International Journal of
Physical Sciences

  • Abbreviation: Int. J. Phys. Sci.
  • Language: English
  • ISSN: 1992-1950
  • DOI: 10.5897/IJPS
  • Start Year: 2006
  • Published Articles: 2570

Full Length Research Paper

Derivation of statistical models to predict roughness parameters during machining process of PEEK composites using PCD and K10 tools

Francisco Mata1*, Issam Hanafi2, Abdellatif  Khamlichi2, Abdallah Jabbouri3 and Mohammed Bezzazi3
1Polytechnic School of Almadén, University of Castilla-La Mancha, Plaza Manuel Meca,1, 13412 Almadén, Spain. 2EMS2M, Faculty of Sciences at Tetouan, BP. 2121, M'hannech, Tetouan, Morocco. 3EMMS, Faculty of Sciences and technology at Tangier, BP 416, Tangier, Morocco
Email: [email protected]

  •  Accepted: 08 June 2009
  •  Published: 30 June 2009


In many scientific fields, non-linear regression based models are of great utility to perform curve adjustment of experimental data. This concept is used in the present study in order to construct adequate adjusted models enabling to make predictions for the different roughness parameters characterizing machining of PEEK composites when using PCD and K10 tools. The adjusted data were obtained by using design of experimental methods and only the main factors affecting roughness during machining of PEEK composites were retained. Since, analysis of variance performed on experimental results has revealed that feed is the main cutting factor that influences surface roughness, nonlinear regression is conducted only in terms of this parameter.


Key words: Non-linear regression, machining process, PEEK composites, PCD tool, K10 tool, roughness parameter.