This article analyzes the current state, practical applications, and promising development directions of artificial intelligence (AI) and machine learning (ML) technologies in mechanical engineering. The study examines the application of AI and ML methods in predictive maintenance, design optimization, manufacturing and process optimization, robotics and automation, quality control, and structural health monitoring. The capabilities of modern approaches, including neural networks, deep learning, reinforcement learning, transfer learning, and Bayesian optimization, in solving complex engineering problems are also discussed. Particular attention is paid to the integration of data-driven models with physics-based models, uncertainty quantification, model interpretability, reliability, and adaptability under different operating conditions. The analysis demonstrates that the integration of AI and ML into mechanical engineering provides significant opportunities for improving production efficiency, reducing maintenance costs, enhancing product quality, optimizing engineering processes, and developing sustainable engineering solutions. The study also identifies promising research directions related to physics-informed artificial intelligence, explainable AI, uncertainty-aware models, hybrid intelligent systems, and adaptive engineering technologies.
This article analyzes the current state, practical applications, and promising development directions of artificial intelligence (AI) and machine learning (ML) technologies in mechanical engineering. The study examines the application of AI and ML methods in predictive maintenance, design optimization, manufacturing and process optimization, robotics and automation, quality control, and structural health monitoring. The capabilities of modern approaches, including neural networks, deep learning, reinforcement learning, transfer learning, and Bayesian optimization, in solving complex engineering problems are also discussed. Particular attention is paid to the integration of data-driven models with physics-based models, uncertainty quantification, model interpretability, reliability, and adaptability under different operating conditions. The analysis demonstrates that the integration of AI and ML into mechanical engineering provides significant opportunities for improving production efficiency, reducing maintenance costs, enhancing product quality, optimizing engineering processes, and developing sustainable engineering solutions. The study also identifies promising research directions related to physics-informed artificial intelligence, explainable AI, uncertainty-aware models, hybrid intelligent systems, and adaptive engineering technologies.
