Parametric modeling of resin-bonded sand mold systems using machine learning-based approaches

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Abstract
This study presents the experimental investigations on modeling of the compression strength and permeability of the resin-bonded sand mold system through Machine Learning approaches. The process of constructing the Fuzzy Logic system was automated by utilizing a data-base and rule-base optimized through genetic algorithms, and the recorded datasets. This research employed three AI models, namely Artificial Neural Networks (ANN), Decision Tree (DT), and Random Forests (RF), using the datasets produced by the GA-tuned Fuzzy model. The objective of this study was to assess and compare the predictive capabilities of three different AI models (Artificial Neural Networks, Decision Tree, and Random Forests) in terms of predicting the values of Compression Strength and Permeability. The complete dataset was divided into two separate subsets, referred to as training data and testing data. Based on the findings, it appears that Random Forest (RF) model exhibits promising potential in accurately predicting the desired mold qualities. The model achieved a high R2 value of 0.9487, indicating a strong correlation with the target values. Additionally, the model demonstrated impressively low Mean Squared Error (MSE) and Mean Absolute Error (MAE) values of 117 and 17.6 points, respectively. Expanding the dataset size may further enhance the efficacy of the models.

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