Abstract

For complex products, as the size, shape, position and other properties of the geometric features changing, the accumulated assembly error or the coordination error between different assemblies would be affected directly. How to keep these key geometric characteristics in a statistical state, especially in the process of batch production, is an important factor to ensure error consistency. Aiming to control the assembly quality, optimization methods for key characteristics of aircraft products with feedback actions and ASFF (Assembly Station Flowing Fluctuation) analysis is proposed. Firstly, by collecting and constructing statistical quality samples, based on SPC (Statistical Process Control) method, criteria on abnormal assembly quality was analyzed, with qualitative practical experience. Secondly, four specific assembly controlling actions with a feedback loop were adopted by quantitative analysis, including PCF (Product Coordination Feature) identification, PCE (Process Coordination Element) mapping, CR (Coordination Relationship) modelling, and assembly error propagation modelling. Thirdly, the concept of ASFF was proposed, and the trajectory chart was plotted to evaluate the deviation and fluctuation of assembly error under one assembly station. This analysis was done by calculating the process offset and stability, according to the dynamic change of assembly quality status data at different time stages. Finally, with the specific improvement actions, i.e. (1) diagnosing the abnormal sources and improving the assembly operating process, (2) analyzing the dynamic deviation and fluctuation of assembly quality data within a specified assembly station, and (3) improving the assembly assurance ability, the out-of-tolerance problem of the skin profile was optimized to verify methodology's feasibility. Benefit results are gained, i.e. the locating state of ending ribs was more accurate, and the assembly process became more stable. With the rapid growth of aircraft production, the quality controlling method would be much helpful especially in the batch manufacturing stage.

Highlights

  • With the appearance of the concept of Key Characteristic (KC), lots of research work has been done in aircraft manufacturing field with the view of KC’s identification and controlling [1]

  • As the size, shape, position and other properties of the geometric features distributing on aircraft products changing, the accumulated assembly error and the coordination error between different assemblies will be affected directly [2]

  • Where the coordination error is defined as the consistency between different assembly error items, both in error accumulation direction and numerical value

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Summary

INTRODUCTION

With the appearance of the concept of Key Characteristic (KC), lots of research work has been done in aircraft manufacturing field with the view of KC’s identification and controlling [1]. With regard to the quality controlling methods, the SPC chart is used to monitor and analyze the fluctuation for the samples of key error factors in the assembly/manufacturing process. According to the detection value of key product elements in a specified manufacturing link or error link, the individual moving range chart, i.e., is used for statistical controlling in aircraft assembly process. (6) During the assembly process among different sorties, when the measurement values of the same characteristic/feature having a relatively fixed relationship with each other, it can be regarded that there is a deterministic system error occurred in the manufacturing process Under this condition, the most important reason may be the inherent positioning deviation state of assembly tooling/fixture, and it‘s necessary to re-calibrate the equipment for satisfying design requirements before starting the detailed assembly work. Section III.D explains the modeling of different kinds of error items, with the analysis on their coupling/accumulation relationship, and the final assembly error chain can be gained

IDENTIFICATION OF THE PCF
CR MODELING
ASSEMBLY ERROR PROPAGATION MODELING
ASSEMBLY STATION FLOWING FLUCTUATION ANALYSIS AT DIFFERENT TIME STAGES
ASSEMBLY QUALITY CONTROLLING WITH SPC AND FEEDBACK IMPROVEMENT ACTIONS
Findings
CONCLUSION
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