Prediction of output Responses in Milling of Casted Aluminum by using ANN

Authors

  • A. Bhargav Asst. Professor, Department of ME, S V College of Engineering , Tirupati
  • N. Rajesh, Asst. Professor, Department of ME, S V College of Engineering , Tirupati
  • Dr. CNV Sridhar Professor, Dept. of ME, AITS, Rajampet.

Keywords:

Taguchi Analysis, DOE, , Milling, ANN

Abstract

The important goal in the modern industries is to manufacture the products with lower cost and with high quality in short span of time. There are two main practical problems that engineers face in a manufacturing process. The first is to determine the values of process parameters that will yield the desired product quality (meet technical specifications) and the second is to maximize manufacturing system performance using the available resources. The increase of customer needs for quality products (more precise tolerances and better product surface roughness) have driven the metal cutting process. The main objective of this project work is to study the effect of surface roughness and Material Removal Rate in a machining of cast aluminum on CNC milling machine with High Speed Steel cutting tool. The feasibility of implementation of design of experiments (DOE), and Artificial Neural network in millingprocess is analyzed.

Downloads

Published

2021-03-27

Issue

Vol. 2 No. 3 (2015): IJIERT

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0 DEED).

You are free to:

  1. Share — copy and redistribute the material in any medium or format
  2. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. NonCommercial — You may not use the material for commercial purposes .
  3. NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
  4. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

 

Rights of Authors

Authors retain the following rights:
1. Copyright and other proprietary rights relating to the article, such as patent rights,
2. the right to use the substance of the article in future works, including lectures and books,
3. the right to reproduce the article for own purposes, provided the copies are not offered for sale,
4. the right to self-archive the article.