# Expert System Based on Integrated Fuzzy AHP for Automatic Cutting Tool Selection
However, it is easier and more accurate for experts to evaluate the priority levels between two alternatives than to simultaneously evaluate all alternatives. Moreover, assessments are performed with respect to only one specific criterion, which is easier than evaluating multiple criteria at the same time. The AHP method can combine the separate evaluation of each criterion to obtain the final priority order in consideration of multiple criteria [8]. Based on the decision maker's pairwise comparisons of the criteria, the AHP method generates a weight for each criterion. The pairwise comparisons of the alternatives or options according to specific criteria are also determined by the experts. The total weight of each option is determined. The higher the weight is, the better the performance of the option. The option with the highest weight is the optimal one. The AHP is a very powerful tool because the final ranking can be easily obtained from the individual evaluations. Since its first introduction by [PERSON] in the 1970s, AHP has been widely used in many applications to help decision makers solve many complex problems with multiple conflicting and subjective criteria [8]. This method has been successfully applied for the remedial modeling of steel bridges through consideration of multiple criteria [9]. The AHP method has been used widely to solve many complex problems in mechanical engineering, such as cutting tool material selection and nontraditional machining process selection [10; 11]. The main problem with the AHP method is that the decision makers have difficultly choosing exact numerical values for pairwise comparison judgments. Thus, the fuzzy AHP method was developed to use the concepts of fuzzy set theory to change fixed value judgments into interval judgments. The fuzzy numbers used in judgments allow decision makers to perform better evaluations. In recent years, the fuzzy AHP method has found a huge number of applications, primarily in the manufacturing, industrial, and government sectors [12]. [PERSON] et al. used fuzzy AHP as an integrated tool to select and evaluate new manufacturing technologies [13]. [PERSON] al. successfully applied fuzzy AHP to assess environmental conflicts [14]. The fuzzy AHP method was proposed as a way to select the best machine tool from the growing number of alternatives on the market [15; 16]. With the same purpose of selecting the optimal machine center, an expert system based on the fuzzy AHP method was effectively developed by [PERSON] et.al. for the evaluation of productivity and flexibility [17]. To select the optimal cutting tool type, the fuzzy AHP method is first applied in our system to solve this problem. However, conventional fuzzy AHP is only suitable for a specific condition. It is not appropriate for multiple case selection processes that include a significant amount of input information. The important issue addressed in this work is the development of an integrated fuzzy AHP that combines multiple cases of initial conditions into one model. The fuzzy AHP model integrates the selection and evaluation criteria systems to solve many complex and diverse problems, such as cutting tool selection, reducing the number of models and calculations required. The details of the proposed methodology are described in Section 2. A case study for cutting tool type selection is presented in Section 3. An expert system based on fuzzy AHP for automatic cutting tool selection and related discussions are presented in Section 4.