Modelling and optimisation of creep feed deep surface grinding using FEM-based NNGA

This paper presents the application of a hybrid approach comprising of neural network and genetic algorithm for modelling and optimisation of creep feed deep surface grinding process. Finite element method has been used to generate dataset for neural network model. Subsequently, NN model has been coupled with genetic algorithm to find optimum input parameters of creep feed deep surface grinding. The proposed hybrid approach is well capable to predict thermal stresses in the workpiece quickly and also minimise it with reasonable accuracy during creep feed deep surface grinding process.