Machining feature recognition based on deep neural networks to support tight integration with 3D CAD systems.
Machining feature recognition based on deep neural networks to support tight integration with 3D CAD systems.
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DOI：
10.1038/s41598-021-01313-3
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发表时间：
2021-11-12
影响因子：
4.6
通讯作者：
Mun D
中科院分区：
文献类型：
--
作者：
Yeo C;Kim BC;Cheon S;Lee J;Mun D
最近，应用深度学习技术来识别三维（3D）计算机辅助设计（CAD）模型的加工特征的研究正在增加。由于在数据结构方面很难直接利用边界表示（B-rep）模型作为神经网络的输入数据，因此B-rep模型通常被转换为体素、网格或点云模型，并用作神经网络的输入，以将3D模型应用于深度学习。然而，在3D模型的格式转换过程中，模型的分辨率会降低，导致一些特征丢失或难以识别与B-rep模型的特定面相对应的转换模型的区域。为了解决这些问题，本研究提出了一种方法，使3D CAD系统与深度神经网络紧密集成，使用特征描述符作为神经网络的输入，用于识别加工特征。特征描述符表示面部的主要属性项的显式表示。我们构建了2236个数据来训练和评估深度神经网络。其中，1430个用于训练深度神经网络，358个用于验证。和448被用来评估训练后的深度神经网络的性能。此外，我们进行了一个实验，以识别共17种类型（16种类型的加工特征和一个非特征）从B-rep模型，并成功地识别了所有75个测试用例的类型。
Recently, studies applying deep learning technology to recognize the machining feature of three-dimensional (3D) computer-aided design (CAD) models are increasing. Since the direct utilization of boundary representation (B-rep) models as input data for neural networks in terms of data structure is difficult, B-rep models are generally converted into a voxel, mesh, or point cloud model and used as inputs for neural networks for the application of 3D models to deep learning. However, the model’s resolution decreases during the format conversion of 3D models, causing the loss of some features or difficulties in identifying areas of the converted model corresponding to a specific face of the B-rep model. To solve these problems, this study proposes a method enabling tight integration of a 3D CAD system with a deep neural network using feature descriptors as inputs to neural networks for recognizing machining features. Feature descriptor denotes an explicit representation of the main property items of a face. We constructed 2236 data to train and evaluate the deep neural network. Of these, 1430 were used for training the deep neural network, and 358 were used for validation. And 448 were used to evaluate the performance of the trained deep neural network. In addition, we conducted an experiment to recognize a total of 17 types (16 types of machining features and a non-feature) from the B-rep model, and the types for all 75 test cases were successfully recognized.
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影响因子：
4.3
作者：
Marchetta, Martin G.;Forradellas, Raymundo Q.
通讯作者：
Forradellas, Raymundo Q.
影响因子：
1.4
作者：
Asiabanpour, Bahram;Mokhtar, Alireza;Nasr, Emad Abouel
通讯作者：
Nasr, Emad Abouel
影响因子：
1.8
作者：
Han, JH;Requicha, AAG
通讯作者：
Requicha, AAG
影响因子：
4.3
作者：
Hong, Taesik;Lee, Kunwoo;Kim, Sungchan
通讯作者：
Kim, Sungchan
影响因子：
10
作者：
Öztürk, N;Öztürk, F
通讯作者：
Öztürk, F