Intelligent support-free additive manufacturing path planning via method library and neural network

Support-free additive manufacturing achieves self-supporting fabrication by adjusting the platform’s posture, effectively reducing material waste and simplifying the post-processing stage. However, the diversity of industrial part geometries requires different approaches to plan the robotic manufacturing paths. Traditional approaches to selecting support-free manufacturing path planning methods rely heavily on expert knowledge. This paper proposes an intelligent path planning system based on a method library. The system utilizes a method library composed of seven approaches to achieve support-free additive manufacturing path planning of various types of parts. The additive manufacturing strategy matching neural network (AMMatcher) is employed to match the optimal path planning method from the method library to a given model and to identify its base surface. AMMatcher can analyze the multi-scale features of the model and leverage a cross-task attention mechanism to propagate classification features into the segmentation task, thereby improving network performance. A newly proposed support-free additive manufacturing model dataset (SFAMDataset) is used to evaluate the performance of AMMatcher and typical samples are validated through fabrication experiments on three different manufacturing platforms. Experimental results demonstrate that AMMatcher effectively identifies suitable manufacturing strategies for various model types and exhibits strong adaptability across different manufacturing platforms.