348
Views
17
CrossRef citations to date
0
Altmetric
Articles

Characteristic length determination of notched woven composites

&
Pages 67-83 | Received 24 Feb 2016, Accepted 31 Aug 2016, Published online: 28 Sep 2016
 

Abstract

In this manuscript, the progressive failure analysis was employed to predict the final failure of notched woven glass/epoxy composite laminates under tensile loading. A user-defined material model (UMAT) in the Abaqus finite-element package was developed to utilize the 3D progressive failure analysis feasible. Three types of stress-based failure criteria as Max. Stress, Yamada-Sun, and Tsai-Wu were implemented in Abaqus to predict the damage initiation in the notched woven composites. Instantaneous and recursive property degradation rules were employed to simulate damage propagation. A numerical procedure was developed to find the characteristic length (CL) in the notched woven composite laminates without test. The various notched and unnotched woven glass/epoxy specimen were fabricated by vacuum-assisted hand layup technique. Four groups of experiments were performed to predict the hole size effect on the strength of the notched laminates and verify the strength predicted by the progressive damage modeling. Numerical results were in good agreement with the test results. More than above, it was verified by the experiment that the CL obtained by the progressive damage analysis is a reliable and simple method to design notched woven laminates.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 751.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.