Review of Surface Roughness Prediction in Cylindrical Grinding process by using RSM and ANN

Abstract

In recent years, one of the innovative technologies which enable automatic computerized devices for the purpose of evaluation, inspection, analyzing of various static and dynamic images is Machine Vision System. MVS provides various benefits in various fields like Surveillance cameras for Security, MAT LAB for analysis of Engineering designs, RSM and ANN for analysis of various parameters for optimizing variables, etc. MVS has some various innovative techniques like automatic capturing of images, evaluation and processing capabilities. This paper is about optimization of various parameters such as depth of cut, feed rate, cutting speed, dressing conditions, etc. For improving the accuracy obtained in Cylindrical (Surface) grinding process. The main objective of our idea is to identify the surface roughness of work piece obtained in Cylindrical grinding process in according to various input parameters like feed rate, depth of cut, dressing speed, etc. The method used here is RSM and ANN which are used to optimize the response of input parameters on output parameters and to provide algorithm for pre-trained model for visualization along with simulation of neural networks. Keywords— Surface Roughness, Response Surface Methodology, Artificial Neural Network, Work piece Materials, Methodology.