Int. J. of Materials and Product Technology   »   2014 Vol.49, No.4

 

 

Title: RMS-based optimisation of surface roughness when turning AISI 420 stainless steel

 

Authors: Lakhdar Bouzid; Mohamed Athmane Yallese; Salim Belhadi; Tarek Mabrouki; Lakhdar Boulanouar

 

Addresses:
Mechanics and Structures Research Laboratory (LMS), May 8th 1945 University, P.O. Box 401, Guelma 24000, Algeria
Mechanics and Structures Research Laboratory (LMS), May 8th 1945 University, P.O. Box 401, Guelma 24000, Algeria
Mechanics and Structures Research Laboratory (LMS), May 8th 1945 University, P.O. Box 401, Guelma 24000, Algeria
University of Tunis El Manar, ENIT, 1002 Tunis, Tunisia; LaMCoS, CNRS, INSA – Lyon, UMR5259, Lyon University, F69621, France
Advanced Technologies in Mechanical Production Research Laboratory (LRTAPM), Badji Mokhtar, University of Annaba, P.O. Box 12, Annaba 23000, Algeria

 

Abstract: The aim of this investigation is to determine the correlation between the cutting conditions such as cutting speed, feed rate, and depth of cut; and surface roughness parameters (Ra, Rq, Rt, Rp, and R3z). The case of turning operation of martensitic stainless steel AISI 420 using CVD coated carbide tool is studied. Full factorial design (4³) was adopted. Statistical analysis ANOVA and response surface methodology (RSM) was used to develop quadratic regression models and to determine optimum cutting conditions. It was found that the feed rate (f) is the highest factor influenced the surface roughness parameters when compared with speed and depth of cut. Moreover, a good agreement was observed between the predicted and the experimental surface roughness criteria. The use of lower cutting speed, lower feed rate, and lower depth of cut ensures minimum surface roughness variations.

 

Keywords: cutting parameters; surface roughness; CVD coated carbide tools; stainless steel; steel turning; ANOVA; modelling; RSM; response surface methodology; desirability function; optimisation; machinability; DoE; design of experiments; steel machining; cutting speed; feed rate; depth of cut; quadratic regression modelling; surface quality.

 

DOI: 10.1504/IJMPT.2014.064934

 

Int. J. of Materials and Product Technology, 2014 Vol.49, No.4, pp.224 - 251

 

Date of acceptance: 10 Mar 2014
Available online: 05 Sep 2014

 

 

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