# Expert System Based on Integrated Fuzzy AHP for Automatic Cutting Tool Selection  
[PERSON]  
1 Hanoi University of Science and Technology, Hanoi 10000, Vietnam; son.truonghoanh@hust. [PERSON] developed a method for automatically choosing a cutting tool for the milling process [2]. A collection of all suitable cutters for each machining feature was constructed. The searching process was performed for the whole Sandvik library to find all available cutting tools for each machining feature, without carrying out a specific evaluation. The cutter's classification is suitable only for the Sandvik library but does not cover other cutting tool manufacturers. [PERSON] constructed a rule-based system that allows the proper cutting tool to be selected according to selected criteria [3]. The huge number of rules were manually fabricated, and the quality of rule structures highly depends on the expertise of the developer. [PERSON] et al. proposed an algorithm to optimize machining parameters, such as cutting data for cutting tool selections, while other cutting tools characteristics were selected by a rule-based method according to Sandvik's technique guide [4]. The main limitation of these studies is that the selection processes used were mainly based on the geometric characteristics of each feature along with complex rules, while ignoring many other important evaluation criteria, such as stability, feature quality, and material removal capacity, as well as tool cost. [PERSON] fabricated a flexible tool selection decision support system for milling operations when considering some important criteria in cutting tool selection [5]. The knowledge-based method considers many criteria, such as the material removal rate, tool life, cost, and time, but it ignores workpiece stability and flexibility when selecting suitable cutting tools. The procedure can cover all steps in cutting tool selection, from the cutter, holder, tool, and insert to the cutting data. However, the cutting tool type selection is not mentioned clearly. Figuring out the optimal cutting tool type in the first step plays a significant role in selecting the cutting tool in a specific cutting tool library. It can significantly reduce the time required for the searching process in the huge cutting tool database. [PERSON] et al. successfully developed a neural network model for cutting tool type selection [6; 7]. In this paper, only shape and quality characteristics are mentioned in the selection procedure, while the effects of other important factors in the selection process are ignored. The determination of the cutting tool type in this paper is only a general selection process, without any evaluation; however, selection should be a multiciteria decision-making problem that includes the evaluation of many attributes, including uncertain criteria. Moreover, another limitation of the method is that the cutting tool type selection result only produces one candidate instead of a list of priorities. In practical manufacturing, the search process is executed in a specific cutting tool library to find the best available option. The lack of cutting tool type priority makes the search process difficult, because if one candidate is absent, there is no substitution for specific cases. The aims of this work are to develop a method for evaluating multiciteria in cutting tool type selection and to generate a list of possible options in priority order. Finding the optimal cutting tool type in the first step makes the whole selection process more efficient and less time consuming. The selection procedure should be suitable for a variety of cutting tool manufacturers. For the past three decades, the analytical hierarchy process (AHP) has been known as a multiciteria decision-making approach for selecting an optimal alternative. Because some of the criteria can be conflicting and uncertain, it is not easy for the decision maker to choose the best, most suitable option.