JPS59146740A - Working abnormality detecting device - Google Patents

Abstract

PURPOSE:To permit to decide instantaneously the abnormality in accordance with a work piece, a working machine, a tool and a working condition comprehensively by detecting a load being applied to the tool by a sensing means provided in the working machine and comparing it with a basic pattern or the like. CONSTITUTION:The detection of the load on a driving shaft is effected in a screw working exclusive machine 1 by the value of the electric power of a motor 4 to detect a load value P at every driving positions or driving times from a starting point and describe an original wave form 11. At first, the wave form is decided whether it is within the arrowable range of the basic pattern and the division of mountains and valleys are coinciding with the number of points of deflections or not, and when the abnormality is existing, a bad product is discharged. Next, the range K, for controlling the load value P, is specified and when the load value P exceeds the upper limit U, P, L, and the lower limit L, P, L, of the abnormality, the product is discharged. Further, the integrated value Q of the load value P in the integrating range W with respect to a control integration load value Q is decided and when it is out of the upper limit and the lower limit, the product is discharged. When these data exceed a working time allowable range, the maximum allowable working number and the allowable maximum faulty product frequency, the emergency stop of the machine is effected and the unmanned operation of the working machine may be promoted.

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a device for detecting abnormalities during machining in specialized machines that process a large number of certain parts or numerically controlled machining machines, and particularly for detecting machining abnormalities due to cutting tool wear and tool life, as well as material quality. The present invention relates to a device for detecting abnormalities in raw material due to excessive or insufficient machining allowance, malfunctions or failures of processing machines, and improper installation when attaching raw materials to processing machines.

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, safety devices generally include fuses, circuit breakers, protective relays, etc., which are damaged if a current exceeding the capacity of the drive device flows, as safety devices depending on the capacity of the drive motor, etc. of the processing machine. 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 only operate according to their respective functions, and do not interfere with the workpiece. It is impossible to detect comprehensive machining abnormalities depending on processing conditions and machining conditions. For example, the safety device that detects a malfunction in the limit switch described above will detect damage to the cutter or the movement of the cutter or table. 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 comparing with past data that is highly accurate and dense.It is only effective as a safety device as described above. There was no.

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 make a determination.

The gist of the present invention is to detect the load applied to the cutting tool on the operating path by a sensing means provided in the processing machine, and to compare the basic pattern based on the waveform of the detected value with the load value for each processing, and The machining abnormality detection device comprises a comparison between the basic pattern and a machining pattern based on a load value for each machining process, and a comparison between the basic pattern and the average load value of the latest plurality of times during machining.

Examples will be described below.

In the processing machine of this embodiment, as an example, for a machine 1 dedicated to thread processing of cast pipe fitting rough materials, thread processing taps 3 at the end of each pipe joint are used so that two pipe fitting erivos 2 shown in Fig. 1 can be simultaneously tapped. It consists of a motor 4 that drives the tap 2 and a lead part that moves the tap 2 back and forth according to the lead of the screw. There is a drive shaft part. Each drive shaft part θ of such a special machine for thread processing
) If the load value P is detected for each drive element vertical position or drive time, and the drive position or drive time is set on the horizontal axis L and the load value P is plotted on the vertical axis, the result will be as shown in Figure 2. The primitive waveform 11 for each process can be drawn. 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 vibrations near the cutting tool using a piezoelectric acceleration pickup, a method of detecting vibrations using a current value of a 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 performed to see if the number of inflection points matches. This will be explained below with reference to FIGS. 2 to 4. In Fig. 2, first take the average from n primitive waveforms to obtain average waveform 12,
If this moving average, which takes the average of a plurality of consecutive load values of the operating path of this average waveform and smoothly corrects the average waveform, is represented in a diagram, a corrected waveform 13 as shown in FIG. 5 is obtained. The moving average is calculated by the following method, for example.

The fourth point of inflection of the peaks and valleys on the corrected waveform obtained in this way is
Image the pattern waveform 14 as shown in the figure, and create a pattern table as shown in the table above.

The method of finding the peak and valley inflection points from the corrected waveform is to continuously find the difference before and after the moving average value shown in the table above, and judge whether the polarity changes as positive or negative. can. In this way, the image of the pattern waveform 14 obtained from a plurality of inflection points is created as the pattern table shown above, and each inflection point, that is, a mountain for each pattern number,
The pattern table is completed by setting the inflection section of the valley, the motion path value, the motion time, and the motion path value tolerance range LB.

From this completed basic pattern and the original parallel shape data obtained during each machining process, a modified waveform 13 is continuously obtained by the moving average obtained by the method described above, and from this modified waveform, the inflection point? Imaged as the desired pattern waveform 14, processing patterns are sequentially created, and it is determined for each pattern IN'0 whether the inflection section of the peaks and troughs and the motion path value are within the allowable range LB. If there is an abnormality in the judgment for each pattern number, the pattern is rejected as abnormal processing and discharged. If there are no abnormalities, proceed to the next check.

In the second step 7, specify the range K for controlling the load value during Renhui's daily operation using parameters as shown in Figure 5 for the basic pattern, and set the upper limit U of the abnormality of the load value within this control range. ，
P, L, lower limit L, P. Determine 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 the load value P during machining is determined within which lamp range. Be sure to show that it has been processed. 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 of abnormality U, P, L are determined.
, if the lower limit values L, P, and L are 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 within the blue B, yellow E, red H, and lamp ranges described above. Always display whether it is in. In this way, you will be able to estimate tool life and tool wear progress at a glance. Of course, when the blue B, yellow E-red R lamp range i exceeded the stop range S, the machine 0) Emergency stop was performed and one worker took one step to stop the process.4)
There should be an exchange of balls. It should be noted that the upper limit of the abnormality μ■ and the lower limit t4p-1j) are determined based on the above-mentioned data 'lσ.
The machine then continues to add the next part.

In the above-mentioned example 19, the load value P'citi control of the operating path A is limited to one range of the operating path lJ, t'
1. However, this control range is 1! The range of the initial number 1161 may be included in the recognition by the pattern ho of the 1<Gerub creation path L-1z.

As the third check, 1i゛ and +i of the 6th item)
The control l on the operation path 17 explained in the second part of Ij.
Specify the integral range W of the load value by setting a certain safety margin T within the range, and calculate the integral load value Q of the primitive waveform 11 being processed. Calculate it. Similarly to the second case, upper limits of abnormality U, P, L, lower limit L,
Determine P and L. Furthermore, the integral value range within these upper limit values U, P, L and lower limit values L, P, L is divided, for example, blue B yellow E,
By providing a red R lamp range and a stop range S, the integrated value Q of the load value P during machining is configured to display the range within which machining is being performed.

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 limits of abnormality U, P, L are determined.
, lower limit values L, P, and L. If they are exceeded, each piece is immediately discharged as defective. 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,
The average value of the load integral value Q for the past M processed waveforms from the current time is calculated, and the range within the blue B, yellow E, and red R lamp ranges is always displayed. The display of the blue, yellow, and red lamps may be linked to the display of the second load value. Furthermore, when the blue B, yellow E, and red R run 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 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 data item and the checking of the average load value and average integral load value of the latest plural times in the past have been confirmed, the next check is successively performed.

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 discharged in the third check to the first and second pieces. Then, as before, a predetermined maximum allowable number of machining is checked, and if the maximum allowable number of machining is exceeded, 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 past 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 will be considered as exceeding the defect class. 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 although they are not direct abnormality checks, they more reliably check tool life 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, 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. , and other rough material abnormalities due to excessive or insufficient machining allowance, as well as machining abnormalities due to processing machine malfunctions or breakdowns, improper installation of rough materials, looseness of attachment parts, etc., can be instantly and accurately detected. It has excellent effects such as being able to reliably discharge non-defective products and predicting tool replacement timing with high accuracy. In addition, by setting the constant interval of load detection to an appropriate value, it is possible to control a plurality of processing machine frames at the same time, thereby providing a device that is excellent in unmanned processing machines.

[Brief explanation of the drawing]

FIG. 1 shows a dedicated machine for machining pipe fitting rough material σ) threads according to an embodiment of the present invention. FIG. 2 shows a graph of variations in load values for the drive path. FIG. 6 shows a modified waveform for the average waveform of FIG. FIG. 4 shows a pattern image obtained from the corrected waveform in FIG. FIG. 5 is a diagram illustrating determination 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, 11: Original waveform, 1
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 comparison between the basic pattern based on the waveform of the detected value and the load value for each process, a comparison between the basic pattern and the load value for each process based on the basic pattern and the load value for each process, and the A machining abnormality detection device comprising a comparison between a basic pattern and a machining pattern based on a load value for each machining process, and a comparison between the basic pattern and an average load value of the latest plurality of times during machining. 2. The machining abnormality detection device according to claim 1, wherein the basic pattern is obtained by determining an inflection/rotation represented by an average of moving load values on an average waveform of a plurality of waveforms of the detected values. 3. The machining abnormality detection device according to claim 1, wherein the basic pattern includes an allowable range of the load value, a control range on the operating path, and an integral range of the load value on the operating path. . 4. The machining abnormality detection device 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 according to claim 1, wherein the machining pattern is obtained by finding a knitting point that can be expressed from an average of moving load values of the load values for each machining process. 6. A machining abnormality detection device according to claim 1, which comprises an integral load value of the average load value. 7. The machining abnormality detection device according to claim 3, wherein the tolerance range is divided within the tolerance range. 8. The machining abnormality detection device according to claim 1, wherein the operation path is comprised of an operation time. 9. The machining abnormality detection device according to claim 1, wherein the sensing means is a power value converter of a cutting drive motor.