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http://dx.doi.org/10.14400/JDC.2019.17.5.217

A Study on the Prediction of Welding Flaw Using Neural Network  

Cho, Jae Hyung (Department of Industrial Engineering, Dankook University)
Ko, Sang Hyun (Department of Industrial Engineering, Dankook University)
Publication Information
Journal of Digital Convergence / v.17, no.5, 2019 , pp. 217-223 More about this Journal
Abstract
A study in predicting defects of spot welding in real time in automotive field is essential for cost reduction and high quality production. Welding quality is determined by shear strength and the size of the nugget, and results depend on different independent variables. In order to develop the real-time prediction system, multiple regression analyses were conducted and the two dependent variables were obtained with sufficient statistical results with three independent variables, however, the quality prediction by the regression formula could not ensure accuracy. In this study, a multi-layer neural network circuit was constructed. The neural network by 10 dynamic resistance variables was constructed with three hidden layers to obtain execution functions and weighting matrix. In this case, the neural network was established with three independent variables based on regression analysis, as there could be difficulties in real-time control due to too many input variables. As a result, all test data were divided into poor, partial, and modalities. Therefore, a real-time welding quality determination system by three independent variables obtained by multiple regression analysis was completed.
Keywords
Neural-Network; Multiple-Regression; Spot-Welding; Dynamic-Resistance; Real-Time Inspection;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
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