Machine Learning-Based Prediction and Optimisation System for Laser Shock Peening

Jino Mathew, Rohit Kshirsagar, Suraiya Zabeen, Niall Smyth, Stratis Kanarachos, Kristina Langer, Michael Fitzpatrick

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10 Citations (Scopus)
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Laser shock peening (LSP) as a surface treatment technique can improve the fatigue life and corrosion resistance of metallic materials by introducing significant compressive residual stresses near the surface. However, LSP-induced residual stresses are known to be dependent on a multitude of factors, such as laser process variables (spot size, pulse width and energy), component
geometry, material properties and the peening sequence. In this study, an intelligent system based on machine learning was developed that can predict the residual stress distribution induced by LSP. The system can also be applied to “reverse-optimise” the process parameters. The prediction system was
developed using residual stress data derived from incremental hole drilling. We used artificial neural networks (ANNs) within a Bayesian framework to develop a robust prediction model validated using a comprehensive set of case studies. We also studied the relative importance of the LSP process parameters using Garson’s algorithm and parametric studies to understand the response of the residual stresses in laser peening systems as a function of different process variables. Furthermore, this study critically evaluates the developed machine learning models while demonstrating the potential benefits of implementing an intelligent system in prediction and optimisation strategies of the laser shock peening process.
Original languageEnglish
Article number2888
Number of pages22
JournalApplied Sciences
Issue number7
Publication statusPublished - 24 Mar 2021

Bibliographical note

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Air Force Office of Scientific Research, Air Force Material Command, USAF, under grant number FA8655-12-1-2084


  • laser shock peening
  • modelling
  • residual stress
  • Bayesian neural networks
  • genetic algorithm
  • optimisation


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