| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Girardin, François
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (13/13 displayed)
- 2018Estimation and optimization of flank wear and tool lifespan in finish turning of AISI 304 stainless steel using desirability function approachcitations
- 2018Estimation and optimization of flank wear and tool lifespan in finish turning of AISI 304 stainless steel using desirability function approachcitations
- 2017Investigation of the performance of the MQL, dry, and wet turning by response surface methodology (RSM) and artificial neural network (ANN)citations
- 2017Predictive modeling and multi-response optimization of technological parameters in turning of Polyoxymethylene polymer (POM C) using RSM and desirability functioncitations
- 2017Predictive modeling and multi-response optimization of technological parameters in turning of Polyoxymethylene polymer (POM C) using RSM and desirability functioncitations
- 2017Modeling and optimization of turning process parameters during the cutting of polymer (POM C) based on RSM, ANN, and DF methodscitations
- 2017Modeling and optimization of turning process parameters during the cutting of polymer (POM C) based on RSM, ANN, and DF methodscitations
- 2017Quality-productivity decision making when turning of Inconel 718 aerospace alloy: A response surface methodology approach
- 2017Quality-productivity decision making when turning of Inconel 718 aerospace alloy: A response surface methodology approachcitations
- 2016Performance of coated and uncoated mixed ceramic tools in hard turning processcitations
- 2016Performance of coated and uncoated mixed ceramic tools in hard turning processcitations
- 2015Modeling and optimization of tool wear and surface roughness in turning of austenitic stainless steel using response surface methodology
- 2015Modeling and optimization of tool wear and surface roughness in turning of austenitic stainless steel using response surface methodology
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document
Quality-productivity decision making when turning of Inconel 718 aerospace alloy: A response surface methodology approach
Abstract
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.