Bending Machine Data Optimization Method and Storage Medium

Abstract

The present invention discloses a method for optimizing bending machine data and a storage medium, which are applied to the field of processing technology, and include: obtaining a historical data set and target bending processing data, where the historical data set includes multiple groups of historical bending processing data, and each group of historical bending processing data at least includes a control angle and a processing diameter, and the target bending processing data at least includes a target processing diameter; obtaining a first data set according to the historical data set; performing curve fitting on the first data set to obtain a standard curve fitting function; calculating target output data corresponding to the target bending processing data according to the first data set and the standard curve fitting function, where the target output data at least includes a target control angle. The method for optimizing bending machine data can obtain satisfactory bending machine processing data by calculating the target output data, can reduce the operation amount in the debugging stage of the bending machine, reduce the difficulty of operating the bending machine, lower the requirements for workers, and reduce the number of consumables in the debugging stage.

Description

Technical Field

[0001] The present invention relates to the field of processing, and particularly relates to a method for optimizing bending machine data and a storage medium. Background Art

[0002] A bending machine is a numerical control machine that can bend thin plates into various shapes, and it can be used for bending arcs, ellipses, and some complex figures of thin plates. Since ellipses and other complex figures can be decomposed into combinations of bending arcs, high-precision bending arcs are one of the main performance indicators for testing numerical control bending machines. However, due to inconsistencies in material batches, material properties, and thicknesses, and the bending machine being very sensitive to the control angle during arc bending, and the material having a springback phenomenon during bending, the theoretical results are seriously inconsistent with the actual results when bending arcs.

[0003] Currently, two methods are used in related technologies to adjust the control angle. One method is to estimate the processing control angle using an empirical formula and then manually adjust the control angle. The other method is to first process several groups of standard data, and then manually adjust the control angle using the dichotomy method according to the actual arc processing requirements for testing until the requirements are met. A large amount of consumables will be wasted during the adjustment process. However, both of these methods have disadvantages such as high requirements for workers and a large amount of consumables during the debugging stage. Summary of the Invention

[0004] The following is an overview of the subject matter described in detail in this article. This overview is not intended to limit the scope of protection of the claims.

[0005] Embodiments of the present application provide a method for optimizing bending machine data and a storage medium, which can reduce the difficulty of operating the bending machine and reduce the amount of consumables during the debugging stage.

[0006] In a first aspect, the present invention provides a method for optimizing bending machine data, including:

[0007] Obtain a historical data set and target bending processing data, where the historical data set includes multiple groups of historical bending processing data, and each group of historical bending processing data includes at least a control angle and a processing diameter, and the target bending processing data includes at least a target processing diameter;

[0008] Obtain a first data set according to the historical data set;

[0009] Perform curve fitting on the first data set to obtain a standard curve fitting function;

[0010] Calculate target output data corresponding to the target bending processing data according to the first data set and the standard curve fitting function, where the target output data includes at least a target control angle.

[0011] According to the above embodiments of the first aspect of the present invention, it has at least the following beneficial effects: taking the historical data set as the first data set, performing curve fitting on the first data set to obtain a standard curve fitting function, and calculating the target output data corresponding to the target bending processing data according to the first data set and the standard curve fitting function. The bending machine data optimization method in the embodiments of the present application can obtain satisfactory bending machine processing data through the calculation of the target output data, reduce the operation amount in the debugging stage of the bending machine, reduce the difficulty of operating the bending machine, reduce the requirements for workers, and reduce the number of consumables in the debugging stage.

[0012] According to some embodiments of the first aspect of the present invention, the historical bending processing data further includes material and thickness. The obtaining of the first data set according to the historical data set includes:

[0013] Filtering and screening the historical data set to obtain a second data set, wherein all the data in the second data set have the same material and thickness, and the historical bending processing data further includes a forgetting factor;

[0014] Calculating the sum of the products of the forgetting factors corresponding to all the data in the second data set and the processing diameter;

[0015] Calculating the sum of the forgetting factors corresponding to all the data in the second data set;

[0016] Dividing the sum of the products of the forgetting factors and the processing diameter by the sum of the forgetting factors to obtain an average diameter, updating the processing diameter of all the data in the second data set to the average diameter, and using the updated data as the first data set.

[0017] According to some embodiments of the first aspect of the present invention, the obtaining of the first data set according to the historical data set includes:

[0018] Obtaining the first control data and the second control data in the historical data set;

[0019] When the control angle and the processing diameter of the first control data are both greater than those of the second control data, and the number of the first control data and the data before it is less than that of the second control data, deleting the first control data;

[0020] Or,

[0021] When the control angle and the processing diameter of the first control data are both greater than those of the second control data, and the number of the first control data and the data before it is greater than that of the second control data, deleting the second control data;

[0022] Repeatedly obtain the data in the historical dataset and process the corresponding data until no new data can be detected in the historical dataset, and use the processed data as the first dataset.

[0023] According to some embodiments of the first aspect of the present invention, the historical bending process data further includes a main thickness flag. Before fitting the first dataset to obtain a standard curve fitting function, it includes:

[0024] Classify the first dataset according to the main thickness flag to obtain a third dataset, and update the third dataset to the first dataset.

[0025] According to some embodiments of the first aspect of the present invention, the classifying the first dataset according to the main thickness flag to obtain a third dataset includes:

[0026] Obtain the third control data in the first dataset;

[0027] When the main thickness flag of the third control data is 1, retain the third control data;

[0028] Or,

[0029] When the main thickness flag of the third control data is 0, delete the third control data;

[0030] Repeatedly obtain the data in the first dataset and process the corresponding data according to the main thickness flag until no new data can be detected in the first dataset, and use the processed data as the third dataset.

[0031] According to some embodiments of the first aspect of the present invention, the historical bending process data further includes material and thickness. The fitting the first dataset to obtain a standard curve fitting function includes:

[0032] Fit the first dataset by the Akima smoothing interpolation method to obtain a standard curve fitting function, where the independent variable of the standard curve fitting function is the processing diameter, and the dependent variable of the standard curve fitting function is the control angle.

[0033] According to some embodiments of the first aspect of the present invention, the calculating the target output data corresponding to the target bending process data according to the first dataset and the standard curve fitting function includes:

[0034] Search for the data in the first dataset that meets the fitting conditions according to the target bending processing data, as the fourth dataset, where the fitting conditions include the same material, the thickness difference being less than or equal to the thickness accuracy, and the processing diameter difference being less than or equal to the diameter accuracy;

[0035] Compare the processing diameter differences corresponding to the data in the fourth dataset to obtain the data with the smallest processing diameter difference;

[0036] Take the data with the smallest processing diameter difference as the target output data corresponding to the target bending processing data.

[0037] According to some embodiments of the first aspect of the present invention, it further includes:

[0038] When the data that meets the fitting conditions cannot be found in the first dataset, take the processing diameter of the target bending processing data as the first diameter, and obtain the second angle and the second diameter, where the value of the second angle is 0, and the control angle value corresponding to the second diameter obtained according to the standard curve fitting function is 0;

[0039] Calculate the control angle corresponding to the first diameter according to the standard curve fitting function, as the first angle;

[0040] Calculate the difference between the second angle and the control angle corresponding to the second diameter obtained according to the standard curve fitting function to obtain the control difference;

[0041] Calculate the sum of the first angle and the control difference as the third angle, and obtain the processing diameter corresponding to the third angle according to the actual operation, as the third diameter;

[0042] When the difference between the third diameter and the processing diameter of the target bending processing data is less than or equal to the diameter accuracy, take the third angle, the third diameter, and the material and thickness as the target output data corresponding to the target bending processing data;

[0043] Or,

[0044] When the difference between the third diameter and the processing diameter of the target bending processing data is greater than the diameter accuracy, take the third angle as the second angle, take the third diameter as the second diameter, and repeat the calculation of the third angle and the third diameter according to the standard curve fitting function and the actual operation until the difference between the third diameter and the processing diameter of the target bending processing data is less than or equal to the diameter accuracy, and take the third angle, the third diameter, and the material and thickness obtained in the last calculation as the target output data corresponding to the target bending processing data.

[0045] According to some embodiments of the first aspect of the present invention, the historical bending process data further includes material, thickness, and forgetting factor, and the bending machine data optimization method further includes:

[0046] According to the material and thickness of the target output data, set the main thickness flag corresponding to the target output data to 1 or 0;

[0047] Set the forgetting factor corresponding to the target output data to 1;

[0048] Search for the data in the historical data set that meets the update conditions according to the target output data, and use it as the fifth data set, where the update conditions include the same material, the thickness difference is less than the thickness accuracy, and the machining diameter difference is less than the diameter accuracy;

[0049] Obtain the fourth control data in the fifth data set, and multiply the forgetting factor corresponding to the fourth control data by the forgetting factor decay coefficient to obtain the updated forgetting factor;

[0050] When the updated forgetting factor is less than or equal to a small amount, delete the fourth control data;

[0051] Or,

[0052] When the updated forgetting factor is greater than the small amount, retain the fourth control data with the updated forgetting factor in the historical data set.

[0053] In a second aspect, the present invention provides a computer storage medium, including computer-executable instructions stored thereon, and the computer-executable instructions are used to execute the bending machine data optimization method as described in the first aspect.

[0054] Since the computer storage medium in the second aspect can execute the bending machine data optimization method of any item in the first aspect, it has all the beneficial effects of the first aspect of the present invention.

[0055] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0057] Figure 1 It is a schematic structural diagram of a bending machine device provided by an embodiment of the present application;

[0058] Figure 2 It is the main step diagram of the bending machine data optimization method provided by an embodiment of the present application;

[0059] Figure 3 It is a partial step diagram of data anti-interference of the bending machine data optimization method provided by an embodiment of the present application;

[0060] Figure 4 It is a partial step diagram of data anti-interference of the bending machine data optimization method provided by an embodiment of the present application;

[0061] Figure 5 It is another main step diagram of the bending machine data optimization method provided by an embodiment of the present application;

[0062] Figure 6 It is the step diagram of data classification of the bending machine data optimization method provided by an embodiment of the present application;

[0063] Figure 7 It is the step diagram of curve fitting of the bending machine data optimization method provided by an embodiment of the present application;

[0064] Figure 8 It is a partial step diagram of data processing of the bending machine data optimization method provided by an embodiment of the present application;

[0065] Figure 9 It is a partial step diagram of data processing of the bending machine data optimization method provided by an embodiment of the present application;

[0066] Figure 10 It is the step diagram of forgetting factor update of the bending machine data optimization method provided by an embodiment of the present application. Detailed implementation manner

[0067] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system architectures and technologies are presented to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the embodiments of the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the embodiments of the present application.

[0068] It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from that in the flowchart. Terms such as "first" and "second" in the specification, claims, and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0069] It should also be understood that references to "one embodiment" or "some embodiments" etc. described in the specification of the embodiments of the present application mean that specific features, structures, or characteristics described in connection with that embodiment are included in one or more embodiments of the embodiments of the present application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0070] A numerically controlled bending machine is a numerically controlled machine that can bend thin plates into various geometric cross-sectional shapes. The numerically controlled bending machine uses the equipped molds to bend metal sheets in a cold state into workpieces of various geometric cross-sectional shapes. Generally, a numerically controlled bending machine uses a special numerical control system for numerically controlled bending machines, which can automatically realize the control of the running depth of the slider, the adjustment of the left and right inclination of the slider, the front and back adjustment, left and right adjustment, pressure tonnage adjustment, and the approach working speed adjustment of the slider by the numerical control system, etc. It can make the numerically controlled bending machine conveniently realize actions such as the slider moving down, jogging, continuous, holding pressure, returning, and stopping midway, and complete multi-bend bending of the same angle or different angles with one loading. Therefore, the numerically controlled bending machine can be used for bending arcs, ellipses, and some complex graphics of thin plates. Since ellipses and other complex graphics can be decomposed into combinations of bending arcs, high-precision bending arcs are one of the main performances for testing numerically controlled bending machines. However, due to the inconsistency of materials in different batches in terms of material quality and thickness, and the numerically controlled bending machine being very sensitive to the control angle when bending arcs, for example, a 0.5-degree difference can cause a difference of dozens of millimeters in the arc diameter. At the same time, there is a springback phenomenon when the material is bent, resulting in a serious mismatch between the theoretical result and the actual situation when bending arcs. In response to this, there are currently two mainstream methods: 1. Estimate the processing control angle using an empirical formula and then adjust it manually; 2. Process several groups of standard data, and then manually adjust the control angle using the dichotomy method according to the actual processing arc requirements for testing until the requirements are met. A large amount of consumables will be wasted during the adjustment process. Both of these methods have disadvantages such as high requirements for workers and more consumables during the debugging stage.

[0071] Based on this, the embodiments of the present application provide a method, device, and storage medium for optimizing bending machine data. The embodiments of the present application perform curve fitting on historical processing data and obtain the control angle required for processing arcs by relying on the standard curve fitting function obtained from the curve fitting.

[0072] The following further elaborates on the embodiments of the present application in conjunction with the accompanying drawings.

[0073] As Figure 1 shownFigure 1 This is a schematic structural diagram of a bending machine device provided by an embodiment of the present application. In Figure 1 the example of, the bending machine device includes a data collection module 100, a data anti-interference module 200, a curve fitting module 300, and a data processing module 400.

[0074] Among them, the data collection module 100 is communicatively connected to the data anti-interference module 200 and the data processing module 400, and the data collection module 100 is used to obtain a historical data set and target bending processing data.

[0075] The data anti-interference module 200 is respectively communicatively connected to the data collection module 100, the curve fitting module 300, and the data processing module 400, and the data anti-interference module 200 is used to obtain a first data set according to the historical data set.

[0076] The curve fitting module 300 is respectively connected to the data anti-interference module 200 and the data processing module 400, and the curve fitting module 300 is used to fit the first data set to obtain a standard curve fitting function.

[0077] The data processing module 400 is respectively communicatively connected to the curve fitting module 300 and the data collection module 100, and the data processing module 400 is used to calculate target output data corresponding to the target bending processing data according to the first data set and the standard curve fitting function.

[0078] In this embodiment, the data collection module 100 sends the obtained historical data set to the data anti-interference module 200, and sends the target bending processing data to the data processing module 400; the data anti-interference module 200 obtains a first data set according to the historical data set, and sends the first data set to the curve fitting module 300 and the data processing module 400; the curve fitting module 300 fits the received first data set to obtain a standard curve fitting function, and sends the standard curve fitting function to the data processing module 400; the data processing module 400 calculates target output data corresponding to the target bending processing data according to the received first data set and the standard curve fitting function.

[0079] The device and application scenario described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation to the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that with the evolution of the system architecture and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0080] Those skilled in the art can understand that Figure 1The device structure shown does not constitute a limitation on the embodiments of the present application, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0081] In Figure 1 the device structure shown, each module can separately call the bending machine data optimization program stored therein to execute the bending machine data optimization method.

[0082] Based on the above device, various embodiments of the bending machine data optimization method of the embodiments of the present application are proposed.

[0083] As Figure 2 described, Figure 2 is the main step diagram of the bending machine data optimization method provided by an embodiment of the present application. The bending machine data optimization method includes but is not limited to the following steps:

[0084] Step S100: Obtain a historical data set and target bending processing data. Among them, the historical data set includes multiple groups of historical bending processing data, and each group of historical bending processing data includes at least a control angle and a processing diameter. The target bending processing data includes at least a target processing diameter.

[0085] Step S200: Obtain a first data set according to the historical data set.

[0086] Step S300: Perform curve fitting on the first data set to obtain a standard curve fitting function.

[0087] Step S400: Calculate the target output data corresponding to the target bending processing data according to the first data set and the standard curve fitting function. The target output data includes at least a target control angle.

[0088] It should be understood that taking the historical data set as the first data set, performing curve fitting on the first data set to obtain a standard curve fitting function, and calculating the target output data corresponding to the target bending processing data according to the first data set and the standard curve fitting function. The bending machine data optimization method of the embodiments of the present application can obtain satisfactory bending machine processing data by calculating the target output data, can reduce the operation amount in the bending machine debugging stage, reduce the difficulty of bending machine operation, reduce the requirements for workers, and reduce the number of consumables in the debugging stage.

[0089] In one embodiment, referring to Figure 3 , step S200 includes but is not limited to the following steps:

[0090] Step S210: Filter and screen the historical data set to obtain a second data set. Among them, the materials and thicknesses of all data in the second data set are equal, and the historical bending processing data further includes a forgetting factor.

[0091] It should be understood that assuming the material is C, the thickness is H, the control angle is u, the machining diameter is D, the forgetting factor is A, and the main thickness mark is F, then the second data set can be expressed as (C, H, u, D, A, F), and the data in the second data set is (C i , H i , u i , D i , A i , F i ), where i is the position of the data in the second data set, and for the second data set, the materials and thicknesses of all data are equal.

[0092] Step S220: Calculate the sum of the products of the forgetting factors and the machining diameters corresponding to all data in the second data set.

[0093] It should be understood that for the second data set (C, H, u, D, A, F), the sum of the products of the forgetting factors and the machining diameters corresponding to all data is expressed as where i is the position of the data in the second data set, and N is the number of data in the second data set.

[0094] Step S230: Calculate the sum of the forgetting factors corresponding to all data in the second data set.

[0095] It should be understood that for the second data set (C, H, u, D, A, F), the sum of the forgetting factors corresponding to all data is expressed as where i is the position of the data in the second data set, and N is the number of data in the second data set.

[0096] Step S240: Divide the sum of the products of the forgetting factors and the machining diameters by the sum of the forgetting factors to obtain the average diameter, update the machining diameters of all data in the second data set to the average diameter, and use the updated data as the first data set.

[0097] It should be understood that dividing the sum of the products of the forgetting factors and the machining diameters by the sum of the forgetting factors to obtain the average diameter, then the average diameter is expressed as:

[0098]

[0099] where represents the average diameter. Update the machining diameters of all data in the second data set to the average diameter, then the data in the second data set can be expressed as

[0100] It should be understood that the average diameter is obtained by dividing the sum of the product of the forgetting factor and the processing diameter by the sum of the forgetting factors. Obtaining the average diameter can prevent errors caused by inaccurate actual measurement of the processing diameter, making the processing data obtained by the bending machine data optimization method of the present application embodiment more accurate.

[0101] In addition, in one embodiment, referring to Figure 4 , step S200 further includes the following steps:

[0102] Step S250, obtaining the first control data and the second control data in the historical dataset.

[0103] Step S260, when the control angle and the processing diameter of the first control data are both greater than those of the second control data, and the number of the first control data and the data before it is less than that of the second control data, deleting the first control data.

[0104] Or,

[0105] Step S270, when the control angle and the processing diameter of the first control data are both greater than those of the second control data, and the number of the first control data and the data before it is greater than that of the second control data, deleting the second control data.

[0106] Step S280, repeatedly obtaining the data in the historical dataset and processing the corresponding data until no new data can be detected in the historical dataset, and using the processed data as the first dataset.

[0107] It should be understood that assuming the first control data is (C i , H i , u i , D i , F i ), and the second control data is (C j , H j , u j , D j , F j ), when u i > u j , D i > D j , and the number of the first control data and the data before it is less than that of the second control data, delete the first control data; when u i > u j , D i > D j , and the number of the first control data and the data before it is greater than that of the second control data, delete the second control data, and in other cases, retain the first control data and the second control data.

[0108] In one embodiment, referring to Figure 5 ,Figure 5 This is another main step diagram of the bending machine data optimization method provided by an embodiment of the present application. Step S500 is set between step S200 and step S300, and step S500 includes but is not limited to the following steps:

[0109] Step S500: Classify the first data set according to the main thickness flag to obtain a third data set, and update the third data set as the first data set.

[0110] It should be understood that for a bending machine that needs to process various different materials and / or materials with different thicknesses, the historical processing data includes not only the control angle and processing diameter, but also the material and thickness. The bending machine data optimization method includes step S100, step S200, step S500, step S300, and step S400; for a bending machine that processes materials with the same material and thickness, the bending machine optimization method includes step S100, step S200, step S300, and step S400.

[0111] In one embodiment, referring to Figure 6 , step S500 includes but is not limited to the following steps:

[0112] Step S510: Obtain the third control data in the first data set.

[0113] Step S520: When the main thickness flag of the third control data is 1, retain the third control data.

[0114] Or,

[0115] Step S530: When the main thickness flag of the third control data is 0, delete the third control data.

[0116] Step S540: Repeatedly obtain the data in the first data set and process the corresponding data according to the main thickness flag until no new data can be detected in the first data set, and use the processed data as the third data set.

[0117] Step S550: Update the third data set as the first data set.

[0118] It should be understood that the value of the main thickness flag F is 1 or 0, where the main thickness flag F being 1 means that the material and thickness represented by this data are the standard thickness of this material, that is, the material and thickness represented by this data are the same as the material and thickness of the target bending processing data, and in other cases, the main thickness flag F is 0.

[0119] It should be understood that classifying the first data set according to the main thickness flag can ensure that the obtained third data set can meet the requirements of curve fitting, that is, for the third data set, only the control angle and processing diameter change, which is convenient for obtaining the standard curve fitting function in the later stage.

[0120] In addition, in one embodiment, referring to Figure 7 , step S300 includes but is not limited to the following steps:

[0121] Step S310: Fit the first data set by the Akima smooth interpolation method to obtain a standard curve fitting function, where the independent variable of the standard curve fitting function is the machining diameter, and the dependent variable of the standard curve fitting function is the control angle.

[0122] It should be understood that using the Akima smooth interpolation technique to perform curve fitting on the data can ensure the smoothness of the curve fitting of the invention, and further ensure that the bending machine can numerically control the machining of ellipses and other complex figures composed of arcs.

[0123] It should be understood that the standard curve fitting function is: where the machining diameter is the independent variable, and the control angle u is the dependent variable.

[0124] In addition, in one embodiment, referring to Figure 8 , step S400 includes but is not limited to the following steps:

[0125] Step S410: Search for the data in the first data set that meets the fitting conditions according to the target bending machining data as the fourth data set, where the fitting conditions include the same material, the thickness difference is less than or equal to the thickness accuracy, and the machining diameter difference is less than or equal to the diameter accuracy.

[0126] It should be understood that the thickness accuracy and the diameter accuracy can be set according to the actual situation. Assuming that the target bending machining data is the thickness accuracy is ΔH, and the diameter accuracy is ΔD, then the fitting condition is to satisfy C i = C r , |H i - H r | ≤ ΔH, |D i - D r | ≤ ΔD, and the data in the first data set that meets the fitting conditions is used as the fourth data set.

[0127] Step S420: Compare the machining diameter differences of the data in the fourth data set to obtain the data with the smallest machining diameter difference.

[0128] It should be understood that by comparing the magnitudes of |D i - D r | values in the fourth data set, the data with the smallest |D i - D r | value is obtained.

[0129] Step S430: Use the data with the smallest difference in machining diameter as the target output data corresponding to the target bending machining data.

[0130] It should be understood that the data with the smallest difference in machining diameter is used as the target output data corresponding to the target bending machining data.

[0131] In addition, in one embodiment, referring to Figure 7 , step S400 further includes the following steps:

[0132] Step S440: When data satisfying the fitting condition cannot be found in the first dataset, use the machining diameter of the target bending machining data as the first diameter, and obtain a second angle and a second diameter. Among them, the value of the second angle is 0, and the control angle value corresponding to the second diameter obtained according to the standard curve fitting function is 0.

[0133] It should be understood that for the target bending machining data when data satisfying the fitting condition cannot be found in the first dataset, use the machining diameter of the target bending machining data as the first diameter, and obtain a second angle u i and a second diameter where u i = 0,

[0134] Step S450: Calculate the control angle corresponding to the first diameter according to the standard curve fitting function as the first angle.

[0135] It should be understood that according to the standard curve fitting function calculate the control angle corresponding to the first diameter, and use the obtained control angle as the first angle

[0136] Step S460: Calculate the difference between the second angle and the control angle corresponding to the second diameter obtained by the standard curve fitting function to obtain a control difference.

[0137] It should be understood that calculate the difference between the second angle u i and the control angle corresponding to the second diameter obtained by the standard curve fitting function to obtain a control difference

[0138] Step S470: Calculate the sum of the first angle and the control difference as the third angle, and obtain the machining diameter corresponding to the third angle according to the actual operation as the third diameter.

[0139] It should be understood that calculate the sum of the first angle and the control difference as the third angle, and the third angle is expressed as:

[0140]

[0141] Obtain the third angle u according to the actual operation r The corresponding machining diameter is used as the third diameter

[0142] Step S480: When the difference between the third diameter and the machining diameter of the target bending machining data is less than or equal to the diameter accuracy, the third angle, the third diameter, the material, and the thickness are used as the target output data corresponding to the target bending machining data.

[0143] It should be understood that when the third diameter satisfies the condition |D i - D r | ≤ ΔD, the third angle, the third diameter, the material, and the thickness are used as the target output data corresponding to the target bending machining data

[0144] Or

[0145] Step S490: When the difference between the third diameter and the machining diameter of the target bending machining data is greater than the diameter accuracy, the third angle is used as the second angle, the third diameter is used as the second diameter, and the third angle and the third diameter are recalculated according to the standard curve fitting function and the actual operation until the difference between the third diameter and the machining diameter of the target bending machining data is less than or equal to the diameter accuracy, and the third angle, the third diameter, the material, and the thickness obtained in the last calculation are used as the target output data corresponding to the target bending machining data.

[0146] It should be understood that when the third diameter does not satisfy |D i - D r | ≤ ΔD, the third angle is used as the second angle, the third diameter is used as the second diameter, and steps S460 and S470 are repeated until the obtained third diameter satisfies |D i - D r | ≤ ΔD, and the third angle, the third diameter, the material, and the thickness obtained in the last calculation are used as the target output data corresponding to the target bending machining data

[0147] In addition, the historical bending machining data also includes a forgetting factor. Referring to Figure 9 , the bending machine data optimization method provided by the embodiments of the present application further includes the following steps:

[0148] Step S610: According to the material and thickness of the target output data, set the main thickness flag corresponding to the target output data to 1 or 0.

[0149] It should be understood that, based on the target output data for the material C r and the thickness H r , determine whether the main thickness flag F corresponding to the target output data is set to 1 or 0.

[0150] Step S620: Set the forgetting factor corresponding to the target output data to 1.

[0151] Step S630: Search for the data in the historical dataset that meets the update conditions according to the target output data, and use it as the fifth dataset. Among them, the update conditions include the same material, the thickness difference is less than the thickness accuracy, and the machining diameter difference is less than the diameter accuracy.

[0152] It should be understood that when the target output data is added to the historical dataset (C, H, u, D, A, F), the data in the target output data that meets C 0 = C r , |H 0 - H r | < ΔH, |D 0 - D r | < ΔD is used as the fifth dataset.

[0153] Step S640: Obtain the fourth control data in the fifth dataset, and multiply the forgetting factor corresponding to the fourth control data by the forgetting factor decay coefficient to obtain the updated forgetting factor.

[0154] It should be understood that for the forgetting factor in the fourth control data (C 0 , H 0 , u 0 , D 0 , A 0 , F 0 ), it is updated using the following formula:

[0155] A 0 = αA 0

[0156] Step S650: When the updated forgetting factor is less than or equal to a small value, delete the fourth control data.

[0157] It can be understood that when the updated forgetting factor A 0 ≤ ε, delete the fourth control data.

[0158] Or,

[0159] Step S660: When the updated forgetting factor is greater than the small value, retain the fourth control data with the updated forgetting factor in the historical dataset.

[0160] It should be understood that when the updated forgetting factor A 0 > ε, the fourth control data after the update of the forgetting factor is retained in the historical data set.

[0161] It should be understood that the value range of the small quantity ε is (0, 1), and the small quantity ε is a preset value.

[0162] It should be understood that by setting the forgetting factor, the forgetting factor attenuation coefficient, and the small quantity to delete or retain the corresponding data, it is possible to delete some data in the historical data set that is close to the target output data of the data, that is, to forget the historical data near the satisfactory result, which can prevent data explosion and interference from historical bad data, and improve the generalization ability of the bending machine data optimization method provided by the embodiments of the present application.

[0163] It should be understood that the bending machine data optimization method of the embodiments of the present application uses the historical data set as the first data set, performs curve fitting on the first data set to obtain a standard curve fitting function, calculates the target output data corresponding to the target bending processing data according to the first data set and the standard curve fitting function, and further strengthens the optimization ability of the bending machine data optimization method through method steps such as data anti-interference, data classification, and forgetting factor update. Moreover, the bending machine data optimization method of the embodiments of the present application obtains satisfactory bending machine processing data through the calculation of the target output data, which can reduce the operation amount in the debugging stage of the bending machine, make the difficulty of operating the bending machine decrease, reduce the requirements for workers, and reduce the number of consumables in the debugging stage.

[0164] In addition, an embodiment of the embodiments of the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it is like the bending machine data optimization method of steps S100 to S400.

[0165] The processor and the memory can be connected through a bus or other means.

[0166] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include high-speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0167] The non-transitory software program and instructions required to implement the bending machine data optimization method of the above embodiments are stored in a memory. When executed by a processor, the bending machine data optimization method in the above embodiments is executed. For example, the method steps S100 to S400 described above are executed. Figure 2 in the method steps S100 to S400.

[0168] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0169] In addition, an embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by a processor or a controller, the processor can execute the bending machine data optimization method in the above embodiments. For example, the method steps S100 to S400 described above are executed. Figure 2 in the method steps S100 to S400, Figure 3 in the method steps S210 to S240, Figure 4 in the method steps 250 to S280, Figure 5 in the method steps S100 to S500, Figure 6 in the method steps S210 to S550, Figure 7 in the method steps S100 to S400, Figure 8 in the method steps S410 to S430, Figure 9 in the method steps S440 to S490, Figure 9 in the method steps S610 to S650.

[0170] Those of ordinary skill in the art will appreciate that all or some of the steps and systems disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and can include any information delivery media.

[0171] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for optimizing bending machine data, characterized in that, it includes: Obtain a historical data set and target bending processing data, wherein the historical data set includes multiple groups of historical bending processing data, and each group of the historical bending processing data includes at least a control angle and a processing diameter, and the target bending processing data includes at least a target processing diameter; Obtain a first data set according to the historical data set; Perform curve fitting on the first data set to obtain a standard curve fitting function; Calculate target output data corresponding to the target bending processing data according to the first data set and the standard curve fitting function, and the target output data includes at least a target control angle; Wherein, the historical bending processing data further includes material and thickness, The calculating the target output data corresponding to the target bending processing data according to the first data set and the standard curve fitting function includes: Search for data in the first data set that meets the fitting conditions according to the target bending processing data as a fourth data set, wherein the fitting conditions include the same material, the thickness difference is less than or equal to the thickness accuracy, and the processing diameter difference is less than or equal to the diameter accuracy; Compare the processing diameter differences of the data in the fourth data set to obtain the data with the smallest processing diameter difference; Use the data with the smallest processing diameter difference as the target output data corresponding to the target bending processing data; Wherein, the method for optimizing bending machine data further includes: When data that meets the fitting conditions cannot be found in the first data set, use the processing diameter of the target bending processing data as the first diameter, and obtain a second angle and a second diameter, wherein the value of the second angle is 0, and the control angle value corresponding to the second diameter obtained according to the standard curve fitting function is 0; Calculate the control angle corresponding to the first diameter according to the standard curve fitting function as the first angle; Calculate the difference between the second angle and the control angle corresponding to the second diameter obtained according to the standard curve fitting function to obtain a control difference; Calculate the sum of the first angle and the control difference as the third angle, and obtain the processing diameter corresponding to the third angle according to actual operation as the third diameter; When the difference between the third diameter and the processing diameter of the target bending processing data is less than or equal to the diameter accuracy, use the third angle, the third diameter, and the material and thickness as the target output data corresponding to the target bending processing data; Or, When the difference between the third diameter and the processing diameter of the target bending processing data is greater than the diameter accuracy, use the third angle as the second angle, use the third diameter as the second diameter, and repeat the calculation of the third angle and the third diameter according to the standard curve fitting function and actual operation until the difference between the third diameter and the processing diameter of the target bending processing data is less than or equal to the diameter accuracy, and use the third angle, the third diameter, and the material and thickness obtained in the last calculation as the target output data corresponding to the target bending processing data.

2. The bending machine data optimization method according to claim 1, wherein, the historical bending process data further includes material and thickness, obtaining a first data set according to the historical data set, including: filtering and screening the historical data set to obtain a second data set, wherein all data in the second data set have the same material and thickness, and the historical bending process data further includes a forgetting factor; calculating the sum of the products of the forgetting factors corresponding to all data in the second data set and the processing diameter; calculating the sum of the forgetting factors corresponding to all data in the second data set; dividing the sum of the products of the forgetting factors and the processing diameter by the sum of the forgetting factors to obtain an average diameter, updating the processing diameter of all data in the second data set to the average diameter, and using the updated data as the first data set.

3. The bending machine data optimization method according to claim 1, wherein, obtaining a first data set according to the historical data set, including: acquiring first control data and second control data in the historical data set; when the control angle and processing diameter of the first control data are both greater than those of the second control data, and the number of the first control data and the data before it is less than that of the second control data, deleting the first control data; or, when the control angle and processing diameter of the first control data are both greater than those of the second control data, and the number of the first control data and the data before it is greater than that of the second control data, deleting the second control data; repeatedly acquiring data in the historical data set and processing the corresponding data until no new data can be detected in the historical data set, and using the processed data as the first data set.

4. The bending machine data optimization method according to claim 1, wherein, the historical bending process data further includes a main thickness flag, before fitting the first data set to obtain a standard curve fitting function, including: classifying the first data set according to the main thickness flag to obtain a third data set, and updating the third data set to the first data set.

5. The bending machine data optimization method according to claim 4, wherein, classifying the first data set according to the main thickness flag to obtain a third data set, and updating the third data set to the first data set, including: acquiring third control data in the first data set; when the main thickness flag of the third control data is 1, retaining the third control data; or, when the main thickness flag of the third control data is 0, deleting the third control data; repeatedly acquiring data in the first data set and processing the corresponding data according to the main thickness flag until no new data can be detected in the first data set, and using the processed data as the third data set; updating the third data set to the first data set.

6. The bending machine data optimization method according to claim 1, wherein, Performing curve fitting on the first data set to obtain a standard curve fitting function includes: Fitting the first data set by the Akima smooth interpolation method to obtain a standard curve fitting function, where the independent variable of the standard curve fitting function is the machining diameter and the dependent variable of the standard curve fitting function is the control angle.

7. The bending machine data optimization method according to claim 1, characterized in that, the historical bending process data further includes material, thickness, and forgetting factor, and the bending machine data optimization method further includes: According to the material and thickness of the target output data, setting the main thickness flag corresponding to the target output data to 1 or 0; Setting the forgetting factor corresponding to the target output data to 1; Searching the historical data set for data that meets the update conditions according to the target output data as the fifth data set, where the update conditions include the same material, the thickness difference being less than the thickness accuracy, and the machining diameter difference being less than the diameter accuracy; Obtaining the fourth control data in the fifth data set, and multiplying the forgetting factor corresponding to the fourth control data by the forgetting factor decay coefficient to obtain an updated forgetting factor; When the updated forgetting factor is less than or equal to a small amount, deleting the fourth control data; Or, When the updated forgetting factor is greater than the small amount, retaining the fourth control data with the updated forgetting factor in the historical data set.

8. A computer storage medium, characterized in that, it includes computer-executable instructions stored, and the computer-executable instructions are used to execute the bending machine data optimization method according to any one of claims 1 to 7.

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