Artificial neural networks for surface roughness prediction when face milling Al 7075-T7351

Patricia Munoz-Escalona, Paul G. Maropoulos

Research output: Contribution to journalArticle

Abstract

In this work, different artificial neural networks (ANN) are developed for the prediction of surface roughness (R a ) values in Al alloy 7075-T7351 after face milling machining process. The radial base (RBNN), feed forward (FFNN), and generalized regression (GRNN) networks were selected, and the data used for training these networks were derived from experiments conducted using a high-speed milling machine. The Taguchi design of experiment was applied to reduce the time and cost of the experiments. From this study, the performance of each ANN used in this research was measured with the mean square error percentage and it was observed that FFNN achieved the best results. Also the Pearson correlation coefficient was calculated to analyze the correlation between the five inputs (cutting speed, feed per tooth, axial depth of cut, chip’s width, and chip’s thickness) selected for the network with the selected output (surface roughness). Results showed a strong correlation between the chip thickness and the surface roughness followed by the cutting speed.
Original languageEnglish
Pages (from-to)185-193
Number of pages9
JournalJournal of Materials Engineering and Performance
Volume19
Issue number2
DOIs
Publication statusPublished - Mar 2010

Keywords

  • face milling
  • feed forward
  • generalized regression
  • radial base
  • surface roughness

Fingerprint Dive into the research topics of 'Artificial neural networks for surface roughness prediction when face milling Al 7075-T7351'. Together they form a unique fingerprint.

Cite this