JPS59102559A - Abnormal working detector and method thereof - Google Patents

Abstract

PURPOSE:To decide reliably in working of dedicated machine, by detecting load to be applied on an edge on the operating path through sensing means then comparing the basic pattern based on that waveform with load level and detecting abnormal working. CONSTITUTION:Two pipe coupling elbows 2 are tapped simultaneously by a tap 3 driven by a motor 4 where the load on drive shaft section is detected for every drive position or drive time by the power level of drive motor 4 and when working with same machine, working condition, cutter on same rough member, approximately same curve is obtained. A microcomputer 9 will check whether the working pattern of working wave is within allowable range on the operating path provided on the basic pattern or the bending points are matched and if there is abnormal decision of each pattern, it is rejected as an abnormal working. In such a manner, abnormal working can be detected accurately to anticipate the tool replacing time accurately.

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a device for detecting abnormalities during machining in special-purpose machines that process a large number of certain parts or numerically controlled machining machines. The present invention relates to a device and a method for detecting processing abnormalities caused by excessive or insufficient material or processing allowance, malfunction or failure of processing machines, poor attachment when mounting rough materials to processing machines, etc.

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, with this kind of abnormality detection based on human sensory judgment, special-purpose machines that process many identical parts 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 were problems that could cause the machine to malfunction. For this reason, there are generally safety devices depending on the capacity of the drive motor of the processing machine, such as fuses, circuit breakers, and protective relays that turn on when 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 cutter if it deviates from a certain predetermined movement path, but these only operate according to their respective functions, and do not affect the workpiece. It is impossible to detect comprehensive machining abnormalities depending on processing conditions and machining conditions.For example, if the limit switch malfunctions as mentioned above, the safety device will not detect it when the cutter is damaged or when the cutter or table moves. It was impossible to detect processing abnormalities such as when the machine stopped due to a failure of the main body. In addition, the latest method is to take the load during one model processing for each motion path, and monitor it by simply moving this waveform vertically in parallel and determining whether the load is within the range. With this method, it is impossible to monitor errors on the motion path at inflection points, and it is not possible to monitor by comparison with past data that is highly accurate and dense, so it is only effective as a safety device as described above. I was bored.

The present invention solves the above problems, and comprehensively and instantly detects machining abnormalities from appropriate machining conditions depending on the workpiece, processing machine, cutter, and machining conditions (cutting speed, feed, depth of cut), etc. The purpose of the present invention is to provide a processing abnormality detection device that can determine whether the

The gist of the present invention is to detect the load applied to the cutting tool on the operating path by means of a sensing means installed in the processing machine, and to compare the basic pattern based on the waveform of the detected value with the load value for each process. Anomaly detection device and method.

Examples will be described below.

As an example of the processing machine of this embodiment, FIG. 1 shows a machine 1 dedicated to thread processing of cast pipe joint rough material. In order to tap the two pipe joint elbows 2 at the same time, there is a drive shaft part consisting of a motor 4 that drives the thread machining tap 3 at the end of each pipe joint, and a lead part that moves the tap 2 back and forth according to the lead of the screw. be. Each drive shaft part of such a dedicated screw processing machine,
The drive position or load value P for each drive time from each start point is detected, and the drive position or drive time is expressed with the horizontal axis L and the load value P on the vertical axis, and the original waveform for each machining is shown in Figure 2. I can draw 11. As a result of experiments, it has been found 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 sound during thread machining using a microphone or an acoustic radiation sensor. , a method of detecting the vibration near the cutting tool using a piezoelectric acceleration pickup, a method of detecting the vibration using the current value of the drive motor, etc., and any of these methods may be used. This curve depends on the processing machine and processing conditions.
Although different curves can be drawn depending on the cutter, etc., if the same processing machine is used, the same processing conditions, the same cutter, and the same rough material are processed, almost the same curves can be obtained. This principle is used to detect processing abnormalities.

First, the first check is whether the machining pattern of the machining waveform obtained by each machining is within the allowable range on the motion path set in the basic pattern, and whether the peaks and valleys at the inflection points are correct. Additionally, a check is made to see if the number of inflection points is correct. 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 load values on the operation path of this average waveform are averaged to smooth the average waveform. If we represent this moving average in a diagram, we get the corrected waveform l3 as shown in Figure 3.
is obtained. The moving average is calculated by the following method, for example.

The inflection points of the peaks and valleys on the corrected waveform obtained in this way are
Image the pattern waveform l4 as shown in the figure, and create a pattern table as shown in the table below.

The inflection points of peaks and valleys can be found from the corrected waveform by continuously finding the difference before and after the moving average value shown in the table above, and determining whether the polarity is positive or negative. . The image of the pattern waveform l4 obtained from a plurality of inflection points in this way is created as the pattern table shown above, and each inflection point, that is, the pattern number, the inflection division of each peak and valley, the motion path value, and The pattern table is completed by setting the operating time and operating path value tolerance range LB.

From this completed basic pattern and the original waveform data obtained in the middle of each processing, a modified waveform 13 is continuously obtained by the moving average obtained by the above method, and an inflection point is determined from the modified waveform, and a pattern waveform 14 is obtained. , sequentially create machining patterns, and judge whether the peak-to-valley inflection division and motion path value for each pattern number are within the allowable range LB. If there is an abnormality in the judgment for each pattern NO%, it is rejected as abnormal machining. If there is no abnormality, proceed to the next check.

Second, as shown in Fig. 5, a range K for controlling the load value on an arbitrary operation path with parameters is specified for the basic pattern, and the upper limit of the abnormality of the load value within this control range, u
, p, l, and determine the lower limit LSI'SL. This upper limit,
In addition to the lower limit value, divide the load value range within this range, for example, specify the blue B, yellow E, red R lamp range and stop range S, and specify which lamp range the load value P during machining is in. Make sure to show if there are any. For the control load value P set in this way, the load value P within the control range K on the operation path L during machining is first determined for each data, and the upper limits U and P of abnormality are determined.
, L, lower limit value L, PSL. If the lower limit value L, P SL is exceeded, each piece is immediately discharged as defective. If it is within the upper and lower limits of the abnormality, then calculate the average load value of the latest D maximum load values processed in the past from the current time, and determine which range this falls within the blue, yellow, and red lamp ranges mentioned above. Always show what is happening. In this way, tool life and tool wear progress can be estimated at a glance. Of course, when the blue B, yellow E, and red R lamp ranges are 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, the upper limit of abnormality U is determined for each piece of data mentioned above.
If the product exceeds the lower limits L, P, and L and is discharged as defective, only the product is discharged as a defective product, but the machine continues to process the next part.

In the above explanation, the range K for controlling the load value P on the operating path L is limited to one range on the operating path, but this control range may be controlled.

As a third check, as shown in Fig. 6, a certain safety margin T is provided within the control range K on the operation path L explained in the second section, and an integral range W of the load value is specified. An integral load value Q of the original waveform 11 is calculated. For the integral load value Q thus obtained, upper limit values U, P, L and lower limit values L, P, L of abnormality are determined in the same manner as in the second case. Furthermore, the integral value range within this upper limit value and lower limit value is divided, for example, blue B,
By providing the yellow E and red R lamp ranges and the stop range S, it is possible to display within which range the integral value Q of the load value P during machining is being processed. The integral value Q of the load value P within the integral range W on the operation path during machining with respect to the control integral load value Q set in this way is first determined for each machining data, and the upper limit of abnormality υ, P, L , lower limit L, PS
Regardless of whether it is within L, if it exceeds it, the defective parts 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, regarding the integration range W, from the present to the past M
The average value of the load integral value Q of the processed waveform is calculated, and the range within the blue B1 yellow E1 red R lamp range is always displayed. The display of the blue, yellow, and red lamps may be linked to the display of the second load value, or may be displayed separately from the display of the load value. Also blue B,
When the yellow E1 red R lamp range is exceeded and the stop range S is reached, the machine is naturally brought to an emergency stop, and the operator changes tools and checks the tools. The integral value Q is calculated by setting a certain safety margin T inside the control range K of the second inflection point, but as a result of experiments, this Since the curve near the bend point is a gentle curve, there are many errors in the integral value, making it difficult to make accurate judgments.For this reason, in order to obtain more accurate judgment results, a safety margin T is provided to cut the gentle part. The integral range is W. In this way, once the checking of the load value P and the integral value Q within the control range K for each piece of data and the checking of the average load value and average integral load value of the latest plural times in the past 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 time 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 number of machining is exceeded and the above-mentioned emergency stop is performed.

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, for example, 5%, the defect frequency will be exceeded. The machine is then brought to an emergency stop. 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 of the above checks are quantity or time checks, and although they are not direct abnormality checks, they more reliably check the life of tools and the quality of processed products, and provide comprehensive information 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 that tool replacement is required when the frequency of machining defects increases even if tool abnormalities and tool wear are within the allowable range. Make a judgment.

As described above, according to the present invention, the wear life of cutting tools in special-purpose machines that process a large number of certain parts, numerically controlled processing machines, and other processing machines such as machining centers, and machining abnormalities due to tool abnormalities such as blade chipping and breakage, can be improved. It instantly and accurately detects abnormalities in the raw material due to the material of the raw material or excessive or insufficient machining allowance, malfunction or failure of the processing machine, improper installation of the raw material, looseness of the attachment part, etc., and identifies defective products. It has excellent effects such as reliable ejection and accurate prediction of tool replacement timing. By setting the constant interval of load detection to an appropriate value, it is possible to control a plurality of processing machines at the same time, thereby providing an apparatus and method that are excellent in unmanned processing machines.

[Brief explanation of drawings]

FIG. 1 shows a machine dedicated to threading pipe fitting raw material according to an embodiment of the present invention. FIG. 2 shows a graph of variations in load values for the drive path. FIG. 3 shows a modified waveform for the average waveform of FIG. FIG. 4 shows a pattern image obtained from the corrected waveform of FIG. 3. FIG. 5 is a diagram illustrating judgment based on load values within the control range. FIG. 6 is a diagram explaining the integral range. FIG. 7 is a diagram illustrating the determination of the integral value in FIG. 6. 5: OT, 6: Power converter, 7: Voltage value, 8: Current value,
9: Microcomputer, 10: Output relay, ll: Original waveform, l
2: Average waveform, 13: Corrected waveform, 15: Pattern No.

Claims (1)

[Claims] 1. In a processing machine that cuts a workpiece with a blade along a set movement path, a load applied to the blade on the movement path is detected by a sensing means provided in the processing machine, A machining abnormality detection device and method comprising a comparison between a basic pattern based on the waveform of the detected value and a load value for each machining process. 2. The machining abnormality detection device and method according to claim 1, wherein the basic pattern has a control range on the movement path. 3. The machining abnormality detection device and method according to claim 1, wherein the basic pattern has an integral range on the motion path. 4. The machining abnormality detection device and method according to claim 1, wherein the load value for each machining is an integral value of the load value for each machining. 5. The machining abnormality detection device and method according to claim 1, wherein the basic pattern is obtained by finding an inflection point represented by an average of moving load values on an average waveform of a plurality of waveforms of the detected values. 6. The machining abnormality detection device and method according to claim 1, wherein the sensing means is a power value converter of a cutting drive motor. 7. The machining abnormality detection device and method according to claim 1, wherein the sensing means is a current value converter of a cutting drive motor. 8. The machining abnormality detection device and method according to claim 1, wherein the sensing means is a cutting sound transducer. 9. In claim 1, the motion path is:
Processing abnormality detection device and method consisting of operation time. 10. The machining abnormality detection device and method according to claims 1 and 4, wherein the basic pattern has a permissible range of the average load value. 11. A machining abnormality detection device and method according to claim 10, wherein the tolerance range is divided into sections within the tolerance range. 12. The machining abnormality detection device and method according to claim 1, wherein the sensing means is a cutting torque converter.