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Optimization of submerged arc welding parameters to improve corrosion resistance and hardness in API 5L X70 steel joins using Support Vector Regression and Multi-Objective Genetic Algorithm

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Abstract

Mathematical techniques such as Support Vector Regression (SVR) and Multi-Objective Genetic Algorithms (MOGA) were used for a multi-objective optimization of corrosion rate (Rcorr) and hardness in an API 5L X70 steel welded by submerged arc welding (SAW) process with a double-V bevel shape. The inner and outer bevels (IB and OB, respectively) were joined at different conditions of voltage (V ), amperage (A) and travel speed in inches per minute (TS (ipm)), with a range in heat input (Q) of 1278–1693 J/mm. As Q is responsible for the microstructural behavior of the welds, their particular characteristics are defined by welding parameters giving as response variables the hardness and Rcorr in the fusion zone (FZ). For the experimental corrosion evaluation, the samples were tested in the FZ and base metal (BM) with potentiodynamic polarization test by three-electrode cell in an H2O + 3.5 wt.% NaCl electrolyte, and the Vickers microhardness (HV ) profiles were measured with a 500 g force. The experimental results (HV and Rcorr) were used for the corresponding prediction and optimization by SVR and MOGA. The main results show that Rcorr using optimized parameters decreases significantly from 2.356 mils per year (mpy) to 0.577 mpy in the FZ with a predominant microstructure of acicular ferrite (AF) and small regions of ferrite at the grain boundary (FGB). For the hardness, the predicted results were 217.36 HV (IB) and 225.63 HV (OB) against the 224.58 HV and 215.75 HV recorded in the validation sample revealing the great effectiveness of the applied method for prediction and optimization.

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Acknowledgements

The author Luis A. Guía-Hernández is indebted to CONACYT for the provided scholarship to pursue a doctoral degree. Also, the authors would like to thank COMIMSA for the provided equipment facilities.

Funding

This research has been funded by the Tecnológico Nacional de México through the research project 14487.22-P.

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Authors and Affiliations

  1. División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/IT Saltillo, Blvd. V. Carranza 2400 Col. Tecnológico, Saltillo, 25280, Coahuila, México

    Luis A. Guía-Hernández, Rocio M. Ochoa-Palacios, Edgar O. Reséndiz-Flores, Patricia S. Costa, Perla J. Reséndiz-Hernández & Gerardo Altamirano-Guerrero

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  1. Luis A. Guía-Hernández
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Contributions

Guía-Hernández, L.A.: experimental methodology, experimental validation, analysis, investigation, writing—original draft, writing review and editing, visualization. Ochoa-Palacios R.M.: experimental methodology, experimental validation, analysis, investigation, writing—original draft, writing review and editing. Reséndiz-Flores, E.O.: conceptualization, mathematical methodology, numerical implementation, numerical analysis, investigation, computational resources, writing—original draft, writing review and editing, supervision, project administration. Costa, P.S.: experimental methodology and resources, investigation, project administration. Reséndiz-Hernández, P.J.: experimental results interpretation. Altamirano-Guerrero, G.: experimental results interpretation.

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Correspondence to Edgar O. Reséndiz-Flores.

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Guía-Hernández, L.A., Ochoa-Palacios, R.M., Reséndiz-Flores, E.O. et al. Optimization of submerged arc welding parameters to improve corrosion resistance and hardness in API 5L X70 steel joins using Support Vector Regression and Multi-Objective Genetic Algorithm. Int J Adv Manuf Technol 126, 531–541 (2023). https://doi.org/10.1007/s00170-023-11070-4

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