A Fuzzy Model for Predicting Surface Roughness in Plasma Arc Cutting of AISI 4140 Steel

Özek, Cebeli; Çaydaş, Ulaş; Ünal, Engin

doi:10.1080/10426914.2011.551952

Cited by 41 publications

(15 citation statements)

References 21 publications

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“…It was evident from the statistical analysis that the cutting speed was the most significant factor which affects the surface roughness [7]. After cutting, it is seen that hardness increases in the areas near the surface of part i.e.…”

Section: Introductionmentioning

confidence: 91%

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Effect of process parameters on cut quality of stainless steel of plasma arc cutting using hybrid approach

Maity

Bagal

2014

Int J Adv Manuf Technol

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An optimization concept of the various machining parameters for the plasma arc cutting procedures on AISI 316 stainless steel conducting a hybrid optimization method has been carried out. A new composition of response surface methodology and grey relational analysis coupled with principal component analysis has been proposed to evaluate and estimate the effect of machining parameters on the responses. The major responses selected for these analyses are kerf, chamfer, dross, surface roughness and material removal rate, and the corresponding machining parameters concentrated for this study are feed rate, current, voltage and torch height. Thirty experiments were conducted on AISI 316 stainless steel workpiece materials based on a facecentered central composite design. The experimental results obtained are applied in grey relational analysis, and the weights of the responses were evaluated by the principal component analysis and further evaluated using response surface method. The results show that the grey relational grade was significantly affected by the machining parameters directly as well as with some interactions. This method is straightforward with easy operability, and the results have also been established by running confirmation tests. The premise attributes beneficial knowledge for managing the machining parameters to enhance the preciseness of machined parts by plasma arc cutting.

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Section: Introductionmentioning

confidence: 91%

“…around 250-350 HVand decreases towards the core of the material [8]. Özek et al performed a Fuzzy model for predicting surface roughness in plasma cutting of AISI-4140 steel plate [7]. Radovanovic and Madic modeled a parametric design of plasma arc cutting process by using ANN to predict surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Effect of process parameters on cut quality of stainless steel of plasma arc cutting using hybrid approach

Maity

Bagal

2014

Int J Adv Manuf Technol

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“…Few researchers have worked on the mathematical modeling of the PAC process and the multi response optimization problem [24] Some of them combined more than one optimization technique to produce optimized cutting. From the literature it was also observed that some multicriteria decision making (MCDM) methods were applied to optimize the PAC process such as, desirability function approach, fuzzy model [14], and grey relational analysis (GRA) [9]. multi-objective optimization on the basis of ratio analysis (MOORA) [29-34] method, were applied.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Plasma Arc Cutting Process Using Moora Combined With Ga

2019

ERJ. Engineering Research Journal

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In this work, cutting parameters were optimized in plasma arc cutting process of mild steel by application of multi-objective optimization by ratio analysis (MOORA) method. Standard deviation (SDV) concept has been used to allocate the weight criteria of each objective being reflected. Cutting speed, arc current, and standoff distance were nominated as setting parameters for optimization the kerf characteristics in the straight-slit cutting (kerf taper, dross, surface roughness and maximization of material removal rate). MOORA was used to transform multiple responses into a single characteristic index known as multi performance characteristic index (MPCI). MPCI was modeled by the use of genetic algorithm (GA). With this action an attempt was made to find more precise dependence of MPCI with cutting parameters. Finally, this was followed by optimization of the MPCI in plasma arc cutting using genetic algorithm. This has also been that, SDV-MOORA-GA method has effectively optimized the plasma arc cutting process parameters used in this study.

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“…The minimum quantity fluid application supplied usually decreases the tool wear rate and as a result enhances tool life. Ozek et al [10] used the Fuzzy logic to investigate the effect of the machining factors on the plasma arc machining process of the AISI 4140 steel. The fuzzy logic model was developed to predict the surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Modeling and optimization of face milling process parameters for AISI 4140 steel

Başar

Akin

Kahraman

et al. 2018

Teh. glas. (Online)

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In this study, the effect of cutting parameters such as the depth of cut, feed rate, cutting speed and the number of inserts on surface roughness were investigated in the milling of the AISI 4140 steel. The optimal control factors for surface quality were detected by using the Taguchi technique. Experimental trials were designed according to the Taguchi L18 (21x33) orthogonal array. The statistical effects of control factors on surface roughness have been established by using the analysis of variance (ANOVA). Optimal cutting parameters were obtained by using the S/N ratio values. The ANOVA results showed that the effective factors were the number of inserts and the feed rate on surface roughness. However, the depth of cut and the cutting speed showed an insignificant effect. Additionally, the First-order and Second-order regression analysis were conducted to estimate the performance characteristics of the experiment. The acquired regression equation results matched with the surface roughness measurement results. The optimal performance characteristics were obtained as a 0.5 mm depth of cut, 0.08 mm/rev feed rate, 325 m/min cutting speed and 1 number of inserts by using the Taguchi method. Additionally, the confirmation test results indicated that the Taguchi method was very prosperous in the optimization of the machining parameters to obtain the minimum surface roughness in the milling of the AISI 4140 steel.

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A Fuzzy Model for Predicting Surface Roughness in Plasma Arc Cutting of AISI 4140 Steel

Cited by 41 publications

References 21 publications

Effect of process parameters on cut quality of stainless steel of plasma arc cutting using hybrid approach

Effect of process parameters on cut quality of stainless steel of plasma arc cutting using hybrid approach

Multi-Objective Optimization of Plasma Arc Cutting Process Using Moora Combined With Ga

Modeling and optimization of face milling process parameters for AISI 4140 steel

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