Deposition and characterization of AZ61A Magnesium alloy in GMAW-based WAAM

Additive Manufacturing (AM) is considered an integral part of the 4th Industrial Revolu- tion, allowing breakthroughs in each step of production. In this category, Direct Energy Depo- sition processes like Wire and Arc Additive Manufacturing (WAAM) have shown capability of high deposition rates, the ability to produce large parts while being economically advanta- geous. However, many typical defects such as pores and cracks and lack of development with some alloys are still present and require more research. Magnesium alloys are among the least developed alloys for the WAAM process, these alloys can provide a weight reduction of parts produced, which can lead to the reduction of emissions in the transportation sector. This study aimed to successfully deposit the AZ61A Magnesium alloy using an in-house built GMAW-Based WAAM machine. By varying the parameters of the process, the Heat Input (HI) during deposition was affected and its impact on the material was studied. During the project four walled-like samples were deposited, each with its own set of parameters and consequently heat input. First, the Wire Feed Speed (WFS) was established, and other parameters were iterated in accordance with deposition stability and weld bead ap- pearance. The samples had significant internal defects and the process was characterized by deposition instability due to the process instability and material reactivity. The mean grain size was similar between the samples (17.49-22.93 μm). As expected from the similar grain size, microhardness was similar as well with a slight tendency to decrease as HI increased. Tensile testing was only conducted for sample S1 due to internal defects. During this project, several obstacles such as, dissimilar materials, deposition instability, and thermal properties of the Magnesium alloy were detrimental to the successful deposition of samples. Acceptable parameters were obtained, and the resulting properties characterized. To further the application of these alloys in GMAW-Based WAAM, development is needed.A Manufatura Aditiva é considerada uma parte integrante da 4ª Revolução Industrial, permitindo avanços em cada etapa da produção. Nesta categoria, os processos de Direct Energy Deposition em específico, Wire and Arc Additive Manufacturing (WAAM) demonstram a capacidade de elevadas taxas de deposição, capacidade de produzir grandes peças. Contudo ainda são presentes defeitos, como poros e fissuras e a falta de desenvolvimento aplicado a outras ligas. As ligas de magnésio estão entre as ligas menos desenvolvidas para o processo WAAM, podem proporcionar uma redução de peso das peças produzidas, o que pode levar à redução das emissões no sector dos transportes. Este estudo visou depositar amostras no formato de uma parede com a liga de Magnésio AZ61A, utilizando a máquina de WAAM desenvolvida no departamento com uso da tecnologia de soldadura GMAW. Ao se estabelecerem os parâmetros para a deposição das amostras houve uma variação da entrega térmica, a sua influência nas propriedades foi analisada. Durante o projeto, foram depositadas quatro amostras. Primeiro, o Wire Feed Speed (WFS) foi estabelecido, os outros parâmetros foram iterados de acordo com a estabilidade de deposição e a aparência do cordão. As amostras tinham defeitos internos significativos e o processo foi caracterizado pela instabilidade de deposição e reatividade do material. O tama- nho médio do grão foi semelhante entre as amostras (17,49-22,93 μm). A microdureza também foi semelhante, com uma ligeira tendência para diminuir à medida que a entrega térmica au- menta. Os ensaios de tração apenas foram realizados para uma amostra devido a defeitos internos. Durante este projeto, vários obstáculos como, materiais diferentes, instabilidades de deposição, e propriedades térmicas da liga de Magnésio, foram prejudiciais para o sucesso da deposição das amostras. Foram obtidos parâmetros aceitáveis, e as propriedades resultantes foram caracteriza- das. Para promover a aplicação destas ligas em WAAM com uso da tecnologia GMAW é ne- cessário mais desenvolvimento

Associação entre a participação num programa de Educação Física estruturado no 1º ciclo, e a qualidade de vida e a motivação para a educação física no 3º ciclo

Orientação: António PalmeiraObjectivo: Este estudo teve como objectivo analisar a associação entre a partipação no projecto PlayGym, que foi providenciado a crianças de 1ºciclo, e a qualidade de vida e motivação para a Educação Física das mesmas crianças no 3º ciclo. Método: Foi realizado um estudo observacional, com recolha de dados quantitativa, em escolas na cidade de Lisboa nos anos letivos de 2005/2006 e 2012/2013. Participaram neste estudo 1211 alunos entre o 9º e o 12º ano de escolaridade, sendo que 325 alunos participaram no projeto PlayGym e 886 não são participantes do projeto PlayGym. No presente estudo, foram utilizados questionários para a recolha de dados: QAD (Questionário de Atividade Física), KidScreen para a qualidade de vida e PLOCQ para os tipos de motivação dos alunos para as aulas de Educação Física. Adicionalmente recolheram-se dados demográficos para caracterizar a amostra. Resultados: Não foram encontradas diferenças significativas entre os alunos que participaram no PlayGym e os que não participaram, em termos de qualidade de vida e regulações motivacionais. Estes resultados contrariam alguma da literatura consultada, que refere que o início da prática de atividade física enquanto jovens, está associado a uma tendência para evitar o sedentarismo quando chegarem à idade adulta.Objective: This study aimed to analyze the association between the participation in the PlayGym project, which was provided to children from elementary school and quality of life and motivation for physical education of those children in middle and high school. Method: An observational study was conducted, collecting quantitative data on schools in the city of Lisbon in 2005/2006 and 2012/2013 school years. 1211 students participated in this study between the 9th and 12th grade, and 325 students participated in the PlayGym project and 886 were not participants in the PlayGym project. In this study, surveys used for data collection were PAF (Physical Activity Survey), KidScreen (quality of life) and PLOCQ for students motivation in physical education classes. Additionally demographic data were collected to characterize the sample. Results: No significant differences were found in quality of life and motivational regulations between students who participated in the PlayGym and the students that did not participate. These results contradict some of the consulted literature, which states that the initiation of physical activity at a young age is associated with a tendency to avoid a sedentary lifestyle when they reach adulthood

Evolutionary design of neural networks for classification and regression

Comunicação aprovada à ICANGA March 2005, Coimbra.The Multilayer Perceptrons (MLPs) are the most popular class of Neural Networks. When applying MLPs, the search for the ideal architecture is a crucial task, since it should should be complex enough to learn the input/output mapping, without overfitting the training data. Under this context, the use of Evolutionary Computation makes a promising global search approach for model selection. On the other hand, ensembles (combinations of models) have been boosting the performance of several Machine Learning (ML) algorithms. In this work, a novel evolutionary technique for MLP design is presented, being also used an ensemble based approach. A set of real world classification and regression tasks was used to test this strategy, comparing it with a heuristic model selection, as well as with other ML algorithms. The results favour the evolutionary MLP ensemble method.Fundação para a Ciência e Tecnologia - Project POSI/ROBO/43904/2002; FEDER

Evolving Time Series Forecasting Neural Network Models

In the last decade, bio-inspired methods have gained an increasing acceptation as alternative approaches for Time Series Forecasting. Indeed, the use of tools such as Artificial Neural Networks (ANNs) and Genetic and Evolutionary Algorithms (GEAs), introduced important features to forecasting models, taking advantage of nonlinear learning and adaptive search. In the present approach, a combination of both paradigms is proposed, where the GEA's searching engine will be used to evolve candidate ANNs topologies, enhancing forecasting models that show good generalization capabilities. A comparison was performed, contrasting bio-inspired and conventional methods, which revealed better forecasting performances, specially when more difficult series were taken into consideration; i.e., nonlinear and chaotic ones.The work of Paulo Cortez was supported by the portuguese Foundation of Science & Technology through the PRAXIS XXI/BD/13793/97 grant. The work of José Neves was supported by the PRAXIS' project PRAXIS/P/EEI/13096/98

Simultaneous evolution of neural network topologies and weights for classification and regression

Artificial Neural Networks (ANNs) are important Data Mining (DM) techniques. Yet, the search for the optimal ANN is a challenging task: the architecture should learn the input-output mapping without overfitting the data and training algorithms tend to get trapped into local minima. Under this scenario, the use of Evolutionary Computation (EC) is a promising alternative for ANN design and training. Moreover, since EC methods keep a pool of solutions, an ensemble can be build by combining the best ANNs. This work presents a novel algorithm for the optimization of ANNs, using a direct representation, a structural mutation operator and Lamarckian evolution. Sixteen real-world classification/regression tasks were used to test this strategy with single and ensemble based versions. Competitive results were achieved when compared with a heuristic model selection and other DM algorithms.Universidade do Minho. Centro Algoritmi.Fundação para a Ciência e a Tecnologia (FCT) - POSI/EIA/59899/2004

Evolution of neural networks for classification and regression

Although Artificial Neural Networks (ANNs) are important Data Mining techniques, the search for the optimal ANN is a challenging task: the ANN should learn the input-output mapping without overfitting the data and training algorithms may get trapped in local minima. The use of Evolutionary Computation (EC) is a promising alternative for ANN optimization. This work presents two hybrid EC/ANN algorithms: the first evolves neural topologies while the latter performs simultaneous optimization of architectures and weights. Sixteen real-world tasks were used to test these strategies. Competitive results were achieved when compared with a heuristic model selection and other Data Mining algorithms.Fundação para a Ciência e a Tecnologia (FCT) - projecto POSI/EIA/59899/2004

Evolutionary neural network learning algorithms for changing environments

Classical Machine Learning methods are usually developed to work in static data sets. Yet, real world data typically changes over time and there is the need to develop novel adaptive learning algorithms. In this work, a number of algorithms, combining Neural Network learning models and Evolutionary Computation optimization techniques, are compared, being held several simulations based on artificial and real world problems. The results favor the combination of evolution and lifetime learning according to the Baldwin effect framework

Evolving time series forecasting ARMA models

Nowadays, the ability to forecast the future, based only on past data, leads to strategic advantages, which may be the key to success in organizations. Time Series Forecasting (TSF) allows the modeling of complex systems as ``black-boxes'', being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. Alternative TSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Evolutionary Algorithms (EAs), are popular. The present work reports on a two-level architecture, where a (meta-level) binary EA will search for the best AutoRegressive Moving-Average (ARMA) model, being the parameters optimized by a (low-level) EA, which encodes real values. The handicap of this approach is compared with conventional forecasting methods, being competitive

Internal diversity and political parties : comparing Expresso’s online and print coverage of the 2022 Portuguese legislative elections

In modern democratic societies, the value of news media content diversity in safeguarding social harmony cannot be underestimated. By providing citizens with a broad range of political, economic and social viewpoints, news media outlets ensure that everyone can participate meaningfully in the democratic process. This task is particularly important in elections, as this is when the public must decide who will be in government. By means of a content analysis of Expresso’s coverage of the 2022 Portuguese legislative elections, this report, which was the culmination of a 6-month internship in Expresso, determined which format, online or print, can better deliver diverse news content. At the article level, the average Expresso print article mentioned more political parties, actors and topics. At the newspaper level, however, both formats displayed strikingly similar levels of content diversity. The results of a multilevel analysis indicate that the size of the article is positively related to content diversity, while game and conflict news frames, often derided as conducive to polarizing and frivolous reporting, can also play a vital role in improving the multiperspectival nature of election coverage.Nas sociedades democráticas modernas, o valor da diversidade dos conteúdos dos meios de comunicação social na salvaguarda da harmonia social não pode ser subestimado. Ao fornecerem aos cidadãos um vasto leque de pontos de vista políticos, económicos e sociais, os meios de comunicação social garantem que todos podem participar de forma significativa no processo democrático. Esta tarefa é particularmente importante durante períodos eleitorais, uma vez que é nesta altura que o público deve decidir quem o vai representar. Através de uma análise de conteúdo da cobertura do jornal Expresso das eleições legislativas portuguesas de 2022, este relatório, que foi o culminar de um estágio de 6 meses no Expresso, determinou qual o formato, online ou papel, que melhor consegue fornecer conteúdos noticiosos diversificados. Ao nível do artigo, o artigo médio da versão impressa do Expresso mencionou mais partidos políticos, actores e tópicos. No entanto, ao nível do jornal, ambos os formatos apresentaram níveis muito semelhantes de diversidade de conteúdo. Os resultados de uma análise multinível indicam que a dimensão do artigo está positivamente relacionada com a diversidade de conteúdo, enquanto que os enquadramentos noticiosos que enfatizam o conflito, muitas vezes referidos como conducentes a reportagens polarizadoras e frívolas, podem também desempenhar um papel vital na melhoria da natureza multiperspectiva da cobertura eleitoral