Breakage determination device and computer-readable storage medium

Abstract

This breakage determination device: obtains samples of the value of the load applied to a shaft of a machine tool; determines whether the samples were detected during a cutting time period or detected during a non-cutting time period; calculates amounts of change between samples during the cutting time period; excludes one or more of the amounts of change; calculates a probability density function from the remaining amounts of change; and determines breakage of a tool on the basis of the probability of occurrence of the excluded one or more amounts of change in the probability density function.

Description

Wear Determination Device and Computer-Readable Storage Medium

[0001] The present disclosure relates to a wear determination device and a computer-readable storage medium.

[0002] Conventionally, in a device for detecting or predicting wear of a tool used in a machine tool, there is a technique of multiplying a coefficient by a feature amount of the load in the cutting time zone to calculate a fluctuating threshold value and determining wear. For example, Patent Document 1.

[0003] Japanese Patent Application Laid-Open No. 2004-130407

[0004] In the technique of Patent Document 1, trial machining is required, so wear cannot be determined from the first machining.

[0005] In the field of numerical control devices, it is desired to improve the efficiency of wear determination.

[0006] The wear determination device includes a data acquisition unit that acquires, as a sample, the value of the load applied to the axis of the machine tool, a determination unit that determines whether the sample acquired by the data acquisition unit is a sample detected in the cutting time zone or a sample detected in the non-cutting time zone, a change amount calculation unit that calculates the change amount of the sample in the cutting time zone, a probability calculation unit that excludes one or more of the change amounts and calculates a probability density function from the remaining change amounts, and a wear detection unit that determines tool wear based on the probability that the excluded change amount occurs in the probability density function.

[0007] Block diagram of the wear determination device. Graph showing changes in the cutting command. Graph of samples in the normal state. Graph of samples when the tool breaks during cutting. Graph showing the relationship between the probability density function and the excluded change amount. Table showing the values of the 3σ interval when one point is excluded and the graph of samples in the normal state. Table showing the values of the 3σ interval when one point is excluded and the graph of samples when the tool breaks during cutting. Flowchart explaining the operation of the wear determination device. Block diagram of a modified example of the wear determination device. Hardware configuration diagram of the wear determination device.

[0008] The following describes the fracture detection device. The fracture detection device is implemented using information processing equipment such as a numerical control device and a PC (personal computer).

[0009] Figure 1 is a block diagram of the breakage detection device 100. The breakage detection device 100 comprises a data acquisition unit 10, a determination unit 11, a change amount calculation unit 12, a probability calculation unit 13, and a breakage detection unit 14.

[0010] The data acquisition unit 10 acquires the load of at least one of the spindle or feed axis. Hereinafter, the load value will be referred to as a sample. The determination unit 11 determines whether the sample acquired by the data acquisition unit 10 was detected during the cutting time or during the non-cutting time. The cutting time is the time when the tool is cutting the workpiece. The non-cutting time is the time when the tool is not cutting the workpiece. One method of determination is to detect a signal. For example, during the cutting time, the numerical control device outputs a "cutting" command signal. Figure 2 shows an example of the "cutting" command signal. The upper graph in Figure 2 shows the load value, and the lower graph in Figure 2 shows the "cutting" command signal. When the numerical control device analyzes the machining program and executes a cutting command such as G01, the "cutting" command signal becomes "ON". This signal allows determination of whether it is the cutting time or the non-cutting time.

[0011] The change amount calculation unit 12 calculates the change in the sample acquired during the cutting time. The change in the sample is the increase or decrease in the sample value. The change amount calculation unit 12 calculates the difference in the sample by subtracting the sample values ​​before or after a certain point in time from the sample value at that point in time. Figure 3 is a graph showing the change in the sample under normal conditions (when the tool has not broken). Under normal conditions, the sample value during the cutting time is approximately constant. The change in the sample also does not fluctuate significantly.

[0012] Figure 4 shows the changes in a sample when the tool breaks during cutting. In Figure 4, a small number of samples (5 points) are shown for illustrative purposes. The actual number of samples should be sufficiently large. The change amount calculation unit 12 calculates the change in the samples. The change from sample 1 to sample 2 is "0", the change from sample 2 to sample 3 is "+0.2", the change from sample 3 to sample 4 is "-1.5", and the change from sample 4 to sample 5 is "-0.1". The change amount calculation unit 12 calculates a sufficient number of change amounts.

[0013] The probability calculation unit 13 calculates the probability density function of the change amount. The probability calculation unit 13 performs preprocessing. In preprocessing, a portion of the change amount is excluded from the sample. The probability calculation unit 13 excludes values ​​with large absolute values. That is, it excludes values ​​with large changes regardless of whether they are positive or negative. The number of values ​​to be excluded may be one or multiple. The criteria for exclusion may be the order of magnitude of the change, or a threshold may be used. The breakage detection unit 14 uses the probability density function of the sample change amount to determine the probability of the excluded value occurring, and if the probability of the excluded value occurring is sufficiently small, it is determined that the tool has broken. That is, if a portion of the change amount is excluded, and the probability density function calculated from the remaining change amount shows that the probability of the excluded value occurring is sufficiently small, it is determined that the tool has broken. Figure 5 is a graph showing the relationship between the probability density function calculated from the remaining change amount after excluding a portion of the change amount, and the probability of the excluded change amount occurring. In Figure 5, the normal distribution on the right is the probability density function, and the points on the left are the excluded change amounts. In the example in Figure 5, the probability of the excluded change occurring is extremely close to "0," so it is determined that the tool has broken.

[0014] Figure 6 shows the samples under normal conditions (when not broken). The values ​​from Sample 1 to Sample 6 remain largely unchanged. Specifically, the change from Sample 1 to Sample 2 is "0.036926", from Sample 2 to Sample 3 is "0.173767", from Sample 3 to Sample 4 is "0.014832", from Sample 4 to Sample 5 is "0.046814", from Sample 5 to Sample 6 is "0.006226", and from Sample 6 to Sample 7 is "0.031311".

[0015] Figure 6 lists the "3σ intervals" when each change is excluded. When the change from sample 1 to sample 2 is excluded, "μ-3σ" is "-0.1094" and "μ+3σ" is "0.218577". When the change from sample 2 to sample 3 is excluded, "μ-3σ" is "-0.05415" and "μ+3σ" is "0.108589". When the change from sample 3 to sample 4 is excluded, "μ-3σ" is "-0.11801" and "μ+3σ" is "0.236033". When the change from sample 4 to sample 5 is excluded, "μ-3σ" is "-0.10553" and "μ+3σ" is "0.21075". When the change from sample 5 to sample 6 is excluded, "μ-3σ" is "-0.12136" and "μ+3σ" is "0.242822". When the change from sample 6 to sample 7 is excluded, "μ-3σ" is "-0.11159" and "μ+3σ" is "0.223017".

[0016] Figure 7 shows samples of tool breakage during cutting. The values ​​from Sample 1 to Sample 6 change rapidly. Specifically, the change from Sample 1 to Sample 2 is "0.032484", the change from Sample 2 to Sample 3 is "0.128205", the change from Sample 3 to Sample 4 is "0.124071", the change from Sample 4 to Sample 5 is "0.197852", the change from Sample 5 to Sample 6 is "3.245687", and the change from Sample 6 to Sample 7 is "15.30834".

[0017] Figure 7 lists the "3σ intervals" when each change is excluded. When the change from sample 1 to sample 2 is excluded, "μ-3σ" is "-7.68314" and "μ+3σ" is "15.2848". When the change from sample 2 to sample 3 is excluded, "μ-3σ" is "-7.64643" and "μ+3σ" is "15.2098". When the change from sample 3 to sample 4 is excluded, "μ-3σ" is "-7.64801" and "μ+3σ" is "15.21304". When the change from sample 4 to sample 5 is excluded, "μ-3σ" is "-7.61971" and "μ+3σ" is "15.15523". When the change from sample 5 to sample 6 is excluded, "μ-3σ" is "-6.44736" and "μ+3σ" is "12.76374". When the change from sample 6 to sample 7 is excluded, "μ-3σ" is "-1.51215" and "μ+3σ" is "3.003473".

[0018] The change from sample 6 to sample 7 exceeds the "3σ interval". The probability of a change occurring outside the "3σ interval" is 0.03%. The fracture detection unit 14 determines that fracture has occurred.

[0019] The operation of the fracture detection device 100 will be explained with reference to Figure 8. The numerical control device is started, setup is completed, and machining is started (step S1). The numerical control device moves the axis at rapid traverse and rotates the spindle at the cutting start position. The data acquisition unit 10 acquires the spindle load value as a sample. The determination unit 11 determines whether the sample acquired by the data acquisition unit 10 is a sample detected during the cutting time or a sample detected during the non-cutting time. The data acquisition unit 10 acquires a sample during the cutting time (step S2).

[0020] The change amount calculation unit 12 calculates the change amount of the sample during the cutting time (step S3). The probability calculation unit 13 excludes some of the change amounts (step S4). One sample may be excluded, or multiple samples may be excluded. Values ​​with large changes, regardless of whether they are positive or negative, are excluded.

[0021] The probability calculation unit 13 calculates a probability density function from the remaining change amounts after excluding a portion (step S5). The probability calculation unit 13 calculates the probability that the excluded change amounts occur in the calculated probability density function (step S6). The probability calculation unit 13 determines that the tool has broken if the probability of the excluded change amounts occurring is sufficiently low (step S7).

[0022] The fracture detection device 100 of this embodiment acquires the current load value as a sample and uses the acquired value to determine fracture, thus eliminating the need for trial processing and streamlining fracture detection.

[0023] (Modified Version) The modified fracture detection device 100 combines two types of detection methods. The fracture detection device 100 in Figure 9 is the fracture detection device 100 in Figure 1 with the addition of a second probability calculation unit 15, a feature calculation unit 16, and a second fracture detection unit 17. The second probability calculation unit 15 calculates the probability density function of the sample acquired during the non-cutting time period. The feature calculation unit 16 determines the features of the sample acquired during the cutting time period. The features are representative values ​​of the sample acquired during the cutting time period. Features include the mean, median, maximum, minimum, and mode. Alternatively, the features may be a moving average, moving median, moving maximum, moving minimum, and moving mode. The type of features is not limited.

[0024] The second fracture detection unit 17 compares the probability density function of the sample during the non-cutting period with the feature quantities of the sample during the cutting period. The second fracture detection unit 17 detects tool fracture if the probability of the feature quantities occurring during the cutting period is sufficiently small in the probability density function of the sample during the non-cutting period.

[0025] The fracture detection unit 14 determines fracture based on the slope of the sample's changes. The second fracture detection unit 17 determines fracture based on whether the characteristic quantity (representative value) of the sample during the cutting period occurs during the non-cutting period. The modified fracture determination device 100 determines fracture by combining these two methods.

[0026] In the modified version, fracture can be determined using both the slope and the sample value, thus broadening the range of fracture detection. Furthermore, by preparing multiple features, fracture can be detected under various conditions.

[0027] The hardware configuration of the fracture detection device 100 to which this disclosure is applied will be described below. Figure 10 is a hardware configuration diagram of the fracture detection device 100. As shown in Figure 10, the fracture detection device 100 includes a CPU 111 that controls the fracture detection device 100 as a whole, a ROM 112 that records programs and data, and a RAM 113 for temporarily expanding data. The CPU 111 reads the system program recorded in the ROM 112 via a bus and determines fracture according to the system program.

[0028] The non-volatile memory 114 is backed up, for example, by a battery (not shown), so that its stored state is maintained even when the power to the fracture detection device 100 is turned off. The non-volatile memory 114 stores various data, such as programs read from the external device 120 via interfaces 115, 118, and 119, and operation inputs input via the input unit 30. The non-volatile memory 114 may also store programs and data for running the fracture detection device 100 of this embodiment.

[0029] Interface 115 is an interface for connecting the fracture detection device 100 to an external device 120 such as an adapter. Programs and various parameters are read from the external device 120. Interface 118 is an interface for connecting the fracture detection device 100 to a display unit 70 such as a liquid crystal display. The display unit 70 displays data obtained as a result of the execution of various data, programs, etc., that have been read into memory. Interface 119 is an interface for connecting the fracture detection device 100 to an input unit 30 such as a keyboard or pointing device. The input unit 30 passes commands, data, etc., based on operations by the operator to the CPU 111 via interface 119.

[0030] While this disclosure has been described in detail, it is not limited to the individual embodiments described above. These embodiments can be added, replaced, modified, partially deleted, etc., in any way that does not depart from the gist of this disclosure or from the spirit of this disclosure derived from the claims and their equivalents. These embodiments can also be implemented in combination. For example, the order of operations and processes in the embodiments described above are given as examples only and are not limited thereto.

[0031] [Correction based on Rule 91 09.01.2026] The following additional notes are disclosed regarding the above embodiments and modified examples. (Note 1) The fracture determination device (100) includes a data acquisition unit (10) that acquires a sample of the load applied to the shaft of a machine tool, a determination unit (11) that determines whether the sample acquired by the data acquisition unit (10) is a sample detected during the cutting time or a sample detected during the non-cutting time, a change amount calculation unit (12) that calculates the change amount of the sample during the cutting time, a probability calculation unit (13) that excludes one or more of the change amounts and calculates a probability density function from the remaining change amounts, and a fracture detection unit (14) that determines tool fracture based on the probability of the excluded change amounts occurring in the probability density function. (Note 2) The determination unit (11) determines the cutting time and the non-cutting time based on the command signal for controlling the machine tool. (Note 3) The fracture detection unit (14) determines that the tool is fractured if the probability of the excluded change occurring is sufficiently small. (Note 4) The probability calculation unit (13) excludes change amounts with large absolute values. (Note 5) The fracture determination device (100) comprises a second probability calculation unit (15) that calculates the probability density function of the sample during the non-cutting time period, a feature calculation unit (16) that calculates the feature quantities of the sample during the cutting time period, and a second fracture detection unit (17) that determines the fracture of the tool based on the probability of the feature quantities occurring in the probability density function of the non-cutting time period. (Note 6) The computer-readable storage medium (112, 113, 114) stores instructions for one or more processors (111) to perform a process of obtaining a sample of the load applied to the axis of the machine tool, determining whether the sample was detected during the cutting time or during the non-cutting time, calculating the amount of change of the sample during the cutting time, excluding one or more of the amounts of change, calculating a probability density function from the remaining amounts of change, and determining tool breakage based on the probability of the excluded amounts of change occurring in the probability density function.

[0032] 100 Fracture detection device 10 Data acquisition unit 11 Determination unit 12 Change amount calculation unit 13 Probability calculation unit 14 Fracture detection unit 111 CPU 112 ROM 113 RAM 114 Non-volatile memory

Claims

1. A breakage detection device comprising: a data acquisition unit that acquires a sample of the load applied to the shaft of a machine tool; a determination unit that determines whether the sample acquired by the data acquisition unit is a sample detected during cutting time or a sample detected during non-cutting time; a change amount calculation unit that calculates the change amount of the sample during cutting time; a probability calculation unit that excludes one or more of the change amounts and calculates a probability density function from the remaining change amounts; and a breakage detection unit that determines tool breakage based on the probability of the excluded change amounts occurring in the probability density function.

2. The fracture determination device according to claim 1, wherein the determination unit determines a cutting time period and a non-cutting time period based on a command signal for controlling the machine tool.

3. The breakage detection unit determines that the tool is broken if the probability of the excluded change occurring is sufficiently small, as described in claim 1.

4. The fracture determination device according to claim 1, wherein the probability calculation unit excludes changes with large absolute values.

5. The fracture determination device according to claim 1, comprising: a second probability calculation unit for calculating a probability density function of a sample during the non-cutting time period; a feature calculation unit for calculating a feature quantity of a sample during the cutting time period; and a second fracture detection unit for determining tool fracture based on the probability of the feature quantity occurring in the probability density function of the non-cutting time period.

6. A computer-readable storage medium that stores instructions for one or more processors to execute a process that involves acquiring a sample of the load applied to the axis of a machine tool, determining whether the sample was detected during the cutting time or during the non-cutting time, calculating the amount of change of the sample during the cutting time, excluding one or more of the amounts of change, calculating a probability density function from the remaining amounts of change, and determining tool breakage based on the probability of the excluded amounts of change occurring in the probability density function.

Images