AE
Ali Reza Eivani
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1
Journal article
(2026)
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Alireza Khanlari, Ali Reza Eivani, Morteza Zakeri, Jie Zhou, Hamid Reza Jafarian, Morteza Tayebi
The optimization of post-processing heat treatment for selective laser melted Ti-6Al-4V remains challenging due to the strong nonlinear coupling between thermal history, microstructure, and hardness. Existing predictive models are typically limited by small datasets and narrow process coverage, particularly for post-heat-treatment hardness. In this study, a machine learning framework was developed to predict the Vickers hardness of heat-treated SLM Ti-6Al-4V using a curated multi-source dataset integrating experimental measurements (19 samples), literature-derived data (42), and 200 synthetically generated samples via Stratified Bootstrap combined with Gaussian Copula Noise. Fifteen regression models were systematically benchmarked using cross-validation. Among them, the Voting Regressor achieved the highest predictive accuracy (R² ≈ 0.92, MAE ≈ 7.8 HV), demonstrating robust generalization across diverse heat-treatment conditions. Explainable artificial intelligence analysis revealed that microstructural characteristics and heat-treatment parameters are the dominant drivers of hardness, in agreement with phase-transformation mechanisms governing α′ decomposition and α + β stabilization. The proposed framework provides a quantitative and interpretable tool for rational heat-treatment design of SLM Ti-6Al-4V, reducing reliance on empirical trial-and-error approaches and enabling data-driven process optimization.
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The optimization of post-processing heat treatment for selective laser melted Ti-6Al-4V remains challenging due to the strong nonlinear coupling between thermal history, microstructure, and hardness. Existing predictive models are typically limited by small datasets and narrow process coverage, particularly for post-heat-treatment hardness. In this study, a machine learning framework was developed to predict the Vickers hardness of heat-treated SLM Ti-6Al-4V using a curated multi-source dataset integrating experimental measurements (19 samples), literature-derived data (42), and 200 synthetically generated samples via Stratified Bootstrap combined with Gaussian Copula Noise. Fifteen regression models were systematically benchmarked using cross-validation. Among them, the Voting Regressor achieved the highest predictive accuracy (R² ≈ 0.92, MAE ≈ 7.8 HV), demonstrating robust generalization across diverse heat-treatment conditions. Explainable artificial intelligence analysis revealed that microstructural characteristics and heat-treatment parameters are the dominant drivers of hardness, in agreement with phase-transformation mechanisms governing α′ decomposition and α + β stabilization. The proposed framework provides a quantitative and interpretable tool for rational heat-treatment design of SLM Ti-6Al-4V, reducing reliance on empirical trial-and-error approaches and enabling data-driven process optimization.
Three commercial stents (Palmaz-Schatz, NIR, and BioMatrix) with either an open-cell (20% open-cell) or a closed-cell (80% closed-cell) design, and one new hybrid stent design were numerically modeled using the ABAQUS/Explicit finite element software (Dassault Systèmes, France) to compare their behaviors during deployment in a stenotic artery. The ABAQUS/Explicit dynamic explicit solver was utilized to efficiently capture the complex interactions between the balloon, stent, artery, and plaque during the stent expansion process. The effect of changing the material from stainless steel (SS 316L) to cobalt-chromium (CoCr) and platinum-chromium (PtCr), as well as the reduced thickness of struts from 0.1 mm to 0.08 mm, were investigated. The new hybrid stent design featured reduced axial strut spacing (from 1.2 mm to 0.8 mm), larger corner radii (from 0.2 mm to 0.3 mm), and smaller amplitudes in the ring (from 1.0 mm to 0.8 mm). For the simulations, a balloon-stent-artery model with plaque and average blood pressure of 80 mmHg was used. The results showed that the new hybrid stent did not perform worse in any of the studied biomechanical parameters compared to the commercial open-cell (20% expansion) and closed-cell (15% expansion) stents, and exhibited better performance in maximum expansion (22%) and recoil responses (5% recoil). Changing the material in the new hybrid stent from SS 316L to CoCr or PtCr improved the biomechanical behavior, such as expansion (25%), recoil (3%), and dogboning (0.9), but increased the maximum von Mises stress on the artery-plaque system by 18%. Reducing the strut thickness from 0.1 mm to 0.08 mm decreased the maximum stress on the artery-plaque system by 12%, but undesirably increased dogboning (1.1) and recoil (7%).
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Three commercial stents (Palmaz-Schatz, NIR, and BioMatrix) with either an open-cell (20% open-cell) or a closed-cell (80% closed-cell) design, and one new hybrid stent design were numerically modeled using the ABAQUS/Explicit finite element software (Dassault Systèmes, France) to compare their behaviors during deployment in a stenotic artery. The ABAQUS/Explicit dynamic explicit solver was utilized to efficiently capture the complex interactions between the balloon, stent, artery, and plaque during the stent expansion process. The effect of changing the material from stainless steel (SS 316L) to cobalt-chromium (CoCr) and platinum-chromium (PtCr), as well as the reduced thickness of struts from 0.1 mm to 0.08 mm, were investigated. The new hybrid stent design featured reduced axial strut spacing (from 1.2 mm to 0.8 mm), larger corner radii (from 0.2 mm to 0.3 mm), and smaller amplitudes in the ring (from 1.0 mm to 0.8 mm). For the simulations, a balloon-stent-artery model with plaque and average blood pressure of 80 mmHg was used. The results showed that the new hybrid stent did not perform worse in any of the studied biomechanical parameters compared to the commercial open-cell (20% expansion) and closed-cell (15% expansion) stents, and exhibited better performance in maximum expansion (22%) and recoil responses (5% recoil). Changing the material in the new hybrid stent from SS 316L to CoCr or PtCr improved the biomechanical behavior, such as expansion (25%), recoil (3%), and dogboning (0.9), but increased the maximum von Mises stress on the artery-plaque system by 18%. Reducing the strut thickness from 0.1 mm to 0.08 mm decreased the maximum stress on the artery-plaque system by 12%, but undesirably increased dogboning (1.1) and recoil (7%).