Quality Identification of the Riveting Process by QNN Model

Author

Yang, Jen-Pin ; Weng, Pin-Hsuin ; Chen, Yu-Ju ; Chuang, Shang-Jen ; Huang, Huang-Chu ; Hwang, Rey-Chue

Author_Institution

Electr. Eng. Dept., I-Shou Univ., Dashu, Taiwan

fYear

2010

fDate

17-19 Sept. 2010

Firstpage

944

Lastpage

947

Abstract

In this paper, an automatic quality inspection system for the riveting process by using quantum neural network (QNN) was proposed. This inspection system not only can monitor the real time riveting process, but also can give the assistance on the riveting quality verification. For demonstrating the superiority of the inspection system we developed, the data provided by the experiment did by Chinese Air Force Institute of Technology was simulated. The method of riveting quality index (RQI) was also performed as a comparison.

Keywords

inspection; mechanical engineering computing; neural nets; quality control; quantum computing; riveting; QNN model; RQI; automatic quality inspection system; quality identification; quantum neural network; riveting process; riveting quality index; Accelerometers; Accuracy; Artificial neural networks; Inspection; Load forecasting; Testing; Training; quality inspection; quantum neural network; riveting process;

fLanguage

English

Publisher

ieee

Conference_Titel

Pervasive Computing Signal Processing and Applications (PCSPA), 2010 First International Conference on

Conference_Location

Harbin

Print_ISBN

978-1-4244-8043-2

Electronic_ISBN

978-0-7695-4180-8

Type

conf

DOI

10.1109/PCSPA.2010.233

Filename

5635929