A combination method for multi-criteria decision making problem in turning process

Abstract

This paper presents a multi-criteria decision making (MCDM) for a turning process. An experimental process was performed according to the sequence of a matrix using the Taguchi method with nine experiments. The parameters including workpiece speed, feed rate, depth of cut, and nose radius were selected as the input variables. At each experiment, three cutting force components that were measured in the three directions X, Y, and Z, were Fx, Fy, and Fz, respectively. The value of Material Removal Rate (MRR) was also calculated at each experiment. The main purpose of this study is determination of an experiment in total performed experiments simultaneously ensuring the minimum Fx, Fy, and Fz and the maximum MRR. The Entropy method was applied to determine the weights for parameters Fx, Fx, Fx, and MRR. Eight MCDM methods were applied for multi-criteria decision making, this has not been performed in any studies. The implementation steps of each method were also presented in this paper. Seven ones of these eight methods determined the best experiment in total nine performed experiments. A new multi-criteria decision-making method as well as orientation for the further works were also proposed in this study.

Comparison R and Curli Methods for Multi-Criteria Decision Making

“…However, both the R and CURLI methods show that A9 is the best option. Especially, A9 was also confirmed to be the best option when using SAW, WASPAS, TOPSIS, VIKOR, MOORA, COPRAS, and PIV methods [30]. Furthermore, the worst alternative shown when using these two methods is option A2, which is similar to when using the SAW, WAPSAS, TOPSIS, VIKOR, and PIV methods.…”

“…Experimental data for a turning process were used in this example (Table 17) [30]. Nine experiments of a turning process were performed.…”

“…The results of ranking options by the methods SAW, WASPAS, TOPSIS, VIKOR, MOORA, COPRAS, and PIV of the study No. [30] have also been summarized in this table.…”

“…R CURLI SAW [30] WASPAS [30] TOPSIS [30] VIKOR [30] MOORA [30] COPRAS [30] PIV [ From Table 18, it can be seen that the ranking results of the alternatives are not the same when implemented by different methods. This has also been confirmed in many studies [21][22][23][24][25][26].…”

Expanding Data Normalization Method to CODAS Method for Multi-Criteria Decision Making

“…If each solution is described by at least two criteria, then this is called multi-criteria decision making. Multi-criteria decision making is essentially the determination of the ranking of the solutions, from which the best solution will be determined, and away from the worst solutions [1][2][3]. If the choice of a certain solution is made according to the subjective opinion of the decision maker, it will easily lead to mistakes.…”