Modeling penetration depth in submerged arc welding using artificial neural networks: A comprehensive approach

Farhad  Rahmati; Ali  Shafipour; Masood  Aghakhani; Farhad  Kolahan

doi:10.59400/mea2511

Modeling penetration depth in submerged arc welding using artificial neural networks: A comprehensive approach

Farhad Rahmati Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad 9177948944, Iran; Department of Mechanical Engineering, Razi University, Kermanshah 6714967346, Iran
Ali Shafipour Department of Mechanical Engineering, Razi University, Kermanshah 6714967346, Iran
Masood Aghakhani Department of Mechanical Engineering, Razi University, Kermanshah 6714967346, Iran
Farhad Kolahan Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad 9177948944, Iran

Article ID: 2511

DOI: https://doi.org/10.59400/mea2511

Keywords: nanoparticles; penetration depth; weld geometry; submerged arc welding; artificial neural networks

Abstract

Penetration depth, defined as the distance from the surface of the base material to the deepest point of the molten zone, is a critical factor influencing the strength and mechanical properties of welds. This study investigates the effects of process parameters in submerged arc welding (SAW) on penetration depth, utilizing a two-hidden-layer artificial neural network (ANN) for modeling. The input parameters include arc voltage, welding current, electrode stick-out, welding speed, and the thickness of a manganese-enriched nanoparticle layer, with penetration depth as the output variable. The results demonstrate that increasing the welding current to 700 amps enhances heat transfer to the molten pool, thereby improving base material melting and penetration depth. Similarly, raising the arc voltage from 24 to 32 volts results in a moderate increase in penetration depth due to higher heat input while maintaining a relatively stable electrode melting rate. These findings highlight the potential of optimizing SAW parameters to achieve consistent weld quality and desirable mechanical properties.

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Published

2025-02-27

How to Cite

Rahmati, F., Shafipour, A., Aghakhani, M., & Kolahan, F. (2025). Modeling penetration depth in submerged arc welding using artificial neural networks: A comprehensive approach. Mechanical Engineering Advances, 3(1), 2511. https://doi.org/10.59400/mea2511

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Issue

Vol. 3 No. 1 (2025)

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Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

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