By Topic

Surface Roughness Prediction and Cutting Parameters Optimization in High-Speed Milling AlMn1Cu Using Regression and Genetic Algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Z. H. Wang ; Sch. of Mech. Eng., Nanjing Univ. of Sci. & Technol., Nanjing, China ; J. T. Yuan ; X. Q. Hu ; W. Deng

Surface roughness is an important indicator of the surface quality of machined workpieces. In this study, in order to find the functional relation between cutting parameters and surface roughness, a series of cutting experiments for AlMn1Cu are conducted to obtain surface roughness values in high-speed peripheral milling. Firstly, this paper presents the predictive mathematic model of surface roughness based on the cutting parameters. Secondly, the optimization model of cutting parameters in order to achieve the maximum material removal rate is built in this paper, and the genetic algorithm is employed to find the optimum cutting parameters leading to maximum material removal rate in the different range of surface roughnesspsilas values.

Published in:

2009 International Conference on Measuring Technology and Mechatronics Automation  (Volume:3 )

Date of Conference:

11-12 April 2009