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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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Athmane, Yallese Mohamed
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Publications (5/5 displayed)
- 2023Minimizing Tool Wear, Cutting Temperature and Surface Roughness in the Intermittent Turning of AISI D3 Steel Using the DF and GRA Methodcitations
- 2020Modeling and Optimization of Cutting Parameters during Machining of Austenitic Stainless Steel AISI304 Using RSM and Desirability Approach
- 2018Estimation and optimization of flank wear and tool lifespan in finish turning of AISI 304 stainless steel using desirability function approach
- 2017Quality-productivity decision making when turning of Inconel 718 aerospace alloy: A response surface methodology approach
- 2015Statistical analysis of AISI304 austenitic stainless steel machining using Ti(C, N)/Al2O3/TiN CVD coated carbide tool
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document
Statistical analysis of AISI304 austenitic stainless steel machining using Ti(C, N)/Al2O3/TiN CVD coated carbide tool
Abstract
The present research work investigated the machining of AISI304 austenitic stainless steel in terms of machining force evolution, power consumption, specific cutting force and surface roughness where a factorial experiment design and analysis of variance technique were used and several factors were evaluated for their effects on each level.The case of dry turning process was studied based on design of experiments in order to obtain empirical equations characterizing material machinability according to cutting conditions such as cutting speed, feed rate and depth of cut and the latter ones were put in relationship with the machining output variables (Ra, Fc, Kc and Pc) through the response surface methodology (RSM).Results revealed that feed rate was the most preponderant factor affecting surface roughness (71.04%).However, the depth of cut affects considerably cutting force and cutting power by (60.74% and 67.11%), respectively.In addition, the specific cutting force was found affected significantly by cutting speed with a contribution of 41.43%.The quadratic model of RSM associated with response optimization technique and composite desirability was used to find optimum values of machining parameters (104.54 m/min, 0.08 mm/rev and 0.295 mm).