Speaker
Description
The integration of hybrid machine learning (ML) models and substructuring methods holds significant potential for advancing dynamic analysis in complex systems. This approach aims to reduce the reliability on extensive physical testing by leveraging data-driven techniques to enhance model accuracy and efficiency.
This work presents a baseline study on integrating machine learning techniques with substructuring methods to predict the dynamic behavior of multi-degree-of-freedom (MDOF) systems. The goal is to develop a hybrid framework that allows for accurate predictions throughout the entire development process, reducing the need for physical prototypes and testing at each phase. A multi-mass oscillator is separated into two linear subsystems, and an ML model is then trained to predict the dynamic behavior of the entire system based on the dynamics of the individual subsystems and their coupling interactions. Additionally, the approach is tested with different numbers of degrees of freedom (DOFs) for both the system and subsystems to ensure flexibility for later application to more complex MDOF systems. Initially, the model is trained using simulation data for the linear subsystems and their couplings. The predictions made by this model are then compared with experimental measurement data to adapt the model and improve its ability to predict real-world behavior, which was not captured by the simulations.
Ultimately, this work lays the initial groundwork for more efficient and reliable product development in complex systems, offering a potential approach to address the challenges of vibration load prediction and dynamic behavior analysis in engineering applications. While this study focuses on a simplified case, it provides a foundation for further research to explore the feasibility and effectiveness of this hybrid approach in more complex scenarios.
