Processing data acquisition method and system applied to numerical control machine tool

Abstract

The invention relates to the technical field of numerical control machine tool operation, in particular to a machining data acquisition method and system applied to a numerical control machine tool, and the method comprises the steps: obtaining a machining precision error value according to the difference between cutting parameters of different dimensions and initial cutting parameters; according to the difference change condition of the cutting parameter and the initial cutting parameter, a tool wear characteristic value is obtained; obtaining a tool wear influence value according to the tool wear characteristic value and the cutting force change influence value; obtaining a machining precision error influence value according to the tool wear influence value and the machining precision error value; acquiring an acquisition frequency regulation factor at the current moment according to the variation trend of the processing precision error influence values of different target error historical time periods; and acquiring the boring and milling data acquisition frequency at the current moment based on the acquisition frequency regulation factor. The boring and milling precision of the numerical control machine tool is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of numerical control machine tool operation, and particularly to a machining data acquisition method and system applied to numerical control machine tools. Background Art

[0002] As a key automated device in modern manufacturing, numerical control machine tools are widely used in fields such as metal processing, mold manufacturing, and precision machining. Especially during boring and milling processes, the machining accuracy of the machine tool directly affects the quality and performance of parts. To ensure machining accuracy, traditional methods usually rely on preset initial cutting parameters for machining control. However, due to factors such as tool wear, workpiece material differences, machine tool vibration, and temperature changes, actual machining parameters deviate over time, leading to the accumulation of machining errors.

[0003] During high-precision numerical control boring and milling machining, both tool wear and vibration generated during the machining process can lead to a deterioration in boring and milling machining accuracy. Existing numerical control machine tool data acquisition methods mostly collect cutting parameters at a fixed frequency, making it difficult to reflect the dynamic changes in the machining state in real time, unable to effectively capture abnormal situations of tool wear and vibration, resulting in low data acquisition efficiency and difficulty in timely correcting machining errors, and making the boring and milling machining accuracy of numerical control machine tools worse and worse. Summary of the Invention

[0004] The present invention provides a machining data acquisition method and system applied to numerical control machine tools to solve the existing problems: existing numerical control machine tool data acquisition methods mostly collect cutting parameters at a fixed frequency, making it difficult to reflect the dynamic changes in the machining state in real time, unable to effectively capture abnormal situations of tool wear and vibration, resulting in low data acquisition efficiency and difficulty in timely correcting machining errors, and making the boring and milling machining accuracy of numerical control machine tools worse and worse.

[0005] The machining data acquisition method and system applied to numerical control machine tools of the present invention adopt the following technical solutions: The present invention proposes a machining data acquisition method applied to numerical control machine tools, which includes the following steps: Obtain the cutting parameter sequence and initial cutting parameters when the numerical control machine tool performs boring and milling machining; By analyzing the difference between the cutting parameter sequence and the initial cutting parameters, obtain several target moments; according to the difference fluctuation between the cutting parameters and the initial cutting parameters at the target moments, obtain several target error history time periods; according to the difference between the cutting parameters and the initial cutting parameters in different dimensions, obtain the machining accuracy error values of the target error history time periods; Obtain the influence value of cutting force change in the target error historical period according to the change range of cutting parameters; obtain the tool wear characteristic value in the target error historical period according to the difference change between the cutting parameters and the initial cutting parameters; obtain the tool wear influence value in the target error historical period according to the tool wear characteristic value and the influence value of cutting force change; obtain the machining accuracy error influence value in the target error historical period according to the tool wear influence value and the machining accuracy error value; Obtain the acquisition frequency adjustment factor at the current moment according to the change trend of the machining accuracy error influence value in different target error historical periods; obtain the data acquisition frequency of boring and milling machining at the current moment based on the acquisition frequency adjustment factor.

[0006] Preferably, the specific method for obtaining several target moments by analyzing the difference between the cutting parameter sequence and the initial cutting parameters is as follows: Record all sampling moments before the current moment as historical moments; Preset an error threshold parameter , and denote the normalized value of the absolute value of the difference between the initial cutting parameter of the th dimension and the cutting parameter of the th historical moment of the th dimension as the error factor of the cutting parameter of the th historical moment of the th dimension; denote the mean value of the error factors of all dimensions of the cutting parameters of the th historical moment as the error amplitude of the th historical moment; record the historical moments with an error amplitude greater than or equal to the error threshold parameter as target moments.

[0007] Preferably, the specific method for obtaining several target error historical periods according to the difference fluctuation between the cutting parameters and the initial cutting parameters at the target moments is as follows: Record the sequence composed of consecutive adjacent target moments as an error sequence; for any error sequence, if the normalized value of the variance of the error amplitudes of all target moments in the any error sequence is greater than or equal to the error threshold parameter , record the time period composed of all target moments in the any error sequence as the target error historical period.

[0008] Preferably, the specific method for obtaining the machining accuracy error value of the target error historical period according to the difference between the cutting parameters of different dimensions and the initial cutting parameters is as follows: In the th target error historical period, all target moments of the The ratio between the mean value of the error factors of the cutting parameters of a certain dimension and the mean value of the error amplitudes at all target moments is used as the weight factor of the cutting parameters of a certain dimension; Multiply the weight factor of the cutting parameters of a certain dimension in the nth target error history period by the mean value of the error factors of the cutting parameters of a certain dimension at all target moments in the mth target error history period, and denote it as the precision error factor of the cutting parameters of a certain dimension; Take the normalized value of the product of the number of all target moments in the nth target error history period and the sum of the precision error factors of all dimensions of cutting parameters as the processing precision error value of the nth target error history period. Preferably, the specific method for obtaining the cutting force change influence value in the target error history period according to the change range of the cutting parameters is as follows: In the

[0009] nth target error history period, the target moments with the same cutting depth and the same feed rate are denoted as comparison moments; The difference between the maximum value and the minimum value of the cutting forces at all target moments in the nth target error history period is denoted as the change range of the cutting force; The absolute value of the difference between the cutting force at the mth comparison moment in the nth target error history period and the mean value of the cutting forces at all comparison moments in the nth target error history period is denoted as the cutting force difference factor at the mth comparison moment; The product of the inverse normalized value of the sum of the cutting force difference factors at all comparison moments in the nth target error history period and the change range of the cutting force is used as the cutting force change influence value in the nth target error history period.

[0010] Preferably, the specific method for obtaining the tool wear characteristic value in the target error history period according to the difference change situation between the cutting parameters and the initial cutting parameters is as follows: Denote the sequence composed of the error amplitudes at all target moments in the nth target error history period as the nth target error sequence; Use the STF decomposition algorithm to decompose the nth target error sequence to obtain several components; Preset a differential threshold parameter , if the difference between the th error magnitude and the th error magnitude in the th component is greater than or equal to the differential threshold parameter , record the th error magnitude as the wear error magnitude; take the ratio of the quantity between all wear error magnitudes and all error magnitudes in the th component as the possibility of belonging to the wear component of the th component; Among all the components after the decomposition of the th target error sequence, record the component corresponding to the maximum value of the possibility of belonging to the wear component as the wear component of the th target error sequence; Take the difference between the maximum value and the minimum value of the wear error magnitudes in the wear component of the th target error sequence as the wear error increment; take the product of the wear error increment and the possibility of belonging to the wear component of the wear component of the th target error sequence as the tool wear characteristic value in the th target error history period.

[0011] Preferably, the specific method for obtaining the tool wear influence value in the target error history period according to the tool wear characteristic value and the cutting force change influence value is as follows: Take the sum of the tool wear characteristic value in the th target error history period and the maximum value of the wear error magnitudes in the wear component of the th target error sequence as the first sum value; take the normalized value of the product of the first sum value and the cutting force change influence value in the th target error history period as the tool wear influence value in the th target error history period.

[0012] Preferably, the specific method for obtaining the machining precision error influence value in the target error history period according to the tool wear influence value and the machining precision error value is as follows: Preset a constant parameter , take the sum value of and the tool wear influence value in the th target error history period as the second sum value; take the product of the second sum value and the machining precision error value in the th target error history period as the machining precision error influence value in the th target error history period.

[0013] Preferably, the specific method for obtaining the acquisition frequency adjustment factor at the current moment according to the change trend of the machining accuracy error influence value in different target error historical time periods is as follows: Take the difference between the machining accuracy error influence values between the th target error historical time period and the th target error historical time period as the error influence difference value of the th target error historical time period; if the error influence difference value is less than or equal to the differential threshold parameter , then the th target error historical time period is recorded as the vibration error historical time period; if the error influence difference value is greater than the differential threshold parameter , then the th target error historical time period is recorded as the wear error historical time period; Take the difference between the machining accuracy error influence value of the last target error historical time period and the machining accuracy error influence value of the first target error historical time period as the first difference; take the ratio between the number of wear error historical time periods and the number of vibration error historical time periods as the first ratio; take the normalized value of the product of the first difference and the first ratio as the acquisition frequency adjustment factor at the current moment.

[0014] The present invention also provides a machining data acquisition system applied to a numerically controlled machine tool, including a memory and a processor. The processor executes the computer program stored in the memory to implement the steps of the above-mentioned machining data acquisition method applied to a numerically controlled machine tool.

[0015] The beneficial effects of the technical solution of the present invention are as follows: According to the differences between cutting parameters in different dimensions and the initial cutting parameters, the machining accuracy error value is obtained; according to the change situation of the differences between cutting parameters and the initial cutting parameters, the tool wear characteristic value is obtained; according to the tool wear characteristic value and the cutting force change influence value, the tool wear influence value is obtained; according to the tool wear influence value and the machining accuracy error value, the machining accuracy error influence value is obtained; according to the change trend of the machining accuracy error influence value in different target error historical time periods, the acquisition frequency adjustment factor at the current moment is obtained; based on the acquisition frequency adjustment factor, the boring and milling machining data acquisition frequency at the current moment is obtained; by automatically adjusting the data acquisition frequency, the numerically controlled machine tool can adapt to the wear process, and adjust the acquisition strategy in real time to ensure that the accuracy remains in the best state; thereby improving the boring and milling machining accuracy of the numerically controlled machine tool. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 It is a flowchart of the steps of the machining data acquisition method applied to a numerically controlled machine tool according to the present invention; Figure 2 It is a flowchart of the characteristic relationship of the machining data acquisition method applied to a numerically controlled machine tool according to the present invention. Detailed implementation manners

[0018] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the drawings and preferred embodiments, details the specific implementation manners, structures, characteristics and effects of the machining data acquisition method and system applied to a numerically controlled machine tool according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. [[ID= / / 17]]

[0020] The following specifically describes the specific solutions of the machining data acquisition method and system applied to a numerically controlled machine tool provided by the present invention with reference to the drawings.

[0021] Please refer to Figure 1 , which shows a flowchart of the steps of the machining data acquisition method applied to a numerically controlled machine tool provided by an embodiment of the present invention. The method includes the following steps: Step S001: Obtain the cutting parameter sequence and the initial cutting parameters when the numerically controlled machine tool performs boring and milling machining.

[0022] It should be noted that in this embodiment, by real-time collecting the cutting parameters and the initial cutting parameters when the numerically controlled machine tool performs boring and milling machining, analyzing the differences between them to evaluate the influence of tool wear on machining accuracy, and obtaining the acquisition frequency adjustment factor at the current moment, the data acquisition frequency of the numerically controlled machine tool is adaptively adjusted.

[0023] In a specific implementation manner of the embodiment of the present invention, the specific method for obtaining the cutting parameter sequence and the initial cutting parameters when the numerically controlled machine tool performs boring and milling machining is as follows: Through the programming interface in the CNC machine tool system, directly read the set cutting force, cutting depth, and feed rate, and record them as the initial cutting parameters when the CNC machine tool performs boring and milling operations; each sampling moment is 1 second, and each time, use the cutting force sensor installed in the CNC machine tool to measure the magnitude of the cutting force to obtain the cutting force, use the displacement sensor to monitor the relative position of the tool to measure the feed rate, and use the optical sensor to measure the specific position of the tool to obtain the distance between the tool and the workpiece to calculate the cutting depth; collect for a total of 10 minutes; record the sequence composed of the cutting force, cutting depth, and feed rate data in all sampling moments as the cutting parameter sequence when the CNC machine tool performs boring and milling operations.

[0024] Among them, during the CNC boring and milling process, the initial cutting parameters are manually set and will be continuously updated over time; that is, the cutting parameters at each sampling moment will correspond to an initial cutting parameter.

[0025] So far, the cutting parameter sequence and the initial cutting parameters when the CNC machine tool performs boring and milling operations are obtained through the above method.

[0026] Step S002: By analyzing the difference between the cutting parameter sequence and the initial cutting parameters, obtain several target moments; according to the difference fluctuation between the cutting parameters and the initial cutting parameters at the target moments, obtain several target error history time periods; according to the difference between the cutting parameters of different dimensions and the initial cutting parameters, obtain the machining accuracy error values of the target error history time periods.

[0027] It should be noted that during CNC boring and milling, not only will the wear of the tool and the vibration generated during the machining process cause certain deviations between the real-time collected cutting parameters and the manually set initial cutting parameters, but also due to the errors existing in the system itself, there will be certain deviations between the real-time collected cutting parameters and the manually set initial cutting parameters; however, the deviation caused by the system error is relatively small and the difference is relatively stable, while the deviation caused by tool wear and vibration is relatively large and the difference is relatively unstable; therefore, it is necessary to exclude the influence of the system error and further analyze the errors caused by tool wear and vibration to obtain the machining accuracy error values within different target error history time periods.

[0028] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for obtaining several target moments by analyzing the difference between the cutting parameter sequence and the initial cutting parameters is as follows: Record all sampling moments before the current moment as historical moments; Preset an error threshold parameter , where this embodiment takes as an example for description, and this embodiment is not specifically limited, where It depends on the specific implementation situation; Take the initial cutting parameters of the nth dimension and the absolute value of the difference between the cutting parameters of the nth dimension at the mth historical moment, and normalize it, which is denoted as the error factor of the cutting parameters of the mth historical moment of the nth dimension; take the mean value of the error factors of the cutting parameters of all dimensions at the mth historical moment, which is denoted as the error amplitude at the mth historical moment; mark the historical moments with the error amplitude greater than or equal to the error threshold parameter as target moments; Preferably, in some implementation manners of the embodiments of the present invention, due to certain errors in the CNC machine tool itself system, there will also be a certain deviation between the real-time collected cutting parameters and the initially set cutting parameters by manual; however, the deviation caused by the system error is relatively small and the difference is relatively stable, while the deviation caused by tool wear and vibration is relatively large and the difference is relatively unstable. Therefore, according to the difference fluctuation situation between the cutting parameters at the target moment and the initial cutting parameters in the cutting parameter sequence, the specific method for obtaining several target error historical time periods is as follows: Mark the sequence composed of continuously adjacent target moments as an error sequence; for any error sequence, if the normalized value of the variance of the error amplitudes of all target moments in the

[0029] any error sequence is greater than or equal to the error threshold parameter , mark the time period composed of all target moments in the any error sequence as a target error historical time period; Among them, sort all target error historical time periods in descending order according to the time interval from the current moment. Preferably, in some implementation manners of the embodiments of the present invention, in the The ratio between the mean value of the error factors of the cutting parameters in a certain dimension and the mean value of the error magnitudes at all target moments is used as the weight factor of the cutting parameters in the th dimension; The product of the weight factor of the cutting parameters in the th dimension in the th target error history period and the mean value of the error factors of the cutting parameters in the th dimension at all target moments in the th target error history period is denoted as the precision error factor of the cutting parameters in the th dimension; The normalized value of the product of the number of all target moments in the th target error history period and the cumulative sum of the precision error factors of all dimensions of cutting parameters is used as the machining precision error value of the th target error history period; In the formula, represents the machining precision error value of the th target error history period; represents the number of all target moments in the th target error history period; represents the number of types of cutting parameters in all dimensions; represents the weight factor of the cutting parameters in the th dimension in the th target error history period; represents the mean value of the error factors of the cutting parameters in the th dimension at all target moments in the th target error history period; represents the linear normalization function.

[0030] Thus, the machining precision error values in each target error history period are obtained through the above method.

[0031] Step S003: Obtain the influence value of cutting force change in the target error history period according to the change range of cutting parameters; obtain the tool wear characteristic value in the target error history period according to the difference change situation between the cutting parameters and the initial cutting parameters; obtain the tool wear influence value in the target error history period according to the tool wear characteristic value and the influence value of cutting force change; obtain the machining precision error influence value in the target error history period according to the tool wear influence value and the machining precision error value.

[0032] It should be noted that the error caused by tool wear is irreversible, while the error caused by vibration will be corrected by the CNC machine tool itself over time. Therefore, the error caused by tool wear is used to further correct the machining accuracy error value to obtain the machining accuracy error influence value; in the cutting process of CNC boring and milling, the cutting force usually changes with the different stages of the machining process, and the cutting force is related to the cutting depth and feed rate; when the cutting depth and feed rate are the same during the machining process, the cutting force should be similar; but when the tool wears, the sharpness of the blade decreases and becomes more blunt, which increases the contact area between the tool and the workpiece material during the cutting process, requiring greater force to overcome the deformation resistance of the material; and after the tool wears, the surface roughness increases, and the friction coefficient between the tool and the workpiece material also increases, which leads to greater friction during the cutting process, thereby increasing the cutting force, that is, when the feed rate and cutting depth are the same, the cutting force becomes greater and greater, indicating that the tool is more likely to wear.

[0033] Preferably, in some implementations of the embodiments of the present invention, a specific method for obtaining the cutting force change impact value in each target error historical time period according to the change amplitude of the cutting force in the target error historical time period is: In the In the target error history time period, the target moment with the same cutting depth and feed amount is recorded as the comparison moment; The first The difference between the maximum and minimum cutting forces at all target moments in the target error history period is recorded as the variation of the cutting force; The target error in the historical period The cutting force at the first comparison moment is The absolute value of the difference between the mean values of the cutting forces at all comparison moments in the target error history period is recorded as The cutting force difference factor at the first comparison moment; The product of the inverse proportional normalized value of the cumulative sum of the cutting force difference factors of all comparison moments in the target error history period and the change amplitude of the cutting force is taken as the first The impact value of cutting force changes in the target error history period; The specific formula is: Where, Indicates the The impact value of cutting force changes in the target error history period; Indicates the The maximum value of the cutting force at all target moments in the target error history period; Indicates the The minimum value of the cutting force at all target moments in a target error history period; Denote the number of all comparison moments in the th target error history period; Denote the cutting force at the th comparison moment in the th target error history period; Denote the exponential function with the natural constant as the base. In the embodiment, model is used to present the inverse proportional relationship and normalization processing, is the input of the model. The implementer can select the inverse proportional function and normalization function according to the actual situation; Among them, if the change range of the cutting force in the th target error history period is larger, and the similarity of the cutting force at the comparison moment is lower, it indicates that the error in the th target error history period is caused by tool wear.

[0034] Preferably, in some implementation manners of the embodiment of the present invention, since both tool wear and vibration generated during the machining process will cause precision errors in boring and milling machining, and the error caused by tool wear is irreversible, it is necessary to identify the error caused by tool wear; according to the difference change situation between the cutting parameters and the initial cutting parameters in the target error history period, the specific method for obtaining the tool wear characteristic value in each target error history period is as follows: Denote the sequence composed of the error amplitudes at all target moments in the th target error history period as the th target error sequence; use the STF decomposition algorithm to decompose the th target error sequence to obtain several components; It should be noted that for any component, since the error caused by wear is irreversible and gradually increases, it means that the difference between adjacent error amplitudes in the component is gradually increasing, that is, the first-order difference value has no negative number, then the greater the possibility that the component belongs to the wear component; the STF decomposition algorithm is a prior art, and no more details are given here in this embodiment.

[0035] Preset a difference threshold parameter , where in this embodiment, is taken as an example for description. This embodiment is not specifically limited, and is determined according to the specific implementation situation; If the th error amplitude in the th component and the The difference between two error margins is greater than or equal to the differential threshold parameter , and the th error margin is recorded as the wear error margin; the ratio of the quantity between all wear error margins and all error margins in the th component is taken as the possibility of belonging to the wear component of the th component; Among all the components after the decomposition of the th target error sequence, the component corresponding to the maximum value of the possibility of belonging to the wear component is recorded as the wear component of the th target error sequence; Wherein, if the difference between the maximum value and the minimum value of the wear error margins in the wear component of the th target error sequence is larger, it indicates that the error increment caused by tool wear is larger.

[0036] The difference between the maximum value and the minimum value of the wear error margins in the wear component of the th target error sequence is recorded as the wear error increment; the product of the wear error increment and the possibility of belonging to the wear component of the th target error sequence is taken as the tool wear characteristic value in the th target error history period; The specific formula is: In the formula, represents the tool wear characteristic value in the th target error history period; represents the product of the wear error increment and the possibility of belonging to the wear component of the th target error sequence; represents the maximum value of the wear error margins in the wear component of the th target error sequence; represents the minimum value of the wear error margins in the wear component of the th target error sequence.

[0037] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for obtaining the tool wear influence value in each target error history period according to the tool wear characteristic value and the cutting force change influence value is: The sum of the tool wear characteristic value in the th target error history period and the maximum value of the wear error margins in the wear component of the th target error sequence is recorded as the first sum value; the first sum value and the The normalized value of the product of the cutting force change influence values in a target error history period is used as the tool wear influence value in the target error history period; The specific formula is: In the formula, represents the tool wear influence value in the target error history period; represents the cutting force change influence value in the target error history period; represents the tool wear characteristic value in the target error history period; represents the maximum value of the wear error amplitude in the wear component of the target error sequence; Linear normalization value.

[0038] Preferably, in some implementation manners of the embodiments of the present invention, since the error caused by tool wear is irreversible, the influence on the boring and milling machining accuracy error is greater than the influence caused by vibration. According to the tool wear influence value and the machining accuracy error value, the specific method for obtaining the machining accuracy error influence value of each target error history period is as follows: Preset a constant parameter , where in this embodiment, is taken as an example for description, and this embodiment does not make specific limitations, where is determined according to the specific implementation situation; The sum value between and the tool wear influence value in the target error history period is denoted as the second sum value; the product of the second sum value and the machining accuracy error value of the target error history period is used as the machining accuracy error influence value of the target error history period; The specific formula is: In the formula, represents the machining accuracy error influence value of the target error history period; represents the tool wear influence value in the target error history period; represents the machining accuracy error value of the target error history period; represents the preset constant parameter.

[0039] At this point, the machining accuracy error impact value of each target error historical time period is obtained through the above method.

[0040] Step S004: obtaining an acquisition frequency adjustment factor at the current moment according to a change trend of the machining accuracy error influence value in different target error historical time periods; and obtaining a boring and milling machining data acquisition frequency at the current moment based on the acquisition frequency adjustment factor.

[0041] It should be noted that both tool wear and vibration generated during machining will lead to deterioration in the accuracy of boring and milling machining, and the error caused by vibration will be corrected by the CNC machine tool itself over time, but the error caused by tool wear will gradually increase over time; therefore, it is necessary to adjust the frequency of collecting cutting parameter data in time according to the tool wear situation for the self-adjustment of the CNC machine tool; it is necessary to analyze the changing trend of the machining accuracy error influence value in different target error historical time periods to obtain the boring and milling machining data collection frequency adjustment factor at the current moment.

[0042] Preferably, in some implementations of the embodiments of the present invention, in the target error historical time period, if the machining accuracy error influence value first increases and then decreases to the initial state, it means that the larger proportion of the boring and milling machining accuracy error influence is caused by the vibration generated during the machining process; on the contrary, if the machining accuracy error influence value continues to rise, so that the final machining accuracy error influence value is too large compared with the initial state, it means that the larger proportion of the boring and milling machining accuracy error influence is caused by tool wear, and in this case, more frequent collection of cutting parameters is required for self-adjustment of the CNC machine tool; according to the changing trend of the machining accuracy error influence value in different target error historical time periods, the specific method for obtaining the collection frequency adjustment factor at the current moment is: The first target error historical period and the The difference in the machining accuracy error impact value between the target error historical time periods is recorded as The error impact difference value of the target error historical time period; if the error impact difference value is less than or equal to the difference threshold parameter , then target error history time period, recorded as the vibration error history time period; if the error impact difference value is greater than the difference threshold parameter , then The target error history time period is recorded as , and the wear error history time period is recorded as ; The difference between the machining precision error influence value of the last target error history time period and the machining precision error influence value of the first target error history time period is denoted as the first difference; the ratio between the number of wear error history time periods and the number of vibration error history time periods is denoted as the first ratio; the normalized value of the product of the first difference and the first ratio is denoted as the acquisition frequency adjustment factor at the current moment. The specific formula is: In the formula, represents the acquisition frequency adjustment factor at the current moment; represents the machining precision error influence value of the last target error history time period; represents the machining precision error influence value of the first target error history time period; represents the number of wear error history time periods; represents the number of vibration error history time periods; linear normalization value.

[0043] Preferably, in some implementation manners of the embodiments of the present invention, if the acquisition frequency adjustment factor at the current moment is larger, it indicates that the machining precision of boring and milling is deteriorated mainly due to tool wear, then it is necessary to increase the acquisition frequency of the boring and milling machining data of the numerical control machine tool for the self-adjustment of the numerical control machine tool; the specific method for obtaining the acquisition frequency of the boring and milling machining data at the current moment based on the acquisition frequency adjustment factor is: Take the sum value between and the initial acquisition frequency of the boring and milling machining data as the third sum value; take the product of the third sum value and the acquisition frequency adjustment factor at the current moment as the acquisition frequency of the boring and milling machining data at the current moment. The specific formula is: In the formula, represents the acquisition frequency of the boring and milling machining data at the current moment; represents the initial acquisition frequency of the boring and milling machining data; represents the acquisition frequency adjustment factor of the boring and milling machining data at the current moment; represents a preset constant parameter.

[0044] It should be noted that taking Take it as the acquisition frequency for the next ten minutes; then collect the cutting parameters for the next 10 minutes, and obtain the acquisition frequency of boring and milling machining data at future moments through the above method. Then, based on the acquisition frequency of boring and milling machining data at future moments, obtain the acquisition frequency for the next future 10 minutes, and so on, continuously iterating; through higher-frequency data acquisition, the changing trend of boring and milling machining errors can be captured more quickly; when the influence of wear gradually increases, the CNC machine tool needs to make rapid adjustments; through more frequent data acquisition, the current machining accuracy can be fed back more timely, and the system can adjust the machining parameters more quickly to avoid accuracy deviations caused by wear.

[0045] Please refer to Figure 2 , which shows the characteristic relationship flowchart of the machining data acquisition method applied to a CNC machine tool; Through the above steps, the machining data acquisition method applied to a CNC machine tool is completed.

[0046] Another embodiment of the present invention provides a machining data acquisition system applied to a CNC machine tool. The system includes a memory and a processor. When the processor executes the computer program stored in the memory, it executes the above method steps S001 to step S004.

[0047] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A processing data acquisition method applied to a CNC machine tool, characterized in that: The method includes the following steps: Obtain the cutting parameter sequence and initial cutting parameters during boring and milling operations of a numerically controlled machine tool; By analyzing the differences between the cutting parameter sequence and the initial cutting parameters, obtain several target times; according to the difference fluctuation between the cutting parameters and the initial cutting parameters at the target times, obtain several target error history time periods; according to the differences between the cutting parameters and the initial cutting parameters in different dimensions, obtain the machining accuracy error values of the target error history time periods; According to the change range of the cutting parameters, obtain the cutting force change influence value in the target error history time period; according to the difference change between the cutting parameters and the initial cutting parameters, obtain the tool wear characteristic value in the target error history time period; according to the tool wear characteristic value and the cutting force change influence value, obtain the tool wear influence value in the target error history time period; according to the tool wear influence value and the machining accuracy error value, obtain the machining accuracy error influence value of the target error history time period; According to the change trend of the machining accuracy error influence values of different target error history time periods, obtain the acquisition frequency adjustment factor at the current time; based on the acquisition frequency adjustment factor, obtain the boring and milling machining data acquisition frequency at the current time.

2. The machining data acquisition method applied to a numerical control machine tool according to claim 1, wherein, The specific method for obtaining several target times by analyzing the differences between the cutting parameter sequence and the initial cutting parameters is as follows: All sampling times before the current time are recorded as historical times; Preset an error threshold parameter , and normalize the absolute value of the difference between the initial cutting parameters of the th dimension and the cutting parameters of the th historical moment of the th dimension, which is denoted as the error factor of the cutting parameters of the th historical moment of the th dimension; take the mean value of the error factors of the cutting parameters of all dimensions at the th historical moment, which is denoted as the error amplitude at the th historical moment; mark the historical moment when the error amplitude is greater than or equal to the error threshold parameter as the target moment.

3. The machining data acquisition method applied to a numerical control machine tool according to claim 2, characterized in that, The specific method for obtaining several target error history time periods according to the difference fluctuation between the cutting parameters and the initial cutting parameters at the target times is as follows: All sequences composed of continuously adjacent target times are denoted as error sequences; for any one of the error sequences, if the normalized value of the variance of the error amplitudes of all target times in the any one of the error sequences is greater than or equal to the error threshold parameter , the time period composed of all target times in the any one of the error sequences is denoted as the target error history time period.

4. The machining data acquisition method applied to a numerically controlled machine tool according to claim 2, wherein The specific method for obtaining the machining accuracy error values of the target error history time periods according to the differences between the cutting parameters and the initial cutting parameters in different dimensions is as follows: In the th target error history time period, the ratio between the mean value of the error factors of the cutting parameters of the th dimension at all target times and the mean value of the error amplitudes at all target times is used as the weight factor of the cutting parameters of the th dimension; Multiply the weight factor of the cutting parameters of the th dimension in the th target error history time period by the mean of the error factors of the cutting parameters of the th dimension at all target times in the th target error history time period, and denote it as the precision error factor of the cutting parameters of the th dimension; Normalize the product of the number of all target moments in the th target error history time period and the cumulative sum of the accuracy error factors of all kinds of dimensional cutting parameters, and use it as the th machining accuracy error value of the target error history time period.

5. The machining data acquisition method applied to a numerically controlled machine tool according to claim 1, wherein, The specific method for obtaining the cutting force change influence value in the target error history time period according to the change range of the cutting parameters is as follows: In the th target error history time period, the target moments with the same cutting depth and the same feed rate are recorded as comparison moments; The difference between the maximum and minimum cutting forces at all target moments in the th target error history time period is denoted as the change range of the cutting force; the absolute value of the difference between the cutting force at the th comparison moment in the th target error history time period and the average value of the cutting forces at all comparison moments in the th target error history time period is denoted as the cutting force difference factor at the th comparison moment; the product of the inverse normalization value of the sum of the cutting force difference factors at all comparison moments in the th target error history time period and the change range of the cutting force is taken as the cutting force change influence value in the th target error history time period.

6. The machining data acquisition method applied to a numerically controlled machine tool according to claim 2, characterized in that, The specific method for obtaining the tool wear characteristic value in the target error history time period according to the difference change between the cutting parameters and the initial cutting parameters is as follows: The first The sequence of error amplitudes of all target moments in the target error history period is recorded as target error sequence; using the STF decomposition algorithm to Decompose the target error sequence to obtain several components; Preset a differential threshold parameter , if the difference between the th error amplitude and the th error amplitude in the th component is greater than or equal to the differential threshold parameter , record the th error amplitude as the wear error amplitude; take the ratio of the quantity between all wear error amplitudes and all error amplitudes in the th component as the possibility of the th component belonging to the wear component; Among all the components after the decomposition of the th target error sequence, the component corresponding to the maximum value of the possibility belonging to the wear component is denoted as the wear component of the th target error sequence; Denote the difference between the maximum value and the minimum value of the wear error amplitude in the wear component of the th target error sequence as the wear error increment; Multiply the wear error increment by the probability of belonging to the wear component of the wear component of the th target error sequence, and use the product as the th tool wear characteristic value in the target error historical time period.

7. The machining data acquisition method applied to a numerically controlled machine tool according to claim 6, wherein, The specific method for obtaining the tool wear influence value in the target error history time period according to the tool wear characteristic value and the cutting force change influence value is as follows: The first The tool wear characteristic value in the target error history period is The sum of the maximum values of the wear error amplitudes in the wear components of the target error sequences is recorded as the first sum value; the first sum value is added to the The normalized value of the product of the cutting force change influence values in the target error history time period is used as the first The tool wear impact value in the target error history period.

8. The machining data acquisition method applied to a numerically controlled machine tool according to claim 1, characterized in that, The specific method for obtaining the machining accuracy error influence value of the target error history time period according to the tool wear influence value and the machining accuracy error value is as follows: Preset a constant parameter , add to the sum of the tool wear influence values in the th target error history time period, and denote it as the second sum value; Multiply the second sum value by the machining accuracy error value of the th target error history time period, and use the result as the machining accuracy error influence value of the th target error history time period.

9. The machining data acquisition method applied to a numerically controlled machine tool according to claim 6, characterized in that The specific method for obtaining the acquisition frequency adjustment factor at the current time according to the change trend of the machining accuracy error influence values of different target error history time periods is as follows: Denote the difference between the machining precision error influence values between the th target error history time period and the th target error history time period as the error influence difference value of the th target error history time period; if the error influence difference value is less than or equal to the differential threshold parameter , then the th target error history time period is denoted as the vibration error history time period; if the error influence difference value is greater than the differential threshold parameter , then the th target error history time period is denoted as the wear error history time period; Record the difference between the machining accuracy error influence value of the last target error history time period and the machining accuracy error influence value of the first target error history time period as the first difference; record the ratio between the number of wear error history time periods and the number of vibration error history time periods as the first ratio; [[ID= 10. A machining data acquisition system applied to a numerically controlled machine tool, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, ​

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