XFEM–ANN approach to predict the fatigue performance of a composite patch repaired aluminium panel

Siddharth Suman, Kishan Dwivedi, Samanvay Anand, Himanshu Pathak (Corresponding Author)

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)

Abstract

A novel extended finite element method (XFEM)-artificial neural network (ANN) approach is proposed to predict the performance of aluminium panels repaired single-sided with a composite patch. The optimized deep neural network model developed using data sets generated from XFEM—benchmarked against experimental results—has the architecture of 4-17-17-2 and it predicts the stress intensity factor and fatigue life cycle with the maximum error of 0.15 % and 0.25 %, respectively. Global sensitivity analysis of the model is performed based on the Shapley values and it reveals that lamina orientation is the least influencing. A comparison of computational time reveals that 100 % reduction is achieved by using the optimized ANN model. The findings establish the use of ANN for aiding as well as accelerating the composite patch repair process for various engineering applications.

Original languageEnglish
Article number100326
JournalComposites Part C: Open Access
Volume9
DOIs
Publication statusPublished - Oct 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Composite patch
  • Failure analysis
  • Neural networks
  • Repair technologies
  • Stress intensity factors

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