Numerical control machine tool control system based on big data

Abstract

The application discloses a numerical control machine tool control system based on big data, a data acquisition module acquires tool wear during production of the numerical control machine tool, load information of the machine tool during machining, machining precision error of workpiece machining and a machining precision error change rate; relates to the technical field of intelligent control, the system comprehensively utilizes big data and a fuzzy self-adaptive control algorithm, the fuzzy self-adaptive control algorithm determines a feed rate adjustment range based on fuzzy information of a machining state, selects a feed rate scheme with the highest fitness function in a scheme set containing all possible feed rates by setting the fitness function, and a feed rate correction adjustment module dynamically adjusts the feed rate according to actual machining precision, so that the difference between the feed rate scheme determined by the fitness function and actual machining is made up. Through the synergistic effect of these technologies, the machining precision and efficiency of the numerical control machine tool are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to a CNC machine tool control system based on big data. Background Technology

[0002] CNC machine tools, short for numerical control machine tools, are automated machine tools equipped with a program control system. This control system can logically process programs with control codes or other symbolic instructions, decode them, represent them with coded numbers, and input them into the CNC device via an information carrier. After processing, the CNC device sends out various control signals to control the machine tool's movements, automatically machining parts according to the shape and dimensions required by the drawings.

[0003] Publication No. CN115639781B discloses a CNC machine tool control method and system based on big data. This method involves: acquiring production data for each workpiece produced by the CNC machine tool; acquiring the tool path error index of the CNC machine tool to filter out abnormal tool path machines; acquiring the position error vector of non-abnormal tool path machines to identify abnormal machines; arbitrarily selecting multiple abnormal machine tools and acquiring multiple vibration-audio sequences for each abnormal machine tool, dividing all selected abnormal machine tools into three groups; determining the group to which each vibration-audio sequence of each abnormal machine tool belongs based on its similarity to different groups of abnormal machine tools, and then implementing corresponding control actions.

[0004] However, the above application still has the following problems: the feed rate adjustment of the above application is only adjusted by a fixed 0.5mm each time based on the machining gear accuracy requirements, without considering the influence of tool wear on the feed rate adjustment. The adjustment strategy lacks flexibility and pertinence, which is not conducive to precise control of machining accuracy. Summary of the Invention

[0005] To address the technical problems existing in the background art, this invention proposes a CNC machine tool control system based on big data.

[0006] This invention proposes a big data-based CNC machine tool control system, comprising:

[0007] Data acquisition module: Collects tool wear during CNC machine tool production, load information of the machine tool during processing, machining accuracy error of the workpiece, and the rate of change of machining accuracy error;

[0008] Infeed adjustment range determination module: Determines the infeed adjustment range through fuzzy adaptive control;

[0009] The feed rate scheme generation and determination module generates a set of schemes containing all possible feed rates within the feed rate adjustment range given by the feed rate adjustment range determination module. Each feed rate value within the feed rate adjustment range corresponds to an independent feed rate scheme.

[0010] Define a fitness function, and select the feed rate scheme with the highest fitness function from the set of schemes containing all possible feed rates;

[0011] Feed rate correction and adjustment module: During the machining process of the feed rate scheme with the highest fitness function, when the actual machining size is greater than the standard size and exceeds the allowable range, or when the actual machining size is less than the standard size and exceeds the allowable range, the feed rate of the feed rate scheme with the highest fitness function is corrected in real time.

[0012] We continuously monitor machining accuracy and adjust the feed rate accordingly.

[0013] Preferably, in the feed rate adjustment range determination module, fuzzy adaptive control determines the feed rate adjustment range as follows:

[0014] The tool wear amount, current machining accuracy error, and rate of change of machining accuracy error are used as input variables for fuzzy adaptive control, and the feed rate adjustment value is used as the output variable.

[0015] The input variables are fuzzified, fuzzy subsets are divided, and the membership function corresponding to each subset is determined.

[0016] The fuzzy subsets include negative large, negative medium, negative small, zero, positive small, positive medium, and positive large;

[0017] The membership function can be determined based on processing experience and experimental data. The membership function is used to accurately describe the degree of belonging of the input variable under different fuzzy states.

[0018] The decision tree algorithm is used to generate fuzzy rules for adjusting the feed rate.

[0019] The decision tree algorithm outputs the following values ​​for the feed rate adjustment: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large.

[0020] The Mamdani inference method is used for fuzzy inference to calculate the fuzzy set of output variables;

[0021] Mamdani inference has unique advantages in handling fuzziness and uncertainty. While the rules for generating decision trees are explicit, they may not fully reflect the fuzzy relationships between input variables and the propagation of fuzziness. When more detailed processing of fuzzy information is needed to accurately characterize the interactions between different fuzzy subsets, Mamdani inference can more accurately calculate the fuzzy set of feed rate adjustment values ​​through steps such as fuzzification of input, fuzzy relation synthesis, and defuzzification, thereby obtaining a more reasonable feed rate adjustment result.

[0022] By performing fuzzy relation synthesis operations, the fuzzy output of the feed rate adjustment value is determined based on the fuzzy values ​​and fuzzy rules of the input variables.

[0023] The centroid method is used to defuzzify the fuzzy output and transform it into a precise feed rate adjustment range. The centroid method determines the precise value by calculating the centroid of the area enclosed by the membership function of the output fuzzy set and the horizontal coordinate, so that the result of fuzzy control can be directly applied to the actual feed rate adjustment.

[0024] Preferably, a fuzzy rule for determining the feed rate adjustment value is generated using a decision tree algorithm, as follows:

[0025] A decision tree model is constructed based on the relationship between the input variables tool wear, current machining accuracy error, rate of change of machining accuracy error, and output variable feed rate adjustment value. By training the decision tree model, the model automatically classifies the output categories corresponding to different combinations of input variables, thereby generating fuzzy rules.

[0026] Preferably, in the feed rate scheme generation and determination module, the fitness function is as follows:

[0027] For each feed rate scheme, during the simulated machining process, the corresponding simulated tool wear, simulated machining accuracy error, and simulated machining accuracy error change rate are calculated.

[0028] The simulated processing described here is based on existing technology;

[0029] The simulated tool wear, simulated machining accuracy error, and simulated machining accuracy error change rate are quantified and normalized.

[0030] Let the weight of tool wear be... The weight of the simulated machining accuracy error is The weight of the simulated machining accuracy error change rate is: ;

[0031] Let the tool wear after quantization and normalization be T, the simulated machining accuracy error be E, and the rate of change of the simulated machining accuracy error be ∆E;

[0032] The fitness function F is: ;

[0033] Wherein, ∈ is used to avoid positive numbers with a denominator of zero. In an optional embodiment, ∈ = ;

[0034] In actual processing, , and The value can be adjusted manually;

[0035] From the set of all possible feed rates, select the feed rate scheme with the highest fitness function.

[0036] Preferably, The value range is 0.2-0.3. The value range is 0.4-0.5. The value range is 0.2-0.3.

[0037] Preferably, in the feed rate correction and adjustment module, during the machining process of the feed rate scheme with the highest fitness function, the initial feed rate determined in the feed rate scheme with the highest fitness function is set as follows: ;

[0038] The allowable accuracy deviation range is ;

[0039] The actual dimensions of the workpiece obtained in real time by the online measurement system are: The preset standard size of the workpiece is ;

[0040] Real-time accuracy deviation is , = - ;

[0041] Let the feed rate after real-time correction be... ;

[0042] The correction factor is This is used to control the range of feed rate adjustment; Adjustments can be made manually based on machine tool performance and the materials being processed.

[0043] when > When the actual machining dimension is larger than the standard dimension and exceeds the allowable range, the feed rate needs to be appropriately reduced, and feed rate correction is required. ;

[0044] The relative proportion exceeding the allowable deviation was calculated and multiplied by the initial feed rate. Obtain the portion of the feed rate that needs to be reduced, and then multiply it by the correction factor. To control the magnitude of the reduction;

[0045] when <- When the actual machining dimension is smaller than the standard dimension and exceeds the allowable range, it is necessary to appropriately increase the feed rate, and thus perform feed rate correction. ;

[0046] The relative proportion exceeding the allowable negative deviation was calculated and multiplied by the initial feed rate. The required increase in feed rate is then multiplied by the correction factor. To control the magnitude of the increase;

[0047] like- ≤ ≤ This means that the deviation between the actual machined dimensions and the standard dimensions is within the allowable range, and the feed rate does not need to be corrected. .

[0048] A CNC machine tool control method based on big data, characterized by comprising the following steps:

[0049] S1. Collect the tool wear during CNC machine tool production, the load information of the machine tool during the machining process, the machining accuracy error of the workpiece, and the rate of change of machining accuracy error;

[0050] S2. Determine the feed rate adjustment range through fuzzy adaptive control;

[0051] S3. Within the feed rate adjustment range given in S2, generate a set of schemes containing all possible feed rates. Each feed rate value within the feed rate adjustment range corresponds to an independent feed rate scheme.

[0052] Define a fitness function, and select the feed rate scheme with the highest fitness function from the set of schemes containing all possible feed rates;

[0053] S4. During the machining process of the feed rate scheme with the highest fitness function, when the actual machining size is greater than the standard size and exceeds the allowable range, or when the actual machining size is less than the standard size and exceeds the allowable range, the feed rate of the feed rate scheme with the highest fitness function is corrected in real time.

[0054] We continuously monitor machining accuracy and adjust the feed rate accordingly.

[0055] The big data-based CNC machine tool control system proposed in this invention has the following beneficial technical effects:

[0056] 1. This system comprehensively utilizes big data and fuzzy adaptive control algorithms. The fuzzy adaptive control algorithm determines the feed rate adjustment range based on fuzzy information of the machining state. By setting a fitness function, it selects the feed rate scheme with the highest fitness function from a set of all possible feed rate schemes. The feed rate correction and adjustment module dynamically adjusts the feed rate according to the actual machining accuracy, compensating for the difference between the feed rate scheme determined by the fitness function and the actual machining. Through the synergistic effect of these technologies, the machining accuracy and efficiency of CNC machine tools are effectively improved, the scrap rate is reduced, production costs are lowered, and the competitiveness of enterprises is enhanced.

[0057] 2. By setting a fitness function, the fuzzy adaptive control algorithm determines the feed rate adjustment range based on the fuzzy information of the machining state. Then, the fitness function calculates the optimal feed rate within the determined feed rate adjustment range, thereby determining the feed rate scheme and improving the accuracy of feed rate adjustment.

[0058] 3. Through the settings of the feed rate correction and adjustment module, the actual dimensions of the workpiece are affected by various factors during the machining process, resulting in deviations. The feed rate correction and adjustment module can monitor these deviations in real time and correct the feed rate promptly. Furthermore, the machining process is dynamic; material properties, tool wear, machine tool performance, and other factors change over time. By continuously monitoring machining accuracy and adjusting the feed rate, the feed rate correction and adjustment module can adapt to these dynamic changes, thereby improving machining accuracy. In addition, because it can correct the feed rate in a timely manner, it greatly reduces rework and scrap caused by insufficient machining accuracy, thus reducing the rework and scrap rate.

[0059] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the system of the present invention;

[0061] Figure 2 This is a flowchart of the method of the present invention;

[0062] Figure 3 Here are some examples of fuzzy rules in the system of this invention;

[0063] Figure 4 This is an example of some fuzzy rules in the system of the present invention. Detailed Implementation

[0064] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar symbols 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 the present invention, and should not be construed as limiting the present invention.

[0065] like Figures 1-2 The CNC machine tool control system shown includes:

[0066] Data acquisition module: Collects tool wear during CNC machine tool production, load information of the machine tool during processing, machining accuracy error of the workpiece, and the rate of change of machining accuracy error;

[0067] Infeed adjustment range determination module: Determines the infeed adjustment range through fuzzy adaptive control;

[0068] The feed rate scheme generation and determination module generates a set of schemes containing all possible feed rates within the feed rate adjustment range given by the feed rate adjustment range determination module. Each feed rate value within the feed rate adjustment range corresponds to an independent feed rate scheme.

[0069] Define a fitness function, and select the feed rate scheme with the highest fitness function from the set of schemes containing all possible feed rates;

[0070] Feed rate correction and adjustment module: During the machining process of the feed rate scheme with the highest fitness function, when the actual machining size is greater than the standard size and exceeds the allowable range, or when the actual machining size is less than the standard size and exceeds the allowable range, the feed rate of the feed rate scheme with the highest fitness function is corrected in real time.

[0071] We continuously monitor machining accuracy and adjust the feed rate accordingly.

[0072] Furthermore, in the feed rate adjustment range determination module, fuzzy adaptive control determines the feed rate adjustment range as follows:

[0073] The tool wear amount, current machining accuracy error, and rate of change of machining accuracy error are used as input variables for fuzzy adaptive control, and the feed rate adjustment value is used as the output variable.

[0074] The input variables are fuzzified, fuzzy subsets are divided, and the membership function corresponding to each subset is determined.

[0075] The fuzzy subsets include negative large, negative medium, negative small, zero, positive small, positive medium, and positive large;

[0076] The membership function can be determined based on processing experience and experimental data. The membership function is used to accurately describe the degree of belonging of the input variable under different fuzzy states.

[0077] The decision tree algorithm is used to generate fuzzy rules for adjusting the feed rate.

[0078] The decision tree algorithm outputs the following values ​​for the feed rate adjustment: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large.

[0079] The Mamdani inference method is used for fuzzy inference to calculate the fuzzy set of output variables;

[0080] Mamdani inference has unique advantages in handling fuzziness and uncertainty. While the rules for generating decision trees are explicit, they may not fully reflect the fuzzy relationships between input variables and the propagation of fuzziness. When more detailed processing of fuzzy information is needed to accurately characterize the interactions between different fuzzy subsets, Mamdani inference can more accurately calculate the fuzzy set of feed rate adjustment values ​​through steps such as fuzzification of input, fuzzy relation synthesis, and defuzzification, thereby obtaining a more reasonable feed rate adjustment result.

[0081] By performing fuzzy relation synthesis operations, the fuzzy output of the feed rate adjustment value is determined based on the fuzzy values ​​and fuzzy rules of the input variables.

[0082] The centroid method is used to defuzzify the fuzzy output and transform it into a precise feed rate adjustment range. The centroid method determines the precise value by calculating the centroid of the area enclosed by the membership function of the output fuzzy set and the horizontal coordinate, so that the result of fuzzy control can be directly applied to the actual feed rate adjustment.

[0083] Furthermore, using a decision tree algorithm, fuzzy rules for determining the feed rate adjustment value are generated, as follows:

[0084] A decision tree model is constructed based on the relationship between the input variables tool wear, current machining accuracy error, rate of change of machining accuracy error, and output variable feed rate adjustment value. By training the decision tree model, the model automatically classifies the output categories corresponding to different combinations of input variables, thereby generating fuzzy rules.

[0085] Furthermore, in the feed rate scheme generation and determination module, the fitness function is as follows:

[0086] For each feed rate scheme, during the simulated machining process, the corresponding simulated tool wear, simulated machining accuracy error, and simulated machining accuracy error change rate are calculated.

[0087] The simulated processing described here is based on existing technology;

[0088] The simulated tool wear, simulated machining accuracy error, and simulated machining accuracy error change rate are quantified and normalized.

[0089] Let the weight of tool wear be... The weight of the simulated machining accuracy error is The weight of the simulated machining accuracy error change rate is: ;

[0090] Let the tool wear after quantization and normalization be T, the simulated machining accuracy error be E, and the rate of change of the simulated machining accuracy error be ∆E;

[0091] The fitness function F is: ;

[0092] Wherein, ∈ is used to avoid positive numbers with a denominator of zero. In an optional embodiment, ∈ = ;

[0093] In actual processing, , and The value can be adjusted manually;

[0094] From the set of all possible feed rates, select the feed rate scheme with the highest fitness function.

[0095] Furthermore, in an optional embodiment, The value range is 0.2-0.3. The value range is 0.4-0.5. The value range is 0.2-0.3.

[0096] By setting a fitness function, the fuzzy adaptive control algorithm determines the feed rate adjustment range based on the fuzzy information of the machining state. Then, the fitness function calculates the optimal feed rate within the determined feed rate adjustment range, thereby determining the feed rate scheme and improving the accuracy of feed rate adjustment.

[0097] Furthermore, in the feed rate correction and adjustment module, during the machining process of the feed rate scheme with the highest fitness function, the initial feed rate determined in the feed rate scheme with the highest fitness function is set as follows: ;

[0098] The allowable accuracy deviation range is ;

[0099] The actual dimensions of the workpiece obtained in real time by the online measurement system are: The preset standard size of the workpiece is ;

[0100] Real-time accuracy deviation is , = - ;

[0101] Let the feed rate after real-time correction be... ;

[0102] The correction factor is This is used to control the range of feed rate adjustment; Adjustments can be made manually based on machine tool performance and the materials being processed.

[0103] when > When the actual machining dimension is larger than the standard dimension and exceeds the allowable range, the feed rate needs to be appropriately reduced, and feed rate correction is required. ;

[0104] The relative proportion exceeding the allowable deviation was calculated and multiplied by the initial feed rate. Obtain the portion of the feed rate that needs to be reduced, and then multiply it by the correction factor. To control the magnitude of the reduction;

[0105] when <- When the actual machining dimension is smaller than the standard dimension and exceeds the allowable range, it is necessary to appropriately increase the feed rate, and thus perform feed rate correction. ;

[0106] The relative proportion exceeding the allowable negative deviation was calculated and multiplied by the initial feed rate. The required increase in feed rate is then multiplied by the correction factor. To control the magnitude of the increase;

[0107] like- ≤ ≤ This means that the deviation between the actual machined dimensions and the standard dimensions is within the allowable range, and the feed rate does not need to be corrected. .

[0108] Online measurement systems are existing technologies, including laser measurement systems, vision measurement systems, contact measurement systems, ultrasonic measurement systems, and capacitance measurement systems.

[0109] If the accuracy deviation exceeds the allowable range, immediately adjust the feed rate to ensure that the machining process always proceeds in the direction of high precision. Continuously optimize the feed rate adjustment to ensure the stability and improvement of machining accuracy.

[0110] By configuring the feed rate correction module, it's possible to mitigate deviations in the workpiece's actual dimensions during machining due to various factors. This module monitors these deviations in real-time and promptly corrects the feed rate. Furthermore, machining is a dynamic process where material properties, tool wear, and machine tool performance all change over time. By continuously monitoring machining accuracy and adjusting the feed rate, the module adapts to these dynamic changes, thereby improving machining precision. Additionally, the timely correction of the feed rate significantly reduces rework and scrap caused by insufficient machining accuracy, thus lowering the rework and scrap rates.

[0111] This system comprehensively utilizes big data and fuzzy adaptive control algorithms. The fuzzy adaptive control algorithm determines the feed rate adjustment range based on fuzzy information of the machining state. By setting a fitness function, it selects the feed rate scheme with the highest fitness function from a set of all possible feed rate schemes. The feed rate correction and adjustment module dynamically adjusts the feed rate according to the actual machining accuracy, compensating for the discrepancy between the feed rate scheme determined by the fitness function and the actual machining. Through the synergistic effect of these technologies, the machining accuracy and efficiency of CNC machine tools are effectively improved, scrap rates are reduced, production costs are lowered, and the competitiveness of enterprises is enhanced.

[0112] A CNC machine tool control method based on big data includes the following steps:

[0113] S1. Collect the tool wear during CNC machine tool production, the load information of the machine tool during the machining process, the machining accuracy error of the workpiece, and the rate of change of machining accuracy error;

[0114] S2. Determine the feed rate adjustment range through fuzzy adaptive control;

[0115] S3. Within the feed rate adjustment range given in S2, generate a set of schemes containing all possible feed rates. Each feed rate value within the feed rate adjustment range corresponds to an independent feed rate scheme.

[0116] Define a fitness function, and select the feed rate scheme with the highest fitness function from the set of schemes containing all possible feed rates;

[0117] S4. During the machining process of the feed rate scheme with the highest fitness function, when the actual machining size is greater than the standard size and exceeds the allowable range, or when the actual machining size is less than the standard size and exceeds the allowable range, the feed rate of the feed rate scheme with the highest fitness function is corrected in real time.

[0118] We continuously monitor machining accuracy and adjust the feed rate accordingly.

[0119] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0120] In the embodiments provided by this invention, it should be understood that the disclosed system or method can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative; for instance, the division of modules is only a logical functional division, and there may be other division methods in actual implementation.

[0121] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0122] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in the form of hardware plus software functional modules.

[0123] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the basic characteristics of the present invention.

[0124] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A CNC machine tool control system based on big data, characterized in that, include: Data acquisition module: Collects tool wear during CNC machine tool production, load information of the machine tool during processing, machining accuracy error of the workpiece, and the rate of change of machining accuracy error; The feed rate adjustment range determination module determines the feed rate adjustment range through fuzzy adaptive control; the feed rate scheme generation and determination module generates a set of schemes containing all possible feed rates within the feed rate adjustment range given by the feed rate adjustment range determination module, and each feed rate value within the feed rate adjustment range corresponds to an independent feed rate scheme. Define a fitness function, and select the feed rate scheme with the highest fitness function from the set of schemes containing all possible feed rates; Feed rate correction and adjustment module: During the machining process of the feed rate scheme with the highest fitness function, when the actual machining size is greater than the standard size and exceeds the allowable range, or when the actual machining size is less than the standard size and exceeds the allowable range, the feed rate of the feed rate scheme with the highest fitness function is corrected in real time. We continuously monitor machining accuracy and adjust the feed rate accordingly.

2. The big data-based CNC machine tool control system according to claim 1, characterized in that, In the feed rate adjustment range determination module, fuzzy adaptive control determines the feed rate adjustment range as follows: The tool wear amount, current machining accuracy error, and rate of change of machining accuracy error are used as input variables for fuzzy adaptive control, and the feed rate adjustment value is used as the output variable. The input variables are fuzzified, fuzzy subsets are divided, and the membership function corresponding to each subset is determined. The fuzzy subsets include negative large, negative medium, negative small, zero, positive small, positive medium, and positive large; The decision tree algorithm is used to generate fuzzy rules for adjusting the feed rate. The decision tree algorithm outputs the following values ​​for the feed rate adjustment: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. The Mamdani inference method is used for fuzzy inference to calculate the fuzzy set of output variables; By performing fuzzy relation synthesis operations, the fuzzy output of the feed rate adjustment value is determined based on the fuzzy values ​​and fuzzy rules of the input variables. The center of gravity method is used to deblur the fuzzy output and convert it into a precise feed rate adjustment range.

3. The big data-based CNC machine tool control system according to claim 2, characterized in that, The decision tree algorithm is used to generate fuzzy rules for adjusting the feed rate, as follows: A decision tree model is constructed based on the relationship between the input variables tool wear, current machining accuracy error, rate of change of machining accuracy error, and output variable feed rate adjustment value. By training the decision tree model, the model automatically classifies the output categories corresponding to different combinations of input variables, thereby generating fuzzy rules.

4. The big data-based CNC machine tool control system according to claim 3, characterized in that, In the module for generating and determining the feed rate scheme, the fitness function is as follows: For each feed rate scheme, during the simulated machining process, the corresponding simulated tool wear, simulated machining accuracy error, and simulated machining accuracy error change rate are calculated. The simulated tool wear, simulated machining accuracy error, and simulated machining accuracy error change rate are quantified and normalized. Let the weight of tool wear be... The weight of the simulated machining accuracy error is The weight of the simulated machining accuracy error change rate is: ; Let the tool wear after quantization and normalization be T, the simulated machining accuracy error be E, and the rate of change of the simulated machining accuracy error be ∆E; The fitness function F is: ; Where ∈ is a positive number to avoid having a denominator of zero; From the set of all possible feed rates, select the feed rate scheme with the highest fitness function.

5. The big data-based CNC machine tool control system according to claim 4, characterized in that, The value range is 0.2-0.

3. The value range is 0.4-0.

5. The value range is 0.2-0.

3.

6. The CNC machine tool control system based on big data according to claim 5, characterized in that, In the feed rate correction and adjustment module, during the machining process of the feed rate scheme with the highest fitness function, let the initial feed rate determined in the feed rate scheme with the highest fitness function be... ; The allowable accuracy deviation range is ; The actual dimensions of the workpiece obtained in real time by the online measurement system are: The preset standard dimensions of the workpiece are ; Real-time accuracy deviation is , = - ; Let the feed rate after real-time correction be... ; The correction factor is This is used to control the range of feed rate adjustment; when > When the actual processed size is larger than the standard size and exceeds the allowable range, then... ; when <- When the actual processed size is smaller than the standard size and exceeds the allowable range, ; like- ≤ ≤ This means that the deviation between the actual machined dimensions and the standard dimensions is within the allowable range, and the feed rate does not need to be corrected. .

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