기계학습을 이용한 3D CAD 모델 형상과 경계조건의 유형 식별

Abstract

In this paper, we propose a machine learning approach for classifying 3D CAD models with boundary conditions. We adopted an extended voxel model to represent a CAD model with its boundary conditions, and to construct the training data. By considering 7 types of part families and 3 types of boundary conditions, we generated 320 similar CAD models for each part family and assigned 3 different boundary conditions to each CAD model, which produces 960 datasets for the part family. We used multi-layer perceptron (MLP) and convolutional neural network (CNN) as machine learning models, which classify the combination type of a given CAD model with its boundary conditions. Using TensorFlow, we trained and tested the models, and compared their performance. We considered the MLP models made of three hidden layers and the CNN models made of two convolutional, two pooling, and three hidden layers. We also conducted a grid search to find the proper number of nodes in hidden layers. From experimental results, we found that the CNN models are better in accuracy than the MLP models. If further enhanced, the proposed approach is expected to become a useful tool for similar case search from archive CAE models.