[pt] Hoje em dia, a enorme quantidade de dados disponíveis online apresenta um novo desafio para os processos de descoberta de conhecimento. As abordagens mais utilizadas para enfrentar esse desafio são baseadas em técnicas de aprendizado de máquina. Apesar de serem muito poderosas, essas técnicas exigem que seus parâmetros sejam calibrados para gerar modelos com melhor qualidade. Esses processos de calibração são demorados e dependem das habilidades dos especialistas da área de aprendizado de máquinas. Neste contexto, esta pesquisa apresenta uma estrutura baseada em agentes de software para automatizar a calibração de modelos de aprendizagem de máquinas. Esta abordagem integra conceitos de Engenharia de Software Orientada a Agentes (AOSE) e Aprendizado de Máquinas (ML). Como prova de conceito, foi utilizado o conjunto de dados Iris para mostrar como nossa abordagem melhora a qualidade dos novos modelos gerados por nosso framework. Além disso, o framework foi instanciado para um dataset de imagens médicas e finalmente foi feito um experimento usando o dataset Grid Sector. / [en] Nowadays, the challenge of knowledge discovery is to mine massive amounts of data available online. The most widely used approaches to tackle that challenge are based on machine learning techniques. In spite of being very powerful, those techniques require their parameters to be calibrated in order to generate models with better quality. Such calibration processes are time-consuming and rely on the skills of machine learning experts. Within this context, this research presents a framework based on software agents for automating the calibration of machine learning models. This approach integrates concepts from Agent Oriented Software Engineering (AOSE) and Machine Learning (ML). As a proof of concept, we first train a model for the Iris dataset and then we show how our approach improves the quality of new models generated by our framework. Then, we create instances of the framework to generate models for a medical images dataset and finally we use the Grid Sector dataset for a final experiment.

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<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>