Comparative analysis of machine learning models for predicting the mechanical properties of aerospace grade fiber composites

Abstract Accurate evaluation of the mechanical properties of aerospace-grade fiber composites is critical for ensuring structural integrity, certification compliance, and operational performance of aircraft and spacecraft. However, significant variability in reported mechanical properties arises from differences in fabrication techniques, process parameters, and testing conditions, necessitating efficient predictive methodologies. While previous machine learning studies have focused predominantly on single property prediction using homogeneous datasets, comprehensive comparative analyses across multiple algorithms utilizing heterogeneous data sources remain limited. This study presents a systematic comparative analysis of machine learning models; Linear Regression (LR), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Instance-based K-Nearest Neighbors (IBK) for predicting both tensile and flexural strength of fiber-reinforced composites. Models were trained using two distinct datasets: in-house laboratory experimental data (28–45 instances) and an expanded dataset combining laboratory results with published literature (98–102 instances). Input attributes encompassed reinforcement type, matrix composition, ply orientation, fiber content, stacking sequence, and specimen dimensions. Statistical evaluation revealed that LR and SVR achieved superior accuracy for smaller datasets (R 2 = 0.998 and 0.996, respectively), attributed to their effectiveness in low-dimensional feature spaces. Conversely, MLP demonstrated robust predictive capability for larger, more heterogeneous datasets (R 2 = 0.9538 for flexural; R 2 = 0.9445 for tensile strength), owing to its capacity for capturing non-linear relationships. These findings establish that appropriately selected ML-based models can effectively predict FRC mechanical properties, providing designers and manufacturers with reliable, data-driven approaches to accelerate material qualification while reducing dependence on extensive experimental testing.