IDEAS home Printed from https://ideas.repec.org/a/bao/ijieis/v2y2022i3p29-41id81.html
   My bibliography  Save this article

Prediction of Weld Strength in Power Ultrasonic Spot Welding Process Using Artificial Neural Network (ANN) and Back Propagation Method

Author

Listed:
  • Ziad Shakeeb Al Sarraf

Abstract

In this presented work, the employment of artificial neural network (ANN) connected with back propagation method was performed to predict the strength of joining materials that carried out by using ultrasonic spot welding process. The models which created in this study were investigated and their process parameters were analysed. These parameters were classified and set as input variables like for example applying pressure, time of duration weld and trigger of vibrating amplitude while weld strength of joining dissimilar materials (Al-Cu) is set as output parameters. The identification from the process parameters are obtained using number of experiments and finite element analyses based prediction. The results of actual and numerical are accurate and reliability, however its complexity has significant effect due to sensitive to the condition variation of welding processes. Therefore, the needed for an efficient technique like artificial neural network coupled with back propagation method is required to use the experiments as an input data in simulation of ultrasonic welding process, finding the adequacy of modeling process in prediction of weld strength and to confirm the performance of using mathematical methods. The results of the selecting non-linear models show a noticeable potency when using ANN with back propagation method in providing high accuracy compared with other results obtained by conventional models.

Suggested Citation

  • Ziad Shakeeb Al Sarraf, 2022. "Prediction of Weld Strength in Power Ultrasonic Spot Welding Process Using Artificial Neural Network (ANN) and Back Propagation Method," International Journal of Innovation in Engineering, International Scientific Network (ISNet), vol. 2(3), pages 29-41.
  • Handle: RePEc:bao:ijieis:v:2:y:2022:i:3:p:29-41:id:81
    as

    Download full text from publisher

    File URL: https://ijie.ir/index.php/ijie/article/view/81/90
    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:bao:ijieis:v:2:y:2022:i:3:p:29-41:id:81. 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: International Scientific Network (ISNet) (email available below). General contact details of provider: https://ijie.ir/index.php/ijie/ .

    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.