| 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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Mabrouki, Tarek
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (22/22 displayed)
- 2024Analysis of the Effect of Substituting Cement With Marble Powder on the Mortar Characteristics Used in 3D Printing
- 2023Selective laser melting of stainless-steel ::a review of process, microstructure, mechanical properties and post-processing treatmentscitations
- 2023Experimental study of morphological defects generated by SLM on 17-4PH stainless steel
- 2023Experimental investigation on the performance of ceramics and CBN cutting materials during dry machining of cast iron: Modeling and optimization study using RSM, ANN, and GAcitations
- 2023Mechanical properties of additively manufactured 17-4PH SS ::heat treatmentcitations
- 2020Modeling and Optimization of Cutting Parameters during Machining of Austenitic Stainless Steel AISI304 Using RSM and Desirability Approachcitations
- 2019Textile composite structural analysis taking into account the forming processcitations
- 2018Estimation and optimization of flank wear and tool lifespan in finish turning of AISI 304 stainless steel using desirability function approachcitations
- 2017Finite Element Analysis of Bend Test of Sandwich Structures Using Strain Energy Based Homogenization Methodcitations
- 2017Predictive modeling and multi-response optimization of technological parameters in turning of Polyoxymethylene polymer (POM C) using RSM and desirability functioncitations
- 2017Quality-productivity decision making when turning of Inconel 718 aerospace alloy: A response surface methodology approachcitations
- 2017Load transfer of graphene/carbon nanotube/polyethylene hybrid nanocomposite by molecular dynamics simulation
- 2016Performance of coated and uncoated mixed ceramic tools in hard turning processcitations
- 2015Mathematical modeling for turning on AISI 420 stainless steel using surface response methodologycitations
- 2015Modeling and optimization of tool wear and surface roughness in turning of austenitic stainless steel using response surface methodology
- 2015A new procedure to increase the orthogonal cutting machining time simulatedcitations
- 2014Load transfer of graphene/carbon nanotube/polyethylene hybrid nanocomposite by molecular dynamics simulations
- 2014Load transfer of graphene/carbon nanotube/polyethylene hybrid nanocomposite by molecular dynamics simulationcitations
- 2014RMS-based optimisation of surface roughness when turning AISI 420 stainless steelcitations
- 2013Three-dimensional finite element modeling of rough to finish down-cut milling of an aluminum alloycitations
- 2012Cutting simulation capabilities based on crystal plasticity theory and discrete cohesive elementscitations
- 2011Application of response surface methodology for determining cutting force model in turning hardened AISI H11 hot work tool steelcitations
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article
Modeling and Optimization of Cutting Parameters during Machining of Austenitic Stainless Steel AISI304 Using RSM and Desirability Approach
Abstract
<jats:p>In the current paper, cutting parameters during turning of AISI 304 Austenitic Stainless Steel are studied and optimized using Response Surface Methodology (RSM) and the desirability approach. The cutting tool inserts used in this work were the CVD coated carbide. The cutting speed (vc), the feed rate (f) and the depth of cut (ap) were the main machining parameters considered in this study. The effects of these parameters on the surface roughness (Ra), cutting force (Fc), the specific cutting force (Kc), cutting power (Pc) and the Material Removal Rate (MRR) were analyzed by ANOVA analysis.The results showed that f is the most important parameter that influences Ra with a contribution of 89.69 %, while ap was identified as the most significant parameter (46.46%) influence the Fc followed by f (39.04%). Kc is more influenced by f (38.47%) followed by ap (16.43%) and Vc (7.89%). However, Pc is more influenced by Vc (39.32%) followed by ap (27.50%) and f (23.18%).The Quadratic mathematical models, obtained by the RSM, presenting the evolution of Ra, Fc, Kc and Pc based on (vc, f, and ap) were presented. A comparison between experimental and predicted values presents good agreements with the models found.Optimization of the machining parameters to achieve the maximum MRR and better Ra was carried out by a desirability function. The results showed that the optimal parameters for maximal MRR and best Ra were found as (vc = 350 m/min, f = 0.088 mm/rev, and ap = 0.9 mm).</jats:p>