Decision tree predictive model for dimensional control of side flange bearing housings

Abstract

Abstract 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.