# Using Fuzzy Control for Feed Rate Scheduling of Computer Numerical Control Machine Tools
## 1 Introduction
[11] proposed a configurable trajectory planning strategy which can be applied to any planar path with a piecewise sufficiently smooth parametric representation. The processing path-based dynamic feed rate scheduling does not need to install an extra sensor. The establishment of a cutting force model does not require cutting experiments; therefore, employing the processing path-based dynamic feed rate scheduling is better than the other method in terms of practicality and versatility. Thus, the present study primary focuses on processing path-based dynamic feed rate scheduling method. An extended cutting experiment is needed to establish and verify the cutting force model. Therefore, this study uses fuzzy theory to control the dynamic cutting feed rate of a machine tool under the same processing software, cutting tool, and processing material to replace manual feed rate adjustment and improve the machining accuracy while shortening the processing time. Fuzzy control was first proposed by [PERSON] in 1964 [12] to develop a versatile control system and to avoid some of the difficulties associated with force-based controllers, it has been widely used in various fields, such as signal processing, servo control, and image processing. [PERSON] et al. [13] developed an adaptive optimizing fuzzy controller for controlling feed rate. The system designed based on the concept of the cutting state and collected expert rules. The experiment has shown that the system performed adequate results. [PERSON] et al. [14] used fuzzy control to realize high cutting rate and high surface quality requirement. The control system is based on controlling the cutting force being milled by digital adaptation of cutting parameters. The experimental results show that the milling system with the designed controller has high robustness and the machining efficiency of the milling system with the adaptive controller is much higher than for the conventional CNC milling system. [PERSON] et al. [15] designed an intelligent fuzzy proportional-integral-derivative (PID) controller to increase the processing efficiency of the CNC machine tool. The main feature of the proposed fuzzy PID controller was to change parameters in different degrees according to time-varying working conditions, thereby improving the adaptability and reliability of the controller. The advantages of fuzzy controller are that it is suitable for nonlinear systems and that it is described by control laws made by experts in their field. [PERSON] and [PERSON] [16] developed a fuzzy control system based on the assumed feed rate, radial and axial depths of cut for CNC profile milling feed rate determination to predict the cutting force. [PERSON] et al. [17] established a fuzzy logic-based torque control system for optimizing the material removal rate in high-speed milling processes. The aforementioned studies have all used fuzzy controllers to achieve good results. The reason is that fuzzy controller has a human-like reasoning mechanism that can solve complex problems with unclear inputs and make decisions accordingly. Therefore, this study uses fuzzy control in processing path-based dynamic feed rate scheduling method. In this study, a fuzzy control for feed rate scheduling based on curvature and curvature variation is proposed. The proposed system is implemented in actual cutting, and to verify the data an optical three-dimensional scanner is used to measure the cutting trajectory of the workpiece. The major contributions of this study are presented as follows: (1) A fuzzy control method is used to effectively schedule a reasonable feed rate; (2) an average filter is developed to reduce jerks to avoid excessive impact on the machine, and (3) the proposed system increases the cutting accuracy under the same cutting time; moreover, it decreases the cutting time under the same cutting accuracy. The remainder of this paper is organized as follows. Section 2 describes materials and methods which includes fuzzy control for feed rate scheduling. Section 3 presents the experimental results.