摘要
钣金装配过程的误差分析对于消除钣金装配质量故障具有重要意义。现有分析建模方法由于受钣金装配零件的材料、几何形状和装配工艺的限制,难于对钣金装配过程进行准确建模和误差分析。与分析建模方法不同,基于装配体关键产品特征的历史测量数据提出进行钣金装配过程误差分析的数据驱动建模方法。所提方法由工程经验和数学推导,建立钣金装配过程的多元一阶自回归模型和多元部分线性模型。基于极大似然估计方法和最小二乘核光滑估计方法给出所建立模型的参数和非参数估计。四元四工序典型汽车引擎盖的装配实例证明,所提方法在钣金装配误差分析过程中具有有效性。基于数据驱动的建模方法易于建模,分析结果准确可靠,可为钣金装配过程的误差分析提供新思路。
Variation analysis in a compliant sheet metal assembly process is of great significance for eliminating assembly quality faults.Existing analytical modeling methods are not competent because they are limited by material,geometry and assembly process of the compliant sheet metal parts.Different from the analytical modeling method,a data-driven modeling-based method for variation analysis in a compliant sheet metal assembly process is proposed based on the measured historical data of key product characteristics.Through engineering experience and mathematical deduction,the multivariate first-order autoregressive model and the multivariate partial linear model are established.The maximum likelihood estimation method and the least squares kernel smoothing estimation method are employed to calculate the parameters and nonparametric estimates of the established models.A typical four-step hood assembly example with four key product characteristics demonstrates the effectiveness of the proposed method in the process of variation analysis.The data-driven modeling method is easy to model and the analysis results are accurate and reliable,which provides a new path for variation analysis of sheet metal assembly process.
作者
张磊
黄传辉
朱恩旭
王磊
董妍
ZHANG Lei;HUANG Chuanhui;ZHU Enxu;WANG Lei;DONG Yan(Jiangsu Key Laboratory of Construction Machinery Detection and Control,Xuzhou University of Technology,Xuzhou 221018)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2019年第10期34-41,共8页
Journal of Mechanical Engineering
基金
江苏省重点研发计划(BE2016047)
江苏省高校自然科学基金(15KJB460016)
徐州市工业科技计划重点研发(KC16GZ015)资助项目
关键词
钣金装配
误差分析
一阶自回归模型
部分线性模型
最小二乘核光滑估计
compliant sheet metal assembly
variation analysis
first-order autoregressive model
partially linear model
least squares kernel smoothing estimation
