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 crosssectional 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.