Authors : B. Sudheer Kumar; K. Kapil Achuth Sheshadri; M. M Jijendra; Dr. V Mahidhar Reddy

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/22e2u5pn

DOI : https://doi.org/10.38124/ijisrt/25apr1802

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Submerged Arc Welding (SAW) is a popular method across many industries because it consistently produces strong, high-quality welds. But getting the best possible performance from this process isn't always straightforward. That's because many different factors—like welding settings and environmental conditions—interact in complex ways. In this study, we take a predictive approach to improve SAW performance. Our goal is to make the welds not only stronger but also more resistant to corrosion and structurally sound at a microscopic level. To test this, we ran salt spray corrosion tests to simulate harsh conditions and see how well the welded joints hold up. We also used a high-tech microscope system, the Metscope Pro, to closely study the microstructure of the welds. We experimented with different welding settings—like current, voltage, travelspeed, and the type of flux used—following a planned experimental design. What we found was pretty clear: by fine-tuning these parameters, we significantly boosted the corrosion resistance and created more uniform and refined microstructures. That means stronger, longer-lasting welds. This work provides practical insights for anyone looking to adapt the SAW process for tough environments where performance really matters.

Keywords : SubmergedArc Welding, Corrosion Testing, Microstructure Analysis, Predictive Modeling, Weld Quality, Metscope Pro, Parameter Tuning.

References :

1. Singh and R. P. Singh, A review of effect of welding parameters on the mechanical properties of weld in submerged arc welding process, Materials Today.
2. S. Sankar Sarkar, A. Das, S. Paul, K. Mali, A. Ghosh, R. Sarkar, A. Kumar, Machine learning method to predict and analyse transient temperature in submerged arc welding, Measurement (2020).
3. V. Sengupta, D. Havrylov, and p. f. Mendez, Physical Phenomena in the Weld Zone of Submerged Arc Welding — A Review, supplement to the welding journal, October 2019.
4. Serdar Karaoglu, Abdullah Secginb, Sensitivity analysis of submerged arc welding process parameters, journal of materials processing technology 202 (2008) 500–507.
5. H. Sharma, B. Rajput and R. P. Singh, A review paper on effect of input welding process parameters on structure and properties of weld in submerged arc welding process, Materials Today: Proceedings.
6. Merenda C, Kim H, Tanous K, et al. (2018) "Augmented Reality Interface Design Approaches for Goal-directed and Stimulus-driven Driving Tasks." IEEE Trans Vis Comput Graph; 24: 2875 2885.
7. Francisco A, Taylor JE (2019) "Understanding citizen perspectives on open urban energy data through the development and testing of a community energy feedback system." Appl Energy 256: 113804.
8. Sidani A, Dinis FM, Sanhudo L, et al. (2021) "Recent Tools and Techniques of BIM-Based Virtual Reality: A Systematic Review." Arch Comput Methods Eng 28: 449–462.
9. Atici-Ulusu H, Ikiz YD, Taskapilioglu O, et al. (2021) "Effects of augmented reality glasses on the cognitive load of assembly operators in the automotive industry." Int J Comput Integr Manuf 34: 487–499.
10. Eiris R, Jain A, Gheisari M, et al. (2020) "Safety immersive storytelling using narrated 360 degree panoramas: A fall hazard training within the electrical trade context." Saf Sci 127: 104703.