Thread Content
【Topic 1: Reliability Analysis and Optimal Design of Equipment Structures Based on AI Intelligent Algorithms】-Starting this Saturday. It integrates AI algorithms with reliability engineering, covering fatigue theory, intelligent agent models, and uncertainty quantification. Through practical applications of ANSYS and Python, the entire process from parameter optimization and reliability assessment to the prediction of aircraft blade life is carried out, enabling structural lightweighting and multi-objective optimization under reliability constraints. 【Special Topic 2: Applications and Practices of Data-Driven Intelligent Fault Diagnosis Techniques】Now available. Focusing on data-driven fault diagnosis, it covers vibration signal analysis, feature learning, and deep learning. Through cases such as bearing fault detection and life prediction, master 1D-CNN, LSTM, and transfer learning techniques to address challenges related to small datasets and varying operating conditions, thereby improving the reliability of diagnostics. 【Topic 3: Fatigue Life and Health Monitoring Techniques of Metal Structures】 Focusing on the energy method, it covers fatigue life prediction and infrared thermography monitoring techniques. Through practical applications of ABAQUS and MATLAB, energy dissipation analysis and crack prediction are carried out; by integrating a CNN-LSTM model, an evaluation framework is developed to assess performance from local damage to the system-level lifetime. 【Topic 4: Multi-scale Modeling and Performance Prediction of Composite Materials Based on AI-Enhanced Finite Element Methods】 Focuses on multi-scale analysis through the integration of AI and finite element methods, covering RVE parametric modeling, damage criteria, and secondary development. By constructing DNN, CNN, and PINN models, rapid prediction of the equivalent properties of composite materials is achieved, and transfer learning is utilized to overcome challenges in prediction across different material systems. 【Topic 5: AI Applications in Polymers and Composite Materials】 Covers AI-driven design and performance prediction of polymer materials. From feature engineering to machine learning and deep learning, practical exercises focus on stress-strain prediction, generative design, and high-throughput screening, while employing tools such as SHAP for explanatory analysis to drive reverse material design. Detailed review: Account name: Yan You Xin er https://mp.weixin.qq.com/s/duXZlI-71SERZjAC0K8e8Q