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Online prediction and monitoring of mechanical properties of industrial galvanised steel coils using neural networks.

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Title: Online prediction and monitoring of mechanical properties of industrial galvanised steel coils using neural networks.
Authors: Lalam, Satyanarayana1 satti.isro@gmail.com, Tiwari, Prabhat Kr1, Sahoo, Sibasis1, Dalal, Achinta Kr1
Source: Ironmaking & Steelmaking. Jan2019, Vol. 46 Issue 1, p89-96. 8p.
Subjects: Galvanized steel, Steel testing, Galvanizing, Artificial neural networks, Tensile strength, Principal components analysis, Decision making
Abstract: In galvanising line of cold rolling mill, mechanical properties, i.e. yield strength (YS) and ultimate tensile strength (UTS), are achieved by controlling the key process parameters within specified limits. In this paper, a feed-forward back-propagation artificial neural network (ANN) is proposed to predict the mechanical properties of a coil from its chemical composition, thickness, width and key galvanising process parameters. Principal component analysis is used to avoid redundancy and collinearity effects in input variables for the ANN. The model predicted the YS and UTS with an accuracy of ±10 megapascal (MPa) for 90% of the data. The model was implemented in the continuous galvanising line of Tata Steel, India. An online quality monitoring system was developed to monitor the predicted mechanical properties and process parameters of a galvanised coil. This system helps quality team in decision making. [ABSTRACT FROM AUTHOR]
Copyright of Ironmaking & Steelmaking is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Galvanized+steel%22">Galvanized steel</searchLink><br /><searchLink fieldCode="DE" term="%22Steel+testing%22">Steel testing</searchLink><br /><searchLink fieldCode="DE" term="%22Galvanizing%22">Galvanizing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Tensile+strength%22">Tensile strength</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink>
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  Data: In galvanising line of cold rolling mill, mechanical properties, i.e. yield strength (YS) and ultimate tensile strength (UTS), are achieved by controlling the key process parameters within specified limits. In this paper, a feed-forward back-propagation artificial neural network (ANN) is proposed to predict the mechanical properties of a coil from its chemical composition, thickness, width and key galvanising process parameters. Principal component analysis is used to avoid redundancy and collinearity effects in input variables for the ANN. The model predicted the YS and UTS with an accuracy of ±10 megapascal (MPa) for 90% of the data. The model was implemented in the continuous galvanising line of Tata Steel, India. An online quality monitoring system was developed to monitor the predicted mechanical properties and process parameters of a galvanised coil. This system helps quality team in decision making. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Ironmaking & Steelmaking is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/03019233.2017.1342424
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 8
        StartPage: 89
    Subjects:
      – SubjectFull: Galvanized steel
        Type: general
      – SubjectFull: Steel testing
        Type: general
      – SubjectFull: Galvanizing
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Tensile strength
        Type: general
      – SubjectFull: Principal components analysis
        Type: general
      – SubjectFull: Decision making
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      – TitleFull: Online prediction and monitoring of mechanical properties of industrial galvanised steel coils using neural networks.
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            NameFull: Lalam, Satyanarayana
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            NameFull: Tiwari, Prabhat Kr
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            NameFull: Sahoo, Sibasis
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            NameFull: Dalal, Achinta Kr
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            – D: 01
              M: 01
              Text: Jan2019
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              Y: 2019
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