AI- and ML-based Models for Predicting Remaining Useful Life (RUL) of Nanocomposites and Reinforced Laminated Structures

Engineering is the material embodiment of scientific principles for greater common good. Therefore, material science provides the foundation for innovation across all domains of engineering and helps the development of new technologies. Artificial intelligence (AI) and machine learning (ML) are being utilized more and more to develop novel materials, creative testing techniques, or models that anticipate the properties of materials. Estimating the remaining useful life (RUL) of contemporary materials, such as nanocomposites and reinforced laminated systems, is crucial for ensuring the robustness and stability of structural elements in a range of applications. Both nanocomposites and reinforced laminated structures have unique mechanical properties, structural integrity, and usage. Utilizing these properties, external conditions, and extent of usage, ML models can be developed for physical data-driven parameters, giving a comprehensive approach to RUL predictions. Data-driven models use past material performance data to identify intricate degradation trends. To find important indicators impacting RUL, feature engineering and selection techniques are investigated.