Modeling and optimization of surface roughness and productivity thru RSM in face milling of AISI 1040 steel using coated carbide inserts

Fnides, Mohamed; Yallese, Mohamed Athmane; Khattabi, Riad; Mabrouki, Tarek; Girardin, François

doi:10.5267/j.ijiec.2017.3.001

Cited by 8 publications

(8 citation statements)

References 22 publications

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“…The desirability function method has been used in numerous research studies [21][22][23][24][25] in order to optimize a manufacturing process pertaining to turning, milling or various other operations [26,27]. This approach allows the combination of multiple responses into a single function (the Desirability Function or DF) through the choice of a value comprised between zero and one (least to most desirable, respectively).…”

Section: Desirability Function (Df) Application and Optimizationmentioning

confidence: 99%

Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy

Khrouf

Tebassi

Yallese

et al. 2022

International Journal of Applied Mechanics and Engineering

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An experimental investigation is carried out to examine the effects of various cutting parameters on the response criteria when turning EN-AW-1350 aluminum alloy under dry cutting conditions. The experiments related to the analysis of the influence of turning parameters on the surface roughness (Ra) and material removal rate (MRR) were carried out according to the Taguchi L27 orthogonal array (313 ) approach. The analysis of variance (ANOVA) was applied to characterizing the main elements affecting response parameters. Finally, the desirability function (DP) was applied for a bi-objective optimization of the machining parameters with the objective of achieving a better surface finish (Ra) and a higher productivity (MRR). The results showed that the cutting speed is the most dominant factor affecting Ra followed by the feed rate and the depth of cut. Moreover, the Artificial Neural Network (ANN) approach is found to be more reliable and accurate than its Response Surface methodology (RSM) counterpart in terms of predicting and detecting the non-linearity of the surface roughness and material removal rate mathematical models. ANN provided prediction models with a precision benefit of 8.21% more than those determined by RSM. The latter is easier to use, and provides more information than ANN in terms of the impacts and contributions of the model terms.

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Section: Desirability Function (Df) Application and Optimizationmentioning

confidence: 99%

Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy

Khrouf

Tebassi

Yallese

et al. 2022

International Journal of Applied Mechanics and Engineering

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“…For this purpose, a number of machining experiments based on statistical three factor and three level factorial experiment designs, completed (L27) with a statistical analysis of variance (ANOVA), were performed in order to develop mathematical models and to identify the significant factors of these technological parameters. It was determined that cutting speed is the most important parameter affecting the surface roughness [33]. In this study, it is aimed at determining suitable cutting tools and cutting parameters for surface roughness and cutting tool wear in face milling Custom 450 stainless steel.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of Cutting Tool and Cutting Parameters in Face Milling of Custom 450 through the Taguchi Method

Gökçe

2019

Advances in Materials Science and Engineering

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Stainless steels with unique corrosion resistance are used in applications with a wide range of fields, especially in the medical, food, and chemical sectors, to maritime and nuclear power plants. The low heat conduction coefficient and the high mechanical properties make the workability of stainless steel materials difficult and cause these materials to be in the class of hard-to-process materials. In this study, suitable cutting tools and cutting parameters were determined by the Taguchi method taking surface roughness and cutting tool wear into milling of Custom 450 martensitic stainless steel. Four different carbide cutting tools, with 40, 80, 120, and 160 m/min cutting speeds and 0.05, 0.1, 0.15, and 0.2 mm/rev feed rates, were selected as cutting parameters for the experiments. Surface roughness values and cutting tool wear amount were determined as a result of the empirical studies. ANOVA was performed to determine the significance levels of the cutting parameters on the measured values. According to ANOVA, while the most effective cutting parameter on surface roughness was the feed rate (% 50.38), the cutting speed (% 81.15) for tool wear was calculated.

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“…The results indicated that feed was a crucial factor on surface roughness. Fnides et al [8] used RSM and DFA based optimizations to find the optimum cutting conditions of minimum surface roughness and maximum material removal rate in face milling of AISI 1040 steel. Palanisamy et al [9] experimented for milling of T6-6061 aluminium alloy to optimize cutting conditions using RSM.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling and Optimization of Milling Parameters in AA 5083 Aluminum Alloy

Başar

Kahraman

Önder

2019

European Mechanical Science

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An experimental study was carried out to determine the effect of different cutting parameters such as feed, spindle speed and depth of cut on surface roughness in face milling of 5083 aluminum alloy. The mathematical model was developed to estimate surface roughness using Response Surface Methodology. The significant contribution of cutting parameters was detected by analysis of variance. Statistical analysis indicated that feed and spindle speed have the most considerable influence on surface roughness. After developed mathematical model, desirability function analysis was performed to minimize the surface roughness. The lowest surface roughness (0.41 µm) was acquired at a feed of 3008 mm/min, a spindle speed of 5981 rpm and a depth of cut of 0.54 mm.

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Modeling and optimization of surface roughness and productivity thru RSM in face milling of AISI 1040 steel using coated carbide inserts

Cited by 8 publications

References 22 publications

Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy

Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy

Optimisation of Cutting Tool and Cutting Parameters in Face Milling of Custom 450 through the Taguchi Method

Mathematical Modeling and Optimization of Milling Parameters in AA 5083 Aluminum Alloy

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