Abstract

In the cell wall of cellulose-based fibers such as wood, the microfibril angle (MFA) in the S2 layer plays a crucial role in determining anisotropic properties. Current Wide-angle X-ray diffraction (WAXD) methods for prediction rely on empirical equations, lacking clear predictive capabilities and remaining stagnant for decades. This study presents a novel approach to predict MFA and its variability using a generalized diffraction equation, Monte Carlo simulations of diffraction patterns, and Machine Learning models, including Random Forest k-Nearest Neighbors (kNN), and Artificial Neural Networks (ANNs). Results show that the commonly Variance Approach generates inaccurate predictions (RMSE=2.61 degrees, MAE=2.12 degrees), while the proposed AI models demonstrate significantly higher accuracy (RF: RMSE=0.72 degrees, MAE=0.29 degrees; kNN: RMSE=0.87 degrees, MAE=0.40 degrees; ANN: RMSE=0.47 degrees, MAE=0.24 degrees). Furthermore, the AI models suggest that empirical cross-section shape is not required for accurate MFA prediction. This innovative approach, leveraging advanced computational methods and AI, addresses long-standing challenges in MFA prediction using WAXD.