Authors: Du, Shichang¹; Xi, Lifeng²; Yu, Jianbo²; Sun, Jiwen²

Source: The International Journal of Advanced Manufacturing Technology, Volume 50, Numbers 1-4, September 2010 , pp. 377-389(13)

Publisher: Springer

Abstract:

The ability to reduce variation for quality improvement in the aircraft horizontal stabilizer assembly processes plays an essential role in the success of an aircraft manufacturing enterprise in today's globally competitive marketplace. Monitoring and identifying variation source(s) of out-of-control signals are important issues for variation reduction in horizontal stabilizer assembly process. Traditional quality control focuses on statistical process control using control charts. However, control charts cannot identify variation source(s) of out-of-control signals. One novel integrated system is developed for monitoring and diagnosis of horizontal stabilizer assembly processes. <EquationSource Format="TEX">$$ left| Sigma right| $$</EquationSource> control charts are firstly designed to be used as the detector of abnormal signals, and then, an improved particle swarm optimization with simulated annealing (PSOSA)-based selective neural network (NN) ensemble approach is explored for identifying the variation source(s) of out-of-control signals. Utilization of selective NN ensemble algorithm is able to improve the generalization performance of neural systems in comparison with using single NN recognizers, and PSOSA algorithm aims to improve the ability to escape from a local optimum. The data from the real-world aircraft horizontal stabilizer assembly processes are collected to validate the developed system. The results indicate that the developed system can perform effectively for monitoring and identifying out-of-control signals of variance increases in terms of correct classification percentage and average run length.

Keywords: Horizontal stabilizer; Multivariate manufacturing; Statistical process control; Neural network

Document Type: Research article

DOI: http://dx.doi.org/10.1007/s00170-009-2490-0

Affiliations: 1: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China, Email: lovbin@sjtu.edu.cn 2: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

Publication date: 2010-09-01

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