Predicting and Optimizing the Mechanical Effects Generated by a Laser-Treated Turbine Blade Using Artificial Neural Networks and ANFIS Techniques

This article discusses the application of artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) to the prediction and optimization of mechanical effects resulting from laser shock processing (LSP) applied to turbine blades made of Inconel 718. LSP treatment is used in the aerospace industry to improve the durability and performance of turbine blades. The use of ANNs and ANFIS offers a data-driven approach to modeling and optimizing the mechanical properties of these treated blades. The development of predictive models that take into account various parameters such as: maximum applied pressure, full-width at half maximum and dimension is explored. In addition, this paper discusses optimization of LP process that harness the power of ANNs and ANFIS to maximize the desired mechanical properties, thereby contributing to the advancement of turbine blade technology in aerospace applications.