Prototyping capabilities are essential in R&D for developing Minimal Viable Prototypes (MVPs), enabling hypothesis testing, consumer feedback, and informing subsequent development cycles. Although we have advanced modeling tools to create digital prototypes, we currently lack a systematic approach to rapidly and effectively translate these insights into physical prototypes. Instead, our MVPs rely on traditional handmaking and pilot manufacturing processes that are costly, time-consuming, and often subject to variability due to their manual nature. Furthermore, these existing prototyping techniques do not fully utilize the modeling insights at our disposal. 

Additive and subtractive manufacturing techniques are widely used to create three-dimensional objects by either adding material layer by layer or removing material from a solid block, based on a digital design file. These approaches enable high-fidelity translation of digital designs into physical parts across many industries. However, they are not well suited to replicating non-woven materials, which rely on stochastic fiber networks formed through bulk processes such as air-laying or meltblowing. In these systems, fiber placement, bonding, and resulting pore structures emerge statistically rather than being explicitly controlled. 

Existing advanced manufacturing approaches, including high-resolution 3D printing and fiber deposition techniques, offer improved spatial control but remain limited in their ability to reproduce the complex architecture of non-wovens, particularly at micron-scale fiber diameters and controlled pore distributions. As a result, there is currently no widely adopted method to directly translate digital models of non-woven structures into physical prototypes with controlled and reproducible microstructure.