A combined numerical control milling machine and control system thereof

Abstract

The application relates to the technical field of industrial control systems, in particular to a composite numerical control milling machine and a control system thereof. The system comprises the following steps: acquiring a vibration sequence, a cutting force sequence and an energy consumption sequence of the numerical control milling machine; acquiring tool additional energy consumption of a single tool according to the energy consumption difference of the single tool under normal conditions and under current wear conditions; acquiring average replacement loss of the single tool according to the energy consumption difference of the single tool in the process of switching tools and replacing tools; acquiring replacement loss of each tool according to the difference between the average energy consumption of each tool under brand-new conditions and the energy consumption under normal conditions, and combining the tool additional energy consumption, the average replacement loss of each tool and whether the current wear condition meets the machining precision to acquire a tool continuous coefficient of each tool, and judging whether each tool needs to be replaced. The application can comprehensively evaluate whether each tool needs to be replaced, realize more accurate tool replacement control and save the energy consumption of the numerical control milling machine.

Description

Technical Field

[0001] This application relates to the field of industrial control system technology, specifically to a composite CNC milling machine and its control system. Background Technology

[0002] A composite CNC milling machine is a high-precision machine tool that uses digital signals to drive cutting tools for machining. It is widely used for machining complex geometries such as planes, grooves, and gear teeth. The introduction of a CNC milling machine control system further enhances its functionality. It can intelligently adjust cutting speed and feed rate based on changes in parameters such as cutting force, spindle current, and temperature during workpiece machining. This not only simplifies the operation process but also dynamically optimizes machining parameters according to working conditions, providing technical support for machining complex parts.

[0003] While existing control systems enable CNC milling machines to achieve high machining accuracy, their energy consumption management typically relies on replacing tools as soon as wear is detected to reduce tool friction and save energy. However, current industrial control systems for CNC milling machines do not adequately consider the energy losses during tool replacement. Besides the energy loss from tool wear, CNC milling machines also experience no-load energy consumption and tool replacement losses. Therefore, frequent tool replacements may lead to increased energy consumption, resulting in ineffective energy control for CNC milling machines. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a composite CNC milling machine and its control system, the specific technical solution of which is as follows:

[0005] This application discloses a composite CNC milling machine and its control system, the system comprising:

[0006] Data acquisition module: acquires the vibration sequence, cutting force sequence, and energy consumption sequence of the CNC milling machine;

[0007] Data analysis module: Obtain cutting operation subsequences; cluster all cutting operation subsequences to obtain multiple tool clusters;

[0008] Energy consumption calculation module: This module obtains the vibration subsequence and energy consumption subsequence for each cutting operation subsequence within the same time range in the vibration and energy consumption sequences. Based on the dispersion and average level of each vibration subsequence, it obtains the vibration factor of each vibration subsequence, thus yielding the undamaged vibration subsequence corresponding to each tool cluster. Based on the average level of the undamaged vibration subsequence corresponding to a single tool cluster and the energy consumption subsequence corresponding to the current vibration subsequence, it obtains the normal energy consumption and current wear energy consumption of a single tool. Based on the difference between the normal energy consumption and current wear energy consumption of a single tool, it obtains the additional tool energy consumption of a single tool. Based on the energy consumption difference of a single tool during tool switching and tool replacement, it obtains the average replacement loss of a single tool. Based on the difference between the average energy consumption and normal energy consumption of each tool in its new condition, it obtains the replacement loss of each tool. Finally, combining the additional tool energy consumption, average replacement loss, and whether the current wear condition meets normal machining accuracy, it obtains the tool continuity coefficient of each tool.

[0009] Tool changing control module: Determines whether each tool needs to be replaced.

[0010] Preferably, the process of obtaining the multiple tool clusters is as follows: the data length and mean of each cutting operation subsequence are used to construct the attribute vector of each cutting operation subsequence; all cutting operation subsequences are clustered according to the attribute vectors of all cutting operation subsequences to obtain multiple tool clusters.

[0011] Preferably, the vibration factor of each vibration subsequence is the product of the variance and the mean of each vibration subsequence.

[0012] Preferably, the undamaged vibration subsequence corresponding to each tool cluster is the vibration subsequence with the smallest vibration factor among all the vibration subsequences corresponding to all cutting operation subsequences in each tool cluster.

[0013] Preferably, the process of obtaining the normal energy consumption and current wear energy consumption of a single tool is as follows: the vibration subsequence corresponding to the last cutting operation subsequence in the time sequence of a single tool cluster is taken as the current vibration subsequence; the average values ​​of the undamaged vibration subsequence corresponding to the single tool cluster and the energy consumption subsequence corresponding to the current vibration subsequence are respectively recorded as the normal energy consumption and current wear energy consumption of a single tool.

[0014] Preferably, the additional energy consumption of a single tool is the difference between the normal energy consumption of the single tool and the current wear energy consumption.

[0015] Preferably, the specific process for obtaining the average replacement loss of a single tool is as follows: obtain the position w2 of the first element of a single cutting operation subsequence in the cutting force sequence, and the position w1 of the last element of the previous cutting operation subsequence in the cutting force sequence; extract the elements of the corresponding positions from the energy consumption sequence according to the position range between w1 and w2 in the cutting force sequence and sum them; record the summation result as the no-load loss of the corresponding single cutting operation subsequence; use the no-load loss of all cutting operation subsequences in a single tool cluster as the input of the threshold segmentation method, divide all no-load losses into two parts, and record the absolute difference between the mean values ​​of the no-load losses of the two parts as the average replacement loss of a single tool.

[0016] Preferably, the process of obtaining the replacement loss of each tool is as follows: obtain the new energy consumption subsequence corresponding to each tool under brand new conditions, and record the difference between the mean of all new energy consumption subsequences corresponding to each tool and the normal energy consumption as the replacement loss of each tool.

[0017] Preferably, the formula for calculating the tool duration coefficient of each tool is: A i =[(B i +C i )-I zc,v ]×σ i In the formula, A i I is the tool sustain coefficient for the i-th tool; zc,v B represents the additional energy consumption of the i-th tool; i C represents the average replacement wear of the i-th tool; i σ represents the replacement wear of the i-th tool; i Let be the tool usage factor of the i-th tool; the tool usage factor of the i-th tool is obtained as follows: if the current wear condition of the i-th tool can meet the normal machining accuracy of the workpiece, then the tool usage factor is set to 1; otherwise, the tool usage factor is set to 0.

[0018] Preferably, the specific method for determining whether each tool needs to be replaced is as follows: if the tool continuity coefficient of a single tool is greater than 0, then the tool does not need to be replaced; otherwise, the tool needs to be replaced.

[0019] This application has the following beneficial effects:

[0020] This application addresses the problem that existing control systems do not adequately consider the no-load loss and replacement loss during tool changes, which may lead to increased energy consumption due to frequent tool replacements. Firstly, by constructing attribute vectors and clustering each cutting operation subsequence, the system can distinguish each tool during the cutting process, providing the necessary data foundation for subsequent energy consumption calculations for each tool. Secondly, by constructing additional tool energy consumption, the system reflects the additional energy consumption caused by each tool under its current wear condition during the cutting process, thus quantifying the impact of current tool wear on machining energy consumption. Thirdly, by constructing a tool durability coefficient, the system can fully balance the relationship between tool wear and replacement loss, reflecting the feasibility of continuing to use the tool under its current wear condition, thereby assessing whether tool replacement is necessary and avoiding increased energy consumption due to frequent tool changes. This achieves more precise tool change control and saves energy on CNC milling machines. Attached Figure Description

[0021] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A block diagram of a composite CNC milling machine and its control system is provided as an embodiment of this application;

[0023] Figure 2 This is a flowchart illustrating the process of obtaining the tool continuity factor for each tool according to one embodiment of this application. Detailed Implementation

[0024] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a composite CNC milling machine and its control system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0026] The following description, in conjunction with the accompanying drawings, details a specific scheme for a composite CNC milling machine and its control system provided in this application.

[0027] Please see Figure 1 The diagram illustrates a block diagram of a composite CNC milling machine and its control system according to an embodiment of this application. The system includes: a data acquisition module, a data analysis module, an energy consumption calculation module, and a tool changing control module.

[0028] Data acquisition module:

[0029] Vibration data of the CNC milling machine is collected in real time using vibration sensors, and cutting force data is acquired in real time through the central control system of the CNC milling machine. Energy consumption data of the CNC milling machine is collected in real time using a power analyzer. All data are collected synchronously and in real time, with a collection time interval of 1 second. The current monitoring period is defined as T minutes prior to the current moment. The duration T of the monitoring period can be selected by the implementer; in this embodiment, T is set to 60.

[0030] To prepare a CNC milling machine for normal machining operations after replacing the cutting tools, a data acquisition module collects new cutting force data and new energy consumption data for a certain period of time under the condition of new cutting tools. The collection time is h minutes, and the entire operation process of the new cutting tools should be included within h minutes. In this embodiment, h is taken as 30. The new cutting force data and new energy consumption data are collected for only one collection period.

[0031] Based on the temporal sequence of vibration data, cutting force data, and energy consumption data during the current monitoring period, the vibration sequence, cutting force sequence, and energy consumption sequence for the current monitoring period are obtained; based on the temporal sequence of new cutting force data and new energy consumption data, the new cutting force sequence and new energy consumption sequence are obtained.

[0032] To eliminate the influence of dimensions between data, all data sequences are normalized separately. The normalization method is not limited to Z-score, maximum value normalization, or maximum and minimum value normalization. This embodiment uses maximum value normalization.

[0033] Data Analysis Module:

[0034] In existing industrial settings, the industrial control systems of CNC milling machines typically replace the tool immediately upon detecting wear to maximize machining efficiency. However, during tool replacement—from selection to alignment and fine-tuning—the CNC milling machine remains running, resulting in idle energy consumption. Furthermore, new tools, with their microscopically uneven surfaces or incompletely stabilized coatings, have a high initial cutting friction coefficient, leading to a short-term spike in energy consumption. Therefore, it is crucial to determine whether immediate tool replacement is necessary based on the actual wear condition, machining accuracy requirements, and energy costs. Delaying tool replacement can avoid the idle energy consumption caused by tool replacement and the additional energy consumption due to the high friction coefficient during the initial cutting with a new tool.

[0035] First, it is necessary to analyze whether the current tool is worn, and to calculate the additional energy consumption generated by the worn tool in the cutting process compared to the unworn tool.

[0036] Since the cutting tool of the CNC milling machine does not contact the workpiece when no cutting operation is being performed, its cutting force should be 0. Therefore, the state of the CNC milling machine during machining operations can be analyzed by using cutting force data.

[0037] The cutting force sequence within the current monitoring period is used as input to a sequence segmentation algorithm for segmentation. The output of the sequence segmentation algorithm is multiple cutting force subsequences. The sequence segmentation algorithm can be, but is not limited to, the BG sequence segmentation algorithm or the MK sequence segmentation algorithm. This embodiment uses the BG sequence segmentation algorithm, which is a well-known technique, and its specific process will not be described in detail. The mean of each cutting force subsequence is calculated, and all cutting force subsequences with a non-zero mean are recorded as cutting operation subsequences.

[0038] Furthermore, since composite CNC milling machines typically process complex parts, they require the use of multiple cutting tools. This means that cutting data within a single monitoring cycle corresponds to multiple tools. The additional losses caused by wear on different tools vary, necessitating the differentiation of these tools to further analyze the additional losses of each tool under different wear conditions.

[0039] During the machining process of a part, different cutting tools have different machining times and cutting forces. Therefore, different cutting tools can be distinguished by their machining time and cutting force. Since cutting force is only generated during the cutting operation, the data length of the cutting operation subsequence can represent the machining time of that cutting tool.

[0040] Considering that the cutting force sequence has already been normalized, in order to further eliminate the influence of dimensions, the data length of each cutting operation subsequence is obtained and normalized.

[0041] Furthermore, the attribute vectors of each cutting operation subsequence are constructed. In this embodiment, the attribute vector of a single cutting operation subsequence is denoted as [a1, a2]; where a1 is the data length of a single cutting operation subsequence and a2 is the mean of a single cutting operation subsequence.

[0042] The attribute vectors of all cutting operation subsequences are used as input to a clustering algorithm for clustering. Clustering algorithms include DPC density clustering, DBSCAN clustering, and hierarchical clustering. This embodiment uses the DPC density clustering algorithm and employs cross-validation to obtain the cutoff distance of the DPC clustering algorithm, outputting all tool clusters. All cutting operation subsequences corresponding to attribute vectors within the same tool cluster represent the cutting data of the same tool.

[0043] Clustering the attribute vectors of cutting operation subsequences using clustering techniques can group cutting operation subsequences with similar characteristics into the same cluster, distinguish different cutting tools, and provide data support for subsequent wear state analysis and energy consumption calculation of different cutting tools. This enables the industrial control system to achieve precise replacement control of cutting tools.

[0044] Thus, the cutting data of each tool in the CNC milling machine cutting process has been distinguished.

[0045] Energy consumption calculation module:

[0046] Furthermore, vibration data can be used to differentiate the additional energy consumption of the tool under current wear conditions compared to normal conditions. When the tool is not worn, the vibration data of the CNC milling machine during cutting should be relatively stable; however, if the tool is worn, the vibration data will fluctuate and become unstable, and the intensity of the vibration will increase significantly.

[0047] Take the j-th cutting operation subsequence in the i-th tool cluster as an example.

[0048] By using the position range of the j-th cutting operation subsequence in the cutting force sequence, the vibration subsequence corresponding to the position range is extracted from the vibration sequence and denoted as the vibration subsequence corresponding to the j-th cutting operation subsequence.

[0049] The dispersion of the vibration subsequence is obtained. The dispersion can be calculated using variance, standard deviation, and coefficient of variation; this embodiment uses variance. The product of the dispersion of the j-th vibration subsequence and its mean is denoted as the vibration factor of the j-th vibration subsequence. The vibration factor reflects whether significant vibration changes occurred in the cutting operation corresponding to the j-th cutting operation subsequence, and whether the vibration fluctuations in the cutting operation were large.

[0050] Using the same operation, obtain the vibration subsequences corresponding to all cutting operation subsequences in the i-th tool cluster, and calculate the vibration factor of each vibration subsequence. Since the vibration amplitude during cutting operations is small and relatively stable when the tool is not worn, its vibration factor is small. Therefore, the vibration subsequence with the smallest vibration factor among all the vibration subsequences corresponding to cutting operation subsequences in the i-th tool cluster is denoted as the undamaged vibration subsequence corresponding to the i-th tool cluster.

[0051] Following the method for extracting vibration subsequences, the energy consumption subsequences corresponding to each vibration subsequence are extracted from the energy consumption sequence. The vibration subsequence corresponding to the last cutting operation subsequence in the time sequence of the i-th tool cluster is taken as the current vibration subsequence. The average of the energy consumption subsequences corresponding to the undamaged vibration subsequences of the i-th tool cluster and the current vibration subsequence is denoted as the normal energy consumption I. zc Current wear energy consumption I v .

[0052] Calculate the current wear energy consumption I v Compared with normal energy consumption I zc The difference between them is denoted as the additional tool energy consumption I of the i-th tool. zc,v .

[0053] Additional tool energy consumption reflects how much additional energy the i-th tool adds to the CNC milling machine due to wear under the current wear condition.

[0054] By calculating the additional energy consumption of the cutting tool, the specific impact of tool wear on machining losses can be quantified, thus providing data support for the subsequent determination of tool replacement.

[0055] Furthermore, after obtaining the additional energy consumption caused by tool wear, it is necessary to analyze the energy consumption impact caused by the idling of the CNC milling machine during the tool replacement process, so as to provide a data basis for subsequent judgment on whether tool replacement is necessary.

[0056] Neither the process of switching tools (changing from one type of tool to another for work) nor the process of replacing tools (removing a tool and installing a new one) generates cutting force. Therefore, the number of elements between two adjacent cutting force subsequences with non-zero mean in the cutting force sequence is the replacement time for various tools. By comparing the energy consumption difference between the tool switching process and the tool replacement process, the no-load loss of various tools under the replacement time can be obtained.

[0057] Let's take the j-th cutting operation subsequence in the i-th tool cluster as an example.

[0058] Obtain the position w2 of the first element of the j-th cutting operation subsequence in the cutting force sequence, and obtain the position w1 of the last element of the preceding cutting operation subsequence in the cutting force sequence. The time interval between w1 and w2 is the changeover time of the j-th cutting operation subsequence for the i-th tool. It should be noted that the obtained changeover time may be the time to switch from any tool to the i-th tool, or it may be the time from disassembly to installation of the i-th tool.

[0059] Based on the position range of the change time of the j-th cutting operation subsequence in the cutting force sequence, extract the corresponding element from the energy consumption sequence and sum them. Record the summation result as the no-load loss of the j-th cutting operation subsequence. The no-load loss reflects the no-load loss of the i-th tool in the CNC milling machine before the j-th cutting operation due to tool switching or tool replacement.

[0060] Since tool replacement requires disassembly and reinstallation, its no-load loss will be much greater than the no-load loss during tool switching. Therefore, the tool replacement loss can be obtained by the difference method.

[0061] Similarly, the idle losses of all cutting operation subsequences in the i-th tool cluster are obtained, and all idle losses are used as input to the Otsu threshold method. All idle losses are divided into two parts, representing the idle loss during normal tool switching and the idle loss during tool disassembly and installation. The absolute difference between the average idle losses of the two parts is recorded as the average replacement loss of the i-th tool. The average replacement loss reflects the wear and tear of the i-th tool in the CNC milling machine caused by tool replacement.

[0062] Furthermore, existing cutting tools are typically coated with one or more layers to improve their hardness, wear resistance, and heat resistance. However, when new cutting tools are first put into use, the coating may not be fully stable, and its lubrication and wear resistance may not be optimal. The coating's lubrication effect may be poor, which could lead to a higher coefficient of friction during the cutting process, resulting in additional wear.

[0063] Based on the novel energy consumption sequence and novel cutting force sequence of various novel cutting tools during the machining process, and using the aforementioned cutting force sequence segmentation method, the novel cutting operation subsequence corresponding to each tool under novel conditions is extracted. Then, based on the position range of the novel cutting operation subsequence, the novel energy consumption subsequence corresponding to each tool under novel conditions is extracted. The novel energy consumption subsequence reflects the energy consumption data generated by each tool during cutting operations under novel conditions.

[0064] The mean of all novel energy consumption subsequences corresponding to the i-th tool is compared with the normal energy consumption I. zc The difference between them is denoted as the replacement loss of the i-th tool.

[0065] By calculating average replacement loss and replacement loss, the energy consumption impact of tool changes can be accurately assessed, and replacement strategies can be optimized. Average replacement loss reflects idling energy consumption, while replacement loss quantifies the initial additional energy consumption of a new tool, providing a basis for replacement decisions, reducing energy consumption and efficiency losses caused by frequent tool changes, and improving the control effect of the CNC milling machine industrial control system.

[0066] Furthermore, based on the replacement wear of each tool, additional tool energy consumption, average replacement wear, and whether the current wear condition meets the machining accuracy requirements, a tool life coefficient is constructed for each tool to determine whether it needs to be replaced. The process for obtaining the tool life coefficient for each tool is as follows: Figure 2 As shown. The specific expression for the tool sustain factor of a single tool is: A i =[(B i +C i )-I zc,v ]×σ i In the formula, A i I is the tool sustain coefficient for the i-th tool; zc,v B represents the additional energy consumption of the i-th tool; i C represents the average replacement wear of the i-th tool; i σ represents the replacement wear of the i-th tool; i Let be the tool usage factor for the i-th tool. The tool usage factor for the i-th tool is obtained as follows: if the current wear condition of the i-th tool meets the normal machining accuracy of the workpiece, then the tool usage factor is set to 1, having no impact on the calculation; otherwise, the tool usage factor is set to 0, indicating that the tool needs to be replaced. Determining whether the tool wear condition meets the normal machining accuracy of the workpiece is a well-known technique in the art and will not be elaborated upon here.

[0067] When A i If the value is greater than zero, it means that the extra energy consumption of the i-th tool in the current wear condition during the cutting process is not as great as the energy consumption of replacing the tool. At this time, the wear of the tool does not affect the machining accuracy, so the tool can still be used without replacement, thus saving energy.

[0068] Tool changing control module:

[0069] Based on the above calculation method for tool durability coefficient, calculate the tool durability coefficient for each tool within the current monitoring period. If the tool durability coefficient of a single tool is greater than 0, it indicates that the tool will not significantly affect machining accuracy and machine tool energy consumption. The additional energy consumption from continued use is less than the no-load loss and replacement loss incurred from replacing the tool once, so the tool can continue to be used. If the tool durability coefficient of a single tool is less than or equal to 0, it indicates that the wear of this type of tool has seriously affected machining efficiency and energy consumption, or can no longer meet machining accuracy requirements, so the tool needs to be replaced immediately.

[0070] By using the above methods to determine whether the tool needs to be replaced, the tool replacement control effect of the CNC milling machine industrial control system is improved, thereby saving energy consumption of the CNC milling machine.

[0071] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0073] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A composite CNC milling machine and its control system, characterized in that, The system includes: Data acquisition module: acquires the vibration sequence, cutting force sequence, and energy consumption sequence of the CNC milling machine; Data analysis module: Obtain cutting operation subsequences; cluster all cutting operation subsequences to obtain multiple tool clusters; Energy consumption calculation module: This module obtains the vibration subsequence and energy consumption subsequence for each cutting operation subsequence within the same time range in the vibration and energy consumption sequences; it obtains the vibration factor of each vibration subsequence based on its dispersion and average level, thus obtaining the undamaged vibration subsequence corresponding to each tool cluster; it obtains the normal energy consumption and current wear energy consumption of a single tool based on the average level of the undamaged vibration subsequence corresponding to a single tool cluster and the energy consumption subsequence corresponding to the current vibration subsequence; it obtains the additional tool energy consumption of a single tool based on the difference between the normal energy consumption and the current wear energy consumption; and it obtains the position w2 of the first element of a single cutting operation subsequence in the cutting force sequence, as well as the last element of the previous cutting operation subsequence. In the cutting force sequence, the element corresponding to the position w1 is extracted from the energy consumption sequence based on the position range between w1 and w2 in the cutting force sequence, and the sum is recorded as the no-load loss of the corresponding single cutting operation subsequence. The no-load loss of all cutting operation subsequences in a single tool cluster is used as the input of the threshold segmentation method to divide all no-load losses into two parts. The absolute difference between the average no-load losses of the two parts is recorded as the average replacement loss of a single tool. Based on the difference between the average energy consumption of each tool under new conditions and normal energy consumption, the replacement loss of each tool is obtained. Combined with the tool's additional energy consumption, average replacement loss, and whether the current wear condition meets the normal machining accuracy, the tool continuity coefficient of each tool is obtained. Tool changing control module: Determines whether each tool needs to be replaced.

2. The composite CNC milling machine and its control system as described in claim 1, characterized in that, The process of obtaining the multiple tool clusters is as follows: the data length and mean of each cutting operation subsequence are used to construct the attribute vector of each cutting operation subsequence; all cutting operation subsequences are clustered according to the attribute vectors of all cutting operation subsequences to obtain multiple tool clusters.

3. The composite CNC milling machine and its control system as described in claim 1, characterized in that, The vibration factor of each vibration subsequence is the product of the variance and the mean of each vibration subsequence.

4. The composite CNC milling machine and its control system as described in claim 1, characterized in that, The undamaged vibration subsequence corresponding to each tool cluster is the vibration subsequence with the smallest vibration factor among all the vibration subsequences corresponding to all cutting operation subsequences in each tool cluster.

5. A composite CNC milling machine and its control system as described in claim 1, characterized in that, The process of obtaining the normal energy consumption and current wear energy consumption of a single tool is as follows: the vibration subsequence corresponding to the last cutting operation subsequence in the time sequence in the single tool cluster is taken as the current vibration subsequence; The mean values ​​of the undamaged vibration subsequence corresponding to a single tool cluster and the energy consumption subsequence corresponding to the current vibration subsequence are respectively denoted as the normal energy consumption and the current wear energy consumption of a single tool.

6. A composite CNC milling machine and its control system as described in claim 1, characterized in that, The additional energy consumption of a single tool is the difference between the normal energy consumption of the single tool and the current wear energy consumption.

7. A composite CNC milling machine and its control system as described in claim 1, characterized in that, The process for obtaining the replacement loss of each tool is as follows: obtain the new energy consumption subsequence corresponding to each tool under brand new conditions, and record the difference between the mean of all new energy consumption subsequences corresponding to each tool and the normal energy consumption as the replacement loss of each tool.

8. A composite CNC milling machine and its control system as described in claim 1, characterized in that, The formula for calculating the tool sustain factor of each tool is as follows: In the formula, Let be the tool sustain coefficient for the i-th tool; The additional energy consumption of the i-th tool; Let be the average replacement wear of the i-th tool; Let $\frac{i}{i}$ be the replacement cost of the $i$-th tool. Let be the tool usage factor of the i-th tool; the tool usage factor of the i-th tool is obtained as follows: if the current wear condition of the i-th tool can meet the normal machining accuracy of the workpiece, then the tool usage factor is set to 1; otherwise, the tool usage factor is set to 0.

9. A composite CNC milling machine and its control system as described in claim 1, characterized in that, The specific method for determining whether each tool needs to be replaced is as follows: if the tool continuity coefficient of a single tool is greater than 0, then the tool does not need to be replaced; otherwise, the tool needs to be replaced.

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