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    Efeito Da Intercamada De Silício Na União Dissimilar A Laser De Ti-6Al-4V E Aa6013-T4
    (Universidade Federal de São Paulo (UNIFESP), 2017-09-06) Moreira, Andre Felipe Ribeiro [UNIFESP]; Oliveira, Aline Capella De [UNIFESP]; Universidade Federal de São Paulo (UNIFESP)
    The main goals of this dissertation were analyze the effects and influence of the deposited silicon at the interface of the laser joint between the dissimilar metals Ti-6Al-4V and AA6013-T4, in order to evaluate the deposition and joining features in microstructural terms and the inhibition the formation of brittle intermetallic compounds, with the aim of improving the mechanical properties of the joint. For this, proposes the deposition of silicon on Ti-6Al-4V substrate using magnetron sputtering technology and subsequent joining with the AA6013-T4 by laser (Yb: fiber) processing that allows greater control of parameters of the bonding process between the dissimilar metals. The results demonstrate that increasing the deposition time, or the utilized power, influences the thickness of the silicon layer deposited on Ti-6Al-4V substrate, with values between 0.9 - 7.5 μm. Furthermore, also shows that better joining condition between the dissimilar alloys in respect of the microstructure region and not significant presence of defects are obtained from 0.2 mm displacement of the beam on the AA6013-T4. The intermetallic layer thickness, with the predominance of fragile intermetallic TiAl3, was reduced from 15 μm to 5 μm. However, such reduction was not sufficient to cause a significant change in the mechanical strength of the assembly. From the results obtained verified the viability of the deposition of silicon on Ti-6Al-4V substrate with the formation of a film of good adhesion in all conditions analyzed. However, the correlation between the introduction of silicon and its influence in the formation of the intermetallic compounds at the joining interface between the dissimilar metals, Ti-6Al-4V and AA6013, was not favorable to increase the mechanical strength of the joint.
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    Programação Kaizen Para Construção De Modelos Interpretáveis: Uma Abordagem Multiobjetivo Para Regressão Simbólica
    (Universidade Federal de São Paulo (UNIFESP), 2017-04-13) Alves, Artur Henrique Goncalves Coutinho [UNIFESP]; Melo, Vinicius Veloso De [UNIFESP]; Universidade Federal de São Paulo (UNIFESP)
    Regression problems are among some of the main current uses for machine learning and pattern recognition techniques, coming from the necessity of identifying relationships between behaviors and explanatory variables in economy, engineering, natural environment and numerous other areas. Traditional machine learning techniques such as artificial neural networks are extensively applied to real problems with good success rates, but present dificulties, such as parameter configuration, and shortcomings, such as the impossibility of interpreting the relationships found in the modeling of a specific system. Enters, then, the symbolic regression, a research subarea focused on methods for building mathematical equations for the correct modeling of different behaviors. This approach is not limited by incorrect structure choices, unlike linear regression, and allows for the analysis of the behavior modeled by the chosen mathematical elements. In this work, a recent automatic programming technique that can be used for symbolic regression is presented: Kaizen Programming. This technique applies continuous improvement concepts in a hyperheuristics structure, allowing for its use in various problems and with various auxiliary heuristics. Besides, it uses deterministic methods to evaluate and decide upon the ideas proposed by these techniques, lessening the negative impact a purely stochastic approach may bring to this kind of application. The implementation used here presents new modifications, specially the inclusion of a new objective: their complexity, defined by the nonlinearity of their mathematical elements. In real problems, it is expected that high quality models will be complex, but not too much, in order to avoid overfitting and to keep interpretability; therefore, the symbolic regression becomes a multiobjective problem, with conflicting objectives. This new version of Kaizen Programming was compared to the original one, to classical machine learning techniques and to another symbolic regression technique in well-known datasets constructed from real problems and in a time series - building autoregressive models for a predictive control to automatically drive a vehicle in a racing simulator. In general, the new technique presents lower predictive power when compared to its original counterpart and the other symbolic regression technique considered here, but offers solutions that are considerably simpler than the ones built by both.