Modelling the correlation between processing parameters and properties in titanium alloys using artificial neural network
- 16 July 2001
- journal article
- Published by Elsevier BV in Computational Materials Science
- Vol. 21 (3), 375-394
- https://doi.org/10.1016/s0927-0256(01)00160-4
Abstract
No abstract availableKeywords
This publication has 17 references indexed in Scilit:
- Differential scanning calorimetry study and computer modeling of β ⇒ α phase transformation in a Ti-6Al-4V alloyMetallurgical and Materials Transactions, 2001
- Resistivity study and computer modelling of the isothermal transformation kinetics of Ti–6Al–4V and Ti–6Al–2Sn–4Zr–2Mo–0.08Si alloysJournal of Alloys and Compounds, 2001
- Application of artificial neural network for prediction of time–temperature–transformation diagrams in titanium alloysMaterials Science and Engineering: A, 2000
- Effects of Carbon Concentration and Cooling Rate on Continuous Cooling Transformations Predicted by Artificial Neural Network.ISIJ International, 1999
- Neural Networks in Materials Science.ISIJ International, 1999
- Comparison of Artificial Neural Networks with Gaussian Processes to Model the Yield Strength of Nickel-base Superalloys.ISIJ International, 1999
- The yield and ultimate tensile strength of steel weldsMaterials Science and Engineering: A, 1997
- Prediction of jominy hardness profiles of steels using artificial neural networksJournal of Materials Engineering and Performance, 1996
- Phase Transformation Kinetics and Mechanisms in Titanium Alloys Ti-6.2.4.6,ß-CEZ and Ti-10.2.3Journal de Physique IV, 1996
- Bayesian Neural Network Analysis of Fatigue Crack Growth Rate in Nickel Base Superalloys.ISIJ International, 1996