Meet your new co-worker: A wood-crafting robot
Though there are huge differences in how humans and robots work, there are similarities in how they learn new crafts. This has significant benefits for using them in design practices.
“The training workflow should not be considered a linear progression from the recording to the fabrication stage but rather as a knowledge platform.”
Each robotic toolpath is a sequence of target frames, which define the position and orientation of the carving gouge along the cut. Given a sequence of target frames, the trained ANN predicts, at each frame, the geometric output parameters of the cut (length, width, depth), considering the influence of material properties determined by the wood species (i.e., grain arrangement and density).
The trained ANN is a knowledge that can be transferred, re-used, extended and, most importantly, integrated within an interface to digitally evaluate multiple design solutions informed by tools and material properties before moving to the production stage. The training workflow should not be considered a linear progression from the recording to the fabrication stage but rather as a knowledge platform that can be remodeled over several cycles with new fabrication data, trained to improve its prediction performance and applied to various design tasks.