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

Abstract

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.

Intersecting Machining Feature Localization and Recognition via Single Shot Multibox Detector

“…As evident in the experiments conducted in [13], segmenting intersecting features in 3D space is rather arduous. Experimental results also demonstrated that it was relatively easy to locate and recognise 3D intersection features from 2D view images [13]. To this end, a one-stage supervised feature segmentation and recognition algorithm based on 2D view images is an ideal solution to the research problem.…”

“…In general, watershed algorithm can yield expected results when segmenting features with low overlap degree, but fails to separate highly intersecting features as the shape information of most features is lost because of the feature intersection. To solve the issues arising from the FeatureNet, Shi et al proposed a novel intersecting feature recognition approach named MsvNet [13]. In this approach, a 3D model with intersecting features was first segmented into separated ones via another unsupervised learning algorithm named selective search algorithm according to the 2D shape information of the features.…”

“…As discussed in Section II, existing learning-based methods (MsvNet [13] and FeatureNet [12]) suffer from a number of limitations, which motivate the research conducted in this paper. Therefore, the main research problem that this paper aims to tackle is: how to locate and recognise highly intersecting machining features from a CAD model efficiently.…”

“…These techniques enable intelligent agents to automatically learn from data without being explicitly programmed. To this end, two novel approaches named FeatureNet [12] and MsvNet [13] that adopt machine learning techniques for intersecting feature recognition have been developed. The two approaches are general purpose method in which adhoc rules are not required any more, such that they can recognise a wide variety of features without imposing the burdens on the recogniser designer.…”

“…However, it is rather difficult to accurately segmenting intersecting features according to the shape information in an unsupervised way, since the topology information of the features might be destroyed because of feature intersection. This will lead to a large amount of features in a CAD model be mis-recognised or mis-located, as evident in the experiments carried out in [13]. Motivated by an effective yet efficient object detection algorithm named single shot multibox detector (SSD) [14], this paper presents a novel method called SsdNet where feature segmentation and recognition are carried out together via supervised learning.…”