Multi criteria decision making through TOPSIS and COPRAS on drilling parameters of magnesium AZ91

Varatharajulu, M.; Duraiselvam, Muthukannan; Kumar, M. Bhuvanesh; Jayaprakash, G.; Baskar, N.

doi:10.1016/j.jma.2021.05.006

Cited by 56 publications

(33 citation statements)

References 45 publications

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“…TOPSIS and MAIRCA methods have been reported to be quite suitable for MCDM problems when CBN grinding on CNC machines. Specifically, these two methods have 11/18 options ranked the same (options 1, 6, 7, 10, 11, 12, 13, 14, 16, 17, and 18) when the weight calculation using the MEREC method and 8/18 options rank the same ( Options 4,5,10,11,12,13,16,17,and 18) when using the Entropy method. Meanwhile, the EAMR method has only the best solution (option 12) similar to other methods.…”

Section: Discussionmentioning

confidence: 99%

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Application of topsis, mairca and EAMR methods for multi-criteria decision making in cubic boron nitride grinding

Huy¹,

Hien

Danh

et al. 2022

EEJET

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Determining the best cutting mode is a common problem for machining processes as well as for CBN (Cubic Boron Nitride) grinding on Computer Numerical Control (CNC) machines. It is even more important when it is necessary to choose a solution that meets many goals, which are in conflict. This paper presents the results of a multi-criteria decision-making (MCDM) study on CBN grinding of cylindrical-shaped parts on CNC milling machines. Three MCDM methods, including TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis), and EAMR (Evaluation by an Area-based Method of Ranking) were applied in this work. Besides, MEREC (Method based on the Removal Effects of Criteria) and Entropy methods were used to determine the weights of the criteria. In addition, the Taguchi method with L18 orthogonal array (6^1+3^3) design was used for the design of an experiment, which has four input factors including the depth of dressing cut, the spindle speed, the feed rate, and the wheel diameter. Two criteria, including the surface roughness (SR) and the material removal speed (MRS) were selected as the response outputs. The reason for choosing these two criteria is because SR and MRS are two very important output factors of a mechanical machining process as well as of the CBN grinding process on a CNC milling machine. In particular, these two criteria are always in conflict with each other. Small SR requirements will require small values of the feed speed and the depth of cut. This will lead to the reduction of MRS. From the results of this study, the use of different methods for MCDM was evaluated. In addition, rankings of alternatives have been given according to MCDM methods. Furthermore, the best alternative to guarantee both the minimum SR and the maximum MRS has been found

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Section: Discussionmentioning

confidence: 99%

“…A plus point of this study is that it has solved the MCDM problem using three different weighting methods: Equal, ROC, and Entropy. In [11], the TOPSIS and COPRAS methods have been chosen for the MCDM problem in drilling magnesium AZ91. Also, the burr and the SR have been chosen as the responses of the study.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Application of topsis, mairca and EAMR methods for multi-criteria decision making in cubic boron nitride grinding

Huy¹,

Hien

Danh

et al. 2022

EEJET

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“…Thakur et al [67] developed a new entropy measure and extended COPRAS under the fuzzy context for hospital evaluation. Varatharajulu et al [68] evaluated the drilling parameters of AZ91 (magnetism) by adopting the COPRAS and TOPSIS methods. Nweze et al [69] analyzed the properties of mild steel weld for improving performance by adopting the integrated COPRAS-ARAS method.…”

Section: Copras Methodsmentioning

confidence: 99%

A New Approach to the Viable Ranking of Zero-Carbon Construction Materials with Generalized Fuzzy Information

Krishankumar

Mishra²,

Cavallaro³

et al. 2022

Sustainability

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This paper aims to put forward an integrated decision approach, with generalized fuzzy information for the viable selection of zero- and low-carbon materials for construction. In countries such as India, the construction sector accounts for high pollution levels and high carbon emissions. To restore sustainability and eco-friendliness, the adoption of low-carbon materials for construction is essential and, owing to the multiple attributes associated with the selection, the problem is viewed as a multi-criteria decision-making problem. Earlier studies on material selection have faced certain issues, such as the following: (i) the modeling of uncertainty is an ordeal task; (ii) the flexibility given to experts during preference elicitation is lacking; (iii) the interactions among the criteria are not well captured; and (iv) a consideration of the criteria type is crucial for ranking. To alleviate these issues, the primary objective of this paper was to develop an integrated framework, with decision approaches for material selection in the construction sector that promote sustainability. To this end, generalized fuzzy information (GFI) was adopted as the preference style as it is both flexible and has the ability to model uncertainty from the following three dimensions: membership, non-membership, and hesitancy grades. Furthermore, the CRITIC approach was extended to the GFI context for calculating criteria weights objectively, by effectively capturing criteria interactions. Furthermore, the COPRAS technique was put forward with the GFI rating for ranking zero- and low-carbon construction materials, based on diverse attributes. The usefulness of the framework was demonstrated via a case example from India and the results showed that the design cost, the financial risk, safety, water pollution, and land contamination were the top five criteria, with blended cement, mud bricks, and bamboo as the top three material alternatives for zero- and low-carbon construction. Finally, a sensitivity analysis and a comparison with other methods revealed the theoretical positives of this framework’s robustness and consistency–but it also revealed some limitations of the proposed framework.

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“…In the actual processing production process, parameter forecasting often requires only one set of process parameters, so in order to obtain intuitive and clear process parameter solutions, the established transverse shrinkage and angular deformation are used as targets, and ( min f 1 , min f 2 ) is used as the process parameter solution performance evaluation index, and TOPSIS is used to perform a multi-attribute decision process on the set of process parameter solutions obtained by multi-objective particle swarm search. TOPSIS is an efficient and advanced MCDM (multi-criteria decision-making) methodology that was proposed to obtain the finest choice based on the compromise solution principle, 17 the main process is shown in Figure 7.…”

Section: Topsis-based Decision Making For Processing Parametersmentioning

confidence: 99%

Line heating process parameter forecasting method based on optimization concept

Zhou

Wang

et al. 2022

Advances in Mechanical Engineering

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In ship construction, line heating is a necessary process for forming ship plates with complex curvature. The determination of line heating process parameters has a significant impact on the accuracy of the process and the efficiency of shipbuilding. However, previous studies on this subject only stay at the level of predicting deformation, which has not meet the actual processing production requirements. In this paper, a new idea of forecasting line heating parameters proposed. Firstly, an Radial Basis Function (RBF) proxy model between processing parameters and deformation was constructed based on experimental data, and then the reliability of the proxy model is verified. The optimization objective for proxy model was set as the minimum difference between the final deformation and the target deformation. Meanwhile, an improved Multi-Objective Particle Swarm Optimization (MOPSO) method was employed to search the optimal-set of processing parameters with non-dominated solutions. The performance of the optimized parameter set was ranked by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and the processing parameters combination ranking first was selected as the parameter forecasting result. The results showed that the predicted processing parameters have some reference for the practical engineering.

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Multi criteria decision making through TOPSIS and COPRAS on drilling parameters of magnesium AZ91

Cited by 56 publications

References 45 publications

Application of topsis, mairca and EAMR methods for multi-criteria decision making in cubic boron nitride grinding

Application of topsis, mairca and EAMR methods for multi-criteria decision making in cubic boron nitride grinding

A New Approach to the Viable Ranking of Zero-Carbon Construction Materials with Generalized Fuzzy Information

Line heating process parameter forecasting method based on optimization concept

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