JPS6144624B2 - - Google Patents

Description

[Detailed description of the invention]

The present invention is a specialized machine that processes a large number of certain parts,
Regarding the method of detecting abnormalities during machining in numerically controlled processing machines, in particular machining abnormalities due to cutting tool wear and tool life, material abnormalities due to material quality or excessive or insufficient machining allowance, and other processing machine malfunctions and failures.
The present invention relates to a method for detecting processing abnormalities due to poor attachment etc. when attaching a material to a processing machine. Traditionally, this type of processing abnormality has been detected by visually observing the dimensions of the part after processing and the surface condition of the machined surface, or by the operator's sense of the processing noise, vibration, and other malfunctions of the processing machine. An abnormality was detected. However, when abnormalities are detected using human sense judgment, special-purpose machines that process many of the same items may be delayed in detecting abnormalities, resulting in a large number of products being defective, cutting tools being damaged, or the processing machine itself being damaged. There was a problem that caused the unit to malfunction. For this reason, there are generally safety devices depending on the capacity of the drive motor, etc. of the processing machine, such as fuses, circuit breakers, and protective relays that turn on if a current exceeding the capacity of the drive device flows. In addition, there are safety devices such as limit switches that stop the operation of the blade if it deviates from a certain predetermined movement path, but these simply operate according to their respective functions and do not affect the workpiece or the workpiece. It is impossible to detect comprehensive machining abnormalities depending on machining conditions. For example, in a safety device that uses the limit switch mentioned above to prevent accidents or breakdowns caused by malfunctions of processing machine operators, it is necessary to It was impossible to detect processing abnormalities. Furthermore, in the latest method, a method is known in which the load during one model processing is taken for each motion path, and this waveform is simply translated vertically and monitored by determining whether the load is within the range. However, the above-mentioned conventional technology performs cutting while determining whether the load value during machining is within the range of load fluctuation tolerance of the reference value.
In other words, when calculating the load curve in the first model machining, and also in subsequent continuous machining, the load values are taken in as data from time to time, and the difference between the reference value and the load value is calculated, and the tolerance value is determined.
This is to determine whether the upper limit or lower limit of the load fluctuation range has been exceeded. Therefore, a computer with a large storage capacity is required, and in order to monitor a large number of machine tools at the same time, the equipment must be large-scale. Furthermore, in the above method, even if a chattering phenomenon occurs in the workpiece or the cutter due to a flaw in the mounting method of the workpiece, for example, it will not be detected as abnormal machining as long as it is within the load fluctuation tolerance range. In addition, we cut the first piece and processed it as a model.
Since the load condition at that time is used as the standard load value, if there are variations in the dimensions of the workpiece or the mounting condition is different, a machining error may occur even though the condition is such that machining can continue. It may be misidentified as In this way, with the conventional method described above, it is impossible to monitor errors on the motion path at the extreme points of peaks and troughs of the waveform, and it is also impossible to monitor errors such as comparison with past data with high accuracy and density. Only the safety device role described above was effective. The present invention solves the above problems and improves the workpiece, processing machine, cutter, and processing conditions (cutting speed, feed,
The present invention provides a machining abnormality detection method that can comprehensively and instantaneously determine machining abnormalities from appropriate machining conditions according to conditions such as depth of cut. The gist of the present invention is to obtain an average waveform by averaging a plurality of primitive waveforms obtained from load values during cutting with a cutting tool, and to average a plurality of moving load values on continuous motion path values of this average waveform. Based on the corrected waveform obtained by taking the waveform, a basic pattern table is created by setting the extreme points of the peaks and valleys of this waveform, the operating path values at the extreme points, the divisions of the peaks and troughs, and the allowable range of the operating path values. The contents of and
Compare the basic pattern table with a machining pattern table obtained by finding the extreme points on the corrected waveform in the same way as the basic pattern table based on the burden value for each process, and find out that the extreme points of each machining pattern table are the extreme points set in the basic pattern table. This is a machining abnormality detection method that checks whether the division of peaks and troughs, the allowable range of motion path values, and the number of extreme points match. The peaks and valleys that appear in the waveform based on the load value on the operating path during normal machining mainly occur when the direction of the cutting tool's operating path changes, but there are also other abnormalities in the cutting tool such as chipping or breakage of the tool. This can also occur due to abnormalities in the material or machining allowance of the workpiece material, errors in the machining procedure, and poor attachment of the workpiece material. In this way, the peaks and valleys appearing in the waveform contain a wealth of information regarding machining as well as abnormalities in the cutting tool. As stated above, this application
Focusing on the peaks and valleys that contain processing abnormality information,
Since the contents are checked, abnormal machining on the movement path can be accurately detected. Examples will be described below. FIG. 1 is an explanatory diagram schematically showing an apparatus in an embodiment of the present invention. In the figure, reference numeral 1 denotes a dedicated thread machining machine, which operates a motor 4 that drives the thread machining tap 3 at the end of each pipe joint and tap 2 according to the lead of the screw so that two pipe fitting elbows 2 can be tapped simultaneously. There is a drive shaft section consisting of a lead section that moves back and forth. Detect the drive position or load value P for each drive time from each start point of each drive shaft part of such a special thread processing machine, and set the drive position or drive time on the horizontal axis L to calculate the load value P. If it is expressed on the vertical axis, the primitive waveform 11 for each process as shown in FIG. 2 can be drawn. Experiments have shown that it is suitable for the present invention to detect the load on the drive shaft using the electric power value of the drive motor 4; however, there is another method of detecting the cutting torque during thread machining using a strain gauge. There are a method of detecting vibrations near the cutting tool using a piezoelectric acceleration pickup, a method of detecting vibrations using a current value of a drive motor, and any of these methods may be used. This curve can be drawn differently depending on the processing machine, processing conditions, cutter, etc., but with the same processing machine, the same processing conditions, the cutter, etc.
If the same material is processed, almost identical curves will be obtained. This principle is used to detect processing abnormalities. First, in the first check, the machining pattern table of the machining waveform obtained from each machining is
It is checked whether the motion path is within the allowable range set in the basic pattern table, whether the peaks and valleys at the poles match, and whether the number of poles matches. This will be explained below with reference to FIGS. 2 to 4. In FIG. 2, first, an average is taken from n primitive waveforms 11 to obtain an average waveform 12, and a plurality of consecutive moving load values on the motion path of this average waveform are averaged to smooth the average waveform 12. . If this moving average is represented in a diagram, a modified waveform 13 as shown in FIG. 3 can be obtained. The moving average is calculated by the method shown in Table 1, as an example.

[Table] The extreme points of the peaks and valleys on the corrected waveform obtained in this way are imagined as a pattern waveform 14 as shown in FIG. 4, and a pattern table as shown in Table 2, for example, is created.

[Table] Regarding the method of finding the extreme points of peaks and valleys from the corrected waveform 13, continuously find the difference before and after the moving average load value shown in Table 1 above, and change the polarity of this value to be positive or negative. can be determined and obtained. The image of the pattern waveform 14 obtained from a plurality of extreme points in this way is created as a pattern table as shown in Table 2, and each extreme point, that is, the pattern number, the classification of peaks and valleys for each, and the operation path value are stored in this table. Alternatively, the basic pattern table is completed by setting the operating time L and the operating path value tolerance range LB. From this completed basic pattern table and the original waveform data obtained during each machining process, a modified waveform 13 is continuously obtained by a moving average obtained using the same method as for the basic pattern table, and the modified waveform 1 is obtained.
Find the extreme points from 3 and visualize them as pattern waveform 14, create a machining pattern table in the same manner as above, and judge whether the peak/valley division and motion path value for each pattern number are within the allowable range LB. Let's do it. If there is an abnormality in the judgment for each pattern table number, it will be rejected as abnormal machining. If there is no abnormality, proceed to the next check. In this way, in this method, the peaks and troughs on the corrected waveform are found, and the contents of these peaks are checked.
It is not necessary to monitor all of the waveforms, the intermediate part can be omitted, and the storage capacity for information processing can be greatly reduced. In addition, by determining the number of extreme points and the classification of peaks and valleys, it is possible to detect an increase in peaks and valleys that should not appear due to chattering phenomena due to a chipped tool edge, poor attachment of the workpiece material, and partial material defects in the workpiece material. Changes in classification can be monitored. Furthermore, the number of pole points does not increase even if the workpiece material fluctuates within the machining allowance range or the mounting position of the material changes within the machining allowance range by determining the motion path value at the pole points. Since the determination is not made based on the magnitude of the load value at the midpoint or the extreme point, but based on the motion path value at the extreme point, if the machining is in a normal machining state, it will not be detected as a machining abnormality. Furthermore, the basic pattern table is obtained by averaging the continuous load values during multiple machining operations to obtain an average waveform, and then using a modified waveform obtained by calculating the moving average of this average waveform. It is highly reliable as it can absorb variations. Therefore, it is possible to accurately check for abnormalities in the cutter, abnormalities in the blade movement path, abnormalities in the shape and material of the workpiece material, mechanical failures, etc., and it is possible to detect a wide range of abnormalities. Furthermore, the following checks can be performed to more precisely check and monitor the progress of the abnormality. Second, as shown in Fig. 5, a range K for monitoring load values on an arbitrary operating path is specified with parameters for the basic pattern, and upper limit values U and P of load value abnormalities within this monitoring range K are specified. , L, lower limit L,
Set P and L. In addition to these upper and lower limit values, the load value range within this range is divided, and for example, blue B, yellow E, red R lamp ranges and stop range S are specified, and within which lamp range the load value P during machining is specified. Be sure to show that it has been processed. Operation path L during machining for the upper and lower limit values set in this way
First, the load value P within the monitoring range K above is determined for each data, and the upper limit values U, P, and L of the abnormality, the lower limit value L,
If P and L are exceeded, each defective product is immediately discharged. If the abnormality is within the upper and lower limit values, then calculate the average maximum load value of the latest D maximum load values processed in the past from the current time, and this is the blue B, yellow E, and red R mentioned above.
Always display which part of the lamp range you are in. In this way, tool life and tool wear progress can be estimated at a glance. Of course, if the range of the blue B, yellow E, and red R lamps is exceeded and the stop range S is reached, the machine is brought to an emergency stop and the operator checks or replaces the tool. In addition, if the above-mentioned data is judged and exceeds the upper limit U, P, L and lower limit value L, P, L of abnormality and is discharged as defective, only the product will be discharged as defective, but the machine will continue to be processed. Then, the next part is processed. In the above explanation, the range K for controlling the load value P on the operating path L is limited to the range of one pole on the operating path L, but this monitoring range K is arbitrary depending on the pattern number on the operating path L. The range of multiple poles may be set and monitored. As a third check, as shown in Fig. 6, a certain safety margin T is provided within the monitoring range K on the operation path L explained in the second section, and the integral range W of the load value is set.
Specify the load integral value Q of the original waveform 11 being processed.
Calculate. For this calculated load integral value Q, the abnormality upper limit values U, P,
L, lower limit values L, P, and L are determined. Furthermore, by dividing the integral value range within these upper limit values U, P, L and lower limit values L, P, L, and providing, for example, blue B, yellow E, red R lamp ranges and stop range S, the load value during machining can be adjusted. The range within which the integral value Q of P is being processed is displayed. For the monitored load integral value Q set in this way, the integral value Q of the load value P within the integral range W on the operation path during machining is first calculated by 1
Judgment is made for each processing data, and the upper limit of abnormality is U, P, L,
It is determined whether or not it is within the lower limit values L, P, and L, and if it is exceeded, the defective pieces are immediately discharged one by one. This defective product follows the same path as the second defective product and is discharged as a defective product. Next, within the integral range W, calculate the average value of the load integral value Q for the past M processing waveforms from the current time, and always check which range this value falls within the blue B, yellow E, and red R lamp ranges. indicate. This blue, yellow,
The red lamp display may be displayed separately from the display based on the second load value. Furthermore, when the blue B, yellow E, and red R lamp ranges are exceeded and the stop range S is reached, the machine is naturally brought to an emergency stop and the operator is required to change tools or check the tools. The integral range W for calculating these integral values is set within the monitoring range by a certain safety margin T than the monitoring range of the second pole, and the integral value Q is calculated, but this is different from the experiment. As a result, it was found that since the curve near the pole is a gentle curve, there are many errors in the integral value and accurate judgment cannot be made.In order to obtain a more accurate judgment result, a safety margin T is set to cut out the gentle part and the integration is performed. The range is W. In this way, once the check of the load value P and the integral value Q within the monitoring range K for each piece of data and the check of the average load value and the average integral load value of the past latest plurality are confirmed, the next check is performed successively. Fourth, the machining time from the start point is sequentially stored and updated, and if the machining time exceeds a predetermined allowable range, an emergency stop is activated in the same manner as described above. If it is within the allowable range, move on to the next check. Fifth, the number of processed pieces from the start is stored, including the number of defective pieces rejected in the first, second, and third checks. Then, as before, a predetermined maximum allowable number of machining is checked, and if this exceeds the maximum allowable number of machining, the above-mentioned emergency stop is performed as the number of machining is exceeded. If this is within the allowable range, proceed to the next check. Sixthly, the number of defective items rejected by the first, second, and third checks is stored in the defect frequency table. In this defect frequency table, the defect frequency for the latest Z number of processes is continuously calculated for each process, and if it exceeds a preset allowable maximum defect frequency, such as 5%, it is considered as exceeding the defect frequency. An emergency stop of the machine is performed. If this sixth check is also within the allowable range, the previous checks are performed again following the start of monitoring of the next machining. The fourth and fifth checks of the above checks are quantity or time checks, and are not direct abnormality checks, but they more reliably check the life of tools and the quality of processed products, and provide a comprehensive check as a whole. Processing abnormalities are monitored more closely, making the effects of the present invention even more reliable. In addition, the sixth check determines whether tool replacement is required when the frequency of machining defects increases even if tool abnormalities and tool wear are within the allowable range. I do. As described above, according to the present invention, processing abnormalities due to the wear life of cutting tools and tool abnormalities such as blade chipping and breakage in special-purpose machines that process a large number of fixed parts, numerically controlled processing machines, and other processing machines such as machining centers can be realized. , Excessive material quality and processing costs,
Instantly and accurately detects material abnormalities due to undersize materials, processing machine malfunctions or breakdowns, poor material attachment, loose attachments, etc., and reliably ejects defective products, as well as accurately predicts when to replace tools. It shows excellent effects such as being able to do things. Note that by setting the load detection interval to an appropriate value, it is possible to control a plurality of processing machines at the same time, which is extremely effective in realizing unmanned processing machines.

[Brief explanation of the drawing]

Fig. 1 is an explanatory diagram schematically showing a device in an embodiment of the present invention, Fig. 2 is a diagram showing the relationship between drive time and load value, Fig. 3 is a diagram showing corrected waveforms, and Fig. 4
The figure shows a pattern image, Figure 5 explains the judgment based on the load value within the monitoring range, Figure 6 explains the integral range, and Figure 7 explains the judgment based on the integral value in Figure 6. This is a diagram. 5: CT, 6: Power converter, 7: Voltage value, 8:
Current value, 9: Microcomputer, 10: Output relay, 1
1: Original waveform, 12: Average waveform, 13: Modified waveform, 15: Pattern No.

Claims (1)

[Claims] 1. Obtain a plurality of loads along the preset motion path when cutting the workpiece with a blade, and use this as a primitive waveform.
An average waveform is obtained by averaging the loads during multiple machining of the original waveform, and the average value of the plurality of load values that have moved within a predetermined range on the continuous motion path values in the average waveform is calculated within the above range. The moving average load value is continuously determined along the operating path value, and this is used as a modified waveform.The difference between the moving average load value and the operating route value of the modified waveform is continuously determined, and the polarity of this difference changes. a polar point, a motion path value at the polar point, and division of peaks and valleys based on changes in the polarity;
Furthermore, a basic pattern table is created by setting a movement path value tolerance range for the movement path value at the extreme point, and the basic pattern table and the basic pattern table are created based on the contents of this basic pattern table and the load value on the movement path for each machining. In the same way, the extreme points on the corrected waveform represented by the moving average load value are compared with the obtained machining pattern table, and the extreme points of each machining pattern table are divided into peaks and valleys at the extreme points set in the basic pattern table. A machining abnormality detection method characterized by checking whether an allowable range of motion path values and the number of extreme points match.