TWI724467B - The diagnosis method of machine ageing - Google Patents

Abstract

The present invention provides the diagnosis method of machine ageing, which excites responses of a drive systems for machine tools by the output of sweep signal to obtain the frequency responses of a velocity loop and a position loop. Through transfer function of the velocity loop and transfer function of the position loop, it calculates and identifies values of variation such as Rotary Inertia—J _M, damping coefficient of the rotary motion—B _M, table mass—M _t,damping coefficient of the lead-screw—C _tand stiffness coefficient of the lead-screw—K. It can procced to record the value of variation through time point. When deviation of the value of variation is greater than parameters, it can diagnose that the drive system of machine tool has aged. Thereby, the present invention can detect immediately vibration of machine tool, variation of bandwidth, variation of frequency response or other circumstances, which can rapidly diagnose the device of actual aging to instantly proceed with servo tuning, maintenance or replacement that can reduce the vibration circumstance of drive system of machine tool to guarantee its function and precision requirements.

Description

Machine aging diagnosis method

The present invention provides a method for diagnosing machine aging, especially a method that can identify various parameters of a tool drive system, and diagnose that the tool drive system is aging when its variation exceeds the threshold, so as to facilitate timely adjustment and maintenance. Maintenance or replacement to reduce its vibration and ensure its performance and work accuracy.

According to the fact that the manufacturing industry’s requirements for the precision of finished products are increasing day by day, in addition to requiring certain processing quality, shortening the processing cycle is also an important goal; therefore, how to make computer numerical control (CNC) machine tools in different processing environments It is an important issue to maintain high speed and high precision under other conditions. At present, there are many documents discussing the mechanical structure, motion control, feed drive system, spindle, CNC servo controller and interpolator in CNC machine tools. Propose different methods to improve processing performance; in the traditional design process, engineers need to spend a lot of time and cost to manufacture and test physical prototypes to detect areas that need improvement to optimize the design; and in the current design process, the machine is Compared with the traditional design method, the simulation prototype technology is adopted through the computer, which reduces a lot of time and cost. However, it is still necessary to rely on experience or trial and error to adjust the machine. This method is inefficient and cannot determine whether the servo parameters have been optimized. .

Since the performance of the machine tool system will directly affect the processing quality, the study of the five-axis feed drive model is of great significance for the development of virtual systems. At present, many documents have discussed the topic of virtual drive system, and proposed a virtual feed model, including motor structure, friction and backlash and other nonlinear characteristics and noise detection, but it assumes that the lead screw is a rigid body, and ignores the work Due to the characteristics of the table structure, the performance of the virtual feed drive system model is still reduced.

In addition, the conventional knowledge has not conducted further research on the aging problem, and the aging of the machine will cause the variation of the bandwidth, resonance frequency and contour error, which will affect the processing quality.

In view of this, our inventors have devoted themselves to further research on the aging diagnosis of the machine tool system, and proceeded to develop and improve, hoping to provide a better invention to solve the above problems, and after continuous testing and modification, the present invention come out.

The purpose of the present invention is to solve the aforementioned problems. In order to achieve the above objectives, our inventors provide a machine aging diagnosis method, the steps of which include: (A) outputting a sweep signal to a tool drive system for sweeping , In order to stimulate the response of the tool driving system, and obtain the frequency response of a speed loop and a position loop, and record the bandwidth BW and the first resonance frequency F _r ; wherein, the tool driving system has at least one servo controller, which Control at least one motor, at least one guide rod driven and rotated by the motor, and at least one drive platform respectively driven by the guide rod; (B) establish a speed loop transfer function, and perform calculations to identify and record The rotational inertia J _m of the motor and the damping coefficient B _{m of the} rotational motion in the tool drive system; (C) Establish a position loop transfer function, and substitute the rotational inertia and the damping coefficient of the rotational motion into the position loop transfer function, The damping coefficient C _t of a guide rod and the stiffness K of the guide rod are identified through calculation; (D) a variable value and a threshold value are defined, and the variable value is selected from at least one of the bandwidth BW and the first resonance The group consisting of frequency F _r , moment of inertia J _m , damping coefficient B _m , damping coefficient C _t and rigidity K; (E) repeat steps (A) to (C), and record the variation values respectively; and When the variation of the variation value is greater than the threshold value, it is diagnosed that the tool driving system is aging.

為

；其中，K_p、K_vp、K_vi為伺服控制器之參數，K_t為馬達轉矩常數，

為馬達轉動慣量之演算法估測值，而

為馬達旋轉運動阻尼係數之演算法估測值；且其係經演算法計算以鑑別該轉動慣量J_m及阻尼係數B_m。 The machine aging diagnosis method described in item 1 of the scope of patent application, wherein the speed loop transfer function

for

; Among them, K _p , K _vp , K _vi are the parameters of the servo controller, K _t is the motor torque constant,

Is the estimated value of the motor's moment of inertia by the algorithm, and

It is the estimated value of the damping coefficient of the motor's rotating motion; and it is calculated by the algorithm to identify the moment of inertia J _m and the damping coefficient B _m .

；並限制

,

>0；其中，G _v (jω _i )| _dB 及∠G _v (jω _i )| _dB 為速度迴路之頻率響應，

及

為速度迴路之頻率響應之演算法估測值，N_v為速度迴路之頻率響應數據之長度、w _v1及w _v2分別為速度迴路之頻率響應的大小及相位權重值。 According to the machine aging diagnosis method described above, the algorithm is a PSO (Particle Swarm Optimization) algorithm, and the optimization problem is defined as

; And limit

,

>0; where G _v ( jω _i )| _dB and ∠ G _v ( jω _i )| _dB are the frequency response of the speed loop,

and

Is the algorithmic estimation value of the frequency response of the speed loop, N _v is the length of the frequency response data of the speed loop, w _{v 1} and w _{v 2} are the magnitude and phase weight value of the frequency response of the speed loop, respectively.

為

其中，

According to the machine aging diagnosis method described above, the motor, the guide rod and the driving platform of the tool driving system are arranged in a linear axis, and the position loop transfer function of the linear axis

for

among them,

且其係經演算法計算以鑑別該驅動平台之質量M_t、導桿之阻尼係數C_t及導桿之剛性K，其中，

為該驅動平台質量之演算法估測值，

為導桿阻尼係數之演算法估測值，

為導桿之剛性之演算法估測值。

And it is calculated by an algorithm to identify the mass M _{t of} the drive platform, the damping coefficient C _t of the guide rod and the rigidity K of the guide rod, among which,

Is the estimated value of the algorithm for the quality of the driving platform,

Is the calculated value of the guide rod damping coefficient,

It is the estimated value of the rigidity of the guide rod by the algorithm.

為

且其係經演算法計算以鑑別該驅動平台質量M_t。 According to the aforementioned method for diagnosing machine aging, wherein the guide rod of the tool driving system is connected to one end of the driving platform, so that the driving platform rotates around the guide rod to form a rocker The setting of the rotary axis A of the bed, and the position loop transfer function of the rotary axis A

for

And it is calculated by an algorithm to identify the quality of the driving platform M _t .

為

且其係經演算法計算以鑑別該轉動慣量J_M及阻尼係數B_M。 According to the aforementioned method for diagnosing machine aging, wherein the guide rod of the tool driving system is connected to the center of the driving platform, so that the driving platform rotates with the guide rod as the rotation axis C , And the position loop transfer function of the rotation axis C

for

And it is calculated by an algorithm to identify the moment of inertia J _M and the damping coefficient B _M.

According to the machine aging diagnosis method described above, the algorithm is a PSO (Particle Swarm Optimization) algorithm, and the optimization problem is defined as

,

,

>0；以及

limit

,

,

>0; and

,

,

>0；其中，G _p (jω _i )| _dB 及∠G _p (jω _i )為位置迴路之頻率響應，

及

為位置迴路之頻率響應之演算法估測值，N_p為位置迴路之頻率響應數據長度、w_p1及w_p2分別為位置迴路的頻率響應之大小及相位權重值。 limit

,

,

>0; where G _p ( jω _i )| _dB and ∠ G _p ( jω _i ) are the frequency response of the position loop,

and

Is the algorithmic estimation value of the frequency response of the position loop, N _p is the data length of the frequency response of the position loop, w _p1 and w _p2 are the magnitude and phase weight value of the frequency response of the position loop, respectively.

According to the machine aging diagnosis method described above, the variation value includes the rigidity K, the frequency response bandwidth BW of the speed loop and the position loop, and the first resonance frequency F _r .

According to the machine aging diagnosis method mentioned above, the threshold value is -20%.

According to the above-mentioned machine aging diagnosis method, the steps further include: (F) define a path for the tool drive system to run; (G) record and compare the running track of the tool drive system; and (H) Set an error value; and diagnose that the tool driving system is aging when the error of the trajectory compared to the path is greater than the error value.

According to the machine aging diagnosis method described above, the steps further include: (I) setting a path for the tool drive system to perform variable speed operation; (J) capturing the command generated by the servo controller to the motor Current data and corresponding speed data; (K) Establish a steady-state friction force function, and use an algorithm to identify the viscous friction coefficient f _a during forward rotation, the viscous friction coefficient f _b during negative rotation, and the difference between viscous friction The initial coefficient f _d and the static friction coefficient f _h , and the variable value includes the steady-state friction force, and when the steady-state friction force is greater than the threshold value, it is diagnosed that the tool driving system is aging.

其中，v為該工具驅動系統之速度，dv為受靜摩擦影響之低速範圍；透過演算法計算以鑑別該正轉時之黏滯摩擦係數f _a 、負轉時之黏滯摩擦係數f _b 、黏滯摩擦之初始係數f _d及靜摩擦係數f _h 。 According to the machine aging diagnosis method described above, the steady-state friction function F _ss,new is

Wherein, v for the speed of the tool drive system, dv is the low speed range is limited by the influence of static friction; Based upon identification of the stick forward stagnation algorithmically friction coefficient f _a, the negative rotation of the lag time of sticky friction coefficient f _b, sticky The initial coefficient of hysteretic friction f _d and the coefficient of static friction f _h .

subject to

,

,

,

,and dv>0 According to the system identification method described above, the algorithm is a PSO (particle swarm optimization) algorithm, and the objective function E _{new that} defines the optimization problem is

subject to

,

,

,

,and dv >0

-

>0；其中，w ₁，及w ₂為誤差權重，

為工具驅動系統速度之演算法估測值，

為正轉時之黏滯摩擦係數之演算法估測值，

為負轉時之黏滯摩擦係數之演算法估測值，

為黏滯摩擦初始係數之演算法估測值，

為靜摩擦係數之演算法估測值。

-

>0; where w ₁ and w ₂ are error weights,

Is the estimated value of the algorithm for the speed of the tool drive system,

Is the calculated value of the viscous friction coefficient during forward rotation,

Is the calculated value of the viscous friction coefficient at the time of negative rotation,

Is the estimated value of the algorithm for the initial coefficient of viscous friction,

It is the calculated value of static friction coefficient.

According to the machine aging diagnosis method mentioned above, the threshold is 20%.

Based on the above description and settings, it is obvious that the present invention mainly has the following advantages and effects, which are described in detail as follows:

1. The present invention stimulates the response of the tool drive system by outputting a frequency sweep signal to obtain the frequency response of a speed loop and a position loop; and uses the transfer function of the speed loop and the transfer function of the position loop to calculate and identify the tool through an algorithm The motor inertia J _{M of the} driving system, the damping coefficient B _{M of} rotational motion, the mass of the driving platform M _t , the damping coefficient C _t of the guide rod and the rigidity K of the guide rod, and can detect vibration, bandwidth changes and system response changes And other phenomena, and record the aforementioned variation value at each point in time, so that when the variation of the aforementioned variation value is greater than the threshold, the specific aging parts of the diagnostic tool drive system can be adjusted, maintained or maintained as quickly and accurately as possible. Replacement to reduce the vibration of the tool drive system and ensure its performance and working accuracy.

1: Tool drive system

11: main computer

12: Servo controller

121: PID controller

122: Feedforward control unit

13: Mechanical structure

131: Motor

132: guide rod

133: drive platform

Figure 1 is a schematic diagram of the basic structure of the tool drive system of the present invention.

Figure 2 is a schematic diagram of the structure of the linear axis model of the present invention.

Figure 3 is a schematic diagram of the structure of the A-axis rotating shaft model of the present invention.

Figure 4 is a schematic diagram of the structure of the C-axis rotating shaft model of the present invention.

Figure 5 is a schematic diagram of the structure of the tool drive system of the present invention.

Figure 6a is the signal diagram of the input signal and output signal of the position loop and the speed loop of the X axis.

Figure 6b is the signal diagram of the input signal and output signal of the Y-axis in the position loop and the speed loop.

Figure 6c is the signal diagram of the input signal and output signal of the Z axis in the position loop and the speed loop.

Figure 6d is the signal diagram of the input signal and output signal of the A axis in the position loop and the speed loop.

Figure 6e is the signal diagram of the input signal and output signal of the C axis in the position loop and the speed loop.

Figure 7 is a schematic diagram of the simplified friction model of the present invention.

Figure 8 is a schematic diagram of the backlash between the guide rod and the driving platform of the present invention.

Figure 9 is a simplified flow chart of the servo tuning method of the present invention.

Figure 10a.1 is a comparison diagram of signals before and after adjustment of the X axis in the speed loop of the present invention.

Figure 10a.2 is a comparison diagram of the signals before and after adjustment of the X axis in the position loop of the present invention.

Figure 10a.3 is a comparison diagram of signals before and after tuning of the X-axis in the feedforward position loop of the present invention.

Figure 10b.1 is a comparison diagram of the signals before and after adjustment of the Y-axis in the speed loop of the present invention.

Figure 10b.2 is a comparison diagram of signals before and after adjustment of the Y-axis in the position loop of the present invention.

Figure 10b.3 is a comparison diagram of signals before and after adjustment of the Y-axis in the feedforward position loop of the present invention.

Figure 10c.1 is a comparison diagram of signals before and after adjustment of the Z axis in the speed loop of the present invention.

Figure 10c.2 is a comparison diagram of signals before and after adjustment of the Z axis in the position loop of the present invention.

Figure 10c.3 is a comparison diagram of signals before and after adjustment of the Z axis in the feedforward position loop of the present invention.

Figure 10d.1 is a comparison diagram of the signals before and after adjustment of the A-axis in the speed loop of the present invention.

Figure 10d.2 is a comparison diagram of the signals before and after adjustment of the A-axis in the position loop of the present invention.

Figure 10d.3 is a comparison diagram of signals before and after adjustment of the A-axis in the feedforward position loop of the present invention.

Figure 10e.1 is a comparison diagram of the signals before and after adjustment of the C axis in the speed loop of the present invention.

Figure 10e.2 is a comparison diagram of signals before and after adjustment of the C axis in the position loop of the present invention.

Figure 10e.3 is a comparison diagram of signals before and after adjustment of the C-axis in the feedforward position loop of the present invention.

Figure 11a is a comparison diagram of the contour error of the trajectory of the present invention in the XY plane before and after the adjustment of the machine.

Figure 11b is a comparison diagram of the contour error of the trajectory before and after adjusting the machine in the XZ plane of the present invention.

Figure 11c is a comparison diagram of the contour error of the trajectory of the present invention in the YZ plane before and after the adjustment of the machine.

Figure 12a is a signal comparison diagram of the attenuation of the damping coefficient of the motor of the present invention.

Figure 12b is a signal comparison diagram of the attenuation of the damping coefficient of the guide rod of the present invention.

Figure 12c is a signal comparison diagram of the attenuation of the rigidity of the guide rod of the present invention.

Regarding the technical means of our inventors, several preferred embodiments are described in detail below in conjunction with the drawings, so as to provide a thorough understanding and approval of the present invention.

The present invention provides a system identification and servo adjustment method. In a specific embodiment, it is mainly used to identify a tool drive system 1. As far as the tool drive system 1 is concerned, it can be a five-axis CNC machine tool. It contains three linear axes (X-axis, Y-axis, and Z-axis) and two rotary axes (A-axis and C-axis). As shown in Figure 1, it mainly includes a host computer 11, a servo controller 12, and A mechanical structure 13; among them, as far as the host computer 11 is concerned, it is mainly created by the operator through a numerical control (NC) program or using a CAD/CAM application program to generate discrete numerical position commands for each servo controller 12; servo control The controller 12 can be divided into a PID (Proportional Integral Derivative) controller 121 and a feedforward control unit 122; in one embodiment, the servo controller 12 is based on three loop control, including current loop control, speed loop control and Position loop control, so high-performance motion control can be provided by adjusting the parameters of the servo controller 12; the mechanical structure 13 can be divided into a motor 131, a guide rod 132 and a drive platform 133. The servo controller 12 is coupled and sends commands to The motor 131 generates torque to rotate the driving guide rod 132 and then drive it to the driving platform 133. The guide rod 132 can be a screw rod; and the linear axis model of the tool driving system 1 is as shown in Figure 2, where T Is the torque of the motor 131 [Nm], J _m is the moment of inertia of the motor 131 [kgm ² ], B _m is the damping coefficient of the rotary motion [Ns/m], M _t is the mass of the drive platform 133 [kg], and C _t is the guide The damping coefficient of the rod 132 [Ns/m], K is the rigidity of the guide rod 132 [N/m], R is the conversion ratio from the rotation angle of the motor 131 to the position of the drive platform 133 [mm/rev], and θ _m is the motor 131 Rotation angle [rad], x _act is the position of the drive platform 133 [m]; in terms of the rotation axis model, the mechanical structure 13 of the A axis is a shaker structure, as shown in Figure 3, the guide rod 132 is connected At one end of the driving platform 133, where m shown in Figure 3 is the mass of the driving platform 133, g is the acceleration of gravity, T is the torque of the motor 131, θ is the angle of the mechanical structure 13, and 1 is the driving platform 133 The vertical length from the center of mass to the axis of the guide rod 132, so that the driving platform 133 rotates around the guide rod 132, thereby forming the setting of the rotation axis A of the rocking table structure. The mechanical structure 13 of the C axis is directly The rotating platform of the driving mechanism, as shown in FIG. 4, the guide rod 132 is connected to the center of the driving platform 133, so that the driving platform 133 rotates with the guide rod 132 as the rotation axis C.

In one embodiment, a complete five-axis CNC tool drive system 1 is shown in Figure 5, where K _p is a position controller, in this embodiment a proportional ( P ) controller is used, and K _v is a speed control , a system using the present embodiment, proportional-integral (PI) controller, K _i is a current controller according to the present embodiment based PI controller is used, L _a is the armature inductance, R _a is the armature resistance, K _t is Torque constant, K _e is the electromagnetic interference and electric field (EMF) constant, ω _cmd is the angular velocity command, x _cmd is the position command, θ _cmd is the angle command, θ _act is the angle of the drive platform 133, θ _act is the angular velocity command, ω _act Is the surface angular velocity; the tool driving system 1 includes a feedforward control unit 122, which can be used to improve the performance of the servo system, the feedforward control unit 122 includes speed and acceleration feedforward control, where VF is the switch of the feedforward control system; VF The value is 1 or 0. If VF =1, it means that the feedforward control system is turned on; on the contrary, if VF =0, it means that the feedforward control system is turned off; AF is the constant coefficient of the acceleration feedforward control system.

In one implementation, the tool drive system 1 of the present invention is a Microcut-MCU-5X five-axis machine tool, which is equipped with a HEIDENHAIN controller and TNC640, and by using HEIDENHAIN software such as TNCOPT, TNCSCOPE, TNCREMO, etc., the computer can be directly connected The controller obtains operating data such as the spindle speed of the motor 131, the position of the driving platform 133, the speed and acceleration of each axis, and so on.

[Identification of speed loop and position loop]

Therefore, the present invention uses a sinusoidal frequency sweep signal for system identification, and obtains the corresponding frequency response through the servo-oriented software TNCOPT, and the sinusoidal frequency sweep function does not include a feedforward controller; the implementation steps are as follows:

S001: Output a frequency sweep signal to sweep the frequency of a tool driving system 1 so as to stimulate the response of the tool driving system 1 and obtain the frequency response of a speed loop and a position loop of each axis; as shown in Figures 6a to 6e.

為

；其中，K_p、K_vp、K_vi為伺服控制器12之參數，K_t為馬達131轉矩常數，

為馬達131轉動 慣量之演算法估測值，而

為馬達131旋轉運動阻尼係數之演算法估測值；且其係經演算法計算以鑑別該轉動慣量J_M及阻尼係數B_M；本發明主要係透過PSO(粒子群優化)演算法進行演算，由於PSO演算法易於實現，並且具有較少的選擇參數以提供計算效率，其主要係藉由定義優化問題及粒子更新標準，其中，粒子更新主要由三部分組成；第一部分係當前的速度影響慣性運動的效果；第二部分是認知部分，根據粒子自己的判斷；第三部分是社會部分，根據群體的最佳解決方案來搜索解空間；本發明係使用PSO演算法的學習因素的線性調整，學習因素的線性調整表示為：c ₁=c _1b +(c _1s -c _1b )/G _max×g S002: Establish a speed loop transfer function, and perform calculations to identify the moment of inertia J _M of the motor 131 and the damping coefficient B _{M of the} rotary motion in the tool drive system 1; in one embodiment, the speed loop transfer function

for

; Among them, K _p , K _vp , K _vi are the parameters of the servo controller 12, and K _t is the torque constant of the motor 131,

Is the calculated value of the motor 131 moment of inertia, and

It is the algorithmic estimated value of the damping coefficient of the rotating motion of the motor 131; and it is calculated by the algorithm to identify the moment of inertia J _M and the damping coefficient B _M ; the present invention is mainly calculated by the PSO (Particle Swarm Optimization) algorithm, Since the PSO algorithm is easy to implement and has fewer selection parameters to provide computational efficiency, it is mainly based on the definition of optimization problems and particle update standards. Among them, the particle update is mainly composed of three parts; the first part is that the current speed affects the inertia The effect of movement; the second part is the cognitive part, based on the particle’s own judgment; the third part is the social part, searching for the solution space according to the best solution of the group; the present invention uses the linear adjustment of the learning factors of the PSO algorithm, The linear adjustment of learning factors is expressed as: c ₁ = c _{1 b} +( c _{1 s} - c _{1 b} )/ G _max × g

c ₂ = c _{2 s} +( c _{2 b} - c _{2 s} )/ G _max × g

Among them, c _{1 b} , c _{1 s} , c _{2 b} and c _{2 s} are normal numbers; c _{1 b} > c _{1 s} and c _{2 b} > c _{2 s} . G _max is the maximum number of iterations allowed, and g is the current number of iterations of the particle.

The current fitness value of each particle is taken as the local best fitness value ( F _i,best ) of each particle; and the current solution of each particle is taken as its local optimal solution ( P _i,best ); in the initial population of particles Find the extreme value of the fitness value as the optimal fitness value of the group ( F _gbest ); use the position of the particle with the optimal fitness value of the group as the optimal solution of the group ( g _best ); update the particle parameters after each iteration When, recalculate the fitness value of the particle; compare the changes in the fitness value and update the best fitness value and solution.

並限制

,

>0； 其中，G _v (jω _i )| _dB 及∠G _v (jω _i )| _dB 為TNCOPT測量速度迴路之頻率響應，

及

為速度迴路之頻率響應之演算法估測值，N_v為速度迴路之頻率響應數據之長度、w _v1及w _v2分別為速度迴路之頻率響應的大小及相位權重值；藉此，即可鑑別速度迴路中之轉動慣量J_M及阻尼係數B_M。 Therefore, regarding the speed loop transfer function, the optimization problem can be expressed as

And limit

,

>0; where G _v ( jω _i )| _dB and ∠ G _v ( jω _i )| _dB are the frequency response of the TNCOPT measurement speed loop,

and

Is the algorithmic estimation value of the frequency response of the speed loop, N _v is the length of the frequency response data of the speed loop, w _{v 1} and w _{v 2} are the magnitude and phase weight value of the frequency response of the speed loop respectively; It can identify the moment of inertia J _M and the damping coefficient B _M in the speed loop.

For the speed loop, the system uses a frequency response of 1-300 [rad/s], because this frequency response range is mainly used for processing.

為

；其中，

,

,

,

S003: Establish a position loop transfer function, and substitute the rotational inertia and the damping coefficient of the rotational motion into the position loop transfer function, and the mass M _{t of} a driving platform 133, the damping coefficient Ct of the guide rod 132, and the guide rod 132 are identified by calculation The rigidity K; in one embodiment, according to the linear axis model, the position loop transfer function

for

;among them,

,

,

,

；且其係經演算法計算以鑑別該驅動平台133質量M_t、導桿132之阻尼係數C_t及導桿132之剛性K，其中，

為該驅動平台133質量之演算法估測值，

為導桿132阻尼係數之演算法估測值，

為導桿132之剛性之演算法估測值。

; And it is calculated by an algorithm to identify the mass M _{t of} the drive platform 133, the damping coefficient C _t of the guide rod 132 and the rigidity K of the guide rod 132, where,

Is the estimated value of the algorithm for the quality of the drive platform 133,

Is the calculated value of the damping coefficient of the guide rod 132,

It is the estimated value of the rigidity of the guide rod 132 by the algorithm.

為

The structure of the rotary axis (A axis and C axis) is different from that of the linear axis; the mechanical structure 13 of the A axis is a shaker structure; and the position loop transfer function of the rotary axis A

for

為

The mechanical structure 13 of the C-axis is a direct drive mechanism, and the power from the motor 131 is not reduced, so it can only identify the moment of inertia J _M and the damping coefficient B _M , the position loop transfer function of the rotating axis C

for

By using the PSO algorithm, the parameters of the position loop can be identified; in addition, the corresponding optimization problems for the linear axis and the rotary axis are

,

,

>0；以及

limit

,

,

>0; and

,

,

>0；其中，G _p (jω _i )| _dB 及∠G _p (jω _i )為位置迴路之頻率響應，

及

為位置迴路之頻率響應之演算法估測值，N_p為位置迴路之頻率響應數據長度、w_p1及w_p2分別為位置迴路的頻率響應之大小及相位權重值；藉此，即可鑑別驅動平台133質量M_t、導桿132之阻尼係數Ct及導桿132之剛性K。 limit

,

,

>0; where G _p ( jω _i )| _dB and ∠ G _p ( jω _i ) are the frequency response of the position loop,

and

Is the algorithmic estimation value of the frequency response of the position loop, N _p is the frequency response data length of the position loop, w _p1 and w _p2 are the magnitude and phase weight value of the frequency response of the position loop respectively; by this, the drive can be identified The mass M _{t of the} platform 133, the damping coefficient Ct of the guide rod 132 and the rigidity K of the guide rod 132.

The goal of identification is to ensure that the overall frequency response, bandwidth and resonance frequency are correct. To speed up identification, use the moment of inertia J _M and the damping coefficient B _{M of the} speed loop identification result as known knowledge to identify the position loop parameters.

Among them, the parameter settings of the PSO algorithm used in the present invention are shown in Table 1 below:

The identification result parameters of each axis are shown in Table 2 below:

[Identification of Friction]

Since the nonlinear phenomenon affects the performance of the tool drive system 1, in the feed drive system, the friction and backlash of the nonlinear phenomenon cannot be ignored; in one embodiment of the present invention, the variable speed data obtained by the circular test is used to To identify friction, it is not necessary to obtain a large amount of speed data and current data at each different speed as known, but only one path is set for the tool drive system 1 to perform variable speed operation; capture the servo controller 12 generates a current command to the motor 131 of the data rate and corresponding data; establishing a steady friction function, and by the algorithm when the differential rotation orthomyxovirus lag coefficient of friction f _a, sticky lag time of negative rotation coefficient of friction f _b . The initial coefficient of viscous friction f _d and the coefficient of static friction f _h .

；其中，v為該工具驅動系統1之速度，dv為受靜摩擦影響之低速範圍；此外，摩擦力在運動轉向時具有更明顯的影響，因此，本發明係於不同速度使用不同的學習權重予以鑑別之，即對於較低速度使用較大的權重並且對較高速度使用較小的權重。因此，選擇優化的目標函數E_new為

subject to

,

,

,

,and dv>0 In a specific embodiment, the present invention uses a simplified friction force model, as shown in Figure 7, and the simplified steady-state friction force function F _{ss,new can be} expressed as

; Among them, v is the speed of the tool drive system 1, dv is the low-speed range affected by static friction; in addition, friction has a more obvious impact on the movement and steering, therefore, the present invention uses different learning weights for different speeds Identify it, that is, use larger weights for lower speeds and use smaller weights for higher speeds. Therefore, the optimized objective function E _new is selected as

subject to

,

,

,

,and dv >0

-

>0；其中，w ₁，及w ₂是誤差權重，

為工具驅動系統1速度之演算法估測值，

為正轉時之黏滯摩擦係數之演算法估測值，

為負轉時之黏滯摩擦係數 之演算法估測值，

為黏滯摩擦初始係數之演算法估測值，

為靜摩擦係數之演算法估測值。

-

>0; where w ₁ and w ₂ are error weights,

Is the estimated value of the algorithm for the speed of the tool drive system 1,

Is the calculated value of the viscous friction coefficient during forward rotation,

Is the calculated value of the viscous friction coefficient at the time of negative rotation,

Is the estimated value of the algorithm for the initial coefficient of viscous friction,

It is the calculated value of static friction coefficient.

The PSO algorithm parameter settings for friction identification are shown in Table 3 below:

The friction identification results of the present invention are shown in Table 4 below:

[Identification of Backlash]

Through the single-axis back and forth movement of the tool driving system 1, the position signal of the driving platform 133 and the speed signal of the motor 131 can be obtained by the linear scale and the rotary encoder. Therefore, by calculating the speed of the motor 131 to obtain the position signal of the guide rod 132, the relationship between the position signal of the guide rod 132 and the position signal of the driving platform 133 can be obtained; as shown in Figure 8, the relationship between the guide rod 132 and the driving platform 133 The backlash indicates that x _act is the position of the driving platform 133, x _l is the position of the guide rod 132, and D _b is the backlash value; it can also indicate the driving platform 133 and the guide rod 132 when moving forward or backward The theoretical error value is half of the backlash value. In order to ensure that the error between the guide rod 132 and the platform remains at half of the backlash value, the moving speed needs to be very slow. During the back and forth movement, the position data of the drive platform 133 is captured. And motor 131 speed data. When the motor 131 reaches the same position, compare the position error of the front and rear platforms, subtract the position between the driving platform 133 and the guide rod 132, and identify a backlash value through the least square method. Expressed as:

Among them, (Δ s ) is the position difference between the driving platform 133 and the guide rod 132, and L is the length of the path data.

[Servo Tuning Method 1 Speed Loop]

Since the response of the tool driving system 1 is too fast, it will often cause structural vibration, so by adjusting its parameters, its stability, performance and working accuracy can be improved.

1.5，限制共振峰值之目的在於避免更大的過衝及振動情形；界定一相依於該速度迴路頻率響應之增益邊限及相位邊限，增益邊限和相位邊限用於確認整體系統之穩定性和穩健性；在一實施例中，該增益邊限G_M為

；其中，GH _v (jω)為所述工具驅動系統1速度迴路之開迴路轉移函數，ω _c 為相位交叉頻率；並限制增益邊限G_M>10Db；該相位邊限P_M為P _M =180°+∠[GH _v (jω _g )]；其中，GH _v (jω)為所述工具驅動系統1速度迴路之開迴路轉移函數，ω _g 為增益交叉頻率；並限制相位邊限P_M>45°。 As shown in Figure 9, the present invention also provides a servo tuning method, which is applied to the system identification method described above, and in one embodiment is actually applied to MCU-5X with a HEIDENHAIN controller. The frequency response of the speed loop is used to adjust the parameters of the speed controller, and its purpose is to maximize the system bandwidth of the closed loop speed loop, so as to obtain better response performance and improve the steady-state error; the adjustment steps of the speed loop include: _{Define the system bandwidth Bw vel} of the speed loop that maximizes the closed loop; limit a resonance peak that depends on the speed loop frequency response; in one embodiment, the resonance peak M _vp is the maximum value of the speed loop frequency response, And define M _vp

1.5. The purpose of limiting the resonance peak is to avoid greater overshoot and vibration; define a gain margin and phase margin that depend on the frequency response of the speed loop. The gain margin and phase margin are used to confirm the stability of the overall system Performance and robustness; in one embodiment, the gain margin G _M is

; Among them, GH _v ( jω ) is the open-loop transfer function of the speed loop of the tool drive system 1, and ω _c is the phase cross frequency; and the gain margin G _M >10Db is restricted; the phase margin P _M is P _M = 180 ° +∠[ GH _v ( jω _g )]; where GH _v ( jω ) is the open-loop transfer function of the speed loop of the tool drive system 1, and ω _g is the gain crossover frequency; and the phase margin P _M > 45°.

_{Defines the ratio between the proportional gain value K vp} and the integral gain value K _{vi in} the parameters of the servo controller 12; in one embodiment, the ratio R _{pi between the proportional gain value K vp} and the integral gain value Kvi is R _pi = K _vi / K _vp =100±5%.

Under the condition that the above constraint conditions are met, the proportional gain value K _vp and the integral gain value K _{vi are obtained} , and the adjustment input is input to the servo controller 12 to achieve the goal of optimizing the speed loop of the tool drive system 1.

[Servo Tuning Method-Position Loop]

100，K_p增加的目標為提高響應速度；惟，K_p過大會導致位置迴路的共振峰變大甚至使系統變得不穩定，故須予限定；限制一相依於該位置迴路頻率響應之共振峰值；在一實施例中，相依於該位置迴路頻率響應之共振峰值M_pp之限制為M _PP

1，其中，共振峰值M_pp為位置迴路頻率響應之最大值；其中，限制共振峰值係可避免更大的過衝和振動情形；由於位置迴路將直接影響產品的軌跡和質量，故過衝的限制比速度迴路更嚴謹。 The servo tuning method of the present invention further includes a position loop step. The frequency response data of the position loop is used to adjust the parameters of the position controller. The steps include: defining the optimization problem to maximize the system frequency of the position loop of the closed loop Width Bw _pos ; limited to the parameter K _p of the servo controller 12; in one embodiment, the parameter K _{p of the} servo controller 12 is limited to K _P

100. _{The goal of increasing K p} is to increase the response speed; however, if K _{p is} too large, the resonance peak of the position loop will become larger or even make the system unstable, so it must be limited; limit a resonance dependent on the frequency response of the position loop Peak; In one embodiment, the resonance peak M _pp of the frequency response of the loop depends on the position, and the limit is M _PP

1. Among them, the resonance peak M _pp is the maximum value of the frequency response of the position loop; among them, limiting the resonance peak can avoid greater overshoot and vibration; because the position loop will directly affect the trajectory and quality of the product, the overshoot is The restriction is more stringent than the speed loop.

Define a gain margin and phase margin that depend on the frequency response of the position loop; in one embodiment, limit the gain margin G _M > 15dB and limit the phase margin P _M >45°; the gain margin of the position loop The limit and phase limit are used to confirm the stability and robustness of the system. During processing, the external load will directly affect the gain limit and phase limit of the position loop. Therefore, the gain limit and phase limit are more limited than the speed loop. tolerant.

；在符合上述約束條件的情況下，求得伺服控制器12之參數K_p，並調整輸入於該伺服控制器12，藉以優化工具驅動系統1之位置迴路。 Limit the system bandwidth of the position loop that maximizes the closed loop

; Under the condition that the above-mentioned constraint conditions are met, the parameter K _{p of} the servo controller 12 is obtained and adjusted and input to the servo controller 12 to optimize the position loop of the tool drive system 1.

[Servo Tuning Method-Position Loop with Feedforward Control Unit 122]

；擷取函數E為最小值時之常數係數AF，並對應輸入於該前饋控制單元122；前饋控制單元122之功能可以改善伺服落後和響應衰減，故加工頻率在位置迴路的頻寬內，藉可避免響應失真。 The aforementioned servo tuning method is to adjust the parameters of the speed controller and the position controller without the feedforward control unit 122. Here, consider the influence of the feedforward control unit 122 on the frequency response of the position loop, and adjust the feedforward control. Therefore, the optimization problem of the parameters of the unit 122 is to make the frequency response close to the level. By appropriately setting the feedforward parameters, the response distortion can be avoided and the servo lag can be eliminated. The steps further include: defining the feedforward control unit 122 dependent and controlling the speed and acceleration A constant coefficient AF; make the frequency response of the position loop close to the level, and define the minimum value of the function E of the optimization problem as

; Capture the constant coefficient AF when the function E is the minimum value, and correspondingly input it to the feedforward control unit 122; the function of the feedforward control unit 122 can improve the servo lag and response attenuation, so the processing frequency is within the bandwidth of the position loop , Which can avoid response distortion.

The parameter settings of the PSO algorithm used in the servo tuning method of the present invention are shown in Table 5 below:

The preset parameters of each axis before adjustment are shown in Table 6 below:

The bandwidth values of each axis before tuning are shown in Table 7 below:

The servo controller 12 parameters of each axis after adjustment are shown in Table 8 below:

The bandwidth values of each axis after tuning are shown in Table 9 below:

Please also refer to the figures 10a.1 to 10e.3. It can be observed that the bandwidth of the speed loop and the position loop has been improved, and the frequency response of the final servo tuning result is better than before the tuning.

In order to verify that the optimization of the servo adjustment method can be effectively applied to the tool drive system 1, the time domain experiment is used to prove the result of the servo adjustment, and the circular test is used here, and the feed rate is 1200 [mm/min] and the radius 1 is set. [mm] Perform verification, as shown in the contour error of the circular trajectory experiment in Figures 11a to 11c and Table 10 below:

From the foregoing, it can be observed that the contour error is significantly improved, while the tracking error is slightly improved. After comparing the contour error and the degree of improvement of the tracking, it is obvious that the servo tuning result of the present invention is reliable.

[Machine aging diagnosis method]

When the machine is aging, the machine is usually more prone to vibration, bandwidth changes, and system response changes. Therefore, the change of the parameters in the aforementioned identification method can be used to diagnose whether the machine is aging. The steps are as follows : S004: Define a variation value and a threshold value. The variation value is selected from at least one of the bandwidth BW, the first resonance frequency F _r , the moment of inertia J _m , the damping coefficient B _m , the damping coefficient C _t and Group consisting of rigidity K; S005: Repeatedly record the variation value at each time point in the aforementioned identification method, and diagnose that the tool drive system 1 is aging when the variation value of the variation value is greater than the threshold.

In a specific embodiment, as shown in Figures 12a to 12c, they are _{the system response of the variation of the damping coefficient B m} of the motor 131, the variation of the damping coefficient C _{t of the} guide rod 132, and the variation of the stiffness K of the guide rod 132, which can be observed When the variation of the damping coefficient B _{m of the} motor 131 corresponds to the deterioration of the system response; when the _{variation of the damping coefficient C t of the} guide rod 132 corresponds to the increase of the system resonance peak; when the variation of the stiffness K of the guide rod 132 corresponds to the decrease of the system resonance frequency and the resonance peak It becomes larger, so the aging of the machine can be judged by identifying the variation of the parameters.

In addition, the changes in bandwidth, resonance frequency, and resonance peak can be observed from the frequency domain signal. In addition, the profile error and tracking error deterioration can be observed from the time domain signal; therefore, in a specific embodiment, the change is The value includes the rigid K, the bandwidth BW of the frequency response of the speed loop and the position loop, and the first resonant frequency F _r , and the threshold is set to -20%; so that the rigid K, the bandwidth BW and the first resonant frequency F _{r are} simultaneously When it drops by 20%, it is judged to be aging and it is recommended to adjust or maintain the machine.

In another embodiment, the tool driving system 1 can also be operated by defining a path, such as a circular test; and recording and comparing the running trajectory of the tool driving system 1; and by setting an error value; and It is diagnosed that the tool driving system 1 is aging when the error of the trajectory compared to the path is greater than the error value.

In another embodiment, it is also possible to identify the steady-state friction force as described above, and diagnose that the tool driving system 1 is aging when it is greater than the threshold.

When it is diagnosed as aging, the aforementioned servo tuning method or related maintenance can be performed to improve the performance and accuracy after aging.

In summary, the technical means disclosed in the present invention can indeed effectively solve the problems of the prior art and achieve the expected purpose and effect. It has not been seen in the publications, has not been used publicly, and has long-term progress before the application. The patent law claims that the invention is correct. Yan filed an application in accordance with the law and prayed for the detailed examination and grant of the invention patent.

However, the above are only a few preferred embodiments of the present invention, and should not be used to limit the scope of implementation of the present invention, that is, all equivalent changes and modifications made in accordance with the scope of the patent application of the present invention and the content of the description of the invention are all It should still fall within the scope of the invention patent.

Claims (13)

A method for diagnosing machine aging. The steps include: (A) outputting a frequency sweep signal in a tool driving system for frequency scanning, so as to stimulate the tool driving system to respond, and obtain the frequency response of a speed loop and a position loop, and record Its bandwidth BW and first resonance frequency F _r ; wherein, the tool driving system has at least one servo controller, which controls at least one motor, at least one guide rod that is driven to rotate by the motor, and at least one respectively driven On the driving platform of the guide rod; (B) establish a speed loop transfer function, the speed loop transfer function

for

; Among them, K _p , K _vp , K _vi are the parameters of the servo controller, K _t is the motor torque constant,

Is the estimated value of the motor's moment of inertia by the algorithm, and

It is the algorithmic estimated value of the damping coefficient of the motor's rotational motion; and the algorithmic operation is used to identify and record the moment of inertia J _m of the motor and the damping coefficient B _{m of the} rotational motion in the tool drive system; (C) establish a position Loop transfer function, and substitute the rotational inertia and the damping coefficient of the rotational motion into the position loop transfer function, and the damping coefficient C _t of a guide rod and the stiffness K of the guide rod are identified by calculation; (D) define a variable value and a Threshold, the variation value is selected from at least one group consisting of the bandwidth BW, the first resonance frequency F _r , the moment of inertia J _m , the damping coefficient B _m , the damping coefficient C _t and the rigidity K; E) Repeat steps (A) to (C), and record the variation values respectively; and diagnose that the tool driving system is aging when the variation of the variation value is greater than the threshold value. The machine aging diagnosis method according to claim 1, wherein the algorithm is a PSO (particle swarm optimization) algorithm, and the optimization problem is defined as

; And limit

,

>0; where G _v ( jω _i )| _dB and ∠ G _v ( jω _i )| _dB are the frequency response of the speed loop,

and

Is the algorithmic estimation value of the frequency response of the speed loop, N _v is the length of the frequency response data of the speed loop, w _{v 1} and w _{v 2} are the magnitude and phase weight value of the frequency response of the speed loop, respectively. The machine aging diagnosis method according to claim 1, wherein the motor, the guide rod and the driving platform of the tool driving system are arranged in a linear axis, and the position loop transfer function of the linear axis

for

;among them,

,

,

,

And it is calculated by an algorithm to identify the mass M _{t of} the drive platform, the damping coefficient C _t of the guide rod and the rigidity K of the guide rod, among which,

Is the estimated value of the algorithm for the quality of the driving platform,

Is the calculated value of the guide rod damping coefficient,

It is the estimated value of the rigidity of the guide rod by the algorithm. The machine aging diagnosis method according to claim 1, wherein the guide rod of the tool driving system is connected to one end of the driving platform, so that the driving platform rotates around the guide rod, thereby Form the setting of the rotary axis A of the shaker, and the position loop transfer function of the rotary axis A

for

And it is calculated by an algorithm to identify the quality of the driving platform M _t . The machine aging diagnosis method according to claim 1, wherein the guide rod of the tool driving system is connected to the center of the driving platform, so that the driving platform uses the guide rod as the rotation axis C Rotate, and the position loop transfer function of the rotation axis C

for

And it is calculated by an algorithm to identify the moment of inertia J _M and the damping coefficient B _M. The machine aging diagnosis method according to claim 3, wherein the algorithm is a PSO (Particle Swarm Optimization) algorithm, and the optimization problem is defined as

limit

,

,

>0; and

limit

,

,

>0; where G _p ( jω _i )| _dB and ∠ G _p ( jω _i ) are the frequency response of the position loop,

and

Is the algorithmic estimation value of the frequency response of the position loop, N _p is the data length of the frequency response of the position loop, w _p1 and w _p2 are the magnitude and phase weight value of the frequency response of the position loop, respectively. The machine aging diagnosis method according to any one of claim 1 to claim 6, wherein the variable value includes the rigidity K, the frequency bandwidth BW of the speed loop and the position loop frequency response, and the first resonance frequency F _r . The machine aging diagnosis method according to claim 7, wherein the threshold value is -20%. For the machine aging diagnosis method described in any one of claim 1 to claim 6, the steps further include: (F) define a path for the tool drive system to run; (G) record and compare the tool drive system The running track; and (H) set an error value; and diagnose that the tool driving system is aging when the error of the track compared to the path is greater than the error value. According to the method for diagnosing machine aging according to any one of claim 1 to claim 6, the steps further include: (I) setting a path for the tool drive system to perform variant speed operation; (J) capturing the said The servo controller generates commands to the current data of the motor and the corresponding speed data; (K) establishes a steady-state friction force function, and uses an algorithm to identify the viscous friction coefficient f _a during forward rotation and the viscosity during negative rotation The coefficient of hysteretic friction f _b , the initial coefficient of viscous friction f _d and the coefficient of static friction f _h , and the variable value includes the steady-state friction force. When the steady-state friction force is greater than the threshold value, it is diagnosed that the tool drive system is aging . The machine aging diagnosis method according to claim 10, wherein the steady-state friction function F _ss,new is

; Wherein, v for the speed of the tool drive system, dv is the low speed range is limited by the influence of static friction; Based upon identification of the stick forward stagnation algorithmically friction coefficient f _a, the negative rotation of the lag time of sticky friction coefficient f _b, The initial coefficient of viscous friction f _d and the coefficient of static friction f _h . The system identification method according to claim 11, wherein the algorithm is a PSO (particle swarm optimization) algorithm, and the objective function E _{new that} defines the optimization problem is

subject to

,

,

,

,and dv >0

-

>0; where w ₁ and w ₂ are error weights,

Is the estimated value of the algorithm for the speed of the tool drive system,

Is the calculated value of the viscous friction coefficient during forward rotation,

Is the calculated value of the viscous friction coefficient at the time of negative rotation,

Is the estimated value of the algorithm for the initial coefficient of viscous friction,

It is the calculated value of static friction coefficient. The machine aging diagnosis method according to claim 10, wherein the threshold value is 20%.

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