Temperature control method for automatically laying carbon fiber thermoplastic composite material through laser heating

Abstract

The invention relates to the field of fiber composite material in-situ forming control, and discloses a temperature control method for automatically laying a carbon fiber thermoplastic composite material through laser heating, which comprises the following steps of: constructing a feedback control model between laser power and heating temperature, and constructing a carbon fiber laser in-situ forming temperature feedback control system; the method comprises the following steps: determining an initial population number and a fitness function in a sparrow search algorithm, calculating an initial fitness value, then updating positions of a discoverer, an addresser and an early warner, recalculating fitness, updating an optimal value, and synchronously judging whether the maximum number of iterations is reached or not; if the optimal solution is not found, continuing to optimize within the range of the number of iterations; if the optimal solution condition is met, outputting an optimal fuzzy rule set, and applying the table to an automatic laying fuzzy PID temperature control system so as to realize temperature control in the fiber laying process; according to the method disclosed by the invention, the temperature in the process of automatically laying the carbon fiber thermoplastic composite material by laser heating is more uniform and stable.

Description

Technical Field

[0001] The invention relates to the technical field of in-situ forming control of fiber composite materials, and in particular to a method for controlling the temperature of a carbon fiber thermoplastic composite material automatically laid by laser heating. Background Art

[0002] With the increasing demand for high-performance composite materials in the aerospace field, automatic placement technology plays a key role in the manufacture of large composite components. In order to achieve efficient manufacturing of large and complex components, improving the efficiency of placement molding has become the key. However, the increase in placement speed has put forward new requirements and challenges for the molding process. Especially during the placement process, in order to ensure that the material can quickly reach and maintain the appropriate processing temperature, it is necessary to provide sufficient heat to the prepreg in a very short time, which places extremely high demands on the response speed and temperature control capability of the heating system.

[0003] At present, the heating methods of automatic placement equipment in China are mainly divided into four types: infrared heat source, hot air gun heat source, laser heat source and ultrasonic heat source. Infrared heat source and hot air gun heat source are widely used in the field of automatic placement because of their low heating cost, but because of their severe heat dissipation, their heating efficiency is low, while ultrasonic heat source is more used in the field of welding. Compared with the above three heating methods, laser heat source is gradually used in the field of automatic placement due to its concentrated energy and fast heating response speed. However, laser heating will produce a heat-affected zone on the material, and its temperature changes with its own speed and the influence of the external environment, which may change the microstructure of the material and thus affect its performance.

[0004] Therefore, during high-speed placement, the heating system must be able to make real-time adjustments to adapt to changes in start, stop and placement speed to ensure accurate heat supply. This requires not only the ability of the heating device to respond quickly, but also advanced temperature control technology to ensure that the temperature of the prepreg can be accurately controlled within the required process window. In addition, uniform temperature distribution is also crucial in the component molding process, because the existence of temperature differences will lead to inconsistent material properties, which will directly affect the performance of the final molded component.

[0005] Therefore, as the laying speed increases, it is necessary to optimize the design of the heating and control system, that is, to develop more efficient and sensitive heating technology and propose more accurate temperature control strategies. This not only involves the innovation of heating technology, but also includes in-depth research on the heat transfer characteristics of materials and the design of high-efficiency thermal management systems to achieve precise control of the temperature of composite materials during high-speed laying and ensure the quality and performance of composite components.

[0006] The purpose of the present invention is to provide a temperature control method for laser-heated automatic placement of carbon fiber thermoplastic composite materials. By constructing a feedback control model between laser power and heating temperature, a temperature feedback control system for carbon fiber laser in-situ forming is developed to ensure the stability of the surface temperature of the prepreg tape, thereby improving the performance of the composite laminate. Summary of the invention

[0007] In view of the defects in the existing automatic tape laying heating field, the purpose of the present invention is to provide a temperature control method for laser heating automatic laying of carbon fiber thermoplastic composite materials based on sparrow search optimization fuzzy PID controller.

[0008] During the high-speed placement process, it is necessary to ensure that the temperature of the prepreg can be precisely controlled within the required process window to achieve a uniform distribution of the temperature field during the in-situ curing of the prepreg and eliminate the influence of temperature differences on the performance of the final molded component.

[0009] In order to achieve the above object, the technical solution provided by the present invention is as follows:

[0010] Firstly, the temperature control system of the laser heating automatic placement process is designed, and the laser heating automatic placement system model is established;

[0011] The temperature control system of the laser heating automatic placement process includes a laser head, a laser controller, an infrared thermometer, an AD data acquisition module, a PLC data processing module, an adaptive fuzzy PID controller, and an AO conversion module;

[0012] The laser head emits light energy to irradiate the carbon fiber thermoplastic composite material, causing the temperature of the composite material to gradually increase; the infrared thermometer detects the radiation temperature signal of the heating point and transmits it to the AD data acquisition module. The PLC data processing module processes and controls the data, converts the signal into a digital signal, and transmits it to the adaptive fuzzy PID controller; the adaptive fuzzy PID controller compares the currently detected temperature with the set target laying temperature, uses the sparrow optimization algorithm SSA to dynamically optimize the fuzzy rules, calculates the temperature error E and the temperature error change rate EC, and generates an adaptive control signal for adjusting the output power of the laser source. The control signal is converted into an analog signal through the AO conversion module, acts on the laser controller, adjusts the output energy of the laser head, and realizes dynamic adjustment of the temperature;

[0013] The entire system forms a closed-loop control loop based on SSA optimization to ensure that the laying temperature of the carbon fiber thermoplastic composite material is stable near the preset target value.

[0014] Then, the control object of the adaptive fuzzy PID controller is established, and the fuzzy set, input quantity, output quantity, domain and membership function of the adaptive fuzzy PID controller are constructed.

[0015] Furthermore, the fuzzy set of the adaptive fuzzy PID controller is {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, that is, {NB, NM, NS, ZE, PS, PM, PB}, the input includes the temperature error E and the error change rate EC, the output is the adjustment of three PID parameters, including ΔKp, ΔKi, ΔKd, which are the adjustment of the proportional adjustment coefficient, the integral adjustment coefficient and the differential adjustment coefficient respectively, the membership function of the input and output are both triangular membership functions, and the basic domain is {-T, T}. The membership of each fuzzy subset is obtained, and the fuzzy rule table is designed according to the fuzzy control rules of each parameter.

[0016] Furthermore, the corrected PID control parameters are obtained according to the correction amount:

[0017]

[0018] Among them, K p ', K i ', K d ' is the PID parameter before correction, ΔK p , ΔK i , ΔK d is the corrected PID parameter.

[0019] Secondly, the fuzzy control rules are taken as the optimization target, and the sparrow search algorithm is used to optimize the fuzzy rules to improve the performance of the fuzzy PID controller so that the laying process can run stably. The specific steps are as follows:

[0020] S1. Generate an initial population of several sparrow individuals according to the fuzzy rules generated by the fuzzy PID in the automatic laying temperature control system, each individual is represented by a set of real vectors, and these vectors correspond to the parameters in the fuzzy rules;

[0021] S2. According to the defined fitness function, calculate the fitness value of each fuzzy rule respectively, and obtain the optimal individuals a, b, c with the minimum fitness. The corresponding position of the individual is X a , X b , X c ;

[0022] S3. Based on the results obtained in S2, explore better fuzzy rules through the discoverer position, joiner position and warning position.

[0023] Furthermore, the discoverer update is performed in the following manner, and its mathematical expression can be expressed as:

[0024]

[0025] Where: t is the number of iterations, iter maxis the maximum number of iterations; R2 and ST are the warning value and the safety value. When R2 < ST, it means the foraging area is safe and the discoverer can forage in the vicinity; when R2 ≥ ST, it means an individual in the foraging area has discovered a predator and the population needs to forage in a safe area.

[0026] Further, the updater updates in the following manner, and its mathematical expression can be represented as:

[0027]

[0028] In the formula: X p and X worst are the best position and the worst position respectively, that is, the positions with the highest and lowest fitness; A is a 1×d matrix. If i > n / 2, it indicates that the i-th joiner has obtained less food and needs to search for food in other areas to improve fitness; when i ≤ n / 2, it indicates that the i-th joiner is foraging in a good foraging area.

[0029] Further, the early warning position updates in the following manner, and its mathematical expression can be represented as:

[0030]

[0031] In the formula: X best is the global best position; f g and f w are the global best and worst fitness; when f i > f g , it means the sparrow is on the edge and is easily preyed upon; when f i = f g , it means a sparrow in a safe position has discovered danger and needs to approach other sparrows.

[0032] S4. Check whether the maximum number of iterations has been reached. If the condition is met, go to step 5; otherwise, return to step 2.

[0033] S5. Output the optimal fuzzy rule set and apply this table to the automatic placement fuzzy PID temperature control system.

[0034] Finally, apply the optimization result to the laser heating temperature control system to achieve temperature field uniformity during the placement process.

[0035] The design idea of the present invention is as follows:

[0036] The present invention discloses a method for optimizing the temperature fuzzy PID controller of laser-heated automatic laying of carbon fiber thermoplastic composite materials based on the sparrow optimization algorithm, which solves the problem that the existing fuzzy rules are not compatible with the automatic laying temperature control system, resulting in poor surface temperature stability of the prepreg tape. The present invention constructs a feedback control model between laser power and heating temperature, builds a carbon fiber laser in-situ forming temperature feedback control system, determines the initial population size and fitness function in the sparrow search algorithm, calculates the initial fitness value, then updates the positions of the discoverer, joiner and early warning, recalculates the fitness and updates the optimal value, and simultaneously determines whether the maximum number of iterations has been reached. If the optimal solution is not found, continue to search for the best within the range of the number of iterations; if the optimal solution conditions are met, output the optimal fuzzy rule set, and apply the table to the automatic laying fuzzy PID temperature control system to achieve temperature control during fiber laying. The method described in the present invention can make the temperature in the process of laser-heated automatic laying of carbon fiber thermoplastic composite materials more uniform and stable.

[0037] By adopting the above technology, compared with the prior art, the present invention has the following beneficial effects:

[0038] 1) The beneficial effects of the present invention are as follows: the present invention uses the sparrow search algorithm to optimize the fuzzy rules in the fuzzy PID controller, which can make the temperature in the process of laser heating automatic heating of carbon fiber thermoplastic composite materials more stable; the present invention makes full use of the global search ability and adaptability of the sparrow optimization algorithm, and the optimized fuzzy rules reduce the dependence on human adjustment and experience, making the control strategy of the system more automated and intelligent. This not only saves time and energy in practical applications, but also reduces the possibility of human error.

[0039] 2) The present invention adopts the sparrow search algorithm to optimize the fuzzy rules in the fuzzy PID controller. Due to the characteristics of fast convergence speed and high precision of the algorithm, the PID controller can be more efficient in regulating temperature while accelerating the process of finding the optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic diagram of the system design for temperature control during the laser heating automatic placement process in the present invention;

[0041] Figure 2 Schematic diagram of temperature control process for laser-heated automatic placement of carbon fiber thermoplastic composites;

[0042] Figure 3 Optimize algorithm flow chart for sparrow search;

[0043] Figure 4 is ΔK p Fuzzy control query table;

[0044] Figure 5 Compare temperature response curves for control system simulations. DETAILED DESCRIPTION

[0045] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0046] like Figure 1 As shown in the figure, the system design schematic diagram of temperature control during the laser heating automatic placement process includes the hardware and software parts of the system. The placement head of the hardware part uses a laser head for heating to provide temperature conditions for the bonding between the prepreg layers, thereby realizing the automatic placement of carbon fiber reinforced thermoplastic composite materials. The infrared thermometer 2 is responsible for real-time collection of temperature information during the placement process; the software part is to write a program running on the host computer, and the infrared temperature measurement module consists of an AD data acquisition module and a PLC data processing module.

[0047] The laser head emits light energy to irradiate the carbon fiber thermoplastic composite material, causing the temperature of the composite material to gradually increase; the infrared thermometer detects the radiation temperature signal of the heating point and transmits it to the AD data acquisition module, the PLC data processing module processes and controls the data, converts the signal into a digital signal, and transmits it to the adaptive fuzzy PID controller; the adaptive fuzzy PID controller compares the currently detected temperature with the set target laying temperature, uses the sparrow optimization algorithm SSA to dynamically optimize the fuzzy rules, calculates the temperature error E and the temperature error change rate EC, and generates an adaptive control signal for adjusting the output power of the laser source, and converts the control signal into an analog signal through the AO conversion module, acts on the laser controller, adjusts the output energy of the laser head, and realizes dynamic adjustment of the temperature; the entire system forms a closed-loop control loop based on SSA optimization to ensure that the laying temperature of the carbon fiber thermoplastic composite material is stable near the preset target value.

[0048] In an embodiment, Figure 2 , 3 The figure shows a schematic diagram of a temperature control system in a temperature control method for laser heating automatic laying of carbon fiber thermoplastic composite materials and a flow chart of a sparrow search optimization algorithm. The temperature control method for laser heating automatic laying of carbon fiber thermoplastic composite materials first needs to establish a laser heating automatic laying system model, establish the control object of the fuzzy PID controller, construct the fuzzy set, input, output, domain and membership function of the fuzzy controller, and use the fuzzy control rule as the optimization target. Secondly, use Figure 3 The sparrow search algorithm optimizes the fuzzy rules and improves the performance of the fuzzy PID controller, so that the laying process can run stably. Finally, the optimization results are applied to the laser heating temperature control system to make the temperature field in the laying process uniform.

[0049] Specifically, the fuzzy set of the fuzzy controller is {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, that is, {NB, NM, NS, ZE, PS, PM, PB}, the input includes the temperature error E and the error change rate EC, the output is the adjustment of the three PID parameters, including ΔKp, ΔKi, ΔKd, which are the adjustment of the proportional adjustment coefficient, the integral adjustment coefficient and the differential adjustment coefficient respectively, the membership function of the input and output are all triangular membership functions, and the basic domain is {-6, 6}. The membership of each fuzzy subset is obtained, and the fuzzy rule table is designed according to the fuzzy control rules of each parameter, where ΔK p The fuzzy control query table is as follows Figure 4 As shown. According to the correction amount, the corrected PID control parameters are obtained:

[0050]

[0051] Among them, K p ', K i ', K d ' is the PID parameter before correction, ΔK p , ΔK i , ΔK d is the corrected PID parameter.

[0052] The sparrow search algorithm is used to optimize the fuzzy rules. The specific steps are as follows:

[0053] S1. Generate an initial population of several sparrow individuals according to the fuzzy rules generated by the fuzzy PID in the automatic laying temperature control system, each individual is represented by a set of real vectors, and these vectors correspond to the parameters in the fuzzy rules;

[0054] S2. According to the defined fitness function, calculate the fitness value of each fuzzy rule respectively, and obtain the optimal individuals a, b, c with the minimum fitness. The corresponding position of the individual is X a , X b , X c ;

[0055] S3. Based on the results obtained in S2, the positions of the remaining sparrows are updated through the discoverer's position and the joiner's position to explore better fuzzy rules.

[0056] Discoverer Update:

[0057]

[0058] Where: t is the number of iterations, iter maxis the maximum number of iterations; R2 and ST are the warning value and the safety value. When R2 < ST, it means the foraging area is safe and the discoverer can forage in the vicinity; when R2 ≥ ST, it means an individual in the foraging area has discovered a predator and the population needs to forage in a safe area.

[0059] Updater update:

[0060]

[0061] In the formula: X p and X worst are the best position and the worst position respectively, that is, the positions with the highest and lowest fitness; A is a 1×d matrix. If i > n / 2, it indicates that the i-th joiner has obtained less food and needs to search for food in other areas to improve fitness; when i ≤ n / 2, it indicates that the i-th joiner is foraging in a good foraging area.

[0062] Early warning position update:

[0063]

[0064] In the formula: X best is the global best position; f g and f w are the global best and worst fitness; when f i > f g it means the sparrow is at the edge and is easily preyed upon; when f i = f g it means that a sparrow in a safe position has discovered danger and needs to approach other sparrows.

[0065] S4. Check whether the maximum number of iterations is reached. If the condition is met, go to step 5; otherwise, return to step 2.

[0066] S5. Output the optimal fuzzy rule set and apply this table to the automatic placement fuzzy PID temperature control system.

[0067] The present invention uses the sparrow search algorithm to optimize the fuzzy rules in the fuzzy PID controller, and establishes a simulation model for temperature control through Simulink, where the simulation time is 10 s. The simulation comparison result graph is as Figure 5 shown. It can be seen from the graph that the response speed of the fuzzy PID temperature control is slow and it is difficult to meet the requirements of laser automatic placement. The control method of using the sparrow search algorithm to optimize the fuzzy rules in the fuzzy PID controller proposed by the present invention has improved in response time compared with the ordinary control, and the overall response speed of the system is fast.

Claims

1. A temperature control method for laser heating automatic placement of carbon fiber thermoplastic composite materials, characterized in that: The following steps are involved: 1.1) Design the temperature control system of the laser heating automatic placement process and establish the laser heating automatic placement system model; 1.2) Establish the control object of the adaptive fuzzy PID controller, and construct the fuzzy set, input quantity, output quantity, domain and membership function of the adaptive fuzzy PID controller; 1.3) Taking the fuzzy control rules as the optimization target, the sparrow search algorithm is used to optimize the fuzzy rules, improve the performance of the adaptive fuzzy PID controller, and enable the laying process to run stably; 1.4) Apply the optimization results to the temperature control system to achieve uniform temperature field during the laying process.

2. According to the temperature control method of the laser heating automatic placement of carbon fiber thermoplastic composite materials in claim 1, the temperature control system of the laser heating automatic placement process includes a laser head, a laser controller, an infrared thermometer, an AD data acquisition module, a PLC data processing module, an adaptive fuzzy PID controller, and an AO conversion module; The laser head emits light energy to irradiate the carbon fiber thermoplastic composite material, causing the temperature of the composite material to gradually increase; the infrared thermometer detects the radiation temperature signal of the heating point and transmits it to the AD data acquisition module. The PLC data processing module processes and controls the data, converts the signal into a digital signal, and transmits it to the adaptive fuzzy PID controller; the adaptive fuzzy PID controller compares the currently detected temperature with the set target laying temperature, uses the sparrow optimization algorithm SSA to dynamically optimize the fuzzy rules, calculates the temperature error E and the temperature error change rate EC, and generates an adaptive control signal for adjusting the output power of the laser source. The control signal is converted into an analog signal through the AO conversion module, acts on the laser controller, adjusts the output energy of the laser head, and realizes dynamic adjustment of the temperature; The entire system forms a closed-loop control loop based on SSA optimization to ensure that the laying temperature of the carbon fiber thermoplastic composite material is stable near the preset target value.

3. The temperature control method of laser-heated automatic placement of carbon fiber thermoplastic composite materials according to claim 1 is characterized by: The fuzzy set of the adaptive fuzzy PID controller is {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, that is, {NB, NM, NS, ZE, PS, PM, PB}, the input includes temperature error E and error change rate EC, the output is the adjustment of three PID parameters, including ΔKp, ΔKi, ΔKd, which are the adjustment of proportional regulation coefficient, integral regulation coefficient and differential regulation coefficient respectively, the membership functions of input and output are triangular membership functions, the basic domain is {-T, T}, thereby obtaining the membership of each fuzzy subset, and designing the fuzzy rule table according to the fuzzy control rules of each parameter; the corrected PID control parameters are obtained according to the correction amount: Among them, K p ', K i ', K d ' is the PID parameter before correction, ΔK p , ΔK i , ΔK d is the corrected PID parameter.

4. The method for temperature control of laser-heated automatic placement of carbon fiber thermoplastic composite materials according to claim 3 is characterized in that: The sparrow search algorithm is used to optimize the fuzzy rules. The specific steps are as follows: S1. Generate an initial population of several sparrow individuals according to the fuzzy rules generated by the fuzzy PID in the temperature control system, each individual is represented by a set of real vectors, which correspond to the parameters in the fuzzy rules; S2. According to the defined fitness function, calculate the fitness value of each fuzzy rule and obtain the optimal individual {a, b, c} with the minimum fitness; a, b, c represent the three parameters ΔK in the adaptive fuzzy PID controller respectively. p , ΔK i , ΔK d ; Each individual {a, b, c} represents a different parameter combination, and the corresponding position of the individual is X a , X b , X c ; S3, based on the results obtained in S2, explore better fuzzy rules through the positions of the discoverer, the joiner and the warning person; S4, check whether the maximum number of iterations is reached, if the condition is met, go to step 5, otherwise return to step 2; S5. Output the optimal fuzzy rule set.

5. The method for temperature control of laser-heated automatic placement of carbon fiber thermoplastic composite materials according to claim 4, characterized in that: When performing step S3, the update of the discoverer, the joiner and the alarmer is performed in the following manner, and a better fuzzy rule is explored by updating the discoverer position, the joiner position and the alarmer position: Finder location update: Where: t is the number of iterations, iter max is the maximum number of iterations; R2 and ST are the warning value and the safety value; α is the learning rate, which determines the size of the adjustment step in each iteration; Q is the coefficient of the update amplitude, which is used to adjust the growth amplitude; L is the step size, which represents the basic amount of change each time when adjusting variables in the iteration process; when R2 < ST, it means that the foraging area is safe and the discoverer can forage in the vicinity; when R2 ≥ ST, it means that an individual in the foraging area discovers a predator and the population needs to forage in the safe area; Joiner location update: Where: X p and X worst are the best and worst positions, i.e. the places with the highest and lowest fitness respectively; A is a 1×d matrix; if i>n / 2, it means that the ith participant gets less food and needs to search for food in other areas to improve fitness; if i≤n / 2, it means that the ith participant is foraging in a good foraging area; Alerter location update: Where: X best is the best global position; f g and f w is the global best and worst fitness; β represents the movement range between the control joiner and the optimal solution, K is a control parameter used to adjust the step size or movement range of the individual during the update process, and ε represents a minimum constant used to prevent zero division errors during division operations. i It represents the fitness of the i-th individual, i.e., the ITAE value, which is used to measure the quality of the individual. i >f g When , it means that the sparrow is at the edge and is easy to be preyed upon; when f i =f g , it means that the sparrow in a safe position has discovered danger and needs to approach other sparrows.

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