CNC (NC) processing technology plays a very important role in modern manufacturing technology. Since the advent of the 1950s, with the rapid development of microelectronics, computer and other technologies, great progress has been made today, for the manufacturing industry and the entire national economy. The development plays an important role. Of course, it is undeniable that there are still many problems in the numerical control technology that need to be further improved. The load adaptive control is that when the load changes in the NC machining, the system can adjust the feed speed of the tool in time to adapt to the load change and maintain the load at a relatively constant level. On the one hand, this technology can improve the efficiency of CNC machining, while also protecting the tools and machine tools and ensuring the quality of the machining. Based on this understanding, this paper proposes a calculation method of load adaptive control in NC machining. The method first obtains the load value in NC machining by indirect measurement method and then uses fuzzy logic method to calculate the load value according to this load value. The corresponding tool feedrate under this load.

First, the measurement of the load As mentioned above, the control of the load is very important in the numerical control, so there are many researches on this, they mainly study from the perspective of how to establish the mathematical model of the load in the NC machining, and A mathematical model of load in various NC machining is proposed.) 51. This method of controlling the load by establishing a load mathematical model including various influencing factors can indeed play a certain role. However, looking at these methods, they all have the following problems: the factors affecting the load in CNC machining, such as the feed rate of the tool, the rotational speed of the spindle, the shape of the machining tool, the shape of the machined part, and the material characteristics, etc. The effects are nonlinear and related, so it is impossible to include all of these factors for accurate modeling. Generally, only a few factors that are considered to have a large influence are used to establish the corresponding model, so that the accuracy of the load mathematical model will be limited. limits.

Mathematical description of the tool to establish its relationship with the load These models are generally only for specific tools (such as ball-end tools) so that they can only be applied to these specific types of tools, so the mathematical model of these loads is very versatile. In particular, it cannot be adapted to a machining center with an automatic tool changer.

Some of the mathematical models of the load mathematical model load need to be solved by an iterative method, which is very time consuming and sometimes difficult to guarantee the convergence of the iteration.

Based on the above analysis, this paper obtains the load value from another angle, that is, it does not directly establish the mathematical model of the load to solve the load, but indirectly characterizes the load by measuring the torque received by the spindle. The principle of the indirect measurement method is that the factor affecting the load size in the numerical control machining must be reflected to the magnitude of the spindle torque, so the magnitude of the spindle torque objectively characterizes the magnitude of the load. This method of indirect measurement avoids the drawback of directly establishing the mathematical model of the load, and the magnitude of the spindle torque can be obtained by measuring the output power of the spindle motor, so the measurement of the method is extremely simple. After measuring the size of the load, the next step is how to adaptively control this. How does BP determine the feed rate of the tool from the measured load value? In this paper, fuzzy logic algorithm is used for load adaptive control.

Second, the adaptive control of the load As mentioned above, this paper uses fuzzy logic algorithm for adaptive control. Since 1965, LAZadeh proposed a system of fuzzy set trapezoidal membership functions and an uncertain system. The adaptive control of the load in the NC is a system that cannot be accurately modeled, so the fuzzy logic control can achieve better results.

â–¡ Load adaptive control principle The principle of adaptive control of CNC machining load is shown in the figure.

The fuzzy controller shown by the dashed line in the figure is the logical algorithm part of the load adaptive control in this paper.

The processing of load adaptive control related technology can be seen from the above schematic diagram, the load adaptive control uses the relevant knowledge of fuzzy logic, and the related technologies are analyzed and processed below.

(1) Establishment of input and output fuzzy sets Establishing input and output fuzzy sets The following steps: Discretization of input and output domains In this NC machining load adaptive control method, the input is the load obtained by indirect measurement. The value output is the feedrate value of the tool. In the process of discretization of input and output domains, the selection of discrete points should be appropriate. The more points, the more delicate the definition of fuzzy subsets, the more delicate the fuzzy reasoning and the fuzzy processing, which will make the fuzzy control The more precise the control and control effects of the device, the higher the M degree, but at the same time it also leads to an increase in the amount of calculation, and even can not be used for real-time control. According to the actual situation, the system takes the input and output universes into nine different fuzzy divisions from the input and output domain. The number of fuzzy subsets is also appropriate. The more subsets, the more delicate the control action. However, too many fuzzy subsets will make the amount of computing unacceptably large. According to the accuracy and real-time requirements, we divide the input and output universe into five fuzzy subsets. For the input of the following subset: large (A1), larger (A3), medium (A3), smaller, smaller (A5). For the output, the following subsets are slow, slow, medium, fast, and fast.

The membership degree of the fuzzy subset of membership functions of fuzzy subsets is the membership degree of each fuzzy subset at each discrete point. Currently, there are commonly used membership functions in the form of triangles, trapezoids and trigonometric functions. We establish the membership degree of the fuzzy subset in the corresponding discrete points according to the method of the trapezoidal membership function (W. So we establish the following input fuzzy subsets: (2) the establishment of the fuzzy inference rules and the division of the output fuzzy subsets According to the actual situation of NC machining, we establish the following five fuzzy inference rules (R), and set the load size to the tool feed rate should be ": rule rule 3 (3) fuzzy inference algorithm to achieve fuzzy set and reasoning of input and output After the rule is established, we can perform the inference operation according to the relevant rules of the fuzzy set. For example, R1 can represent R1=01x as shown in Table 1: In the fuzzy fuzzy control of the input data, the detected input data is generally an exact number, and The data processed in the fuzzy controller is the amount of fuzzy, so the input data must be blurred, including range conversion and quantization, and the fuzzy method is selected in two steps: range conversion and quantization. The load value obtained by indirect measurement of the input signal must be converted first. For the discrete points in the domain mentioned above, we use the scale factor! According to the specific high-speed CNC machining situation, we obtain the maximum allowable load as /. Then =8//, for any measured load value /, the discrete point in the corresponding domain is =INT(/+!) =INT(/8//.) where INT represents the rounding function.

The selection of the fuzzification method is to directly blur a certain precise point into a fuzzy single point, which is a kind of fuzzy subset, which has a membership degree of 1, and the domain All other points whose degree of membership is the defuzzified fuzzy inference of the output data is the amount of blur, and the actuator can only receive the exact amount, so the defuzzification must be performed, that is, the amount of blur is converted into an accurate quantity. Defuzzification is the inverse process of fuzzification, which includes two steps: the choice of the defuzzification method and the transformation of the range.

1 The choice of the defuzzification method The system uses the weighted average method to solve the fuzzy, that is, the membership degree of the point-to-output fuzzy set on the fuzzy output domain is the weighted average of the weight coefficient to solve the fuzzy result. The specific method is to set a point on the output domain, and its membership in the output fuzzy subset is /! 6 (7), the result of the deblurring is 8 = (. + called (7)) A. called (7)), for example, when the above load value is 3, the corresponding tool feed speed value is: 2 range The conversion of the data obtained by the above 1 defuzzification is still the point on the output domain, not the physical quantity used to control the actuator action, so we must perform the range conversion again. We use the scaling factor of 2 to convert according to the specific high-speed CNC machining situation, we get the maximum feed speed of the tool when machining is 2=./8. Thus, for any data obtained by the above 1 defuzzification, the corresponding Physical quantity velocity for actual control III. Conclusion This paper applies fuzzy logic theory to the adaptive control of load in NC machining. The method first uses an indirect method to measure the load size, avoiding the complexity and inaccuracy of the traditional load-building mathematical model, and then adaptively controlling the measured load using fuzzy logic. In this way, the feed rate of the tool can be changed correspondingly with the load, so that the load remains relatively stable throughout the machining process. Therefore, the load adaptive control method can improve the quality and efficiency of the processing on the one hand and protect the tool and the machine tool to improve their service life on the other hand.

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