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%.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
