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Article Dans Une Revue International Journal of Industrial Engineering Computations Année : 2017

Quality-productivity decision making when turning of Inconel 718 aerospace alloy: A response surface methodology approach

Résumé

Inconel 718 is among difficult to machine materials because of its abrasiveness and high strength even at high temperature. This alloy is mainly used in aircraft and aerospace industries. Therefore, it is very important to reveal and evaluate cutting tools behavior during machining of this kind of alloy. The experimental study presented in this research work has been carried out in order to elucidate surface roughness and productivity mathematical models during turning of Inconel 718 superalloy (35 HRC) with SiC Whisker ceramic tool at various cutting parameters (depth of cut, feed rate, cutting speed and radius nose). A small central composite design (SCCD) including 16 basics runs replicated three times (48 runs), was adopted and graphically evaluated using Fraction of design space (FDS) graph, completed by a statistical analysis of variance (ANOVA). Mathematical models for surface roughness and productivity were developed and normality was improved using the Box-Cox transformation. Results show that surface roughness criterion Ra was mainly influenced by cutting speed, radius nose and feed rate, and that the depth of cut had major effect on productivity. Finally, ranges of optimized cutting conditions were proposed for serial industrial production. Industrial benefit was illustrated in terms of high surface quality accompanied with high productivity. Indeed, results show that the use of optimal cutting condition had an industrial benefit to 46.9 % as an improvement in surface quality Ra and 160.54 % in productivity MRR.

Dates et versions

hal-01999573 , version 1 (30-01-2019)

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Hamid Tebassi, Mohamed Athmane Yallese, Salim Belhadi, François Girardin, Tarek Mabrouki. Quality-productivity decision making when turning of Inconel 718 aerospace alloy: A response surface methodology approach. International Journal of Industrial Engineering Computations, 2017, pp.347-362. ⟨10.5267/j.ijiec.2016.12.003⟩. ⟨hal-01999573⟩
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