IDEAS home Printed from https://ideas.repec.org/a/dbk/datame/v4y2025ip666id1056294dm2025666.html
   My bibliography  Save this article

Predicting the tensile strength of a new fabric using artificial intelligence (fuzzy logic)

Author

Listed:
  • Redouane Messnaoui
  • Mhammed El Bakkali
  • Mostafa Elkhaoudi
  • Omar Cherkaoui
  • Aziz Soulhi

Abstract

One of the most important characteristics of a warp and weft fabric is its tensile strength. The aim of this research is to develop a practical fuzzy logic model that could anticipate the ideal tensile strength of new fabrics by modifying only the weave structure. An experimental part was carried out on different weave structures to obtain the results that will enable the development of this new model. We then used the fuzzy logic model to compare its results with those of the experimental tests. The calculated mean absolute error of the fuzzy model was 1.83% for tensile strength in the warp direction and 1.99% for tensile strength in the weft direction. This result also confirmed that the fuzzy model was not only effective, but also reliable in predicting the strength of the new fabric.

Suggested Citation

Handle: RePEc:dbk:datame:v:4:y:2025:i::p:666:id:1056294dm2025666
DOI: 10.56294/dm2025666
as

Download full text from publisher

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a
for a similarly titled item that would be available.

More about this item

Statistics

Access and download statistics

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:dbk:datame:v:4:y:2025:i::p:666:id:1056294dm2025666. 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: Javier Gonzalez-Argote (email available below). General contact details of provider: https://dm.ageditor.ar/ .

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.