Box-Behnken deneysel tasarım metodunun boyalı sulardan kitosan koagülasyonu ile renk giderimine uygulanması

Akdemir, Ezgi Oktav

doi:10.46239/ejbcs.782611

Cited by 1 publication

(3 citation statements)

References 11 publications

(2 reference statements)

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“…However, this approach is time-consuming, especially for multivariate systems and also when considering multiple responses. The statistical design of experiments, on the other hand, reduces the number of experiments that need to be conducted, considers the interactions between variables, and can be applied to optimize the process parameters of multivariate systems [16].…”

Section: Experimental Design Methodsmentioning

confidence: 99%

“…Box-Behnken design: This is a very important experimental design that allows for calculating the response function and estimating the system performance at any experimental point in the examined range by performing a small number of studies [16].…”

Section: Response Surface Methods (Rsm)mentioning

confidence: 99%

“…Additionally, the implementation of the second-level model is seen as the second-best option in the Box-Behnken design. An advantage of the Box-Behnken design is that successful results can be obtained with fewer experiments [16]. Box-Behnken designs are used effectively in the quadratic estimation of models, construction of sequential models, analyses of confidence, blocks, and experimental designs [15].…”

Section: Response Surface Methods (Rsm)mentioning

confidence: 99%

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Optimization of the Cutting Parameters Affecting the Turning of AISI 52100 Bearing Steel Using the Box-Behnken Experimental Design Method

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2022

Applied Sciences

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In this study, we aimed to optimize the cutting parameters that affect the minimum temperature and power consumption in the turning of AISI 52100 bearing steel. For this, the Box-Behnken experimental design method, which was used for the lowest number of experiments in the experimental systems created using the response surface method (RSM), was used. The cutting parameters affecting the turning of the AISI 52100 bearing steel were determined as the cutting speed, depth of cut, and feed rate based on a literature research. The temperature and power consumption values were obtained via analyses according to the experimental design method determined by the finite element analysis (FEM) method. The results obtained were analyzed in Design Expert 13 software. According to the analysis results, the parameter values were determined for the minimum temperature and power consumption. The temperature and power consumption variables were affected by all three parameters, namely the cutting speed, depth of cut, and feed rate. For the minimum temperature and power consumption, a cutting speed of 162.427 m/min, depth of cut of 1.395 mm, and feed rate of 0.247 mm/rev, as well as the feed rate parameters, affected both the temperature and power consumption the most. In addition, it was determined that the cutting speed parameter had the least effect on both the temperature and power consumption variables. In addition, validation experiments were carried out in a real experimental environment with optimum values for the cutting parameters. The results showed that the output values obtained within the limits of the study with the obtained equation were quite close (3.3% error for temperature, 6.6% error for power consumption) to the real experimental outputs.

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Section: Experimental Design Methodsmentioning

confidence: 99%

Section: Response Surface Methods (Rsm)mentioning

confidence: 99%