ABSTRACT
This study presents a concise analysis on applying machine learning (ML) algorithms to predict the mechanical properties of Polyphenylene Sulfide (PPS) composites reinforced with carbon fiber fillers. This paper aims to improve the accuracy and interpretability of predicting major components like flexural modulus and impact strength, which are essential for composite material design. The utilization of different ML models such as Random Forest, Gradient Boosting, and XGBoost to explore the effectiveness of multi-stacking learning techniques over traditional methods. Gradient Boosting surfaced as the most performing model, obtaining R² values of 0.977 for flexural modulus and 0.681 for impact strength, demonstrating its ability to capture complex, non-linear interaction within composite materials. As revealed by the process of feature engineering and analysis, filler content is the dominant constituent influencing mechanical properties, overshadowing the effect of processing temperature. This study also highlights the merits of adopting ensemble models based on their ability to capture intricate data patterns, providing satisfactory predictive power. While the results highlight the capability of ML in advancing composite materials research, there is also need to emphasize on the robustness of the datasets and comprehensive feature representations to further enhance model performance. This study contributes to the growing field of artificial intelligence in material science by providing a scalable platform for predicting composite properties, and eventually ushering engineers and material scientists in promoting sustainable materials.
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