# Expert System Based on Integrated Fuzzy AHP for Automatic Cutting Tool Selection
## 4 Expert System and Discussion
The database for the cutting tool selection system based on [PERSON]'s classification is created and managed using Microsoft SQL Server Express 2012. The relationship among tables in the cutting tool database is shown in Figure 11. This cutting tool database is suitable for many cutting tool libraries, including many cutting tool manufacturers and tool types. When the cutting tool library is selected, all searching processes are executed in the specific tool library. The cutting tool library used for the case study was Sandvik, which contains over 15 milling tool types, 800 cutting tools, and 350 inserts in the database. The final cutting tool selection result is shown in Figure 12. The following were selected for shoulder milling in the case study in less than 1 s: cutting tool R390-016A16-11L, insert R390-11T-304E-PL with the edge geometry L type (Light), and grade GC1025. This searching result is suitable for the specific conditions of the case study and is compatible with expert recommendations. This concept was also applied in our CAPP system and successfully tested by the Mekamic joint stock company in Vietnam, which has diverse cutting tool manufacturers, including YG-1, CMTec, and Sumitomo. Thus, the expert system can successfully convert expert knowledge into a computational method. The main advantage of the expert system is the quick searching ability in a huge and diverse cutting tool library, which is very difficult to achieve with a process planner. Figure 6: Initial selection criteria for the expert system. Figure 7: Comparison matrix and priority levels according to specific criteria. Compared with the neutral-network-based method for cutting tool type selection mentioned in the literature [6; 7], the integrated fuzzy AHP method has great merit in terms of its simplicity and applicability. The proposed method can be used to obtain a list of candidates in priority order based on pair-wise comparison matrices, instead of just providing a single option from the huge amount of neutral network training data. Moreover, all alternatives in the proposed hierarchy structure and database are constructed in association with the cutting tool classification manufacturers in the market. Thus, it is suitable for application in practical manufacturing. In the introduced procedure, the specific cutting tool types (e.g., the solid end mill, long edge end mill, and indexable end mill), as substitutes for general types (e.g., end mill and face mill), are determined in the first step, which can restrict the searching domain in the database, as well as the processing time. In addition, the proposed expert system can automatically generate rules for cutting tool type selection depending on the given conditions, in comparison with other manually generated rules used in previous research [1; 2; 4]. It can significantly reduce errors while manually constructing a variety of complex rules. Finally, some new attributes, including stability and flexibility, are also considered. Considering the structure of the fuzzy AHP and the number of calculations, the proposed fuzzy AHP exhibits outstanding benefits. If using the conventional fuzzy AHP, where each specific case has one hierarchy structure [15; 16; 17], 162 hierarchy structures need to be established for all cases of initial conditions in shoulder milling. The number of calculations increases because of the many duplicate calculations from duplicate pair-wise comparison matrixes, whereas with the integrated structure, all cases are presented in a single model and each matrix appears and is calculated once, so the number of calculations is significantly reduced. This proposed approach can expand the applicability of the fuzzy AHP to solve many other complex problems that have multiple given conditions.