Analysis of the effects of processing parameters on mechanical properties and formability of cold rolled low carbon steel sheets using neural networks

✍ Scribed by H. Monajati; D. Asefi; A. Parsapour; Sh. Abbasi

• Book ID: 116375753
• Publisher: Elsevier Science
• Year: 2010
• Tongue: English
• Weight: 946 KB
• Volume: 49
• Category: Article
• ISSN: 0927-0256
• DOI: 10.1016/j.commatsci.2010.06.040

✦ Synopsis

In the present study, an artificial neural network (ANN) is used to describe the effects of processing parameters on the evolution of mechanical properties and formability of deep drawing quality (DDQ) steel sheets. This model is a feed forward back-propagation neural network (BPNN) with a set of 19 parameters including chemical composition, hot and cold rolling parameters, and subsequent batch annealing process parameters to predict the final properties, including yield strength (YS), work hardening exponent (n), and plastic strain ratio (

r

¯

), of sheets. ANN system was trained using the prepared training set. After training process, the test data were used to check system accuracy. The results show that the model can be used as a quantitative guide to control the final formability properties of commercial low carbon steel products.