This paper introduces a prediction model based on machine learning techniques for dimensional control in the manufacturing process of side ange bearing housings, according to the technical standard DIN 31693. The process is implemented in a journal-bearing manufacturing industry positioned among the three brands with the highest participation in the international market in 2023. The manufacturing process consists of rigid machining processes composed of a universal horizontal machining center and dimensional control composed of a coordinate measuring machine. After machining, the piece is measured, and its dimensional report is generated. Quali ed professionals use deviations obtained from this report to support the decision-making. The method used is based on the holistic monitoring of the surface geometry of the machined piece. The approach used to compensate for dimensional deviations is based on monitoring and modeling the total deviation. In this context, the effects of all sources of systematic errors are compensated regardless of their origin. The heuristic is used for the steps that make up the decision-making process. The way to implement the predictive model in the production line is based on the interaction between human and machine experience. This paper proposes using the regression decision trees for de ning the displacement parameters of the machining center axes from the dimensional results of housings obtained in the coordinate measuring machine. The model is validated if the mean absolute error is less than or equal to 0.003mm. A comparison between an assembled model is performed to verify the performance between different predictive models.

This paper introduces a prediction model based on machine learning techniques for dimensional control in the manufacturing process of side flange bearing housings, according to the technical standard DIN 31693. The process is implemented in a journal-bearing manufacturing industry positioned among the three brands with the highest participation in the international market in 2023. The manufacturing process consists of rigid machining processes composed of a universal horizontal machining center and dimensional control composed of a coordinate measuring machine. After machining, the piece is measured, and its dimensional report is generated. Qualified professionals use deviations obtained from this report to support the decision-making. The method used is based on the holistic monitoring of the surface geometry of the machined piece. The approach used to compensate for dimensional deviations is based on monitoring and modeling the total deviation. In this context, the effects of all sources of systematic errors are compensated regardless of their origin. The heuristic is used for the steps that make up the decision-making process. The way to implement the predictive model in the production line is based on the interaction between human and machine experience. This paper proposes using the regression decision trees for defining the displacement parameters of the machining center axes from the dimensional results of housings obtained in the coordinate measuring machine. The model is validated if the mean absolute error is less than or equal to 0.003mm. A comparison between an assembled model is performed to verify the performance between different predictive models.

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Este trabalho apresenta os processos para a construção do curso de desenvolvimento de aplicativos para dispositivos móveis, utilizando o MIT App Inventor. O objetivo do curso foi introduzir o pensamento computacional nos alunos envolvidos de forma criativa, utilizando o próprio celular para o desenvolvimento de aplicações, estimulando-os, com os conteúdos abordados, a resolver problemas de seu cotidiano. Este artigo apresenta toda a fundamentação teórica utilizada para a construção, metodologia de ensino e avaliação aplicados no curso, o trabalho  apresenta também os desafios enfrentados no ensino devido à pandemia de Covid-19 e, por fim, discute os resultados obtidos pelo curso, através do formulário de autoavaliação aplicado aos estudantes.

Recognized by law, the Brazilian Sign Language (LIBRAS), is thesecond Brazilian official language and, according to IBGE (BrazilianInstitute of Geography and Statistics), Brazil has a large communityof hearing-impaired people, with approximately nine million ofdeaf people. Besides that, most of the non-deaf community cannotcommunicate or understand this language. Considering that, theuse of LIBRAS’ interpreters becomes extremely necessary in orderto allow a greater inclusion of people with this type of disabilitywith the whole community. However, an alternative solution tothis problem would be to use artificial neural network methods forthe LIBRAS recognition and translation. In this work, a processof LIBRAS’ recognition and translation is presented, using videosas input and a convolutional-recurrent neural network, known asConvLSTM. This type of neural network receives the sequence offrames from the videos and analyzes, frame by frame, if the framebelongs to the video and if the video belongs to a specific class.This analysis is done in two steps: first, the image is analyzed inthe convolutional layer of the network and, after that, it is sent tothe network recurrent layer. In the current version of the implementednetwork, data collection has already been carried out, theconvolutional-recurrent neural network has been trained and it ispossible to recognize when a given LIBRAS’ video represents ornot a specific sentence in this language.