Abstract
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.
Highlights
Studies applying deep learning technology to recognize the machining feature of threedimensional (3D) computer-aided design (CAD) models are increasing
The above problem makes it difficult to integrate information obtained from deep learning and 3D CAD systems that have to deal with boundary representation (B-rep) models when realizing online manufacturing support platforms
In a previous s tudy[30], we proposed feature descriptors to recognize machining features based on similarity comparison
Summary
Studies applying deep learning technology to recognize the machining feature of threedimensional (3D) computer-aided design (CAD) models are increasing. 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. Due to the loss or change of the model’s geometric information during the conversion, finding a set of faces corresponding to the detected area is difficult This problem can be seen as a highly studied problem of persistent naming in the CAD field[27,28]. The above problem makes it difficult to integrate information obtained from deep learning and 3D CAD systems that have to deal with B-rep models when realizing online manufacturing support platforms
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