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Related Concept Videos

Yield Criteria for Ductile Materials under Plane Stress01:25

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In designing structural elements and machine parts using ductile materials, it is crucial to ensure that these components withstand applied stresses without yielding. Yielding is initially determined through a tensile test, which evaluates the material's response to uniaxial stress. However, tensile stress is insufficient when components face biaxial or plane stress conditions This condition requires advanced criteria to predict failure.
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Related Experiment Video

Updated: Jul 29, 2025

Generating Lap Joints Via Friction Stir Spot Welding on DP780 Steel
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Optimization of Friction Stir Spot Welding Process Using Bonding Criterion and Artificial Neural Network.

Deok Sang Jo1, Parviz Kahhal1,2,3, Ji Hoon Kim1

  • 1School of Mechanical Engineering, Pusan National University, Geumjeong-gu, Busan 46241, Republic of Korea.

Materials (Basel, Switzerland)
|May 27, 2023
PubMed
Summary

This study optimized friction stir spot welding (FSSW) parameters using artificial neural networks and finite element analysis (FEA). The pressure-time-flow criterion proved most suitable, leading to accurate predictions for bonding strength and hardness.

Keywords:
artificial neural networkbonding criterioncoupled eulerian–lagrangianfriction stir spot welding

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Computational Modeling

Background:

  • Solid-state joining processes require robust bonding criteria to ensure weld integrity.
  • Friction stir spot welding (FSSW) is a promising solid-state joining technique with complex process dynamics.
  • Traditional bonding criteria may not fully capture the intricacies of FSSW.

Purpose of the Study:

  • To analyze and validate bonding criteria for FSSW using finite element analysis (FEA).
  • To determine optimal FSSW process parameters for enhanced weld quality using artificial neural networks (ANNs).
  • To compare predicted outcomes with experimental results for validation.

Main Methods:

  • Finite element analysis (FEA) using ABAQUS-3D Explicit with coupled Eulerian-Lagrangian formulation for large deformations.
  • Application of pressure-time and pressure-time-flow bonding criteria to FEA results.
  • Optimization of process parameters (including tool rotational speed) via ANNs based on bonding criteria.
  • Experimental validation of optimized parameters for bonding strength and hardness.

Main Results:

  • The pressure-time-flow criterion was identified as more suitable for FSSW compared to the pressure-time criterion.
  • Artificial neural networks successfully optimized process parameters, predicting bonding strength and hardness.
  • Tool rotational speed was found to be the most influential parameter on bonding strength and hardness.
  • Experimental validation showed high accuracy, with bonding strength error of 3.675% and hardness error of 3.197%.

Conclusions:

  • The FEA-driven approach combined with ANNs provides an effective method for optimizing FSSW parameters.
  • The pressure-time-flow criterion is a reliable metric for assessing FSSW joint quality.
  • Optimized parameters significantly improve weld zone hardness and bonding strength, validated by experimental data.