AI-Powered Crack Propagation Predictions

Abstract

Artificial intelligence (AI) is transforming the forecasting and control of crack propagation of essential engineering materials and structures. Traditional methods, such as finite element and probabilistic fracture mechanics, are helpful but often fail to provide real-time flexibility, computational speed, and the variability of real-world situations. Machine learning, deep learning, and physics-informed neural networks —solutions based on AI —address these limitations by directly learning the relationships between stress, material properties, and crack growth, utilizing large, complex sensor datasets and complex historical records. With the combination of Internet of Things (IoT) sensor networks and digital twins, AI systems can be used to obtain prediction accuracy over 90 percent and drop the time needed by more than half, bringing the capability to monitor in real-time and detect failures before they happen. The aerospace, civil infrastructure, and energy industries have demonstrated the high economic and safety advantages of using the technology in case studies, including reduced inspection times, lower maintenance costs, and the elimination of disastrous failures. The paper includes theoretical beliefs, data collection and preprocessing approaches, model development and validation, and main findings, as well as describes prospects in hybrid AI-physics modeling, federated learning, and designing the next-generation computing. It also highlights the significance of international standards, open data materials, and interdisciplinary team integration in securing a transparent, dependable, and scalable implementation of AI-based crack propagation prediction.