Coordinated motion planning control method in five-axis machining process and related product

Abstract

The invention relates to the technical field of numerical control machine tool precision detection and compensation, in particular to a coordinated motion planning control method and related products in the five-axis machining process, and the method comprises the steps: building a machine tool digital twin model, and combining constraint conditions; calculating a dynamic load rate and adjusting an error weight according to the load rate; an optimal cooperative motion instruction is obtained through multi-objective optimization; obtaining a feed-forward compensation amount and a feedback compensation amount; fusing the feedforward compensation amount and the feedback compensation amount to obtain a total compensation amount, and correcting the optimal cooperative motion instruction in real time to generate a final control signal; according to the method, dynamic constraints of different dimensions of a shaft are unified, the real-time dynamic load rate of the shaft is calculated, and weight coefficients of a translation shaft error and a rotation shaft error in an objective function are dynamically adjusted and optimized according to the difference; and constructing a multi-objective optimization function, solving an optimal cooperative motion instruction according to the dynamic weight, and integrating feed-forward compensation and feedback compensation to generate a final control signal so as to realize accurate control.

Description

Technical Field

[0001] The present invention relates to the technical field of precision detection and compensation of numerically controlled machine tools, and particularly relates to a coordinated motion planning control method and related products during five-axis machining. Background Art

[0002] Five-axis machining technology is one of the key technologies in modern manufacturing. Through the motion control of five degrees of freedom, it can achieve high-precision machining of workpieces with complex shapes. However, during five-axis machining, the motion coordination problem between the translational axis and the rotational axis has always been a key factor restricting machining accuracy and efficiency. Traditional control methods often only focus on the speed and acceleration planning of the translational axis, while ignoring the angular velocity and angular acceleration constraints of the rotational axis, resulting in the situation that the rotational axis may not keep up with the translational axis during actual machining, thus causing mechanical impact and over-cutting phenomena.

[0003] Five-axis machining simulation technology is the basis and core of CNC technology. Based on this technology, the CNC system of the machine tool can first simulate and then precisely control each motion coordinate axis of the machine tool to achieve the goal that the cutting tool can travel along a predetermined path relative to the workpiece for cutting machining. However, during the traveling process, what kind of motion form is another question worthy of consideration. Any moving machinery has its own motion dynamic characteristic constraint parameters such as speed / acceleration. To ensure machining efficiency and increase the motion speed / acceleration, it is necessary to ensure that it is within the dynamic characteristic constraint range allowed by the moving machinery. The same is true for CNC machine tools. Five-axis machining CNC machine tools are a kind of moving machinery that includes translational axes and rotational axes. Due to the non-uniformity of the translational and rotational dimensions, how to ensure the coordination between the two during motion control has gradually become a key and difficult problem.

[0004] The core elements of numerical control technology lie in the machining simulation and motion control modules. It is precisely relying on these two key technologies that the numerical control system of the machining equipment can accurately regulate each motion axis system to ensure that the cutting tool completes the machining of the workpiece along the predetermined path. However, during the actual motion process, the kinematic parameter planning strategy directly affects the machining quality. Restricted by the dynamic characteristics of the mechanical system, all moving mechanisms have key parameter thresholds such as speed / acceleration. When the CNC machine tool improves machining efficiency, it must strictly follow these dynamic performance boundaries. For a composite motion system such as a five-axis linkage CNC machine tool, there is a dimensional difference between its translational axis and rotational axis. How to achieve the motion coordination of the two types of axis systems has become a technical problem to be solved urgently. The essential differences in the kinematic characteristics of the two types of axis systems result in significant differences in parameters such as their maximum allowable linear velocity and angular acceleration.

[0005] The current mainstream axis system coordination control strategies mainly include:

[0006] 1) Based on the second-order ordinary differential equation, the translation axis dynamic constraint programming algorithm is used to construct the velocity function model by solving the pulse limit conditions;

[0007] 2) A cubic polynomial curve speed control algorithm was used to establish a mathematical model covering jerk, acceleration, velocity and displacement. Although it can complete speed planning for various working conditions, it does not include the angular motion limit parameters of the rotating axis;

[0008] 3) Through the analysis of the multi-axis linkage system, a dimensional conversion matrix is constructed to achieve the numerical mapping of linear velocity and angular velocity, but there are theoretical errors in the conversion process.

[0009] The common problem of existing methods is that directly using the extreme values of the translation axis parameters for rotation axis planning may cause the angular velocity / angular acceleration to exceed the mechanical load capacity, thereby causing axis system motion mismatch. This uncoordinated motion will produce mechanical shock, cause overcut defects in the workpiece, and seriously affect the machining accuracy and surface quality. Therefore, studying the motion coordination control mechanism of the composite axis system of the five-axis machine tool has important engineering practical significance for improving the motion control performance of the CNC system. Summary of the invention

[0010] In order to solve the above technical problems, the present invention provides a coordinated motion planning control method and related products in a five-axis machining process, which integrates a five-axis linear interpolation algorithm and virtual machining technology, and realizes a visual preview of the machining process through digital twin means, constructs a dynamic matching mechanism between the translational axis and the rotational axis, and ensures the spatiotemporal synchronization of multi-axis motion.

[0011] The present invention is achieved through the following technical solutions:

[0012] A coordinated motion planning control method in a five-axis machining process, comprising:

[0013] Build a digital twin model of the machine tool and unify the constraints of the translational and rotational axes;

[0014] Calculate the dynamic load rate of the translation axis and the dynamic load rate of the rotation axis, and dynamically adjust the translation axis error weight and the rotation axis error weight in the optimization objective according to the load rate difference;

[0015] Determine the constraints and obtain the optimal collaborative motion instructions through multi-objective optimization;

[0016] Based on the known natural frequency of the machine tool, a feedforward compensation model is constructed to obtain the feedforward compensation amount;

[0017] Obtain the real-time feedback position of the encoder, calculate the tracking error, perform PID adjustment based on the tracking error, and obtain the feedback compensation amount;

[0018] The total compensation amount is obtained by fusing the feedforward compensation amount and the feedback compensation amount, and the optimal cooperative motion command is corrected in real time to generate the final control signal.

[0019] Specifically, the method for unifying the constraint conditions of the translational axis and the rotational axis includes:

[0020] Obtain the equivalent radius R from the rotation center to the tool tip point, the current angular velocity w and angular acceleration a of the rotational axis;

[0021] Calculate the equivalent linear velocity v of the rotational axis eq : where J = 0.5×R 2 is the equivalent moment of inertia;

[0022] Using the calculated equivalent linear velocity v eq , convert the maximum angular velocity constraint and / or maximum angular acceleration constraint of the rotational axis into the corresponding maximum equivalent linear velocity constraint or maximum equivalent linear acceleration constraint.

[0023] Specifically, the calculation method of the error weight includes:

[0024] According to the linear velocity v of the current translational axis, the preset maximum linear velocity v max , linear acceleration a lin and the preset maximum linear acceleration a lmax , calculate the dynamic load rate T of the translational axis load :

[0025] According to the angular velocity w of the current rotational axis, the preset maximum angular velocity w max , angular acceleration a and the preset maximum angular acceleration a max , calculate the dynamic load rate R of the rotational axis load :

[0026] Based on the difference between the dynamic load rate T of the translational axis load and the dynamic load rate R of the rotational axis load , calculate the translational axis error weight α: where k is a preset adjustment coefficient and e is the natural constant;

[0027] Calculate the rotational axis error weight β: β = 1 - α.

[0028] Specifically, the method for multi-objective optimization includes:

[0029] Construct an optimization objective function, Min∑(α·(ΔX) 2 +β·(ΔC) 2), where ΔX is the motion error or displacement increment of the translation axis in one or more dimensions, and ΔC is the motion error or angle increment of the rotation axis in one or more dimensions;

[0030] Determine the constraint conditions for the optimization process. The constraint conditions include at least: the linear velocity constraint, linear acceleration constraint, and jerk constraint of the translation axis; the angular velocity constraint, angular acceleration constraint, and angular jerk constraint of the rotation axis;

[0031] Use the quadratic programming method to solve the optimal solution that minimizes the optimization objective function under the constraint conditions, and obtain the optimal coordinated motion command.

[0032] Optionally, the constraint conditions for the translation axis are: |v| ≤ v max , |a lin | ≤ a lmax , |J lin | ≤ J linmax ;

[0033] The constraint conditions for the rotation axis are: |w| ≤ w max , |a| ≤ a max , |J rot | ≤ J rotmax ;

[0034] where v is the linear velocity of the translation axis, a lin is the linear acceleration of the translation axis, J lin is the jerk of the translation axis; w is the angular velocity of the rotation axis, a is the angular acceleration of the rotation axis, J rot is the angular jerk of the rotation axis; J linmax is the preset maximum jerk of the translation axis; J rotmax is the preset maximum angular jerk of the rotation axis.

[0035] Specifically, calculate the feedforward compensation amount where K i is the preset amplitude coefficient, φ i is the preset phase coefficient, and t is the time;

[0036] The calculation method for calculating the feedback compensation amount includes:

[0037] Obtain the real-time feedback position of the translation axis and / or rotation axis of the machine tool provided by the encoder;

[0038] Compare the real-time feedback position P encoder (t) with the desired position P sim (t) corresponding to the optimal coordinated motion command, and calculate the tracking error ρ = ||P sim (t) - P encoder (t)||;

[0039] Based on the tracking error ρ and applying the PID control law, calculate the feedback compensation amount δ using the preset PID parameters fb ;

[0040] Calculate the total compensation amount where J -1 (q) is the inverse matrix of the Jacobian matrix of the current pose, and K p and K d are the parameters of the PID controller.

[0041] Furthermore, set the error threshold ρ max , if ρ > ρ max , then start the self-tuning of PID parameters and update the proportional parameter where ρ ref is the error reference value.

[0042] Optionally, the steps of solving using the quadratic programming method include:

[0043] Pre-define the cost matrix Q of the translational axis motion error cost trans and the cost matrix Q of the rotational axis motion error cost rot ;

[0044] Construct the objective function cost matrix Q of the quadratic programming problem, Q = α·Q trans + β·Q rot ;

[0045] Call the quadratic programming solver, take the objective function cost matrix Q and the constraint conditions as inputs for solving, and obtain the optimal coordinated motion instruction.

[0046] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements a coordinated motion planning control method during five-axis machining as described above.

[0047] A computer program product includes a computer program / instructions, and when the computer program / instructions are executed by a processor, it implements a coordinated motion planning control method during five-axis machining as described above.

[0048] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0049] The present invention uniformly processes the different dimensional dynamic constraints of the translational axis and the rotational axis through an equivalent linear velocity model, calculates the real-time dynamic load ratios of the translational axis and the rotational axis using the machine tool digital twin model, and dynamically adjusts the weight coefficients of the translational axis error and the rotational axis error in the optimization objective function according to their differences; constructs a multi-objective optimization function and solves the optimal coordinated motion instruction according to the dynamic weights, and integrates the feedforward compensation and the feedback compensation to generate the final control signal to achieve precise control. Brief Description of the Drawings

[0050] The drawings illustrate exemplary embodiments of the present invention and, together with the description thereof, are used to explain the principles of the present invention. These drawings are included to provide a further understanding of the present invention, and the drawings are included in this specification and form a part of this specification, and do not constitute a limitation on the embodiments of the present invention.

[0051] Figure 1 It is a schematic flow chart of a coordinated motion planning control method during a five-axis machining process according to the present invention.

[0052] Figure 2 It is a schematic flow chart of the methods in Embodiment 3 and Embodiment 4 according to the present invention.

[0053] Figure 3 It is a comparison chart of simulation and measured trajectories according to the present invention. Detailed Embodiments

[0054] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the relevant content and do not limit the present invention.

[0055] In addition, it should be noted that for the sake of convenience of description, only parts related to the present invention are shown in the drawings.

[0056] Without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and embodiments.

[0057] Embodiment 1

[0058] As Figure 1 shown, this embodiment describes a coordinated motion planning control method during a five-axis machining process. First, a digital machine tool model is established and the constraints of different axes are uniformly processed; then, preliminary motion instructions are generated based on the real-time load conditions of each axis; finally, the preliminary instructions are refined by combining predictive compensation and compensation based on actual errors to generate the final control signal for driving the motion of the machine tool. The specific method includes:

[0059] Construct a digital twin model of the machine tool and unify the constraint conditions of the translational axis and the rotational axis; solve the problem that it is difficult to directly compare and process the constraint conditions (such as maximum speed and acceleration limits) brought about by the different dimensions of the translational axis (the unit is usually millimeters) and the rotational axis (the unit is usually degrees or radians).

[0060] During the motion planning process, the translational axis dynamic load rate and the rotational axis dynamic load rate are calculated, which reflect the degree to which the current operating states (such as speed, acceleration, etc.) of each axis approach their own performance limits; and the translational axis error weight and the rotational axis error weight in the optimization objective are dynamically adjusted according to the load rate difference.

[0061] Define various constraint conditions that must be observed during the operation of the machine tool, and obtain the optimal coordinated motion instructions through multi-objective optimization; that is, calculate the optimal motion instructions for the translational axis and the rotational axis while achieving multiple objectives and satisfying all the determined constraint conditions.

[0062] Based on the known natural frequencies of the machine tool (i.e., the frequencies at which the machine tool structure is prone to resonance), construct a feedforward compensation model to obtain the feedforward compensation amount; it is used to predict and compensate in advance for the repeatable dynamic errors caused by these natural frequencies.

[0063] Install an encoder on the machine tool axis, obtain the real-time feedback position of the encoder, calculate the tracking error, and perform PID adjustment based on the tracking error to obtain the feedback compensation amount; that is, calculate the feedback compensation according to the current error, the accumulated error, and the error change rate.

[0064] Fuse the feedforward compensation amount and the feedback compensation amount to obtain the total compensation amount, and perform real-time correction on the optimal coordinated motion instructions to generate the final control signal.

[0065] Embodiment 2

[0066] The objective of this embodiment is to solve the problem that it is difficult to directly compare and coordinate the performance constraints (such as maximum speed, acceleration) due to the different dimensions of the translational axis (such as millimeters per second) and the rotational axis (such as radians per second). That is, by calculating the equivalent linear velocity generated by the rotational axis motion at the tool point, the angular motion constraints of the rotational axis (such as maximum angular velocity) are converted into equivalent linear velocity constraints, so as to consider the constraint conditions of all axes in a unified linear motion framework.

[0067] The method for unifying the constraint conditions of the translational axis and the rotational axis includes:

[0068] Obtain the equivalent radius R from the rotation center to the tool tip point, the current angular velocity w and angular acceleration a of the rotational axis;

[0069] Calculate the equivalent linear velocity v of the rotational axis eq : where J = 0.5×R 2 is the equivalent moment of inertia;

[0070] Using the calculated equivalent linear velocity v eq , convert the maximum angular velocity constraint and / or the maximum angular acceleration constraint of the rotational axis into the corresponding maximum equivalent linear velocity constraint or maximum equivalent linear acceleration constraint.

[0071] For example, convert the C-axis rotational speed constraint of 15 rpm to an equivalent linear velocity constraint: v constraint = 2πR × (15 / 60) = 0.5πR (mm / s).

[0072] Embodiment III

[0073] As Figure 2 shown, this embodiment provides a process of how to dynamically determine the relative importance of the translational axis and the rotational axis after obtaining unified constraint conditions, and how to use these weights for multi-objective optimization to obtain the optimal collaborative motion instruction. This process mainly includes two stages: error weight calculation and multi-objective optimization.

[0074] The calculation method of the error weight includes:

[0075] According to the current linear velocity v of the translational axis, the preset maximum linear velocity v max , linear acceleration a lin and the preset maximum linear acceleration a lmax , calculate the dynamic load rate T of the translational axis load : It is used to measure the degree to which the current motion state of the translational axis approaches its performance limit.

[0076] According to the current angular velocity w of the rotational axis, the preset maximum angular velocity w max , angular acceleration a and the preset maximum angular acceleration a max , calculate the dynamic load rate R of the rotational axis load : It is used to measure the degree to which the current motion state of the rotational axis approaches its performance limit.

[0077] Based on the difference between the dynamic load rate T of the translational axis load and the dynamic load rate R of the rotational axis load , calculate the error weight α of the translational axis: where k is a preset adjustment coefficient, calibrated through the machine tool frequency response experiment, and taken as 5.0 in this embodiment. e is the natural constant; when the load T of the translational axis load is much greater than the load R of the rotational axis load , α approaches 1, indicating that the optimization will pay more attention to the translational axis error; conversely, when the load R of the rotational axis load is much greater than the load T of the translational axis load , α approaches 0, indicating that the optimization will pay more attention to the rotational axis error; when the loads of the two are close, α approaches 0.5, and both are equally concerned.

[0078] Calculate the error weight β of the rotational axis: β = 1 - α, that is, ensure that the sum of the error weights of the two axes is 1.

[0079] The methods for multi-objective optimization include:

[0080] The goal of optimization is to minimize a comprehensive cost function, which is a weighted sum of the translational axis error term and the rotational axis error term, Min∑(α·(ΔX) 2 +β·(ΔC) 2 ), where ΔX is the motion error or displacement increment of the translational axis in one or more dimensions, and ΔC is the motion error or angle increment of the rotational axis in one or more dimensions; the purpose of the cost function is to find a set of motion instructions that minimize the total error (or the sum of squares of the total displacement / angle change) after adjustment according to the weights.

[0081] Determine the constraints of the optimization process. The constraints include at least: the linear velocity constraint, linear acceleration constraint, and jerk constraint of the translational axis; the angular velocity constraint, angular acceleration constraint, and angular jerk constraint of the rotational axis;

[0082] Use the quadratic programming method to solve the optimal solution that minimizes the optimization objective function under the constraints. The steps include:

[0083] Pre-define the cost matrix Q of the translational axis motion error cost trans and the cost matrix Q of the rotational axis motion error cost rot ;

[0084] Construct the objective function cost matrix Q of the quadratic programming problem, Q = α·Q trans +β·Q rot ;

[0085] Call the quadratic programming solver, and use the objective function cost matrix Q and the constraints as inputs to solve, and obtain the optimal coordinated motion instructions.

[0086] Provide an example of the constraints:

[0087] The constraints of the translational axis are: |v|≤v max , |a lin |≤a lmax , |J lin |≤J linmax ;

[0088] The constraints of the rotational axis are: |w|≤w max , |a|≤a max , |J rot |≤J rotmax ;

[0089] where v is the linear velocity of the translational axis, a lin is the linear acceleration of the translational axis, J lin is the jerk of the translational axis; w is the angular velocity of the rotational axis, a is the angular acceleration of the rotational axis, Jrot is the jerk of the rotation axis angle; J linmax is the maximum linear jerk of the preset translation axis; J rotmax is the maximum angular jerk of the preset rotation axis.

[0090] Provide specific numerical examples: J linmax = 3000mm / s 3 , J rotmax = 5rad / s 3 .

[0091] In addition, S-curve optimization under jerk constraint can be performed to improve the traditional seven-segment S-curve and add rotation axis angle jerk constraint: Through fourth-order derivative smoothing, the angular jerk fluctuation is reduced by 80%.

[0092] Example 4

[0093] As Figure 2 shown, this embodiment combines feedforward compensation and feedback compensation to correct the motion command. By calculating two types of compensation signals in parallel: one is the feedforward compensation based on prediction, which is used to cancel known periodic errors; the other is the feedback compensation based on real-time measurement, which is used to correct the deviation between the actual motion and the desired trajectory.

[0094] Calculate the feedforward compensation amount Among them, K i is the preset amplitude coefficient, φ i is the preset phase coefficient, t is time, f i is the natural frequency of the machine tool structure, measured by the hammering method: f1 = 235Hz, f2 = 487Hz, and n is the total number of natural frequencies of the machine tool incorporated into the feedforward compensation model.

[0095] The calculation method for calculating the feedback compensation amount includes:

[0096] Obtain the real-time feedback position of the translation axis and / or rotation axis of the machine tool provided by the encoder;

[0097] Compare the real-time feedback position P encoder (t) with the desired position P sim (t) corresponding to the optimal cooperative motion command, and calculate the tracking error ρ = ||P sim (t) - P encoder (t)||, and the magnitude of the error is usually represented by the norm of the difference between the two position vectors.

[0098] Based on the tracking error ρ and applying the PID control law, using the preset PID parameters (optimized for a 0.01mm error band, proportional K p = 12.5, integral K i= 0.8, differential K d = 0.05) to calculate the feedback compensation amount δ fb ;

[0099] Fuse feedforward and feedback compensation to calculate the total compensation amount Among them, J -1 (q) is the inverse matrix of the Jacobian matrix of the current pose q, K p and K d are the parameters of the PID controller. The Jacobian matrix describes the relationship between joint velocity / displacement and the velocity / displacement of the end effector (tool tip point), and its inverse matrix is used to convert the feedback correction (mainly the PD part) calculated based on the end error into the joint space of the drive motor.

[0100] To improve the robustness and adaptability of the control system, a parameter self-tuning mechanism is added.

[0101] Set the error threshold ρ max , if ρ > ρ max , then start the PID parameter self-tuning and update the proportional parameter Among them, ρ ref is the error reference value used to calibrate the adjustment amplitude. An example is ρ ref = 0.2.

[0102] Numerical example ρ max = 0.005 mm.

[0103] Example 5

[0104] Provide a specific example.

[0105] 1. Machine tool platform configuration

[0106] Machine type selection:

[0107] Adopt a five-axis linkage machining center (example: DMU 50, configured with X / Y / Z linear axes, A / C rotary axes).

[0108] Key parameters:

[0109] Linear axes: maximum speed 2000 mm / s, acceleration 1.5g.

[0110] Rotary axis (C axis): maximum rotational speed 15 rpm, angular acceleration 1.8 rad / s 2 .

[0111] Sensor configuration:

[0112] Linear scale (resolution 0.1 μm, X / Y / Z axes)

[0113] Circular grating encoder (resolution 0.001°, C axis)

[0114] Vibration sensor (frequency response range 0 - 1 kHz, installed at the spindle nose)

[0115] 2. Control system architecture

[0116] Motion controller: Select a multi-axis motion control card (example: Galil DMC-4080), supporting the EtherCAT communication protocol.

[0117] Real-time requirement: Control cycle ≤ 1 ms, synchronization jitter < 2 μs.

[0118] Interface configuration:

[0119] Digital twin simulation interface: OPC UA protocol.

[0120] Physical axis control interface: High-speed pulse output (differential signal).

[0121] 3. Digital twin modeling

[0122] Physical engine: Adopt the Adams dynamics simulation kernel.

[0123] Accuracy calibration:

[0124] Geometric error: ±0.002 mm (calibrated by laser tracker).

[0125] As Figure 3 shown, dynamic error: ±0.005 mm (compared with the actual machining trajectory).

[0126] 4. Verification case (aeronautical impeller machining).

[0127] Machining parameters:

[0128] Material: Ti-6Al-4V.

[0129] Tool: φ6 ball end mill.

[0130] Cutting parameters: Rotation speed 8000 rpm, feed rate 1500 mm / min.

[0131]

[0132] Through the technology of the present invention, the achieved technical effects are:

[0133]

[0134] Beneficial effects are:

[0135] 1. Breakthrough improvement in machining accuracy

[0136] Profile error control: Through the dynamic weight allocation strategy and the equivalent linear velocity model, the profile error in complex surface machining is reduced from 0.02 - 0.05 mm of traditional methods to 0.012 mm (actual measured data in impeller machining), with a reduction rate of 68%.

[0137] Surface quality optimization: Based on the feedforward-feedback compound control, the surface roughness Ra value is stabilized below 0.35 μm (0.82 μm by traditional methods), and the waviness Wz is reduced by 57%.

[0138] Theoretical error elimination: The dimension-unified compensation algorithm compresses the dimension conversion error from ±0.008 mm to ±0.002 mm (simulation verification data).

[0139] 2. Dramatic improvement in motion coordination performance

[0140] Dynamic synchronization accuracy: The spatio-temporal synchronization error between the linear axis and the rotary axis < 2 μs (measured value by laser interferometer).

[0141] Acceleration jerk suppression: The fluctuation of angular jerk is reduced by 80%, and the mechanical tremor energy decays by 42% (results of vibration spectrum analysis).

[0142] Axis system overrun risk control: The overrun rate of angular acceleration is reduced from 23% to 4.7% (based on 100-hour continuous machining statistics).

[0143] 3. Significantly improved process efficiency

[0144] Shorter machining cycle: The non-cutting time is reduced by 18.5%, and the program block processing speed is increased to 2500 blocks / second (1200 blocks / second by traditional methods).

[0145] Enhanced dynamic stiffness: The adaptive adjustment of the feed rate under the spindle load fluctuation improves the dynamic stiffness of the cutting process by 25% (results of frequency response function test).

[0146] Extended equipment life: The bearing wear rate is reduced by 62% (2000-hour comparison experiment in a certain automotive mold factory).

[0147] 4. Advantages of intelligent technology

[0148] Digital twin prediction ability: Predict the trajectory deviation 10 ms in advance (the time delay between the simulation-physical system < 1 ms).

[0149] Multi-condition adaptability: Support the self-matching of cutting parameters for 6 types of materials (such as titanium alloy / superalloy, etc.), and the changeover time is shortened by 40%.

[0150] Cross-platform compatibility: Has been adapted to mainstream CNC systems such as FANUC and SIEMENS, and the interface transformation takes less than 8 man-hours.

[0151] Embodiment Six

[0152] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements a coordinated motion planning control method in a five-axis machining process as described above.

[0153] Without loss of generality, computer-readable media can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes RAM, ROM, EPROM, EEPROM, flash memory, or other solid-state storage technologies, CD-ROM, DVD, or other optical storage, magnetic tape cartridges, magnetic tapes, disk storage, or other magnetic storage devices. Of course, those skilled in the art will know that computer storage media is not limited to the above several types. The above-mentioned system memory and mass storage devices can be collectively referred to as memory.

[0154] A computer program product includes a computer program / instructions, and when the computer program / instructions are executed by a processor, it implements a coordinated motion planning control method in a five-axis machining process as described above.

[0155] A computer program product includes a computer program or instruction set for performing a specific task or implementing a specific function. These programs or instructions are designed to be executable by a processor to achieve a series of predefined steps or operations. The program product may be stored in various forms of computer storage media, such as memory, hard disk, solid-state drive, optical disc, or other forms of digital storage devices. It may exist in the form of compiled binary code or in the form of scripts or bytecodes executable by an interpreter. Through carefully designed algorithms and logical instructions, the program product enables the processor to process data in a specific order and manner to complete various functions such as data analysis, user interaction, and device control.

[0156] In the description of this specification, the description with reference to terms such as "one embodiment / way", "some embodiments / ways", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment / way or example are included in at least one embodiment / way or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment / way or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments / ways or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments / ways or examples described in this specification and the features of different embodiments / ways or examples.

[0157] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0158] Those skilled in the art should understand that the above embodiments are merely for clearly illustrating the present invention and are not intended to limit the scope of the present invention. For those skilled in the art, other changes or modifications can be made based on the above invention, and these changes or modifications are still within the scope of the present invention.

Claims

1. A coordinated motion planning and control method during five-axis machining, characterized in that, Including: Construct a digital twin model of the machine tool and unify the constraint conditions of the linear axes and the rotary axes; Calculate the dynamic load ratios of the linear axes and the rotary axes, and dynamically adjust the error weights of the linear axes and the rotary axes in the optimization objective according to the load ratio differences; Determine the constraint conditions and obtain the optimal coordinated motion instructions through multi-objective optimization; Based on the known natural frequencies of the machine tool, construct a feedforward compensation model to obtain the feedforward compensation amount; Obtain the real-time feedback position of the encoder, calculate the tracking error, and perform PID adjustment based on the tracking error to obtain the feedback compensation amount; Fuse the feedforward compensation amount and the feedback compensation amount to obtain the total compensation amount, and perform real-time correction on the optimal coordinated motion instructions to generate the final control signal.

2. The coordinated motion planning control method during five-axis machining according to claim 1, wherein, The method for unifying the constraint conditions of the linear axes and the rotary axes includes: Obtain the equivalent radius R from the rotation center to the tool tip point, the current angular velocity w and angular acceleration a of the rotary axis; Calculate the equivalent linear velocity v of the rotation axis eq : where J = 0.5 × R 2 is the equivalent moment of inertia; Using the calculated equivalent linear velocity v eq , the maximum angular velocity constraint and / or the maximum angular acceleration constraint of the rotating shaft are converted into corresponding maximum equivalent linear velocity constraints or maximum equivalent linear acceleration constraints.

3. A coordinated motion planning control method during five-axis machining according to claim 1, characterized in that The calculation method of the error weight includes: According to the linear velocity v of the current translation axis, the preset maximum linear velocity v max , the linear acceleration a lin and the preset maximum linear acceleration a lmax , calculate the dynamic load rate T of the translation axis load : According to the angular velocity w of the current rotation axis and the preset maximum angular velocity w max 、the angular acceleration a and the preset maximum angular acceleration a max , calculate the dynamic load rate R of the rotation axis load : Based on the difference between the translational axis dynamic load rate T load and the rotational axis dynamic load rate R load , calculate the translational axis error weight α: where k is a preset adjustment coefficient and e is the natural constant; Calculate the error weight β of the rotary axis: β = 1 - α.

4. A coordinated motion planning control method during five-axis machining according to claim 3, characterized in that, The method for performing multi-objective optimization includes: Construct an optimization objective function, Min∑(α·(ΔX) 2 +β·(ΔC) 2 ), where ΔX is the motion error or displacement increment of the translational axis in one or more dimensions, and ΔC is the motion error or angular increment of the rotational axis in one or more dimensions; Determine the constraint conditions of the optimization process, and the constraint conditions at least include: the linear velocity constraint, linear acceleration constraint and jerk constraint of the linear axes; the angular velocity constraint, angular acceleration constraint and jerk constraint of the rotary axes; Adopt the quadratic programming method to solve the optimal solution that minimizes the optimization objective function under the constraint conditions to obtain the optimal coordinated motion instructions.

5. A coordinated motion planning and control method during five-axis machining according to claim 4, characterized in that, The constraint conditions for the translational axis are: |v| ≤ v max , |a lin | ≤ a lmax , |J lin | ≤ J linmax ; The constraint conditions for the rotation axis are: |w| ≤ w max , |a| ≤ a max , |J rot | ≤ J rotmax ; Among them, v is the translational axis linear velocity, a lin is the translational axis linear acceleration, J lin is the translational axis jerk; w is the rotational axis angular velocity, a is the rotational axis angular acceleration, J rot is the rotational axis angular jerk; J linmax is the preset maximum translational axis linear jerk; J rotmax is the preset maximum rotational axis angular jerk.

6. A coordinated motion planning and control method during five-axis machining according to claim 1, characterized in that, Calculate the feedforward compensation amount where K i is the preset amplitude coefficient, φ i is the preset phase coefficient, t is time, f i is the natural frequency of the machine tool structure, and n is the total number of natural frequencies of the machine tool incorporated into the feedforward compensation model; The calculation method for calculating the feedback compensation amount includes: Obtain the real-time feedback position of the linear axes and / or rotary axes of the machine tool provided by the encoder; Compare the real-time feedback position P encoder (t) with the desired position P sim (t) corresponding to the optimal cooperative motion command, and calculate the tracking error ρ = ||P sim (t) - P encoder (t)||; Based on the tracking error ρ and applying the PID control law, calculate the feedback compensation amount δ using the preset PID parameters fb ; Calculate the total compensation amount where J -1 (q) is the inverse matrix of the Jacobian matrix of the current pose q, and K p and K d are the parameters of the PID controller.

7. A coordinated motion planning control method during five-axis machining according to claim 6, characterized in that, Set the error threshold ρ max , if ρ > ρ max , then start the PID parameter self-tuning and update the proportional parameter where ρ ref is the error reference value.

8. A coordinated motion planning and control method during five-axis machining according to claim 4, characterized in that, The steps of adopting the quadratic programming method to solve include: Cost matrix Q for predefined translational axis motion error cost trans and cost matrix Q for rotational axis motion error cost rot ; Construct the objective function cost matrix \(Q\) of the quadratic programming problem, \(Q = \alpha\cdot Q\) trans +\(\beta\cdot Q\) rot ; Call the quadratic programming solver, and use the objective function cost matrix Q and the constraint conditions as inputs to solve to obtain the optimal coordinated motion instructions.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements a coordinated motion planning control method in a five-axis machining process as described in any one of claims 1-8.

10. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by a processor, it implements a coordinated motion planning control method in a five-axis machining process as described in any one of claims 1-8.

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