Using Digital Image Correlation to Analyze Deformation in Wood-Based Liquid Deposition Modelling (2025-03)¶

Upcycling wood waste is a long-standing challenge in manufacturing, particularly in creating precise and durable products from leftover wood. 3D printing offers a sustainable solution by utilising recycled wood waste, reducing the need for new wood, and lowering environmental impact. However, issues like deformation and shrinkage after printing remain a challenge, affecting result precision. Current methods like 3D scanning provide volumetric data but fail to track the deformation of specific points. This paper introduces a Digital Image Correlation (DIC) based method to overcome these limitations. DIC measures 3D displacements without contact, offering detailed point-to-point insights into shrinkage, which could be used to develop a dataset for a machine learning model that can predict deformation and compensate for it. We used DIC to track the deformations of 3D wood prints over 24 hours and compared the results to 3D scans of the same prints. The findings show that DIC improves deformation tracking, enhancing understanding and supporting sustainable manufacturing with recycled wood.