Applied Sciences, Vol. 13, Pages 3181: A Semi-Supervised Learning Framework for Machining Feature Recognition on Small Labeled Sample

Applied Sciences, Vol. 13, Pages 3181: A Semi-Supervised Learning Framework for Machining Feature Recognition on Small Labeled Sample

Applied Sciences doi: 10.3390/app13053181

Authors: Hongjin Wu Ruoshan Lei Pei Huang Yibing Peng

Automated machining feature recognition is an essential component linking computer-aided design (CAD) and computer-aided process planning (CAPP). Deep learning (DL) has recently emerged as a promising method to improve machining feature recognition. However, training DL-based recognition models typically require annotating large amounts of data, which is time-consuming and labor-intensive for researchers. Additionally, DL models struggle to achieve satisfactory results when presented with small labeled datasets. Furthermore, existing DL-based approaches require significant memory and processing time, thus hindering their real-world application. To address these challenges, this paper presents a semi-supervised learning framework that leverages both labeled and unlabeled data to learn meaningful visual representations. Specifically, self-supervised learning is utilized to extract prior knowledge from a large dataset without annotations, which is then transferred to improve downstream feature recognition tasks. Furthermore, we apply lightweight network techniques to two established feature recognizers, FeatureNet and MsvNet, to develop reduced-memory, computationally efficient models termed FeatureNetLite and MsvNetLite, respectively. To validate the effectiveness of the proposed approaches, we conducted comparative studies on the FeatureNet dataset. With only one training sample per class, MsvNetLite outperformed MsvNet by about 19%, whereas FeatureNetLite outperformed FeatureNet by approximately 20% in machining feature classification. On a common X86 CPU, MsvNetLite gained 6.68× improvement in speed over MsvNet, and FeatureNetLite was 2.49× faster than FeatureNet. The proposed semi-supervised learning framework shows a significant improvement in machining feature recognition on small labeled data while achieving the optimal balance between recognition accuracy and inference speed compared to other DL-based approaches.