Grey-based fuzzy algorithm for the optimization of the ball burnishing process

Eşme, Uğur; Külekçi, Mustafa Kemal; Üstün, Deniz; Kahraman, Funda; Kazançoğlu, Yiğit

doi:10.3139/120.110763

Cited by 4 publications

(12 citation statements)

References 24 publications

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“…Table 8 shows the response table for the mean of GFRG. Higher is the value of GFRG, the corresponding factor combination is said to be close to the optimal [13,14]. Analysis of the means is performed for the GRFG.…”

Section: Resultsmentioning

confidence: 99%

“…The optimal parametric combination is then evaluated which would result highest Grey relational grade. The optimal factor setting for maximizing overall Grey relational grade can be performed by Taguchi method [13,16,17]. In Grey relational generation, the normalized F and Ra corresponding to the smaller-the-better (SB) criterion which can be expressed as given in Eq.…”

Section: Grey Relational Analysis (Gra)mentioning

confidence: 99%

“…For GRG estimation, two approaches were employed to compare output performance. One is the traditional GRG function while the other is the fuzzy inference system (FIS) [13,16,[28][29]. In the first step, fuzzifier uses the membership function to fuzzify inputs which are obtained before GRG calculations.…”

Section: Fuzzy Inference System (Fis) Modelingmentioning

confidence: 99%

“…Next, based on normalized experimental data, Grey relational coefficient is calculated to represent the correlation between the desired and actual experimental data. Then overall Grey relational grade is determined by averaging the Grey relational coefficient corresponding to selected responses [13][14][15][16]. The overall performance characteristic of the multiple response process depends on the calculated Grey relational grade (GRG) [14].…”

Section: Grey Relational Analysis (Gra)mentioning

confidence: 99%

“…This uncertainty can be effectively checked by using fuzzy logic [13,[19][20][21][22][23][24][25][26][27][28][29]. The grey-fuzzy method was created and applied by Lin in 2004 [15].…”

Section: Fuzzy Inference System (Fis) Modelingmentioning

confidence: 99%

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A hybrid approach for the prediction and optimization of cutting forces using grey-based fuzzy logic

Eşme¹

2017

European Mechanical Science

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This study focused on the Grey-Based Fuzzy Logic Algorithm for the prediction and optimization of multiple performance characteristics of oblique turning process. Experiments have been constructed according to Taguchi's L16 orthogonal array design matrix. Cutting speed, rate of feed and depth of cut were selected as input parameters, whereas material removal rate, cutting force and surface roughness were selected as output responses. Using grey relation analysis (GRA), grey relational coefficient (GRC) and grey relation grade (GRG) were obtained. Then, Grey based fuzzy algorithm was applied to obtain grey fuzzy reasoning grade (GFRG). Analysis of variance (ANOVA) carried out to find the significance and contribution of parameters on multiple performance characteristics. Finally, confirmation test was applied at the optimum level of GFRG to validate the results. The results also show the application feasibility of the grey based fuzzy algorithm for continuous improvement in product quality in complex manufacturing processes.

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Section: Resultsmentioning

confidence: 99%

Section: Grey Relational Analysis (Gra)mentioning

confidence: 99%

Section: Fuzzy Inference System (Fis) Modelingmentioning

confidence: 99%

Section: Grey Relational Analysis (Gra)mentioning

confidence: 99%

“…This uncertainty can be effectively checked by using fuzzy logic [13,[19][20][21][22][23][24][25][26][27][28][29]. The grey-fuzzy method was created and applied by Lin in 2004 [15].…”

Section: Fuzzy Inference System (Fis) Modelingmentioning

confidence: 99%

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A hybrid approach for the prediction and optimization of cutting forces using grey-based fuzzy logic

Eşme¹

2017

European Mechanical Science

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Analysis of ball-burnishing impact on barrier properties of wood workpieces

Vukelić

Tadić

Džunić

et al. 2017

Int J Adv Manuf Technol

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The Modelling of Surface Roughness after the Ball Burnishing Process with a High-Stiffness Tool by Using Regression Analysis, Artificial Neural Networks, and Support Vector Regression

Kanović

Vukelić

Šimunović

et al. 2022

Metals

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Surface roughness is an important indicator of the quality of the machined surface. One of the methods that can be applied to improve surface roughness is ball burnishing. Ball burnishing is a finishing process in which a ball is rolled over the workpiece surface. Defining adequate input variables of the ball burnishing process to ensure obtaining the required surface roughness is a typical problem in scientific research. This paper presents the results of experiments to investigate ball burnishing of AISI 4130 alloy steel with a high-stiffness tool and a ceramic ball. The experiments were conducted following a randomized full factorial design for different levels of input variables. The input variables included the initial arithmetic mean roughness (the initial surface roughness), the depth of ball penetration, the burnishing feed, and the burnishing ball diameter, while the output variable was the arithmetic mean roughness after ball burnishing (the final surface roughness). The surface roughness modeling was performed based on the experimental results, using regression analysis (RA), artificial neural network (ANN), and support vector regression (SVR). The regression model displayed large prediction errors at low surface roughness values (below 1 μm), but it proved to be reliable for higher roughness values. The ANN and SVR models have excellently predicted roughness across a range of input variables. Mean percentage error (MPE) during the experimental training research was 29.727%, 0.995%, and 1.592%, and MPE in the confirmation experiments was 34.534%, 1.559%, and 2.164%, for RA, ANN, and SVR, respectively. Based on the obtained MPEs, it can be concluded that the application of ANN and SVR was adequate for modeling the ball burnishing process and prediction of the roughness of the treated surface in terms of the possibility of practical application in real industrial conditions.

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Grey-based fuzzy algorithm for the optimization of the ball burnishing process

Cited by 4 publications

References 24 publications

A hybrid approach for the prediction and optimization of cutting forces using grey-based fuzzy logic

A hybrid approach for the prediction and optimization of cutting forces using grey-based fuzzy logic

Analysis of ball-burnishing impact on barrier properties of wood workpieces

The Modelling of Surface Roughness after the Ball Burnishing Process with a High-Stiffness Tool by Using Regression Analysis, Artificial Neural Networks, and Support Vector Regression

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