Name
Abstract | ||
|---|---|---|
Over the last four years efforts have been devoted towards the development and validation of mechanical test result models relating to a range of alloy steels. Several neural-network based models have been developed, two of which are related to the mechanical test results of Ultimate Tensile Strength (UTS) and Reduction of Area (ROA). The ultimate aim of developing these models is to pave the way to process optimisation through better predictions of mechanical properties. In this research we propose to exploit such neural network models in order to determine the optimal alloy composition and heat treatment temperatures required, given certain predefined mechanical properties such as the UTS and ROA. Generic Algorithms are used for this purpose. The results obtained are very encouraging |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/KES.2000.885830 | KES |
Keywords | Field | DocType |
optimisation,alloy steels,evolutionary computation,physics computing,heat treatment temperatures,alloy steel,neural-network based models,mechanical properties,optimal alloy composition,mechanical engineering computing,evolutionary computing,temperature,ultimate tensile strength,neural network model,genetic algorithms,optimal design,generic algorithm,neural networks,neural network,predictive models,system testing,heat treatment | Alloy composition,Computer science,Mechanical engineering,Alloy steel,Evolutionary computation,Optimal design,Alloy,Ultimate tensile strength,Artificial neural network | Conference |
Volume | ISBN | Citations |
1 | 0-7803-6400-7 | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahdi Mahfouf | 1 | 235 | 33.17 |
| J. Tenner | 2 | 1 | 1.29 |
| Derek A. Linkens | 3 | 215 | 25.36 |
| Maysam F. Abbod | 4 | 224 | 28.14 |