A Review of AI-Assisted Stress Prediction Models in Mechanical Design

Abstract

Stress analysis constitutes an essential part of mechanical design, by which the safety, reliability, and performance of the components engineered to operate under complex loadings and environmental conditions can be ascertained. Conventional methods for stress prediction, such as analytical approaches, finite element analysis (FEA), and experimental testing, have been implemented extensively in applications like piping systems, pressure vessels, and rotating machinery. these methods are frequently associated with significant computational costs, prolonged solution times, and extensive modeling efforts, thereby hindering their effectiveness in rapid design iterations and real, time applications. In a landmark departure from traditional methods, the advent of artificial intelligence (AI) and machine learning (ML) has spawned data, driven alternatives that can efficiently predict stress responses by learning the nonlinear relationships between geometrical features, materials, and loading conditions. AI, driven stress prediction models, which encompass machine learning and deep learning methods, are capable of quick and precise stress estimations, thus facilitating early, stage design decisions and diminishing the need for a multitude of simulation runs a comprehensive review of AI, assisted stress prediction models in mechanical design, their interaction with traditional stress analysis methods, the main advantages, and the limitations intrinsic to them regarding computational efficiency, predictive accuracy, and obstacles stemming from data quality, generalization, and physical interpretability, thereby sketching the next steps towards intelligent stress analysis frameworks in engineering practice.