Effect of in-situ heat treatment on flow stress behavior of additive manufacturing of Ti–6Al–4V alloy under quasi-static and dynamic deformation: a comparative study through experimental and modelling through J–C model and ANN approach

Abstract Ti6Al4V is considered a promising candidate material for bioimplant applications, including bone implants, skull covers, and surgical equipment, due to its high specific strength and non-reactive nature with human body fluids. The aim of this study is to understand the effect of in-situ heat treatment on flow stress behavior, thereby to eliminate the need for traditional heat treatment methods, thereby reducing costs associated with equipment, time, and energy consumption. In the present study, the flow stress behavior of Additive Manufacturing (AM) Ti–6Al–4V alloy was investigated using two different sample preparation methods: single scanning and in-situ heat treatment (double scanning), aimed at controlling the mechanical properties during fabrication via the selective laser melting (SLM) process. Using analytical techniques such as Field emission scanning electron microscopy (FESEM) and Electron Backscatter Diffraction (EBSD) images the phase present such as α, β, α′, and α + β and their evolution mechanisms were discussed critically in single scanned and in-situ heat treatment. The uniaxial tensile tests were carried out at different quasi-static strain rates in a range of 0.001 to 1 s −1 and dynamic strain rates in the range of 10 to 500 s −1 . Further, improved version of the Johnson–Cook model has been formulated by using the experimental parameters for the Ti6Al4V alloy, furthermore, an artificial neural network-based (ANN) predictive model was established to evaluate the flow stress response of the Ti–6Al–4V alloy across diverse loading conditions. The results demonstrated that the AI-based ANN model performs comparably to the modified Johnson–Cook model, with the predicted flow stress values showing strong agreement with those obtained using the Johnson–Cook (J–C) formulation.

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Journal
Progress in Additive Manufacturing
Published
2026-06-25
DOI
https://doi.org/10.1007/s40964-026-01780-y
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
Field-Weighted Citation Impact
0.00

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article

Effect of in-situ heat treatment on flow stress behavior of additive manufacturing of Ti–6Al–4V alloy under quasi-static and dynamic deformation: a comparative study through experimental and modelling through J–C model and ANN approach

Hung‐Wei Yen, H. H. Liu, Y.T. Lee, Venkat Chilla et al.
Progress in Additive Manufacturing
Additive Manufacturing Materials and Processes
article

Effect of in-situ heat treatment on flow stress behavior of additive manufacturing of Ti–6Al–4V alloy under quasi-static and dynamic deformation: a comparative study through experimental and modelling through J–C model and ANN approach

Hung‐Wei Yen, H. H. Liu, Y.T. Lee, Venkat Chilla, Chi-Wei Wang, B N Yadav, Zhi-Hong Zhang, Pei-Cheng Wu, Pai Chen Lin, Akram Alfantazi
article en

Abstract

Abstract Ti6Al4V is considered a promising candidate material for bioimplant applications, including bone implants, skull covers, and surgical equipment, due to its high specific strength and non-reactive nature with human body fluids. The aim of this study is to understand the effect of in-situ heat treatment on flow stress behavior, thereby to eliminate the need for traditional heat treatment methods, thereby reducing costs associated with equipment, time, and energy consumption. In the present study, the flow stress behavior of Additive Manufacturing (AM) Ti–6Al–4V alloy was investigated using two different sample preparation methods: single scanning and in-situ heat treatment (double scanning), aimed at controlling the mechanical properties during fabrication via the selective laser melting (SLM) process. Using analytical techniques such as Field emission scanning electron microscopy (FESEM) and Electron Backscatter Diffraction (EBSD) images the phase present such as α, β, α′, and α + β and their evolution mechanisms were discussed critically in single scanned and in-situ heat treatment. The uniaxial tensile tests were carried out at different quasi-static strain rates in a range of 0.001 to 1 s −1 and dynamic strain rates in the range of 10 to 500 s −1 . Further, improved version of the Johnson–Cook model has been formulated by using the experimental parameters for the Ti6Al4V alloy, furthermore, an artificial neural network-based (ANN) predictive model was established to evaluate the flow stress response of the Ti–6Al–4V alloy across diverse loading conditions. The results demonstrated that the AI-based ANN model performs comparably to the modified Johnson–Cook model, with the predicted flow stress values showing strong agreement with those obtained using the Johnson–Cook (J–C) formulation.

Progress in Additive Manufacturing
Advanced Materials and Processes Research Institute (IN), National Chung Cheng University (TW), National Taiwan University (TW), Khalifa University of Science and Technology (AE)
National Science and Technology Council, Ministry of Education, India, National Science and Technology Council
Affordable and clean energy
Openalex Percentile: Top 12%
Additive Manufacturing Materials and Processes
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