چكيده لاتين
Predicting the tensile properties of thermoplastic polymers such as PLA and ABS, including elastic modulus and tensile strength, significantly reduces material waste and manufacturing costs through the application of machine learning and deep learning algorithms. In this study, machine learning and deep learning algorithms were developed for predicting the tensile properties of PLA and ABS. A grid search method was employed for hyperparameter tuning of the machine learning algorithms to predict the tensile properties of PLA, leveraging the sufficient amount of data available in various publications. Meanwhile, the efficient computational Taguchi method was utilized for tuning the hyperparameters of the deep learning algorithms. Ultimately, the deep deterministic policy gradient method, a part of deep reinforcement learning, was used for hyperparameter tuning of the machine learning algorithms for predicting the tensile properties of ABS due to the scarcity of data. Innovative aspects of this research include the comparison of traditional and state-of-the-art deep learning algorithms, as well as the integration of machine learning algorithms for predicting the tensile properties of 3D-printed polymers. The results indicated that the combination of the CatBoost-GPR and CatBoost-GBM-XGBoost-LGBM algorithms yielded the best performance in predicting the elastic modulus and tensile strength of PLA parts, achieving prediction accuracies of 99.476% and 98.051%, respectively. Additionally, the combined CatBoost-KNN-KR algorithm demonstrated a prediction accuracy of 93.925%, the SVR-XGBoost combined algorithm achieved an accuracy of 88.309%, and the CatBoost-KR-SVR-XGBoost combined algorithm attained an accuracy of 90.18% for predicting tensile strength, elastic modulus, and simultaneous predictions of both properties for 3D-printed ABS. Furthermore, the TabNet algorithm, as an advanced and updated neural network, exhibited a more accurate predictive performance compared to traditional methods such as ANN and LSTM. Finally, utilizing a response surface methodology allowed for the simultaneous maximization of the elastic modulus and tensile strength of 3D-printed ABS parts, with a discrepancy of less than ten percent observed between the values obtained from the response surface method and experimental values. A limitation of this research was the lack of sufficient data for 3D-printed ABS in the literature, resulting in a limited dataset due to the costs associated with experiments and material fabrication.
