A tool monitoring method and device, electronic equipment and storage medium

Abstract

This invention provides a tool monitoring method, device, electronic device, and storage medium. The method includes: acquiring spindle load data corresponding to the tool, the spindle load data including a spindle load curve corresponding to the tool and a machining type, the spindle load curve representing the correspondence between time and spindle load; truncating the spindle load curve according to the machining type corresponding to the tool to obtain a first filtering result, the first filtering result including N data points, where N is an integer greater than 1, the data points representing the correspondence between time and spindle load; and monitoring the tool based on the first filtering result. The features of this invention can improve the processing efficiency of tool monitoring in CNC machine tools.

Description

Technical Field

[0001] This invention relates to the field of CNC machine tool technology, and in particular to a tool monitoring method, device, electronic device, and storage medium. Background Technology

[0002] In order to accurately monitor the real-time status of the cutting tool during the product processing of CNC machine tools, the tool monitoring system in CNC machine tools collects data about the cutting tool, including spindle load data directly related to the cutting tool, temperature and vibration data in the machining environment, and other data. The collected data is then stored, processed and analyzed to determine the current status of the cutting tool.

[0003] In existing technologies, the tool monitoring system in CNC machine tools stores, processes, and analyzes all collected data. For example, process engineers can analyze the load on the cutting tools used in product machining and add start and end markers for real-time tool status monitoring to the CNC machine tool's process program, thereby enabling the tool monitoring system to monitor the complete machining process within the marked area.

[0004] It is evident that the existing technology for monitoring cutting tools in CNC machine tools suffers from low processing efficiency due to data redundancy. Summary of the Invention

[0005] This invention provides a tool monitoring method, device, electronic device, and storage medium to solve the problem of low data processing efficiency in tool monitoring in the prior art.

[0006] In a first aspect, embodiments of the present invention provide a tool monitoring method, including:

[0007] Obtain spindle load data corresponding to the tool. The spindle load data includes the spindle load curve corresponding to the tool and the machining type. The spindle load curve represents the correspondence between time and spindle load.

[0008] The spindle load curve is truncated according to the machining type corresponding to the tool to obtain a first filtering result. The first filtering result includes N data points, where N is an integer greater than 1, and the data points represent the correspondence between time and spindle load.

[0009] The cutting tool is monitored based on the first filtering result.

[0010] Optionally, the step of truncating the spindle load curve according to the machining type corresponding to the tool to obtain the first filtering result includes:

[0011] A status marker is determined based on the machining type. The set of status markers represents the machining process of the tool. The status marker includes at least one of the following: spindle speed reached marker, cutting feed marker, tapping status marker, and tool downward feed marker.

[0012] The data segment marked with the status on the spindle load curve is retained to obtain the first filtering result.

[0013] Optionally, after truncating the spindle load curve according to the machining type corresponding to the tool to obtain the first filtering result, the method further includes:

[0014] Based on the first filtering result, the target monitoring data segment is determined, and a second filtering result is obtained. The second filtering result includes M data points, where M is an integer greater than 1 and N is greater than M.

[0015] Optionally, determining the target monitoring data segment based on the first filtering result to obtain the second filtering result includes:

[0016] Obtain the preset normal baseline and the preset abnormal baseline;

[0017] Based on the first filtering result, the preset normal baseline, and the preset abnormal baseline, a valid monitoring data segment is determined.

[0018] The effective monitoring data segment is optimized to obtain the target monitoring data segment;

[0019] The spindle load curve is extracted based on the target monitoring data segment to obtain the second filtering result.

[0020] Optionally, determining the valid monitoring data segment based on the first filtering result, the preset normal baseline, and the preset abnormal baseline includes:

[0021] An auxiliary baseline is determined based on the preset normal baseline and the preset abnormal baseline;

[0022] Based on the auxiliary baseline and the first filtering result, data points less than 0 are filtered to obtain effective monitoring data segments.

[0023] Optionally, the auxiliary baseline is obtained in the following manner:

[0024] Subtract the value of the preset abnormal baseline from the value of the preset normal baseline, and then divide by the value of the preset normal baseline to obtain the value of the corresponding auxiliary baseline;

[0025] The position of the auxiliary reference line in the spindle load curve is determined based on the value of the auxiliary reference line.

[0026] Optionally, optimizing the effective monitoring data segment to obtain the target monitoring data segment includes:

[0027] The median is determined based on the effective monitoring data segment, and data points greater than the median in the auxiliary baseline are extracted to obtain the data point index;

[0028] The index column is determined based on the data point index;

[0029] The starting point and ending point of the interval are determined based on the index sequence, and the interval represented between the starting point and the ending point is the target monitoring data segment.

[0030] Secondly, embodiments of the present invention provide a tool monitoring device, comprising:

[0031] The acquisition module is used to acquire spindle load data corresponding to the tool. The spindle load data includes the spindle load curve corresponding to the tool and the machining type. The spindle load curve represents the correspondence between time and spindle load.

[0032] The first processing module is used to extract the spindle load curve according to the machining type corresponding to the tool to obtain a first filtering result. The first filtering result includes N data points, where N is an integer greater than 1, and the data points represent the correspondence between time and spindle load.

[0033] The monitoring module is used to monitor the cutting tool based on the first filtering result.

[0034] The first processing module includes:

[0035] The first determining unit is used to determine a status marker based on the machining type. The set of status markers represents the machining process of the tool. The status markers include at least one of the following: spindle speed reached marker, cutting feed marker, tapping status marker, and tool downward feed marker.

[0036] The first processing unit is used to retain the data segment of the status marker on the spindle load curve to obtain the first filtering result.

[0037] Optionally, the device further includes:

[0038] The second processing module is used to determine the target monitoring data segment based on the first filtering result and obtain the second filtering result, which includes M data points, where M is an integer greater than 1 and N is greater than M.

[0039] Optionally, the second processing model includes:

[0040] The first acquisition unit is used to acquire the preset normal baseline and the preset abnormal baseline;

[0041] The second processing unit is used to determine the valid monitoring data segment based on the first filtering result, the preset normal baseline, and the preset abnormal baseline.

[0042] The third processing unit is used to optimize the effective monitoring data segment to obtain the target monitoring data segment;

[0043] The fourth processing unit is used to extract the spindle load curve based on the target monitoring data segment to obtain the second filtering result.

[0044] Optionally, the second processing unit includes:

[0045] An auxiliary baseline is determined based on the preset normal baseline and the preset abnormal baseline;

[0046] Based on the auxiliary baseline and the first filtering result, data points less than 0 are filtered to obtain effective monitoring data segments.

[0047] Optionally, the auxiliary baseline is obtained in the following manner:

[0048] Subtract the value of the preset abnormal baseline from the value of the preset normal baseline, and then divide by the value of the preset normal baseline to obtain the value of the corresponding auxiliary baseline;

[0049] The position of the auxiliary reference line in the spindle load curve is determined based on the value of the auxiliary reference line.

[0050] Optionally, the third processing unit includes:

[0051] The median is determined based on the effective monitoring data segment, and data points greater than the median in the auxiliary baseline are extracted to obtain the data point index;

[0052] The index column is determined based on the data point index;

[0053] The starting point and ending point of the interval are determined based on the index sequence, and the interval represented between the starting point and the ending point is the target monitoring data segment.

[0054] Thirdly, embodiments of the present invention provide an electronic device, including:

[0055] At least one processor; and

[0056] A memory communicatively connected to the at least one processor; wherein,

[0057] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the tool monitoring method described in the first aspect.

[0058] Fourthly, embodiments of the present invention provide a storage medium, comprising:

[0059] A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to perform the tool monitoring method described in the first aspect.

[0060] In this embodiment of the invention, the spindle load data of the tool in the CNC machine tool is first acquired. The spindle load data includes a spindle load curve that reflects the relationship between time and spindle load, as well as the machining type of the tool in the working state. Then, the data segment corresponding to the machining type is extracted and retained on the spindle load curve to obtain a first filtering result. This data segment includes N data points. Finally, the tool is monitored based on the first filtering result. By obtaining the first filtering result through filtering, the data segment that has the most significant impact on the tool state can be accurately located, thereby simplifying the amount of data analysis and improving the data processing efficiency of tool monitoring.

[0061] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0062] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is one of the flowcharts illustrating a tool monitoring method provided in an embodiment of the present invention;

[0064] Figure 2 This is a second schematic flowchart of a tool monitoring method provided in an embodiment of the present invention;

[0065] Figure 3 This is the third flowchart of a tool monitoring method provided in an embodiment of the present invention;

[0066] Figure 4 This is the fourth flowchart of a tool monitoring method provided in an embodiment of the present invention;

[0067] Figure 5 This is a schematic diagram of the structure of a tool monitoring device provided in an embodiment of the present invention;

[0068] Figure 6 This is a block diagram of an electronic device used to implement the tool monitoring method of this invention. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] In the embodiments of this invention, the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0071] Please see Figure 1 , Figure 1 This is one of the flowcharts illustrating a tool monitoring method provided in an embodiment of the present invention, such as... Figure 1 As shown, it includes the following steps:

[0072] Step 101: Obtain the spindle load data corresponding to the tool. The spindle load data includes the spindle load curve and machining type corresponding to the tool. The spindle load curve represents the correspondence between time and spindle load.

[0073] Step 102: Extract the spindle load curve according to the machining type corresponding to the tool to obtain a first filtering result. The first filtering result includes N data points, where N is an integer greater than 1, and the data points represent the correspondence between time and spindle load.

[0074] Step 103: Monitor the cutting tool based on the first filtering result.

[0075] The steps 101, 102 and 103 of the above tool monitoring method can be executed by electronic devices, such as CNC machine tools and computers. This embodiment of the invention does not limit the scope of the invention.

[0076] In step 101, the spindle load curve represents the correspondence between time and spindle load. The spindle load curve can be represented in a coordinate system, for example, the horizontal axis represents the time variable and the vertical axis represents the spindle load variable.

[0077] In addition, the above-mentioned machining type can be understood as the working state of the above-mentioned tool within a time period or data segment. The above-mentioned machining type includes, but is not limited to, tapping machining type, drilling machining type, turning machining type and milling machining type.

[0078] In step 102, each of the above processing types may include several processing features. Different processing types correspond to different combinations of processing features. Then, the spindle load curve is truncated according to the combination of processing features. This can be understood as retaining the data segments corresponding to the above processing features and filtering the remaining data segments.

[0079] In addition, the spindle load data mentioned above can also include data on tool vibration, temperature and other dimensions in the machining scenario. Through the processing in step 102 above, these data can also be filtered so that the final data obtained is the one that best matches the abnormal state of the tool.

[0080] In step 103, CNC machine tools or computers can monitor the cutting tool based on the first filtering result, that is, monitor the data segment represented by the first filtering result. This data segment can be understood as the machining range and program range that have the most significant impact on the cutting tool state.

[0081] In this implementation scheme, the spindle load data of the tool in the CNC machine tool is first acquired. The spindle load data includes a spindle load curve that reflects the relationship between time and spindle load, as well as the machining type of the tool in the working state. Then, the data segment corresponding to the machining type is extracted and retained from the spindle load curve to obtain the first filtering result. This data segment includes N data points. Finally, the tool is monitored based on the first filtering result. By obtaining the first filtering result through filtering, the data segment that has the most significant impact on the tool state can be accurately located, thereby simplifying the amount of data analysis and improving the data processing efficiency of tool monitoring.

[0082] It should be noted that, when the spindle load data mentioned above also includes other dimensions such as vibration and temperature, the tool monitoring method mentioned above collects multi-dimensional state data during the product processing, namely the spindle load data, and filters and cleans the data according to the processing type, thereby extracting the data with the highest degree of correlation with the abnormal state of the tool. Furthermore, while ensuring the accuracy of tool monitoring, it greatly reduces the pressure on data processing, thereby improving the responsiveness of the monitoring results output.

[0083] In addition, before step 101, the tool type of the aforementioned tool can be determined, and steps 101, 102, and 103 of the aforementioned tool monitoring method can be performed based on the aforementioned tool type, thereby improving the accuracy of the aforementioned tool monitoring.

[0084] Optionally, the step of truncating the spindle load curve according to the machining type corresponding to the tool to obtain the first filtering result includes:

[0085] A status marker is determined based on the machining type. The set of status markers represents the machining process of the tool. The status marker includes at least one of the following: spindle speed reached marker, cutting feed marker, tapping status marker, and tool downward feed marker.

[0086] The data segment marked with the status on the spindle load curve is retained to obtain the first filtering result.

[0087] In this implementation scheme, since different machining types correspond to different machining features, the status markers of the machining features included can first be determined based on the machining type. The status markers can be understood as whether the machining features have reached a preset state. Then, the spindle load data is filtered, that is, the data segments on the spindle load curve corresponding to the status markers are retained, thereby accurately locating the data segments that have the most significant impact on the tool status, thus simplifying the amount of data analysis and improving the data processing efficiency of tool monitoring.

[0088] Please refer to Table 1, which shows the correspondence between processing types and status flags:

[0089] Table 1

[0090]

[0091] Among them, tapping, drilling, turning, and milling can be understood as the above-mentioned machining types, while spindle speed, cutting feed, tapping status, and tool downward feed can be understood as the above-mentioned machining features. The numbers 1 and 0 in Table 1 indicate whether the above-mentioned machining features are completed. Taking drilling as an example: when the machining type is drilling, as shown in Table 1, the machining features included in the drilling machining type are spindle speed reaching a certain preset value, cutting feed, and tool downward feed. Then, the spindle load curve is filtered, specifically, the data segments corresponding to spindle speed reaching a certain preset value, cutting feed, and tool downward feed are retained, thereby obtaining the above-mentioned first filtering result.

[0092] In some alternative implementations, please refer to Figure 2 , Figure 2 This is a second flowchart illustrating a tool monitoring method provided in an embodiment of the present invention, as shown below. Figure 2 As shown, the machining features are first identified based on the spindle load data, and then the corresponding filtering algorithm is matched to obtain the first filtering result. The state filtering algorithm can be a preset state filtering algorithm, which completes the filtering by matching the state flag bit of the machining type.

[0093] Optionally, after truncating the spindle load curve according to the machining type corresponding to the tool to obtain the first filtering result, the method further includes:

[0094] Based on the first filtering result, the target monitoring data segment is determined, and a second filtering result is obtained. The second filtering result includes M data points, where M is an integer greater than 1 and N is greater than M.

[0095] In this implementation scheme, the first filtering result is filtered again, and then the target monitoring data segment is determined to obtain the second filtering result. This method can find the data segment that can optimally segment normal samples and abnormal samples, thereby performing data filtering and finally obtaining the second filtering result. While ensuring the accuracy of tool monitoring, it reduces the amount of data, thereby greatly reducing the pressure of data processing. On the other hand, it improves the responsiveness of the monitoring result output while improving processing efficiency.

[0096] It should be noted that the first filtering result mentioned above includes N data points, while the second filtering result mentioned above includes M data points. Since the second filtering result is based on the first filtering result, the number of data points in the second filtering result can be less than the number of data points in the first filtering result, in order to obtain a more accurate data segment.

[0097] Please see Figure 3 , Figure 3 This is a third flowchart illustrating a tool monitoring method provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the data input is the input of the spindle load data of the corresponding tool. First, the spindle load data is input into the first-level filter, that is, the status mark is determined according to the machining type, and the data segment of the status mark on the spindle load curve is retained to obtain the first filtering result. Then, the first filtering result is input into the second-level filter, that is, the target monitoring data segment is determined according to the first filtering result to obtain the second filtering result. Finally, the second filtering result is output, and the tool monitoring is completed according to the second filtering result.

[0098] Optionally, determining the target monitoring data segment based on the first filtering result and obtaining the second filtering result includes:

[0099] Obtain the preset normal baseline and the preset abnormal baseline;

[0100] Based on the first filtering result, the preset normal baseline, and the preset abnormal baseline, a valid monitoring data segment is determined.

[0101] The effective monitoring data segment is optimized to obtain the target monitoring data segment;

[0102] The spindle load curve is extracted based on the target monitoring data segment to obtain the second filtering result.

[0103] In this implementation scheme, the aforementioned preset normal baseline and preset abnormal baseline are first obtained. These baselines are then used to further filter the first filtering result to obtain the effective monitoring data segment. This effective monitoring data segment is then used as a basis for further optimization to obtain the target monitoring data segment. Finally, the spindle load curve is extracted based on the target monitoring data segment to obtain the second filtering result. The spindle load curve can be understood as the curve after the first filtering process. This method further filters the first filtering result to obtain data that best matches the abnormal tool condition. After re-integration, a second monitoring interval is automatically selected, simplifying the data analysis and improving the data processing efficiency for tool monitoring.

[0104] It should be noted that the aforementioned preset normal baseline and the aforementioned preset abnormal baseline can be set according to the actual spindle load curve, and this embodiment of the present invention does not limit this.

[0105] Optionally, determining the valid monitoring data segment based on the first filtering result, the preset normal baseline, and the preset abnormal baseline includes:

[0106] An auxiliary baseline is determined based on the preset normal baseline and the preset abnormal baseline;

[0107] Based on the auxiliary baseline and the first filtering result, data points less than 0 are filtered to obtain effective monitoring data segments.

[0108] In this implementation scheme, the auxiliary baseline is determined by the preset normal baseline and the preset abnormal baseline. The first filtering result can be represented as a coordinate curve corresponding to time and spindle load. The auxiliary baseline is introduced into this coordinate curve, and data points less than 0 in the auxiliary baseline are filtered out to obtain the filtered sequence, i.e., the effective monitoring data segment. This method reduces the impact of error data on the monitoring results and improves the effectiveness of tool monitoring.

[0109] It should be noted that the above auxiliary baseline can be represented as a set of values, that is, the values ​​corresponding to the intersection of the above auxiliary baseline and the spindle load curve.

[0110] In some alternative implementations, please refer to Figure 4 , Figure 4 This is a fourth flowchart illustrating a tool monitoring method provided in an embodiment of the present invention, as shown below. Figure 4 As shown, firstly, based on the first filtering result, the preset normal baseline and the preset abnormal baseline, the effective monitoring data segment is determined. The effective monitoring data segment is then optimized, that is, the optimal data segment is selected to obtain the target monitoring data segment. The data corresponding to the target monitoring data segment in the spindle load curve is extracted and used as the second filtering result.

[0111] The spindle load curve is extracted based on the target monitoring data segment to obtain the second filtering result.

[0112] Optionally, the auxiliary baseline is obtained in the following manner:

[0113] Subtract the value of the preset abnormal baseline from the value of the preset normal baseline, and then divide by the value of the preset normal baseline to obtain the value of the corresponding auxiliary baseline;

[0114] The position of the auxiliary reference line in the spindle load curve is determined based on the value of the auxiliary reference line.

[0115] The above-mentioned preset normal baseline is represented as: base_line, the above-mentioned preset abnormal baseline is represented as: ab_line, and the above-mentioned auxiliary baseline is represented as: xecm_line, where xecm_line:[x1,x2,x3,…,x n ], where n represents the number of sampling points or data points within a processing cycle. The above auxiliary baseline can be calculated using the following formula:

[0116] xecm_line=(base_line-ab_line) / base_line

[0117] Optionally, optimizing the effective monitoring data segment to obtain the target monitoring data segment includes:

[0118] The median is determined based on the effective monitoring data segment, and data points greater than the median in the auxiliary baseline are extracted to obtain the data point index;

[0119] The index column is determined based on the data point index;

[0120] The starting point and ending point of the interval are determined based on the index sequence, and the interval represented between the starting point and the ending point is the target monitoring data segment.

[0121] In this implementation scheme, the median is first found within the aforementioned effective monitoring data segment. Then, data points greater than the median in the aforementioned auxiliary baseline are extracted to obtain their indices. Next, a first-order difference is calculated on these indices to obtain the corresponding index sequence, i.e., the aforementioned index sequence. Finally, the start and end points of the aforementioned intervals are determined based on the aforementioned index sequence. For each pair of start and end points, the integral value of the difference between that data segment and the idle state is calculated. The interval segment with the largest integral is the target data segment to be selected, which can also be understood as the monitoring interval. This method performs a second filtering process on the aforementioned first filtering results to obtain data with the highest correlation to the abnormal tool status. After re-integration, a second automatic selection of the monitoring interval is performed, thereby improving the data processing efficiency for tool monitoring.

[0122] In some optional implementations, the second filtering result can be obtained by determining the target monitoring data segment based on the first filtering result in the following manner:

[0123] 1. The above-mentioned preset normal baseline is set as: base_line, the above-mentioned preset abnormal baseline is set as: ab_line, and the above-mentioned auxiliary baseline is represented as: xecm_line, where xecm_line: [x1, x2, x3, ..., x n ], where n represents the number of sampling points or data points within a processing cycle. The above auxiliary baseline can be calculated using the following formula:

[0124] xecm_line=(base_line-ab_line) / base_line

[0125] 2. Filter the data points in xecm_line that are less than 0, and represent the filtered sequence as: xecm_above0;

[0126] 3. Calculate the median of the sequence xecm_above0 to obtain xecm_threshold;

[0127] 4. Extract data points in xecm_line that are greater than xecm_threshold, and obtain the index of these data points;

[0128] 5. Calculate the first difference of the index to obtain the sequence index_diff. It should be noted that the purpose of performing the first difference on the index is to determine the continuous index. When the value is 1, it means that the index is continuous. When the value is greater than 1, it means that the index is not continuous.

[0129] 6. Extract data points greater than 10 from index_diff, and set the extracted index column as index_diff_1. It should be noted that 10 here is a set value, which can be set according to the actual situation. This embodiment of the invention does not limit this value.

[0130] 7. Determine the start and end points of the interval, where location_start represents the index sequence of the start point and location_end represents the index sequence of the end point, and can be obtained using the following algorithm:

[0131] location_end={index[value==index_diff_1]}U{index[-1]}, location_start={index[0]}U{index[value==index_diff_1+1]};

[0132] 8. Calculate the starting and ending points of each group. Specifically, calculate the integral value of the difference between each segment of data and the no-load. The segment with the largest integral is the target monitoring data segment we want to select, which is the monitoring interval.

[0133] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a tool monitoring device provided in an embodiment of the present invention, as shown below. Figure 5 As shown, the tool monitoring device 500 includes:

[0134] The acquisition module 501 is used to acquire spindle load data corresponding to the tool. The spindle load data includes the spindle load curve corresponding to the tool and the machining type. The spindle load curve represents the correspondence between time and spindle load.

[0135] The first processing module 502 is used to extract the spindle load curve according to the machining type corresponding to the tool to obtain a first filtering result. The first filtering result includes N data points, where N is an integer greater than 1, and the data points represent the correspondence between time and spindle load.

[0136] The monitoring module 503 is used to monitor the cutting tool based on the first filtering result.

[0137] The first processing module 502 includes:

[0138] The first determining unit is used to determine a status marker based on the machining type. The set of status markers represents the machining process of the tool. The status markers include at least one of the following: spindle speed reached marker, cutting feed marker, tapping status marker, and tool downward feed marker.

[0139] The first processing unit is used to retain the data segment of the status marker on the spindle load curve to obtain the first filtering result.

[0140] Optionally, the tool monitoring device 500 also includes:

[0141] The second processing module is used to determine the target monitoring data segment based on the first filtering result and obtain the second filtering result, which includes M data points, where M is an integer greater than 1 and N is greater than M.

[0142] Optionally, the second processing model includes:

[0143] The first acquisition unit is used to acquire the preset normal baseline and the preset abnormal baseline;

[0144] The second processing unit is used to determine the valid monitoring data segment based on the first filtering result, the preset normal baseline, and the preset abnormal baseline.

[0145] The third processing unit is used to optimize the effective monitoring data segment to obtain the target monitoring data segment;

[0146] The fourth processing unit is used to extract the spindle load curve based on the target monitoring data segment to obtain the second filtering result.

[0147] Optionally, the second processing unit includes:

[0148] An auxiliary baseline is determined based on the preset normal baseline and the preset abnormal baseline;

[0149] Based on the auxiliary baseline and the first filtering result, data points less than 0 are filtered to obtain effective monitoring data segments.

[0150] Optionally, the auxiliary baseline is obtained in the following manner:

[0151] Subtract the value of the preset abnormal baseline from the value of the preset normal baseline, and then divide by the value of the preset normal baseline to obtain the value of the corresponding auxiliary baseline;

[0152] The position of the auxiliary reference line in the spindle load curve is determined based on the value of the auxiliary reference line.

[0153] Optionally, the third processing unit includes:

[0154] The median is determined based on the effective monitoring data segment, and data points greater than the median in the auxiliary baseline are extracted to obtain the data point index;

[0155] The index column is determined based on the data point index;

[0156] The starting point and ending point of the interval are determined based on the index sequence, and the interval represented between the starting point and the ending point is the target monitoring data segment.

[0157] According to embodiments of the present invention, the present invention also provides an electronic device and a readable storage medium.

[0158] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0159] like Figure 6 As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 602 or a computer program loaded from storage unit 608 into random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0160] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0161] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as tool monitoring methods.

[0162] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard parts (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0163] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0164] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0165] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0166] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0167] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0168] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0169] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A tool monitoring method, characterized in that, include: Obtain spindle load data corresponding to the tool. The spindle load data includes the spindle load curve corresponding to the tool and the machining type. The spindle load curve represents the correspondence between time and spindle load. The spindle load curve is truncated according to the machining type corresponding to the tool to obtain a first filtering result. The first filtering result includes N data points, where N is an integer greater than 1, and the data points represent the correspondence between time and spindle load. The cutting tool is monitored based on the first filtering result; After truncating the spindle load curve according to the machining type corresponding to the tool to obtain the first filtering result, the method further includes: Based on the first filtering result, the target monitoring data segment is determined, and a second filtering result is obtained. The second filtering result includes M data points, where M is an integer greater than 1 and N is greater than M. The step of determining the target monitoring data segment based on the first filtering result and obtaining the second filtering result includes: Obtain the preset normal baseline and the preset abnormal baseline; Based on the first filtering result, the preset normal baseline, and the preset abnormal baseline, a valid monitoring data segment is determined. The effective monitoring data segment is optimized to obtain the target monitoring data segment; The spindle load curve is extracted based on the target monitoring data segment to obtain the second filtering result; The determination of valid monitoring data segments based on the first filtering result, the preset normal baseline, and the preset abnormal baseline includes: An auxiliary baseline is determined based on the preset normal baseline and the preset abnormal baseline. The auxiliary baseline is represented as a set of values, which includes the values ​​corresponding to the intersection points of the auxiliary baseline and the spindle load curve. Based on the auxiliary baseline and the first filtering result, data points less than 0 are filtered to obtain effective monitoring data segments; The auxiliary baseline is obtained in the following way: Subtract the value of the preset abnormal baseline from the value of the preset normal baseline, and then divide by the value of the preset normal baseline to obtain the value of the corresponding auxiliary baseline; The position of the auxiliary reference line in the spindle load curve is determined based on the value of the auxiliary reference line.

2. The tool monitoring method according to claim 1, characterized in that, The step of truncating the spindle load curve according to the machining type corresponding to the tool to obtain the first filtering result includes: A status marker is determined based on the machining type. The set of status markers represents the machining process of the tool. The status marker includes at least one of the following: spindle speed reached marker, cutting feed marker, tapping status marker, and tool downward feed marker. The data segment marked with the status on the spindle load curve is retained to obtain the first filtering result.

3. The tool monitoring method according to claim 1, characterized in that, The optimization of the effective monitoring data segment to obtain the target monitoring data segment includes: The median is determined based on the effective monitoring data segment, and data points greater than the median in the auxiliary baseline are extracted to obtain the data point index; The index column is determined based on the data point index; The starting point and ending point of the interval are determined based on the index sequence, and the interval represented between the starting point and the ending point is the target monitoring data segment.

4. A tool monitoring device, characterized in that, include: The acquisition module is used to acquire spindle load data corresponding to the tool. The spindle load data includes the spindle load curve corresponding to the tool and the machining type. The spindle load curve represents the correspondence between time and spindle load. The first processing module is used to extract the spindle load curve according to the machining type corresponding to the tool to obtain a first filtering result. The first filtering result includes N data points, where N is an integer greater than 1, and the data points represent the correspondence between time and spindle load. A monitoring module is used to monitor the cutting tool based on the first filtering result; The device further includes: The second processing module is used to determine the target monitoring data segment based on the first filtering result and obtain the second filtering result, which includes M data points, where M is an integer greater than 1 and N is greater than M. The second processing model includes: The first acquisition unit is used to acquire the preset normal baseline and the preset abnormal baseline; The second processing unit is used to determine the valid monitoring data segment based on the first filtering result, the preset normal baseline, and the preset abnormal baseline. The third processing unit is used to optimize the effective monitoring data segment to obtain the target monitoring data segment; The fourth processing unit is used to extract the spindle load curve based on the target monitoring data segment to obtain the second filtering result; The second processing unit includes: An auxiliary baseline is determined based on the preset normal baseline and the preset abnormal baseline. The auxiliary baseline is represented as a set of values, which includes the values ​​corresponding to the intersection points of the auxiliary baseline and the spindle load curve. Based on the auxiliary baseline and the first filtering result, data points less than 0 are filtered to obtain effective monitoring data segments; The auxiliary baseline is obtained in the following way: Subtract the value of the preset abnormal baseline from the value of the preset normal baseline, and then divide by the value of the preset normal baseline to obtain the value of the corresponding auxiliary baseline; The position of the auxiliary reference line in the spindle load curve is determined based on the value of the auxiliary reference line.

5. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the tool monitoring method according to any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, in, The computer instructions are used to cause the computer to execute the tool monitoring method according to any one of claims 1 to 3.

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