Temperature‐Dependent Tensile Deformation, Microstructural Evolution, and Machine Learning Prediction of Wire Laser Metal‐Deposited Ti–6Al–4V Alloy

Abstract

In this study, the hot tensile deformation behavior of additive manufacturing‐wire laser metal deposition (AM‐WLMD) Ti–6Al–4V alloy was investigated at different deformation temperatures (25, 100, 200, and 300 °C) under a quasistatic strain rate (0.1 mm min −1 ). The microstructural evolution of the deformed Ti–6Al–4V alloy was analyzed using electron backscatter diffraction technique. The results revealed that the as‐deposited WLMD Ti–6Al–4V alloy showed a predominantly acicular microstructure, which gradually broke down with increasing deformation temperature. This behavior was attributed to enhanced plastic deformation and microstructural rearrangement. The mechanical properties of yield strength, ultimate tensile strength, and Young's modulus decreased with increasing temperature. At 300 °C, these properties were reduced by approximately 37.67%, 34.97%, and 34.96%, respectively, when compared to those obtained at 25 °C. In addition, machine learning models based on multiple linear regression (MLR), artificial neural networks (ANNs), and support vector regression (SVR) were developed to predict the UTS. The SVR and ANN model showed a higher coefficient of determination ( R 2 ) and better agreement with experimental results than the MLR model. The results suggest that the combined experimental and machine learning (ML) approach can be used to predict the temperature‐dependent mechanical behavior of WLMD‐fabricated Ti–6Al4V alloy.