基于STEP和改进神经网络的STEP-NC制造特征识别方法

张禹; 董小野; 李东升; 曾奇峰; 杨树华; 巩亚东

doi:10.7527/S1000-6893.2018.22687

航空学报 >

2019 , Vol. 40 >Issue 7: 422687 - 422687

DOI: https://doi.org/10.7527/S1000-6893.2018.22687

材料工程与机械制造

基于STEP和改进神经网络的STEP-NC制造特征识别方法

张禹 ,
董小野 ,
李东升 ,
曾奇峰 ,
杨树华 ,
巩亚东

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1. 东北大学机械工程与自动化学院, 沈阳 110819;
2. 沈阳鼓风机集团股份有限公司, 沈阳 110869

收稿日期: 2018-09-18

修回日期: 2018-10-18

网络出版日期: 2018-12-19

基金资助

国家自然科学基金（51205054）；中国博士后科学基金（2017M611245）；中央高校基本科研业务费专项资金（N180313010，N160304009）；东北大学博士后基金

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Method for STEP-NC manufacturing feature recognition based on STEP and improved neural network

ZHANG Yu ,
DONG Xiaoye ,
LI Dongsheng ,
ZENG Qifeng ,
YANG Shuhua ,
GONG Yadong

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1. School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China;
2. Shenyang Blower Works Group Corporation, Shenyang 110869, China

Received date: 2018-09-18

Revised date: 2018-10-18

Online published: 2018-12-19

Supported by

National Natural Science Foundation of China (51205054);China Postdoctoral Science Foundation(2017M611245); the Fundamental Research Funds for the Central Universities (N180313010, N160304009); Postdoctoral Fund of Northeastern University

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摘要

特征识别是实施STEP-NC重要的一步，也是实现开放式、智能化和网络化STEP-NC数控系统的关键。本文提出了一种基于STEP和改进神经网络的STEP-NC制造特征识别方法。该方法首先在对STEP AP203中性文件进行几何拓扑信息提取后，基于边的凹凸性判断构建了零件最小子图。然后，将混沌算法、遗传算法与BP神经网络算法有机相结合提出了改进的BP神经网络。最后，通过将获得的零件模型最小子图信息数据输入到改进的BP神经网络，实现了对STEP-NC制造特征高效精准地识别。通过实例验证了该方法的有效性和可行性。

关键词： STEP-NC; 特征识别; STEP AP203文件; 最小子图; 改进BP神经网络

本文引用格式

张禹 , 董小野 , 李东升 , 曾奇峰 , 杨树华 , 巩亚东 . 基于STEP和改进神经网络的STEP-NC制造特征识别方法[J]. 航空学报, 2019 , 40(7) : 422687 -422687 . DOI: 10.7527/S1000-6893.2018.22687

Abstract

Feature recognition is an important step to implement STEP-NC theory and a key to realize the open, intelligent and networked STEP-NC CNC system. A feature recognition method based on STEP and improved neural network for STEP-NC manufacturing features is presented in this paper. This method first extracts the geometric and topological information from the STEP AP203 file of a part, and builds the minimum subgraph of the part based on the judgment of the convexity of edges. Then, an improved BP neural network is proposed by combining the chaos algorithm, the genetic algorithm, and the BP neural network algorithm. Finally, by inputting the information data from the minimum subgraph of the part to the improved BP neural network, efficient and accurate feature recognition for STEP-NC manufacturing features in the part is achieved. The validity and feasibility of the proposed method are verified by two case studies.

Key words： STEP-NC; feature recognition; STEP AP203 file; minimum subgraph; improved (BP) neural network

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