## 1 Introduction
They proposed a support model for deciding the potential application of seven processes: Laser, Plasma, Waterjet, Abrasive Waterjet, Electrochemical Machining, Electrical Discharge Machining, and Wire Discharge Machining when cutting carbon structural steel with a plate width of 10 mm. [PERSON] and [PERSON] solved the problem of selecting nonconventional machining processes by using the importance of criteria through the correlation between criteria and the combined distance-based evaluation of multi-criteria decision methods. By combining the two methods, they successfully created a decision support model for eight different processes [28]. Productivity, shape property, workpiece type, accuracy and surface roughness, power consumption, and cost were used as criteria for evaluating the processes. There are some techniques developed in the literature to solve MCDM problems. Mainly, AHP, VIKOR, TOPSIS, etc., are used, which are classical methods for solving multi-criteria problems. To improve the classical methods, they are often combined with artificial intelligence tools [29; 30]. The combination of the above approaches is called hybrid MCDM technique. However, when many articles talk about hybrid techniques, little emphasis is placed on explaining the use of artificial intelligence methods. Fuzzy set theory has been proposed by many researchers to solve imprecise and indeterminate problems. However, it is not yet clear to what extent fuzzy logic and grey relational analysis can be applied. The use of fuzzy sets is not only due to the indeterminacy of judgments, but also due to the hesitation of experts on which decisions to make. Although it is still controversial to what extent fuzzy sets with grey relational analysis solve the problem of uncertainty, it is a simple and useful method that is widely used. Therefore, this article provides guidelines for selecting appropriate techniques for building fuzzy models in terms of representing fuzzification, aggregation, and defuzzification. In any grey fuzzy approach (GFA), it is crucial to establish an appropriate rule base that covers a wide range of possible solutions depending on the input criteria, as shown in this article. Independently applied grey analysis characterizes the observed system as partially known or partially unknown. This kind of system with very little data can be used until a solution is created. Due to the small amount of data, there is a chance that the system based on grey relational analysis cannot fully present the final solution. In order to avoid these shortcomings, grey analysis is combined with the fuzzy logic method. The advantage of this method is designing a simple model that essentially replaces the mathematically based grey relational analysis. However, the model based on fuzzy logic largely depends on the acquired knowledge base. The integrated grey analysis and fuzzy logic technique has been successfully applied to solving many complicated problems such as the selection cutting process [31; 32]. In applying this approach, the final solution is achieved in several steps. The first step is normalization, which serves as data preparation for grey relational analysis. The next step is the evaluation of the grey relational coefficient, depending on the established criteria: 'low is better' or 'high is better' [33]. In order to avoid some degree of uncertainty, fuzzy logic is used in the next step. In this process, the previously determined GRC values are fuzzified using membership functions. There are several types of membership functions, such as triangular, Gaussian, G-bell, etc. The choice of this function is intuitive and depends on the creator of the model. The number of observed variables also depends on the number of criteria, i.e., it represents the GRC values for each of them with an output of GFR. Then, a set of IF-THEN rules is formulated to represent the relationships between the GRC input values and the GFR output.