Development of Predictive Model for Surface Roughness Using Artificial Neural Networks

EasyChair Preprint 5396

Abstract

The need for quality products has been a constant driving force for manufacturing industries. The surface properties are  the  determinant of the product quality. Surface roughness prediction is now an area of interest in the machining industry.speed of cutting , feed rate and depth of cut  are some of the parameters that influence  the prediction of surface roughness.The combined effect of all the three parameters influence  the Surface Roughness to a much significant extent.Data driven prediction is the way ahead.In this study, an Artificial Neural Network is developed fusing the speed of cutting , feed rate and depth of cut.The ANN model is trained using the experimental data already present in the research papers  for the prediction  as well as  optimisation  of depth of cut ,   cutting speed and feed rate  in CNC lathe  for the least possible Surface Roughness of mild steel using statistical techniques and regression models. Further, the ANN model is validated on the basis of two other  unseen sets of experimental  data on mild steel. From the validation it has been found that prediction of surface roughness by the ANN has higher accuracy as compared to other existing methods.

Keyphrases: Artificial Neural Network, Mild steel, Surface roughness

@booklet{EasyChair:5396,
  author    = {Nikhil Rai and Ms Niranjan and Prateek Verma and Prince Tyagi},
  title     = {Development of Predictive Model for Surface Roughness Using Artificial Neural Networks},
  howpublished = {EasyChair Preprint 5396},
  year      = {EasyChair, 2021}}