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

Applied Sciences

Kristina Langer¹  Niall Smyth²  Rohit Kshirsagar²  Jino Mathew²  Stratis Kanarachos²  Suraiya Zabeen²  MichaelE. Fitzpatrick² 

[1] Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH 45433, USA;Faculty of Engineering, Environment and Computing, Coventry University, Priory Street, Coventry CV1 5FB, UK;

关键词: laser shock peening;    modelling;    residual stress;    Bayesian neural networks;    genetic algorithm;    optimisation;   

DOI  :  10.3390/app11072888

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown