Process regulation method and device based on robot milling stability improvement

Abstract

The application discloses a process control method and device based on robot milling stability improvement. The method comprises the following steps: optimizing the tool feed direction based on a modal coupling flutter prediction model; optimizing the redundant angle through a regenerative flutter prediction model; and optimizing the machining parameters, including the spindle speed and the axial depth of cut, according to the optimized tool feed direction and the redundant angle. It can be seen that the application comprehensively considers the feed direction, the machining pose and the process parameters, comprehensively considers the modal coupling flutter and the regenerative flutter effects, avoids the flutter effect by controlling the process, and realizes the improvement of the robot milling stability.

Description

Technical Field

[0001] This invention relates to the field of processing, and in particular to a process control method, apparatus, electronic device, and computer-readable storage medium based on improving the stability of robotic milling. Background Technology

[0002] With the continuous development of the manufacturing industry, robots are no longer limited to simple welding, handling, and painting—areas that do not require high precision—but are gradually expanding into fields such as grinding, polishing, drilling, and milling. Compared to machine tool processing, robots have significant advantages. Their wider processing range makes it possible to process large and complex structures, and they offer better cost-effectiveness and greater flexibility. Therefore, they are more suitable for processing large and complex structural components in fields such as shipbuilding, aerospace, wind power, and automobile manufacturing, and play a significant role in improving the level of automated manufacturing and reducing processing costs.

[0003] However, compared to grinding, polishing, and drilling, robots are currently used less in the milling process. In addition to the fact that the problem of machining accuracy has not been well solved, the main reason for its limited application is the chatter effect caused by the robot's weak rigidity structure. This not only seriously restricts the quality of the machined surface, but also accelerates tool wear and may even damage the accuracy of the machining equipment.

[0004] How to control the process to avoid chatter and improve the stability of robotic milling has become an urgent problem to be solved. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a process control method, apparatus, electronic device, and computer-readable storage medium based on improving the stability of robotic milling to overcome or at least partially solve the above problems.

[0006] One embodiment of the present invention provides a process control method based on improving the stability of robotic milling, the method comprising:

[0007] The tool feed direction is optimized based on the modal coupling chatter prediction model;

[0008] The redundancy angle is optimized using a regenerative flutter prediction model;

[0009] The machining parameters are optimized based on the optimized tool feed direction and redundancy angle. The machining parameters include spindle speed and axial depth of cut.

[0010] Optionally, the optimization of the tool feed direction based on the modal coupling chatter prediction model includes:

[0011] A modal coupling chatter prediction model is established. Given a fixed spindle speed, feed rate, and radial depth of cut, the model predicts whether modal coupling chatter will occur, and obtains the modal coupling chatter prediction results for different tool feed directions and axial depths of cut.

[0012] The tool feed direction is optimized based on the modal coupling chatter prediction results of different tool feed directions and axial depth of cut.

[0013] Optionally, the modal coupling flutter prediction model includes:

[0014] when At this time, modal coupling flutter occurs;

[0015] Among them, k' x =k x -k p sinα0cosγ0,k' y =k y -k p cosα0sinγ0;k x and k y For the principal stiffness of the robotic machining system; k p The cutting stiffness is defined as follows: the x-axis of the workpiece coordinate system is aligned with the tool feed direction; α0 is the angle between the principal stiffness coordinate system and the tool coordinate system; γ0 is the angle between the resultant milling force and the x-axis of the principal stiffness coordinate system, and γ0 = β0 + γ - α0; β0 is the angle between the resultant milling force and the x-axis of the workpiece coordinate system, and γ is the angle between the workpiece coordinate system and the tool coordinate system, i.e., the tool feed direction.

[0016] Optionally, the optimization of the redundancy angle using the regenerative flutter prediction model includes:

[0017] A regenerative chatter prediction model is established. Given the spindle speed, feed rate, and radial depth of cut, the limit cutting depth corresponding to each achievable redundant angle is determined based on the regenerative chatter prediction model, and a stability prediction diagram of the redundant angle and the limit cutting depth is obtained.

[0018] The redundant angle is optimized based on the stability prediction diagram of the redundant angle and the limit cutting depth.

[0019] Another embodiment of the present invention provides a process control device based on improving the stability of robotic milling, comprising:

[0020] The tool feed direction optimization unit is used to optimize the tool feed direction based on the modal coupling chatter prediction model;

[0021] Redundancy angle optimization unit, used to optimize redundancy angle through regenerative flutter prediction model;

[0022] The machining parameter optimization unit is used to optimize the machining parameters based on the optimized tool feed direction and redundancy angle. The machining parameters include spindle speed and axial depth of cut.

[0023] Optionally, the tool feed direction optimization unit is further configured to:

[0024] A modal coupling chatter prediction model is established. Given a fixed spindle speed, feed rate, and radial depth of cut, the model predicts whether modal coupling chatter will occur, and obtains the modal coupling chatter prediction results for different tool feed directions and axial depths of cut.

[0025] The tool feed direction is optimized based on the modal coupling chatter prediction results of different tool feed directions and axial depth of cut.

[0026] Optionally, the modal coupling flutter prediction model includes:

[0027] when At this time, modal coupling flutter occurs;

[0028] Among them, k' x =k x -k p sinα0cosγ0,k' y =k y -k p cosα0sinγ0;k x and k y For the principal stiffness of the robotic machining system; k p The cutting stiffness is defined as follows: the x-axis of the workpiece coordinate system is aligned with the tool feed direction; α0 is the angle between the principal stiffness coordinate system and the tool coordinate system; γ0 is the angle between the resultant milling force and the x-axis of the principal stiffness coordinate system, and γ0 = β0 + γ - α0; β0 is the angle between the resultant milling force and the x-axis of the workpiece coordinate system, and γ is the angle between the workpiece coordinate system and the tool coordinate system, i.e., the tool feed direction.

[0029] Optionally, the redundancy angle optimization unit is further used for:

[0030] A regenerative chatter prediction model is established. Given the spindle speed, feed rate, and radial depth of cut, the limit cutting depth corresponding to each achievable redundant angle is determined based on the regenerative chatter prediction model, and a stability prediction diagram of the redundant angle and the limit cutting depth is obtained.

[0031] The redundant angle is optimized based on the stability prediction diagram of the redundant angle and the limit cutting depth.

[0032] Another embodiment of the present invention provides an electronic device, wherein the electronic device includes:

[0033] Processor; and,

[0034] A memory is configured to store computer-executable instructions, which, when executed, cause the processor to perform the methods described above.

[0035] Another embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs that, when executed by a processor, implement the method described above.

[0036] The beneficial effects of this invention are that it optimizes the tool feed direction based on a modal coupling chatter prediction model, optimizes the redundancy angle through a regenerative chatter prediction model, and optimizes the machining parameters based on the optimized tool feed direction and redundancy angle. This invention comprehensively considers the feed direction, machining pose, and process parameters, and integrates both modal coupling chatter and regenerative chatter effects. By controlling the process, it avoids chatter effects and improves the stability of robotic milling. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating a process control method for improving the stability of robotic milling according to an embodiment of the present invention.

[0038] Figure 2 This is a schematic diagram of the tool coordinate system, workpiece coordinate system, and principal stiffness coordinate system according to an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of the modal coupling flutter prediction result according to an embodiment of the present invention;

[0040] Figure 4 This is a stability prediction diagram of redundancy angle and limiting cutting depth according to an embodiment of the present invention;

[0041] Figure 5 This is a lobe diagram illustrating the stability of regenerative chatter milling under two machining poses before and after optimization, according to an embodiment of the present invention.

[0042] Figure 6 This is a schematic diagram of a process control device based on improving the stability of robotic milling according to an embodiment of the present invention;

[0043] Figure 7 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention is shown;

[0044] Figure 8 A schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present invention is shown. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0046] Machining chatter is mainly caused by two effects: modal coupling chatter and regenerative chatter. Modal coupling chatter is caused by the simultaneous vibration of the robot machining system's mass in all degrees of freedom directions, with different amplitudes and phases. The low stiffness of the robot structure and the significant stiffness differences in different directions are the main factors leading to the easy generation of modal coupling chatter. Regenerative chatter refers to the change in chip thickness and cutting force during machining due to the phase difference between the wavy surface left by the previous cutting tooth and the wavy surface generated by the current cutting tooth.

[0047] Current methods for predicting chatter in robotic milling only consider single modal coupling chatter effects or regenerative chatter effects. However, the structural characteristics of robots mean that both types of chatter may exist during the milling process. Therefore, both types of chatter should be considered comprehensively for robotic milling process control.

[0048] Figure 1 This is a flowchart illustrating a process control method for improving the stability of robotic milling according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0049] S11: Optimize the tool feed direction based on the modal coupling chatter prediction model;

[0050] Understandably, for the same robotic machining system, there exists a relatively fixed range of tool feed directions prone to modal coupling chatter, which does not change with variations in machining pose. This invention can determine the range of tool feed directions prone to modal coupling chatter in a given robotic machining system based on a modal coupling chatter prediction model, and avoid selecting tool feed directions prone to modal coupling chatter while meeting machining accuracy, quality, and efficiency requirements. Therefore, modal coupling chatter can be avoided by adjusting the tool feed direction.

[0051] S12: Optimize the redundancy angle using a regenerative flutter prediction model;

[0052] It should be noted that rotating the robot around the tool axis coordinate system by any angle along the tool axis direction can achieve machining in different poses without changing the machining position and the tool axis direction. This rotation angle is defined as the redundancy angle.

[0053] The machining pose used by a robotic machining system has a significant impact on the tool tip dynamics; therefore, optimizing the machining pose is one method to avoid regenerative chatter. Robotic machining systems have the advantage of motion redundancy, enabling different robot postures for the same machining position and tool axis direction. Optimizing the redundancy angles further optimizes the machining pose.

[0054] S13: Optimize the machining parameters based on the optimized tool feed direction and redundancy angle. The machining parameters include spindle speed and axial depth of cut.

[0055] Understandably, this invention optimizes machining parameters using a regenerative chatter prediction model under optimized tool feed direction and redundancy angle, thereby determining the preferred process combination to avoid chatter.

[0056] This invention discloses a process control method for improving robot milling stability. It optimizes the tool feed direction based on a modal coupling chatter prediction model and optimizes the redundancy angle using a regenerative chatter prediction model. Based on the optimized tool feed direction and redundancy angle, the machining parameters are then optimized. This invention comprehensively considers feed direction, machining pose, and process parameters, taking into account both modal coupling chatter and regenerative chatter effects. By controlling the process, chatter effects are avoided, thus improving the stability of robot milling.

[0057] In an optional embodiment of the present invention, the optimization of the tool feed direction based on the modal coupling chatter prediction model includes:

[0058] A modal coupling chatter prediction model is established. Given a fixed spindle speed, feed rate, and radial depth of cut, the model predicts whether modal coupling chatter will occur, and obtains the modal coupling chatter prediction results for different tool feed directions and axial depths of cut.

[0059] The tool feed direction is optimized based on the modal coupling chatter prediction results of different tool feed directions and axial depth of cut.

[0060] Specifically, the modal coupling flutter prediction model includes:

[0061] when At this time, modal coupling flutter occurs;

[0062] Among them, k' x =k x -k p sinα0cosγ0,k' y =k y -k p cosα0sinγ0;k x and k y For the principal stiffness of the robotic machining system; k pThe cutting stiffness is defined as follows: the x-axis of the workpiece coordinate system is aligned with the tool feed direction; α0 is the angle between the principal stiffness coordinate system and the tool coordinate system; γ0 is the angle between the resultant milling force and the x-axis of the principal stiffness coordinate system, and γ0 = β0 + γ - α0; β0 is the angle between the resultant milling force and the x-axis of the workpiece coordinate system, and γ is the angle between the workpiece coordinate system and the tool coordinate system, i.e., the tool feed direction.

[0063] In practical applications, such as Figure 2 The tool coordinate system {c}, workpiece coordinate system {w}, and principal stiffness coordinate system {k} are established. The workpiece coordinate system {w} has a x-axis x... w Consistent with the tool feed direction, γ is the angle between the workpiece coordinate system {w} and the tool coordinate system {c}, i.e., the tool feed direction; α0 is the angle between the principal stiffness coordinate system {k} and the tool coordinate system {c}; β0 is the angle between the milling resultant force F and the x-axis of the workpiece coordinate system {w}. w The angle between the milling resultant force and the tool coordinate system {c}x-axis; β is the angle between the milling resultant force and the tool coordinate system {c}x-axis. c The angle between the resultant milling force F and the principal stiffness coordinate system {k}x-axis, and β=β0+γ; γ0 is the angle between the resultant milling force F and the principal stiffness coordinate system {k}x-axis. k The angle between them is given by γ0 = β0 + γ - α0.

[0064]

[0065] Based on the above formula (1), the conditions for the generation of modal coupling flutter can be analyzed. First, when λ is a negative real number, the system is stable; when λ is a complex number, the system is unstable. Therefore, when...

[0066]

[0067] At the time of establishment, the unstable region of the system was

[0068]

[0069] Only when k p >|k' x -k' y Formula (3) is only valid under certain conditions. Therefore, formula (3) can be used as the basis for determining whether modal coupling flutter has occurred.

[0070] According to formula (2), the principal stiffness k of the robot machining system x and k y The angle α0 between the principal stiffness coordinate system {k} and the tool coordinate system {c} is determined by the machining pose, and the machining pose can be optimized by optimizing the redundant angle; cutting stiffness k p It is mainly related to the machining parameters. γ0 is related to the tool feed direction. Therefore, the occurrence of modal coupling chatter during milling can be avoided by adjusting the machining parameters and the tool feed direction.

[0071] In a specific implementation of the practical application, the robot pose is selected as (-70.86°, -65.67°, 112.68°, -28.35°, -52.38°, -75.63°), the spindle speed n is 500 r / min, the feed rate f is 0.05 m / s, and the radial depth of cut a is... e The value is 5mm. Based on the aforementioned modal coupling flutter prediction model, it is predicted whether modal coupling flutter will occur. Figure 3 As shown, the relationship between different feed directions γ and axial depth of cut a is plotted. p The image below shows the predicted modal coupling chatter during reverse milling. The region enveloped by the curve represents the predicted modal coupling chatter area, while the remaining areas represent the predicted stable machining region. From... Figure 3 It can be seen that modal coupling chatter is prone to occur in the feed direction range of 90°–120° and 270°–300°. Therefore, it is necessary to determine the tool feed direction outside of the above ranges.

[0072] Furthermore, the optimization of the redundancy angle using the regenerative flutter prediction model includes:

[0073] A regenerative chatter prediction model is established. Given the spindle speed, feed rate, and radial depth of cut, the limit cutting depth corresponding to each achievable redundant angle is determined based on the regenerative chatter prediction model, and a stability prediction diagram of the redundant angle and the limit cutting depth is obtained.

[0074] The redundant angle is optimized based on the stability prediction diagram of the redundant angle and the limit cutting depth.

[0075] In practical applications, the regenerative flutter prediction model is a two-degree-of-freedom flutter prediction model that considers the regenerative effect, and it can be solved using the fully discrete method.

[0076] Specifically, the two-degree-of-freedom flutter prediction model considering the regeneration effect is shown in Equation (4):

[0077]

[0078] Where m, c, and k are modal mass, modal damping, and modal stiffness, respectively, and a p h is the axial depth of cut. xx h xy h yx h yy As shown in formula (5):

[0079]

[0080] Among them, K tc and K rc These are the tangential and radial cutting force coefficients, respectively; Let θ be the angular position of the j-th tooth of the milling cutter, which can be expressed by formula (6):

[0081]

[0082] Where Ω is the rotational speed and N is the number of cutter teeth;

[0083] Window function g(φ) j (t) is used to determine whether the j-th tooth of the milling cutter is in the cutting state, and can be expressed as formula (7):

[0084]

[0085] in, and Let the entry angle and exit angle of the j-th cutting tooth be respectively:

[0086]

[0087] Among them, a e D is the radial depth of cut, and D is the tool diameter.

[0088] definition Converting formula (4) into a state equation yields:

[0089]

[0090] Where A0 is a constant matrix representing the time-invariant characteristics of the system, B(t) is a periodic coefficient matrix, and they are represented as follows; U(t) is the state term, and U(tT) is the time delay term.

[0091]

[0092] In practical applications, there are an infinite number of selectable redundancy angles within the reach of the robot system. Different redundancy angles θ can be achieved by rotating the robot body around the tool axis coordinate system by different angles. x The selection of [the angle]. In this embodiment of the invention, the inverse kinematics solution is solved for the machining pose corresponding to each reachable redundant angle. After determining each joint angle, the corresponding limiting cutting depth α can be determined based on the aforementioned regenerative chatter prediction model. plim A stability prediction map of the redundancy angle and the limiting cutting depth is obtained. Then, the redundancy angle is optimized based on the stability prediction map of the redundancy angle and the limiting cutting depth.

[0093] In a specific implementation of the practical application, the spindle speed n is selected as 3000 r / min, the feed rate f is 0.05 m / s, and the radial depth of cut a is... eThe value is 1mm. Using the above method, the stability prediction diagram of the redundancy angle and the limit cutting depth is shown in Figure 4. In the figure, the sector represents the achievable pose area of ​​the robot at the selected machining position. The gray area is the stable machining area, and the white area is the regenerative chatter area.

[0094] The following section verifies the process control method proposed in this invention through milling. For the machining position and tool axis direction at machining poses of (-81.18, -56.37, 87.77, -3.63, -31.87, 4.02), the preferred tool feed direction is 0° based on modal coupling chatter prediction results. Figure 4 The regenerative chatter prediction results considering the redundancy angle shown are preferably such that the redundancy angle with a large stable limit cutting depth is 100°, and the machining pose at this time is (-73.65, -52.35, 79.86, 65.98, -51.48, -53.14). Figure 5 To optimize the regenerative chatter milling stability lobe diagram under the two machining poses before and after optimization, the dashed line in the figure represents the prediction result under the unoptimized pose, and the solid line represents the prediction result under the optimized pose. '●' indicates the optimized combination of machining parameters. Figure 5 It is evident that the optimized milling stability domain expands, allowing for a larger axial depth of cut, thereby increasing material removal rate and improving machining efficiency. Based on the chatter prediction results, the tool feed direction was optimized to 0°, and the machining system redundancy angle was optimized to 100°. A comparison was then made between the regenerative chatter stability domains before and after optimization. The results show that the proposed process control method can effectively expand the milling stability domain, thereby increasing material removal rate and improving machining efficiency by selecting a larger axial depth of cut.

[0095] Figure 6 This is a schematic diagram of a process control device for improving the stability of robotic milling according to an embodiment of the present invention. Figure 6 As shown, the device includes:

[0096] The tool feed direction optimization unit 61 is used to optimize the tool feed direction based on the modal coupling chatter prediction model;

[0097] Redundancy angle optimization unit 62 is used to optimize the redundancy angle through the regenerative flutter prediction model;

[0098] The machining parameter optimization unit 63 is used to optimize the machining parameters based on the optimized tool feed direction and redundancy angle. The machining parameters include spindle speed and axial depth of cut.

[0099] This invention discloses a process control device for improving robot milling stability. It optimizes the tool feed direction based on a modal coupling chatter prediction model and optimizes the redundancy angle using a regenerative chatter prediction model. Based on the optimized tool feed direction and redundancy angle, the machining parameters are further optimized. This invention comprehensively considers feed direction, machining pose, and process parameters, taking into account both modal coupling chatter and regenerative chatter effects. By controlling the process, it avoids chatter effects and improves the stability of robot milling.

[0100] In an optional embodiment of the present invention, the tool feed direction optimization unit 61 is further configured to:

[0101] A modal coupling chatter prediction model is established. Given a fixed spindle speed, feed rate, and radial depth of cut, the model predicts whether modal coupling chatter will occur, and obtains the modal coupling chatter prediction results for different tool feed directions and axial depths of cut.

[0102] The tool feed direction is optimized based on the modal coupling chatter prediction results of different tool feed directions and axial depth of cut.

[0103] Specifically, the modal coupling flutter prediction model includes:

[0104] when At this time, modal coupling flutter occurs;

[0105] Among them, k' x =k x -k p sinα0cosγ0,k' y =k y -k p cosα0sinγ0;k x and k y For the principal stiffness of the robotic machining system; k p The cutting stiffness is defined as follows: the x-axis of the workpiece coordinate system is aligned with the tool feed direction; α0 is the angle between the principal stiffness coordinate system and the tool coordinate system; γ0 is the angle between the resultant milling force and the x-axis of the principal stiffness coordinate system, and γ0 = β0 + γ - α0; β0 is the angle between the resultant milling force and the x-axis of the workpiece coordinate system, and γ is the angle between the workpiece coordinate system and the tool coordinate system, i.e., the tool feed direction.

[0106] Redundancy angle optimization unit 62 is further used for:

[0107] A regenerative chatter prediction model is established. Given the spindle speed, feed rate, and radial depth of cut, the limit cutting depth corresponding to each achievable redundant angle is determined based on the regenerative chatter prediction model, and a stability prediction diagram of the redundant angle and the limit cutting depth is obtained.

[0108] The redundant angle is optimized based on the stability prediction diagram of the redundant angle and the limit cutting depth.

[0109] It should be noted that the process control device based on robot milling stability improvement in the above embodiments can be used to execute the methods in the foregoing embodiments, so they will not be described in detail one by one.

[0110] In summary, this invention optimizes the tool feed direction based on a modal coupling chatter prediction model, optimizes the redundancy angle through a regenerative chatter prediction model, and optimizes machining parameters based on the optimized tool feed direction and redundancy angle. This invention comprehensively considers feed direction, machining pose, and process parameters, taking into account both modal coupling chatter and regenerative chatter effects. By controlling the process, it avoids chatter effects and improves the stability of robotic milling.

[0111] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] It should be noted that:

[0113] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. Various general-purpose devices can also be used in conjunction with the teachings herein. The required structure for constructing such devices is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0114] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0115] Similarly, it should be understood that, in order to simplify the invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0116] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0117] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0118] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the device for detecting the wearing status of an electronic device according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can take the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0119] For example, Figure 7 A schematic diagram of an electronic device according to an embodiment of the present invention is shown. The electronic device conventionally includes a processor 71 and a memory 72 arranged to store computer-executable instructions (program code). The memory 72 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Figure 1 Storage space 73 for program code 74 of any method steps shown and in any of the embodiments. For example, storage space 73 for storing program code may include various program codes 74 for implementing the various steps in the methods above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. Such computer program products are typically, for example, Figure 8 The aforementioned computer-readable storage medium. This computer-readable storage medium may have the same characteristics as... Figure 7 The memory 72 in the electronic device is similarly arranged as a storage segment, storage space, etc. The program code can be compressed, for example, in a suitable form. Typically, the storage space stores program code 81 for performing the steps of the method according to the invention; that is, it can contain program code, such as that read by a processor 71, which, when run by the electronic device, causes the electronic device to perform the various steps of the method described above.

[0120] The above description is merely a specific embodiment of the present invention. Under the teachings of the present invention, those skilled in the art can make other improvements or modifications based on the above embodiments. Those skilled in the art should understand that the above specific description is only to better explain the purpose of the present invention, and the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A process control method based on improving the stability of robotic milling, characterized in that, include: The tool feed direction is optimized based on the modal coupling chatter prediction model; The redundancy angle is optimized using a regenerative flutter prediction model; The machining parameters are optimized based on the optimized tool feed direction and redundancy angle, including spindle speed and axial depth of cut; The optimization of the tool feed direction based on the modal coupling chatter prediction model includes: A modal coupling chatter prediction model is established. Given a fixed spindle speed, feed rate, and radial depth of cut, the model predicts whether modal coupling chatter will occur, and obtains the modal coupling chatter prediction results for different tool feed directions and axial depths of cut. The tool feed direction is optimized based on the modal coupling chatter prediction results of different tool feed directions and axial depth of cut. The modal coupling flutter prediction model includes: when At this time, modal coupling flutter occurs; in, , ; and For the principal stiffness of the robot machining system; For cutting stiffness; the x-axis of the workpiece coordinate system is aligned with the tool feed direction; α 0 is the angle between the principal stiffness coordinate system and the tool coordinate system; Let be the angle between the resultant milling force and the x-axis of the principal stiffness coordinate system, and ; β 0 represents the angle between the resultant milling force and the x-axis of the workpiece coordinate system. γ The angle between the workpiece coordinate system and the tool coordinate system is the tool feed direction.

2. The method according to claim 1, characterized in that, The optimization of the redundancy angle using the regenerative flutter prediction model includes: A regenerative chatter prediction model is established. Given the spindle speed, feed rate, and radial depth of cut, the limit cutting depth corresponding to each achievable redundant angle is determined based on the regenerative chatter prediction model, and a stability prediction diagram of the redundant angle and the limit cutting depth is obtained. The redundant angle is optimized based on the stability prediction diagram of the redundant angle and the limit cutting depth.

3. A process control device for improving the stability of robotic milling, characterized in that, include: The tool feed direction optimization unit is used to optimize the tool feed direction based on the modal coupling chatter prediction model; Redundancy angle optimization unit, used to optimize redundancy angle through regenerative flutter prediction model; The machining parameter optimization unit is used to optimize the machining parameters based on the optimized tool feed direction and redundancy angle. The machining parameters include spindle speed and axial depth of cut. The tool feed direction optimization unit is further used for: A modal coupling chatter prediction model is established. Given a fixed spindle speed, feed rate, and radial depth of cut, the model predicts whether modal coupling chatter will occur, and obtains the modal coupling chatter prediction results for different tool feed directions and axial depths of cut. The tool feed direction is optimized based on the modal coupling chatter prediction results of different tool feed directions and axial depth of cut. The modal coupling flutter prediction model includes: when At this time, modal coupling flutter occurs; in, , ; and For the principal stiffness of the robot machining system; For cutting stiffness; the x-axis of the workpiece coordinate system is aligned with the tool feed direction; α 0 is the angle between the principal stiffness coordinate system and the tool coordinate system; Let be the angle between the resultant milling force and the x-axis of the principal stiffness coordinate system, and ; β 0 represents the angle between the resultant milling force and the x-axis of the workpiece coordinate system. γ The angle between the workpiece coordinate system and the tool coordinate system is the tool feed direction.

4. The apparatus according to claim 3, characterized in that, The redundancy angle optimization unit is further used for: A regenerative chatter prediction model is established. Given the spindle speed, feed rate, and radial depth of cut, the limit cutting depth corresponding to each achievable redundant angle is determined based on the regenerative chatter prediction model, and a stability prediction diagram of the redundant angle and the limit cutting depth is obtained. The redundant angle is optimized based on the stability prediction diagram of the redundant angle and the limit cutting depth.

5. An electronic device, characterized in that, The electronic device includes: Processor; and, A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1-2.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs that, when executed by a processor, implement the method of any one of claims 1-2.

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