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Prediction of mechanical properties and fractography examination of AZ91 magnesium composites reinforced with graphene using a random forest machine learning model: experimental validation.

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Název: Prediction of mechanical properties and fractography examination of AZ91 magnesium composites reinforced with graphene using a random forest machine learning model: experimental validation.
Autoři: Huang, Song-Jeng, Adityawardhana, Yudhistira
Zdroj: Archives of Civil & Mechanical Engineering (Elsevier Science); Oct2025, Vol. 25 Issue 5/6, p1-26, 26p
Témata: MACHINE learning, RANDOM forest algorithms, GRAPHENE, FRACTOGRAPHY, MECHANICAL behavior of materials, MAGNESIUM alloys, HEAT treatment
Abstrakt: Machine learning (ML), a prominent branch of artificial intelligence, is increasingly applied in material design, particularly for magnesium composites. In this study, random forest models were used to predict mechanical properties and fractographic behavior using regression classification, respectively. Both the regression and classification models of the random forest demonstrated high accuracy in predicting new optimal mechanical properties for a composite containing 0.16 wt% graphene, which was enhanced through T6 heat treatment and equal channel angular pressing (ECAP). The predictions were further validated through laboratory experiments. Although not all predicted mechanical property values exceeded the optimal values obtained from the experiments, the strain-hardening capacity of the ML-recommended samples was higher than that of the experimental samples. In addition, the predicted surface features using fractography closely matched the experimental validation, indicating consistent ductile behavior. [ABSTRACT FROM AUTHOR]
Copyright of Archives of Civil & Mechanical Engineering (Elsevier Science) is the property of Springer Nature 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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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Prediction of mechanical properties and fractography examination of AZ91 magnesium composites reinforced with graphene using a random forest machine learning model: experimental validation.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Huang%2C+Song-Jeng%22">Huang, Song-Jeng</searchLink><br /><searchLink fieldCode="AR" term="%22Adityawardhana%2C+Yudhistira%22">Adityawardhana, Yudhistira</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Archives of Civil & Mechanical Engineering (Elsevier Science); Oct2025, Vol. 25 Issue 5/6, p1-26, 26p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22RANDOM+forest+algorithms%22">RANDOM forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22GRAPHENE%22">GRAPHENE</searchLink><br /><searchLink fieldCode="DE" term="%22FRACTOGRAPHY%22">FRACTOGRAPHY</searchLink><br /><searchLink fieldCode="DE" term="%22MECHANICAL+behavior+of+materials%22">MECHANICAL behavior of materials</searchLink><br /><searchLink fieldCode="DE" term="%22MAGNESIUM+alloys%22">MAGNESIUM alloys</searchLink><br /><searchLink fieldCode="DE" term="%22HEAT+treatment%22">HEAT treatment</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Machine learning (ML), a prominent branch of artificial intelligence, is increasingly applied in material design, particularly for magnesium composites. In this study, random forest models were used to predict mechanical properties and fractographic behavior using regression classification, respectively. Both the regression and classification models of the random forest demonstrated high accuracy in predicting new optimal mechanical properties for a composite containing 0.16 wt% graphene, which was enhanced through T6 heat treatment and equal channel angular pressing (ECAP). The predictions were further validated through laboratory experiments. Although not all predicted mechanical property values exceeded the optimal values obtained from the experiments, the strain-hardening capacity of the ML-recommended samples was higher than that of the experimental samples. In addition, the predicted surface features using fractography closely matched the experimental validation, indicating consistent ductile behavior. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Archives of Civil & Mechanical Engineering (Elsevier Science) is the property of Springer Nature 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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    Identifiers:
      – Type: doi
        Value: 10.1007/s43452-025-01308-1
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 26
        StartPage: 1
    Subjects:
      – SubjectFull: MACHINE learning
        Type: general
      – SubjectFull: RANDOM forest algorithms
        Type: general
      – SubjectFull: GRAPHENE
        Type: general
      – SubjectFull: FRACTOGRAPHY
        Type: general
      – SubjectFull: MECHANICAL behavior of materials
        Type: general
      – SubjectFull: MAGNESIUM alloys
        Type: general
      – SubjectFull: HEAT treatment
        Type: general
    Titles:
      – TitleFull: Prediction of mechanical properties and fractography examination of AZ91 magnesium composites reinforced with graphene using a random forest machine learning model: experimental validation.
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            NameFull: Huang, Song-Jeng
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            NameFull: Adityawardhana, Yudhistira
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          Dates:
            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
          Identifiers:
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              Value: 25
            – Type: issue
              Value: 5/6
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            – TitleFull: Archives of Civil & Mechanical Engineering (Elsevier Science)
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