JP2693664B2 - Machining state judgment device in end mill machining - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION The present invention is applicable to end mill processing.
A processing state determination device that determines whether the processing state is normal
Related.

[0002]

2. Description of the Related Art In recent years, in processing machines, so-called processing has become more intelligent, and the state of processing is detected by a sensor such as a six-component force table, and correction is made in real time according to the processing state, thereby improving processing. Higher precision is being attempted. For example, if chattering occurs during cutting with an end mill, the processing accuracy will be impaired. Therefore, in order to detect chatter, conventionally, the magnitude of the cutting force is detected by a force sensor installed on the tool side or the workpiece side, and the detected value is FFT.
(Fast Fourier transform) may be performed to extract and determine the vibration frequency component of chatter. FIG. 13 shows a change in the cutting force (X-axis direction) measured in the actual groove cutting process. The processing conditions in this case are as follows.

Tool: Square end mill (2 blades) Tool diameter: φ5 mm Twist angle: 30 ° Workpiece: S45C Spindle speed: 2200 rpm Feed rate: 60 mm / min Feed direction: Y-axis positive direction Depth of cut: 2.5 mm Machining type: Groove cutting

FIG. 14 shows a frequency distribution (power spectrum) obtained by performing a fast Fourier transform on the change in cutting force shown in FIG. FIG. 15 shows changes in the cutting force in the Y-axis direction during the cutting shown in FIG. FIG. 16 shows a frequency distribution (power spectrum) obtained by performing a fast Fourier transform on the change in cutting force shown in FIG. In these FIGS.
It is in the state where normal cutting is being performed.
In No. 5, the peak value of the cutting force is the rotation cycle of the tool (about 0.02
It appears twice every 7 seconds). Also, FIGS.
In FIG. 6, it can be seen that the components of multiples of the frequency of the peak value appearing in FIGS. 13 and 15 stand out.

FIGS. 17 and 19 show changes in the cutting force when the chattering phenomenon occurs in the cutting under the same conditions as described above in the X-axis and Y-axis directions, respectively. 18 and 20 show frequency distributions (power spectra) obtained by fast Fourier transforming the changes in the cutting force in FIGS. 17 and 19, respectively. 17 and 19, the change component of the cutting force due to the chattering phenomenon is superimposed on the change of the cutting force due to normal cutting. Further, in FIGS. 18 and 20, as is clear from comparison with FIGS. 14 and 16, in addition to the frequency component corresponding to the original peak value of the cutting force, a large amount of the chattering phenomenon appears (especially about 800 Hz). Around). In this way, the occurrence of the chattering phenomenon can be detected by performing the fast Fourier transform on the detected change in the cutting force.

[0006]

By the way, as a method of judging the occurrence of the chattering phenomenon from the waveform of the cutting force and the result of the fast Fourier transform of the cutting force, the simplest method is to visually check the human. However, since there is a demand for automation and unmanned processing, it must be determined by a computer. It is possible to use a pattern recognition method as a specific determination method therefor, but the image processing time becomes long and it is impossible to make determination in real time during processing.

It is also possible to pass the result of the fast Fourier transform through a low-pass filter and take out only the required waveform to make a decision, but similarly the processing time becomes long and it is impossible to make a decision in real time. Moreover, as is well known, the result of the fast Fourier transform has an error, and the processing / judgment is performed while the error is included.
It may be insufficient in terms of accuracy.

As described above, conventionally, a change in cutting force during end mill processing can be detected by using a six-component force sensor or the like, but sufficient processing speed and accuracy can be obtained at the stage of judging the presence or absence of processing abnormality from the detected value. However, there is a problem in terms of practicability because the method for determining the above is not established. The present invention has been made to solve the above problems, and an object thereof is to determine whether or not there is an abnormality in the processing state in end mill processing at high speed and with high accuracy, and to perform optimum processing control. It is to provide a processing state determination device in end mill processing .

[0009]

Means for Solving the Problems Therefore, the above problems are solved.
For this purpose, the present invention provides a machine tool for end milling.
In addition, the processing reaction force generated between the end mill and the workpiece is large.
Install a sensor that detects the size and the direction.
The detected processing reaction force value by as parametric time
Instead of looking at the average force of torque,
The trajectory of a vector on a multidimensional space containing the directions forming the plane.
The center of gravity of the figure surrounded by the trace while drawing as a trace
Ask for. Next, each point on the vector trajectory from the center of gravity of the figure
Calculate the variance of the distance to
By comparing with
judge.

[0010]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below with reference to the drawings. FIG. 1 is a diagram showing an overall configuration when the present invention is applied to end mill machining of a machining center. In the figure, the processing condition determination unit 1 determines the processing conditions (material of the work, type of tool, cutting speed, feed speed, depth of cut) with reference to the database 22 based on the input design information. Then, the specific operation amount of each actuator for operating the machining center 5 is determined and sent to the machining machine controller 2, the machining physical model 3, and the sensor information predictor 4.

The machining machine controller 2 drives the rotating mechanism 7 and the moving mechanism 6 of the tool 9 and the moving mechanism 11 of the workpiece 14 based on the operation amount to control the machining of the workpiece 14 by the tool 9. The drive amount of the rotation mechanism 7 and the movement mechanisms 6 and 11 is corrected by the operation signal from the adaptive controller 21. The force sensor 8 installed in the tool 9 detects a processing reaction force generated in the tool 9 during processing and sends a detection signal proportional to the processing reaction force to the strain amplifier 16. The force sensor 8 is composed of a six-component force sensor or the like and detects the processing reaction force generated in the tool 9 as a six-axis component.

Similarly, the force sensor 13 installed on the side of the workpiece 14 detects a processing reaction force generated on the workpiece 14 during processing and outputs a detection signal proportional to the processing reaction force to the strain amplifier 1.
Send to 7. A fail-safe mechanism 15 is provided between the rotation mechanism 7 and the force sensor 8, and a moving mechanism 11 and the force sensor 1 are provided.
A fail-safe mechanism 12 is installed between each of the 3 and 3, and when an excessive load is applied to the tool 9 and the workpiece 14 during machining, the tool 9, the workpiece 14 and the force sensor 8,
Prevent damage to 13. The distortion amplifiers 16 and 17 respectively amplify the detection signals and send them to the sensor information acquisition device 18.

The sensor information acquisition device 18 sends each detection signal to A
/ D-converted and sent to the state determiner 19 at a predetermined timing. On the other hand, a physical model 3 of machining corresponding to the machining center 5 is created from the operation amount output from the machining condition determining unit 1, and the machining phenomenon between the tool 9 and the workpiece 14 is reproduced or predicted. . The model data obtained by the processing physical model 3 is sent to the sensor information predictor 4.
The sensor information predictor 4 predicts the detection values of the force sensors 8 and 13 from the operation amount and the model data, and sends the prediction value to the state determiner 19.

Here, the state determiner 19 compares the input actual detection value of the force sensors 8 and 13 with the predicted value, and if the detected value exceeds the allowable range of the predicted value, it is determined as a machining abnormality. Then, the state determination signal is sent to the adaptive controller 21 and the database 22. Even if the allowable range is not exceeded, how appropriate the current machining state is is determined by the magnitude of the value. In the state determiner 19,
Although the detection values of both the input force sensors 8 and 13 are used as the determination reference, it is also possible to use only the detection values of one sensor to make the determination.

When the state determination signal is input, the adaptive controller 21 creates an operation signal for improving the processing state and sends it to the processing machine controller 2. The database 22 stores in advance rules regarding machining, knowledge regarding tools and workpieces, knowledge regarding machining conditions and machining results, and further acquires and accumulates knowledge regarding machining from the state determination signal from the state determiner 19. Although not shown, the knowledge in the database 22 is also used by the adaptive controller 21 and the physical model 3 for machining, which is not shown, in addition to being called by the machining condition determining unit 1.

Incidentally, the rules regarding machining in the database 22 are those which, when a chattering phenomenon occurs during machining, for example, reduce the feed rate and increase the number of revolutions so that concrete measures are taken when an abnormality occurs. Is. Further, the knowledge about the tool and the workpiece is a usage condition for performing appropriate machining such as the type of machining that can be performed by the tool and the machining range by the combination of the tool and the workpiece.

Similarly, the knowledge about the machining conditions and the machining results is already known empirically such as "a chattering phenomenon occurs with a certain tool at any rotation speed and feed rate". In addition to that, the knowledge actually gained by experience at the machining center 5 is also accumulated. That is, when the result of processing under a certain processing condition is defective, the processing result is fed back and the processing condition is corrected. In this way, the learning is repeated as the processing experience is gained, and the accuracy of the accumulated knowledge is improved. In addition, rules regarding processing are also accumulated.
The specific procedure for this learning, the paper "Warisawa Shinichi by the present inventors, has announced, Mitsuishi Mamoru, Takaaki Nagao," Machining
Construction of optimum machining condition determination system for robot ”, Procee
ding of the Symposium on Industrial Applications o
f PROLOG, 1989 (INAP'89), Tokyo, 1989. The method and the like already known can be used.

Next, a method for determining a machining abnormality will be specifically described. First, the value of the machining reaction force generated between the tool 9 and the workpiece 14 when the workpiece 14 is machined is obtained by the physical model 3 based on the machining condition determined by the machining condition determination unit 1. FIG. 2 shows changes in the machining reaction force obtained based on the physical model 3 of machining under the following machining conditions as two-direction components on the X and Y planes perpendicular to the axial direction of the tool 9. , Is a vector locus with time as a parameter.

Tool: Square end mill (2 blades) Tool diameter: φ5 mm Twist angle: 30 ° Workpiece: S45C Spindle speed: 2200 rpm Feed rate: 60 mm / min Feed direction: Y-axis positive direction Depth of cut: 2.5 mm Machining type: Groove cutting

As is apparent from the figure, the change in the machining reaction force has a periodicity and the locus of its vector is a closed curve. Since the tool 9 is a double-edged tool, it halves the closed curve. Make one lap. Next, the center of gravity of the figure enclosed by the closed curve drawn by the vector locus and the variance of the vector locus are obtained. FIG. 3 is an enlarged view of the vector locus of FIG. 2. After obtaining the center of gravity (Gx, Gy), each point on the vector locus (Fx1, Fy1) from the center of gravity (Fx1, Fy1) ...
The variance of the distance to (Fxi, Fyi) ...
The value of this variance is a small value if the vector locus is close to a circle and has a smooth shape, and is large if the shape has many undulations different from the circle, and the change in the outer shape of the rotating figure is estimated. Can be used as a guide.

Next, let us compare the values of the processing reaction force detected from the actual processing. FIG. 4 shows the tool 9 when the workpiece 14 is actually machined under the same machining conditions as the machining shown in FIG.
The detected value of the processing reaction force generated between the workpiece and the workpiece 14 is represented as a vector locus. Tool 9 in the figure
1 is represented by one rotation, and the vector trajectory is a double elliptical trajectory. Compared with the locus of the model shown in FIG. 2, a slight undulation is seen on the outer shape, which indicates a normal working state. The center of gravity and the variance are similarly obtained from this trajectory.

FIG. 5 shows the locus of the vector when the chattering phenomenon occurs in the processing shown in FIG. As is clear from the figure, when the chattering phenomenon occurs, the amplitude appears in the locus and the value of dispersion increases. Also for this locus, the center of gravity and amplitude for one rotation are obtained. FIGS. 6 and 7 show the temporal change of the dispersion obtained from the values actually measured in this way. FIG. 6 shows the change in dispersion in the normal processing shown in FIG. 4 in comparison with the dispersion value (161N ² ) obtained from the physical model.

FIG. 7 shows the variation of dispersion due to the abnormal processing shown in FIG. As is clear from a comparison between the two figures, in the normal case, the variance value is within several times the value obtained from the physical model, and the degree of change is moderate.
On the other hand, in an abnormal case, the chattering phenomenon itself appears as a waveform, and the dispersion value is extremely larger than the model value, and the change is drastic. As a result, it is possible to determine whether the machining is normal or abnormal based on the magnitude of the variance and the degree of change. That is, the ratio of the variance between the measured value and the model value is set in advance, the variation of the variance value obtained during machining is monitored, and if it exceeds the set ratio, it is determined that machining is abnormal. The values of the center of gravity and the variance are for one half rotation of the tool 9,
Alternatively, it can be calculated for each detection data for two rotations.

FIGS. 8 and 9 show changes in the value of dispersion in the normal processing and the abnormal processing in other processing examples, respectively. In this processing example, the feed rate is 120 mm / min, and the other processing conditions are the same as those in the processing example described above. By setting the feed rate to 120 mm / min, the dispersion value obtained from the model is 349 N ² . 10 and 11 respectively show a normal case and an abnormal case in other processing examples. In this processing example, the feed rate is 90 mm / min
Other processing conditions are the same as the processing example described above. By setting the feed rate to 90 mm / min, the dispersion value obtained from the model is 251 N ² .

In each of the machining examples described above, the center of gravity and the variance were obtained from the two-dimensional vector locus, focusing on the biaxial component in which the machining reaction force changes remarkably. If it is conspicuous only for one axis, it is possible to determine the machining abnormality by the same process even for only one axis. Further, if the change cycle is the same, it is possible to determine the machining abnormality by obtaining the center of gravity and the variance as the trajectory of the vector in the multidimensional space using the detection values of three or more axes.

FIG. 12 is a flow chart systematically showing the determination processing of the above-mentioned embodiment, and the following description will be made with reference to the drawing. First, the force sensors 8 and 13 detect the processing reaction force generated on the tool 9 and the workpiece 14 during processing (S
1). Next, the center of gravity of the figure drawn by the trajectory when the detected one cycle of the processing reaction force value is represented as the trajectory of the vector is obtained (S2), and the variance of the distance from the center of gravity to each point on the trajectory of the vector is obtained. (S3).

The obtained dispersion value is compared with a reference value set in advance in accordance with the processing conditions. If the obtained dispersion exceeds the reference value (S4 yes), a processing abnormality has occurred. As a matter of course, the machining conditions such as the rotational speed of the tool 9 and the feed speed of the workpiece 14 are changed to avoid abnormal machining (S
5). In this way, even if a chattering phenomenon occurs during machining, the tool 9 and the work piece 14 are prevented from being damaged by being immediately detected and returned to the normal machining state, and the life of the tool 9 is increased. However, the yield of processed products is also improved. In the embodiment, the presence / absence of abnormality is determined by paying attention to the change in the two-direction components of the generated processing reaction force of the X-axis and the Y-axis. Even if there is, it is similarly possible, and the measured values are combined so as to make the determination most easily according to the characteristics of the actual determination target.

In the embodiment, the chattering phenomenon has been described as a machining abnormality, but other machining abnormalities, for example, machining abnormalities due to wear or damage of the tool 9 can be detected in the same manner. Furthermore, as another processing machine,
It can also be applied to lathes, grinders, drilling machines, etc. Further, it is also applicable to objects other than processing machines. That is,
If the measured value is the state quantity of the object that changes with time and has periodicity, the measured value is used as the vector locus to determine the center of gravity and variance, and it can be compared with the reference value to determine whether there is an abnormality. Is. As the reference value, a physical model may be used, or a fixed value may be set in advance.

[0029]

According to the present invention as described above, according to the present invention, determine the centroid and variance of the values of the processing reaction force generated between the cell down <br/> Sa end mill and the workpiece detected, obtained By comparing the variance value and the reference value to determine whether or not there is an abnormality in the processing, it is possible to make the determination if the detected data amount is only one cycle in which the center of gravity and the variance are obtained. Therefore, it is possible to determine the presence or absence of an abnormality in a short time of operation, ing so can determine the working status of the working machine in real time.

[Brief description of the drawings]

FIG. 1 is a diagram showing an overall configuration of an embodiment of the present invention.

FIG. 2 is a graph showing a machining reaction force obtained based on a physical model of machining as a vector locus.

FIG. 3 is a graph showing the trajectory of the vector of FIG. 2 in an enlarged manner and showing the center of gravity of the trajectory and the distance from the center of gravity to the trajectory of the vector.

FIG. 4 is a graph showing a machining reaction force actually generated as a vector locus.

FIG. 5 is a graph showing a locus of a vector when a machining abnormality occurs.

FIG. 6 is a graph showing changes in dispersion during normal processing.

FIG. 7 is a graph showing changes in dispersion due to abnormal processing.

FIG. 8 is a graph showing another example of changes in dispersion during normal processing.

FIG. 9 is a graph showing another example of changes in dispersion due to abnormal processing.

FIG. 10 is a graph showing another example of changes in dispersion during normal processing.

FIG. 11 is a graph showing another example of changes in dispersion due to abnormal processing.

FIG. 12 is a flowchart systematically showing the determination processing of the embodiment.

FIG. 13 is a graph showing changes in the X-axis component of the actually measured cutting force.

FIG. 14 is a graph showing the result of fast Fourier transform of the change in cutting force in FIG.

FIG. 15 is a graph showing changes in the Y-axis component of the actually measured cutting force.

16 is a graph showing the result of fast Fourier transform of the change in cutting force in FIG.

FIG. 17 is a graph showing changes in the X-axis component of the cutting force when a cutting abnormality occurs.

18 is a graph showing the result of fast Fourier transform of the change in cutting force in FIG.

FIG. 19 is a graph showing changes in the Y-axis component of the cutting force when a cutting abnormality occurs.

20 is a graph showing the result of fast Fourier transform of the change in cutting force in FIG.

[Explanation of symbols]

 1 Machining condition determination unit 2 Machining machine controller 3 Physical model of machining 4 Sensor information predictor 5 Machining center 6 Moving mechanism 7 Rotating mechanism 8 Force sensor 9 Tool 11 Moving mechanism 12 Fail-safe mechanism 13 Force sensor 14 Workpiece 15 Fail Safe mechanism 16 Distortion amplifier 17 Distortion amplifier 18 Sensor information acquisition device 19 State determination device 21 Adaptive controller 22 Database

Claims (1)

(57) [Claims] 1. A machine tool for end mill processing
Of the processing reaction force generated between the end mill and the workpiece.
A sensor that detects the size and direction, and the processing reaction force detected by the sensor with time as a parameter.
Instead of looking at the value as an average force called torque, the value is dropped on the spindle.
Vector in multidimensional space containing directions forming a straight plane
Of the figure surrounded by the locus
Means to find the center of gravity and variance of the distance from the center of gravity of the figure to each point on the vector trajectory
And whether the end milling is normal by comparing the variance value with the reference value.
Machining state determination apparatus of the end milling, characterized in that it comprises a means for determining whether.