Quality Inspection of the Riveting Process by Neural Networks

Abstract:

This paper presents an automatic quality inspection system for the riveting process by using neural network (NN) techniques. Two types of neural models were used in studies. One is the conventional neural network and the other one is the quantum neural network which is expected to deal with the signals with fuzziness and uncertainty. The well-trained neural network could make an immediate diagnosis of the riveting quality based on the impact signals sensed. Thus, such NN inspection system can not only monitor the real time riveting process, but also give the assistance on the riveting quality verification. In order to demonstrate the superiority of neural network inspection system developed, the experimental data provided Chinese Air Force Institute of Technology was studied and simulated. The method of riveting quality index (RQI) was also performed as a comparison. From the simulation results shown, both of the proposed neural network inspection systems have the better verification accuracy than RQI method.

• Periodical: Applied Mechanics and Materials (Volumes 284-287)
• Pages: 2276-2280
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.284-287.2276
• Online since: January 2013
• Authors: Huang Chi Chen, Yu Ju Chen, Hsing Ling Wang, Chuo Yean Chang, Pin Hsuin Weng, Rey Chue Hwang
• Keywords: Automatic, Neural Network (NN), Quality Inspection, Riveting Process

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