Bending machine as well as self-learning method and device for lead of rear stopper of bending machine

Abstract

The invention discloses a bending machine and a rear stop lead self-learning method and device thereof, and relates to the technical field of mechanical control. The method comprises the following steps: controlling a rear stopper to move to a first target position, and obtaining an actual measurement value of the first target position; controlling the rear stopper to move to a second target position, and acquiring an actual measurement value of the second target position; based on the current set value of the rear material blocking lead, the calculated value of the first target position and the calculated value of the second target position are determined, and the rear material blocking lead is the product of the ratio of the number of teeth of the driving wheel to the number of teeth of the driven wheel and the lead of the lead screw; and according to the actual measurement value of the first target position, the calculation value of the first target position, the actual measurement value of the second target position, the calculation value of the second target position and the current set value of the rear material blocking lead, the self-learning value of the rear material blocking lead is determined. According to the method and the device, self-learning of the rear stop lead is realized, the workload is reduced, the accuracy of the rear stop lead is improved, and the debugging efficiency is improved.

Description

Technical Field

[0001] The present application belongs to the field of mechanical control technology, and in particular relates to a bending machine and a back gauge lead self-learning method and device thereof. Background Art

[0002] A bending machine is a machine that can bend thin plates. The back gauge (i.e., the workbench) in a bending machine is generally driven by a pulley and a screw. The position of the back gauge needs to be calculated using mathematical formulas based on the parameters of the motor encoder (including the reading and resolution of the motor encoder) and the mechanical structure parameters (including the number of teeth on the driving wheel, the number of teeth on the driven wheel, and the lead of the screw).

[0003] Due to the large number of bending machine manufacturers, the back gauge structures of bending machines from different manufacturers vary greatly. Even for the same manufacturer, the back gauge structures of bending machines of different tonnages are different. Therefore, when debugging a new machine, the debugging personnel need to obtain the mechanical structure parameters of the back gauge structure of the machine every time. If any of the number of teeth on the driving wheel, the number of teeth on the driven wheel, and the lead of the screw rod are inaccurate, the position calculation of the back gauge will be wrong, which will lead to abnormal control. In the related technology, the debugging personnel generally obtain the mechanical structure parameters of the back gauge structure by communicating with the manufacturer, or by actual measurement. However, the manufacturer often cannot accurately provide the mechanical structure parameters of the back gauge structure, and when the parameters provided by the manufacturer are used for debugging and it is found that the parameters are incorrect, it is impossible to determine which of the three parameters is incorrect, and they need to be confirmed one by one, which increases the workload and reduces the debugging efficiency. The actual measurement method has a large workload and low accuracy. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a bending machine and a back gauge lead self-learning method and device thereof, so as to solve the problem that the mechanical structure parameters of the back gauge structure obtained in the related art have low accuracy, increased workload, and reduced debugging efficiency.

[0005] To achieve the above objectives, the present application embodiment adopts the following technical solutions: In a first aspect, an embodiment of the present application provides a self-learning method for a rear stop lead of a bending machine, comprising: controlling the rear stop to move to a first target position and obtaining a measured value of the first target position; controlling the rear stop to move to a second target position and obtaining a measured value of the second target position; determining a calculated value of the first target position and a calculated value of the second target position based on a current setting value of the rear stop lead, the rear stop lead being the product of the ratio of the number of teeth of the driving wheel to the number of teeth of the driven wheel and the lead of the screw; determining a self-learning value of the rear stop lead according to the measured value of the first target position, the calculated value of the first target position, the measured value of the second target position, the calculated value of the second target position and the current setting value of the rear stop lead.

[0006] In the second aspect, an embodiment of the present application provides a self-learning device for the lead of a rear stopper of a bending machine, comprising: a first control module, used to control the movement of the rear stopper to a first target position, and obtain a measured value of the first target position; a second control module, used to control the movement of the rear stopper to a second target position, and obtain a measured value of the second target position; a first determination module, used to determine a calculated value of the first target position and a calculated value of the second target position based on a current setting value of the lead of the rear stopper, the lead of the rear stopper being the product of the ratio of the number of teeth of the driving wheel to the number of teeth of the driven wheel and the lead of the screw; a second determination module, used to determine the self-learning value of the lead of the rear stopper according to the measured value of the first target position, the calculated value of the first target position, the measured value of the second target position, the calculated value of the second target position and the current setting value of the lead of the rear stopper.

[0007] In the third aspect, an embodiment of the present application provides a bending machine, comprising: a rear stop, a driving wheel, a driven wheel, a screw and a self-learning device, wherein the self-learning device comprises a processor, a memory and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in the first aspect.

[0008] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: When the embodiment of the present application performs self-learning on the rear stop lead of the bending machine, the rear stop is controlled to move to a first target position and a measured value of the first target position is obtained, the rear stop is controlled to move to a second target position and a measured value of the second target position is obtained, and based on the current setting value of the rear stop lead, a calculated value of the first target position and a calculated value of the second target position are determined, the rear stop lead is the product of the ratio of the number of teeth on the driving wheel to the number of teeth on the driven wheel and the lead of the screw, and the self-learning value of the rear stop lead is determined according to the measured value of the first target position, the calculated value of the first target position, the measured value of the second target position, the calculated value of the second target position and the current setting value of the rear stop lead. In the embodiment of the present application, the product of the three parameters of the back gauge structure (i.e., the ratio of the number of teeth on the driving wheel to the number of teeth on the driven wheel and the lead of the screw) is regarded as a whole as the back gauge lead, and based on the current setting value of the back gauge lead, the self-learning value of the back gauge lead is determined according to the measured values ​​and calculated values ​​at two different positions of the back gauge, thereby realizing self-learning of the back gauge lead, reducing the workload, improving the accuracy of the back gauge lead, and avoiding the problem of increased workload and reduced debugging efficiency caused by an error in at least one parameter in the back gauge lead. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A schematic flow chart of a method for self-learning the lead of a rear gauge of a bending machine provided in one embodiment of the present application; Figure 2 A schematic diagram of the structure of a bending machine provided for one embodiment of the present application; Figure 3 A schematic diagram of a back gauge lead self-learning interface provided for one embodiment of the present application; Figure 4 A schematic diagram of the overall process of a bending machine rear gauge lead self-learning method provided in another embodiment of the present application; Figure 5 A schematic structural diagram of a rear gauge lead self-learning device for a bending machine provided in one embodiment of the present application. DETAILED DESCRIPTION

[0010] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0011] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here. In addition, the "and / or" in this application represents at least one of the connected objects, and the character " / " generally represents that the front and back associated objects are in an "or" relationship. It should be noted that the data involved in this application are all obtained on the premise of obtaining user authorization.

[0012] A bending machine is a machine that can bend thin plates. The back gauge (i.e., the workbench) in a bending machine is generally driven by a pulley and a screw. The position of the back gauge needs to be calculated using mathematical formulas based on the parameters of the motor encoder (including the reading and resolution of the motor encoder) and the mechanical structure parameters (including the number of teeth on the driving wheel, the number of teeth on the driven wheel, and the lead of the screw).

[0013] Due to the large number of bending machine manufacturers, the back gauge structures of bending machines from different manufacturers are very different. Even for the same manufacturer, the back gauge structures of bending machines of different tonnages are also different. Therefore, when debugging a new machine, the commissioning personnel need to obtain the mechanical structure parameters of the back gauge structure of the machine every time. If any of the number of teeth on the driving wheel, the number of teeth on the driven wheel, and the lead of the screw are inaccurate, the position calculation of the back gauge will be wrong, which will lead to abnormal control.

[0014] In the related technology, the debugging personnel generally obtain the mechanical structure parameters of the back gauge structure by communicating with the manufacturer, or by actual measurement. However, the back gauge structures of different bending machines vary greatly. The selection of the driving wheel, driven wheel, and screw of the same bending machine may be different. Therefore, the manufacturer is often unable to accurately provide the mechanical structure parameters of the back gauge structure, and use the parameters provided by the manufacturer for debugging. If it is found that the actual measurement position of the back gauge is inconsistent with the position calculated by the system, although it can be determined that the parameters are incorrect, it is impossible to determine which of the three parameters is incorrect. It is necessary to confirm them one by one, which increases the workload and reduces the debugging efficiency. In terms of actual measurement methods, the back gauge structure of some bending machines is a dustproof structure, and the dust cover needs to be removed for measurement. The actual measurement of the number of teeth on the driving wheel, the number of teeth on the driven wheel, and the lead of the screw requires a large workload and low accuracy, because the measurement error of the lead of the screw needs to be accurate to 0.01 mm.

[0015] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0016] Figure 1 A schematic diagram of a process flow of a bending machine back gauge lead self-learning method provided by an embodiment of the present application. Figure 1 As shown, the bending machine rear gauge lead self-learning method of the embodiment of the present application may specifically include the following steps: S101, controlling the rear stopper to move to a first target position, and obtaining a measured value of the first target position.

[0017] In the embodiment of the present application, the executor of the bending machine rear gauge lead self-learning method of the embodiment of the present application is a bending machine rear gauge lead self-learning device, and the bending machine rear gauge lead self-learning device can be arranged in the control system of the bending machine.

[0018] Figure 2 This is a schematic diagram of the structure of the bending machine according to an embodiment of the present application. Figure 2 As shown, the bending machine of the embodiment of the present application at least includes: a slider ( Figure 2 ), motor 21, motor encoder (integrated in motor 21, Figure 2), driving wheel 22, driven wheel 23, belt 24, screw 25, nut 26 and back stop 27. Among them, the slider is an executive component in the bending machine that drives the upper mold to slide downward to perform the bending action. The motor 21 is an executive component in the bending machine used to drive the slider to slide. The motor encoder is generally integrated inside the motor 21. It is a sensor in the bending machine that converts the angular displacement of the motor 21 into an electrical signal, which is used to collect the position of the motor 21. The rotation of the motor 21 drives the driving wheel 23 to rotate. The rotation of the driving wheel 23 drives the driven wheel 23 to rotate through the belt 24. The rotation of the driven wheel 23 drives the screw 25 to rotate, thereby causing the back stop 27 to move along the screw 25. The back stop 27 is a mechanical structure on the bending machine that is responsible for supporting and positioning the thin plate to be bent. It is used to accurately control the bending size during the bending process.

[0019] The position of the back gauge can be calculated using the following formula: Pw=(Ev / Es)*(G1 / G2)*Sp(1) Where Pw is the position of the back gauge, Ev is the motor position reading of the motor encoder, Es is the resolution of the motor encoder (i.e. the number of pulses measured when the motor encoder rotates one circle), G1 is the number of teeth on the driving wheel (i.e. the number of teeth on the motor end in the gear transmission or pulley transmission structure, for example Figure 2 G2 is the number of teeth on the driven wheel 22 in the pulley transmission structure), G3 is the number of teeth on the driven wheel (i.e. the number of teeth on the moving end in the gear transmission or pulley transmission structure, for example Figure 2 The number of teeth of the driven wheel 23 in the pulley transmission structure of the screw), Sp is the lead of the screw (i.e. the axial distance between the corresponding points of two adjacent teeth on the same spiral line of the screw). The motor position reading Ev of the motor encoder and the resolution Es of the motor encoder are known parameters of the control system, and the number of teeth of the driving wheel G1, the number of teeth of the driven wheel G2 and the lead of the screw Sp are the mechanical structure parameters of the rear gauge transmission mechanism.

[0020] By observing formula (1), we can see that the motor position reading Ev of the motor encoder and the resolution Es of the motor encoder are known parameters of the control system and can be regarded as a whole, that is, (Ev / Es) can be simplified to E; the number of driving gear teeth G1, the number of driven gear teeth G2 and the lead screw Sp are mechanical constants and can be regarded as a coefficient as a whole, that is, (G1 / G2)*Sp can be simplified to kSp. Then formula (1) can be simplified to the following formula (2): Pw=E*kSp(2) From the simplified formula (2), we can see that the originally unknown number of teeth on the driving gear G1, the number of teeth on the driven gear G2, and the lead of the screw Sp are simplified to an unknown coefficient kSp, where kSp is a multiple of Sp. The values ​​of the number of teeth on the driving gear G1 and the number of teeth on the driven gear G2 are simplified to 1. kSp is recorded as the back gauge lead to distinguish it from the lead of the screw Sp.

[0021] Based on the above formula (2), before self-learning the back gauge lead kSp, the back gauge lead needs to be set to a certain value kSp1 (which can be a default value such as 10, or a value entered by the user), which is recorded as the current setting value.

[0022] After the self-learning starts, the rear stop is controlled to move to the first target position, and the measured value Pt1 of the first target position is obtained. The measured value of the first target position is the value of the first target position actually measured, for example, Pt1=266.00. The rear stop is controlled to move to the first target position, which can be controlled to move to one side of the stroke (the position is arbitrary), or it can be controlled to move to the first target position according to the first target position input by the user.

[0023] S102, controlling the rear stopper to move to a second target position, and obtaining a measured value of the second target position.

[0024] In the embodiment of the present application, after the rear stop is controlled to move to the first target position, the rear stop is controlled to move to the second target position, and the measured value of the second target position is obtained. The measured value of the second target position is the value of the second target position actually measured, for example, Pt2=520.00. Controlling the rear stop to move to the second target position can be to control the rear stop to move to the other side of the stroke (the position is arbitrary, but different from the first target position), or to control the rear stop to move to the second target position according to the second target position input by the user.

[0025] S103, determining a calculated value of the first target position and a calculated value of the second target position based on a current setting value of a back gauge lead, wherein the back gauge lead is a product of a ratio of the number of teeth of the driving wheel to the number of teeth of the driven wheel and a lead of the screw.

[0026] In the embodiment of the present application, when the back gauge moves to the first target position, the calculated value Ps1 of the first target position is determined based on the current setting value kSp1 of the back gauge lead, for example, Ps1 = 217.81. When the back gauge moves to the second target position, the calculated value Ps2 of the second target position is determined based on the current setting value kSp1 of the back gauge lead, for example, Ps2 = 409.74.

[0027] Furthermore, the above step of "determining the calculated value Ps1 of the first target position based on the current setting value kSp1 of the rear stop lead" may specifically include the following steps: obtaining the current first parameter value E1 of the motor encoder when the rear stop moves to the first target position; determining the calculated value Ps1 of the first target position based on the first parameter value E1 and the current setting value kSp1 of the rear stop lead.

[0028] Among them, the current first parameter value E1 of the motor encoder is the ratio of the current first motor position reading Ev1 of the motor encoder and the resolution Es1 of the motor encoder. Since Ev1 and Es1 are currently known parameters of the control system, the first parameter value E1 can be calculated as Ev1 / Es1.

[0029] Furthermore, the above step of “determining the calculated value Ps1 of the first target position based on the first parameter value E1 and the current setting value kSp1 of the back gauge lead” may specifically include the following steps: substituting the first parameter value E1 and the current setting value kSp1 into the above formula (2), that is, using the following formula (3) to determine the calculated value Ps1 of the first target position: Ps1=E1*kSp1(3) Correspondingly, the above-mentioned step of "determining the calculated value Ps2 of the second target position based on the current setting value kSp1 of the rear stop lead" can specifically include the following steps: obtaining the current second parameter value E2 of the motor encoder when the rear stop moves to the second target position; determining the calculated value Ps2 of the second target position based on the second parameter value E2 and the current setting value kSp1 of the rear stop lead.

[0030] Among them, the current second parameter value E2 of the motor encoder is the ratio of the current second motor position reading Ev2 of the motor encoder and the resolution Es1 of the motor encoder. Since Ev2 and Es1 are currently known parameters of the control system, the second parameter value E2 can be calculated as Ev2 / Es1.

[0031] Furthermore, the above step of “determining the calculated value Ps2 of the second target position based on the second parameter value E2 and the current setting value kSp1 of the back gauge lead” may specifically include the following steps: substituting the second parameter value E2 and the current setting value kSp1 into the above formula (2), that is, using the following formula (4) to determine the calculated value Ps2 of the second target position: Ps2=E2*kSp1(4) S104, determining a self-learning value of the back gauge lead according to the measured value of the first target position, the calculated value of the first target position, the measured value of the second target position, the calculated value of the second target position and the current setting value of the back gauge lead.

[0032] In the embodiment of the present application, the self-learning value kSp2 of the back gauge lead kSp is determined according to the measured value Pt1 of the first target position obtained in step S101, the measured value Pt2 of the second target position obtained in step S102, the calculated value Ps1 of the first target position determined in step S103, the calculated value Ps2 of the second target position and the current setting value kSp1 of the back gauge lead. The self-learning value kSp2 of the back gauge lead is the value of the back gauge lead kSp obtained through the above self-learning process.

[0033] Specifically, the following formula (5) can be used to determine the self-learning value kSp2 of the back gauge lead kSp: kSp2=((Pt2-Pt1) / (Ps2-Ps1))*kSp1 (5) For example, when the measured value of the second target position Pt2 = 520.00, the measured value of the first target position Pt1 = 266.00, the calculated value of the second target position Ps2 = 409.74, the calculated value of the first target position Ps1 = 217.81 and the current setting value of the back gauge lead kSp1 = 10, the self-learning value of the back gauge lead kSp can be calculated by the above formula (5) to obtain kSp2 = ((520.00-266.00) / (409.74-217.81)) * 10 = 13.23. The corresponding back gauge lead self-learning interface can be as follows Figure 3 As shown, position 1 is the first target position, and position 2 is the second target position.

[0034] It should be noted here that when the reference point position of the back gauge is uncertain, the measured value and calculated value of the target position of the back gauge are meaningless, so the values ​​of the two target positions need to be measured and calculated.

[0035] To clearly illustrate the bending machine rear gauge lead self-learning method of the present application embodiment, the following is combined with Figure 4 The overall process of the bending machine rear gauge lead self-learning method of the embodiment of the present application is described. Figure 4 As shown, the self-learning method for the lead of the rear gauge of the bending machine of the embodiment of the present application may specifically include the following steps: setting the lead of the rear gauge to the current setting value kSp1, after the control system of the bending machine receives the first action command, controls the motor to rotate, the motor rotation drives the driving wheel to rotate, the driving wheel rotation drives the driven wheel to rotate, the driven wheel rotation drives the screw to rotate, and the rear gauge moves to the first target position, obtains the actual measured value Pt1 of the first target position and the first motor position reading Ev1 of the motor encoder at this time, and based on the first motor position reading Ev1, the resolution Es of the motor encoder and the current setting value kSp1, the formula Ps1=E1*kSp1 is used to determine the calculated value Ps1 of the first target position. After receiving the second action command, the control system of the bending machine controls the motor to rotate again, so that the rear stopper moves to the second target position, and takes the actual measured value Pt2 of the second target position and the second motor position reading Ev2 of the motor encoder at this time, and based on the second motor position reading Ev1, the resolution Es of the motor encoder and the current setting value kSp1, the formula Ps2=E2*kSp1 is used to determine the calculated value Ps2 of the second target position, and the formula kSp2=((Pt2-Pt1) / (Ps2-Ps1))*kSp1 is used to determine the self-learning value kSp2 of the rear stopper lead.

[0036] In summary, the self-learning method for the rear gauge lead of the bending machine in the embodiment of the present application, when self-learning the rear gauge lead of the bending machine, controls the rear gauge to move to the first target position and obtains the measured value of the first target position, controls the rear gauge to move to the second target position, and obtains the measured value of the second target position, based on the current setting value of the rear gauge lead, determines the calculated value of the first target position and the calculated value of the second target position, the rear gauge lead is the product of the ratio of the number of teeth of the driving wheel to the number of teeth of the driven wheel and the lead of the screw, and determines the self-learning value of the rear gauge lead according to the measured value of the first target position, the calculated value of the first target position, the measured value of the second target position, the calculated value of the second target position and the current setting value of the rear gauge lead. In the embodiment of the present application, the product of the three parameters of the back gauge structure (i.e., the ratio of the number of teeth on the driving wheel to the number of teeth on the driven wheel and the lead of the screw) is regarded as a whole as the back gauge lead, and based on the current setting value of the back gauge lead, the self-learning value of the back gauge lead is determined according to the measured values ​​and calculated values ​​at two different positions of the back gauge, thereby realizing self-learning of the back gauge lead, reducing the workload, improving the accuracy of the back gauge lead, and avoiding the problem of increased workload and reduced debugging efficiency caused by an error in at least one parameter in the back gauge lead.

[0037] Figure 5 This is a schematic diagram of a structure of a bending machine rear gauge lead self-learning device provided by an embodiment of the present application. Figure 5 As shown, the bending machine rear gauge lead self-learning device 500 of the embodiment of the present application may specifically include: a first control module 501, a second control module 502, a first determination module 503 and a second determination module 504. Among them: The first control module 501 is used to control the rear stopper to move to the first target position and obtain the actual measured value of the first target position.

[0038] The second control module 502 is used to control the rear stopper to move to the second target position and obtain the actual measured value of the second target position.

[0039] The first determination module 503 is used to determine the calculated value of the first target position and the calculated value of the second target position based on the current setting value of the back gauge lead, and the back gauge lead is the product of the ratio of the number of teeth of the driving wheel to the number of teeth of the driven wheel and the lead of the screw.

[0040] The second determination module 504 is used to determine the self-learning value of the rear stop lead according to the measured value of the first target position, the calculated value of the first target position, the measured value of the second target position, the calculated value of the second target position and the current setting value of the rear stop lead.

[0041] In the embodiment of the present application, the specific process of each module and unit in the bending machine rear gauge lead self-learning device of the embodiment of the present application realizing its function can be referred to the relevant description in the above-mentioned bending machine rear gauge lead self-learning method embodiment, which will not be repeated here.

[0042] In summary, the self-learning device for the rear gauge lead of the bending machine in the embodiment of the present application regards the product of the three parameters of the rear gauge structure (i.e., the ratio of the number of teeth on the driving wheel to the number of teeth on the driven wheel and the lead of the screw) as a whole as the rear gauge lead, and based on the current setting value of the rear gauge lead, determines the self-learning value of the rear gauge lead according to the measured values ​​and calculated values ​​at two different positions of the rear gauge, thereby realizing self-learning of the rear gauge lead, reducing workload, improving the accuracy of the rear gauge lead, and avoiding the problem of increased workload and reduced debugging efficiency caused by at least one parameter error in the rear gauge lead.

[0043] The present application also provides a bending machine. Figure 2 As shown, the bending machine includes a back gauge 27, a driving wheel 22, a driven wheel 23, a screw rod 25 and a self-learning device ( Figure 2 (not shown), the self-learning device includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the self-learning method for the rear stopper guide of the bending machine in any of the above-mentioned embodiments of the present application are implemented.

[0044] In the embodiment of the present application, the specific process of the bending machine in the embodiment of the present application to realize its function can refer to the relevant description in the embodiment of the self-learning method of the rear stopper guide of the bending machine mentioned above, which will not be repeated here.

[0045] The bending machine in the embodiment of the present application regards the product of the three parameters of the back gauge structure (i.e., the ratio of the number of teeth on the driving wheel to the number of teeth on the driven wheel and the lead of the screw) as a whole as the back gauge lead, and based on the current setting value of the back gauge lead, determines the self-learning value of the back gauge lead according to the measured values ​​and calculated values ​​at two different positions of the back gauge, thereby realizing self-learning of the back gauge lead, reducing the workload, improving the accuracy of the back gauge lead, and avoiding the problem of increased workload and reduced debugging efficiency caused by an error in at least one parameter in the back gauge lead.

[0046] The embodiment of the present application further proposes a readable storage medium, on which one or more computer programs are stored, and the one or more computer programs include instructions. When the program or instruction is executed by a processor in a bending machine including multiple application programs, the processor in the bending machine can execute each process of the embodiment of the above-mentioned bending machine rear gauge lead self-learning method, and is specifically used to execute: Controlling the back gauge to move to a first target position and obtaining a measured value of the first target position; Controlling the back gauge to move to a second target position, and obtaining a measured value of the second target position; Determining a calculated value of the first target position and a calculated value of the second target position based on a current setting value of a back gauge lead, wherein the back gauge lead is a product of a ratio of the number of teeth of the driving wheel to the number of teeth of the driven wheel and a lead of the screw; The self-learning value of the back gauge lead is determined according to the measured value of the first target position, the calculated value of the first target position, the measured value of the second target position, the calculated value of the second target position and the current setting value of the back gauge lead.

[0047] In the embodiment of the present application, the specific process of the readable storage medium in the embodiment of the present application realizing its function can be referred to the relevant description in the embodiment of the self-learning method for the rear stopper lead of the bending machine mentioned above, which will not be repeated here.

[0048] The readable storage medium of the embodiment of the present application regards the product of the three parameters of the rear gauge structure (i.e., the ratio of the number of teeth on the driving wheel to the number of teeth on the driven wheel and the lead of the screw) as a whole as the rear gauge lead, and based on the current setting value of the rear gauge lead, determines the self-learning value of the rear gauge lead according to the measured values ​​and calculated values ​​at two different positions of the rear gauge, thereby realizing self-learning of the rear gauge lead, reducing the workload, improving the accuracy of the rear gauge lead, and avoiding the problem of increased workload and reduced debugging efficiency caused by an error in at least one parameter in the rear gauge lead.

[0049] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0050] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0051] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0052] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0053] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0054] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0055] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0056] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0057] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined in this article, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0058] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0059] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0060] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0061] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for self-learning the lead of the back gauge of a bending machine, characterized in that: include: Controlling the back stop to move to a first target position, and obtaining a measured value of the first target position; Controlling the back stop to move to a second target position, and obtaining a measured value of the second target position; Determining a calculated value of the first target position and a calculated value of the second target position based on a current setting value of a back gauge lead, wherein the back gauge lead is a product of a ratio of the number of teeth of the driving wheel to the number of teeth of the driven wheel and a lead of the screw; The self-learning value of the back gauge lead is determined according to the measured value of the first target position, the calculated value of the first target position, the measured value of the second target position, the calculated value of the second target position and the current setting value of the back gauge lead.

2. The method according to claim 1, characterized in that Before controlling the back stop to move to the first target position, the method further includes: The back gauge lead is set to the current setting.

3. The method according to claim 1, characterized in that: The step of determining the calculated value of the first target position based on the current setting value of the back gauge lead comprises: Acquire a current first parameter value of a motor encoder when the rear stopper moves to the first target position; Based on the first parameter value and the currently set value of the back gauge lead, a calculated value of the first target position is determined.

4. The method according to claim 1, characterized in that: Determining a calculated value of the second target position based on a current setting value of the back gauge lead includes: Acquire a current second parameter value of the motor encoder when the rear stopper moves to the second target position; Based on the second parameter value and the currently set value of the back gauge lead, a calculated value of the second target position is determined.

5. The method according to claim 3 or 4, characterized in that: The current first parameter value of the motor encoder is a ratio of the current first motor position reading of the motor encoder to the resolution of the motor encoder; The current second parameter value of the motor encoder is the ratio of the current second motor position reading of the motor encoder to the resolution of the motor encoder.

6. The method according to claim 3, characterized in that The step of determining a calculated value of the first target position based on the first parameter value and the current setting value of the back gauge lead comprises: The calculated value of the first target position is determined using the following formula: Ps1=E1*kSp1; Wherein, the Ps1 is the calculated value of the first target position, the E1 is the first parameter value, and the kSp1 is the current setting value of the back gauge lead.

7. The method according to claim 4, characterized in that The step of determining a calculated value of the second target position based on the second parameter value and the current setting value of the back gauge lead comprises: The calculated value of the second target position is determined using the following formula: Ps2=E2*kSp1; Wherein, Ps2 is the calculated value of the second target position, E2 is the second parameter value, and kSp1 is the current setting value of the back gauge lead.

8. The method according to claim 1, characterized in that Determining the self-learning value of the back gauge lead according to the measured value of the first target position, the calculated value of the first target position, the measured value of the second target position, the calculated value of the second target position and the current setting value of the back gauge lead includes: The following formula is used to determine the self-learning value of the back gauge lead: kSp2=((Pt2-Pt1) / (Ps2-Ps1))*kSp1; Among them, kSp2 is the self-learning value of the rear stop lead, Pt2 is the measured value of the second target position, Pt1 is the measured value of the first target position, Ps2 is the calculated value of the second target position, Ps1 is the calculated value of the first target position, and kSp1 is the current setting value of the rear stop lead.

9. A bending machine rear gauge lead self-learning device, characterized in that: include: A first control module, used for controlling the rear stopper to move to a first target position and obtaining a measured value of the first target position; A second control module, used for controlling the back stop to move to a second target position and obtaining a measured value of the second target position; A first determination module, configured to determine a calculated value of the first target position and a calculated value of the second target position based on a current setting value of a back gauge lead, wherein the back gauge lead is a product of a ratio of a number of teeth of a driving wheel to a number of teeth of a driven wheel and a lead of a screw rod; The second determination module is used to determine the self-learning value of the rear stop lead according to the measured value of the first target position, the calculated value of the first target position, the measured value of the second target position, the calculated value of the second target position and the current setting value of the rear stop lead.

10. A bending machine, characterized in that: It includes a back stop, a driving wheel, a driven wheel, a screw and a self-learning device. The self-learning device includes a processor, a memory and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the method as described in any one of claims 1 to 8 are implemented.

Images