Analysis of the possibility of using exploration and learning algorithms in the production of castings

Abstract

The research presented in the article indicates the process of building models based on machine learning algorithms, linear regression, decision trees, ensemble learning, random forest, ensemble averaging, Boosting, stacking, and support vector regression (SVR) algorithms. The basis for building these models are experimental data collected during research conducted at the Łukasiewicz Research Network-Krakow Institute of Technology. An analysis of the initial state and the analysis of the state of correlation in the set were performed, which facilitated the development of models. To increase the amount of data, augmentation was performed using the Bootstrapping. For selected results, castings were made and tested in real conditions. The research results indicate the possibility of identifying the most appropriate input parameters for specific production processes of austempered ductile iron (ADI), the possibility of predicting the expected mechanical parameters based on the indicated parameters of the production process, chemical composition, specific parameters of the heat treatment process, and the thickness of the target product. A set of such models constitutes the basis of the system, enabling the end user to estimate the final parameters of the casting planned to be produced.