Predicting Interference of Headless Rivet Using Multiple Nonlinear Regression

LI Xiaofeng; CHANG Zhengping; GAO Yazhi; HUO Yongxing; SONG Jiansheng; WANG Zhongqi

doi:10.7652/xjtuxb202401015

您当前的位置：

首页 >

文章列表页 >

Predicting Interference of Headless Rivet Using Multiple Nonlinear Regression

更新时间：2025-05-13

- Predicting Interference of Headless Rivet Using Multiple Nonlinear Regression
- Vol. 58, Issue 1, Pages: 157-166(2024)
- 作者机构：
  
  1. 西北工业大学机电学院,西安,710072
  2. 中航西安飞机工业集团股份有限公司,西安,710089
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202401015
  CLC： V262.4
- Online First：10 January 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
LI Xiaofeng, CHANG Zhengping, GAO Yazhi, et al. Predicting Interference of Headless Rivet Using Multiple Nonlinear Regression[J]. 2024, 58(1): 157-166.
DOI：

LI Xiaofeng, CHANG Zhengping, GAO Yazhi, et al. Predicting Interference of Headless Rivet Using Multiple Nonlinear Regression[J]. 2024, 58(1): 157-166. DOI： 10.7652/xjtuxb202401015.

摘要

为明晰被连接件材料性能对安装干涉量的影响规律

并进一步为新材料扩展应用提供可靠性预测

在铆接过程有限元仿真数据基础上

提出了一种采用多元非线性回归模型的无头铆钉安装干涉量预测方法。首先

根据实际铆接过程建立有限元仿真模型

通过铆接试验验证模型有效性。然后

采用有限元和正交试验法

研究了被连接件弹性模量、屈服强度、强化系数和应变强度指数及其交互作用对铆接干涉量水平的显著性

明确了各因素对安装干涉量的影响效果。最后

选用幂函数作为多元非线性回归的函数形式

剔除显著性较低因素项

建立了待测位置干涉量回归预测模型。结果表明

在一定铆接工艺条件下

屈服强度和应变强度指数及其交互作用是影响干涉量水平的主要因素。对比模拟值与回归模型预测值发现两者变化趋势一致

且误差不超过10%

表明干涉量多元回归预测模型具有有效性。

Abstract

This study proposes a method for predicting the installation interference of headless rivet using a multiple nonlinear regression model based on the finite element simulation data of the riveting process

with the aim to elucidate the influence of material properties on the interference during installation and to provide reliable predictions for the use of new materials. Firstly

a finite element simulation model is established based on the actual riveting process and its validity is verified through riveting experiments. Subsequently

finite element analysis and orthogonal testing techniques are employed to examine the significance of various factors

such as elastic modulus

yield strength

strengthening coefficient

and strain strength index

along with their interactions on installation interference

determining the influence of each factor on the riveting interference. Finally

the multiple nonlinear regression model is formulated using a power function

and less significant factors are eliminated. The results show that

under specific riveting process conditions

yield strength and strain strength index

as well as their interaction

are the primary factors affecting the interference. By comparing the simulated values and the predicted values of the regression model

it is observed that the two values exhibit consistent variation trends

with errors remaining below 10%

demonstrating the effectiveness of the multiple regression models in predicting riveting interference.

关键词

Keywords

references

吕九九, 张秀艳, 马兴海, 等. 无头铆钉双面鼓形镦头成形工艺与性能分析 [J]. 机械工程与技术, 2018, 7(5): 344-355.

LÜ Jiujiu, ZHANG Xiuyan, MA Xinghai, et al. Forming technology and performance analysis of headless rivet with double drum upsetting head [J]. Mechanical Engineering and Technology, 2018, 7(5): 344-355.

王仲奇, 常正平, 郭飞燕, 等. 大型飞机壁板无头铆钉干涉连接技术 [J]. 航空制造技术, 2015(4): 34-39.

WANG Zhongqi, CHANG Zhengping, GUO Feiyan, et al. Interference fit technology of slug rivet in large aircraft panel [J]. Aeronautical Manufacturing Technology, 2015(4): 34-39.

DE RIJCK J J M, HOMAN J J, SCHIJVE J, et al. The driven rivet head dimensions as an indication of the fatigue performance of aircraft lap joints [J]. International Journal of Fatigue, 2007, 29(12): 2208-2218.

KIM K Y, SIM J, JANNAT N E, et al. Challenges in riveting quality prediction: a literature survey [J]. Procedia Manufacturing, 2019, 38: 1143-1150.

曹增强, 冯东格. 理想干涉配合铆接 [J]. 航空科学技术, 2012(4): 84-86.

CAO Zengqiang, FENG Dongge. Ideal interference fit riveting [J]. Aeronautical Science Technology, 2012(4): 84-86.

PAN Minghui, TANG Wencheng, XING Yan, et al. Numerical simulation analysis for deformation deviation and experimental verification for an antenna thin-wall parts considering riveting assembly with finite element method [J]. Journal of Central South University, 2018, 25(1): 60-77.

CHERAGHI S H, KRISHNAN K, BAJRACHARYA B. Effect of variations in the riveting process on the quality of riveted joints [J]. The International Journal of Advanced Manufacturing Technology, 2008, 39(11): 1156-1156.

刘连喜, 李西宁, 王仲奇, 等. 无头铆钉自动钻铆工艺试验研究 [J]. 西北工业大学学报, 2013, 31(1): 77-82.

LIU Lianxi, LI Xining, WANG Zhongqi, et al. Semi-empirical research on automatic drilling and riveting process of headless rivet [J]. Journal of Northwestern Polytechnical University, 2013, 31(1): 77-82.

李超, 王仲奇, 常正平, 等. 基于BP神经网络和多目标粒子群算法的自动钻铆工艺参数优化方法 [J]. 航空制造技术, 2021, 64(23): 94-102.

LI Chao, WANG Zhongqi, CHANG Zhengping, et al. Automatic drilling and riveting process parameter optimization method based on BP neural network and multi-objective particle swarm optimization algorithm [J]. Aeronautical Manufacturing Technology, 2021, 64(23): 94-102.

李艳, 于克杰, 李小雷. 铆钉材料对铆接变形影响的有限元分析 [J]. 机床与液压, 2013, 41(4): 50-52.

LI Yan, YU Kejie, LI Xiaolei. Finite element analysis for the influence of rivet materials on rivet deformation [J]. Machine Tool Hydraulics, 2013, 41(4): 50-52.

AMAN F, CHERAGHI S H, KRISHNAN K K, et al. Study of the impact of riveting sequence, rivet pitch, and gap between sheets on the quality of riveted lap joints using finite element method [J]. The International Journal of Advanced Manufacturing Technology, 2013, 67(1): 545-562.

陈贵坤, 曾凯, 邢保英, 等. 铝合金自冲铆工艺参数的多元非线性回归模型 [J]. 机械工程学报, 2022, 58(22): 227-234.

CHEN Guikun, ZENG Kai, XING Baoying, et al. Multiple nonlinear regression model of process parameters for aluminum alloy self-piercing riveting [J]. Journal of Mechanical Engineering, 2022, 58(22): 227-234.

CHANG Zhengping, WANG Zhongqi, ZHANG Jinming, et al. Investigation of riveting parameters influence on the riveted joints deformation during slug rivet installation [C]//ASME 2016 International Mechanical Engineering Congress and Exposition. New York, NY, USA: ASME, 2016: V002T02A027.

陈超, 赵升吨, 崔敏超, 等. 铆压重塑形工艺的铆钉优化与试验研究 [J]. 西安交通大学学报, 2016, 50(3): 94-100.

CHEN Chao, ZHAO Shengdun, CUI Minchao, et al. Rivet optimization and experimental research on the reshaping method [J]. Journal of Xi'an Jiaotong University, 2016, 50(3): 94-100.

王雷, 李玉龙, 索涛, 等. 航空常用铝合金动态拉伸力学性能探究 [J]. 航空材料学报, 2013, 33(4): 71-77.

WANG Lei, LI Yulong, SUO Tao, et al. Mechanical behavior of commonly used aeronautical aluminum alloys under dynamic tension [J]. Journal of Aeronautical Materials, 2013, 33(4): 71-77.

王运, 张昌明, 张昱. 航空Al7050合金的静动态力学特性研究及JC本构模型构建 [J]. 材料导报, 2021, 35(10): 10096-10102.

WANG Yun, ZHANG Changming, ZHANG Yu. Study on static and dynamic mechanical properties of aviation Al7050 alloy and construction of JC constitutive model [J]. Materials Reports, 2021, 35(10): 10096-10102.

冯振宇, 李恒晖, 刘义, 等. 中低应变率下7075-T7351铝合金本构与失效模型对比 [J]. 材料导报, 2020, 34(12): 12088-12093.

FENG Zhenyu, LI Henghui, LIU Yi, et al. Comparison of constitutive and failure models of 7075-T7351 alloy at intermediate and low strain rates [J]. Materials Reports, 2020, 34(12): 12088-12093.

BLANCHOT V, DAIDIE A. Riveted assembly modelling: study and numerical characterisation of a riveting process [J]. Journal of Materials Processing Technology, 2006, 180(1/3): 201-209.

常正平, 王仲奇, 张津铭, 等. 采用局部-整体映射模型的壁板铆接变形预测 [J]. 西安交通大学学报, 2018, 52(1): 115-122.

CHANG Zhengping, WANG Zhongqi, ZHANG Jinming, et al. Prediction of riveting deformation of aircraft panel using local-global mapping approach [J]. Journal of Xi'an Jiaotong University, 2018, 52(1): 115-122.[20] 常正平, 王仲奇, 王斌斌, 等. 基于镦头不均匀变形的压铆力建模 [J]. 航空学报, 2016, 37(7): 2312-2320.

CHANG Zhengping, WANG Zhongqi, WANG Binbin, et al. Riveting force computation model based on formed head inhomogeneous deformation [J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(7): 2312-2320.

刘平, 张开富, 李原. 铆接变形及其有限元分析 [J]. 航空制造技术, 2008(22): 94-97.

LIU Ping, ZHANG Kaifu, LI Yuan. Rivet deformation and its FE analysis [J]. Aeronautical Manufacturing Technology, 2008(22): 94-97.

袁立. 航空制造工程手册: 飞机装配 [M]. 2版. 北京: 航空工业出版社, 2010: 248-264.

BOX G E P, HUNTER J S, HUNTER W G. Statistics for experimenters: design, innovation, and discovery [M]. 2nd ed. Hoboken, NJ, USA: Wiley-Interscience, 2005.

魏世丽. 直接观测值回归与间接观测值回归方法的应用研究 [D]. 太原: 太原理工大学, 2015.

马涛, 马源, 黄晓明. 基于多元非线性回归的智能压实关键参数最优解 [J]. 吉林大学学报(工学版), 2023, 53(7): 2067-2077.

MA Tao, MA Yuan, HUANG Xiaoming. Optimal combination of key parameters of intelligent compaction based on multiple nonlinear regression [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(7): 2067-2077.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

I/O Performance Modeling and Prediction Method for Large-Scale Parallel Applications Using Machine Learning

Knowledge Graph Representation Learning Driven by Semantic Discrimination and Subgraph Integration

Windage Loss and Flow Characteristics in Impeller Back Gaps of sCO2 Radial Inflow Turbines

Diffusion Characteristics and Influencing Factors in Case of Hydrogen Leakage from Fuel Cell Vehicles Running in Tunnels

A Fast Calculation Method for Thermal Flow Fields in Transformer Windings Based on PI-DeepONet

Related Author

No data

Related Institution

No data

⁰