Abstract
Due to the widespread use of highly automated machine tools, manufacturing requires reliable models and methods for the prediction of output performance of machining processes. The prediction of optimal machining conditions for good surface finish and dimensional accuracy plays a very important role in process planning. The present work deals with the study and development of a surface roughness prediction model for machining Al7075-T6, using Response Surface Methodology (RSM). Machining operations of work pieces made by Al7075-T6 covering a wide range of machining conditions have been carried out with by flat end mill with four teeth made by High Speed Steel. A RS model, in terms of machining parameters, was developed for surface roughness prediction using the Radial Basis Functions (RBF) technique. This model gives the process response sensitivity to the individual process parameters. An attempt has also been made to optimize the surface roughness prediction model using Genetic Algorithms (GA).
Article PDF
Avoid common mistakes on your manuscript.
References
M. Sander, A Practical Guide to the Assessment of Surface Texture, Gottingeti, Germany (1991).
Taramen, K., Multi-machining output-multi independent variable turning research by response surface methodology. International Journal of Production Research, 12(2), pp. 233–245, 1974.
Boothroyd G, Knight WA. Fundamentals of machining and machine tools. New York: Marcel Dekker Inc.; 1989.
Alauddin et al.End milling Machinability of Inconel 718. Int J Eng Manufacture 1996;210:11–23.
U. Tetsutaro, M. Naotake, Prediction and detection of cutting tool failure by modified group method of data handling, International Journal of Machine Tools and Manufacture 26 (1986) 69–110.
S. Das et al. Simple approach for online tool wear monitoring using the analytical hierarchy process. J of Eng. Man. 211 (1997) 19–27.
C.A. Van Luttervelt, T.H.C. Childs, I.S. Jawahir, F. Klocke, P.K.Venuvinod. Progress Report ‘Modelling of machining operations’, Annals of the CIRP, 47/2 (1998) 587–626.
Engineous Software - iSIGHT Version 3.0 User's Guide - 2008.
Weissinger, J. Lift distribution of swept-back wings. NACA pp.1120, 1947.
A. Del Prete, A.A. De Vitis, A. Spagnolo. Experimental development of RSM techniques for surface quality prediction in metal cutting applications. Esaform Conference 2010.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Del Prete, A., De Vitis, A.A. & Anglani, A. Roughness improvement in machining operations through coupled metamodel and genetic algorithms technique. Int J Mater Form 3 (Suppl 1), 467–470 (2010). https://doi.org/10.1007/s12289-010-0808-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12289-010-0808-y