ScrapAR: Democratizing Woodworking Education Through Contemporary Computational Advancements

This thesis introduces a computational workflow that integrates computer vision (CV) and augmented reality (AR) to democratize woodworking education for beginners. Grounded in ethnographic interviews with professionals, iterative prototyping, and systematic testing, the proposed approach enables users to catalog and repurpose scrap materials as volumetric “ingredients” within AR assisted recipes. Leveraging machine-learning-based object detection and accessible AR game engines, the system delivers interactive user-interface overlays – including tool tutorials, nested part visualizations, and step-by-step assembly guidance – for constructing a pre-designed chair. By automating tasks such as scrap management, spatial planning, and measurement, the workflow reduces barriers to entry to woodworking as a form of craft and fosters creative reuse of existing materials. To evaluate its effectiveness, users – ranging from a novice to an experienced woodworker – completed an illustrative project. Their case studies demonstrate improvements in user confidence, material efficiency, and exploratory learning. The findings suggest that integrating CV and AR can enhance craft-education accessibility and support innovative, sustainable practices in alternative learning