三种用于加工特征识别的神经网络方法综述

doi:10.7527/S1000-6893.2019.22840

Abstract

Abstract: Automatic manufacturing feature recognition is a crucial support for intelligent design and its manufacturing. However, existing practical algorithms expose common problems such as poor learning ability, limited recognition range, and slow recognition speed. The neural network method has achieved great success in the fields of computer vision and pattern recognition. At the same time, its self-learning and self-adaptive capabilities and high-speed computing have also been preliminarily demonstrated in manufacturing feature recognition. Three different neural network methods with potential applications in manufacturing feature recognition are studied in this paper. The key issues of feature preprocessing and coding and architecture design of neural networks in manufacturing feature recognition based on neural networks are analyzed. Meanwhile, the similarities and differences of different neural network methods are analyzed. In addition, the development direction of current feature recognition approach using neural networks is summarized. This overview can provide some theoretical guidance and technical support for the research in related fields.

Key words: feature recognition, neural networks, multilayer perceptron, self-organizing neural network, convolutional neural network

CLC Number:

V262.3

SHI Yenan, ZHENG Guolei. A review of three neural network methods for manufacturing feature recognition[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(9): 22840-022840.

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A review of three neural network methods for manufacturing feature recognition

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