Tool failure detection method, device and equipment in machine tool spindle and storage medium

Abstract

The application relates to a tool fault detection method, device and equipment in a machine tool spindle and a storage medium, wherein the method comprises the following steps: acquiring a plurality of spindle load values of the machine tool spindle in a monitoring time period; wherein each spindle load value is a load value corresponding to machining each workpiece under the condition that a current tool is installed in the machine tool spindle; determining a load average value and a load peak value based on the plurality of spindle load values; in the case that the load average value exceeds a preset load average value interval or the load peak value exceeds a preset peak value interval, it is determined that the current tool in the machine tool spindle has a fault. By adopting the method, the efficiency of tool fault detection in the machine tool spindle can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fault detection, in particular to a tool fault detection method, device and equipment in a machine tool spindle and a storage medium. BACKGROUND

[0002] Nowadays, in the field of large-scale mechanical processing, in order to realize the continuous production and efficient processing of products, production enterprises use a large number of assembly lines for automatic processing of complex workpieces. However, with the increase in the number of workpieces, the parts in the mechanical processing equipment, such as the tool in the machine tool spindle, will have a certain degree of failure (such as the tool in the machine tool spindle being worn). When the tool in the machine tool spindle has a large degree of failure, it will result in poor quality of the processed workpiece. Therefore, it is necessary to detect the running state of the tool in the machine tool spindle.

[0003] At present, when detecting the tool fault in the machine tool spindle, the detection equipment can first collect the running data of the machine tool spindle obtained through various sensors, and then analyze the running data through machine learning, deep learning, artificial intelligence algorithm and other methods to obtain the detection result, and inform the user of the fault detection result. However, using this method, the running data needs to go through two stages of data processing and decision reasoning to obtain the final detection result, and since these two stages often require a lot of time, the timeliness of the output detection result is affected. Especially in application scenarios with high real-time requirements, this problem is particularly prominent.

[0004] Therefore, how to improve the efficiency of tool fault detection in the machine tool spindle has become a problem to be solved. SUMMARY

[0005] The embodiments of the present application provide a tool fault detection method, device, equipment and storage medium in a machine tool spindle, which can improve the efficiency of tool fault detection in the machine tool spindle.

[0006] In a first aspect, the present application provides a tool fault detection method in a machine tool spindle, applied to an edge processing device, the method comprising:

[0007] Obtaining a plurality of spindle load values of the machine tool spindle in a monitoring time period; wherein each spindle load value is a load value corresponding to processing each workpiece under the condition that the current tool is installed in the machine tool spindle;

[0008] Determining a load average value and a load peak value based on the plurality of spindle load values;

[0009] In a case where the load average value exceeds a preset load average value interval, or the load peak value exceeds a preset peak value interval, it is determined that the current tool in the machine tool spindle has a fault.

[0010] In one of the embodiments, the acquiring the plurality of spindle load values of the machine tool spindle in the monitoring time period comprises: acquiring, in real time, running data of the machine tool spindle in the monitoring time period, the running data comprising initial spindle load values corresponding to a plurality of points of a target workpiece to be machined by the machine tool spindle with a current tool installed in the machine tool spindle; and acquiring positions of the machine tool spindle when each of the points is machined; slicing the plurality of initial spindle load values based on the positions of the machine tool spindle when each of the points is machined to obtain a plurality of sliced spindle load values; and taking the plurality of sliced spindle load values as the plurality of spindle load values of the machine tool spindle.

[0011] In one of the embodiments, the slicing the plurality of initial spindle load values based on the positions of the machine tool spindle when each of the points is machined to obtain a plurality of sliced spindle load values comprises: determining machining positions of the machine tool spindle based on the positions of the machine tool spindle when each of the points is machined; and slicing the plurality of initial spindle load values based on the machining positions to obtain the plurality of sliced spindle load values.

[0012] In one of the embodiments, the preset load average value range and the preset peak value range are determined by: acquiring a plurality of historical spindle load values corresponding to machining of a preset first number of workpieces in a previous monitoring time period of the monitoring time period; determining a historical load average value and a historical load peak value based on the plurality of historical spindle load values; determining the preset load average value range based on the historical load average value, and determining the preset peak value range based on the historical load peak value.

[0013] In one of the embodiments, the acquiring, in real time, running data of the machine tool spindle in the monitoring time period comprises: acquiring, in real time, a plurality of running data of the machine tool spindle from a device in which the machine tool spindle is located; the plurality of running data being acquired by the device based on a predetermined sampling frequency, the sampling frequency being determined based on signal characteristics, dynamic response requirements, storage capacity and processing capability, and actual requirements; wherein the signal characteristics comprise a fast-slow degree, a frequency range and a fluctuation amplitude of spindle load signal changes of the machine tool spindle, the dynamic response requirements refer to a fast-slow degree of spindle load signal changes to be monitored, the storage capacity refers to an available storage space of an edge processing device, the processing capability refers to a data processing rate of the edge processing device, and the actual requirements comprise a cost required for collecting data.

[0014] In one of the embodiments, the method further comprises: determining a current spindle load value after the current tool processes a preset second number of workpieces; obtaining a target spindle load value after processing the preset second number of workpieces in a case that the last tool is installed in the spindle of the machine tool; outputting an alarm information in a case that a difference between the current spindle load value and the target spindle load value is greater than a preset threshold, the alarm information being used to indicate that the current tool fails; and the preset threshold being associated with the preset second number.

[0015] In a second aspect, the present application provides a tool failure detection device in a spindle of a machine tool, applied to an edge processing equipment, the device comprising:

[0016] an obtaining module, configured to obtain a plurality of spindle load values of the spindle of the machine tool in a monitoring time period; wherein each spindle load value is a load value corresponding to processing of each workpiece in a case that a current tool is installed in the spindle of the machine tool;

[0017] a determining module, configured to determine a load average value and a load peak value based on the plurality of spindle load values;

[0018] The determining module is further configured to determine that the current tool in the spindle of the machine tool fails in a case that the load average value exceeds a preset load average value interval or the load peak value exceeds a preset peak value interval.

[0019] In a third aspect, the present application provides an electronic device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0020] obtaining a plurality of spindle load values of the spindle of the machine tool in a monitoring time period; wherein each spindle load value is a load value corresponding to processing of each workpiece in a case that a current tool is installed in the spindle of the machine tool;

[0021] determining a load average value and a load peak value based on the plurality of spindle load values;

[0022] determining that the current tool in the spindle of the machine tool fails in a case that the load average value exceeds a preset load average value interval or the load peak value exceeds a preset peak value interval.

[0023] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0024] obtaining a plurality of spindle load values of the spindle of the machine tool in a monitoring time period; wherein each spindle load value is a load value corresponding to processing of each workpiece in a case that a current tool is installed in the spindle of the machine tool;

[0025] determining a load average value and a load peak value based on the plurality of spindle load values;

[0026] In a case where it is determined that the load average value exceeds the preset load average value interval or the load peak value exceeds the preset peak value interval, it is determined that the current tool in the machine tool spindle has a failure.

[0027] In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the following steps:

[0028] obtaining a plurality of spindle load values of the machine tool spindle within a monitoring time period; wherein each spindle load value is a load value corresponding to processing each workpiece in a case where the current tool is installed in the machine tool spindle;

[0029] determining a load average value and a load peak value based on the plurality of spindle load values;

[0030] In a case where it is determined that the load average value exceeds the preset load average value interval or the load peak value exceeds the preset peak value interval, it is determined that the current tool in the machine tool spindle has a failure. The tool failure detection method, device, equipment and storage medium of the machine tool spindle provided in the above embodiments, the edge processing equipment obtains a plurality of spindle load values of the machine tool spindle within a monitoring time period; wherein each spindle load value is a load value corresponding to processing each workpiece in a case where the current tool is installed in the machine tool spindle; a load average value and a load peak value are determined based on the plurality of spindle load values; in a case where it is determined that the load average value exceeds the preset load average value interval or the load peak value exceeds the preset peak value interval, it is determined that the current tool in the machine tool spindle has a failure. In this way, compared with the method of analyzing the running data by machine learning, deep learning, artificial intelligence algorithm and the like to obtain a detection result, the tool failure detection method provided in the embodiments of the present application does not need to go through two stages of data processing and decision reasoning, so that the efficiency of tool failure detection in the machine tool spindle can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technical solutions, the drawings needed to be used in the description of the embodiments of the present application or the related technical solutions will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without any creative effort.

[0032] Figure 1 is an application scenario diagram of a tool failure detection method in a machine tool spindle provided by the embodiments of the present application;

[0033] Figure 2 is a flowchart of a tool failure detection method in a machine tool spindle provided by the embodiments of the present application;

[0034] Figure 3 is a schematic diagram of running data of a machine tool spindle when processing a single workpiece provided by an embodiment of the present application;

[0035] Figure 4 is a schematic diagram of a boring tool processing load trend after being sliced by an edge processing device provided by an embodiment of the present application;

[0036] Figure 5 is a schematic diagram of a load average fitting control curve provided by an embodiment of the present application;

[0037] Figure 6 is a schematic diagram of a load peak value change provided by an embodiment of the present application;

[0038] Figure 7 is a schematic diagram of a flow of another tool fault detection method in a machine tool spindle provided by an embodiment of the present application;

[0039] Figure 8 is a schematic diagram of an anomaly detection provided by an embodiment of the present application;

[0040] Figure 9 is a schematic diagram of a tool trend early warning provided by an embodiment of the present application;

[0041] Figure 10 is a schematic diagram of a structure of a tool fault detection device in a machine tool spindle provided by an embodiment of the present application;

[0042] Figure 11 is a schematic diagram of a structure of a device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0044] The application scenario of the tool fault detection method in a machine tool spindle provided by an embodiment of the present application will be introduced below.

[0045] Please refer to Figure 1 Please refer to Figure 1 , Figure 1 is a schematic diagram of an application scenario of a tool fault detection method in a machine tool spindle provided by an embodiment of the present application. As Figure 1As shown, the system includes an edge processing device 101, a machine tool spindle belonging device 102, a network cable 103, a protocol converter 104, and a data server 105. The edge processing device 101 can communicate with the machine tool spindle belonging device 102 through the network cable 103 via the protocol converter 104.

[0046] The edge processing device 101 can obtain a plurality of spindle load values of the machine tool spindle within a monitoring time period from the machine tool spindle belonging device 102 via the network cable 103 and the protocol converter 104. Each spindle load value is a load value corresponding to processing each workpiece when the current tool is installed in the machine tool spindle. Based on the plurality of spindle load values, a load average value and a load peak value are determined. If the load average value exceeds a preset load average value interval or the load peak value exceeds a preset peak value interval, it is determined that the current tool in the machine tool spindle has failed. In this way, the efficiency of tool failure detection in the machine tool spindle can be improved.

[0047] Optionally, the edge processing device 101 can upload the obtained plurality of historical spindle load values of the machine tool spindle to the data server 105 via the network cable 103.

[0048] Optionally, the edge processing device 101 can be an edge PLC device, wherein the edge PLC device can use a data analysis module of a programmable controller.

[0049] Optionally, the machine tool spindle belonging device 102 can be a device using a numerical control machine tool program commonly used in a numerical control machine tool workshop.

[0050] Optionally, the network used by the network cable 103 can be a local area network (LAN) network. The LAN network is a common and mature network architecture, suitable for data transmission within a local area, with low cost and simple deployment and maintenance. In this application, the edge processing device 101 and the device 102 where the machine tool spindle is located are relatively close, so the LAN network can provide a data transmission speed that meets the requirements.

[0051] Please refer to Figure 2 , Figure 2 is a flowchart of a tool failure detection method in a machine tool spindle provided by an embodiment of the present application. The method can be executed by an edge processing device (for example, the edge processing device 101 described above). As shown in Figure 2 , the tool failure detection method in the machine tool spindle can include but is not limited to the following steps:

[0052] S201. Obtain multiple spindle load values ​​of the machine tool spindle within the monitoring time period; wherein, each spindle load value is the load value corresponding to machining each workpiece when the current tool is installed in the machine tool spindle.

[0053] In one optional implementation, the edge processing device acquires multiple spindle load values ​​of the machine tool spindle within a monitoring period, which may include: acquiring real-time operating data of the machine tool spindle within the monitoring period, the operating data including initial spindle load values ​​corresponding to multiple points of the target workpiece being machined when the current tool is installed on the machine tool spindle; acquiring the position of the machine tool spindle when machining each point; slicing the multiple initial spindle load values ​​based on the position of the machine tool spindle when machining each point to obtain multiple sliced ​​spindle load values; and using the multiple sliced ​​spindle load values ​​as the multiple spindle load values ​​of the machine tool spindle.

[0054] In this embodiment, the edge processing device acquires the machine tool spindle's operating data in real time during the monitoring period. This can include: acquiring multiple operating data points of the machine tool spindle from the device where the machine tool spindle is located in real time. These multiple operating data points are acquired by the device based on a predetermined sampling frequency. The sampling frequency is determined based on signal characteristics, dynamic response requirements, storage capacity, processing capability, and actual needs. Among these, signal characteristics include the rate of change, frequency range, and fluctuation amplitude of the spindle load signal of the machine tool spindle; dynamic response requirements refer to the rate of change of the spindle load signal to be monitored; storage capacity refers to the available storage space of the edge processing device; processing capability refers to the rate at which the edge processing device processes data; and actual needs include the cost required to acquire the data.

[0055] In other words, the equipment containing the machine tool spindle can collect the operating data of the machine tool spindle through its own sensors based on the sampling frequency predetermined by the user.

[0056] When determining the sampling frequency, the following factors can be considered:

[0057] (1) Signal characteristics and dynamic response requirements

[0058] First, understand the characteristics of the spindle load signal. For example, the rate of change, frequency range, and fluctuation amplitude of the spindle load signal. If the signal changes very rapidly or contains high-frequency components, a higher sampling frequency is needed to capture subtle changes. If precise control or monitoring of instantaneous changes in the spindle load is required, the sampling frequency should be high enough to acquire data in real time and make timely adjustments.

[0059] (2) Storage capacity and processing power

[0060] Consider the limitations of data storage capacity and processing power. Higher sampling frequencies will generate more data and may require more storage space and computational resources for processing. Therefore, the appropriate sampling frequency needs to be determined based on the available storage capacity and processing power.

[0061] (3) Cost and actual demand

[0062] Finally, consider the cost factor and actual demand. Higher sampling frequencies will increase the cost of data acquisition equipment, as well as the complexity of data processing and analysis.

[0063] In practical applications, the most appropriate output waveform can be observed by adjusting different data sampling frequencies directly from the data tracking page of the FANUC system in the equipment where the machine tool spindle is located. And according to the influence of sampling frequency on data quality and system performance, the best sampling frequency is determined, as shown in Table 1.

[0064] Table 1 Sampling frequency requirements (unit: ms)

[0065]

[0066] See Figure 3 , Figure 3 is a schematic diagram of the running data of the machine tool spindle when processing a single workpiece provided by an embodiment of the present application. Among them, Figure 3 the running data shown in the figure is the spindle load value and the position of the machine tool spindle (horizontal axis coordinate and vertical axis coordinate) when processing the workpiece, which is collected by the equipment where the machine tool spindle is located at a sampling frequency of 100 ms. As Figure 3 shown, the running data before the dashed line is the running data of the machine tool spindle when the current tool (i.e. boring tool) is installed on the machine tool spindle, and the running data after the dashed line is the running data of the machine tool spindle when the next tool (milling tool) is installed on the machine tool spindle.

[0067] In this embodiment, the edge processing device performs slicing processing on the plurality of initial spindle load values based on the position of the machine tool spindle when processing each point, to obtain a plurality of sliced spindle load values, including: determining the processing position of the machine tool spindle based on the position of the machine tool spindle when processing each point; based on the processing position, slicing processing is performed on the plurality of initial spindle load values to obtain a plurality of sliced spindle load values.

[0068] See Figure 4 , Figure 4 is a schematic diagram of the boring tool processing load trend after slicing by the edge processing device provided by an embodiment of the present application. As Figure 4 shown, the horizontal coordinate represents the number of processing points, and the vertical coordinate represents the spindle load value. Among them, Figure 4The edge processing device includes the case of installing a boring tool on the main shaft of the machine tool, processing 705 workpieces, and each workpiece processing 4 points (a total of 2820 points) corresponding to the main shaft load value after slicing.

[0069] S202, based on the plurality of main shaft load values, determining a load average value and a load peak value.

[0070] Among them, the plurality of main shaft load values are the main shaft load values corresponding to the preset number of workpieces processed by the current tool installed in the main shaft of the machine tool.

[0071] In an optional implementation, the edge processing device determines the load average value based on the plurality of main shaft load values, which can include: determining the load average value of the main shaft of the machine tool based on the main shaft load values corresponding to the preset number of workpieces processed under the condition that the preset number of workpieces are processed; and the load average value is used for smoothing processing of the main shaft load value corresponding to the workpiece processed in the time period after the historical time period, and filtering high-frequency noise processing.

[0072] For example, assuming that the preset number is 50, after processing 50 workpieces, the edge processing device determines the load average value of the main shaft of the machine tool based on the main shaft load values f1, f2,..., f50 corresponding to the 50 workpieces processed.

[0073] In an optional implementation, the edge processing device determines the load peak value based on the plurality of historical main shaft load values, which can include: determining the load peak value of the main shaft of the machine tool based on the main shaft load values corresponding to the preset number of workpieces processed under the condition that the preset number of workpieces are processed.

[0074] For example, assuming that the preset number is 50, after processing 50 workpieces, the edge processing device determines the load maximum value from the main shaft load values corresponding to the 50 workpieces processed as the load peak value of the main shaft of the machine tool.

[0075] S203, in the case that the load average value exceeds the preset load average value interval, or the load peak value exceeds the preset peak value interval, determining that the current tool in the main shaft of the machine tool fails.

[0076] Among them, the load average value can be used for smoothing data, filtering high-frequency noise and detecting the overall trend of the load. The peak value can be used for detecting the burst time and abnormal situation of the load.

[0077] In an optional embodiment, the preset average load value interval and the preset peak value interval can be determined by the edge processing device in the following manner: obtaining a plurality of historical spindle load values corresponding to a preset first number of workpieces processed by the machine tool spindle in a previous monitoring time period of the monitoring time period; determining a historical average load value and a historical peak load value based on the plurality of historical spindle load values; determining the preset average load value interval based on the historical average load value, and determining the preset peak value interval based on the historical peak load value.

[0078] In this embodiment, the edge processing device determines the preset average load value interval based on the historical average load value, which can include determining the preset average load value interval based on the historical average load value and preset first upper limit variation value and first lower limit variation value. Optionally, the first upper limit variation value can be 3 and the first lower limit variation value can be -3.

[0079] For example, assuming that the historical average load value is 35, the first upper limit variation value is 3, and the first lower limit variation value is -3, the edge processing device can determine the preset average load value interval to be [32, 38]. In this case, if the average load value is 39, the edge processing device can determine that the average load value 39 exceeds the preset average load value interval [32, 38], and at this time, the edge processing device can determine that the current tool in the machine tool spindle has failed.

[0080] Optionally, the edge processing device can fit an upper and lower limit control curve of ±3 based on the historical average load value. Please refer to Figure 5 , Figure 5 which is a schematic diagram of a load average value fitting control curve provided by an embodiment of the present application. As shown in Figure 5 , the horizontal coordinate represents the number of machining points, and the vertical coordinate represents the spindle load value.

[0081] Optionally, the edge processing device can also output the fitted control curve for the user to view.

[0082] In this embodiment, the edge processing device determines the preset peak value interval based on the historical peak load value, which can include determining the preset peak value interval based on the historical peak load value and preset second upper limit variation value and second lower limit variation value.

[0083] For example, assuming that the historical peak load value is 43, the second upper limit variation value is 2, and the second lower limit variation value is -2, the edge processing device can determine the preset peak value interval to be [41, 45]. In this case, if the load peak value of the monitoring time period is 46, the edge processing device can determine that the load peak value 46 exceeds the preset peak value interval [41, 45], and at this time, the edge processing device can determine that the current tool in the machine tool spindle has failed.

[0084] Please refer to Figure 6 ,Figure 6 is a schematic diagram of a load peak value variation provided by an embodiment of the present application. As shown in Figure 6 , the load peak value in the preset time period is in the preset peak value interval, that is, the load peak value in the preset time period is in the range of the peak value upper limit value / peak value lower limit value. Thus, it can be determined that the current tool in the machine tool spindle does not appear abnormal (or does not occur failure).

[0085] In another alternative embodiment, the preset load average value interval and the preset peak value interval can be preset by the user according to experience value.

[0086] In the embodiment of the present application, the edge processing device obtains a plurality of spindle load values of the machine tool spindle in a monitoring time period; wherein each spindle load value is a load value corresponding to processing each workpiece under the condition that the current tool is installed in the machine tool spindle; based on the plurality of spindle load values, a load average value and a load peak value are determined; in the case that the load average value exceeds the preset load average value interval, or the load peak value exceeds the preset peak value interval, it is determined that the current tool in the machine tool spindle has occurred failure. In this way, compared with the method of analyzing the running data by machine learning, deep learning, artificial intelligence algorithm and other methods to obtain the detection result, the tool failure detection method provided by the present application does not need to go through the two stages of data processing and decision reasoning, thereby improving the efficiency of tool failure detection in the machine tool spindle.

[0087] In addition, the edge processing device is used to process and analyze the running data in the present application, which can reduce the delay time of transmitting the running data to the cloud and reduce the consumption of network resources; at the same time, it can also improve the security of the running data.

[0088] Please refer to Figure 7 , Figure 7 is a flowchart of another tool failure detection method in a machine tool spindle provided by an embodiment of the present application. Different from Figure 2 , the method shown in Figure 7 , the method shown in the method illustrates that the edge processing device can detect the failure of the tool in the machine tool spindle based on the preset load threshold value and the current spindle load value.

[0089] S701, obtaining a current spindle load value corresponding to processing a current workpiece.

[0090] In an alternative embodiment, the edge processing device can obtain the current spindle load value of the machine tool spindle from the device to which the machine tool spindle belongs through the protocol converter via the network cable.

[0091] S702, determining whether the current spindle load value is greater than the preset load threshold value, if yes, executing step S703, if not, executing step S704.

[0092] The preset load threshold is an instantaneous value, and the current spindle load value is also an instantaneous value. In this way, by monitoring whether the load state exceeds the preset load threshold in real time, potential tool failure or Brown reaction can be warned.

[0093] In an optional embodiment, the preset load threshold can be a load peak value in a preset time period, or an experience value set by a user, which is not limited here.

[0094] That is, the edge processing device can monitor whether the spindle load value exceeds the preset load threshold.

[0095] In an optional embodiment, the edge processing device can also detect abnormal conditions to monitor the load state in real time and warn potential failure or improper operation.

[0096] S703, determine that the current tool in the machine tool spindle has failed.

[0097] For example, assuming that the current spindle load value is 42 and the preset load threshold is 41, the edge processing device can determine that the current spindle load value 42 is greater than the preset load threshold 41, and at this time, the edge processing device can determine that the current tool in the machine tool spindle has failed.

[0098] In an optional embodiment, the edge processing device can also output an abnormal detection waveform diagram and abnormal condition information. Please refer to Figure 8 , Figure 8 is an abnormal detection schematic diagram provided by an embodiment of the present application. As shown in (1) of Figure 8 , the horizontal axis represents time, and the vertical axis represents the spindle load value. As shown in (2) of Figure 8 , it is a log list of the spindle load value monitored by the edge processing device.

[0099] S704, determine that the current tool in the machine tool spindle has not failed, and continue to obtain the next spindle load value of the current spindle load value.

[0100] In an embodiment of the present application, the edge processing device can obtain the corresponding current spindle load value when processing the current workpiece; determine whether the current spindle load value is greater than the preset load threshold, if yes, determine that the current tool in the machine tool spindle has failed, and if not, determine that the current tool in the machine tool spindle has not failed, and continue to obtain the next spindle load value of the current spindle load value. In this way, by monitoring whether the current spindle load value is greater than the preset load threshold in real time, the tool in the machine tool spindle can be detected for failure, which can improve the detection efficiency.

[0101] In an optional embodiment, Figure 2 and Figure 7In the tool failure detection method in the machine tool spindle shown, the edge processing device can also output alarm information in the case where it is determined that the current tool in the machine tool spindle has failed. In this way, relevant personnel can take appropriate measures, such as replacing the tool. The alarm information is used to indicate that the current tool in the machine tool spindle has failed.

[0102] Optionally, the alarm information can be an alarm sound automatically issued by the edge processing device, or the edge processing device can output information indicating that the current tool has failed to the client, and the like, which is not limited here.

[0103] In an optional implementation, the edge processing device can also send the load state of the machine tool spindle to the user device. In this way, relevant personnel can understand the production situation in real time and perform data analysis and comparison.

[0104] In an optional implementation, the edge processing device can also generate a spindle load report based on the historical spindle load value and the current spindle load value, and send the spindle load report to the user device. In this way, it is beneficial for relevant personnel to make subsequent analysis and decision based on the spindle load report.

[0105] Optionally, the user device can be a remote monitoring device, or a large screen or monitoring interface in the production site. In the case where the user device is a remote monitoring device, the edge processing device and the remote monitoring device can communicate through a bastion host, so that relevant personnel can remotely monitor and remotely adjust the spindle load value of the machine tool spindle. In the case where the user device is a large screen or monitoring interface in the production site, relevant personnel can monitor the spindle load value of the machine tool spindle in real time through the on-site GOT, so that the personnel can make corresponding adjustments based on the demand to optimize the efficiency and quality of the production workpiece.

[0106] In an optional implementation, the edge processing device can also determine the current spindle load value after the current tool processes a preset second number of workpieces, obtain a target spindle load value after a preset second number of workpieces are processed in the case where the last tool is installed in the machine tool spindle, and output alarm information in the case where the difference between the current spindle load value and the target spindle load value is greater than a preset threshold. The alarm information is used to indicate that the current tool has failed. The preset threshold is associated with the preset second number.

[0107] Please refer to Figure 9 , Figure 9 is a tool trend early warning schematic diagram provided by an embodiment of the present application. As shown in Figure 9As shown, the first row of waveform diagram is a schematic diagram of the change trend of the spindle load value corresponding to the plurality of slice processing, the second row is a schematic diagram of the change trend of the spindle load value corresponding to the current tool machining workpiece, and the third row is a schematic diagram of the change trend of the spindle load value corresponding to the last tool machining workpiece. Among them, in the position marked by the circle, the spindle load value change trend after the spindle load value when the current tool machines 2000 workpieces is the spindle load value after the last tool machines 2000 workpieces, so that the change amount between the spindle load value when the current tool machines 2000 workpieces and the spindle load value when the last tool machines 2000 workpieces can be reflected. After calculation, the spindle load value when the current tool machines 2000 workpieces is 10% higher than the spindle load value when the last tool machines 2000 workpieces. Assuming that the preset threshold is 12%, the edge processing device can output alarm information when the 2000th spindle load value is obtained.

[0108] In an optional embodiment, the edge processing device can output alarm information in the case that the current tool has not reached the tool use number threshold and the current spindle load value belongs to the preset load interval when the tool is replaced.

[0109] It should be understood that, although each step in the flowchart involved in each of the above-described embodiments is shown in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each of the above-described embodiments can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0110] Based on the same inventive concept, the embodiments of the present application also provide a tool failure detection device in a machine tool spindle for implementing the tool failure detection method in the machine tool spindle as described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, and therefore the specific limitations in one or more tool failure detection device embodiments in a machine tool spindle provided below can refer to the limitations of the tool failure detection method in the machine tool spindle described above, which will not be described here again.

[0111] Please refer to Figure 10 , Figure 10 is a structural schematic diagram of a tool failure detection device in a machine tool spindle provided by the embodiments of the present application. As Figure 10As shown, the tool failure detection device in the machine tool spindle can include but is not limited to: an acquisition module 1001 and a determination module 1002.

[0112] The acquisition module 1001 is configured to acquire a plurality of spindle load values of the machine tool spindle within a monitoring time period; wherein each spindle load value is a load value corresponding to machining each workpiece when the current tool is installed in the machine tool spindle;

[0113] The determination module 1002 is configured to determine a load average value and a load peak value based on the plurality of spindle load values.

[0114] The determination module 1002 is further configured to determine that the current tool in the machine tool spindle has failed when the load average value exceeds a preset load average value interval or the load peak value exceeds a preset peak value interval. In an embodiment, the device further includes a processing module. Wherein, when the acquisition module 1001 is used to acquire a plurality of spindle load values of the machine tool spindle within a monitoring time period, it is specifically used to: acquire running data of the machine tool spindle within the monitoring time period in real time, and the running data includes a plurality of initial spindle load values corresponding to machining each point of the target workpiece when the current tool is installed in the machine tool spindle; and, acquire the position of the machine tool spindle when machining each point; the processing module is configured to slice process the plurality of initial spindle load values based on the position of the machine tool spindle when machining each point to obtain a plurality of sliced spindle load values; and the plurality of sliced spindle load values are used as the plurality of spindle load values of the machine tool spindle.

[0115] In an embodiment, when the processing module is used to slice process the plurality of initial spindle load values based on the position of the machine tool spindle when machining each point to obtain a plurality of sliced spindle load values, it is specifically used to: determine the machining position of the machine tool spindle based on the position of the machine tool spindle when machining each point; and slice process the plurality of initial spindle load values based on the machining position to obtain a plurality of sliced spindle load values.

[0116] In an embodiment, the acquisition module 1001 is further configured to acquire a plurality of historical spindle load values corresponding to machining a preset first number of workpieces within a previous monitoring time period of the monitoring time period; the determination module 1002 is further configured to determine a historical load average value and a historical load peak value based on the plurality of historical spindle load values; determine the preset load average value interval based on the historical load average value, and determine the preset peak value interval based on the historical load peak value.

[0117] In one embodiment, the acquisition module 1001, when acquiring the running data of the machine tool spindle in real time within the monitoring time period, specifically configured to: acquire a plurality of running data of the machine tool spindle in real time from the device where the machine tool spindle is located; the plurality of running data is collected by the device based on a predetermined sampling frequency, and the sampling frequency is determined based on signal characteristics and dynamic response requirements, storage capacity and processing capacity, and actual requirements; wherein the signal characteristics include the fast and slow degree, frequency range and fluctuation amplitude of the spindle load signal change of the machine tool spindle, the dynamic response requirement refers to the fast and slow degree of the spindle load signal change required to be monitored, the storage capacity refers to the available storage space of the edge processing device, the processing capacity refers to the rate of the edge processing device processing data, and the actual requirement includes the cost required for collecting data.

[0118] In one embodiment, the determination module 1002 is further configured to determine a current spindle load value after the current tool processes a preset second number of workpieces; the acquisition module 1001 is further configured to acquire a target spindle load value after processing the preset second number of workpieces when the last tool is installed in the machine tool spindle; and the processing module is further configured to output an alarm information when the difference between the current spindle load value and the target spindle load value is greater than a preset threshold value, the alarm information being used to indicate that the current tool has failed; the preset threshold value is associated with the preset second number.

[0119] The above-mentioned various modules in the tool failure detection device in the machine tool spindle can be realized by software, hardware and combinations thereof, in whole or in part. The above-mentioned various modules can be embedded in or independent of the processor in the terminal device in hardware form, or can be stored in the memory in the terminal device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned various modules.

[0120] In one exemplary embodiment, the embodiments of the present application provide a device, which can be an edge processing device, and the internal structure diagram of the device can be as shown in Figure 11As shown in the figure. The device includes a processor, a memory, an input / output interface, a communication interface and an input device. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface and the input device are connected to the system bus through the input / output interface. Among them, the processor of the device is used to provide computing and control capability. The memory of the device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the device is used to exchange information between the processor and external devices. The communication interface of the device is used for wired or wireless communication with external terminals, and wireless communication can be realized through WIFI, mobile cellular network, Near Field Communication (NFC) or other technologies. The computer program is executed by the processor to realize a tool fault detection method in a machine tool spindle.

[0121] Those skilled in the art can understand that, Figure 11 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the device to which the scheme of the present application is applied. The specific device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0122] In an exemplary embodiment, the present application provides a device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to realize the following steps:

[0123] Obtaining a plurality of spindle load values of the machine tool spindle within a monitoring time period; wherein each spindle load value is a load value corresponding to processing each workpiece under the condition that the current tool is installed in the machine tool spindle;

[0124] Determining a load average value and a load peak value based on the plurality of spindle load values;

[0125] In the case where the load average value exceeds the preset load average value interval, or the load peak value exceeds the preset peak value interval, it is determined that the current tool in the machine tool spindle has failed.

[0126] It can be understood that the steps that can be realized and the beneficial effects that can be achieved when the processor executes the computer program can be referred to the description of the foregoing related tool fault detection method in the machine tool spindle, which will not be described here.

[0127] In an exemplary embodiment, the present application provides a computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to realize the following steps:

[0128] obtaining a plurality of spindle load values of the machine tool spindle within a monitoring time period; wherein each spindle load value is a load value corresponding to processing each workpiece in a case that the current tool is installed in the machine tool spindle;

[0129] determining a load average value and a load peak value based on the plurality of spindle load values;

[0130] determining that the current tool in the machine tool spindle is in failure in a case that the load average value exceeds a preset load average value interval, or the load peak value exceeds a preset peak value interval.

[0131] It can be understood that the steps that can be implemented and the beneficial effects that can be achieved when the computer program is executed by the processor can refer to the description of the foregoing related tool failure detection method embodiments of the machine tool spindle, and will not be described here.

[0132] In one exemplary embodiment, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the following steps:

[0133] obtaining a plurality of spindle load values of the machine tool spindle within a monitoring time period; wherein each spindle load value is a load value corresponding to processing each workpiece in a case that the current tool is installed in the machine tool spindle;

[0134] determining a load average value and a load peak value based on the plurality of spindle load values;

[0135] determining that the current tool in the machine tool spindle is in failure in a case that the load average value exceeds a preset load average value interval, or the load peak value exceeds a preset peak value interval.

[0136] It can be understood that the steps that can be implemented and the beneficial effects that can be achieved when the computer program is executed by the processor can refer to the description of the foregoing related tool failure detection method embodiments of the machine tool spindle, and will not be described here.

[0137] It should be noted that the data (including but not limited to operating data, spindle load values, horizontal axis coordinates, vertical axis coordinates, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the related data need to comply with relevant regulations.

[0138] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0139] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0140] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A method for detecting tool faults in a machine tool spindle, characterized in that, Applied to edge processing devices, the method includes: The machine tool spindle's operating data is acquired in real time during the monitoring period. The operating data includes the initial spindle load values ​​corresponding to multiple points of the target workpiece being machined when the current tool is installed on the machine tool spindle; and the position of the machine tool spindle when machining each point. Based on the position of the machine tool spindle when machining each of the aforementioned points, the initial spindle load values ​​are sliced ​​to obtain multiple sliced ​​spindle load values, and these multiple sliced ​​spindle load values ​​are used as multiple spindle load values ​​of the machine tool spindle; wherein, each spindle load value is the load value corresponding to machining each workpiece when the current tool is installed in the machine tool spindle; Based on multiple spindle load values, an average load and a peak load are determined; the average load is used to smooth the data and filter high-frequency noise; the peak load is used to detect sudden load events and anomalies. If the average load value exceeds a preset average load value range, or the peak load value exceeds a preset peak load range, it is determined that the current tool in the machine tool spindle has malfunctioned. The preset average load range and the preset peak range are determined in the following manner: Obtain multiple historical spindle load values ​​corresponding to the processing of a preset first number of workpieces by the machine tool spindle in the previous monitoring time period of the monitoring time period; Based on the multiple historical spindle load values, determine the historical average load and the historical peak load; Based on the historical load average, the preset load average range is determined, and based on the historical load peak, the preset peak range is determined.

2. The method according to claim 1, characterized in that, Based on the position of the machine tool spindle at each machining point, the initial spindle load values ​​are sliced ​​to obtain multiple sliced ​​spindle load values, including: The machining position of the machine tool spindle is determined based on the position of the machine tool spindle when machining each of the aforementioned points; Based on the processing position, multiple initial spindle load values ​​are sliced ​​to obtain multiple sliced ​​spindle load values.

3. The method according to claim 1, characterized in that, The real-time acquisition of the machine tool spindle's operating data during the monitoring period includes: Multiple operating data of the machine tool spindle are acquired in real time from the equipment where the machine tool spindle is located; The multiple operational data are collected by the device based on a predetermined sampling frequency, which is determined based on signal characteristics and dynamic response requirements, storage capacity and processing capabilities, as well as actual needs. The signal characteristics include the rate of change, frequency range, and fluctuation amplitude of the spindle load signal of the machine tool spindle; the dynamic response requirement refers to the rate of change of the spindle load signal to be monitored; the storage capacity refers to the available storage space of the edge processing device; the processing capability refers to the data processing rate of the edge processing device; and the actual requirements include the cost required for data acquisition.

4. The method according to claim 1, characterized in that, The edge processing device can also determine the current spindle load value after the current tool has processed a preset second number of workpieces; The target spindle load value after machining the preset second number of workpieces is obtained when a cutting tool is installed in the machine tool spindle. If the difference between the current spindle load value and the target spindle load value is greater than a preset threshold, an alarm message is output, which is used to indicate that the current tool has malfunctioned. The preset threshold is associated with the preset second quantity.

5. The method according to claim 1, characterized in that, Determining the preset load average range based on the historical load average includes: Based on the historical average load, the preset first upper limit change value, and the preset lower limit change value, the preset average load range is determined.

6. The method according to claim 1, characterized in that, Determining the preset peak range based on the historical load peak includes: The preset peak range is determined based on the historical load peak value, the preset second upper limit change value, and the preset second lower limit change value.

7. A tool fault detection device for a machine tool spindle, characterized in that, Applied to edge processing devices, the apparatus includes: The acquisition module is used to acquire the operating data of the machine tool spindle in real time during the monitoring period. The operating data includes the initial spindle load values ​​corresponding to multiple points of the target workpiece being machined when the current tool is installed on the machine tool spindle; and to acquire the position of the machine tool spindle when machining each point. The processing module is used to slice multiple initial spindle load values ​​based on the position of the machine tool spindle when machining each of the points, to obtain multiple sliced ​​spindle load values, and to use the multiple sliced ​​spindle load values ​​as multiple spindle load values ​​of the machine tool spindle; wherein, each spindle load value is the load value corresponding to machining each workpiece when the current tool is installed in the machine tool spindle; The determination module is used to determine the average load and the peak load based on multiple spindle load values; the average load is used to smooth the data and filter high-frequency noise; the peak load is used to detect sudden load events and abnormal conditions. The determining module is further configured to determine that the current tool in the machine tool spindle has malfunctioned when the average load exceeds a preset average load range or the peak load exceeds a preset peak load range. The preset average load range and the preset peak range are determined in the following manner: Obtain multiple historical spindle load values ​​corresponding to the processing of a preset first number of workpieces by the machine tool spindle in the previous monitoring time period of the monitoring time period; Based on the multiple historical spindle load values, determine the historical average load and the historical peak load; Based on the historical load average, the preset load average range is determined, and based on the historical load peak, the preset peak range is determined.

8. A device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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