Information contributes to the improvement of decision-making, process improvement, error detection, and prevention. The new requirements of the coming Industry 4.0 will make these new information technologies help in the improvement and decision-making of industrial processes. In case of the welding processes, several techniques have been used. Welding processes can be analyzed as a stochastic system with several inputs and outputs. This allows a study with a data analysis perspective. Data mining processes, machine learning, deep learning, and reinforcement learning techniques have had good results in the analysis and control of systems as complex as the welding process. The increase of information acquisition and information quality by sensors developed at present, allows a large volume of data that benefits the analysis of these techniques. This research aims to make a bibliographic analysis of the techniques used in the welding area, the advantages that these new techniques can provide, and how some researchers are already using them. The chapter is organized according to some stages of the data mining process. This was defined with the objective of highlighting evolution and potential for each stage for welding processes.
The dataset was collected from experiments using the gas metal arc welding (GMAW) process. The experiments were planned with Central Composite Design to obtain a greater variety of data. This variability helps to develop a predictive model more generalistic with machine learning techniques. It was collected welding arc images and weld bead geometry images. Welding arc images were processed with a deep learning technique to detect drop detachment and short circuit transfer mode. These detections were useful to calc drop detachment frequency, short circuit frequency, and molten volume in every moment of GMAW process time. It was obtained the weld bead geometry parameters by process time too. All these data, joining input parameters were correlated, resulting in the datasets shown in this article.
O artigo discute o contexto de produção de dados quantitativos sobre evasão escolar na Rede Federal de Educação Profissional e Tecnológica, no Brasil, com especial atenção para o ensino médio técnico, entre 2003 e 2015. Por meio de análise documental e entrevistas a gestores, são apresentadas as ações do governo federal ao tratarem da formulação de indicadores que quantifiquem a evasão escolar. Especial atenção é dada às mudanças nas bases de dados, nas formas de cálculos e na estrutura e cultura institucional necessárias para se medir a evasão escolar. As escolhas institucionais do governo federal para lidar com a evasão escolar revelam a complexidade do problema e os limites e possibilidades das políticas públicas implementadas. Explicita-se um Estado regulador e avaliador, distante dos modos de combate à evasão.
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