Title :
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
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;
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
DOI :
10.1109/PCSPA.2010.233
