NDLI: Quality monitoring of resistance spot welding based on electrode displacement characteristics analysis

Content Provider	Springer Nature Link
Author	Zhang, Pengxian Zhang, Hongjie Chen, Jianhong Ma, Yuezhou
Copyright Year	2007
Abstract	A new method is developed to monitor joint quality based on the information collection and process in spot welding. First, twelve parameters related to weld quality are mined from electrode displacement signal on the basis of different phases of nugget formation marked by simultaneous dynamic resistance signal. Second, through correlation analysis of the parameters and taking tensile-shear strength of the spot-welded joint as evaluation target, different characteristic parameters are reasonably selected. At the same time, linear regression, nonlinear regression and radial basis function (RBF) neural network models are set up to evaluate weld quality between the selected parameters and tensile-shear strength. Finally, the validity of the proposed models is certified. Results show that all of the models can be used to monitor joint quality. For the RBF neural network model, which is more effective for monitoring weld quality than the others, the average error validated is 2.88% and the maximal error validated is under 10%.
Starting Page	330
Ending Page	335
Page Count	6
File Format	PDF
ISSN	16733479
Journal	Frontiers of Mechanical Engineering
Volume Number	2
Issue Number	3
e-ISSN	16733592
Language	English
Publisher	Higher Education Press
Publisher Date	2007-01-01
Publisher Institution	Chinese Universities
Publisher Place	Beijing
Access Restriction	Subscribed
Subject Keyword	resistance spot welding correlation analysis regression analysis radial basis function (RBF) neural network quality monitoring Mechanical Engineering
Content Type	Text
Resource Type	Article
Subject	Mechanical Engineering

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