面向MBD模型的特征信息自动识别算法研究

郭亮; 杨滔; 李湉; 郑娟

doi:10.13433/j.cnki.1003-8728.20220078

面向MBD模型的特征信息自动识别算法研究

doi: 10.13433/j.cnki.1003-8728.20220078

郭亮^1,,
杨滔¹,
李湉²,
郑娟²

1.
西南石油大学机电工程学院，成都　610500
2.
中国航发成都发动机有限公司，成都　610503

基金项目: 中国航发自主创新专项基金项目（ZZCX-2017-039）、四川省科技计划项目（2022YFQ0016）、成都市国际科技合作项目（2020-GH02-00040-HZ）及西南石油大学青年科技创新团队（2019CXTD02）。

详细信息

作者简介:
郭亮（1985−），副教授，博士，研究方向为云制造和智能制造，gl@swpu.edu.cn

中图分类号: TH166
计量
- 文章访问数: 100
- HTML全文浏览量: 36
- PDF下载量: 14
- 被引次数: 0
出版历程
- 收稿日期: 2021-08-17
- 刊出日期: 2023-09-30

An Automatic Feature Information Recognition Algorithm Based on MBD Model

1.
School of Mechanical Engineering, Southwest Petroleum University, Chengdu 610500, China
2.
AECC Chengdu Engine Co., Ltd., Chengdu 610503, China

摘要

摘要: 针对特征识别技术对产品制造信息识别的缺失以及匹配算法的复杂性问题，提出了一种基于MBD模型的特征信息自动识别算法。首先，通过EXPRESS和Java映射，构建STEP文件解析器解析MBD模型信息。其次，建立基于图和提示的几何特征识别算法，用表征面邻接图作为匹配算法的数据结构，以提示作为搜索与特征库进行匹配。然后，提出基于关系树的产品制造信息识别方法识别产品制造信息。最后，以轴和机匣作为测试对象，在Intellij IDEA平台中对所提方法进行了验证，分析了匹配算法时间复杂度。结果表明，该方法能够识别几何特征信息和产品制造信息，本匹配算法时间复杂度为O(n)，优于时间复杂度为O(n²)的传统匹配算法。
- MBD /
- 产品制造信息 /
- 特征识别 /
- 图和提示
Abstract: To solve the problems that feature recognition technology are the lack of product manufacturing information recognition and the complexity of matching algorithm, an automatic feature information recognition algorithm based on MBD (Model-based definition) model is proposed. Firstly, a STEP file parser is built to parse the MBD model information through the Express and Java mappings. Secondly, a geometric feature recognition algorithm based on graph and hint was established. The representation surface adjacency graph was used as the data structure of the matching algorithm, and the hint was used to search and match the predefined library. Then, the recognition algorithm of product manufacturing information based on the relational tree is proposed to recognize product manufacturing information. Finally, the shaft and the casing were taken as test objects to verify the proposed method with the IntelliJ IDEA platform, and the time complexity of the matching algorithm was analyzed. The results show that this method can recognize the geometric feature information and the product manufacturing information. The time complexity of the recognition algorithm is O(n), being better than that of the traditional matching algorithm, which is O(n²).
- model based definition (MBD) /
- product manufacturing information /
- feature recognition /
- graphs and hints

HTML全文

图 1 特征识别流程图

Figure 1. Flowchart for the characteristics recognition process

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图 2 全局映射框架

Figure 2. Global mapping framework

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图 3 表征面邻接图

Figure 3. Characterization surface adjacency graph

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图 4 相交情况1

Figure 4. Intersection case 1

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图 5 相交情况2

Figure 5. Intersection case 2

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图 6 矩阵转换

Figure 6. Matrix transformation

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图 7 公差和尺寸规则树

Figure 7. Tolerance and dimension rule tree

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图 8 定位规则树

Figure 8. Localization rule tree

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图 9 公差等级流程图

Figure 9. Tolerance grade flowchart

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图 10 6与9的差异

Figure 10. Difference between 6 and 9

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图 11 粗糙度规则树

Figure 11. Surface roughness rule tree

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图 12 测试零件

Figure 12. Test part

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图 13 识别信息展示（单位：mm）

Figure 13. Recognition information (unit: mm)

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图 14 本算法运行时间函数

Figure 14. Algorithm runtime function

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图 15 二维矩阵算法运行时间函数

Figure 15. Two-dimensional matrix algorithm runtime function

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表 1 数据类型映射

Table 1. Data type mapping

EXPRESS数据类型	Java 数据类型
NUMBER	double
REAL	double
INTEGER	int
STRING	String
BINARY	byte[]
BOOLEAN,LOGICAL	boolean,enum（User-Defined）
ARRAY	Arrays
LIST	ArrayList
SET	Set

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表 2 点数法定义

Table 2. Point method definition

符号	数组	符号	数组
0	（13）	5	（10）
1	（3,2）	6	（12）
2	（8）	7	（3）
3	（7,6）	8	（16）
4	（4）	9	（12）
·	（8,2）	R	（7,2）
a	（2,16）		（2,2,2,2）

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