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<p>This study evaluates the accuracy, complexity, and sensitivity of the Hollomon, Swift, Voce, and Menegotto-Pinto models in predicting the stress–strain behavior of A36 steel under plastic deformation. All models achieved high accuracy, with <i>R</i><sup>2</sup> &gt; 0.99 and mean absolute percentage errors (Mape) below 1.68%. The Swift and Voce models demonstrated the highest accuracy with the lowest mean squared errors (5.85 MPa<sup>2</sup> and 11.06 MPa<sup>2</sup>, respectively). The Menegotto-Pinto model, despite its higher complexity, exhibited lower sensitivity to parameter variations, followed by the Voce model. Conversely, the Hollomon and Swift models were more sensitive to parameter uncertainties. In terms of math and calibration complexity, the models ranked from simplest to most complex as follows: Hollomon, Swift, Voce, and Menegotto-Pinto. It is expected that this research will serve as a basis for the selection of these models for the implementation of simulations of manufacturing processes and failure in A36 steels, by means of finite element or analytical models.</p>

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Comparative analysis of stress–strain models for A36 steel in monotonic loading: performance, complexity, and sensitivity

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月23日

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  • Camilo Andrés Gonzalez Olier,
  • Jorge Enrique Gonzalez Coneo

摘要

This study evaluates the accuracy, complexity, and sensitivity of the Hollomon, Swift, Voce, and Menegotto-Pinto models in predicting the stress–strain behavior of A36 steel under plastic deformation. All models achieved high accuracy, with R2 > 0.99 and mean absolute percentage errors (Mape) below 1.68%. The Swift and Voce models demonstrated the highest accuracy with the lowest mean squared errors (5.85 MPa2 and 11.06 MPa2, respectively). The Menegotto-Pinto model, despite its higher complexity, exhibited lower sensitivity to parameter variations, followed by the Voce model. Conversely, the Hollomon and Swift models were more sensitive to parameter uncertainties. In terms of math and calibration complexity, the models ranked from simplest to most complex as follows: Hollomon, Swift, Voce, and Menegotto-Pinto. It is expected that this research will serve as a basis for the selection of these models for the implementation of simulations of manufacturing processes and failure in A36 steels, by means of finite element or analytical models.