Location of Repository

Design of Optimum ANN for Prediction of response parameters in drilling of En8 steel Under MQL environment

By 

Abstract

Abstract — the present paper focused on development of an ANN to predict the responses in drilling of En-8 steel with coated tools. ANN is trained with experimental data which is generated during drilling operation at different conditions. Further ANN is optimized by performance evaluation by changing its parameters. This optimized ANN can measure the responses torque, cutting force, surface roughness, material removal rate, power required. Finally ANN predicted responses are compared with experimental results and found that these are closely matched

Topics: Minimum Quantity Lubrication, Torque, Cutting Force, MRR, Power and Surface Roughness
Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.413.5134
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.ijetae.com/files/Vo... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.