Journal
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE
Volume 228, Issue 2, Pages 233-244Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0954405413498582
Keywords
End milling; process optimisation; cutting parameters; surface roughness; neural network modelling
Funding
- European Union
- Greek national funds
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A mastering of surface quality issues during machining helps avoiding failure, enhances component integrity and reduces overall costs. Surface roughness significantly affects the quality performance of finished components. A number of parameters, both material and process oriented, influence at a different extend the surface quality of the finished product. Aluminium alloy 5083 component surface quality, achieved in side end milling, constitutes the subject of the present case study. The design of experiment method is employed: that is, 18 carbide two-flute end mill cutters - manufactured by a five-axis grinding machine - have been assigned to mill 18 pockets in finishing conditions - having different combinations of geometry and cutting parameters values, according to the L-18 (2(1)x 3(7)) standard orthogonal array. Process performance is estimated using the statistical surface texture parameters R, R-y and R-z- measured during three different passes on the side surface of the pockets. The results indicate that process parameters - such as the cutting speed, the peripheral second relief angle and the core diameter - mostly influence surface texture. The experimental values are used to train a feed forward back-propagation artificial neural network for the prediction of the yield surface roughness magnitude.
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