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Modeling the weld bead penetration in the presence of Cr2O3 nanoparticles in the submerged arc welding process using a modified neuro-fuzzy system

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Multiscale and Multidisciplinary Modeling, Experiments and Design Aims and scope Submit manuscript

Abstract

Submerged arc welding (SAW) is a widely used technique in various industries for welding thick plates. The quality of welded joints produced by this process depends on the selection of appropriate parameters that yield weldments with desirable mechanical properties. Among these parameters, weld bead penetration is a crucial indicator of weld quality. In this study, the effect of arc voltage, welding current, distance between the contact tip and the workpiece, and arc travel speed in the presence of Chromium oxide (Cr2O3) nanoparticles on penetration has been investigated. In addition, a new hybrid optimization algorithm has been developed by combining the differential evolution (DE) and wingsuit flying search (WFS) algorithms. The hybrid algorithm is evaluated by using standard benchmark functions and combined with the adaptive network-based fuzzy inference system (ANFIS) to create a new model. The model predicts weld bead penetration in the SAW process based on input parameters. The proposed hybrid algorithm improved the effectiveness of the main ANFIS in predicting weld bead penetration. The results showed that the addition of Cr2O3 nanoparticles to the weld pool increased its penetration.

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All the data are provided in the paper.

Abbreviations

ANFIS:

Adaptive network-based fuzzy inference system

ANN:

Artificial neural network

AI:

Artificial intelligence

CCRD:

Central composite rotatable design

DCEP:

Direct current electrode positive

DE:

Differential algorithm

DLS:

Dynamic light scattering

EDX:

Energy dispersive X-ray

FA-ACO:

Firefly algorithm-ant colony optimization

FA-DE:

Firefly algorithm-differential evolution

FIS:

Fuzzy Inference System

GD:

Gradient descent

GWO:

Grey Wolf Optimization

LSE:

Least squares estimation

MRE:

Mean Relative Error

R2 :

Correlation determination

RSW:

Resistance spot welding

SAW:

Submerged arc welding

SEM:

Scanning electron microscope

Std Dev:

Standard deviation

WFS:

Wingsuit Flying Search

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Funding

This research received no external funding.

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Authors and Affiliations

  1. Mechanical Engineering Department, Southern Illinois University Edwardsville, Edwardsville, USA

    Pooria Naderian & Neaman Sohrabi

  2. Mechanical Engineering Department, Razi University, Kermanshah, Iran

    Pooria Naderian & Masood Aghakhani

  3. Mechanical Engineering Department, Kermanshah University of Technology, Kermanshah, Iran

    Alimohammad Karami

  4. Department of Chemistry, Southern Illinois University Edwardsville, Edwardsville, USA

    Hadis Ghasemi

Authors
  1. Pooria Naderian
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  2. Alimohammad Karami
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  3. Masood Aghakhani
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  4. Hadis Ghasemi
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  5. Neaman Sohrabi
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Contributions

P.N. Conducting the experiments, writing the manuscript, developing model - A.K. Developing the model - M.A Supervising the research - H.G Preparing the nanoparticles - N.S. Conducting the experiments

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Correspondence to Pooria Naderian.

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Naderian, P., Karami, A., Aghakhani, M. et al. Modeling the weld bead penetration in the presence of Cr2O3 nanoparticles in the submerged arc welding process using a modified neuro-fuzzy system. Multiscale and Multidiscip. Model. Exp. and Des. (2024). https://doi.org/10.1007/s41939-024-00386-7

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