Optimization and Modelling of Resistance Spot Welding Process Parameters for Quality Improvement Using Taguchi Method and Artificial Neural Network

• Main Authors: Soomro, I.A., Pedapati, S.R., Awang, M., Soomro, A.A., Alam, M.A., Bhayo, B.A.
• Format: Article
• Published: 2022
• Online Access: http://scholars.utp.edu.my/id/eprint/34025/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144139703&doi=10.22068%2fijmse.2709&partnerID=40&md5=aa407d0363324fd43f4fa816ad00d9ab

This paper investigated the optimization, modeling, and effect of welding parameters on the tensile shear load-bearing capacity of double pulse resistance spot-welded DP590 steel. Optimization of welding parameters was performed using the Taguchi design of experiment method. A relationship between input welding parameters i.e., second pulse welding current, second pulse welding current time, and first pulse holding time and output response i.e, tensile shear peak load was established using regression and neural network. Results showed that the maximum average tensile shear peak load of 26.47 was achieved at optimum welding parameters i.e., second pulse welding current of 7.5 kA, second pulse welding time of 560 ms, and first pulse holding time of 400 ms. It was also found that the ANN model predicted the tensile shear load with higher accuracy than the regression model. © 2022, Iran University of Science and Technology. All rights reserved.