A Unified Mixed Deep Neural Network for Fatigue Damage Detection in Components with Different Stress Concentrations

Susheel Dharmadhikari, Riddhiman Raut, Asok Ray, Amrita Basak

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The article presents a mixed deep neural network (DNN) approach for detecting micron-scale fatigue damage in high-strength polycrystalline aluminum alloys. Fatigue testing is conducted using a custom-designed apparatus integrated with a confocal microscope and a moving stage to accurately pinpoint the instance of micron-scale crack emergence. The specimens are monitored throughout the duration of the experiment using a pair of high-frequency ultrasonic transducers. The mixed DNN is trained with ultrasonic time-series data that are obtained from two sets of specimens categorized by different stress concentration factors. To understand the effects of mixing the data from both types of specimens, a parametric analysis is performed by varying the amount of training data from each specimen to develop a series of mixed DNNs. The mixed DNN, when tested on unseen data from both specimens, exhibits an accuracy of over 95%. This article, therefore, demonstrates a successful alternative to customized DNNs for new types, geometries, or stress concentration factors in the materials under consideration.

Original languageEnglish (US)
Article number1542
JournalApplied Sciences (Switzerland)
Issue number3
StatePublished - Feb 2023

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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