Presentation

· Presenters · Organizations · Search Program · My Schedule · Happening Now

DeepMill: Neural Accessibility Learning for Subtractive Manufacturing

DescriptionManufacturability is crucial in product design and production, especially for subtractive manufacturing, where geometric accessibility analysis is time-consuming and hard to scale. Existing deep learning methods often overlook geometric challenges and are limited to specific models. This paper introduces DeepMill, a neural framework that accurately and efficiently predicts inaccessible and occluded regions under various machining cutters for both CAD and freeform models. By developing a cutter-aware dual-head octree convolutional network and generating datasets with diverse cutter sizes, DeepMill effectively addresses cutter-collision issues and data scarcity.

Contributors