A novel learning-based feature recognition method using multiple sectional view representation

Peizhi Shi, Qunfen Qi*, Yuchu Qin, Paul J. Scott, Xiangqian Jiang

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

65 Citations (Scopus)


In computer-aided design (CAD) and process planning (CAPP), feature recognition is an essential task which identifies the feature type of a 3D model for computer-aided manufacturing (CAM). In general, traditional rule-based feature recognition methods are computationally expensive, and dependent on surface or feature types. In addition, it is quite challenging to design proper rules to recognise intersecting features. Recently, a learning-based method, named FeatureNet, has been proposed for both single and multi-feature recognition. This is a general purpose algorithm which is capable of dealing with any type of features and surfaces. However, thousands of annotated training samples for each feature are required for training to achieve a high single feature recognition accuracy, which makes this technique difficult to use in practice. In addition, experimental results suggest that multi-feature recognition part in this approach works very well on intersecting features with small overlapping areas, but may fail when recognising highly intersecting features. To address the above issues, a deep learning framework based on multiple sectional view (MSV) representation named MsvNet is proposed for feature recognition. In the MsvNet, MSVs of a 3D model are collected as the input of the deep network, and the information achieved from different views are combined via the neural network for recognition. In addition to MSV representation, some advanced learning strategies (e.g. transfer learning, data augmentation) are also employed to minimise the number of training samples and training time. For multi-feature recognition, a novel view-based feature segmentation and recognition algorithm is presented. Experimental results demonstrate that the proposed approach can achieve the state-of-the-art single feature performance on the FeatureNet dataset with only a very small number of training samples (e.g. 8–32 samples for each feature), and outperforms the state-of-the-art learning-based multi-feature recognition method in terms of recognition performances.

Original languageEnglish
Pages (from-to)1291-1309
Number of pages19
JournalJournal of Intelligent Manufacturing
Issue number5
Publication statusPublished - 1 Jun 2020

Bibliographical note

Funding Information:
This research was supported by the EPSRC Future Advanced Metrology Hub (Ref. EP/P006930/1), EPSRC UKRI Innovation Fellowship (Ref. EP/S001328/1), and EPSRC Fellowship in Manufacturing (Ref. EP/R024162/1). The authors would like to thank two anonymous reviewers for their valuable comments and suggestions. The authors would like to thank Zhibo Zhang for providing the dataset and source code of FeatureNet on Github used in this research.

Publisher Copyright:
© 2020, The Author(s).


  • Data augmentation
  • Deep learning
  • Feature recognition
  • Multiple sectional views
  • Transfer learning


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