IDEAS home Printed from https://ideas.repec.org/a/gam/jjopen/v8y2025i2p15-d1646236.html
   My bibliography  Save this article

Inferring Mechanical Properties of Wire Rods via Transfer Learning Using Pre-Trained Neural Networks

Author

Listed:
  • Adriany A. F. Eduardo

    (Escola de Engenharia de Lorena, Universidade de São Paulo, Lorena 12602-810, SP, Brazil)

  • Gustavo A. S. Martinez

    (Escola de Engenharia de Lorena, Universidade de São Paulo, Lorena 12602-810, SP, Brazil)

  • Ted W. Grant

    (College of Arts and Sciences, California Baptist University, Riverside, CA 92504, USA)

  • Lucas B. S. Da Silva

    (Escola de Engenharia de Lorena, Universidade de São Paulo, Lorena 12602-810, SP, Brazil)

  • Wei-Liang Qian

    (Escola de Engenharia de Lorena, Universidade de São Paulo, Lorena 12602-810, SP, Brazil
    Faculdade de Engenharia de Guaratinguetá, Universidade Estadual Paulista, Guaratinguetá 12516-410, SP, Brazil
    Center for Gravitation and Cosmology, College of Physical Science and Technology, Yangzhou University, Yangzhou 225009, China)

Abstract

The primary objective of this study is to explore how machine learning techniques can be incorporated into the analysis of material deformation. Neural network algorithms are applied to the study of mechanical properties of wire rods subjected to cold plastic deformations. Specifically, this study explores how pre-trained neural networks with appropriate architecture can be exploited to predict apparently distinct but internally related features. Tentative predictions are made by observing only an insignificant cropped fraction of the material’s profile. The neural network models are trained and calibrated using 6400 image fractions with a resolution of 120 × 90 pixels. Different architectures are developed with a focus on two particular aspects. Firstly, different possible architectures are compared, particularly between multi-output and multi-label convolutional neural networks (CNNs). Moreover, a hybrid model is employed, essentially a conjunction of a CNN with a multi-layer perceptron (MLP). The neural network’s input constitutes combined numerical and visual data, and its architecture primarily consists of seven dense layers and eight convolutional layers. By proper calibration and fine-tuning, observed improvements over the standard CNN models are reflected by good training and test accuracies in order to predict the material’s mechanical properties, with efficiency demonstrated by the loss function’s rapid convergence. Secondly, the role of the pre-training process is investigated. The obtained CNN-MLP model can inherit the learning from a pre-trained multi-label CNN, initially developed for distinct features such as localization and number of passes. It is demonstrated that the pre-training effectively accelerates the learning process for the target feature. Therefore, it is concluded that appropriate architecture design and pre-training are essential for applying machine learning techniques to realistic problems.

Suggested Citation

  • Adriany A. F. Eduardo & Gustavo A. S. Martinez & Ted W. Grant & Lucas B. S. Da Silva & Wei-Liang Qian, 2025. "Inferring Mechanical Properties of Wire Rods via Transfer Learning Using Pre-Trained Neural Networks," J, MDPI, vol. 8(2), pages 1-18, April.
  • Handle: RePEc:gam:jjopen:v:8:y:2025:i:2:p:15-:d:1646236
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-8800/8/2/15/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2571-8800/8/2/15/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjopen:v:8:y:2025:i:2:p:15-:d:1646236. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.