A constant tension control method for continuous fiber composite 3D printing forming process

Abstract

This invention provides a constant tension control method for the 3D printing process of continuous fiber composite materials, including modeling the tension control process and optimizing the PID controller using the cucurbit optimization algorithm. By real-time monitoring and dynamic adjustment of fiber tension during 3D printing, a mathematical model is established based on the characteristics of the magnetic powder actuator to describe and predict tension changes. The cucurbit optimization algorithm is then used to optimize the parameters of the PID controller. This algorithm simulates the foraging behavior of cucurbits in nature, finding the optimal PID parameter settings through iterative calculation, thereby achieving rapid response and system stability. Compared with the BP optimization algorithm and the traditional PID algorithm, the cucurbit optimization algorithm has higher global search capability and convergence speed. Through the above method, this invention can maintain the stability of fiber tension during 3D printing, reduce printing defects caused by tension fluctuations, improve the molding quality and printing efficiency of continuous fiber composite materials in 3D printing, and has broad application prospects.

Description

Technical fields:

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

[0002] Tension control is a crucial step in the 3D printing process of continuous fiber composite materials. Lack of fiber tension or unstable tension can cause fiber loosening, resulting in uneven fiber distribution, porosity in the matrix, and decreased mechanical properties after molding. Therefore, it is necessary to maintain stable tension control for continuous fibers.

[0003] Current tension control methods often fail to 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 environmental and load conditions. When the external environment or load changes, the magnetic powder brake cannot adjust quickly, leading to 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 adaptability and optimization potential of tension controllers for continuous fiber composite material 3D printing in complex environments.

[0004] The Crested Porcupine Optimizer (CPO) is a novel nature-inspired meta-optimization algorithm inspired by the various defensive behaviors of crested porcupines in nature. This algorithm simulates the optimization mechanism of crested porcupines' swarm behavior during foraging, optimizing the objective function through a series of iterative processes to find the optimal solution. Compared to other algorithms, the CPO utilizes four defense strategies for mathematical modeling and uniquely reduces the optimization time through a loop size reduction strategy. Summary of the Invention:

[0005] To address the tension control problem in 3D printing of continuous fiber composite materials, this invention provides an optimized tension control method for 3D printing of continuous fiber composite materials based on an improved PID controller. The method optimizes the tension PID controller using the Crown Porcupine optimization algorithm to solve the problems existing in the background technology, achieve stable tension control in 3D printing of continuous fiber composite materials, and improve the printing quality of continuous fiber composite materials.

[0006] To achieve the above objectives, the present invention employs a constant tension control method for the 3D printing process of continuous fiber composite materials, the specific steps of which are as follows.

[0007] S1. The tension controller for continuous fiber composite material 3D printing, which uses a magnetic powder brake as the actuator, is modeled using a reverse torque method to model the tension control process.

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

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

[0010] S4. Introduce the Crown Porcupine optimization algorithm to optimize PID parameters, take tension error as the input of the optimization algorithm, and output the braking torque of the magnetic powder brake.

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

[0012] 2. More specifically, the tension controller for continuous fiber composite material 3D printing described in step S1 is characterized in that, during the tension control process, after the continuous fiber roller is connected to the magnetic powder brake, the continuous fiber is pulled downward by the filament 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, where F0 is the initial tension of the fiber before contacting the magnetic powder brake. Figure 2 As shown.

[0013] 3. More specifically, the tension control process in S1, which uses a magnetic powder brake as the actuating element, is modeled as follows:

[0014] S11, The torque equation for the magnetic powder brake is:

[0015] f(t)RM Z (t)+M N (t)=Jα(t) (1)

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

[0017] S12, the relationship between angular acceleration and fiber displacement is shown in equation (2):

[0018]

[0019] In the formula, L1(t) is the length of the same fiber segment before pre-stretching, and L2(t) is the length of the fiber after pre-stretching.

[0020] S13. Fiber strain before and after applying pretension:

[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 fiber elastic modulus, σ represents the viscosity coefficient, and fiber tension is the additional internal force on the cross section caused by external force.

[0025] S15. Assuming negligible changes in cross-section, the final tension of the fiber is the sum of the initial tension and static friction:

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

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

[0028]

[0029] S17 and F1(t) are the system outputs, M Z (t) represents the system input, M N (t) represents the system disturbance, and the other parameters are constants. Let t be the system disturbance, and let t be the constant.

[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 a Laplace transform:

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

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

[0035]

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

[0037] 5. More specifically, the optimization of PID parameters using the Crowned Porcupine optimization algorithm described in step S4 includes the following steps:

[0038] S41. The parameters for initializing the hog optimization algorithm include the current iteration number T, the maximum iteration number Tmax, the initial maximum population size N, the minimum hog population size Nmin, and the upper bound ub and lower bound lb for the improved hog optimization algorithm.

[0039] S42. Set the Kp, Ki, and Kd parameters of the tension controller PID control algorithm to a spatial solution set and encode them as the positions of each individual porcupine. Each individual porcupine position represents a candidate solution of a set of Kp, Ki, and Kd solution sets.

[0040] S43. Simulate the visual, auditory, olfactory, and aggressive behaviors of the hooded porcupine to establish a mathematical model of the defense mechanism of the hooded porcupine optimization algorithm, and use the mathematical model to update the individual position of the hooded porcupine;

[0041] S44. Calculate the fitness value of each individual porcupine position in the current iteration; retain the porcupine position corresponding to the smallest fitness value in the current iteration;

[0042] S45. Compare the minimum fitness value of the current iteration with the minimum fitness value of the previous iteration, and retain the position of the hog with the smaller fitness value.

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

[0044] 6. More specifically, the fitness function is calculated using time multiplied by the integral of the absolute error, and the mathematical model is as follows:

[0045]

[0046] In Equation 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, a mathematical model of the defense mechanism of the crowned porcupine's optimization algorithm is established by simulating its visual, auditory, olfactory, and aggressive behaviors. This model is then used to update the individual positions of the crowned porcupine population, i.e., to update the solution of the proportional parameter Kp, integral parameter Ki, and derivative parameter Kd set of the tension controller PID algorithm. The specific steps are as follows:

[0048] S411. The dimensions of the problem are randomly initialized within the search space. The mathematical model is as follows:

[0049]

[0050] N ′ Indicates population size, For the i-th candidate solution, and These represent the upper and lower boundaries of the search, respectively. It can be any value between 0 and 1;

[0051] S412. The described 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, in order to accelerate the convergence speed and avoid getting trapped in local minima. The mathematical model is as follows:

[0052]

[0053] In the formula, T is the variable that determines the number of iterations, t is the current function evaluation, Tmax is the maximum number of function evaluations, % is the modulo operator, and Nmin is the minimum number of individuals in the newly generated population, such that the population size cannot be less than Nmin.

[0054] S421. Simulate the crested porcupine detecting a predator. It can choose to approach or move away. A normal distribution is used to generate random values. If these random values ​​are less than 1 or greater than -1, the predator is encouraged to approach; otherwise, it will move away. This establishes the mathematical model for the first defense strategy in the crested porcupine optimization algorithm.

[0055]

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

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

[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: the crested porcupine secretes a foul odor that spreads in the surrounding area to prevent predators from approaching it. The mathematical model is as follows:

[0061]

[0062] r3 is a random number between [1, N], and δ is a parameter controlling the search direction, defined by equation (19). Let t be the position of the i-th individual at iteration t, γt be the defense factor defined by equation (20), and τ3 be a random value in the interval [0,1]. The odor diffusion factor is defined by equation (21);

[0063] Specifically

[0064]

[0065] in This 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, The optimal solution represents the position of the i-th individual at iteration t, α is the velocity factor, and τ4 is a random value within the interval [0,1]. Let be the average force exerted by the crested porcupine on the i-th predator. This is provided by the inelastic collision law.

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

[0070] 8.1 A mathematical model of the tension control system for continuous fiber composite material 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 optimization algorithm of the crown porcupine was applied to the optimization of the tension PID controller for continuous fiber composite material 3D printing. Constant tension control was achieved in the continuous fiber composite material 3D printing molding process, which improved 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 quality of continuous fiber composite material 3D printing. Attached image description:

[0072] Figure 1 Flowchart of tension control for continuous fiber composite material 3D printing using the cauda pig optimization algorithm to tune PID parameters;

[0073] Figure 2 A schematic diagram of a tension controller for continuous fiber composite material 3D printing using a magnetic powder brake as the actuator;

[0074] Figure 3 Comparison curves of the tension control system response after PID parameters were tuned for the optimized algorithm of the Guanhao pig; Detailed implementation method:

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments; based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] Please see Figures 1-3 This embodiment provides a constant tension control algorithm for the 3D printing process of continuous fiber composite materials, including the establishment of a mathematical model for the tension control process and the application of the Crown Porcupine optimization algorithm to tune 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 continuous fiber composite material 3D printing, which uses a magnetic powder brake as the actuator, is modeled using a reverse torque method to model the tension control process.

[0078] More specifically, the tension controller for continuous fiber composite material 3D printing described in step S1 is characterized in that, during the tension control process, after the continuous fiber roller is connected to the magnetic powder brake, the continuous fiber is pulled downward by the filament 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, where F0 is the initial tension of the fiber before contacting the magnetic powder brake. Figure 2 As shown.

[0079] More specifically, the tension control process in S1, which uses a magnetic powder brake as the actuating element, is modeled as follows:

[0080] S11, The torque equation for the magnetic powder brake is:

[0081] f(t)RM Z (t)+M N(t)=Jα(t) (1)

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

[0083] S12, the relationship between angular acceleration and fiber displacement is shown in equation (2):

[0084]

[0085] In the formula, L1(t) is the length of the same fiber segment before pre-stretching, and L2(t) is the length of the fiber after pre-stretching.

[0086] S13. Fiber strain before and after applying pretension:

[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 fiber elastic modulus, σ represents the viscosity coefficient, and fiber tension is the additional internal force on the cross section caused by external force.

[0091] S15. Assuming negligible changes in cross-section, the final tension of the fiber is the sum of the initial tension and static friction:

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

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

[0094]

[0095] S17 and F1(t) are the system outputs, M Z (t) represents the system input, M N (t) represents the system disturbance, and the other parameters are constants. Let t be the system disturbance, and let t be the constant.

[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 a Laplace transform:

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

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

[0101]

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

[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 Caucasian 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 target tension value, the tension data detection module is used to detect the real-time tension value, and the Caucasian optimization algorithm module is used to tune the proportional parameter Kp, integral parameter Ki, and derivative parameter Kd of the tension controller PID control algorithm.

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

[0105] S4. Introduce the Crown Porcupine optimization algorithm to optimize PID parameters, take tension error as the input of the optimization algorithm, and output the braking torque of the magnetic powder brake.

[0106] More specifically, step S4, which involves optimizing the PID parameters using the Crowned Porcupine optimization algorithm, includes the following steps:

[0107] S41. The parameters for initializing the hog optimization algorithm include the current iteration number T, the maximum iteration number Tmax, the initial maximum population size N, the minimum hog population size Nmin, and the upper bound ub and lower bound lb for the improved hog optimization algorithm.

[0108] S42. Set the Kp, Ki, and Kd parameters of the tension controller PID control algorithm to a spatial solution set and encode them as the positions of each individual porcupine. Each individual porcupine position represents a candidate solution of a set of Kp, Ki, and Kd solution sets.

[0109] S43. Simulate the visual, auditory, olfactory, and aggressive behaviors of the hooded porcupine to establish a mathematical model of the defense mechanism of the hooded porcupine optimization algorithm, and use the mathematical model to update the individual position of the hooded porcupine;

[0110] S44. Calculate the fitness value of each individual porcupine position in the current iteration; retain the porcupine position corresponding to the smallest fitness value in the current iteration;

[0111] S45. Compare the minimum fitness value of the current iteration with the minimum fitness value of the previous iteration, and retain the position of the hog with the smaller fitness value.

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

[0113] More specifically, the fitness function is calculated using time multiplied by the integral of the absolute error, and the mathematical model is as follows:

[0114]

[0115] In Equation 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, a mathematical model of the defense mechanism of the crowned porcupine's optimization algorithm is established by simulating its visual, auditory, olfactory, and aggressive behaviors. This model is then used to update the individual positions of the crowned porcupine population, i.e., to update the solution of the proportional parameter Kp, integral parameter Ki, and derivative parameter Kd set of the tension controller PID algorithm. The specific steps are as follows:

[0117] S411. The dimension of the problem is randomly initialized within the search space, and the mathematical model is as follows:

[0118]

[0119] N ′ Indicates population size, For the i-th candidate solution, and These represent the upper and lower boundaries of the search, respectively. It can be any value between 0 and 1;

[0120] S412. The described 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, in order to accelerate the convergence speed and avoid getting trapped in local minima. The mathematical model is as follows:

[0121]

[0122] In the formula, T is the variable that determines the number of iterations, t is the current function evaluation, Tmax is the maximum number of function evaluations, % is the modulo operator, and Nmin is the minimum number of individuals in the newly generated population, such that the population size cannot be less than Nmin.

[0123] S421. Simulate the crested porcupine detecting a predator. It can choose to approach or move away. A normal distribution is used to generate random values. If these random values ​​are less than 1 or greater than -1, the predator is encouraged to approach; otherwise, it will move away. This establishes the mathematical model for the first defense strategy in the crested porcupine optimization algorithm.

[0124]

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

[0126] S422. The second defense strategy involves the crested porcupine making noise to threaten predators. The mathematical model is as follows:

[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: the crested porcupine secretes a foul odor that spreads in the surrounding area to prevent predators from approaching it. The mathematical model is as follows:

[0130]

[0131] r3 is a random number between [1, N], and δ is a parameter controlling the search direction, defined by equation (19). Let t be the position of the i-th individual at iteration t, γt be the defense factor defined by equation (20), and τ3 be a random value in the interval [0,1]. The odor diffusion factor is defined by equation (21).

[0132] Specifically

[0133]

[0134] in This 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, The optimal solution is represented by the position of the i-th individual at iteration t, α is the convergence rate factor, and τ4 is a random value in the interval [0,1]. Let be the average force exerted by the crested porcupine on the i-th predator. This is provided by the inelastic collision law.

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

[0139] Furthermore, following the steps described above, program models of the improved porcupine optimization algorithm and the standard porcupine optimization algorithm were established in MATLAB, and a constant tension control system model for continuous fiber composite material 3D printing was established in Siumlink. The Kp, Ki, and Kd parameters of the tension PID controller were tuned using the improved and unimproved porcupine optimization algorithms.

[0140] Furthermore, using the unit step signal as the target tension value, the optimal Kp, Ki, and Kd parameters are input into the constant tension control system model for continuous fiber composite material 3D printing established in Siumlink, resulting in the following... Figure 3 The control effect diagram shown demonstrates that, compared with the BP optimization algorithm and the traditional PID algorithm, the application of the Crown Porcupine optimization algorithm to control the tension of continuous fiber composite material 3D printing has a faster response speed and smaller overshoot, indicating that the constant tension control algorithm for the continuous fiber composite material 3D printing process proposed in this invention is innovative and practical.

Claims

1. A constant tension control method for the 3D printing process of continuous fiber composite materials, which optimizes the constant tension PID controller for continuous fiber composite material 3D printing using the Crowned Porcupine optimization algorithm, specifically including: S1. The tension controller for continuous fiber composite material 3D printing, which uses a magnetic powder brake as the actuator, is modeled using a reverse torque method for tension control process modeling. S2. Collect real-time tension data through a tension sensor and input the real-time tension data into the tension controller for continuous fiber composite material 3D printing; S3. Set the target tension value in the tension controller for the continuous fiber composite material 3D printing, and calculate the current difference between the real-time tension data and the target tension data; S4. Introduce the Crown Porcupine optimization algorithm to optimize PID parameters, take tension error as the input of the optimization algorithm, and output the braking torque of the magnetic powder brake. S5. Repeat steps S2-S4 until the tension error is zero, thus achieving constant tension control in the 3D printing process of continuous fiber composite materials. In step S1, the tension controller for continuous fiber composite material 3D printing describes a process where, after the continuous fiber roller is connected to the magnetic powder brake, the continuous fiber is pulled downwards by the filament feeding device inside the 3D printing head. During the printing process, static friction is generated between the continuous fiber and the magnetic powder brake. Used to overcome the braking torque of magnetic powder brake The static friction force generated by the magnetic powder brake on the continuous fiber is used to generate the target tension on the continuous fiber. ; The tension control process modeling in step S1 specifically includes the following steps: S11, The torque equation for the magnetic powder brake is: （1）， in The static friction force between the continuous fiber and the magnetic powder brake. Indicates the radius of the magnetic particle brake. The braking torque of the magnetic powder brake. For disturbance, The moment of inertia of the magnetic powder brake. This refers to the angular acceleration of the magnetic powder brake. S12, the relationship between angular acceleration and fiber displacement is shown in equation (2): （2）， wherein, Lpre is the length of the same segment of continuous fiber prior to pre-stretching; S13. Strain of continuous fiber before and after applying pretension: （3）， Where L2(t) is the length of the fiber after pre-stretching; S14. The fiber stress-strain model is equivalent to the Maxwell model, satisfying the following relationship: （4）， in, Indicates the fiber's elastic modulus. Indicates the viscosity coefficient; S15. Assuming negligible changes in cross-section, the final target tension of the continuous fiber is the sum of the initial tension and the static friction: ) (5)， in, The initial tension of the continuous fiber before it comes into contact with the magnetic powder brake; S16. Substituting (2), (3), (4), and (5) into (1) yields: （6）， wherein is a perturbation; Step 17, Order: （7）， （8）， （9）， （10）； S18, will , , , Substitute into equation (6) and perform the Laplace transform: （11）， The transfer function is then obtained as: （12）， This is the model obtained by modeling the tension control process.

2. A constant tension control method for a continuous fiber composite 3D printing forming process according to claim 1, characterized in that, The tension controller for continuous fiber composite material 3D printing includes a tension data setting module, a tension data detection module, a tension error calculation module, a PID control algorithm module for the tension controller, and a torque output module for the magnetic powder brake. The tension data setting module is used to set the target tension value, the tension data detection module is used to detect the real-time tension value, and the PID control algorithm module is used to tune the proportional parameter Kp, integral parameter Ki, and derivative parameter Kd of the tension controller's PID control algorithm.

3. The constant tension control method for 3D printing of continuous fiber composite materials according to claim 1, characterized in that, Step S4 optimizes the PID parameters using the Crowned Porcupine optimization algorithm. The specific steps are as follows: S41. The parameters for initializing the hog optimization algorithm include the current iteration number T, the maximum iteration number Tmax, the initial maximum population size N, the minimum hog population size Nmin, and the upper bound ub and lower bound lb for the improved hog optimization algorithm. S42. Set the Kp, Ki, and Kd parameters of the tension controller PID control algorithm to a spatial solution set and encode them as the positions of each individual porcupine. Each individual porcupine position represents a candidate solution of a set of Kp, Ki, and Kd solution sets. S43. Simulate the visual, auditory, olfactory, and aggressive behaviors of the hooded porcupine to establish a mathematical model of the defense mechanism of the hooded porcupine optimization algorithm, and use the mathematical model to update the individual position of the hooded porcupine; S44. Calculate the fitness value of each individual porcupine position in the current iteration; retain the porcupine position corresponding to the smallest fitness value in the current iteration; S45. Compare the minimum fitness value of the current iteration with the minimum fitness value of the previous iteration, and retain the position of the hog with the smaller fitness value. S46. Decode the position of the hog with the smallest fitness value into the Kp, Ki, and Kd parameter solutions of the PID algorithm for the tension controller of continuous fiber composite material 3D printing. If the current iteration count equals the maximum iteration count, exit the entire process; otherwise, execute the cyclic population reduction strategy to reduce the size of candidate solutions and return to step S42.

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