A data-driven framework for engineering design research: combining virtual and physical testing

This paper presents a hybrid framework that integrates physical and virtual testing to enhance cross-sectional studies in the field of engineering design. The framework addresses the critical challenge that valid inferences in realistic cross-sectional studies are often hampered by the manufacturing constraints of physical prototypes and the limitations of virtual prototypes. Using the example of a snap-fit system, the framework shows how predictive modelling and parametric design enable efficient iterations for building design knowledge. By combining the empirical accuracy of physical testing with the scalability of virtual simulations, the framework reduces iteration times, improves resource efficiency and adapts to different study conditions.