Computer-aided process planning (CAPP) is an important link between computer-aided design (CAD) and computer-aided manufacturing (CAM). The amount of design information in CAD is insufficient for process planning and manufacturing, while CAM only helps engineers generate a toolpath according to the manual selection of cutting tools and cutting data for each machining feature. Selection of the optimal cutting tool that can meet multiple criteria, such as productivity, quality, and cost, is a significant step in process planning. There are many kinds of cutting tools, and their applications vary depending on their features, such as their geometric characteristics, stability, and the quality of the machining feature. The decision makers need to consider many factors and objectives, including conflicting and uncertain criteria from a large amount of cutting tool data at one time. It is very difficult to choose the appropriate cutting tools for a machining feature. Moreover, manual selection is a time-consuming and difficult process requiring a lot of process planning experience. Thus, a computer-aided cutting tool selection system based on an available cutting tool library is essential in modern manufacturing, especially for CAPP. Oral et al. developed an automatic tool selection system for the turning process based on rules [
1]. With this method, according to feature information, selection is made from an appropriate cutting tool library, but the evaluation of multicriteria is ignored. Elmesbahi 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. Vukelica 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. Zubair 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. Carpenter 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. Amaitik 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 multicriteria 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 multicriteria 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 multicriteria 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. 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 Saaty 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]. Jaganathan et al. used fuzzy AHP as an integrated tool to select and evaluate new manufacturing technologies [
13]. Dao 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 Tansel İç 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.