Upcycling wood waste remains a significant challenge in manufacturing, particularly when aiming for precision and durability in products made from recycled material. Large-scale 3D printing offers a promising, sustainable approach by reusing wood waste, reducing reliance on virgin resources, and minimizing environmental impact. Yet, issues such as post-printing deformation and shrinkage continue to compromise accuracy and consistency.
This research explores a novel method using Digital Image Correlation (DIC) to address these challenges. While conventional 3D scanning provides volumetric data, it does not track localized point-by-point deformation over time. DIC, a non-contact optical technique, captures detailed 3D displacements and enables continuous monitoring of shrinkage in printed wood composites.
In the study, DIC was applied to track deformations in wood-based 3D prints over a 24-hour period. The results were then compared to conventional 3D scan data. The research demonstrates that DIC significantly improves the ability to monitor and understand deformation behavior, paving the way for the development of predictive models—potentially powered by machine learning—that can anticipate and compensate for shrinkage. This advancement contributes to more precise, sustainable fabrication processes using recycled wood materials.
