Creep Feed Grinding Forces Prediction Using Neural Network

The aim of this paper is to model and predict the grinding forces of the creep feed grinding using the neural network. This model is then used to select the working conditions (such as depth of cut, the wheel speed and workpiece speeds) to prevent the surface burning and to maximize the material removal rate. The material used in this study is nickel-based supperalloy. These materials are usually used in aircrafts, gas turbines, rocket engines, petrochemical equipments and other high temperature applications. The results show that the grinding forces can be accurately predicted by the application of neural network. The outcomes of the paper are now used to select appropriate working conditions for grinding the turbine blades.

Keywords:

Neural network , Creep feed grinding , Workpiece burning , Nickel-based Supperalloys