Prediction of mechanical properties of A357 alloy using artificial neural network

Xia Wei Yang, Jing Chuan Zhu, Zhi Sheng Nong, Dong He, Zhong Hong Lai, Ying Liu, Fa Wei Liu

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

The workpieces of A357 alloy were routinely heat treated to the T6 state in order to gain an adequate mechanical property. The mechanical properties of these workpieces depend mainly on solid-solution temperature, solid-solution time, artificial aging temperature and artificial aging time. An artificial neural network (ANN) model with a back-propagation (BP) algorithm was used to predict mechanical properties of A357 alloy, and the effects of heat treatment processes on mechanical behavior of this alloy were studied. The results show that this BP model is able to predict the mechanical properties with a high accuracy. This model was used to reflect the influence of heat treatments on the mechanical properties of A357 alloy. Isograms of ultimate tensile strength and elongation were drawn in the same picture, which are very helpful to understand the relationship among aging parameters, ultimate tensile strength and elongation.

Original languageEnglish
Pages (from-to)788-795
Number of pages8
JournalTransactions of Nonferrous Metals Society of China (English Edition)
Volume23
Issue number3
DOIs
StatePublished - Mar 2013
Externally publishedYes

Keywords

  • A357 alloy
  • artificial neural network
  • heat treatment parameters
  • mechanical properties

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