Showcase 4: Structural Adhesives - Fatigue Life

In structural engineering, predicting the fatigue life of structural adhesives is essential for various industrial applications, but it's challenging due to the complex nature of material properties and loading conditions. Traditional modeling methods are time-consuming, making virtual design optimization difficult for industrial products. To address this, researchers are exploring hybrid models that blend physics-based and data-driven approaches, leveraging artificial intelligence (AI) to analyze existing data. Showcased in the DOME 4.0 project, this hybrid model collaboration between Fraunhofer IFAM, Siemens Digital Industries Software, and Citrine Informatics aims to enhance fatigue predictions for structural adhesives. By combining machine learning with physics-based models, these hybrid models offer insights into material behavior, guiding the selection of optimal adhesives for various applications. As research and industry collaboration continue, hybrid models are expected to advance our understanding of structural adhesive joints, bridging data insights with physics principles for improved product performance.