Determining the Best Dressing Parameters for External Cylindrical Grinding Using MABAC Method

Le, H. K.; Hoang, Xuan-Tu; Trieu, Quy-Huy; Pham, Duc-Lam; Le, Xuan-Hung

doi:10.3390/app12168287

Cited by 18 publications

(13 citation statements)

References 37 publications

(43 reference statements)

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“…The sensitivity analysis toward the best alternative when the weight of the criteria changes is a necessary thing to do. The Spearman's rank correlation coefficients can be used to complete this work [37,38]; 4. Currently, the DN formulas can not be applied to qualitative criteria (colors, hobbies etc.).…”

Section: Discussionmentioning

confidence: 99%

Selection of Suitable Data Normalization Method to Combine with the CRADIS Method for Making Multi-Criteria Decision

Hà¹

2023

Applied Engineering Letters

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Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) is a new MCDM method (discovered in 2022). It is built on a combination of three well-known methods, including Additive Ratio Assessment (ARAS), Measurement Alternatives and Ranking according to Compromise Solution (MARCOS), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This method has the advantage of being resistant to the rank inversion phenomenon. However, if only the available data normalization (DN) method in this method is used, this method will only be usable in some cases. This study investigated the suitability of twelve data normalization methods combined with the CRADIS method. The solutions in four cases of four different fields were ranked using these twelve combination methods. Using these methods, the ranked results were compared with those of other MCDM methods. Four DN methods were appropriate in combination with the CRADIS method. The application scope of CRADIS method can be extended when using this DN method.

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Section: Discussionmentioning

confidence: 99%

Selection of Suitable Data Normalization Method to Combine with the CRADIS Method for Making Multi-Criteria Decision

Hà¹

2023

Applied Engineering Letters

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“…Let's observe that when comparing CURLI with five methods: COCOSO, MABAC, MAIR-CA, EAMR, and TOPSIS, SÎ[0.9÷1]. This strongly affirms that the rankings of experiments when ranked by CURLI method differ very little compared to rankings by the other five methods [26,27]. Conversely, when comparing PSI with the same five methods (COCOSO, MABAC, MAIRCA, EAMR, and TOPSIS), S has a very small value, S Î[-0.6088÷-0.3706].…”

Section: Mathematicsmentioning

confidence: 61%

Comparision of both methods psi and curli: applied in solving multi-objective optimization problem of turning process

Trung,

Phuong Giang,

Son

2024

Eureka: PE

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Solving a multi-objective optimization problem involves finding the best solution to simultaneously satisfy multiple predefined objectives. Currently, various mathematical methods are available for solving optimization problems in general, and multi-objective optimization in particular. The comparison of mathematical methods when addressing the same problem has been explored in numerous studies. In this study, let’s conduct a comparison of two multi-objective optimization methods: the PSI method and the CURLI method. These two methods were applied collectively to tackle a multi-objective optimization problem related to a turning process. Experimental data were borrowed from a previous study, and a total of sixteen experiments were conducted. Roughness average (Ra), Roundness Error (RE), Tool Wear (VB), and Material Removal Rate (MRR) were the four output parameters measured in each experiment. The objective of solving the multi-objective optimization problem was to identify an experiment among the sixteen existing experiments that simultaneously minimized the three parameters of Ra, RE, and VB while maximizing MRR. The optimal results determined using the PSI and CURLI methods were also compared with the optimal results obtained through other methods (COCOSO, MABAC, MAIRCA, EAMR and TOPSIS) in published documents. The comparison results indicate that the optimal experiment found using the CURLI method consistently matches that of other methods. In contrast, the optimal results obtained through the PSI method differ significantly from those obtained through other methods. The Spearman correlation ranking coefficient between CURLI and the five methods COCOSO, MABAC, MAIRCA, EAMR, and TOPSIS is very high, ranging from 0.9 to 1. In contrast, this coefficient is very small when comparing PSI with the aforementioned five methods, falling within the range of –0.6088 to –0.3706 in this case. Ultimately, this study concludes that the CURLI method is suiTable for solving the multi-objective optimization problem in the turning process, whereas the PSI method is deemed unsuitable

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“…To determine which MCDM method is more suitable than the other two, it is necessary to test the stability of ranking options. The Spearman rank correlation coefficient (S) has been used for this task [27,28]. This coefficient is calculated using formula (46), where D i represents the difference in the ranking of option i between scenarios, and m is the number of options to be ranked:…”

Section: Fig 2 Ranking Of Lubricant Typesmentioning

confidence: 99%

Integration of objective weighting methods for criteria and MCDM methods: application in material selection

Dua,

Duc,

Bao

et al. 2024

Eureka: PE

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Determining weights for criteria is an extremely crucial step in the process of selecting an option based on multiple criteria, also known as Multi-Criteria Decision Making (MCDM). This article presents the combination of five objective weighting methods for criteria with three MCDM methods in the context of material selection. The five objective weighting methods considered are Entropy, MEREC (Method based on the Removal Effects of Criteria), LOPCOW (Logarithmic Percentage Change-driven Objective Weighting), CRITIC (Criteria Importance Through Intercriteria Correlation), and MEAN. The three MCDM methods employed are MARA (Magnitude of the Area for the Ranking of Alternatives), RAM (Root Assessment Method), and PIV (Proximity Indexed Value). Material selection investigations were conducted in three different cases, including lubricant selection for two-stroke engines, material selection for manufacturing screw shafts, and material selection for manufacturing gears. The Spearman's rank correlation coefficient was calculated to assess the stability of ranking the alternatives using different MCDM methods. The combinations of objective weighting methods and MCDM methods were evaluated based on factors such as consistency in identifying the best material type, range, average value, and median of each set of Spearman's rank correlation coefficients. Two significant findings were identified. First, the weights of criteria calculated using LOPCOW method appear to be inversely related to those calculated using the Entropy method. Second, among the three MCDM methods used, MARA was identified as the most suiTable for lubricant selection for two-stroke engines, RAM was found to be the most suiTable for material selection for screw shafts and gears. The best material type in each case was also determined

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Determining the Best Dressing Parameters for External Cylindrical Grinding Using MABAC Method

Cited by 18 publications

References 37 publications

Selection of Suitable Data Normalization Method to Combine with the CRADIS Method for Making Multi-Criteria Decision

Selection of Suitable Data Normalization Method to Combine with the CRADIS Method for Making Multi-Criteria Decision

Comparision of both methods psi and curli: applied in solving multi-objective optimization problem of turning process

Integration of objective weighting methods for criteria and MCDM methods: application in material selection

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