TWI665051B - Method of detecting cutter wear for machine tools - Google Patents

Abstract

The method for detecting tool wear of a machine tool according to the present invention includes a preparation step, Measurement steps, calculation steps, drawing steps, model building steps, and judgment steps; it is through an acceleration gauge attached to the fixture of the machine tool, and the signals measured by the acceleration gauge are performed through a slave system and the main system, respectively. Chaotic synchronization signal processing to generate a dynamic error signal, use the above dynamic error to draw the distribution map of the center of gravity points of each state of the dynamic error, and create a matter-element model based on the characteristics of each tool wear situation for the center of gravity generated during subsequent operations The point distribution map can be compared with the matter element model to determine the status of the tool; therefore, only a single accelerometer sensor can be used to reduce the calculation amount and quickly analyze the results to achieve a good result. Accuracy, and the cost-effectiveness required to effectively reduce detection.

Description

Method for detecting tool wear of machine tools

The invention relates to a detection method, in particular to a method for detecting tool wear of a machine tool.

A machine tool is an automated tool for cutting workpieces. Under long-term operating conditions, the damage and wear of cutting tools is unavoidable. When the tool is worn, the contact surface between the cutting surface and the tool will increase. In addition to increasing the resistance, at the same time, the finished product will not meet the original design specifications, leading to the generation of scrap products. In order to reduce this situation, generally, the finished product is mainly inspected by the operator manually. When it meets the specifications, it can be judged that the current tool is worn, and then a new tool is replaced.

However, the practice of inspecting finished products at any time through human labor not only increases labor costs, but also causes unnecessary waste when personnel do not detect defective products in real time, and costs increase, and there is a need for improvement. Therefore, if the Detecting and monitoring tool wear can effectively reduce the occurrence of defective products, and can also reduce the labor costs required for monitoring, thereby improving the efficiency and production yield of the machine tool. Therefore, how to make the tool wear of the machine tool detect in real time It is the goal of everyone's efforts to know the tool wear situation and quickly and correctly diagnose the degree of tool wear.

Therefore, an object of the present invention is to provide a method for detecting tool wear of a machine tool, which only needs to use a single sensor for monitoring, can quickly analyze the results, achieve a good accuracy rate, and reduce the monitoring facility. Required cost.

Therefore, the method for detecting tool wear of a machine tool according to the present invention includes a preparation step, a measurement step, a calculation step, a drawing step, a model creation step, and a judgment step; wherein the preparation step is tied to the fixture An accelerometer is attached to the accelerometer, and the accelerometer can be divided into a main system and a slave system. In addition, the measurement step uses the accelerometer to measure the vibration of the tool on the fixture, so that the main system can be used. Receive the initial vibration signal generated by the acceleration gauge to detect the initial rotation of the spindle, and the turning vibration signal generated by the tool when cutting the first cutter; as for the slave system, it can receive the rotation vibration signal that the acceleration gauge detects the continuous rotation of the spindle. And the cutting vibration signal generated by the tool's continuous cutting; in addition, the calculation step has a processing device connected to the acceleration gauge signal, and the processing device can input the initial vibration signal, the turning vibration signal, and the rotation vibration signal. And the cutting vibration signal are calculated, and a fractional-order chaotic self-synchronization method is used to successively obtain a dynamic error signal. After the center of gravity points of each dynamic error signal state are marked, the center of gravity points of each dynamic error signal state are successively displayed to form a distribution pattern of the center of gravity points of each dynamic error state (that is, a drawing step), and then After establishing a matter-element model of the aforementioned center-of-gravity point distribution pattern, different tool types are erected, and the aforementioned measurement steps, calculation steps, and drawing steps are repeated to establish different matter-element models. The iso-element model can be stored in the processing device (model establishment step); finally, the judgment step is provided with another tool for cutting processing on the machine, and the aforementioned tool undergoes the measurement step, the calculation step and the Drawing steps, etc. to obtain a center-of-gravity point distribution pattern, and the center-of-gravity point distribution pattern is then combined with the matter element models Compare with the processing device to determine the use status of the tool; therefore, only a single accelerometer sensor can be used to reduce the amount of calculation and quickly analyze the results, achieve good accuracy, and effectively reduce detection Required cost effectiveness.

FIG. 1 is a flow block diagram of a preferred embodiment of the present invention.

FIG. 2 is a two-dimensional diagram of a Chen-Lee chaotic system according to a preferred embodiment of the present invention.

FIG. 3 is a three-dimensional view of a Chen-Lee chaotic system according to a preferred embodiment of the present invention.

FIG. 4 is a classic domain and node domain diagram of each state of a preferred embodiment of the present invention.

FIG. 5 shows the variation of the error value e1 in a preferred embodiment of the present invention.

FIG. 6 shows a variation of the difference e2 of a preferred embodiment of the present invention.

FIG. 7 is a dynamic trajectory diagram of each state of a fractional order (order 0.1) according to a preferred embodiment of the present invention.

FIG. 8 is a dynamic trajectory diagram of each state of a fractional order (0.2 order) according to a preferred embodiment of the present invention.

FIG. 9 is a dynamic trajectory diagram of each state of a fractional order (0.3 order) according to a preferred embodiment of the present invention.

FIG. 10 is a dynamic trajectory diagram of each state of a fractional order (0.4 order) according to a preferred embodiment of the present invention.

FIG. 11 is a dynamic trajectory diagram of each state of a fractional order (0.5 order) according to a preferred embodiment of the present invention.

FIG. 12 is a dynamic trajectory diagram of each state of a fractional order (order 0.6) according to a preferred embodiment of the present invention.

FIG. 13 is a dynamic trajectory diagram of each state of a fractional order (0.7 order) according to a preferred embodiment of the present invention.

FIG. 14 is a dynamic trajectory diagram of each state of a fractional order (0.8 order) according to a preferred embodiment of the present invention.

FIG. 15 is a dynamic trajectory diagram of each state of a fractional order (order 0.9) according to a preferred embodiment of the present invention.

FIG. 16 is a dynamic trajectory diagram of each state of an integer order according to a preferred embodiment of the present invention.

Fig. 17 shows the signal of the status monitoring system as normal.

Figure 18 shows the signal of the condition monitoring system to show a slight wear.

Figure 19 shows the signal of the state monitoring system showing moderate wear.

Figure 20 shows the signal of the state monitoring system for severe wear.

The foregoing and other technical contents, features, and effects of the present invention will be clearly understood in the following detailed description of the preferred embodiments with reference to the accompanying drawings.

Referring to FIG. 1, a preferred embodiment of the present invention is a method for detecting tool wear on a machine tool, which is used to detect a tool wear condition on a machine tool, and the machine tool has a rotatable spindle, The fixture rotated by the main rotation, and a tool held by the fixture, the detection method sequentially includes a preparation step, a measurement step, a calculation step, a drawing step, a model building step, and a judgment step. Among them, the An accelerometer, such as a three-axis accelerometer, is attached to the fixture step. The accelerometer can be divided into a master system and a slave system. The so-called master system and slave system in the present invention It uses chaos theory to perform calculations.

Continuing the foregoing, the measuring step uses the acceleration gauge to measure the vibration of the tool on the fixture, and the main system can receive the initial vibration signal generated by the acceleration gauge to detect the initial rotation of the spindle, and the tool cutting first The turning vibration signal (that is,) generated by the tool, and the slave system can receive the turning vibration signal that the acceleration gauge detects the continuous rotation of the spindle, and the cutting vibration signal generated by the tool continuously cutting.

Continuing the foregoing, the calculation step has a processing device connected to the acceleration gauge signal, and the processing device can calculate the input initial vibration signal, the turning vibration signal, the rotation vibration signal, the cutting vibration signal, etc., A fractional-order chaotic self-synchronization method is adopted to successively obtain a dynamic error signal, and the center of gravity for each dynamic error signal state The points are marked; in addition, the drawing step is to successively display the center of gravity points of each dynamic error signal state, and form a center of gravity point distribution pattern (also referred to as a dynamic trajectory diagram) of each dynamic error state.

Continuing the foregoing, the step of establishing a model is to establish a matter-element model (a dynamic trajectory map of a specific state) of the aforementioned center-of-gravity point distribution pattern, and then prepare different tool types for erection, and repeat the aforementioned measurement steps, The calculation step and the drawing step to establish different matter-element models, and the aforementioned matter-element models can be stored in the processing device; in addition, the judgment step is provided with another tool for cutting processing on the machine tool, The aforementioned tool passes the measurement step, the calculation step, the drawing step, etc. to obtain a center-of-gravity point distribution pattern, and the center-of-gravity point distribution pattern is compared with the matter-element models in the processing device to determine The tool usage status.

Therefore, in the actual operation of the present invention, an acceleration gauge is used to capture vibration signals into the master and slave systems. Among them, the master system (MS) is a vibration signal with ideal tool health and the slave system (SS) is real-time captured vibration. Signal, the chaotic dynamic error and its chaotic attractor can be obtained by subtracting the master and slave systems. The master-slave chaotic system is shown in Equation 1:

among them The vibration signal is the ideal tool state. The state vector calculated by the chaotic system, For real-time acquisition of vibration signals, the state vector calculated by the chaotic system is an M × N matrix, F (X) and F (Y) are non-linear matrices, and u is a non-linear control term. The dynamic error of the servo system is used as the identification feature of tool wear, so u is 0, and the dynamic equation of the chaotic system of Equation 2 is converted into the main, and the type of the servo system can be expressed as Equation 3:

In Equation 3, α , β, and γ are system parameters of the Chen-Lee system. After Lyapunov exponent verification, if the conditions α > 0, β <0, 0 < α <-( β + γ ) are satisfied, the system has chaos. Characteristics of attractors, and in this embodiment, the signal with ideal health status is substituted into the master system, and the slave system is substituted into the real-time extracted vibration signal, and the results generated by the chaos system are subtracted by individual chaos calculations. The chaotic dynamic error of the master-servant and the chaotic attractor can be obtained, and the two results can be used as the characteristic basis for classification; and the embodiment of the present invention exemplifies the above parameters α , β , and γ as (5, -10, -3.8 ), And the initial conditions of x , y , and z are 0.001, the system generates chaos, as shown in Figures 2 and 3, and the main architecture of its chaotic system, X R ^N , Y R ^N , where X = [ x ₁ , x ₂ , x ₃ ] ^T , Y = [ y ₁ , y ₂ , y ₃ ] ^T , U = [ u ₁ , u ₂ , u ₃ ] ^T , its main system With slave system Is a 3 × 3 system matrix, the main system X is as shown in equation (4), and the slave system Y is as shown in equation (5):

At the same time, in order to realize the synchronous dynamic error of the master system in the present invention, the control item u ₁ = u ₂ = u ₃ = 0 is designed, and the error state e = [ e ₁ , e ₂ , e ₃ ] ^{T is} defined, where e ₁ = x ₁ - y ₁ , e ₂ = x ₂ - y ₂ , e ₃ = x ₃ - y ₃ , the error system can be expressed as equation (5) after finishing:

Its parameters need to meet the conditions of a > 0, b <0, c <0 and 0 < a <-( b + c ), then the system error dynamic equation has the characteristics of singular attractors.

Its parameters need to meet the conditions of a > 0, b <0, c <0 and 0 < a <-( b + c ), then the system error dynamic equation has the characteristics of singular attractors.

In addition, in the present invention, the fractional-order chaotic self-synchronized dynamic error generates a dynamic error. In order to more accurately represent the characteristics of each state of the tool vibration signal, it is written as a formula (6) in fractional order, where m is an arbitrary real number and e is a dynamic error. , Γ can choose the required order.

q is a fractional order, where q = (1- γ ) and satisfies 0 < q <1, and Γ (˙) is a Gamma function. In order to generate chaotic attractors in equation (5), the equation is rewritten as equation (7 ),

The system parameters a ' , b' , and c ' are non-zero constants. The phase trajectory shows various dynamic behaviors when the fractional order is q . In order to make equation (7) achieve self-synchronous tracking, its dynamic error is set to: e ₁ [ i ] = x [ i ] -y [ i ], e ₂ [ i ] = x [ i +1] -y [ i +1], e ₃ [ i ] = x [ i +2] -y [ i +2 ], I = 1,2,3, ..., n -2, then Φ ₁ [ i ], Φ ₂ [ i ], Φ ₃ [ i ] can be defined as formula (8)

Finally, the establishment of the matter-element model is to use extension theory as a method for determining the degree of tool damage. By determining the size of the classical domain correlation function, and comparing the set matter-element model, the matter-element model of extension theory can be expressed as: (9)

Where R is a thing, N is a self-defined thing name, C is a feature of the thing, and V is a magnitude of the thing. The present invention uses the chaotic attractor phase plane coordinate system generated by the dynamic error of the fractional chaotic system to establish Extend the magnitude of the theoretical matter-element model, and in this embodiment, the set matter-element model has four states: normal state, light wear, moderate wear, and heavy wear. The matter-element model is as follows:

After the matter-element model is established, the fractional chaotic attractor phase plane coordinate divisions of each state are defined. The phase plane coordinate system of the matter-element model can define the classical domain and node domain in the extension set. As shown in Fig. 4, the extension correlation degree between -1 and 1 can be obtained through the operation of Equations 10 and 11.

In the figure above, the blue frame is the nodal field, the green frame is the heavily worn classic domain, the red frame is the moderately worn classic domain, the orange frame is the lightly worn classic domain, and the yellow frame is the classic domain in good condition. After the definition of the node domain and the classical domain is completed, other vibration signals can be subsequently substituted. After chaos analysis, the chaotic attractor is taken. The equations 10 and 11 are used to calculate the states of each state. The correlation degree value of the canonical domain can be defined as the state if the correlation degree is greater than 0, otherwise it is not. In this case, it can be determined that the state meets the characteristics of the best degree, and the state of the tool compared to the correlation function is diagnosed.

Therefore, when in use, that is, through the equipment step, measurement step, calculation step, and model building step to change the tools in different states, the present invention is in a normal state, light wear, The matter-element model is established as the target of comparison in the four states of moderate wear and severe wear. Therefore, after the tool is replaced, the vibration condition is measured by the acceleration gauge, that is, the source of the signal of the main system is is electrically opened by the spindle and the tool rest turning first knife vibration signal, the signal source for the slave system when the motion signal of the current measurement of the tool spindle and the operation signal, and to enter the above equation, the generated e 1, e The trajectory diagram of the differential error of 2, e 3, according to the four states specified: normal, slight wear, moderate wear, and severe wear. when the state signal is a state of cutting through the dynamic error e 1, e 2 in FIG plotted dynamic trajectory observed differences, in order to establish matter of extension theory, as the basis for determining the state of the signal, and finally According to the state of different tools to do continuous cutting action, and continuously capturing signal, and finally an error can be obtained as shown in FIG. 5 and 6 e1.e2 value of the amount of change.

With reference to Figures 7 to 17, using the fractional and integer order chaotic systems to process the dynamic error trajectory of the tool vibration signal, a relatively linear change can be found in the 0.7 to integer order, and the 0.1 to 0.6 order is too divergent, so first exclude Order 0.1 to 0.6, and then calculate the dynamic error characteristic amount from order 0.7 to integer, and build a matter-element model for each order. Finally, compare the accuracy of each order, and use wavelet packet analysis and discrete Fourier transform. The methods are compared, and the results of troubleshooting are as follows:

It is still the same as above, so in the experiment, you can first install the cutters in each state and run at the same speed and feed rate, capture the vibration signals to establish a database of each state, and draw a chaotic dynamic error trajectory map according to the database to establish each Extensible matter-element model of states and the state-extended matter-element model table:

After the database is constructed, the present invention uses a human-machine interface for establishing a smart tool condition monitoring system, and the user can know the current tool vibration signal and For the state, it can be seen in Fig. 17 to Fig. 20 that the system can accurately judge the tool state, and according to the established light bulb display, each state is calculated by using the matter-element model of each state's matter-element model table to calculate the extension correlation function. The results are obtained afterwards, and each time the vibration signal of the tool is retrieved, the user can judge the state of the machine tool. Therefore, only a single accelerometer sensor can be used to reduce the calculation amount and quickly analyze the results. Achieve good accuracy, and effectively reduce the cost-effectiveness required for detection.

To sum up, the method for detecting tool wear of a machine tool according to the present invention includes a preparation step, a measurement step, a calculation step, a drawing step, a model building step, and a judgment step; it is affixed through the fixture of the machine tool. There is an accelerometer, and the signals measured by the accelerometer are processed through a slave system and a master system respectively to generate chaotic synchronization signals. After generating a dynamic error signal, use the above dynamic error to draw the distribution map of the center of gravity points of each state of the dynamic error. And based on the characteristics of the wear conditions of each tool, a matter-element model is created, so that the center-of-gravity point distribution map generated during subsequent operations can be compared with the matter-element model to determine the state of the tool; so, so only By using a single accelerometer sensor, the calculation amount can be reduced and the results can be quickly analyzed to achieve good accuracy and effectively reduce the cost-effectiveness required for detection.

However, the above are only for describing the preferred embodiments of the present invention. When the scope of implementation of the present invention cannot be limited by this, that is, the simple equivalent changes and modifications made according to the scope of the patent application and the content of the invention specification of the present invention , All should still fall within the scope of the invention patent.

Claims (3)

A tool wear detection method for a machine tool, which is used to detect the tool wear status on a machine tool, and the machine tool has a rotatable main shaft, a jig rotated by the main rotation, and a The detection method of the tool held by the fixture includes: a preparation step, an acceleration gauge is attached to the fixture, and the acceleration gauge can be divided into a main system and a slave system; a measuring step, The acceleration gauge is used to measure the vibration of the tool on the fixture, and the main system can receive the initial vibration signal generated by the acceleration gauge to detect the initial rotation of the spindle, and the turning vibration signal generated by the tool when cutting the first cutter. As for the slave system, it can receive the rotational vibration signal that the acceleration gauge detects the continuous rotation of the spindle, and the cutting vibration signal generated by the tool continuous cutting; a calculation step, which has a processing device connected to the acceleration gauge signal, the The processing device can calculate the input initial vibration signal, the turning vibration signal, the rotation vibration signal, and the cutting vibration signal, etc. In a synchronous manner, a dynamic error signal is successively obtained, and the center of gravity points of each dynamic error signal state are marked; a drawing step, the aforementioned center of gravity points of each dynamic error signal state are successively displayed to form the each dynamic A distribution diagram of the center of gravity points of the error state; and a model building step, which establishes a matter element model of the distribution map of the center of gravity points, and then sets up different tool types for erection, and repeats the foregoing measurement steps and calculation steps And the drawing step to establish different matter-element models, and the aforementioned matter-element models can be stored in the processing device; a judgment step is provided with another tool for cutting processing on the machine, and the tool is The measurement step, the calculation step, the drawing step, etc., obtain a center-of-gravity point distribution pattern, and the center-of-gravity point distribution pattern is compared with the matter-element model on the processing device to determine the use state of the tool. According to the method for detecting tool wear of a machine tool described in item 1 of the scope of the patent application, the fractional order chaotic self-synchronization method is used to calculate the optimal fractional order from the 0.7th order to the integer order dynamic error feature. According to the method for detecting tool wear of a machine tool described in item 1 of the scope of the patent application, the matter element model can be divided into normal state, slight wear, moderate wear, and heavy wear.