Artificial Neural Network Modeling of Ti-6Al-4V Alloys to Correlate Their Microstructure and Mechanical Properties
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection and Input–Output Variables of the Model
2.2. Training Procedure of the ANN Model
3. Results and Discussion
3.1. Validation of ANN Model and Weights Distribution
3.2. Prediction of the Relationship Between Process Parameters and Mechanical Properties
W = 0.1008M2 − 0.3547M + 0.53 (M > 0.7)
3.3. Graphical User Interface
3.4. Index of the Relative Importance
4. Conclusions
- In this study, an ANN model was developed to study the influence of microstructural parameters on the mechanical properties of Ti-6Al-4V alloys. The developed model shows a high Adj. R2 value for the training and testing datasets for UTS, YS, and %EL. This shows that the model is highly accurate, with predictions of output values near the experimental values.
- The effects of microstructural parameters on the mechanical properties of Ti-6Al-4V alloys was explained with the help of 2D plots. The model’s predicted results show that the UTS of Ti-6Al-4V decreases with increases in prior β grain size, Widmanstatten α lath thickness, grain boundaries α thickness, colony scale factor, while UTS increases with mean edge length.
- The significance of each microstructural parameter on mechanical properties was described with the help of an index of relative importance. The Wida, Vfwid, MEL, and CSF are the parameters which most affect UTS.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Model | Learning Rate | Momentum Term | Iterations | Hidden Layers and Respective Neurons in Hidden Layer | RMSE | Etr |
---|---|---|---|---|---|---|
Model-1 (YS) | 0.5 | 0.6 | 13,254 | 2 9 9 | 0.00001 | 0.675 |
Model-2 (UTS) | 0.6 | 0.7 | 17,346 | 2 6 6 | 0.00001 | 3.42 |
Model-3 (%EL) | 0.7 | 0.5 | 15,561 | 2 6 6 | 0.00001 | 0.268 |
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Maurya, A.K.; Narayana, P.L.; Yeom, J.-T.; Hong, J.-K.; Subba Reddy, N.G. Artificial Neural Network Modeling of Ti-6Al-4V Alloys to Correlate Their Microstructure and Mechanical Properties. Materials 2025, 18, 1099. https://doi.org/10.3390/ma18051099
Maurya AK, Narayana PL, Yeom J-T, Hong J-K, Subba Reddy NG. Artificial Neural Network Modeling of Ti-6Al-4V Alloys to Correlate Their Microstructure and Mechanical Properties. Materials. 2025; 18(5):1099. https://doi.org/10.3390/ma18051099
Chicago/Turabian StyleMaurya, Anoop Kumar, Pasupuleti Lakshmi Narayana, Jong-Taek Yeom, Jae-Keun Hong, and Nagireddy Gari Subba Reddy. 2025. "Artificial Neural Network Modeling of Ti-6Al-4V Alloys to Correlate Their Microstructure and Mechanical Properties" Materials 18, no. 5: 1099. https://doi.org/10.3390/ma18051099
APA StyleMaurya, A. K., Narayana, P. L., Yeom, J.-T., Hong, J.-K., & Subba Reddy, N. G. (2025). Artificial Neural Network Modeling of Ti-6Al-4V Alloys to Correlate Their Microstructure and Mechanical Properties. Materials, 18(5), 1099. https://doi.org/10.3390/ma18051099