Key Deviation Source Diagnosis for Aircraft Structural Component Assembly Driven by Small Sample Inspection Data

Abstract

Abstract: There were many transitive relationships among aircraft structural component assembly quality and the deviation sources such as nonlinearity, multilevel strong coupling and large uncertainty, which could not be directly built to identify the key deviation sources using the equation of assembly dimension chain. Thus, the measurement information theory was introduced, and a key deviation source diagnosis was presented for aircraft structural component assembly driven by small sample inspection data based on the integrated use of the entropy weight method and the synthetic grey correlation. Firstly, the potential informations were mined from assembly quality inspection data and entropy method was used to measure assembly quality attribute differences. Secondly, the correlation between each deviation source and the assembly quality was quantized by the synthetic grey correlation. Thirdly, the synthetic grey correlation between each deviation sources and assembly quality was modified by the weight of assembly quality attribute. Finally, according to the modified correlation, each deviation source was sorted to determine the key deviation source which affected assembly quality quantitatively. The case of assembly application verifies the accuracy and computational feasibility of the key deviation source diagnosis method based on entropy method and synthetic grey correlation.

Key words: aircraft, assembly, deviation source, small sample, entropy weight, synthetic grey correlation

摘要： 飞机结构件的装配质量与其偏差源之间呈现非线性、多层级强耦合、不确定度大的传递关系，不能通过构建装配尺寸链方程的方法诊断出影响装配质量的关键偏差源，为此，以装配偏差实测数据为基础，引入测量信息论，将熵权法和灰色综合关联度进行融合，提出小样本数据驱动的影响结构件装配质量的关键偏差源诊断方法。挖掘结构件装配质量检测数据的潜在信息，利用熵权法对结构件装配质量属性进行差异化度量；利用灰色综合关联度量化结构件各偏差源与结构件装配质量之间的关联度；利用装配质量属性权重对各偏差源与装配质量之间的灰色综合关联度进行修正；最后，按照修正后的关联度对各偏差源的重要程度进行排序，量化确定影响结构件装配质量的关键偏差源。装配应用案例证明了基于熵权法和灰色综合关联度的装配质量关键偏差源诊断方法的准确性和计算可行性。

关键词: 飞机, 装配, 偏差源, 小样本, 熵权, 灰色综合关联度

CLC Number:

V262.4

ZHU Yongguo1;DENG Bin1;HUO Zhengshu1; ZHOU Jiehua2. Key Deviation Source Diagnosis for Aircraft Structural Component Assembly Driven by Small Sample Inspection Data[J]. China Mechanical Engineering.

朱永国1;邓斌1;霍正书1;周结华2. 小样本检测数据驱动的飞机结构件装配关键偏差源诊断[J]. 中国机械工程.

References

[1]赵爽, 谢石林, 邓正平, 等.基于装配过程的关键特性识别与控制方法研究[J]. 航空制造技术, 2016(8): 56-59.
ZHAO Shuang, XIE Shilin, DENG Zhengping, et al. Research on Key Characteristics Identifying and Controlling Method Based on Assembly Process[J]. Aeronautical Manufacturing Technology, 2016(8): 56-59.
[2]CHASE K W, MAGLEBY S P, GAO J. ToleranceAnalysis of 2-D and 3-D Mechanical Assemblies with Small Kinematic Adjustments[C]∥Advanced Tolerancing Techniques. New York: John Wiley & Sons, 1997: 103-137.
[3]朱永国, 张文博, 刘春锋, 等. 基于SDT和间接平差的中机身自动调姿精度分析[J]. 航空学报, 2017, 12(38): 296-309.
ZHU Yongguo, ZHANG Wenbo, LIU Chunfeng, et al. Accuracy Analysis for Aircraft Fuselage Automatical Posture Adjustment Based on SDT and Indirect Adjustment[J]. Acta Aeronautica et Astronautica Sinica, 2017, 12(38): 296-309.
[4]刘伟东, 宁汝新, 刘检华, 等. 基于偏差有向图和DH方法的产品装配精度预测技术[J]. 机械工程学报, 2012, 48(7): 125-140.
LIU Weidong, NING Ruxin, LIU Jianhua, et al. Precision Predicting Based on Directed Deviation Graph Modeling and D-H Methodology[J]. Journal of Mechanical Engineering, 2012, 48(7): 125-140.
[5]唐文斌.飞机非线性装配偏差分析与容差协同分配方法研究[D]. 西安: 西北工业大学, 2015.
TANG Wenbin. Non-linear Assembly Deviation Analysis and Tolerance Co-allocation for Aircraft[D]. Xi'an: Northwestern Polytechnical University, 2015.
[6]孙辉鹏. 飞机壁板类柔性部件的装配偏差分析与预测[D]. 南京: 南京航空航天大学, 2015.
SUN Huipeng. Assembly Variation Analysis and Prediction of Flexible Parts of Aeronautical Panels[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2015.
[7]朱怡心, 乔立红. 装配过程快速仿真建模服务平台[J]. 航空制造技术, 2016(12): 38-43.
ZHU Yixin, QIAO Lihong. Service Platform of Rapid Simulation Modeling for Assembly Process[J]. Journal of Mechanical Engineering, 2016(12): 38-43.
[8]MAROPOULOS P G, MUELANER J E, SUMMERS M D, et al. A New Paradigm in Large Scale Assembly-research Priorities in Measurement Assisted Assembly[J]. The International Journal of Advanced Manufacturing Technology, 2014, 70(1): 621-633.
[9]CHEN Z H, DU F Z, TANG X Q, et al. A Framework of Measurement Assisted Assembly for Wing-fuselage Alignment Based on Key Measurement Characteristics [J].International Journal of Manufacturing Research, 2015, 10(2): 107-128.
[10]林雪竹. 基于全三维模型的飞机大部件装配对接测量方法及实验[D]. 长春: 长春理工大学, 2016.
LIN Xuezhu. Assembly and Joint Measurement Method and Experimental Study of Large Aircraft Components Based on Full 3-D Model[D]. Changchun: Changchun University of Science and Technology, 2016.
[11]WANG H, DING X. Identifying Sources of Variation in Horizontal Stabilizer Assembly Induced by Rib Using Finite-element Analysis and Full Factorial Design Method[J]. Journal of Aerospace Engineering, 2014, 27(4): 158-164.
[12]CHENG H, WANG R X, LI Y. Modeling and Analyzing of Variation Propagation in Aeronautical Thin-walled Structures Automated Riveting[J]. Assembly Automation, 2012(32): 25-37.
[13]LIU G, HUAN H L, KE Y L. Study on Analysis and Prediction of Riveting Assembly Variation of Aircraft Fuselage Panel[J]. The International Journal of Advanced Manufacturing Technology, 2014, 75(5): 991-1003.
[14]HU S, WU S W. Identifying Root Cause of Variation in Automobile Body Assembly Using Principal Component Analysis [J]. Trans. of NAMRI, 1992, 20(3): 311-316.
[15]周正龙, 马本江, 胡凤英. 基于熵值法与灰色关联决策的最佳响应方案[J]. 统计与决策, 2017 (8): 46-49.
ZHOU Zhenglong, MA Benjiang, HU Fengying. Optimal Response Scheme Based on Entropy Method and Grey Relational Decision[J].Statistics and Decision, 2017 (8): 46-49.
[16]付忠广, 刘炳含, 刘璐, 等. 融合熵权TOPSIS法与灰色关联度法的火电机组综合评价方法[J]. 华北电力大学学报(自然科学版), 2018, 45(6): 68-75.
FU Zhongguang, LIU Binghan, LIU Lu, et al. Comprehensive Evaluation Method of Thermal Power Unit on Basis of Entropy Weight TOPSIS Method and Grey Relational Grade Method[J]. Journal of North China Electric Power University, 2018, 45(6): 68-75.
[17]田民,刘思峰,卜志坤.灰色关联度算法模型的研究综述[J]. 统计与决策, 2008(1): 24-27.
TIAN Min，LIU Sifeng, BU Zhikun. A Review of Research on Grey Correlation Algorithm Model[J]. Statistics and Decision, 2008(1): 24-27.