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<p>Optimizing hot-working process requires an accurate prediction of the flow stress behavior of aluminum matrix composites (AMCs) at high temperatures. In this study, hot-compression tests were conducted on 15% SiCp/AA2024 composites to evaluate three constitutive models: Arrhenius, Double Multiple Nonlinear Regression (DMNR), and Modified Johnson-Cook (mJ-C). Experiments were performed on a Gleeble−3500 simulator at temperatures ranging from 673 to 753&#xa0;K, strain rates between 0.01 and 1&#xa0;s<sup>−1</sup>, and true strains up to 0.7. Based on the statistical indicators, including correlation coefficient (<i>R</i>), average absolute relative error (AARE), and root-mean-square error (RMSE), the DMNR model demonstrated the highest predictive accuracy (<i>R</i> = 0.99467, AARE = 1.8080%, and RMSE = 1.7968&#xa0;MPa) outperforming both the mJ-C and Arrhenius models. A key contribution of this work is the direct construction of hot-processing maps using the DMNR model, which has not been reported previously. Using this model, the strain-rate sensitivity (<i>m</i>), the strain-hardening exponent (<i>n</i>), and <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({s}^{"}\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mi>s</mi> </mrow> <mo>"</mo> </msup> </math></EquationSource> </InlineEquation>, enabling clear identification of stable and unstable deformation regions. These maps highlight processing windows with high energy-dissipation efficiency and provide a practical basis for process optimization. Furthermore, the microstructural observations confirmed that DMNR-predicted optimal zones correspond to regions with fine, recrystallized grains, supporting the reliability and applicability of the DMNR-based processing maps.</p> Graphical Abstract <p></p>

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Comprehensive Assessment of Constitutive Models for Precise Flow Stress Prediction in Aluminum Matrix Composites under Thermomechanical Loading at Elevated Temperatures

  • 期刊论文

  • 发表日期：

    2026年1月29日

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  • Anisah Farooq Hashmi,
  • Fuguo Li,
  • Qian Zhao,
  • Muhammad Tanveer,
  • Tarek Khelfa,
  • E. Zhu

摘要

Optimizing hot-working process requires an accurate prediction of the flow stress behavior of aluminum matrix composites (AMCs) at high temperatures. In this study, hot-compression tests were conducted on 15% SiCp/AA2024 composites to evaluate three constitutive models: Arrhenius, Double Multiple Nonlinear Regression (DMNR), and Modified Johnson-Cook (mJ-C). Experiments were performed on a Gleeble−3500 simulator at temperatures ranging from 673 to 753 K, strain rates between 0.01 and 1 s−1, and true strains up to 0.7. Based on the statistical indicators, including correlation coefficient (R), average absolute relative error (AARE), and root-mean-square error (RMSE), the DMNR model demonstrated the highest predictive accuracy (R = 0.99467, AARE = 1.8080%, and RMSE = 1.7968 MPa) outperforming both the mJ-C and Arrhenius models. A key contribution of this work is the direct construction of hot-processing maps using the DMNR model, which has not been reported previously. Using this model, the strain-rate sensitivity (m), the strain-hardening exponent (n), and \({s}^{"}\) s " , enabling clear identification of stable and unstable deformation regions. These maps highlight processing windows with high energy-dissipation efficiency and provide a practical basis for process optimization. Furthermore, the microstructural observations confirmed that DMNR-predicted optimal zones correspond to regions with fine, recrystallized grains, supporting the reliability and applicability of the DMNR-based processing maps.

Graphical Abstract