Multi-criteria decision-making is important and it affects the efficiency of a mechanical processing process as well as an operation in general. It is understood as determining the best alternative among many alternatives. In this study, the results of a multi-criteria decision-making study are presented. In which, sixteen experiments on turning process were carried out. The input parameters of the experiments are the cutting speed, the feed speed, and the depth of cut. After conducting the experiments, the surface roughness and the material removal rate (MRR) were determined. To determine which experiment guarantees the minimum surface roughness and maximum MRR simultaneously, four multi-criteria decision-making methods including the MAIRCA, the EAMR, the MARCOS, and the TOPSIS were used. Two methods the Entropy and the MEREC were used to determine the weights for the criteria. The combination of four multi-criteria making decision methods with two determination methods of the weights has created eight ranking solutions for the experiments, which is the novelty of this study. An amazing result was obtained that all eight solutions all determined the same best experiment. From the obtained results, a recommendation was proposed that the multi-criteria making decision methods and the weighting methods using in this study can also be used for multi-criteria making decision in other cases, other processes.

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

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 F_x, F_y, and F_z, 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 F_x, F_y, and F_z and the maximum MRR. The Entropy method was applied to determine the weights for parameters F_x, F_x, F_x, 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.

The efficiency of cutting machining methods is generally evaluated through many parameters such as surface roughness, material removal rate, cutting force, etc. A machining process is considered highly efficient when it meets the requirements for these parameters, such as ensuring small surface roughness, high material removal rate, or small cutting force, etc. However, for each specific machining condition, sometimes the objective functions give contradictory requirements. In this case, it is necessary to implement multi-criteria decision making, i.e., make a decision to ensure harmonization of all required objectives. In this paper, a multi-criteria decision-making study is presented for three common machining methods: milling, grinding, and turning. In each machining method, the weights of the criteria were determined by four different methods, including Equal weight, ROC weight, RS weight and Entropy weight. The MARCOS method was applied for multi-criteria decision making. The best alternative was found to be the same as the weights were determined using the Equal weight and Entropy weight methods. In the remaining two weighting methods, the best alternative found depends on the order where the criteria were arranged, not these methods themselves. Direction for further research has been suggested in this study as well.

Low surface roughness and high Material Removal Rate (MRR) are expected in most machining methods in general and milling method in particular. However, they sometimes do not occur, for example, the MRR is often small as the surface roughness is low. In this case, the decisions made should ensure that desires are simultaneously satisfied. This situation leads to a problem known as multi-criteria decision making (MCDM). In this study, five methods including EDAS, MARCOS, PIV, MOORA and TOPSIS are used together for the decision-making in the milling process. The purpose of the research is to determine the value of cutting parameters for both the low surface roughness and large MRR. The comparison of these methods for finding the best is carefully discussed as well.

In this study, TOPSIS and PIV methods were applied for multi-criteria decision making in hard turning process. Experiments have been conducted in accordance with an experimental matrix designed by the Taguchi method with a total of twenty-seven experiments. At each experiment, the values of...