Name
Abstract | ||
|---|---|---|
Deep-drawn interfacial free (IF) steel is one of the important raw materials in the automotive industry. Due to the complex production processes and numerous influence factors, it is difficult to construct the predicted model between microstructure and yield strength using the quantitative mathematical method. So, it is proposed to use BP neural network to construct the model to describe the relationship between the microstructure and yield strength of the IF steel. And the learning properties of the BP neural network under the different inputs are surveyed by means of simulations. The results of simulation show when the size, distribution uniformity degree, shape factor of the ferrite grain and the size, distribution uniformity degree of the second phase particle are used as the input, the average relative error of the BP neural network can arrives at 2.2%, which can meet the need of practical production. © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICAwST.2011.6163122 | Proceedings of 2011 3rd International Conference on Awareness Science and Technology, iCAST 2011 |
Keywords | DocType | Volume |
bp neural network,if steel,microstructure,yield strength,automobile industry,materials,neural nets,predictive models,backpropagation,mathematical model | Conference | null |
Issue | ISSN | ISBN |
null | null | 978-1-4577-0887-9 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jin Wang | 1 | 1 | 0.35 |
| qiang qu | 2 | 83 | 12.15 |
| Yandong Liu | 3 | 1 | 0.35 |