基于数字孪生的装配误差建模与溯源分析方法

门松辰; 周光辉; 张超; 常丰田; 邹永成

doi:10.7652/xjtuxb202301017

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基于数字孪生的装配误差建模与溯源分析方法

—————————————————————— | 更新时间：2025-07-09

- 基于数字孪生的装配误差建模与溯源分析方法
- Assembly Error Modeling and Traceability Analysis Method Based on Digital Twin
- 西安交通大学学报 2023年57卷第1期页码：175-184
- 作者机构：
  
  1. 西安交通大学机械工程学院,西安,710049
  2. 西安交通大学机械制造系统工程国家重点实验室,西安,710054
  3. 长安大学工程机械学院,西安,710064
- 作者简介：
  
  : 2022-05-19。作者简介: 门松辰(1998—),男,硕士生
  张超(通信作者),男,博士,助理教授。基金项目: 国家自然科学基金资助项目(52105530)
- 基金信息：
  
  : 2022-05-19。作者简介: 门松辰(1998—),男,硕士生;张超(通信作者),男,博士,助理教授。基金项目: 国家自然科学基金资助项目(52105530);中国博士后创新人才支持计划资助项目(BX2021244);陕西省高校科协青年人才托举计划资助项目(20210409)。
- DOI：10.7652/xjtuxb202301017
  中图分类号： TH166
- 网络首发：2023-01-10，
  
  纸质出版：2023
- 稿件说明：
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门松辰, 周光辉, 张超, 等. 基于数字孪生的装配误差建模与溯源分析方法[J]. 西安交通大学学报, 2023,57(1):175-184.

MEN Songchen, ZHOU Guanghui, ZHANG Chao, et al. Assembly Error Modeling and Traceability Analysis Method Based on Digital Twin[J]. 2023, 57(1): 175-184.
门松辰, 周光辉, 张超, 等. 基于数字孪生的装配误差建模与溯源分析方法[J]. 西安交通大学学报, 2023,57(1):175-184. DOI： 10.7652/xjtuxb202301017.

MEN Songchen, ZHOU Guanghui, ZHANG Chao, et al. Assembly Error Modeling and Traceability Analysis Method Based on Digital Twin[J]. 2023, 57(1): 175-184. DOI： 10.7652/xjtuxb202301017.

摘要

针对因装配误差溯源分析时效性差、准确率低而导致的装配质量控制不及时等问题

提出基于数字孪生的装配误差建模与溯源分析方法。首先

结合数字孪生技术搭建面向装配误差分析的数字孪生框架

在此基础上

构建装配物理层、数据感知模型、几何模型与设备运动模型

为后续装配误差传递模型的构建与溯源分析提供支持; 其次

将装配工艺信息中提取的零部件特征、质量特征与特征间连接关系

分别作为误差传递网络中节点与节点的连边关系

构建自适应生长误差传递网络

并通过数据模型获取装配现场数据

用于误差传递网络权重赋值

得到自适应生长误差传递模型; 然后

采用多算法动态融合排序方法对自适应生长误差传递模型进行关键装配节点识别

为装配质量控制提供依据; 最后

结合逆回溯算法与误差传递强度

计算误差传递路径上各节点的误差传递强度

实现装配误差溯源分析。采用所提方法在装配实验平台进行误差溯源分析实验

结果表明:在各装配工序发生质量问题概率相等的情况下

所提方法相比传统离线溯源分析方法的时效性提高了约40%

通过所提方法能有效提高装配质量控制时效性

并降低装配质量控制成本。

Abstract

Aiming at the problems of untimely assembly quality control due to poor timeliness and low accuracy of assembly error traceability analysis

an assembly error modeling and traceability analysis method based on digital twin is proposed. Firstly

a digital twin framework for assembly error analysis is built by the digital twin technology. On this basis

the assembly physical layer

data perception model

geometric model

and equipment motion model are constructed to provide support for subsequent assembly error transfer model construction and traceability analysis. Secondly

an adaptive growth error transfer network is constructed by taking the component features

quality features

and connection relationship among the features extracted from the assembly process information respectively as the nodes and connection relationship among the nodes in the error transfer network

and then an adaptive growth error transfer model is generated by applying the assembly site data obtained from the data model to the weight assignment of the error transfer network. Then

the multi-algorithm dynamic fusion sorting method is introduced to identify the key assembly nodes of the adaptive growth error transfer model

which provides a basis for assembly quality control. Finally

the inverse backtracking algorithm and error transfer intensity algorithm are combined to calculate the error transfer intensity of each node on the error transfer path and to realize the assembly error traceability analysis. The proposed method is used to conduct error traceability analysis experiments on the assembly experimental platform. The experimental results show that the timeliness using the proposed method could be improved by about 40% compared with the traditional offline traceability analysis method under the condition that the probability of quality problems in each assembly process is equal

which indicates that the proposed method can effectively improve the timeliness of assembly quality control and reduce the cost thereof.

关键词

Keywords

references

郭飞燕, 刘检华, 邹方, 等. 数字孪生驱动的装配工艺设计现状及关键实现技术研究 [J]. 机械工程学报, 2019, 55(17): 110-132.

GUO Feiyan, LIU Jianhua, ZOU Fang, et al.Research on the state-of-art, connotation and key implementation technology of assembly process planning with digital twin [J]. Journal of Mechanical Engineering, 2019, 55(17): 110-132.

戴宏玮, 陈琨, 于慧, 等. 雅可比旋量的装配体并联结构公差分析方法研究 [J]. 西安交通大学学报, 2022, 56(5): 156-165, 222.

DAI Hongwei, CHEN Kun, YU Hui, et al. Tolerance analysis of partial parallel assemblies based on Jacobian-Torsor model [J]. Journal of Xi'an Jiaotong University, 2022, 56(5): 156-165, 222.

WANG Hongwei, ZHANG Genbao, TAO Guibao, et al. A novel structural decomposition method for error propagation mechanism analysis based on meta-action unit theory [J]. IEEE Access, 2019, 7: 95108-95121.

LI Yunyong, ZHAO Yong, YU Haidong, et al. Modeling deviation propagation of compliant assembly considering form defects based on basic deviation fields [J]. Assembly Automation, 2019, 39(1): 226-242.

余德文, 赵强强, 陈飞飞, 等. 利用结构等效转换的空间可展机构装配误差建模与灵敏度分析 [J]. 西安交通大学学报, 2019, 53(4): 15-23.

YU Dewen, ZHAO Qiangqiang, CHEN Feifei, et al. Assembly error modeling and sensitivity analysis for spatial deployable mechanisms with structural equivalent transformation [J]. Journal of Xi'an Jiaotong University, 2019, 53(4): 15-23.

祝鹏, 余建波, 郑小云, 等. 机械装配过程的偏差传递网络建模与误差溯源 [J]. 浙江大学学报(工学版), 2019, 53(8): 1582-1593.

ZHU Peng, YU Jianbo, ZHENG Xiaoyun, et al. Variation propagation network-based modeling and error tracing in mechanical assembling process [J]. Journal of Zhejiang University(Engineering Science), 2019, 53(8): 1582-1593.

ZHANG Chao, ZHOU Guanghui, HE Jun, et al. A data- and knowledge-driven framework for digital twin manufacturing cell [J]. Procedia CIRP, 2019, 83: 345-350.

胡兴, 刘检华, 庄存波, 等. 基于数字孪生的复杂产品装配过程管控方法与应用 [J]. 计算机集成制造系统, 2021, 27(2): 642-653.

HU Xing, LIU Jianhua, ZHUANG Cunbo, et al.Digital twin-based management method and application for the complex products assembly process [J]. Computer Integrated Manufacturing Systems, 2021, 27(2): 642-653.

ZHUANG Cunbo, GONG Jingcheng, LIU Jianhua. Digital twin-based assembly data management and process traceability for complex products [J]. Journal of Manufacturing Systems, 2021, 58, Part B: 118-131.

张超, 周光辉, 肖佳诚, 等. 数字孪生制造单元多维多尺度建模与边—云协同配置方法 [J/OL]. 计算机集成制造系统[2022-05-13]. http://kns.cnki.net/kcms/detail/11.5946.TP.20210601.0907.004.html.

ZHANG Chao, ZHOU Guanghui, XIAO Jiacheng, et al.Multi-dimensional and multi-scale modeling and edge-cloud collaborative configuration method for digital twin manufacturing cell [J/OL]. Computer Integrated Manufacturing Systems [2022-05-13]. http://kns.cnki.net/kcms/detail/11.5946.TP.20210601.0907.004.html.

林海龙, 周雅光, 张超, 等. 基于工业互联网的燃料乙醇生产线数字孪生框架与建模方法 [J]. 应用科技, 2022, 49(1): 20-26.

LIN Hailong, ZHOU Yaguang, ZHANG Chao, et al. A digital twin framework and a modeling method of fuel ethanol production line based on industrial internet [J]. Applied Science and Technology, 2022, 49(1): 20-26.

常丰田, 周光辉, 李锦涛, 等. 边-云协同下智能制造单元的物联网络协调配置方法 [J]. 西安交通大学学报, 2022, 56(6): 184-194.

CHANG Fengtian, ZHOU Guanghui, LI Jintao, et al.Coordinated configuration method for internet of things network of smart manufacturing cells under edge-cloud collaboration [J]. Journal of Xi'an Jiaotong University, 2022, 56(6): 184-194.

张超, 周光辉, 李晶晶, 等. 新一代信息技术赋能的数字孪生制造单元系统关键技术及应用研究 [J/OL]. 机械工程学报[2022-08-31]. http://kns.cnki.net/kcms/detail/11.2187.TH.20220727.1035.004.html.

ZHANG Chao, ZHOU Guanghui, LI Jingjing, et al.Research on key technologies and application of new IT-driven digital twin manufacturing cell system [J/OL]. Journal of Mechanical Engineering[2022-08-31]. http://kns.cnki.net/kcms/detail/11.2187.TH.20220727.1035.004.html.

刘显. 时序网络节点重要性研究 [D]. 江苏徐州: 中国矿业大学, 2020: 17-19.

张嘉锟. 汽轮机通流间隙装配质量网络建模与分析方法研究 [D]. 西安: 西安交通大学, 2021: 43-44.

张崇兴. 基于预测和D-S证据理论的多传感器数据融合研究 [D]. 成都: 电子科技大学, 2021: 7-16.

WANG Xinhe, WANG Zhen, SHEN Hao. Dynamical analysis of a discrete-time SIS epidemic model on complex networks [J]. Applied Mathematics Letters, 2019, 94: 292-299.

任晓龙, 吕琳媛. 网络重要节点排序方法综述 [J]. 科学通报, 2014, 59(13): 1175-1197.

REN Xiaolong, LV Linyuan. Review of ranking nodes in complex networks [J]. Chinese Science Bulletin, 2014, 59(13): 1175-1197.

先九洲. 基于多指标融合的复杂网络节点重要度评估方法 [D]. 云南大理: 大理大学, 2021: 17-42.

CUI Xiaoyan, ZHAO Tao, WANG Juan. Allocation of carbon emission quotas in China's provincial power sector based on entropy method and ZSG-DEA [J]. Journal of Cleaner Production, 2021, 284: 124683.

LI Huicong, PENG Rui, WANG Fengbin. Varying total population enhances disease persistence: qualitative analysis on a diffusive SIS epidemic model [J]. Journal of Differential Equations, 2017, 262(2): 885-913.

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