Abstract
The aging hardening process makes it possible to get higher hardness and electrical conductivity of lead frame copper alloy. The process has only been studied empirically by trial-and-error method so far. The use of a supervised artificial neural network (ANN) was proposed to model the non-linear relationship between parameters of aging process with respect to hardness and conductivity properties of Cu-Cr-Zr alloy. The improved model was developed by the Levenberg-Marquardt training algorithm. A basic repository on the domain knowledge of aging process was established via sufficient data mining by the network. The results show that the ANN system is effective and successful for predicting and analyzing the properties of Cu-Cr-Zr alloy.
| Original language | English |
|---|---|
| Pages (from-to) | 1419-1423 |
| Number of pages | 5 |
| Journal | Transactions of Nonferrous Metals Society of China (English Edition) |
| Volume | 13 |
| Issue number | 6 |
| State | Published - Dec 2003 |
Keywords
- Aging process
- Artificial neural network
- Copper alloy
- Levenberg-Marquardt algorithm