcelana elétrica e porcelanato, contendo número de matérias-primas similar ao utilizado nas indústrias de fabricação de isoladores elétricos e de revestimentos cerâmicos, respectivamente. As matérias-primas industriais disponíveis foram caracterizadas quanto à composição química e propriedades físicas, as quais foram utilizadas para o delineamento de misturas. Após processamento e obtenção de propriedades das misturas planejadas, em laboratório preparado para reprodução de condições industriais, efetuou-se análise pelos métodos de regressão polinomial e treinamento de redes neurais artificiais. A capacidade preditiva dos dois métodos de análise foi avaliada com experimentos de verificação a partir de misturas não contidas no planejamento inicial. No sistema #porcelana elétrica# modelos polinomiais de segunda ordem ajustaramse para relação entre teor de matérias-primas e propriedades finais das misturas, havendo melhor capacidade preditiva das redes neurais em comparação aos modelos resultantes da regressão polinomial. No sistema #porcelanato#, modelos lineares ajustaram-se bem para relação entre variáveis de entrada e saída, assim como as redes neurais, as quais apresentam menor desvio padrão ao prever propriedades a partir de entradas não contidas no planejamento. De maneira geral, as RNAs #aprendem# a relação entre o teor das matérias-primas e as propriedades finais das misturas cerâmicas tradicionais, independente do grau da função que a descreve e do número de matérias-primas envolvidas, podendo ser utilizadas para prever propriedades de misturas não contidas no treinamento. Artificial neural networks were applied to the prediction of properties of two multicomponent ceramic systems: electrical porcelain and porcelain stoneware. Design of mixture experime the number of raw materials commonly used in ceramic industries of electrical insulators and floor tiles, respectively. Physical and chemical properties were determined for available raw materials, which were used in the design of mixture experiments. Designed mixtures were processed in laboratory according to an industrial approach. After reaching the properties of the designed ceramic mixtures, data analyses were performed by linear regression and artificial neural networks. The predictive ability of both data analysis methods was evaluated by verification experiments. In the electrical porcelain system, second order polynomial model fitted the relation between the raw materials content and final properties of mixtures. Artificial neural networks were successfully used for prediction of properties, compared with polinomial regression. In the floor tile system, linearity was achieved for the relation between raw materials content and final properties. Artificial neural networks were successful too and provided lower standard deviation compared to the fitted linear model. Artificial neural networks #learn# the relationship between raw materials content and ceramic mixture properties, independently of the number of raw materials used and the mathematical model that describes this relationship

EXPERT SYSTEM BASED APPROACH FOR MATERIAL SELECTION OF AUTOMOBILE BODY-IN-WHITE STRUCTURAL PANELS USING NUMERICAL RANKING AND SUSTAINABILITY INDICES

The goal of this work is to establish a set of quantifiable measures for design for sustainability (DFS) that can be applied to automotive applications in terms of environmental, social, economic and technical aspects. In this study, a comprehensive analysis was made in order to develop a methodology that can evaluate different body-in-white designs in terms of major sustainability aspects. Besides the complete life cycle analysis, environmental impacts and cost factors will be analyzed over vehicle\u27s entire life-cycle (fuel extraction and refining, Pre-manufacturing, Manufacturing, Use, and Post-use stages). The considered material options include: conventional steel, high strength steel, aluminum, magnesium, titanium and composites that are currently used in body-in-white (BIW) structures and exterior body panels. Sustainability scoring method was developed and used to decide on how using lighter materials in auto body applications is beneficial or not. The proposed major sustainable factors are categorized into four major groups: environmental, economical, social and technical groups. Also, each group has corresponding factors which were chosen by extensive search and screening, so only important sustainability aspects for auto body design have been selected in this study. Then the dissertation proceeds to show some sustainability scoring methods in order to get better understanding as well as relative ranking for different materials from sustainability point of view. Moreover, this work discusses the role and application of some multi-criteria decision making methods in materials selection, namely quality function deployment (QFD) and analytic hierarchy process (AHP). However, multi-criteria decision making methods are efficient tools to choose alternative from large set of alternatives, especially when two or more conflicting goals are present. Besides that, knowledge based system (KBS) was established for eco-material selection for auto-body structural panels. The goal behind using KBS is to help designers in material selection process which usually needs experience, time and effort

Prédiction et compréhension de la densification des poudres commerciales d'alumine et de fer grâce à une approche par réseau de neurones artificiels

The influence of the properties of commercial powders on the densification during their packing, compaction and sintering process is still not understood in detail. With regard to the sintering process, neither the well-known sintering equation for the first sintering step nor the relation between the density and grain size at the final sintering step can describe the influence of powder characteristics on its densification behaviour. For improving the sintered density of a ceramic powder, it is known to be crucial to start with a highly dense and homogeneous green body. Therefore, the powder has to fulfil different requirements such as being agglomerate free, reasonably spherical and having a narrow size distribution (but not mono-dispersed). The aim of this work is to develop a better understanding of the relation between the powder properties and the densification behaviour during the packing, compaction and sintering process, of commercial, micron sized, metallic and ceramic powders. Another aim of this work is to evaluate if prediction of the packed, green and sintered densities based only on the known powder characteristics is possible via a neural network approach. The presented results show that a well learnt neural network is a useful tool for the prediction of green and sintered densities of granulated alumina powder produced either by milling (Bayer process) or by chemical processes. Moreover, the simulated influences of characteristics, on the green and sintered densifications, fit well literature models behaviours and intrinsic properties of such powders. Concerning the green densification, Bayer powders are denser for coarser particles and/or a broader size distribution. Relating to the chemically produced powders, those tendencies are inversed, due a stronger agglomeration with a broader size distribution and coarser particles. Regarding the sintered density, the neuronal approach highlights a better sinterability for finer powders. Limits of the artificial neural network tool are emphasized with its application to metallic powders: the learning stage seems to be primeval and simulated results are to be analysed and interpreted with care and inside the validity domain of each specific artificial neural network