Machine Learning for Identifying Damage and Predicting Properties in 3D-Printed PLA/Lygeum Spartum Biocomposites

This paper offers an experimental approach that integrates acoustic emission (AE) monitoring with machine learning (ML) to identify damage mechanisms and predict the mechanical properties of 3D-printed biocomposites. Specimens were fabricated using a bio-filament composed of a PLA matrix reinforced with 10% wt. of Lygeum spartum fibers and were subjected to tensile and flexural tests. The processed dataset, comprising six normalized features (cumulative rise, duration, count, frequency, energy, and amplitude) was used to train four ML models: Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Decision Trees (DT) implemented in Python using libraries such as scikit-learn, pandas, and numpy. The prediction models were developed using an 80/20 train–test split and further validated by 5-fold cross-validation, with performance evaluated by R-squared (R2) and Mean Squared Error (MSE) metrics. Our results demonstrate robust prediction capabilities, with the RFR model achieving the highest accuracy (R2 > 0.98 and MSE as low as 0.013 for tensile stress prediction). Additionally, unsupervised clustering using K-means was applied to group AE signals into distinct clusters corresponding to different damage modes. This comprehensive methodology not only enhances our understanding of damage evolution in composite materials but also establishes a data-driven framework for non-destructive evaluation and structural health monitoring.