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 work introduces an approach for making decisions on buying and selling stocks in the Brazilian Stock Exchange to maximize profits in each operation. The proposed approach was built using the Neo-Fuzzy-Neuron (NFN) network to predict the future value of stocks and the Hurwicz criterion for decision analysis under risk and uncertainty, considering different degrees of optimism and pessimism. The approach was applied to Petrobras stocks (PETR4), and the results obtained were compared with the ”buy and hold”strategy. The computational results and comparisons suggest that the proposed approach is promising and provides a significant return on investment

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