A method, system and apparatus for fuzzy adaptive constant force polishing

Abstract

The application relates to a fuzzy self-adaptive constant force polishing method, system and device, wherein the method comprises the following steps: based on a force sensor digital signal received in a workpiece polishing process, actual contact force in the polishing process is calculated in real time; based on the actual contact force, a damping coefficient of a mobility control model is adjusted through a fuzzy control theory; based on the actual contact force, the mobility control model is optimized through an adaptive control theory; and through the adjusted and optimized mobility control model, a target polishing track of a polishing tool is updated. Through the application, the problem of large tracking error of workpiece automatic polishing is solved, the best force control parameter dynamic rapid adjustment based on a fuzzy control algorithm is realized, and real environment position and stiffness compensation based on an adaptive control algorithm is realized, so that the effect of eliminating steady-state error and improving workpiece polishing precision is achieved.

Description

Technical Field

[0001] This application relates to the field of machine automation technology, and in particular to a fuzzy adaptive constant force grinding method, system and apparatus. Background Technology

[0002] With the booming development of industrial automation, people's requirements for machine manufacturing have risen from the past of simple, repetitive labor to intelligence, greenness, low cost, high quality, and high efficiency. Most companies have begun to use robots to replace human labor. Among these processes, grinding, as an indispensable and important step in parts processing, is key to ensuring the surface quality of parts and is an important link in the machine manufacturing industry.

[0003] Currently, the grinding of workpiece surfaces is mainly carried out by operators using manual sanders or surface polishing tools. This manual grinding method cannot guarantee the personal safety of operators, nor can it guarantee the stability and consistency of grinding quality.

[0004] Currently, no effective solution has been proposed to address the large tracking error problem in automated workpiece grinding in related technologies. Summary of the Invention

[0005] This application provides a fuzzy adaptive constant force grinding method, system, and apparatus to at least solve the problem of large tracking error in automated workpiece grinding in related technologies.

[0006] In a first aspect, embodiments of this application provide a fuzzy adaptive constant force grinding method, the method comprising:

[0007] Based on the digital signals from the force sensor received during the workpiece grinding process, the actual contact force during the grinding process is calculated in real time.

[0008] Based on the actual contact force, the damping coefficient of the admittance control model is adjusted using fuzzy control theory;

[0009] Based on the actual contact force, the admittance control model is optimized using adaptive control theory;

[0010] The target grinding trajectory of the grinding tool is updated by adjusting and optimizing the admittance control model.

[0011] In some embodiments, before calculating the actual contact force during the grinding process in real time based on the force sensor digital signal received during the workpiece grinding process, the method includes:

[0012] Based on the expected polishing location, determine the expected polishing trajectory;

[0013] Based on the expected grinding trajectory and the pre-input expected contact force, the grinding tool is controlled to grind the workpiece through the admittance control model.

[0014] In some embodiments, adjusting the damping coefficient of the admittance control model based on the actual contact force using fuzzy control theory includes:

[0015] Based on the actual contact force, the desired grinding position, and the desired contact force, the admittance control model is adjusted using fuzzy control theory. The damping coefficients in the figure, where M, B, and K represent the mass coefficient, damping coefficient, and stiffness coefficient of the second-order system, respectively, and F... e F represents the actual contact force. d Indicates the expected contact force, x r The expected polishing position is represented by x, the actual polishing position is represented by t, and the polishing time point is represented by t.

[0016] In some embodiments, the fuzzy control theory includes:

[0017] If admittance control model Force tracking error e f =F e (t)-F d Position tracking error e = x(t) - x r If (t) is greater than the corresponding preset threshold, then the damping coefficient of the admittance control model is reduced;

[0018] If admittance control model Force tracking error e f =F e (t)-F d Position tracking error e = x(t) - x r If (t) is less than the corresponding preset threshold, then the damping coefficient of the admittance control model is increased.

[0019] In some embodiments, optimizing the admittance control model based on the actual contact force using adaptive control theory includes:

[0020] Based on the actual contact force, the desired grinding position, and the desired contact force, the admittance control model is optimized using adaptive control theory to obtain the optimized admittance control model. Where M and B represent the mass coefficient and damping coefficient of the second-order system, respectively; force tracking error e f =F e (t)-F d F e F represents the actual contact force. d Indicates an expectation of contact force. The force error integral term is represented by η, which represents the adaptive factor; the position tracking error is e = x(t) - x r (t), x r The expected polishing position is indicated by x, the actual polishing position is indicated by t, and the polishing time point is indicated by t.

[0021] In some embodiments, determining the desired polishing trajectory based on the desired polishing location includes:

[0022] Obtain the calibration position of the grinding tool and the workpiece as the expected grinding position;

[0023] Based on the desired polishing location, the desired polishing trajectory is automatically generated using a trajectory planning algorithm.

[0024] In some embodiments, obtaining the calibration positions of the grinding tool and the workpiece includes:

[0025] Position the center point of the grinding tool relative to the surface of the workpiece. Record the X-axis, Y-axis, and Z-axis position information of the grinding tool displayed on the teach pendant for each positioning point, as the calibration position.

[0026] In some embodiments, updating the target grinding trajectory of the grinding tool through the adjustment and the optimized admittance control model includes:

[0027] The target grinding trajectory X of the grinding tool is updated using the adjusted and optimized admittance control model. d =x r +e, where e represents the position tracking error, x r This indicates a desire to have the area polished.

[0028] Based on inverse kinematics theory, the target grinding trajectory is converted into joint angles, which are used to control the grinding tool to grind the workpiece.

[0029] Secondly, embodiments of this application provide a fuzzy adaptive constant force grinding system, the system including a real-time acquisition module, a fuzzy control module, an adaptive control module, and a grinding update module;

[0030] The real-time acquisition module is used to calculate the actual contact force during the grinding process in real time based on the digital signal from the force sensor received during the workpiece grinding process.

[0031] The fuzzy control module is used to adjust the damping coefficient of the admittance control model based on the actual contact force using fuzzy control theory.

[0032] The adaptive control module is used to optimize the admittance control model based on the actual contact force using adaptive control theory.

[0033] The grinding update module is used to update the target grinding trajectory of the grinding tool through the adjustment and the optimized admittance control model.

[0034] Thirdly, embodiments of this application provide a grinding control device, including a force controller and a position controller, wherein the force controller and the position controller are configured to control a grinding tool to grind a workpiece based on the method described in any one of the first aspects above.

[0035] Compared to related technologies, the embodiments of this application provide a fuzzy adaptive constant force grinding method, system, and apparatus. This method calculates the actual contact force during the grinding process in real time based on the digital signal from the force sensor received during the workpiece grinding process; adjusts the damping coefficient of the admittance control model using fuzzy control theory based on the actual contact force; optimizes the admittance control model using adaptive control theory based on the actual contact force; and updates the target grinding trajectory of the grinding tool by adjusting and optimizing the admittance control model. This solves the problem of large tracking errors in automated workpiece grinding, achieves dynamic and rapid adjustment of optimal force control parameters based on fuzzy control algorithms, and compensates for real-world environmental position and stiffness based on adaptive control algorithms, thereby eliminating steady-state errors and improving workpiece grinding accuracy. Attached Figure Description

[0036] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0037] Figure 1 This is a flowchart of the steps of the fuzzy adaptive constant force grinding method according to an embodiment of this application;

[0038] Figure 2 This is a schematic diagram of a constant force grinding system according to an embodiment of this application;

[0039] Figure 3 This is a schematic diagram of the robot end effector connection according to an embodiment of this application;

[0040] Figure 4 This is a flowchart illustrating the fuzzy adaptive constant force grinding method according to an embodiment of this application;

[0041] Figure 5 This is a schematic diagram of the algorithm of the fuzzy adaptive constant force grinding method according to an embodiment of this application;

[0042] Figure 6 This is a structural block diagram of a fuzzy adaptive constant force grinding system according to an embodiment of this application;

[0043] Figure 7This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0045] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0046] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0047] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0048] Before describing specific embodiments of the present invention, it should be noted that grinding refers to reprocessing the surface material of a part using grinding tools. The purpose of grinding is not to change the geometry of the surface, but to improve the surface finish, that is, to remove surface irregularities caused by tooling, forging, and burr formation during the casting process. Therefore, grinding does not require the large processing force of machining operations such as drilling, but rather requires maintaining a stable state in terms of the direction and magnitude of the force to contact the workpiece surface; when processing workpieces with complex curved surfaces, maintaining the stability of the contact force is even more crucial. Grinding can generally be divided into two main categories: free-form surface machining and curved surface grinding and polishing.

[0049] Currently, although some progress has been made in the machining of free-form surfaces, surface grinding and polishing remains a weak point in finishing processes. The finishing of many free-form surface workpieces still relies on manual operation by skilled workers. Taking precision molds as an example, manual finishing of surface grinding and polishing accounts for approximately 37% to 42% of the total machining time in developed industrial countries such as the United States, Japan, and Germany, while in my country, manual processing accounts for about 50% of the manufacturing time. Manual operation is time-consuming, labor-intensive, and inefficient, significantly impacting processing progress and making it difficult to achieve good shape accuracy and surface quality. It has become a bottleneck hindering the development of high-quality free-form surface manufacturing technology. Therefore, there is an urgent need for an easy-to-operate, high-precision robotic automation solution. The solution described in the embodiments of this invention is applicable to both complex and irregular surface grinding scenarios and simple surface grinding scenarios such as planes and inclined surfaces.

[0050] This application provides a fuzzy adaptive constant force grinding method. Figure 1 This is a flowchart of the steps of the fuzzy adaptive constant force grinding method according to an embodiment of this application, as follows: Figure 1 As shown, the method includes the following steps:

[0051] Step S102: Based on the digital signal from the force sensor received during the workpiece grinding process, the actual contact force during the grinding process is calculated in real time.

[0052] Before performing step S102, the method further includes step S101, which determines the expected grinding trajectory based on the expected grinding position (key point of the grinding trajectory); and controls the grinding tool to grind the workpiece through an admittance control model based on the expected grinding trajectory and the pre-input expected contact force.

[0053] Specifically, step S101 involves obtaining the calibration position of the grinding tool and the workpiece as the desired grinding position; automatically generating the desired grinding trajectory based on the desired grinding position using a trajectory planning algorithm; and controlling the grinding tool to grind the workpiece using an admittance control model based on the desired grinding trajectory and the pre-input desired contact force.

[0054] Step S101 preferably, Figure 2 This is a schematic diagram of a constant force grinding system according to an embodiment of this application, as shown below. Figure 2 As shown, the grinding system includes a six-dimensional force sensor, a six-axis robot, grinding tools, a mounting station, a workpiece to be ground, a worktable, a robot control cabinet, and a teach pendant. First, the workpiece is fixed within the robot's workspace. Then, trajectory planning is performed based on the robot's teaching points. Next, the force control algorithm is activated to output the trajectory to be adjusted to maintain a constant force. Finally, the mounting station (such as a computer) issues control commands to control the robot's movement. Figure 3 This is a schematic diagram of the robot end effector connection according to an embodiment of this application, such as... Figure 3 As shown, the force sensor is connected to the end flange of the robot, and the force sensor is connected to the grinding tool via an adapter.

[0055] Figure 4 This is a flowchart illustrating the fuzzy adaptive constant force grinding method according to an embodiment of this application, as shown below. Figure 4 As shown, before the constant force control in steps S102 to S108 generates the adjusted trajectory in this method, step S101 preferably includes the following steps:

[0056] Step 1: Demonstrate and determine the key points for polishing.

[0057] The workpiece is mounted on the worktable for positioning and clamping, completing the coordinate calibration of the process system. The robot uses a calibration pin to calibrate the coordinates of the end effector, installs the grinding tool, and sets the end effector information in the robot teach pendant, completing the robot calibration. Then, the center point of the grinding tool is positioned relative to the fixed workpiece surface on the worktable. For each point located, the X, Y, and Z axis position information of the end effector grinding tool displayed on the teach pendant is recorded (calibration position), completing the determination of key points in the grinding path and the calibration of the robot's base coordinates with the worktable coordinates, achieving coordinate calibration unification. It should be noted that, unlike current constant force control strategies, the initial values ​​of this invention only require setting the position points X, Y, and Z; the attitude does not need to be considered and is assumed to be an identity matrix. The robot can automatically adjust its attitude based on the feedback force from the contact surface to adapt to surfaces with different curvatures.

[0058] Step two, refine the path trajectory planning.

[0059] Input the location information recorded in step one into the trajectory planning algorithm of the upper position in sequence. The algorithm will automatically generate the trajectory between two adjacent points of the input points to obtain the initial trajectory for grinding.

[0060] Step 3: Set the desired polishing trajectory and desired contact force.

[0061] The initial trajectory planned in step two is input into the constant force control algorithm as the desired grinding trajectory, and the desired contact force is set as the input of the constant force control algorithm to participate in the calculation.

[0062] Step S104: Based on the actual contact force, adjust the damping coefficient of the admittance control model using fuzzy control theory;

[0063] Specifically, in step S104, based on the actual contact force, the desired grinding position, and the desired contact force, the admittance control model is adjusted using fuzzy control theory. The damping coefficients in the figure, where M, B, and K represent the mass coefficient, damping coefficient, and stiffness coefficient of the second-order system, respectively, and F... e F represents the actual contact force.d Indicates the expected contact force, x r Let x represent the expected grinding position, t represent the actual grinding position, and t represent the grinding time. The fuzzy control theory includes:

[0064] If admittance control model Force tracking error e f =F e (t)-F d Position tracking error e = x(t) - x r If (t) is greater than the corresponding preset threshold, then reduce the damping coefficient of the admittance control model;

[0065] If admittance control model Force tracking error e f =F e (t)-F d Position tracking error e = x(t) - x r If all (t) are less than the corresponding preset threshold, then increase the damping coefficient of the admittance control model.

[0066] Step S104 is preferably... Figure 5 This is a schematic diagram of the algorithm for the fuzzy adaptive constant force grinding method according to an embodiment of this application, as shown below. Figure 5 As shown, the constant force control algorithm in this method is specifically an admittance control algorithm that combines fuzzy control theory (step S104) and adaptive control theory (step S106). In step S104, the upper control unit acquires the force sensor digital signal F... m The data is processed, and gravity compensation calculations are performed according to the gravity compensation algorithm. The force sensor digital signal F is then processed. m Converted to actual contact force F e The basic algorithm formula for the admittance control model is as follows: Where M, B, and K represent the mass coefficient, damping coefficient, and stiffness coefficient of the second-order system, respectively, and F e F represents the actual contact force (the force exerted by the environment on an object). d Indicates the expected contact force, x r Let represent the expected polishing position, x represent the actual polishing position, and t represent the polishing time. Define e = x(t) - x r (t), then and At this point, the admittance control model transforms into...

[0067] The fuzzy control in step S104 addresses situations where the environmental stiffness changes. In such cases, the fixed parameters are insufficient to meet the accuracy requirements of constant force tracking. For example, when the stiffness of the contacting environmental system is high, a smaller correction to the force-controlled displacement is desired to compensate for the force error; conversely, when the stiffness is low, a larger correction is desired to compensate for the force error. Therefore, single admittance control is insufficient to meet practical control requirements. To address this, the fuzzy control in step S104 directly adjusts the parameters of the admittance control model using the feedback force error and position information, thereby indirectly adapting to the system stiffness. Since modifying the mass coefficient M can easily cause system oscillations, the fuzzy control in step S104 only modifies the damping coefficient B. The basic principle is: when the rate of change of force tracking error and position tracking error is large, the damping coefficient B decreases accordingly, thereby accelerating the control system's response speed; when the rate of change of force tracking error and position tracking error is small, the damping coefficient B decreases accordingly, thereby reducing the overshoot of the control system. The established fuzzy control rules are shown in Table 1, where the force tracking error e f =F e -F d The first derivative of the position tracking error NB represents negative big, NS represents negative middle, ZE represents zero, PS represents positive small, and PB represents positive big. In the field of fuzzy control, NB, NS, ZE, PS, and PB are used to represent membership relationships.

[0068] Table 1

[0069]

[0070] It should be noted that, unlike the current constant force control strategy, this invention benefits from the fuzzy control in step S104. In the variable stiffness environment, instead of adjusting the stiffness coefficient K, it autonomously adjusts to obtain the optimal damping coefficient B to achieve a fast dynamic response speed, thereby further realizing the effect of rapid attitude adjustment.

[0071] Step S106: Based on the actual contact force, optimize the admittance control model using adaptive control theory;

[0072] Specifically, in step S106, based on the actual contact force, the desired grinding position, and the desired contact force, the admittance control model is optimized using adaptive control theory to obtain the optimized admittance control model. Where M and B represent the mass coefficient and damping coefficient of the second-order system, respectively; force tracking error e f =F e (t)-F dF e F represents the actual contact force. d Indicates an expectation of contact force. The force error integral term is represented by η, which represents the adaptive factor; the position tracking error is e = x(t) - x r (t), x r The expected polishing position is indicated by x, the actual polishing position is indicated by t, and the polishing time point is indicated by t.

[0073] Step S106 preferably, as follows: Figure 5 As shown, the constant force control algorithm in this method is specifically an improved admittance control algorithm that combines fuzzy control theory (step S104) and adaptive control theory (step S106). The adaptive control in step S106 addresses the situation where environmental information is unknown, aiming to solve the modeling error problem caused by uncertain environmental information. Its core idea is to minimize the force error directly by designing a simple adaptive control law when tracking an unknown environment. This adaptive control algorithm does not require information about the environmental stiffness (i.e., K = 0), at which point the admittance control model... Transformed into Simultaneously consider x r Compared with the actual environment x e There exists an error δx=x e -x r Define e' = e + δx, then the admittance control model becomes

[0074] If x e If it is a plane, then It can achieve precise force tracking; however, if x e It is an inclined plane or a more complex curved surface, x e And δx is time-varying, at which point it exists and or and Force tracking error will always exist. To eliminate the error, the admittance control model is optimized based on the adaptive control in step S106. Where M and B represent the mass coefficient and damping coefficient of the second-order system, respectively; force tracking error e f =F e (t)-F d F e F represents the actual contact force. d Indicates an expectation of contact force. The force error integral term is represented by η, which represents the adaptive factor; the position tracking error is e = x(t) - x r (t), x r The expected polishing position is indicated by x, the actual polishing position is indicated by t, and the polishing time point is indicated by t.

[0075] An adaptive factor η is introduced to compensate for the force tracking error term. η should be selected with an appropriate value within the range [0,1] based on the force control system. Furthermore, as the system's operating time increases, the force error integral will... Continuous accumulation, even reaching an upper limit of 1, cannot eliminate the force error. To avoid this situation, the force error integral term should be adjusted. Limit the amplitude, that is, restrict The upper limit.

[0076] It should be noted that, unlike the current constant force control strategy, which does not require prior information about the environment for unknown environments, this invention benefits from the adaptive control in step S106, which compensates for inaccuracies in the environmental position and stiffness as well as the robot dynamics modeling, thereby achieving the purpose of eliminating steady-state errors and high-precision force-controlled grinding. The maximum force tracking error can be controlled within ±1.5N.

[0077] Step S108: Update the target grinding trajectory of the grinding tool by adjusting and optimizing the admittance control model.

[0078] Specifically, step S108 involves updating the target grinding trajectory X of the grinding tool by adjusting and optimizing the admittance control model. d =x r +e, where e represents the position tracking error, x r This indicates a desire to have the area polished.

[0079] Based on inverse kinematics theory, the target grinding trajectory is converted into joint angles, which are used to control the grinding tool to grind the workpiece.

[0080] Through steps S101 to S108 in the embodiments of this application, the problem of large tracking error in automated workpiece grinding is solved, and the optimal force control parameters based on fuzzy control algorithm are dynamically and quickly adjusted, as well as the real environment position and stiffness compensation based on adaptive control algorithm are achieved, thereby eliminating steady-state error and improving workpiece grinding accuracy.

[0081] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0082] This application provides a fuzzy adaptive constant force grinding system. Figure 6 This is a structural block diagram of a fuzzy adaptive constant force grinding system according to an embodiment of this application, as shown below. Figure 6 As shown, the system includes a real-time acquisition module, a fuzzy control module, an adaptive control module, and a polishing and updating module;

[0083] The real-time acquisition module is used to calculate the actual contact force during the grinding process in real time based on the digital signal from the force sensor received during the workpiece grinding process.

[0084] The fuzzy control module is used to adjust the damping coefficient of the admittance control model based on the actual contact force using fuzzy control theory.

[0085] The adaptive control module is used to optimize the admittance control model based on the actual contact force using adaptive control theory.

[0086] The grinding update module is used to update the target grinding trajectory of the grinding tool by adjusting and optimizing the admittance control model.

[0087] The real-time acquisition module, fuzzy control module, adaptive control module, and grinding update module in this application embodiment solve the problem of large tracking error in automated workpiece grinding, realize the dynamic and rapid adjustment of the optimal force control parameters based on the fuzzy control algorithm, and the real environmental position and stiffness compensation based on the adaptive control algorithm, thereby achieving the effect of eliminating steady-state error and improving workpiece grinding accuracy.

[0088] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0089] This application provides a grinding control device, including a force controller and a position controller, which are configured to control the grinding tool to grind the workpiece based on the method described in the above method embodiment.

[0090] This embodiment also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0091] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0092] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0093] Furthermore, in conjunction with the fuzzy adaptive constant force grinding method in the above embodiments, this application embodiment can provide a storage medium for implementation. This storage medium stores a computer program; when executed by a processor, the computer program implements any one of the fuzzy adaptive constant force grinding methods in the above embodiments.

[0094] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, a network interface, a display screen, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a fuzzy adaptive constant force grinding method. The display screen may be a liquid crystal display (LCD) or an e-ink display. The input device may be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0095] In one embodiment, Figure 7 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application, such as... Figure 7 As shown, an electronic device is provided, which can be a server, and its internal structure diagram can be as follows. Figure 7 As shown, the electronic device includes a processor, a network interface, internal memory, and non-volatile memory connected via an internal bus. The non-volatile memory stores the operating system, computer programs, and a database. The processor provides computing and control capabilities, the network interface communicates with external terminals via a network connection, the internal memory provides an environment for the operation of the operating system and computer programs, the computer programs are executed by the processor to implement a fuzzy adaptive constant-force grinding method, and the database stores data.

[0096] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0097] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0098] Those skilled in the art should understand that the technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0099] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A fuzzy adaptive constant force grinding method, characterized in that, The method includes: Based on the digital signals from the force sensor received during the workpiece grinding process, the actual contact force during the grinding process is calculated in real time. Based on the actual contact force, adjust the damping coefficient of the admittance control model; Based on the actual contact force, the desired grinding position, and the desired contact force, the admittance control model is optimized using adaptive control theory to obtain the optimized admittance control model. ,in, M and B These represent the mass coefficient and damping coefficient of the second-order system, respectively; force tracking error. e f =F e ( t )- F d , F e Indicates the actual contact force. F d Indicates an expectation of contact force. This represents the integral term of force error. Indicates the adaptive factor; position tracking error e=x ( t )- x r ( t ), x r They expressed their expectation for the position to be refined. x Indicates the actual polishing location; t Indicates the time point of polishing; The adaptive factor The force control system selects an appropriate value within the range [0, 1] to compensate for the force tracking error. e f =F e ( t )- F d And the force error integral term exists Upper limit; The target grinding trajectory of the grinding tool is updated by adjusting and optimizing the admittance control model.

2. The method according to claim 1, characterized in that, Before calculating the actual contact force during the grinding process in real time based on the digital signal from the force sensor received during the workpiece grinding process, the method includes: Based on the expected polishing location, determine the expected polishing trajectory; Based on the expected grinding trajectory and the pre-input expected contact force, the grinding tool is controlled to grind the workpiece through the admittance control model.

3. The method according to claim 1, characterized in that, Based on the actual contact force, the damping coefficient of the admittance control model is adjusted as follows: Based on the actual contact force, the desired grinding position, and the desired contact force, the admittance control model is adjusted using fuzzy control theory. The damping coefficient in, where, M , B and K These represent the mass coefficient, damping coefficient, and stiffness coefficient of the second-order system, respectively. F e Indicates the actual contact force. F d Indicates an expectation of contact force. x r They expressed their expectation for the position to be refined. x Indicates the actual polishing location. t This indicates the time point for polishing.

4. The method according to claim 3, characterized in that, The fuzzy control theory includes: If admittance control model Force tracking error e f =F e ( t )- F d and position tracking error e=x ( t )- x r ( t If all values ​​are greater than the corresponding preset threshold, then the damping coefficient of the admittance control model is reduced. If admittance control model Force tracking error e f =F e ( t )- F d and position tracking error e=x ( t )- x r ( t If all values ​​are less than the corresponding preset threshold, then the damping coefficient of the admittance control model is increased.

5. The method according to claim 2, characterized in that, Based on the expected polishing location, the expected polishing trajectory is determined as follows: Obtain the calibration position of the grinding tool and the workpiece as the expected grinding position; Based on the desired polishing location, the desired polishing trajectory is automatically generated using a trajectory planning algorithm.

6. The method according to claim 5, characterized in that, Obtaining the calibration positions of the grinding tools and workpiece includes: Position the center point of the grinding tool relative to the surface of the workpiece. Record the X-axis, Y-axis, and Z-axis position information of the grinding tool displayed on the teach pendant for each positioning point, as the calibration position.

7. The method according to claim 1, characterized in that, The target grinding trajectory of the grinding tool is updated using the adjusted and optimized admittance control model, including: The target grinding trajectory of the grinding tool is updated using the adjusted and optimized admittance control model. X d = x r + e ,in, e Indicates position tracking error. x r This indicates a desire to have the area polished. The target grinding trajectory is converted into joint angles to control the grinding tool to grind the workpiece.

8. A fuzzy adaptive constant force grinding system, characterized in that, The system is used to execute the method according to any one of claims 1 to 7, and the system includes a real-time acquisition module, a fuzzy control module, an adaptive control module, and a polishing and updating module; The real-time acquisition module is used to calculate the actual contact force during the grinding process in real time based on the digital signal from the force sensor received during the workpiece grinding process. The fuzzy control module is used to adjust the damping coefficient of the admittance control model according to the actual contact force. The adaptive control module is used to optimize the admittance control model based on the actual contact force using adaptive control theory. The grinding update module is used to update the target grinding trajectory of the grinding tool through the adjustment and the optimized admittance control model.

9. A grinding control device, comprising a force controller and a position controller, characterized in that, The force controller and position controller are configured to control the grinding tool to grind the workpiece based on the method of any one of claims 1 to 7.

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