Multi-response Optimization of Wire EDM of Inconel 718 Using a Hybrid Entropy Weighted GRA-TOPSIS Method

Abhilash, P. M.; Chakradhar, D.

doi:10.1007/s41660-021-00202-6

Cited by 27 publications

(6 citation statements)

References 38 publications

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“…The corresponding normalized decision matrix is shown in Table 16.

a_{italicij} = \frac{x_{ij}}{\sqrt{\sum_{i = 1}^{m} x_{ij}^{2}}}

Here,

x_{italicij}

is the element in the decision matrix presenting the performance of i th alternative with respect to the j th attribute and a ij is the corresponding normalized value. Equation (16): [93].…”

Section: Proposed Working Methodologymentioning

confidence: 99%

“…n is the number of attributes considered in the study, while W j symbolizes a common representation for the weights calculated using different weighing methodologies in the present work, that is,

W_{j}^{I}

W_{j}^{italicII}

W_{j}^{italicIII}

, and

W_{j}^{int}

. Equations (21) to (23): [93].…”

Section: Proposed Working Methodologymentioning

confidence: 99%

“…For beneficial criteria

V_{j}^{+} = Max (A_{ij}) V_{j}^{-} = Min (A_{ij})

For non‐beneficial criteria Equation (17): [93]

V_{j}^{+} = Min (A_{ij}) V_{j}^{-} = Max (A_{ij})

{GRC}_{italicij}^{+} = (\frac{{Δ_{worst}|}_{V_{j}^{+}} + ξ \times {Δ_{best}|}_{V_{j}^{+}}}{{Δ_{ij}|}_{V_{j}^{+}} + ξ \times {Δ_{best}|}_{V_{j}^{+}}})

{GRC}_{italicij}^{-} = (\frac{{Δ_{best}|}_{V_{j}^{-}} + ξ \times {Δ_{worst}|}_{V_{j}^{-}}}{{Δ_{ij}|}_{V_{j}^{-}} + ξ \times {Δ_{worst}|}_{V_{j}^{-}}})

Here, Δ ij is the deviations of actual normalized results to corresponding ideal values and ξ is the distinguishing coefficient

(0 \leq ξ \leq 1)

. Generally, its value is taken as 0.5.…”

Section: Proposed Working Methodologymentioning

confidence: 99%

“…Here, x ij is the element in the decision matrix presenting the performance of ith alternative with respect to the jth attribute and a ij is the corresponding normalized value. Equation ( 16): [93]. Equations ( 17) and ( 18) are used to calculate the ideal best (V þ j ) and worst (V À j ) reference solutions with respect to which the Grey relational coefficients (GRC þ and GRC À ) are determined using Equations ( 19) and ( 20), respectively as tabulated in Table 17 for all adopted schemes.…”

Section: Gra-topsis Methodsmentioning

confidence: 99%

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Selection of nanoparticles for battery thermal management system using integrated multiple criteria decision‐making approach

Dwivedi

Kumar

Goel

2022

Intl J of Energy Research

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Summary The selection of suitable nanoparticles is an important task for their applications as heat transfer promoters for the mitigation of thermal issues in energy storage systems. This paper aims to present a systematic framework for the selection of nanoparticles based on multiple attribute decision‐making tools. A hybrid Grey relational analysis‐based technique for order preference by similarity to ideal solution (GRA‐TOPSIS) approach combining the advantages of both GRA and TOPSIS methodologies is proposed and opted for evaluation, comparison, and ranking of different alternatives under the influence of several objective and subjective criteria weights. The criteria importance through inter‐criteria correlation (CRITIC) and entropy weighing methods are employed to determine the objective weights, while the subjective weights are calculated using the analytic hierarchy process (AHP). Also, an attempt is made to present a generalized weight integration formula that combines several objective and subjective weights of attributes. The results demonstrate Al2O3 nanoparticles as the most acceptable alternative. The sensitivity analysis performed proves the robustness and feasibility of the adopted decision framework. Additionally, the rank reversal issue is also addressed to present the qualitative assessment of estimated results. Thus, the evaluation methodology discussed in this paper seems to provide a theoretical reference strategy for nanoparticle selection in heat transfer enhancement applications. Highlights A novel integrated multiple criteria decision making scheme for nanoparticle selection is presented An attempt is made to present a generalized weight integration formula combining several objective and subjective weights of attributes Thermal conductivity and specific surface area are reported as the most significant attributes. Al2O3 is highlighted as the best choice. The sensitivity analysis and rank‐reversal test have also been performed to check the robustness of suggested procedures and Al2O3 has outperformed all other alternatives under consideration.

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“…The corresponding normalized decision matrix is shown in Table 16.

a_{italicij} = \frac{x_{ij}}{\sqrt{\sum_{i = 1}^{m} x_{ij}^{2}}}

Here,

x_{italicij}

is the element in the decision matrix presenting the performance of i th alternative with respect to the j th attribute and a ij is the corresponding normalized value. Equation (16): [93].…”

Section: Proposed Working Methodologymentioning

confidence: 99%

W_{j}^{I}

W_{j}^{italicII}

W_{j}^{italicIII}

, and

W_{j}^{int}

. Equations (21) to (23): [93].…”

Section: Proposed Working Methodologymentioning

confidence: 99%

“…For beneficial criteria

V_{j}^{+} = Max (A_{ij}) V_{j}^{-} = Min (A_{ij})

For non‐beneficial criteria Equation (17): [93]

V_{j}^{+} = Min (A_{ij}) V_{j}^{-} = Max (A_{ij})

{GRC}_{italicij}^{+} = (\frac{{Δ_{worst}|}_{V_{j}^{+}} + ξ \times {Δ_{best}|}_{V_{j}^{+}}}{{Δ_{ij}|}_{V_{j}^{+}} + ξ \times {Δ_{best}|}_{V_{j}^{+}}})

{GRC}_{italicij}^{-} = (\frac{{Δ_{best}|}_{V_{j}^{-}} + ξ \times {Δ_{worst}|}_{V_{j}^{-}}}{{Δ_{ij}|}_{V_{j}^{-}} + ξ \times {Δ_{worst}|}_{V_{j}^{-}}})

Here, Δ ij is the deviations of actual normalized results to corresponding ideal values and ξ is the distinguishing coefficient

(0 \leq ξ \leq 1)

. Generally, its value is taken as 0.5.…”

Section: Proposed Working Methodologymentioning

confidence: 99%

Section: Gra-topsis Methodsmentioning

confidence: 99%

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Selection of nanoparticles for battery thermal management system using integrated multiple criteria decision‐making approach

Dwivedi

Kumar

Goel

2022

Intl J of Energy Research

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show abstract

“…(Rehman and Alkatani 2017) attempted multi criteria approach for ranking manufacturing strategies for automobile firms. (Abhilash and Chakradhar (2022)) studied a unique MCDM method used to optimize the machining parameters for milling Inconel 718 using WEDM. A hybrid GRA-TOPSIS algorithm was used to optimize the wire electric discharge parameters of cutting speed, SR and flatness inaccuracy.…”

Section: Introductionmentioning

confidence: 99%

Expert opinion based optimziation of CNC machining parameters for Al-CNT composites using fuzzy TOPSIS method

Nand,

Singh,

Chimata

2023

Eng. Res. Express

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Machinability of Al-CNT composites at optimum conditions of machining parameters saves a lot of time, improves quality of machined components at minimum possible cost of machining. In conventional methods, controlled experimentation is performed. In order to obtain best machining parameters, optimization methods are applied but they are inadequate in many situations. Hence, an attempt was made in this work to collect subjective data for response parameters such as, quality, cost, and time, from selected experts in this field for various combinations of input process parameters of CNC milling machine for the case of Al-CNT composites with high-speed steel as tool material. 27 sets of experiments were devised for various combinations of machining parameters systematically by taking cutting speed (in rpm), feed, step over ratio, and depth of cut each at 3 levels. Two experts were invited for collection of data for the response parameters and three experts were involved for giving weightages for the response parameters. Fuzzy trapezoidal membership functions were made use of, for representing fuzzy set and fuzzy Technique and TOPSIS method was applied for finding the best solution. Results revealed that best solution was obtained for a spindle speed of 3000 rpm, feed of 150 mm rev−1, step-over ratio of 0.5, and depth of cut of 0.2 mm.

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Process Enhancement of WEDM Operational Parameters of Inconel 718 Superalloy Using RSM Based MOPSO Algorithm

Kumar,

Chakradhar

2024

Springer Proceedings in Materials

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Multi-response Optimization of Wire EDM of Inconel 718 Using a Hybrid Entropy Weighted GRA-TOPSIS Method

Cited by 27 publications

References 38 publications

Selection of nanoparticles for battery thermal management system using integrated multiple criteria decision‐making approach

Selection of nanoparticles for battery thermal management system using integrated multiple criteria decision‐making approach

Expert opinion based optimziation of CNC machining parameters for Al-CNT composites using fuzzy TOPSIS method

Process Enhancement of WEDM Operational Parameters of Inconel 718 Superalloy Using RSM Based MOPSO Algorithm

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