Seven Machine Learning Methods for Selecting Connecting Rods in the Machining Process

Luciana Claudia Martins Ferreira Diogenes

Frutal - MG, Brazil

Email: [email protected]

*Corresponding Author: Luciana Claudia Martins Ferreira Diogenes, Email: [email protected]

The use of machine learning (ML) has been widely used to control part dimensions during production. Parts manufactured in the automotive sectors also use ML to obtain better accuracy when selecting parts within the customer's specification limit. In this work, it will be simulated how connecting rods can be selected during the machining process inside a metallurgical plant through the application of seven ML algorithms: Decision Tree Classifier, Logistic Regression, k-Nearest Neighbors, Random Forest Classifier, Gaussian NB (Naïve Bayes), SVC (Support Vector Clustering) and Neural Network. The values for the diameter of the small connecting rod eye and the outer diameter are based on data from the literature. Simulations of training and testing data were obtained through Python programming and this data was entered into each of the seven ML techniques.

Connecting rods, control, machine learning, production, manufacturing, confusion matrix

Luciana Claudia Martins Ferreira Diogenes (2023). Seven Machine Learning Methods for Selecting Connecting Rods in the Machining Process. Journal of Artificial Intelligence and Systems, 5, 91–114. https://doi.org/10.33969/AIS.2023050107.

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