Optimization and Modelling of Resistance Spot Welding Process Parameters for Quality Improvement Using Taguchi Method and Artificial Neural Network
| Autor: | Imtiaz Ali Soomro, Srinivasa Rao Pedapati, Mokhtar Awang, Afzal Ahmed Soomro, Mohammad Azad Alam, Bilawal Ahmed Bhayo |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Iranian Journal of Materials Science and Engineering, Vol 19, Iss 4, Pp 1-10 (2022) |
| Druh dokumentu: | article |
| ISSN: | 1735-0808 2383-3882 |
| Popis: | This paper investigated the optimization, modelling 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 paramaters 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 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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