Plasticity and fracture of metals under tensile loading at elevated temperatures

Kiani, Mohammad Sajjad

doi:10.22075/ijnaa.2026.40067.5599

Plasticity and fracture of metals under tensile loading at elevated temperatures

Articles in Press

Document Type : Research Paper

Author

Mohammad Sajjad Kiani

Department of Mechanical Engineering, Tehran University, Tehran, Iran

10.22075/ijnaa.2026.40067.5599

Abstract

This study presents a comprehensive investigation into the plasticity and ductile fracture behavior of high-strength AA7075-T6 aluminum alloy sheets under uniaxial tensile loading across a broad spectrum of strain rates and elevated temperatures. An extensive experimental program was conducted, encompassing quasi-static, intermediate, and high strain rate regimes (from 0.001 s⁻¹ to 150 s⁻¹) and temperatures ranging from room temperature (20 °C) to 360 °C, relevant to hot forming and impact applications. Low and intermediate strain rate tests were performed using a servo-hydraulic universal testing machine (Instron 8801), while high strain rate experiments utilized a modified Split Hopkinson Pressure Bar (SHPB) setup equipped with a Load Inversion Device (LID) for tensile loading and a custom induction heating system for rapid, uniform temperature control. Full-field deformation and strain measurements were achieved through digital image correlation (DIC) employing high-speed cameras and stochastic speckle patterns. Temperature uniformity was monitored and verified using infrared thermography and pyrometer feedback. The experimental results revealed pronounced coupled effects of temperature and strain rate on flow stress, strain hardening, and fracture characteristics. Flow stress decreased markedly with rising temperature across all strain rates, while strain rate sensitivity transitioned from negligible at lower temperatures to significant at elevated temperatures (above ~120 °C), with notable hardening at the highest rates and temperatures. To model these complex thermo-viscoplastic responses, a modified Johnson–Cook constitutive equation was initially calibrated but found limited in capturing the non-separable interactions. Subsequently, a machine learning-enhanced formulation, integrating a neural network with a baseline Swift–Voce hardening law, was developed and optimized. This data-driven approach demonstrated superior accuracy in representing the intricate, non-linear dependencies without predefined functional constraints.

Keywords

Ductile fracture

Tensile loading

Elevated temperature

Strain rate effect

Metal plasticity

Machine learning constitutive modeling

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