Point cloud self-supervised learning for machining feature recognition

Abstract

Machining feature recognition serves as a foundational step in process planning, crucial for translating design information into manufacturing information. Traditional rule-based methods require extensive manual rule definition, prompting researchers to develop learning-based methods using data-driven algorithms. However, existing learning-based methods typically demand substantial data annotation and show limitations in machining feature segmentation. To address these issues, this paper introduces a novel learning-based machining feature recognition method. The proposed method leverages self-supervised learning to autonomously extract valuable intrinsic information from unlabeled data and incorporates a discriminative loss function to improve feature segmentation performance, thereby enhancing feature recognition results under conditions of limited labeled data. Specifically, the self-supervised learning network is first pre-trained on a large amount of unlabeled point cloud data representing CAD models and then fine-tuned with labeled data using the discriminative loss function. The fine-tuned network can be employed for recognizing machining features. Experimental results demonstrate that the proposed approach is effective during pre-training and improves feature recognition performance with limited amounts of labeled data, potentially reducing annotation efforts and associated costs.