Highly interacting machining feature recognition via small sample learning
Peizhi Shi, Qunfen Qi, Yuchu Qin, Paul J. Scott, Xiangqian Jiang
Abstract
In the area of intelligent manufacturing, recognising the interacting features on a CAD model is a critical yet challenging task as topology structures of features are damaged due to the feature interaction. Some of the learning-based feature recognition methods produce less favourable results when recognising highly interacting features, while some require a significant amount of 3D models for training, which present an increasing challenge in a real world scenario, especially whenever collecting large training data becomes too difficult and time-consuming. To this end, effective highly interacting feature recognition via small sample learning becomes bottleneck for learning-based methods. To tackle the above issue, the paper proposes a novel method named RDetNet based on single-shot refinement object detection network (RefineDet) which is capable of recognising highly interacting features with small training samples. In addition, the paper further utilises several data augmentation (DA) strategies to increase the amount of relevant 3D training models. Experiments carried out in this paper show that the proposed method yields favourable results in recognising highly interacting features by using small training samples (e.g. 32 models per class).