Machine learning-driven prediction of tensile strength in 3D-printed PLA parts

• Published in: Expert systems with applications 2025-03, Vol.264, p.125836, Article 125836
• Main Authors: Nikzad, Mohammad Hossein, Heidari-Rarani, Mohammad, Rasti, Reza, Sareh, Pooya
• Format: Article
• Language: English
• Subjects: Ensemble machine learning; Fused deposition modeling; Machine learning; Ultimate tensile strength
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.125836

[Display omitted] Additive manufacturing (AM) has become a transformative technology in modern production, enabling complex geometric designs with minimal material waste. A significant aspect of AM, particularly in fused deposition modeling (FDM), is the need for precise prediction of mechanical properties, such as ultimate tensile strength (UTS), which is crucial for industrial applications. This study examines whether simple machine learning (ML) algorithms can accurately predict the UTS of 3D-printed polylactic acid (PLA) parts, and evaluates the effectiveness of ML techniques, especially ensemble methods, in enhancing prediction accuracy. To this end, the study compares simple ML algorithms to identify the most accurate model for predicting the UTS of 3D-printed PLA parts. Subsequently, an average ensemble technique combines four ML algorithms, namely categorical boosting (CatBoost), extreme gradient boosting (XGBoost), gradient boosting machine (GBM), and light gradient boosting machine (LGBM), to predict UTS. In this technique, the average predicted UTS values of CatBoost, XGBoost, GBM, and LGBM are taken as the final predicted UTS value. Additionally, 11 ensemble configurations of these algorithms are analyzed to determine the optimized ensemble configuration. The results show that the CatBoost algorithm, with an R2 of 94.46%, achieved the highest predictive accuracy among individual ML algorithms. Moreover, the CatBoost-XGBoost-GBM-LGBM ensemble was the most accurate configuration, achieving an R2 of 98.05% with less than 10% error in predicting 37 external data points not included in the training and testing sets. This study advances predictive modeling in AM by demonstrating that ML, particularly ensemble techniques, can reliably predict material properties, paving the way for more robust applications of AM in industry.