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首页> 外文期刊>Journal of Intelligent Manufacturing >A novel learning-based feature recognition method using multiple sectional view representation
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A novel learning-based feature recognition method using multiple sectional view representation

机译：一种使用多截面视图表示的基于新的基于学习的功能识别方法

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摘要

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.
机译：在计算机辅助设计（CAD）和流程规划（CAPP）中，要素识别是一个基本任务，它识别计算机辅助制造（CAM）的3D模型的特征类型。通常，基于传统的规则的特征识别方法是计算昂贵的，并且依赖于表面或特征类型。此外，设计适当的规则以识别交叉功能是非常具有挑战性的。最近，已经提出了一种基于学习的方法，名为featureenet，用于单一和多重特征识别。这是一种通用算法，能够处理任何类型的功能和表面。然而，训练需要数千种注释的训练样本来实现高单位特征识别准确性，这使得该技术难以在实践中使用。此外，实验结果表明，这种方法中的多重特征识别部分非常好地对具有小重叠区域的交叉特征，但在识别高度交叉的特征时可能会失败。为了解决上述问题，提出了一种基于多截面视图（MSV）表示的深度学习框架，名为MSVNET的特征识别。在MSVNet中，将3D模型的MSV被收集为深网络的输入，并且通过神经网络组合从不同视图实现的信息进行识别。除了MSV表示之外，还采用了一些高级学习策略（例如转移学习，数据增强）来最小化培训样本和培训时间的数量。对于多重特征识别，提出了一种新的基于视图的特征分割和识别算法。实验结果表明，所提出的方法可以在Featureenet数据集中实现最先进的单一特征性能，只有非常少量的训练样本（例如，每个特征的8-32个样本），并且优于状态 - 在识别性能方面的基于学习的多重特征识别方法。

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