Abstract
Electrode misalignment in resistance spot welding can be caused by poor fitting or deformation of electrode with continuous usage. This leads to asymmetric weld nugget formation, porosity and expulsion. This paper presents a novel low-cost real-time inspection system for angular misalignment using an image processing approach. The proposed solution can effectively segment the electrode tips even from the image captured at noisy industrial background such as automotive assembly line, by using a regional convolutional neural network based object identification method. The trained model has a mean average precision and recall of 99.01% and 96.6%, respectively. A series of image processing tools and mathematical operations were used to identify the edge line contours of electrode tips accurately from the detection mask, and determine the angular misalignment with a maximum deviation of less than 0.06°. Experimental results showed that the weld nuggets exhibited porosity, shrinkage voids, and cracks when performed under angular misalignment conditions.
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.
