Constant tension control method for 3D printing forming process of continuous fiber composite material

Abstract

The invention provides a constant tension control method for a 3D printing forming process of a continuous fiber composite material. The constant tension control method comprises the steps of modeling in a tension control process and optimizing PID by applying a crown porcupine optimization algorithm. Through real-time monitoring and dynamic adjustment of the fiber tension in the 3D printing process, a mathematical model is established according to the characteristics of the magnetic powder brake, and the tension change is described and predicted. And optimizing parameters of the PID controller by adopting a crown porcupine optimization algorithm. According to the algorithm, the foraging behavior of natural crown porcupines is simulated, and optimal PID parameter setting is found through iterative calculation, so that quick response and system stability are realized. Compared with a BP (Back Propagation) optimization algorithm and a traditional PID (Proportion Integration Differentiation) algorithm, the crown porcupine optimization algorithm has higher global search capability and convergence speed. By means of the method, the stability of fiber tension can be maintained in the 3D printing process, printing defects caused by tension fluctuation are reduced, the 3D printing forming quality and printing efficiency of the continuous fiber composite material are improved, and the method has wide application prospects.

Description

Technical field:

[0001] The present invention belongs to the field of constant tension control in a continuous fiber composite material 3D printing molding process, and in particular relates to a tension control optimization method using a magnetic powder brake as a tension control actuator. Background technology:

[0002] Tension control is an important step in the 3D printing process of continuous fiber composites. Lack of fiber tension or unstable fiber tension will cause loose fibers, leading to uneven fiber distribution after molding, porosity in the matrix, and decreased mechanical properties. Therefore, stable tension control of continuous fibers is required.

[0003] Current tension control methods often cannot accurately achieve stable output of magnetic powder brake torque, resulting in unstable tension control. In addition, existing control methods often cannot adapt well to different environments and load conditions. When the external environment or load changes, the magnetic powder brake cannot be adjusted quickly, resulting in a decrease in tension control effect and affecting molding quality. Most tension control methods still rely on manual operation or simple control logic, which limits the adaptive ability and optimization potential of continuous fiber composite 3D printing tension controllers in complex environments.

[0004] Crested Porcupine Optimizer (CPO) is a new type of nature-inspired meta-optimization algorithm, which is inspired by the various defensive behaviors of crested porcupines in nature. This algorithm simulates the optimization mechanism of the clustering behavior of crested porcupines during foraging, and optimizes the objective function through a series of iterative processes to find the optimal solution. Compared with other algorithms, the crested porcupine optimization algorithm uses four defense strategies to implement the mathematical modeling of the algorithm, and uses the unique characteristics of the loop size reduction strategy to reduce the algorithm's optimization time. Summary of the invention:

[0005] In view of the problem of tension control in 3D printing of continuous fiber composite materials, the present invention provides a method for optimizing tension control in 3D printing of continuous fiber composite materials based on improved PID. The tension PID controller is optimized by a crown porcupine optimization algorithm to solve the problems existing in the background technology, realize stable tension control in 3D printing of continuous fiber composite materials, and improve the 3D printing quality of continuous fiber composite materials.

[0006] To achieve the above-mentioned purpose, the present invention adopts a constant tension control method for the continuous fiber composite material 3D printing molding process, and the specific steps are as follows.

[0007] S1. The tension controller for 3D printing of continuous fiber composite materials with a magnetic powder brake as the actuator adopts the reverse torque method to model the tension control process.

[0008] S2. Collecting real-time tension data through a tension sensor, and inputting the real-time tension data into a continuous fiber composite material 3D printing tension controller.

[0009] S3. Setting a target tension value in the continuous fiber composite material 3D printing tension controller, and calculating a current difference between the real-time tension data and the target tension data.

[0010] S4. The crown porcupine optimization algorithm is introduced to optimize the PID parameters, the tension error is used as the input of the optimization algorithm, and the braking torque of the magnetic powder brake is output.

[0011] S5. Repeat steps S2-S4 until the tension error is zero, thereby achieving constant tension control in the continuous fiber composite material 3D printing process.

[0012] 2. More specifically, the continuous fiber composite material 3D printing tension controller described in step S1 is characterized in that during the tension control process, the continuous fiber line roller is connected to the magnetic powder brake, and the continuous fiber is pulled downward by the wire feeding device inside the 3D printing head. During the printing process, a static friction force f is generated between the fiber and the magnetic powder brake to overcome the braking torque Mz of the magnetic powder brake. The static friction force generated by the magnetic powder brake on the fiber is used to generate a target tension F1 on the fiber, and F0 is the initial tension of the fiber before contacting the magnetic powder brake. Figure 2 shown.

[0013] 3. More specifically, the tension control process in S1 with the magnetic powder brake as the actuator is modeled:

[0014] S11. The torque equation of magnetic powder brake is:

[0015] f(t)RM Z (t)+M N (t) = Jα(t) (1)

[0016] Where f(t) is the static friction between the fiber and the magnetic powder brake, R is the radius of the magnetic powder brake, M Z (t) is the braking torque of the magnetic powder brake, M N (t) is the disturbance, J is the moment of inertia of the magnetic powder brake, and α(t) is the angular acceleration of the magnetic powder brake;

[0017] The relationship between S12, angular acceleration and fiber displacement is as shown in formula (2):

[0018]

[0019] Where L1(t) is the length of the same fiber before pre-stretching, and L2(t) is the length of the fiber after pre-stretching;

[0020] S13, fiber strain before and after pre-tensioning:

[0021] ΔL(t)=L2(t)-L1(t) (3)

[0022] S14. The fiber stress-strain model is equivalent to the Maxwell model, satisfying the following relationship:

[0023]

[0024] Where E represents the elastic modulus of the fiber, σ represents the viscosity coefficient, and the fiber tension is expressed as the additional internal force on the cross section caused by the external force;

[0025] S15. Assuming that the cross-sectional change is negligible, the final tension of the fiber is the sum of the initial tension and the static friction:

[0026] F1(t)=F0+f(t) (5)

[0027] S16. Substituting (2), (3), (4), and (5) into (1), we obtain:

[0028]

[0029] S17, F1(t) are system outputs, M Z (t) is the system input, M N (t) is the system disturbance, and the other parameters are constants.

[0030]

[0031] Q1(t) and Q2(t) are system inputs;

[0032] S18. Substitute Q1(t), Q2(t), P1, and P2 into equation (6) and perform Laplace transform:

[0033] F1(s)(P1s 2 +P2s+1)=Q1(s)+Q2(s) (11)

[0034] Then the transfer function of the tension control system is:

[0035]

[0036] 4. More specifically, the continuous fiber composite material 3D printing tension controller described in step S1 includes a tension data setting module, a tension data detection module, a tension error calculation module, a crown porcupine optimization algorithm module, a tension controller PID control algorithm module and a magnetic powder brake torque output module. The tension data setting module is used to set the tension target value, the tension data detection module is used to detect the real-time tension value, and the crown porcupine optimization algorithm module is used to adjust the proportional parameter Kp, integral parameter Ki and differential parameter Kd of the tension controller PID control algorithm.

[0037] 5. More specifically, the PID parameters are optimized using the crown porcupine optimization algorithm described in step S4, and the specific steps are as follows:

[0038] S41, initializing the parameters of the crown porcupine optimization algorithm including the current number of iterations T, the maximum number of iterations Tmax, the maximum size of the initial population N, the minimum size of the crown porcupine population Nmin, and the optimization upper bound ub and lower bound lb of the improved crown porcupine optimization algorithm;

[0039] S42, setting the Kp, Ki, and Kd parameters of the tension controller PID control algorithm to a spatial solution set and encoding them as the positions of each individual crested porcupine, each individual position of the crested porcupine representing a set of candidate solutions of the Kp, Ki, and Kd solution sets;

[0040] S43, simulating the vision, hearing, smell and attack behavior of the crested porcupine to establish a mathematical model of the defense mechanism of the crested porcupine optimization algorithm, and using the mathematical model to update the individual positions of the crested porcupine;

[0041] S44, calculating the fitness value of each crested porcupine individual position in the current iteration; retaining the crested porcupine position corresponding to the minimum fitness value in the current iteration;

[0042] S45, comparing the minimum fitness value of the current iteration with the minimum fitness value of the previous iteration, and retaining the crested porcupine position with the smaller fitness value;

[0043] S46. Decode the position of the crowned porcupine with the smallest fitness value as the Kp, Ki, and Kd parameter solutions of the PID algorithm of the continuous fiber composite material 3D printing tension controller; if the current number of iterations is equal to the maximum number of iterations, exit the entire process, otherwise execute the cyclic population reduction strategy, reduce the scale of candidate solutions, and return to execute step S42.

[0044] 6. More specifically, the fitness function uses time multiplied by absolute error integral, and the mathematical model is:

[0045]

[0046] In formula (13), T is the maximum integration time, t is the current integration time, and e(t) is the difference between the target tension value and the actual tension value.

[0047] 7. More specifically, the visual, auditory, odor and attacking behaviors of crested porcupines are simulated to establish a mathematical model of the defense mechanism of the crested porcupine optimization algorithm. The mathematical model of the defense mechanism is used to update the individual positions of the crested porcupine population, that is, to update the solution of the proportional parameter Kp, integral parameter Ki and differential parameter Kd set of the tension controller PID algorithm. The specific steps are:

[0048] S411, the dimension of the random initialization problem in the search space, the mathematical model is,

[0049]

[0050] N ′ represents the population size, is the i-th candidate solution, and Respectively represent the upper and lower boundaries of the search Any value between 0 and 1;

[0051] S412, the cyclic population reduction strategy is characterized by simulating the idea that not all crested porcupines activate the defense mechanism, but only those crested porcupines that are threatened will activate the defense mechanism to speed up the convergence speed and avoid falling into the local minimum. The mathematical model is:

[0052]

[0053] Where T is the variable that determines the number of loops, t is the current function evaluation, Tmax is the maximum number of function evaluations, % is the modulus operator, and Nmin is the minimum number of individuals in the newly generated population, so that the population size cannot be less than Nmin;

[0054] S421, simulate the crested porcupine discovering a predator. At this time, it can choose to approach or stay away. Use normal distribution to generate random values. If these random values ​​are less than 1 or greater than -1, it is encouraged to approach the crested porcupine, otherwise the predator will stay away. Thus, the mathematical model of the first defense strategy of the crested porcupine optimization algorithm is established:

[0055]

[0056] is the optimal solution, represents the position of the predator during iteration, τ1 is a random number based on normal distribution, and τ2 is a random value in the interval [0,1];

[0057] S422. The second defense strategy is that the crested porcupine makes noise and threatens predators. The mathematical model is:

[0058]

[0059] Where r1 and r2 are two random integers between [1,N], and τ3 is a random value generated between 0 and 1;

[0060] S423. The third defense strategy is that the crested porcupine will secrete a foul odor that spreads in the surrounding area to prevent predators from approaching it. The mathematical model is:

[0061]

[0062] r3 is a random number between [1, N], δ is a parameter that controls the search direction, defined by equation (19), is the position of the ith individual at iteration t, γt is the defense factor defined by equation (20), τ3 is a random value in the interval [0,1], is the odor diffusion factor, defined by formula (21);

[0063] Specifically

[0064]

[0065] in Represents the objective function value of the i-th individual during iteration;

[0066] S424, the fourth defense strategy is physical attack, the mathematical model is

[0067]

[0068] in, is the optimal solution, which represents the position of the i-th individual at iteration t, α is the speed factor, and τ4 is a random value in the interval [0,1] is the average force of the crested porcupine affecting the i-th predator. It is provided by the law of inelastic collision.

[0069] 8. In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0070] 8.1. A mathematical model of the tension control system for continuous fiber composite 3D printing with magnetic powder brake as the actuator was established, which can provide an in-depth understanding of the system's dynamic characteristics, stability, response speed and other important parameters, and thus guide the design and optimization of the system;

[0071] 8.2. The crowned porcupine optimization algorithm is applied to the optimization of the tension PID controller for continuous fiber composite 3D printing, which realizes constant tension control in the continuous fiber composite 3D printing process and improves the stability and speed of tension control. The optimized controller can adjust the output torque of the magnetic powder brake according to actual needs, better adapt to different environments and load conditions, improve the tension stability control effect, and thus improve the 3D printing quality of continuous fiber composites. Description of the drawings:

[0072] Figure 1 Flow chart of tension control for 3D printing of continuous fiber composites using the crown porcupine optimization algorithm to adjust PID parameters;

[0073] Figure 2 Schematic diagram of a continuous fiber composite 3D printing tension controller using a magnetic powder brake as an actuator;

[0074] Figure 3 The response comparison curve of the tension control system using the crown porcupine optimization algorithm to adjust the PID parameters; Specific implementation method:

[0075] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] See also Figure 1-Figure 3 This embodiment provides a constant tension control algorithm for the continuous fiber composite material 3D printing molding process, including the establishment of a mathematical model of the tension control process and the application of the crown porcupine optimization algorithm to adjust the PID parameters. The specific process is as follows: Figure 1 As shown, the method includes the following steps.

[0077] S1. The tension controller for 3D printing of continuous fiber composite materials with a magnetic powder brake as the actuator adopts the reverse torque method to model the tension control process.

[0078] More specifically, the continuous fiber composite material 3D printing tension controller described in step S1 is characterized in that during the tension control process, the continuous fiber line roller is connected to the magnetic powder brake, and the continuous fiber is pulled downward by the wire feeding device inside the 3D printing head. During the printing process, a static friction force f is generated between the fiber and the magnetic powder brake to overcome the braking torque Mz of the magnetic powder brake. The static friction force generated by the magnetic powder brake on the fiber is used to generate a target tension F1 on the fiber. F0 is the initial tension of the fiber before contacting the magnetic powder brake, such as Figure 2 shown.

[0079] More specifically, the tension control process in S1 with the magnetic powder brake as the actuator is modeled as follows:

[0080] S11. The torque equation of magnetic powder brake is:

[0081] f(t)RM Z (t)+M N(t) = Jα(t) (1)

[0082] Where f(t) is the static friction between the fiber and the magnetic powder brake, R is the radius of the magnetic powder brake, M Z (t) is the braking torque of the magnetic powder brake, M N (t) is the disturbance, J is the moment of inertia of the magnetic powder brake, and α(t) is the angular acceleration of the magnetic powder brake;

[0083] The relationship between S12, angular acceleration and fiber displacement is as shown in formula (2):

[0084]

[0085] Where L1(t) is the length of the same fiber before pre-stretching, and L2(t) is the length of the fiber after pre-stretching;

[0086] S13, fiber strain before and after pre-tensioning:

[0087] ΔL(t)=L2(t)-L1(t) (3)

[0088] S14. The fiber stress-strain model is equivalent to the Maxwell model, satisfying the following relationship:

[0089]

[0090] Where E represents the elastic modulus of the fiber, σ represents the viscosity coefficient, and the fiber tension is expressed as the additional internal force on the cross section caused by the external force;

[0091] S15. Assuming that the cross-sectional change is negligible, the final tension of the fiber is the sum of the initial tension and the static friction:

[0092] F1(t)=F0+f(t) (5)

[0093] S16. Substituting (2), (3), (4), and (5) into (1), we obtain:

[0094]

[0095] S17, F1(t) are system outputs, M Z (t) is the system input, M N (t) is the system disturbance, and the other parameters are constants.

[0096]

[0097] Q1(t) and Q2(t) are system inputs;

[0098] S18. Substitute Q1(t), Q2(t), P1, and P2 into equation (6) and perform Laplace transform:

[0099] F1(s)(P1s 2 +P2s+1)=Q1(s)+Q2(s) (11)

[0100] Then the transfer function of the tension control system is:

[0101]

[0102] S2. Collecting real-time tension data through a tension sensor, and inputting the real-time tension data into a continuous fiber composite material 3D printing tension controller.

[0103] More specifically, the continuous fiber composite material 3D printing tension controller includes a tension data setting module, a tension data detection module, a tension error calculation module, a crown porcupine optimization algorithm module, a tension controller PID control algorithm module and a magnetic powder brake torque output module. The tension data setting module is used to set the tension target value, the tension data detection module is used to detect the real-time tension value, and the crown porcupine optimization algorithm module is used to adjust the proportional parameter Kp, integral parameter Ki and differential parameter Kd of the tension controller PID control algorithm.

[0104] S3. Setting a target tension value in the continuous fiber composite material 3D printing tension controller, and calculating a current difference between the real-time tension data and the target tension data.

[0105] S4. The crown porcupine optimization algorithm is introduced to optimize the PID parameters, the tension error is used as the input of the optimization algorithm, and the braking torque of the magnetic powder brake is output.

[0106] More specifically, the optimization of PID parameters using the crown porcupine optimization algorithm described in step S4 comprises the following specific steps:

[0107] S41, initializing the parameters of the crown porcupine optimization algorithm including the current number of iterations T, the maximum number of iterations Tmax, the maximum size of the initial population N, the minimum size of the crown porcupine population Nmin, and the optimization upper bound ub and lower bound lb of the improved crown porcupine optimization algorithm;

[0108] S42, setting the Kp, Ki, and Kd parameters of the tension controller PID control algorithm to a spatial solution set and encoding them as the positions of each individual crested porcupine, each individual position of the crested porcupine representing a set of candidate solutions of the Kp, Ki, and Kd solution sets;

[0109] S43, simulating the vision, hearing, smell and attack behavior of the crested porcupine to establish a mathematical model of the defense mechanism of the crested porcupine optimization algorithm, and using the mathematical model to update the individual positions of the crested porcupine;

[0110] S44, calculating the fitness value of each crested porcupine individual position in the current iteration; retaining the crested porcupine position corresponding to the minimum fitness value in the current iteration;

[0111] S45, comparing the minimum fitness value of the current iteration with the minimum fitness value of the previous iteration, and retaining the crested porcupine position with the smaller fitness value;

[0112] S46. Decode the position of the crowned porcupine with the smallest fitness value as the Kp, Ki, and Kd parameter solutions of the PID algorithm of the continuous fiber composite material 3D printing tension controller; if the current number of iterations is equal to the maximum number of iterations, exit the entire process, otherwise execute the cyclic population reduction strategy, reduce the scale of candidate solutions, and return to execute step S42.

[0113] More specifically, the fitness function uses time multiplied by absolute error integral, and the mathematical model is:

[0114]

[0115] In formula (13), T is the maximum integration time, t is the current integration time, and e(t) is the difference between the target tension value and the actual tension value.

[0116] 7. More specifically, the visual, auditory, odor and attacking behaviors of crested porcupines are simulated to establish a mathematical model of the defense mechanism of the crested porcupine optimization algorithm. The mathematical model of the defense mechanism is used to update the individual positions of the crested porcupine population, that is, to update the solution of the proportional parameter Kp, integral parameter Ki and differential parameter Kd set of the tension controller PID algorithm. The specific steps are:

[0117] S411, the dimension of the random initialization problem in the search space, the mathematical model is:

[0118]

[0119] N ′ represents the population size, is the i-th candidate solution, and Respectively represent the upper and lower boundaries of the search Any value between 0 and 1;

[0120] S412, the cyclic population reduction strategy is characterized by simulating the idea that not all crested porcupines activate the defense mechanism, but only those crested porcupines that are threatened will activate the defense mechanism to speed up the convergence speed and avoid falling into the local minimum. The mathematical model is:

[0121]

[0122] Where T is the variable that determines the number of loops, t is the current function evaluation, Tmax is the maximum number of function evaluations, % is the modulus operator, and Nmin is the minimum number of individuals in the newly generated population, so that the population size cannot be less than Nmin;

[0123] S421, simulate the crested porcupine discovering a predator. At this time, it can choose to approach or stay away. Use normal distribution to generate random values. If these random values ​​are less than 1 or greater than -1, it is encouraged to approach the crested porcupine, otherwise the predator will stay away. Thus, the mathematical model of the first defense strategy of the crested porcupine optimization algorithm is established:

[0124]

[0125] is the optimal solution, represents the position of the predator during iteration, τ1 is a random number based on normal distribution, and τ2 is a random value in the interval [0,1];

[0126] S422. The second defense strategy is that the crested porcupine makes noise and threatens predators. The mathematical model is:

[0127]

[0128] Where r1 and r2 are two random integers between [1,N], and τ3 is a random value generated between 0 and 1;

[0129] S423. The third defense strategy is that the crested porcupine will secrete a foul odor that spreads in the surrounding area to prevent predators from approaching it. The mathematical model is:

[0130]

[0131] r3 is a random number between [1, N], δ is a parameter that controls the search direction, defined by equation (19), is the position of the ith individual at iteration t, γt is the defense factor defined by equation (20), τ3 is a random value in the interval [0,1], is the odor diffusion factor, defined by formula (21).

[0132] Specifically

[0133]

[0134] in Represents the objective function value of the i-th individual during iteration;

[0135] S424. The fourth defense strategy is physical attack, and the mathematical model is:

[0136]

[0137] in, is the optimal solution, represents the position of the i-th individual at iteration t, α is the convergence speed factor, τ4 is a random value in the interval [0,1], is the average force of the crested porcupine affecting the i-th predator. It is provided by the law of inelastic collision.

[0138] S5. Repeat steps S2-S4 until the tension error is zero, thereby achieving constant tension control in the continuous fiber composite material 3D printing process.

[0139] Furthermore, according to the above steps, the program models of the improved crown porcupine optimization algorithm and the standard crown porcupine optimization algorithm were established in MATLAB, and the continuous fiber composite material 3D printing constant tension control system model was established in Siumlink, and the Kp, Ki, and Kd parameters of the tension PID controller were adjusted using the improved crown porcupine optimization algorithm.

[0140] Furthermore, the unit step signal is used as the target tension value, and the optimal Kp, Ki, and Kd parameters are input into the constant tension control system model of continuous fiber composite 3D printing established by Siumlink, and the following is obtained: Figure 3 As shown in the control effect diagram, compared with the BP optimization algorithm and the traditional PID algorithm, the crown porcupine optimization algorithm for controlling the tension of continuous fiber composite 3D printing has a faster response speed and a smaller overshoot, indicating that the constant tension control algorithm for the continuous fiber composite 3D printing molding process proposed in the present invention is innovative and practical.

Claims

1. A constant tension control method for continuous fiber composite 3D printing molding process, which optimizes the constant tension PID controller of continuous fiber composite 3D printing by using the crown porcupine optimization algorithm, specifically comprising: S1. The tension controller for continuous fiber composite 3D printing with magnetic powder brake as the actuator adopts the reverse torque method to model the tension control process; S2, collecting real-time tension data through a tension sensor, and inputting the real-time tension data into a continuous fiber composite material 3D printing tension controller; S3, setting a target tension value in the continuous fiber composite material 3D printing tension controller, and calculating a current difference between the real-time tension data and the target tension data; S4, introducing the crown porcupine optimization algorithm to optimize the PID parameters, taking the tension error as the input of the optimization algorithm, and outputting the braking torque of the magnetic powder brake; S5. Repeat steps S2-S4 until the tension error is zero, thereby achieving constant tension control in the continuous fiber composite material 3D printing process.

2. According to claim 1, a constant tension control method for continuous fiber composite material 3D printing molding process, the continuous fiber composite material 3D printing tension controller in step S1 is characterized in that During the tension control process, the continuous fiber line roller is connected to the magnetic powder brake and the continuous fiber is pulled downward by the wire feeding device inside the 3D printing head. During the printing process, a static friction force f is generated between the fiber and the magnetic powder brake to overcome the braking torque Mz of the magnetic powder brake. The static friction force generated by the magnetic powder brake on the fiber is used to generate a target tension F1 for the fiber. F0 is the initial tension of the fiber before it contacts the magnetic powder brake, as shown in Figure 2.

3. The continuous fiber composite material 3D printing tension controller according to claim 2 comprises a tension data setting module, a tension data detection module, a tension error calculation module, a crown porcupine optimization algorithm module, a tension controller PID control algorithm module and a magnetic powder brake torque output module. The tension data setting module is used to set the tension target value, the tension data detection module is used to detect the real-time tension value, and the crown porcupine optimization algorithm module is used to adjust the proportional parameter Kp, integral parameter Ki and differential parameter Kd of the tension controller PID control algorithm.

4. A constant tension control method for continuous fiber composite material 3D printing molding process according to claim 1, characterized in that: The step S4 uses the crown porcupine optimization algorithm to optimize the PID parameters, and the specific steps are: S41, initializing the parameters of the crown porcupine optimization algorithm including the current number of iterations T, the maximum number of iterations Tmax, the maximum size of the initial population N, the minimum size of the crown porcupine population Nmin, and the optimization upper bound ub and lower bound lb of the improved crown porcupine optimization algorithm; S42, setting the Kp, Ki, and Kd parameters of the tension controller PID control algorithm to a spatial solution set and encoding them as the positions of each individual crested porcupine, each individual position of the crested porcupine representing a set of candidate solutions of the Kp, Ki, and Kd solution sets; S43, simulating the vision, hearing, smell and attack behavior of the crested porcupine to establish a mathematical model of the defense mechanism of the crested porcupine optimization algorithm, and using the mathematical model to update the individual positions of the crested porcupine; S44, calculating the fitness value of each crested porcupine individual position in the current iteration; retaining the crested porcupine position corresponding to the minimum fitness value in the current iteration; S45, comparing the minimum fitness value of the current iteration with the minimum fitness value of the previous iteration, and retaining the crested porcupine position with the smaller fitness value; S46, decoding the position of the crested porcupine with the smallest fitness value into the Kp, Ki, and Kd parameter solutions of the PID algorithm of the continuous fiber composite material 3D printing tension controller; If the current number of iterations is equal to the maximum number of iterations, the entire process is exited; otherwise, a cyclic population reduction strategy is executed to reduce the size of candidate solutions, and the process returns to step S42.

5. The cyclic population reduction strategy according to claim 4, characterized in that it simulates the idea that not all crested porcupines activate the defense mechanism, but only those crested porcupines that are threatened will activate the defense mechanism to speed up the convergence speed and avoid falling into a local minimum.

Images