Online prediction of surface roughness in peripheral milling processes

Author

Vallejo, Antonio J. ; Morales-Menendez, Ruben ; Ramirez-Mendoza, Ricardo ; Garza-Castanon, Luis

Author_Institution

Inst. de Autom. Ind., Madrid, Spain

fYear

2009

fDate

23-26 Aug. 2009

Firstpage

3695

Lastpage

3700

Abstract

An online surface roughness prediction module for peripheral end milling in High Speed Machining was developed. An Artificial Neural Network framework integrated five cutting parameters and one process variable signal. Vibration signal in the workpiece showed high correlation with the surface roughness. This signal was pre-processed as Mel Frequency Cesptrum Coefficients. This could be a practical solution for a wide cutting conditions with several Aluminium alloys and cutting tools. Results were validated by using an industrial High Speed Machining center.

Keywords

aluminium alloys; cutting; cutting tools; milling; milling machines; neural nets; production engineering computing; quality control; signal processing; surface roughness; vibrations; aluminium alloys; artificial neural network; cutting parameters; cutting tools; high speed machining; mel frequency cesptrum coefficients; peripheral end milling processes; signal preprocessing; surface roughness online prediction; vibration signal; Accelerometers; Artificial neural networks; Cutting tools; Machining; Rough surfaces; Sensors; Surface roughness; Artificial Neural Networks; High Speed Machining; Modelling; Surface Roughness;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ECC), 2009 European

Conference_Location

Budapest

Print_ISBN

978-3-9524173-9-3

Type

conf

Filename

7074974