Automatic tool changing control method, device, equipment and readable storage medium
By analyzing the vibration characteristic matrix of CNC machine tool cutting tools, and utilizing an autonomous mobile robot to automatically change CNC cutting tools, the problems of low tool changing efficiency and poor accuracy in existing technologies have been solved, achieving efficient and accurate automatic tool changing control.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHENSHI YUZHAN PRECISION TECH CO LTD
- Filing Date
- 2024-12-27
- Publication Date
- 2026-08-04
AI Technical Summary
Uncontrollable factors exist in the current CNC tool changing process, resulting in low tool changing efficiency and poor accuracy, which affects machining efficiency.
By acquiring the vibration timing data of the current tool on the CNC machine tool, analyzing the vibration characteristic matrix, determining the tool status, and using an autonomous mobile robot to automatically replace the spare tool.
Automatic tool changing is achieved, which improves tool changing efficiency and accuracy, and ensures the continuity and safety of machining.
Smart Images
Figure CN119952508B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of cutting tool technology, and in particular to an automatic tool changing control method, apparatus, device, and readable storage medium. Background Technology
[0002] Currently, CNC (Computerized Numerical Control Machine) cutting tools are handled in a rough manner. In addition to relying on the experience of technicians and regular tool setting tests to detect tool breakage, the wear condition of the tools is also assessed by personnel based on experience, making it impossible to accurately determine whether tool replacement is necessary. The traditional tool changing process is operated by technicians, which is complex and involves many uncontrollable factors, such as changing to the wrong tool or not changing the tool in time. These uncontrollable factors have a significant impact on the efficiency of normal tool changing, resulting in long tool preparation and changing times, long downtime, and thus affecting machining efficiency. Summary of the Invention
[0003] In view of this, it is necessary to provide an automatic tool changing control method, device, equipment, and readable storage medium to achieve automatic tool changing and improve tool changing efficiency and accuracy.
[0004] The first aspect of this application provides an automatic tool changing control method, comprising:
[0005] Obtain the current data of the current tool in the CNC machine tool, and determine the current state of the current tool based on the current data;
[0006] In response to the current tool being in an abnormal state, a backup tool is determined based on the parameter information of the current tool;
[0007] The autonomous mobile robot is controlled to perform a tool changing operation, so that the autonomous mobile robot can move the spare tool and replace the current tool with the spare tool on the CNC machine tool.
[0008] In some embodiments, acquiring the current data of the current tool in the CNC machine tool and determining the current state of the current tool based on the current data includes:
[0009] Obtain the vibration timing data of the previous two machining processes and the vibration timing data of the current tool.
[0010] Based on the previous vibration time series data and the current vibration time series data, vibration feature matrices corresponding to the previous vibration time series data and the current vibration time series data are obtained respectively; wherein, the vibration feature matrix includes vibration time domain features and vibration frequency domain features;
[0011] Based on the vibration feature matrix corresponding to the previous vibration time sequence data and the current vibration time sequence data, the current feature difference degree of the current tool between the two most recent processing processes is obtained;
[0012] Based on the comparison results between the current feature difference degree and the current difference degree threshold, the current state of the current tool is determined.
[0013] In some embodiments, obtaining the vibration feature matrices corresponding to the previous vibration time-series data and the current vibration time-series data based on the previous vibration time-series data and the current vibration time-series data respectively includes:
[0014] The previous vibration time series data and the current vibration time series data are filtered based on the first target frequency band;
[0015] The fundamental frequency for time-frequency domain conversion is determined based on the current set rotational speed of the tool.
[0016] Based on the current number of cutting edges of the tool, determine the maximum harmonic of the time-frequency domain conversion;
[0017] Based on the fundamental frequency and the maximum harmonic, at least two second target frequency bands for time-frequency domain conversion are determined;
[0018] The filtered previous vibration time series data and the current vibration time series data are respectively converted into time and frequency domains to obtain the vibration frequency domain features corresponding to each second target frequency band in the previous vibration time series data and the current vibration time series data.
[0019] In some embodiments, before determining the current state of the current tool based on the comparison result of the current feature difference degree and the current difference degree threshold, the method further includes:
[0020] Acquire multiple vibration timing data of the processing procedure with a previously set number of cycles;
[0021] Obtain the multiple vibration feature matrix corresponding to the multiple vibration time series data;
[0022] Based on the vibration feature matrix corresponding to the multiple vibration time series data, the feature difference degree between two adjacent processing operations in the previous set number of processing operations of the CNC machine tool is obtained;
[0023] The current difference threshold is updated based on the feature difference between two adjacent processing operations during the previously set number of processing operations.
[0024] In some embodiments, updating the current difference threshold based on the feature difference between two adjacent processes in a previous number of processing steps includes:
[0025] Obtain the feature difference between two adjacent processes in the previous processing steps, and construct a similarity set;
[0026] Based on the mean and variance of the feature differences corresponding to the similarity set, and the size parameters of the current tool, obtain the threshold to be updated;
[0027] Determine whether the threshold to be updated is greater than the initial difference threshold;
[0028] If it is greater than the threshold to be updated, then the threshold to be updated will be used as the current difference threshold.
[0029] If it is not greater than, then the initial difference threshold is used as the current difference threshold.
[0030] In some embodiments, after determining the current state of the tool based on the comparison result between the current feature difference degree and the current difference degree threshold, the method further includes:
[0031] In response to the comparison result between the current feature difference degree and the current difference degree threshold, which indicates that the current state of the current tool is abnormal, the remaining service life of the current tool is obtained based on the current data; wherein, the current data also includes the maximum number of times the current tool has been used, the number of times it has been used, the time of each cutting, and the processing time of the current workpiece;
[0032] In response to the fact that the remaining service life of the current tool is less than a set service life threshold, the current state of the current tool is ultimately determined to be an abnormal state.
[0033] In some embodiments, obtaining the remaining service life of the current tool based on the current data includes:
[0034] The remaining service life T of the current tool is calculated according to the first calculation formula; wherein, the first calculation formula includes:
[0035] T = (c1 - c2) * t1 - t2;
[0036] Where c1 is the maximum number of times the current tool can be used, c2 is the number of times the current tool has been used, t1 is the time for each cutting operation of the current tool, and t2 is the processing time of the current tool.
[0037] A second aspect of this application provides an automatic tool changer control device, comprising:
[0038] The first acquisition unit is used to acquire the current data of the current tool in the CNC machine tool, and determine the current state of the current tool based on the current data;
[0039] A matching unit is used to match and determine a backup tool based on the parameter information of the current tool in response to the current tool being in an abnormal state.
[0040] The tool changing unit is used to control the autonomous mobile robot to perform a tool changing operation, so that the autonomous mobile robot can remove the spare tool and replace the current tool with the spare tool on the CNC machine tool.
[0041] A third aspect of this application provides an electronic device, the electronic device comprising:
[0042] Memory, which stores computer-readable instructions; and
[0043] The processor executes computer-readable instructions stored in the memory to implement the automatic tool changing control method as described in any of the above embodiments.
[0044] A fourth aspect of this application provides a computer-readable storage medium storing computer-readable instructions that are executed by a processor in an electronic device to implement the automatic tool changing control method as described in any of the above embodiments.
[0045] The aforementioned automatic tool changing control method, device, equipment, and readable storage medium, when in use, first acquire the current data of the current tool in the CNC machine tool and determine the current state of the current tool based on the current data; then, in response to the current tool being in an abnormal state, a spare tool is matched and determined based on the parameter information of the current tool; finally, the autonomous mobile robot is controlled to perform a tool changing operation, so that the autonomous mobile robot moves the spare tool and replaces the current tool with the spare tool on the CNC machine tool. This can realize automatic tool changing, and can monitor the current data of the current tool in the CNC machine tool in real time and perform tool changing in a timely manner, resulting in high tool changing efficiency. In addition, the matching of the information of the current tool and the spare tool is ensured during tool changing, resulting in high tool changing accuracy. Attached Figure Description
[0046] Figure 1 This is a flowchart of an automatic tool changing control method provided in an embodiment of this application.
[0047] Figure 2 This is a flowchart of a method for obtaining the current data of the current tool in a CNC machine tool and determining the current state of the current tool based on the current data.
[0048] Figure 3 This is a flowchart illustrating the method for obtaining the current difference threshold through iterative model training.
[0049] Figure 4 This is a flowchart illustrating the method for determining the current state of the cutting tool.
[0050] Figure 5This is a flowchart illustrating a method for obtaining vibration feature matrices corresponding to the previous and current vibration time series data, respectively.
[0051] Figure 6 This is a flowchart illustrating the method for determining the current state of the tool based on the comparison between the current feature difference and the current difference threshold.
[0052] Figure 7 This is a flowchart illustrating a method for updating the current difference threshold based on the feature difference between two adjacent processing operations within a previously set number of processing steps.
[0053] Figure 8 This is a flowchart illustrating the method for determining the current state of the tool based on the comparison between the current feature difference and the current difference threshold.
[0054] Figure 9 This is a functional block diagram of an automatic tool changer control device provided in an embodiment of this application.
[0055] Figure 10 This is a schematic diagram of the architecture of an electronic device that implements an automatic tool changing control method according to an embodiment of this application.
[0056] Explanation of key component symbols: Automatic tool changer control device 100, first acquisition unit 110, matching unit 120, tool changer unit 130, electronic device 1, memory 12, processor 13. Detailed Implementation
[0057] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.
[0058] In the description of this application, it should be understood that the terms indicating orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, in the description of this application, it should be noted that "a plurality of" means two or more, unless otherwise explicitly specified.
[0059] In the description of this application, it should be noted that, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or a connection that allows communication between the components; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.
[0060] The embodiments of this application will be described in detail below with reference to the accompanying drawings.
[0061] like Figure 1 The diagram shown is a flowchart of an embodiment of the automatic tool change control method of this application. Depending on different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted. The automatic tool change control method includes:
[0062] S10: Obtain the current data of the current tool in the CNC machine tool, and determine the current state of the current tool based on the current data.
[0063] The current data can be the usage data of the current tool, such as the maximum number of times the tool can be used is 30 times and the current number of times it has been used is 20 times. The current data can also be the vibration time sequence data generated by the tool collected by the vibration sensor installed on the spindle of the CNC machine tool.
[0064] The current status of a tool can be normal, abnormal, or suspected abnormal. A normal status means that the tool is still usable and does not need to be replaced. An abnormal status means that the tool is unusable and needs to be replaced. A suspected abnormal status means that the tool's usage frequency / duration exceeds a preset threshold limit, but has not yet reached the abnormal status standard.
[0065] S20, in response to the current tool being in an abnormal state, a backup tool is matched and determined based on the parameter information of the current tool.
[0066] When the current tool is abnormal, such as when the current tool breaks, a spare tool that matches the current tool can be matched based on the parameter information of the current tool, which can include the tool's model and size.
[0067] S30 controls the autonomous mobile robot to perform a tool changing operation, so that the autonomous mobile robot can move the spare tool and replace the current tool with the spare tool on the CNC machine tool.
[0068] After the spare tool is determined, the autonomous mobile robot can move the spare tool to the preset position and replace the current tool on the CNC machine tool. The autonomous mobile robot can be an AGV (Automated Guided Vehicle) or an AMR (Autonomous Mobile Robot).
[0069] Here, we take AMR as an example to introduce the specific practical steps of automatic tool changing in the embodiments of this application.
[0070] First, the control processing terminal in this application embodiment communicates with the CNC machine tool and the storage system respectively to obtain relevant information of the target CNC machine tool that needs to have its tool replaced (machine number, machine position, tool model, tool replacement priority, etc.) and relevant information of the spare tool (storage location, tool number, tool model, etc.).
[0071] Then, the control processing terminal of this application embodiment will calculate the time matrix of the AMR moving to the target CNC machine tool and optimize the path planning to reduce the waiting time of the AMR.
[0072] Subsequently, the control processing terminal of this application embodiment will send a specific tool change request to the AMR, and the AMR will then go to the tool holder to retrieve the tool based on the provided response information.
[0073] Finally, the AMR, carrying a spare tool, navigates to the target CNC machine tool for tool changing. When the AMR approaches the target CNC machine tool and is ready to perform the tool change, the target CNC machine tool immediately opens its safety door after the current machining cycle ends to allow the AMR to change the tool. During this process, the AMR uses precise positioning technology to accurately remove the old tool and install the new tool on the target CNC machine tool. After the tool change is complete, the AMR sends a completion signal to the target CNC machine tool and then leaves to continue other tasks. Upon receiving the completion signal, the target CNC machine tool closes its safety door and continues its machining operation. Throughout the entire AMR movement and tool change process, the two communicate with each other to ensure the synchronization and safety of the tool change process.
[0074] The above-mentioned automatic tool changing control method first acquires the current data of the current tool in the CNC machine tool and determines the current state of the current tool based on the current state. Then, in response to the current tool being in an abnormal state, a spare tool is matched and determined based on the parameter information of the current tool. Finally, the autonomous mobile robot is controlled to perform the tool changing operation, so that the autonomous mobile robot moves the spare tool and replaces the current tool with the spare tool on the CNC machine tool. This can realize automatic tool changing, and can monitor the current data of the current tool in the CNC machine tool in real time and perform tool changing in a timely manner, with high tool changing efficiency. In addition, the matching of the information of the current tool and the spare tool is ensured during tool changing, resulting in high tool changing accuracy.
[0075] Please see below. Figure 2 The system acquires the current data of the current tool in the CNC machine tool and determines the current state of the current tool based on the current data, including:
[0076] S11A: Obtain the vibration timing data of the previous two machining processes and the current vibration timing data generated by the current tool.
[0077] The vibration timing data collected by the vibration sensor, along with the corresponding processing attribute information, will be transmitted to the electronic equipment. For example, the processing attribute information may include machine number, material number, tool number, program segment, timestamp, machine status (whether it is waiting for material, alarm, or processing), and machine operation task (whether it is inspection or material processing).
[0078] S12A: Based on the previous vibration time series data and the current vibration time series data, obtain the vibration feature matrices corresponding to the previous vibration time series data and the current vibration time series data, respectively; wherein, the vibration feature matrix includes vibration time domain features and vibration frequency domain features.
[0079] In this embodiment, the vibration feature matrix includes vibration time-domain features and vibration frequency-domain features. The vibration time-domain features and vibration frequency-domain features are described in detail below.
[0080] Vibration time-domain characteristics include: amplitude characteristic indicators, statistical characteristic indicators, waveform characteristic indicators, and impact characteristic indicators. Amplitude characteristic indicators include: peak value, trough value, and peak-to-peak value; statistical characteristic indicators include: mean, variance, standard deviation, and root mean square value; waveform characteristic indicators include: kurtosis, skewness, and waveform indices; impact characteristic indicators include: peak value index, impulse index, and marginal coefficient. The specific indicators can be selected according to actual needs.
[0081] Vibration frequency domain characteristics include: power spectrum characteristics, statistical characteristics, and waveform characteristics. Power spectrum characteristics include: peak power density and peak frequency; statistical characteristics include: average power spectral density, power spectral density variance, frequency standard deviation, and root mean square frequency; waveform characteristics include: spectral kurtosis, spectral skewness, frequency skewness, frequency kurtosis, centroid frequency, D-factor, E-factor, and G-factor. The specific characteristics can be selected according to actual needs.
[0082] S13A: Based on the vibration feature matrix corresponding to the previous vibration time series data and the current vibration time series data, obtain the current feature difference degree of the current tool between the last two machining processes.
[0083] In this embodiment, the characteristic difference degree of the current tool between the last two machining processes can be obtained by calculating the Euclidean distance between the vibration feature matrices corresponding to the previous vibration time series data and the current vibration time series data. It should be understood that the Euclidean distance is negatively correlated with the characteristic difference degree; a larger Euclidean distance indicates a lower characteristic difference degree, and vice versa. Of course, other methods can also be used to determine the characteristic difference degree, and this application does not impose any particular limitations on this.
[0084] S14A: Based on the comparison results between the current feature difference degree and the current difference degree threshold, determine the current state of the tool.
[0085] In this embodiment, if the difference in current characteristics between the current vibration time series data and the previous vibration time series data is high, it indicates that the change in the current vibration time series data compared to the previous vibration time series data is significant. If the tool state corresponding to the previous vibration time series data was normal, then it can be inferred that the tool state at the time of generating the current vibration time series data was abnormal. If the tool state corresponding to the previous vibration time series data was abnormal, and if the current difference threshold of the current tool is greater than the current difference threshold, then it can be inferred that the tool state at the time of generating the current vibration time series data was still abnormal.
[0086] If the difference in current characteristics between the current vibration time series data and the previous vibration time series data is low, it indicates that the change in the current vibration time series data compared to the previous vibration time series data is small. If the tool condition corresponding to the previous vibration time series data was normal, then it can be inferred that the tool condition when the current vibration time series data was generated was normal. If the tool condition corresponding to the previous vibration time series data was abnormal, and if the current difference threshold of the tool is not greater than the current difference threshold, then it can be inferred that the tool condition when the current vibration time series data was generated was still normal.
[0087] The current dissimilarity threshold can be obtained either through empirical calculations or through iterative model training. Please refer to [link to relevant documentation]. Figure 3 Methods for obtaining the current difference threshold through iterative model training include:
[0088] S141A acquires the historical vibration timing data and tool status data generated by the tool during the machining process of the CNC machine tool.
[0089] In this embodiment, vibration timing data generated by the tool within a preset time period and corresponding machining attribute information can be obtained. Specific machining attribute information can be found above and will not be repeated here. Then, the vibration timing data is filtered based on the machining attribute information. For example, vibration timing data not within a machining program segment is filtered out; vibration timing data with excessively long or short machining times is filtered out; vibration timing data where the machine tool is not in a machining state is filtered out; and vibration timing data where the machine tool's operating task is not material processing is filtered out, thus obtaining the vibration timing data generated by the tool during the machining process.
[0090] Then, the historical detection frequency, tool parameters, and alarm information of the tool setter can be obtained. Based on the number of vibration timing data points generated by the tool during the machining process and the detection frequency of the tool setter, the vibration timing data is grouped. For example, if the detection frequency is 5, a tool check is performed every 5 pieces of material processed. If the number of acquired vibration timing data points is 10, then the 10 vibration timing data points can be divided into 2 groups. For each group of vibration timing data, the tool setter performs a tool check after generating the last vibration data point in that group. If a broken tool is detected, an alarm message will be output.
[0091] For the vibration timing data of group n, if there is no alarm message after the last vibration timing data, the tool status corresponding to the vibration timing data of group n is determined to be normal. If there is an alarm message after the last vibration timing data, and the tool parameters indicate that the tool has not been replaced, the tool status corresponding to the vibration timing data of group n is determined to be suspected abnormal. If there is an alarm message after the last vibration timing data, and the tool parameters indicate that the tool has been replaced, the tool status corresponding to the last vibration timing data is determined to be abnormal, and the tool status corresponding to the other vibration timing data in group n, excluding the last vibration timing data, is determined to be suspected abnormal.
[0092] S142A, obtain the vibration feature matrix corresponding to the vibration time series data of each vibration.
[0093] In this embodiment of the application, the characteristics of each vibration time series data are extracted to obtain the corresponding vibration feature matrix. The vibration feature matrix includes vibration time domain features and vibration frequency domain features. For details on which vibration time domain features and vibration frequency domain features are included, please refer to the above description, which will not be repeated here.
[0094] It is worth noting that the tool status in each vibration time series data can be normal, abnormal, or suspected abnormal, and the appropriate status can be selected according to actual needs.
[0095] S143A: Based on the vibration feature matrix corresponding to the vibration time series data of each vibration, obtain the feature difference degree corresponding to two adjacent processing times in the historical processing of the CNC machine tool, and construct a set of feature difference degrees of each vibration.
[0096] In this embodiment, the feature difference degree can be obtained by calculating the Euclidean distance between the vibration feature matrices corresponding to adjacent vibration time series data, thereby constructing a set of feature difference degrees for each time series. Of course, other methods can also be used to obtain the feature difference degree, and this application does not impose any particular limitations on this.
[0097] S144A aims to distinguish between abnormal and non-abnormal tool states by using an initial difference threshold. Based on the tool states in previous feature difference sets, the current difference threshold is iteratively adjusted until the iteration ends, and the initial difference threshold is obtained. This initial difference threshold is then used as the initial value for the current set difference threshold.
[0098] Two possible implementation schemes for threshold iterative adjustment are provided here.
[0099] For the first feature difference in each feature difference set, if this first feature difference is less than the initial value of the initial difference threshold, the predicted tool state is determined to be abnormal. Since the actual tool state is normal while the predicted tool state is abnormal, this indicates that the initial value of the initial difference threshold is set too high. In this case, the initial difference threshold should be updated to decrease. If the first feature difference is greater than the initial value of the initial difference threshold, the predicted tool state is determined to be normal. Since both the actual and predicted tool states are normal, this indicates that the initial value of the initial difference threshold is set appropriately. In this case, the threshold should be kept unchanged or updated to increase. Subsequent iterations follow this pattern until the latest initial difference threshold is obtained.
[0100] The second approach involves using the current initial difference threshold to determine the tool status for n sets of vibration time series data, obtaining the judgment index (such as accuracy, false kill rate, etc.) corresponding to the n sets of vibration time series data, and then iteratively adjusting the initial difference threshold based on the judgment index.
[0101] The current status of the tool can be categorized into three types: normal, suspected abnormal, and abnormal. Please refer to [link / reference]. Figure 4 Methods for determining the current state of the tool include:
[0102] S145A, determine whether the current feature difference is greater than the current difference threshold.
[0103] S146A, if it is greater than the current difference threshold, the tool is considered to be in an abnormal state.
[0104] S147A, if it is less than the current difference threshold, then determine whether the current number of machining operations of the tool exceeds the recommended number of tool changes.
[0105] S148A: If the recommended number of tool changes is exceeded, the current tool is considered to be in a suspected abnormal state.
[0106] S149A: If the recommended number of tool changes is not exceeded, the current tool is considered to be in normal condition.
[0107] This application embodiment takes into account that the vibration frequency of the cutting tool during the current machining process may be mainly concentrated in a specific frequency band. Therefore, in this application embodiment, the vibration feature matrix can be extracted only from the vibration time series data located within the machining frequency band, thereby improving the accuracy of the extracted vibration feature matrix.
[0108] For details, please see Figure 5 Step S13 involves obtaining the vibration feature matrices corresponding to the previous vibration time series data and the current vibration time series data, respectively, based on the previous vibration time series data and the current vibration time series data. This includes:
[0109] S131A filters the previous vibration time series data and the current vibration time series data based on the first target frequency band.
[0110] In this embodiment, the first target frequency band can be considered as the working frequency band of the tool during the current machining stage. For example, the first target frequency band can be [50Hz, 1950Hz]. Filtering the previous vibration timing data and the current vibration timing data generated in the two most recent machining processes using the first target frequency band helps to eliminate other frequency bands unrelated to the machining stage, making the extracted time-frequency domain features for the machining stage more accurate based on the previous vibration timing data and the current vibration timing data.
[0111] S132A determines the fundamental frequency for time-frequency domain conversion based on the current tool's set rotational speed.
[0112] In this embodiment of the application, taking the current tool's set rotation speed of 6000 r / min as an example, the fundamental frequency of the time-frequency domain conversion is 6000 / 60 = 100 Hz.
[0113] S133A determines the maximum harmonic of the time-frequency domain conversion based on the current number of cutting edges of the tool.
[0114] In this embodiment of the application, taking a tool with four cutting edges as an example, if the base frequency is 100Hz, then the maximum harmonic frequency of the time-frequency domain conversion is 100*4=400Hz.
[0115] S134A determines at least two second target frequency bands for time-frequency domain conversion based on the fundamental frequency and the maximum harmonic.
[0116] In this embodiment of the application, if the base frequency is 100Hz and the maximum harmonic frequency is 400Hz, then the second target frequency bands can be directly determined as [50Hz, 250Hz] and [250Hz, 450Hz].
[0117] Of course, in another possible embodiment, multiple intermediate harmonics can be determined between the fundamental frequency and the maximum harmonic. Then, based on the fundamental frequency, the multiple intermediate harmonics, and the maximum harmonic, at least two second target frequencies for time-frequency domain conversion can be determined. Continuing with the example of a fundamental frequency of 100Hz and a maximum harmonic of 400Hz, the multiple intermediate harmonics that can be determined are 200Hz and 300Hz. Based on this, the determined second target frequency bands are [50Hz, 150Hz], [150Hz, 250Hz], [250Hz, 350Hz], and [350Hz, 450Hz].
[0118] It is worth noting that each second target frequency band needs to cover the fundamental frequency, intermediate harmonics, or maximum harmonics.
[0119] S135A performs time-frequency domain conversion on the filtered previous vibration time series data and the current vibration time series data respectively, and obtains the vibration frequency domain features corresponding to each second target frequency band in the previous vibration time series data and the current vibration time series data respectively.
[0120] In this embodiment of the application, after determining multiple second target frequency bands containing effective signals of tool vibration based on the current tool's set rotation speed and number of cutting edges, the vibration frequency characteristics of the previous vibration time series data and the current vibration time series data in each second target frequency band can be obtained during time-frequency domain conversion, without needing to obtain vibration frequency domain characteristics of other frequency bands besides the second target frequency band, thereby improving the accuracy of the extracted vibration frequency domain characteristics.
[0121] The purpose of steps S131A-S135A is to extract useful features from vibration data through a series of preprocessing and analysis steps, so as to monitor the tool condition and diagnose anomalies, thereby improving the accuracy and reliability of automatic tool change control methods.
[0122] In actual online operation, considering that the tool itself will be continuously worn during the machining process, the current difference threshold used to judge the tool status should also be updated adaptively to improve the accuracy of tool status judgment.
[0123] Please see below. Figure 6 Before determining the current state of the tool based on the comparison results of the current feature difference degree and the current difference degree threshold, the method further includes:
[0124] S136A, acquires multiple vibration timing data of a previously set number of processing steps.
[0125] S137A, obtain the multiple vibration feature matrix corresponding to multiple vibration time series data.
[0126] S138A: Based on the vibration feature matrix corresponding to multiple vibration time series data, obtain the feature difference degree between two adjacent processing steps in the previously set number of processing steps of the CNC machine tool.
[0127] S139A, Update the current difference threshold based on the feature difference between two adjacent processing steps in the previously set number of processing steps.
[0128] In this embodiment of the application, due to the differences in the workpiece during the current tool processing, the current difference threshold may not be able to distinguish the current tool state well. Therefore, after each set number of processing operations, the current difference threshold will be updated to improve the accuracy of tool state judgment.
[0129] The following section details how to update the current difference threshold. Please refer to [link / reference]. Figure 7 Step S139A includes:
[0130] S1391A: Obtain the feature difference between two adjacent processes in the previous processing stages and construct a similarity set.
[0131] S1392A: Obtain the threshold to be updated based on the mean and variance of the feature differences corresponding to the similarity set, as well as the current tool size parameters.
[0132] S1393A, determine whether the threshold to be updated is greater than the initial difference threshold.
[0133] If S1394A is greater than the threshold, then the threshold to be updated will be used as the current difference threshold.
[0134] If S1395A is not greater than, then the initial difference threshold will be used as the current difference threshold.
[0135] In this embodiment, for several previous machining processes, the overall similarity mean and overall similarity variance corresponding to multiple feature differences can be determined based on the feature differences between the vibration time-series data generated by two adjacent machining processes. Then, an update threshold is determined based on the overall similarity mean, overall similarity variance, and the current tool size parameters. If the update threshold is greater than the initial difference threshold obtained from previous training, the update threshold is used as the new current difference threshold, and the detection adaptability is changed to better detect the tool state with a larger current difference threshold; if the update threshold is not greater than the initial difference threshold obtained from previous training, the initial difference threshold is used as the new current difference threshold, that is, the lower limit of the threshold is used to detect the tool state.
[0136] Then, the threshold to be updated is determined based on the mean, variance, and current tool size parameters. The formula for calculating the threshold to be updated is:
[0137]
[0138] Where y represents the threshold to be updated, σ represents the mean of the feature differences corresponding to the similarity set, μ represents the variance of the feature differences corresponding to the similarity set, and cl represents the tool size parameter. Of course, in the above embodiments, the standard deviation can also be used instead of the variance, and this application does not impose any particular restriction on this.
[0139] If the threshold to be updated is greater than the initial difference threshold obtained from previous training, then the threshold to be updated is used as the new current difference threshold. It should be understood that the tool anomaly judgment mechanism in this application is based on the current feature difference being greater than the current difference threshold. Therefore, if the threshold to be updated is greater than the initial difference threshold obtained from previous training, the threshold to be updated is used as the new current difference threshold. This means that the current feature difference needs to be greater than the threshold to be updated to be judged as a tool anomaly. Here, a larger current difference threshold is used to modify the detection adaptability to better detect the tool state.
[0140] In practical applications, this application embodiment believes that the above-mentioned tool status determination results may be inaccurate. Before finally determining that the tool is in an abnormal state, a new verification process is added, specifically including S11B to S12B.
[0141] In this embodiment of the application, please refer to Figure 8 After determining the current state of the tool based on the comparison results of the current feature difference degree and the current difference degree threshold, the following steps are also included:
[0142] S11B, in response to the comparison result based on the current feature difference degree and the current difference degree threshold, the current state of the current tool is determined to be an abnormal state. Based on the current data, the remaining service life of the current tool is obtained. The current data includes the maximum number of times the current tool has been used, the number of times it has been used, the time of each cutting, and the processing time of the current workpiece.
[0143] S12B, in response to the fact that the remaining service life of the current tool is less than the set service life threshold, ultimately determines that the current state of the current tool is an abnormal state.
[0144] This system analyzes the current tool's usage history and current data (such as maximum number of uses, number of uses, time per cut, and machining time of the current workpiece) to assess the current tool's wear level and remaining service life. This assessment helps predict when the current tool needs to be replaced, thus avoiding decreased machining quality or sudden tool failure due to tool wear. If the current tool's remaining service life is too short, it is marked as an abnormal state, triggering a tool change process. Such status monitoring and judgment ensure machining continuity and safety.
[0145] Among these, based on the current data, the remaining service life of the current tool is obtained, including:
[0146] The remaining tool life T is calculated according to the first calculation formula; wherein the first calculation formula includes:
[0147] T = (c1 - c2) * t1 - t2;
[0148] Where c1 is the maximum number of times the current tool can be used, c2 is the number of times the current tool has been used, t1 is the time for each cutting operation of the current tool, and t2 is the processing time of the current tool.
[0149] The first calculation formula can monitor the wear and usage of the tool in real time, predict when the tool needs to be replaced, and thus assist in the detection of tool anomalies through tool vibration time series data, thereby improving the accuracy of tool anomaly detection.
[0150] like Figure 9 The diagram shown is a functional block diagram of an embodiment of the automatic tool changer control device 100 of this application. The automatic tool changer control device 100 includes a first acquisition unit 110, a matching unit 120, and a tool changer unit 130. The term "unit" in this application refers to a series of computer-readable instruction segments that can be acquired by the processor 13 and perform a fixed function, and which are stored in the memory 12. In this embodiment, the functions of each unit will be described in detail in subsequent embodiments.
[0151] The first acquisition unit 110 is used to acquire the current data of the current tool in the CNC machine tool, and determine the current state of the current tool based on the current data.
[0152] The matching unit 120 is used to match and determine a backup tool based on the parameter information of the current tool in response to the current tool being in an abnormal state.
[0153] The tool changing unit 130 is used to control the autonomous mobile robot to perform tool changing operations, so that the autonomous mobile robot can move the spare tool and replace the current tool with the spare tool on the CNC machine tool.
[0154] The various units described above in the device embodiments of this application can cooperate with each other to realize the various functions mentioned in the method embodiments of this application and achieve the corresponding technical effects, which will not be elaborated here.
[0155] like Figure 10 The diagram shown is a schematic diagram of the architecture of an electronic device 1 that implements the automatic tool changing control method of this application.
[0156] In one embodiment of this application, the electronic device 1 includes, but is not limited to, a memory 12, a processor 13, and computer-readable instructions, such as an automatic tool changer control program, stored in the memory 12 and executable on the processor 13.
[0157] Combination Figure 1 The memory 12 in electronic device 1 stores computer-readable instructions to implement an automatic tool changing control method, and the processor 13 can execute the computer-readable instructions to achieve the following:
[0158] Obtain the current data of the current tool in the CNC machine tool, and determine the current state of the current tool based on the current data;
[0159] In response to the current tool being in an abnormal state, a backup tool is determined based on the parameter information of the current tool;
[0160] Control the autonomous mobile robot to perform a tool changing operation, so that the autonomous mobile robot can move the spare tool and replace the current tool with the spare tool on the CNC machine tool.
[0161] Specifically, the specific implementation method of the processor 13 for the above-mentioned computer-readable instructions can be found in [reference]. Figure 1 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.
[0162] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.
[0163] A computer-readable storage medium stores computer-readable instructions, which, when executed by processor 13, are used to perform the following steps:
[0164] Obtain the current data of the current tool in the CNC machine tool, and determine the current state of the current tool based on the current data;
[0165] In response to the current tool being in an abnormal state, a backup tool is determined based on the parameter information of the current tool;
[0166] Control the autonomous mobile robot to perform a tool changing operation, so that the autonomous mobile robot can move the spare tool and replace the current tool with the spare tool on the CNC machine tool.
[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.
Claims
1. An automatic tool changer control method characterized by, include: Obtain the current data of the current tool in the CNC machine tool, and determine the current state of the current tool based on the current data; In response to the current tool being in an abnormal state, a backup tool is determined based on the parameter information of the current tool; Control the autonomous mobile robot to perform a tool changing operation, so that the autonomous mobile robot can move the spare tool and replace the current tool with the spare tool on the CNC machine tool; The step of acquiring the current data of the current tool in the CNC machine tool and determining the current state of the current tool based on the current data includes: Obtain the vibration timing data of the previous two machining processes and the vibration timing data of the current tool. Based on the previous vibration time series data and the current vibration time series data, vibration feature matrices corresponding to the previous vibration time series data and the current vibration time series data are obtained respectively; wherein, the vibration feature matrix includes vibration time domain features and vibration frequency domain features; Based on the vibration feature matrix corresponding to the previous vibration time sequence data and the current vibration time sequence data, the current feature difference degree of the current tool between the two most recent processing processes is obtained; Based on the comparison result between the current feature difference degree and the current difference degree threshold, the current state of the current tool is determined; The step of obtaining vibration feature matrices corresponding to the previous vibration time series data and the current vibration time series data, respectively, based on the previous vibration time series data and the current vibration time series data, includes: The previous vibration time series data and the current vibration time series data are filtered based on the first target frequency band; The fundamental frequency for time-frequency domain conversion is determined based on the current set rotational speed of the tool. Based on the current number of cutting edges of the tool, determine the maximum harmonic of the time-frequency domain conversion; Based on the fundamental frequency and the maximum harmonic, at least two second target frequency bands for time-frequency domain conversion are determined; The filtered previous vibration time series data and the current vibration time series data are respectively converted into time and frequency domains to obtain the vibration frequency domain features corresponding to each second target frequency band in the previous vibration time series data and the current vibration time series data.
2. The automatic tool changer control method according to claim 1, characterized in that, Before determining the current state of the tool based on the comparison result between the current feature difference degree and the current difference degree threshold, the method further includes: Acquire multiple vibration timing data of the processing procedure with a previously set number of cycles; Obtain the multiple vibration feature matrix corresponding to the multiple vibration time series data; Based on the vibration feature matrix corresponding to the multiple vibration time series data, the feature difference degree between two adjacent processing operations in the previous set number of processing operations of the CNC machine tool is obtained; The current difference threshold is updated based on the feature difference between two adjacent processing operations during the previously set number of processing operations.
3. The automatic tool changing control method according to claim 2, characterized in that, The step of updating the current difference threshold based on the feature difference between two adjacent processing operations within the previously set number of processing operations includes: Obtain the feature difference between two adjacent processing operations during the previously described set number of processing operations, and construct a similarity set; Based on the mean and variance of the feature differences corresponding to the similarity set, and the size parameters of the current tool, obtain the threshold to be updated; Determine whether the threshold to be updated is greater than the initial difference threshold; If it is greater than the threshold to be updated, then the threshold to be updated will be used as the current difference threshold. If it is not greater than, then the initial difference threshold is used as the current difference threshold.
4. The automatic tool changer control method according to claim 1, characterized in that, After determining the current state of the tool based on the comparison result between the current feature difference degree and the current difference degree threshold, the method further includes: In response to the comparison result based on the current feature difference degree and the current difference degree threshold, the current state of the current tool is determined to be an abnormal state. Based on the current data, the remaining service life of the current tool is obtained. The current data also includes the maximum number of times the current tool has been used, the number of times it has been used, the time of each cutting, and the current processing time. In response to the fact that the remaining service life of the current tool is less than a set service life threshold, the current state of the current tool is ultimately determined to be an abnormal state.
5. The automatic tool changer control method according to claim 4, characterized in that, The step of obtaining the remaining service life of the current tool based on the current data includes: The remaining service life T of the current tool is calculated according to the first calculation formula; wherein, the first calculation formula includes: T = (c1 - c2) * t1 - t2; Where c1 is the maximum number of times the current tool can be used, c2 is the number of times the current tool has been used, t1 is the time for each cutting operation of the current tool, and t2 is the processing time of the current tool.
6. An automatic tool changer control device using the automatic tool changer control method according to any one of claims 1 to 5, characterized by include: The first acquisition unit is used to acquire the current data of the current tool in the CNC machine tool, and determine the current state of the current tool based on the current data; A matching unit is used to match and determine a backup tool based on the parameter information of the current tool in response to the current tool being in an abnormal state. The tool changing unit is used to control the autonomous mobile robot to perform a tool changing operation, so that the autonomous mobile robot can remove the spare tool and replace the current tool with the spare tool on the CNC machine tool.
7. An electronic device, comprising: The electronic device includes: Memory, which stores computer-readable instructions; and The processor executes computer-readable instructions stored in the memory to implement the automatic tool changing control method as described in any one of claims 1 to 5.
8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which are executed by a processor in an electronic device to implement the automatic tool changing control method as described in any one of claims 1 to 5.