Modelling and optimisation of creep feed deep surface grinding using FEM-based NNGA. (2016)

• Record Type: Journal Article
• Authors: Narayan, Audhesh; Yadava, Vinod
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 8:Number 1(2016)
• Page Start: 65
• Page End: 74
• Publication Date: 2016
• Subjects: creep feed grinding -- creep feed deep surface grinding -- CFDSG -- thermal stress -- finite element method -- FEM -- neural networks -- genetic algorithms -- modelling -- optimisation; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

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.