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Application of Artificial Neural Networks in Cold Rolling Process

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  • Atuwo, Tamaraebi

Abstract

Rolling is one of the most complicated processes in metal forming. Knowing the exact amount of basic parameters, especially inter-stand tensions can be effective in controlling other parameters in this process. Inter-stand tensions affect rolling pressure, rolling force, forward and backward slips and neutral angle. Calculating this effect is an important step in continuous rolling design and control. Since inter-stand tensions cannot be calculated analytically, attempt is made to describes an approach based on artificial neural network (ANN) in order to identify the applied parameters in a cold tandem rolling mill. Due to the limited experimental data, in this subject a five stand tandem cold rolling mill is simulated through finite element method. The outputs of the FE simulation are applied in training the network and then, the network is employed for prediction of tensions in a tandem cold rolling mill. Here, after changing and checking the different designs of the network, the 11-42-4 structure by one hidden layer is selected as the best network. The verification factor of ANN results according to experimental data are over R=0.9586 for training and testing the data sets. The experimental results obtained from the five stands tandem cold rolling mill. This paper proposed new ANN for prediction of inter-stand tensions. Also, this ANN method shows a fuzzy control algorithm for investigating the effect of front and back tensions on reducing the thickness deviations of hot rolled steel strips. The average of the training and testing data sets is mentioned 0.9586. It means they have variable values which are discussed in details in section 4. According to Table 7, this proposed ANN model has the correlation coefficients of 0.9586, 0.9798, 0.9762 and 0.9742, respectively for training data sets and 0.9905, 0.9798, 0.9762 and 0.9803, respectively for the testing data sets. These obtained numbers indicate the acceptable accuracy of the ANN method in predicting the inter-stand tensions of the rolling tandem mill. This method provides a highly accurate solution with reduced computational time and is suitable for on-line control or optimization in tandem cold rolling mills. Due to the limited experimental data, for data extraction for the ANN simulation, a 2D tandem cold rolling process is simulated using ABAQUS 6.9 software. For designing a network for this rolling problem, various structures of neural networks are studied in MATLAB 7.8 software.

Suggested Citation

  • Atuwo, Tamaraebi, 2018. "Application of Artificial Neural Networks in Cold Rolling Process," MPRA Paper 88520, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:88520
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    References listed on IDEAS

    as
    1. Rahmani, Fatemeh & Razaghian, Farhad & Kashaninia, Alireza, 2014. "High Power Two- Stage Class-AB/J Power Amplifier with High Gain and Efficiency," MPRA Paper 86867, University Library of Munich, Germany, revised May 2014.
    2. Moghaddam, Amir Kamyab & Ketabdar, Milad & Amir Ahmadian, Sepideh & Hoseini, Pedram & Pishdadakhgari, Mohammadali, 2017. "Experimental survey of energy dissipation in nappe flow regime in stepped spillway equipped with inclined steps and sill," MPRA Paper 94690, University Library of Munich, Germany, revised Apr 2017.
    3. Sadeghian, Ehsan, 2018. "Modeling and Checking the Power Quality of High Pressure Sodium Vapor Lamp," MPRA Paper 86668, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Artificial neural networks; Computational time; On-line control; Finite element modeling; Training and testing data; Tandem cold rolling mill; Hidden layer;
    All these keywords.

    JEL classification:

    • L16 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Industrial Organization and Macroeconomics; Macroeconomic Industrial Structure
    • L61 - Industrial Organization - - Industry Studies: Manufacturing - - - Metals and Metal Products; Cement; Glass; Ceramics
    • L63 - Industrial Organization - - Industry Studies: Manufacturing - - - Microelectronics; Computers; Communications Equipment
    • L71 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Mining, Extraction, and Refining: Hydrocarbon Fuels
    • L72 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Mining, Extraction, and Refining: Other Nonrenewable Resources

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