Mapping the input–output relationship in HSLA steels through expert neural network
- 7 March 2006
- journal article
- Published by Elsevier in Materials Science and Engineering: A
- Vol. 420 (1-2), 254-264
- https://doi.org/10.1016/j.msea.2006.01.037
Abstract
No abstract availableKeywords
This publication has 13 references indexed in Scilit:
- Optimizing parameters of supervised learning techniques (ANN) for precise mapping of the input-output relationship in TMCP steelsScandinavian Journal of Metallurgy, 2004
- Kohonen Network Modelling for the Strength of Thermomechanically Processed HSLA SteelISIJ International, 2004
- Advances in Physical Metallurgy and Processing of Steels. Physical Metallurgy of Modern High Strength Steel Sheets.ISIJ International, 2001
- The Application of Constitutive and Artificial Neural Network Models to Predict the Hot Strength of Steels.ISIJ International, 1999
- Petri Neural Network Model for the Effect of Controlled Thermomechanical Process Parameters on the Mechanical Properties of HSLA Steels.ISIJ International, 1999
- Tensile properties of mechanically alloyed oxide dispersion strengthened iron alloys Part 2 – Physical interpretation of yield strengthMaterials Science and Technology, 1998
- A study on the causes of deviation in mechanical properties of thin steel sheetsJournal of Materials Processing Technology, 1998
- Statistical Modelling of Mechanical Properties of Microalloyed Steels by Application of Artificial Neural NetworksMaterials Science Forum, 1998
- Prediction of the mechanical properties of hot-rolled CMn steels using artificial neural networksJournal of Materials Processing Technology, 1996
- Optimisation of Composition and Rolling Variables of Tough, Ductile and Readily Weldable Medium Strength (30-42 kgf/mm2 Y. S.) Low Copper-Niobium SteelsTransactions of the Iron and Steel Institute of Japan, 1974