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

Patricia Muñoz-Escalona*, Paul G. Maropoulos

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    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. © ASM International.

    Original languageEnglish
    Pages (from-to)185-193
    Number of pages9
    JournalJournal of Materials Engineering and Performance
    Issue number2
    Early online date21 Mar 2009
    Publication statusPublished - Mar 2010


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


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