Prediction of Mechanical Properties of Cold Rolled Steel Using Genetic Expression Programming

• Authors: E. Kanca , F. Çavdar , M. Erşen
• Languages of publication: EN

Abstracts

EN

A new model was developed to predict the mechanical properties of St22 grade cold rolled deep drawing steel by gene expression programming. To obtain a dataset to find out the effect of reduction rate on the mechanical properties of cold rolled and galvanized steel sheet, an experimental program was constructed in the real production plant by keeping all other process parameters constant. The training and testing data sets of gene expression programming model were obtained from the test results. For gene expression programming model, mechanical properties (yield strength, ultimate tensile strength and elongation) before cold rolling, chemical composition, initial sheet thickness and reduction rate were used as independent input variables, while mechanical properties after cold rolling (yield strength, ultimate tensile strength and elongation) were used as dependent output variables. Before constructing the gene expression programming models for dependent variables, dataset was analyzed using the analysis of variance and statistically significant (P ≤ 0.1) independent parameters, i.e. initial sheet thickness, reduction rate, initial yield strength, initial tensile strength, elongation and Mn content were used in gene expression programming model. Different models were obtained for each dependent variable depending on the significant independent variables using the training dataset and accuracy of the best models was verified with testing data set. The predicted values were compared with experimental results and it was found that models are in good agreement with the experimentally obtained results.

Keywords

EN

81.20.Hy; 81.05.U; 07.05.Mh; 81.40.Ef; 89.20.Bb

Discipline

• 81.40.Ef: Cold working, work hardening; annealing, post-deformation annealing, quenching, tempering recovery, and crystallization
• 89.20.Bb: Industrial and technological research and development
• 07.05.Mh: Neural networks, fuzzy logic, artificial intelligence
• 81.20.Hy: Forming; molding, extrusion, etc.[see also, 83.50.Uv Material processing (extension, molding, etc.)]

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2016
• Volume: 130
• Issue: 1
• Pages: 365-369

Dates

published

2016-07

Contributors

author

E. Kanca

• İskenderun Technical University, Mechanical Engineering Department, Hatay, Turkey

author

F. Çavdar

• İskenderun Technical University, Dörtyol Vocational School, Machinery Department, Hatay, Turkey

author

M. Erşen

• MMK Metalurji San. Tic. ve Liman İsletmeciligi A.S., Hatay, Turkey

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Identifiers

DOI

10.12693/APhysPolA.130.365