Dynamic regulation and control method and system for state and structural morphology of laser arc composite additive manufacturing molten pool

Abstract

The invention discloses a dynamic regulation and control method for the state and the structure morphology of a laser arc composite additive manufacturing molten pool, which comprises a multi-physical field coupling model used for simulating the temperature, the flow mode and the structure solidification process of the molten pool under a composite heat source, constructing a dynamic regulation and control model, and performing closed-loop control on the state and the structure morphology of the molten pool. And based on control parameters in closed-loop control, carrying out visual presentation and real-time monitoring on the state and the structure morphology of the molten pool by utilizing a multi-physics field coupling model. According to the scheme, the control precision of the molten pool and the morphology precision of the lattice structure in the laser arc composite additive manufacturing process can be effectively improved, technological parameters can be adjusted according to real-time data, accurate control over the state and the structure morphology of the molten pool is achieved, and therefore the stability and quality of products are greatly improved; particularly, the method has remarkable application advantages in the fields with high requirements on structural precision and surface quality, such as the industries of ships, aviation, spaceflight, medical instruments and the like.

Description

Technical Field

[0001] The present invention belongs to the field of additive manufacturing, and specifically relates to a method and system for dynamically controlling the state and structural morphology of a molten pool in laser arc composite additive manufacturing. Background Art

[0002] With the rapid development of modern manufacturing, the demand for high-quality, high-precision, and high-efficiency additive manufacturing technologies is increasing. Laser arc hybrid additive manufacturing, an advanced manufacturing process that combines the advantages of both laser and arc heat sources, has garnered widespread attention in recent years. This technology combines the high precision and high quality of laser additive manufacturing with the high efficiency and low cost of arc additive manufacturing, demonstrating significant potential for application in a wide range of fields, including aerospace, automotive manufacturing, and energy equipment.

[0003] During the laser arc hybrid additive manufacturing (LAM) process, the state and structural morphology of the melt pool have a crucial impact on the quality of the final product. Parameters such as the melt pool's temperature distribution, flow characteristics, and solidification behavior directly determine the microstructure and mechanical properties of the manufactured part. Furthermore, characteristics such as the rod diameter, roughness, and node defects of the lattice structure are also key indicators for evaluating the quality of AM parts. These parameters interact in a complex and dynamic manner with the LAM process parameters, making precise control of the melt pool state and lattice structure morphology a highly challenging task.

[0004] Traditional additive manufacturing control methods often rely on empirical formulas or static models, which cannot accurately reflect the dynamic changes in the melt pool state and structural morphology, resulting in significant uncertainty in the manufacturing process. Therefore, developing a control method that can monitor the melt pool state and structural morphology in real time and dynamically adjust process parameters based on the monitoring results is of great significance for improving the quality and efficiency of laser arc hybrid additive manufacturing.

[0005] As the trend toward intelligent manufacturing intensifies, achieving intelligent control of the additive manufacturing process is a current research hotspot. The application of mobile additive manufacturing robots offers the potential for intelligent control. However, enabling robots to autonomously adjust their motion trajectory and process parameters based on environmental information, as well as achieving multi-robot collaborative additive manufacturing, remain pressing technical challenges. Summary of the Invention

[0006] In response to the above problems, the purpose of the present invention is to provide a method and system for dynamically controlling the melt pool state and structural morphology in laser arc composite additive manufacturing. The method aims to achieve precise control of the melt pool behavior and morphology accuracy by dynamically adjusting the process parameters through real-time monitoring of the melt pool state and structural morphology, while promoting the intelligent development of the additive manufacturing process.

[0007] The specific technical solutions for achieving the purpose of the present invention are as follows:

[0008] A method for dynamically controlling the state and structural morphology of a molten pool in laser arc composite additive manufacturing comprises the following steps:

[0009] Step 1: Construct a multi-physics field coupling model to simulate the molten pool temperature, flow pattern and structural solidification process under the composite heat source;

[0010] Step 2: Build a dynamic control model to perform closed-loop control on the molten pool state and structural morphology;

[0011] Step 3: Based on the control parameters in the closed-loop control, the multi-physics field coupling model is used to visualize and monitor the molten pool state and structural morphology in real time.

[0012] The present invention also provides a dynamic control system for the state and structural morphology of the molten pool in laser arc composite additive manufacturing, comprising the following modules:

[0013] Multi-physics coupling modeling module: used to build a multi-physics coupling model to simulate the melt pool temperature, flow pattern and structural solidification process under a composite heat source;

[0014] Structural morphology dynamic control module: used to build a structural morphology dynamic control model and perform closed-loop control of the molten pool state and structural morphology.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] (1) The solution of the present invention constructs a multi-physics field coupling model that can analyze the complex interaction between the temperature distribution, flow characteristics and solidification behavior of the molten pool under different laser arc composite additive manufacturing process conditions, as well as the characteristics of the lattice structure such as rod diameter, roughness and node defects and process parameters. The model can reveal the specific impact of changes in composite heat source process parameters on the molten pool behavior and structural morphology accuracy, thereby providing a theoretical basis for subsequent process adjustments;

[0017] (2) The solution of the present invention constructs a dynamic control model. Combined with the multi-physics field coupling model, it can deeply reveal the dynamic changes in the state and structural morphology of the molten pool due to changes in the composite heat source process parameters (such as laser power, laser beam width, movement speed, molten pool atmosphere, etc.), especially the changes in the morphology of the unsupported suspended molten pool and the lattice structure. By studying the fluidity, solidification behavior and lattice structure accuracy of the molten pool under different process conditions, it can effectively predict and control the quality defects that may occur in the manufacturing process;

[0018] (3) The solution of the present invention, through a multi-physics field coupling model, can predict the chain reactions that may be triggered by a single change in process parameters and analyze the complex impact of these changes on the entire additive manufacturing process. For example, an increase in laser power may cause the melt pool temperature to rise, thereby affecting the flow characteristics and solidification behavior of the melt pool, and thus affecting the accuracy of the structural morphology. By comprehensively predicting and analyzing these chain reactions, it is ensured that when adjusting process parameters, their impact on the manufacturing process can be fully evaluated, reducing the occurrence of adverse reactions and quality defects.

[0019] (4) Dynamically adjust process parameters. Combining the multi-physics field coupling model and the dynamic control model, the process parameters of laser arc composite additive manufacturing can be dynamically adjusted according to the dynamic change law of the molten pool state and structural morphology predicted by the model and the preset goals (such as molten pool temperature, flow characteristics, structural accuracy, etc.), and the molten pool temperature distribution, flow characteristics, solidification behavior, as well as the diameter, roughness and node defects of the lattice structure rods can be precisely controlled. By dynamically adjusting the process parameters, the entire additive manufacturing process can be operated within the target accuracy range, thereby improving product quality;

[0020] (5) The solution of the present invention can optimize the additive manufacturing process and improve product quality. By implementing the above-mentioned dynamic control technology, the process of laser arc composite additive manufacturing can be effectively optimized and the product quality in the manufacturing process can be improved. By real-time monitoring and adjustment of process parameters, the present invention can optimize the molten pool state and structural morphology in the additive manufacturing process in real time, avoid the occurrence of quality problems, and provide a theoretical basis for the subsequent feedback control system to achieve closed-loop precise control of the additive manufacturing process.

[0021] This paper establishes a mathematical model linking process parameters with melt pool behavior and structural morphology, enabling systematic optimization and control. This model simulates the impact of a composite heat source on the melt pool and structural morphology under varying process conditions and provides corresponding parameter adjustment strategies. This mathematical model enables systematic optimization of the additive manufacturing process, ensuring stability, precision, and efficiency throughout.

[0022] The present invention will be further described below with reference to specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Schematic diagram of laser arc composite additive manufacturing in an embodiment of the present invention.

[0024] Figure 2 Schematic diagram of the system architecture for dynamically controlling the state and structural morphology of the molten pool in laser arc composite additive manufacturing in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] Example

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. The described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0027] As used in this application and the claims, the words "a," "an," "an," and / or "the" are not intended to refer to the singular but may include the plural unless the context clearly indicates otherwise. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0028] Unless otherwise specifically stated, the relative arrangement of the parts and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to actual proportional relationships. The techniques, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods and equipment should be considered as part of the authorization specification. In all examples shown and discussed here, any specific values ​​should be interpreted as being merely exemplary and not as limitations. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that similar numbers and letters represent similar items in the following figures, and therefore, once an item is defined in one figure, it does not need to be further discussed in subsequent figures.

[0029] Combine Figure 1 A method for dynamically controlling the state and structural morphology of a molten pool in laser arc composite additive manufacturing comprises the following steps:

[0030] Step 1: Construct a multi-physics coupling model to simulate the melt pool temperature, flow pattern, and structural solidification process under the composite heat source:

[0031] Step 1-1: Based on the unsteady Navier-Stokes equations and Boussinesq approximation, describe the flow characteristics inside the molten pool and construct a partial differential equation system for the dynamic behavior of the molten pool under the action of the composite heat source:

[0032]

[0033] Where ρ is the density of the molten metal (kg / m 3), u is the velocity field vector inside the molten pool (m / s), t is the time (s), P is the pressure field inside the molten pool (Pa), μ is the dynamic viscosity of the molten metal (Pa·s), g is the gravity acceleration vector (m / s 2 ), β is the thermal expansion coefficient of the metal (K-1), T is the molten pool temperature field (K), and T0 is the reference temperature (usually ambient temperature or melting point, K). F Marangoni is the Marangoni force term, caused by the surface tension gradient, and is expressed as γ is the surface tension coefficient (N / m·K);

[0034] Step 1-2: Couple the heat input of the laser and arc to construct a composite heat source conduction model:

[0035]

[0036] Among them, the laser heat source term Q laser Using a Gaussian distribution:

[0037]

[0038] Arc heat source term Q arc Using the double ellipse distribution model:

[0039]

[0040] Where C p is the constant-pressure specific heat capacity of the molten metal (J / (kg·K)). k is the thermal conductivity of the metal (W / (m·K)). Q laser is the laser heat source power density (W / m 3 );η l is the laser energy absorption efficiency (dimensionless, 0.82±0.03). l is the laser power (W). l is the laser spot radius (value is 200μm). r is the radial distance from the center of the laser spot (m). Q arc is the arc heat source power density (W / m 3 );η a is the arc energy efficiency (dimensionless, 0.65±0.05). a is the arc current (A). V arc is the arc voltage (V). a, b the length of the ellipse semi-axis of the arc heat source distribution (m, 1.2 mm and 2.0 mm respectively). x, y the local coordinates of the arc action area (m);

[0041] Steps 1-3: Track the evolution of the solid-liquid interface based on the Phase Field Method and construct a solidification interface tracking model:

[0042]

[0043] Among them, φ is the phase field variable, ranging from [0,1], which is used to distinguish the solid phase (φ=1) and the liquid phase (φ=0), M φ is the interface mobility coefficient (m 3 / (J·s)), characterizes the interface movement rate. ∈Interface thickness parameter (value 10 -5 m). f(φ) is the double-well potential function, defined as f(φ) = φ 2 (1-φ) 2 (J / m3). λ is the phase change driving force coupling coefficient (valued at 1.5×10 6 K -1 ). m is the equilibrium melting point of the metal (K).

[0044] Step 2: Construct a dynamic control model, use numerical simulation technology combined with actual additive manufacturing experiments, and analyze the temperature distribution, flow characteristics and solidification behavior of the molten pool under different laser arc composite additive manufacturing process conditions. The mathematical model used is based on heat conduction, fluid mechanics and phase change theory, and can simulate the influence of composite heat sources (laser and arc) on the molten pool temperature, flow pattern and structural solidification process. For example, under specific process conditions (laser power, welding speed, etc.), the model simulates the temperature field and velocity field of the molten pool through numerical calculation, and combines the physical properties of the material (such as thermal conductivity, specific heat, etc.) to deduce the cooling rate and solidification behavior of the molten pool, and perform closed-loop control of the molten pool state and structural morphology:

[0045] Step 2-1: real-time acquisition of morphological features of the molten pool and pre-processing;

[0046] This embodiment uses a high-speed CCD camera (frame rate ≥ 2000fps) in conjunction with a laser line scanner to extract the following features through image processing algorithms:

[0047] Molten pool geometric parameters, including melt pool length Lp, width Wp, collapse amount Δh, lattice structure morphology of the melt pool, including rod diameter Di (accuracy ±5μm), surface roughness Ra (resolution 0.1μm), node ellipticity; melt pool temperature.

[0048] Step 2-2: Based on multi-objective optimization and dynamic compensation, achieve real-time closed-loop control of the molten pool state and structural morphology:

[0049] Based on the real-time extracted molten pool morphological features, a multi-objective optimization function of morphological accuracy and process stability is constructed to optimize the process parameters:

[0050]

[0051] Constraints:

[0052]

[0053] Among them, D i is the measured diameter of the i-th rod, D0 is the target diameter, R a Surface roughness, E j is the ellipticity of the jth node, defined as Ej = D major / D minor , T max is the maximum temperature of the molten pool, is the maximum temperature gradient, v s is the scanning speed, R a Surface roughness, E j is the ellipticity of the jth node, defined as Ej = D major / D minor , T max is the maximum temperature of the molten pool, is the maximum temperature gradient, v s is the scanning speed;

[0054] The multi-objective optimization function is solved to obtain the optimized process parameters, including laser power, arc current and scanning speed;

[0055] The NSGA-II algorithm can be used to solve the Pareto optimal solution set of laser power Pl, arc current Ia, and scanning speed vs;

[0056] For unsupported suspended structures, the compensation relationship between the molten pool drop and the arc current is determined, and the compensated arc current is obtained:

[0057]

[0058] Among them, ΔIa(t) is the real-time adjustment of the arc current, Δh(t) is the drop of the molten pool, that is, the deviation between the actual forming height and the target height, K p is the proportional gain coefficient, K d is the differential gain coefficient, K i is the integral gain coefficient;

[0059] Based on feedforward-feedback composite control, the optimized process parameters are output according to the set control cycle, realizing real-time monitoring and closed-loop dynamic control of the molten pool state and structural morphology:

[0060] Real-time monitoring includes:

[0061] Molten pool temperature monitoring: two-color infrared thermometer (wavelength 1.5-2.0μm, sampling rate 1kHz, accuracy ±10K);

[0062] Dynamic monitoring of morphology: fringe projection 3D scanner (resolution 10 μm);

[0063] Process parameter collection: laser power meter (error ±0.5%), arc voltage sensor (error ±0.2V);

[0064] The closed-loop control strategy adopts a feedforward-feedback composite control architecture:

[0065] Among them: Feedforward control: Based on the process parameter-morphology response database, the parameter adjustment amount is predicted;

[0066] Feedback control: Using fuzzy PID algorithm to correct the control quantity in real time:

[0067]

[0068] Where ΔPl(t) is the adjustment amount of laser power, e(t) is the diameter deviation, e(t) = D target -D measured

[0069] During feedback control, if the melt pool temperature deviates from the target value, the system automatically adjusts parameters such as laser power and welding speed to ensure a stable melt pool and that the structural topography meets precision requirements. This closed-loop control enables dynamic optimization of melt pool behavior and structural topography, effectively controlling the quality of the additive manufacturing process.

[0070] Step 3: Based on the control parameters in the closed-loop control, the multi-physics field coupling model is used to visualize and monitor the molten pool state and structural morphology in real time.

[0071] In this implementation, a digital mapping relationship between process parameters, melt pool behavior, and structural morphology is constructed, and the following functions are achieved through the OPC UA protocol: real-time synchronization of physical equipment and virtual model data; abnormal morphology traceability analysis (based on graph neural networks); and process parameter self-correction (error tolerance ±3%).

[0072] This embodiment also includes a human-computer interaction interface, which is developed based on the Qt framework and includes the following modules: process parameter setting, input of target melt pool temperature, structural dimensions and other parameters; real-time monitoring panel, display of melt pool temperature field distribution, flow velocity vector diagram, and three-dimensional point cloud of structural morphology; historical data backtracking, storage and analysis of key parameter change curves during the control process.

[0073] The present invention also provides a dynamic control system for the state and structural morphology of the molten pool in laser arc composite additive manufacturing, comprising the following modules:

[0074] Multi-physics coupling modeling module: used to build a multi-physics coupling model to simulate the melt pool temperature, flow pattern and structural solidification process under a composite heat source, including:

[0075] include:

[0076] Based on the unsteady Navier-Stokes equations and Boussinesq approximation, the flow characteristics inside the molten pool are described, and a partial differential equation system for the dynamic behavior of the molten pool under the action of a composite heat source is constructed:

[0077]

[0078] Where ρ is the density of the molten metal, u is the velocity field vector inside the molten pool, t is time, P is the pressure field inside the molten pool, μ is the dynamic viscosity of the molten metal, g is the gravity acceleration vector, β is the thermal expansion coefficient of the metal, T is the molten pool temperature field, T0 is the reference temperature, F Marangoni is the Marangoni force term, caused by the surface tension gradient, and is expressed as γ is the surface tension coefficient;

[0079] Couple the heat input of the laser and arc to construct a composite heat source conduction model:

[0080]

[0081] Among them, C p is the constant pressure specific heat capacity of the molten metal, and k is the thermal conductivity of the metal. laser is the laser heat source power density, η l is the laser energy absorption efficiency, P l is the laser power, r l is the laser spot radius, r is the radial distance from the center of the laser spot, Q arc is the arc heat source power density, η a is the arc energy efficiency, I a is the arc current, V arc is the arc voltage, a, b are the lengths of the semi-axis of the arc heat source ellipse, and x, y are the local coordinates of the arc action area;

[0082] Based on the phase field method, the evolution of the solid-liquid interface is tracked and a solidification interface tracking model is constructed:

[0083]

[0084] Among them, φ is the phase field variable, ranging from [0,1], which is used to distinguish the solid phase (φ=1) and the liquid phase (φ=0), M φ is the interface mobility coefficient, which characterizes the interface movement rate,∈interface thickness parameter, f(φ) is the double-well potential function, defined as f(φ)=φ 2 (1-φ) 2 , λ is the phase change driving force coupling coefficient, T m The equilibrium melting point of the metal.

[0085] Structural morphology dynamic control module: used to build a structural morphology dynamic control model and perform closed-loop control of the molten pool state and structural morphology, including:

[0086] include:

[0087] Collect the morphological characteristics of the molten pool in real time and perform preprocessing;

[0088] The morphological characteristics of the molten pool include: molten pool geometric parameters, including molten pool length Lp, width Wp, collapse amount Δh, molten pool lattice structure morphology, including rod diameter Di, surface roughness Ra, node ellipticity; molten pool temperature

[0089] Based on multi-objective optimization and dynamic compensation, real-time closed-loop control of the melt pool state and structural morphology is achieved:

[0090] Based on the real-time extracted molten pool morphological features, a multi-objective optimization function of morphological accuracy and process stability is constructed to optimize the process parameters:

[0091]

[0092] Constraints:

[0093]

[0094] Among them, D i is the measured diameter of the i-th rod, D0 is the target diameter, R a Surface roughness, E j is the ellipticity of the jth node, defined as Ej = D major / D minor , T max is the maximum temperature of the molten pool, is the maximum temperature gradient, v s is the scanning speed, R a Surface roughness, E j is the ellipticity of the jth node, defined as Ej = D major / D minor , T max is the maximum temperature of the molten pool, is the maximum temperature gradient, v s is the scanning speed;

[0095] The multi-objective optimization function is solved to obtain the optimized process parameters, including laser power, arc current and scanning speed;

[0096] For unsupported suspended structures, the compensation relationship between the molten pool drop and the arc current is determined, and the compensated arc current is obtained:

[0097] The compensation relationship between the molten pool drop and the arc current is:

[0098]

[0099] Among them, ΔIa(t) is the real-time adjustment of the arc current, Δh(t) is the drop of the molten pool, that is, the deviation between the actual forming height and the target height, K p is the proportional gain coefficient, K d is the differential gain coefficient, K i is the integral gain coefficient;

[0100] Based on feedforward-feedback composite control, the optimized process parameters are output according to the set control cycle to achieve dynamic control of the molten pool state and structural morphology:

[0101]

[0102] Where ΔPl(t) is the adjustment amount of laser power, e(t) is the diameter deviation, e(t) = D target -D measured .

[0103] In general, this approach, based on the calculation results of the dynamic control model, further reveals the specific effects of changes in process parameters on the behavior and structural morphology of the molten pool. By varying parameters such as laser power, laser beam width, and welding speed, their effects on the temperature distribution, flow characteristics, solidification rate, and final lattice structure of the molten pool are studied. For example, when the laser power increases, the temperature of the molten pool rises, the flow characteristics change, and the solidification rate slows down, which may lead to an increase in the width of the molten pool and the roughness of the lattice structure. By simulating the changes in the molten pool and structure under different process conditions, real-time control of the molten pool state can be achieved during the manufacturing process.

[0104] Furthermore, the chain reaction caused by changes in process parameters (such as laser power, arc current, welding speed, etc.) was simulated through a dynamic control model. For example, when the welding speed increases, the flow characteristics of the molten pool may change, and the temperature distribution of the molten pool changes, resulting in an increase in the roughness of the structural morphology and node defects. By predicting these changes, the process parameters can be adjusted in a timely manner during the actual manufacturing process to avoid uncontrollable quality defects. Specifically, the temperature changes caused by the increase in laser power may change the flow characteristics of the molten pool, thereby affecting the solidification rate of the molten pool and ultimately affecting the accuracy of the lattice structure. Based on these chain reactions, the implementation model can provide a scientific basis for the adjustment of process parameters, thereby improving the stability and quality of the product;

[0105] After conducting an in-depth analysis of the relationship between process parameters, melt pool behavior, and structural morphology, this embodiment dynamically adjusts process parameters based on the predictions of the dynamic control model. For example, if the model predicts that the melt pool temperature is too high, which may lead to excessive melt pool fluidity and incomplete solidification, resulting in an inaccurate structure, the melt pool temperature can be lowered by reducing the laser power or slowing the welding speed, thereby optimizing the melt pool fluidity and solidification process.

[0106] In addition, using real-time monitoring data, this solution can adjust parameters such as laser power, welding speed, and heat input according to set targets (such as melt pool temperature and structural accuracy), ensuring that the melt pool state and structural morphology always remain within the expected range, thereby achieving precise additive manufacturing.

[0107] In summary, the dynamic control technology of the present invention can effectively improve the control accuracy of the molten pool and the morphological accuracy of the lattice structure in the laser arc composite additive manufacturing process, and can adjust the process parameters according to real-time data to achieve precise control of the molten pool state and structural morphology, thereby greatly improving the stability and quality of the product. It has significant application advantages, especially in fields with high requirements on structural accuracy and surface quality, such as shipbuilding, aviation, aerospace, medical equipment and other industries.

[0108] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for dynamically controlling the state and structural morphology of a molten pool in laser arc composite additive manufacturing, characterized in that: The following steps are involved: Step 1: Construct a multi-physics field coupling model to simulate the molten pool temperature, flow pattern and structural solidification process under the composite heat source; Step 2: Build a dynamic control model to perform closed-loop control on the molten pool state and structural morphology; Step 3: Based on the control parameters in the closed-loop control, the multi-physics field coupling model is used to visualize and monitor the molten pool state and structural morphology in real time.

2. The method for dynamically controlling the state and structural morphology of the molten pool in laser arc composite additive manufacturing according to claim 1, characterized in that: The multi-physics coupling model in step 1 includes: Step 1-1: Based on the unsteady Navier-Stokes equations and Boussinesq approximation, describe the flow characteristics inside the molten pool and construct a partial differential equation system for the dynamic behavior of the molten pool under the action of the composite heat source: Where ρ is the density of the molten metal, u is the velocity field vector inside the molten pool, t is time, P is the pressure field inside the molten pool, μ is the dynamic viscosity of the molten metal, g is the gravity acceleration vector, β is the thermal expansion coefficient of the metal, T is the molten pool temperature field, T0 is the reference temperature, F Marangoni is the Marangoni force term, caused by the surface tension gradient, and is expressed as γ is the surface tension coefficient; Step 1-2: Couple the heat input of the laser and arc to construct a composite heat source conduction model: Among them, C p is the constant pressure specific heat capacity of the molten metal, and k is the thermal conductivity of the metal. laser is the laser heat source power density, η l is the laser energy absorption efficiency, P l is the laser power, r l is the laser spot radius, r is the radial distance from the center of the laser spot, Q arc is the arc heat source power density, η a is the arc energy efficiency, I a is the arc current, V arc is the arc voltage, a, b are the lengths of the semi-axis of the arc heat source ellipse, and x, y are the local coordinates of the arc action area; Steps 1-3: Track the evolution of the solid-liquid interface based on the phase field method and build a solidification interface tracking model: Among them, φ is the phase field variable, ranging from [0,1], which is used to distinguish the solid phase (φ=1) and the liquid phase (φ=0), M φ is the interface mobility coefficient, which characterizes the interface movement rate,∈interface thickness parameter, f(φ) is the double-well potential function, defined as f(φ)=φ 2 (1-φ) 2 , λ is the phase change driving force coupling coefficient, T m The equilibrium melting point of the metal.

3. The method for dynamically controlling the state and structural morphology of the molten pool in laser arc composite additive manufacturing according to claim 2, characterized in that: The dynamic control model in step 2 is constructed to perform closed-loop control on the molten pool state and structural morphology, specifically: Step 2-1: real-time acquisition of morphological features of the molten pool and pre-processing; Step 2-2: Based on multi-objective optimization and dynamic compensation, real-time closed-loop control of the molten pool state and structural morphology is achieved.

4. The method for dynamically controlling the state and structural morphology of the molten pool in laser arc composite additive manufacturing according to claim 3, characterized in that: The morphological characteristics of the molten pool collected in step 2-1 include: The geometric parameters of the molten pool include the length Lp, width Wp, and collapse Δh of the molten pool; the lattice structure morphology of the molten pool includes the rod diameter Di, surface roughness Ra, and node ellipticity; and the temperature of the molten pool.

5. The method for dynamically controlling the state and structural morphology of the molten pool in laser arc composite additive manufacturing according to claim 3, characterized in that: The real-time monitoring and closed-loop control of the molten pool state and structural morphology in step 2-2 are specifically as follows: Based on the real-time extracted molten pool morphological features, a multi-objective optimization function of morphological accuracy and process stability is constructed to optimize the process parameters; For unsupported suspended structures, determine the compensation relationship between the molten pool drop and the arc current, and obtain the compensated arc current; Based on feedforward-feedback composite control, the optimized process parameters are output according to the set control cycle to achieve dynamic control of the molten pool state and structural morphology.

6. The method for dynamically controlling the state and structural morphology of the molten pool in laser arc composite additive manufacturing according to claim 5, characterized in that: The multi-objective optimization function of morphology accuracy and process stability is specifically: Constraints: Among them, D i is the measured diameter of the i-th rod, D0 is the target diameter, R a Surface roughness, E j is the ellipticity of the jth node, defined as Ej = D major / D minor , T max is the maximum temperature of the molten pool, is the maximum temperature gradient, v s is the scanning speed, R a Surface roughness, E j is the ellipticity of the jth node, defined as Ej = D major / D minor , T max is the maximum temperature of the molten pool, is the maximum temperature gradient, v s is the scanning speed; The multi-objective optimization function is solved to obtain the optimized process parameters, including laser power, arc current and scanning speed; The compensation relationship between the molten pool drop and the arc current is: Among them, ΔIa(t) is the real-time adjustment of the arc current, Δh(t) is the drop of the molten pool, that is, the deviation between the actual forming height and the target height, K p is the proportional gain coefficient, K d is the differential gain coefficient, K i is the integral gain coefficient; The feedforward-feedback composite control predicts the parameter adjustment amount and uses the fuzzy PID algorithm to correct the control amount in real time: Where ΔPl(t) is the adjustment amount of laser power, e(t) is the diameter deviation, e(t) = D target -D measured .

7. A dynamic control system for the state and structural morphology of the molten pool in laser arc composite additive manufacturing, characterized in that: Includes the following modules: Multi-physics coupling modeling module: used to build a multi-physics coupling model to simulate the melt pool temperature, flow pattern and structural solidification process under a composite heat source; Structural morphology dynamic control module: used to build a structural morphology dynamic control model and perform closed-loop control of the molten pool state and structural morphology.

8. The dynamic control system for the laser arc composite additive manufacturing molten pool state and structural morphology according to claim 7 is characterized in that: The multi-physics coupling modeling module includes: Based on the unsteady Navier-Stokes equations and Boussinesq approximation, the flow characteristics inside the molten pool are described, and a partial differential equation system for the dynamic behavior of the molten pool under the action of a composite heat source is constructed: Where ρ is the density of the molten metal, u is the velocity field vector inside the molten pool, t is time, P is the pressure field inside the molten pool, μ is the dynamic viscosity of the molten metal, g is the gravity acceleration vector, β is the thermal expansion coefficient of the metal, T is the molten pool temperature field, T0 is the reference temperature, F Marangoni is the Marangoni force term, caused by the surface tension gradient, and is expressed as γ is the surface tension coefficient; Couple the heat input of the laser and arc to construct a composite heat source conduction model: Among them, C p is the constant pressure specific heat capacity of the molten metal, and k is the thermal conductivity of the metal. laser is the laser heat source power density, η l is the laser energy absorption efficiency, P l is the laser power, r l is the laser spot radius, r is the radial distance from the center of the laser spot, Q arc is the arc heat source power density, η a is the arc energy efficiency, I a is the arc current, V arc is the arc voltage, a, b are the lengths of the semi-axis of the arc heat source ellipse, and x, y are the local coordinates of the arc action area; Based on the phase field method, the evolution of the solid-liquid interface is tracked and a solidification interface tracking model is constructed: Among them, φ is the phase field variable, ranging from [0,1], which is used to distinguish the solid phase (φ=1) and the liquid phase (φ=0), M φ is the interface mobility coefficient, which characterizes the interface movement rate,∈interface thickness parameter, f(φ) is the double-well potential function, defined as f(φ)=φ 2 (1-φ) 2 , λ is the phase change driving force coupling coefficient, T m The equilibrium melting point of the metal.

9. The system for dynamically controlling the state and structural morphology of the molten pool in laser arc composite additive manufacturing according to claim 7, characterized in that: The structural morphology dynamic control module includes: Collect the morphological characteristics of the molten pool in real time and perform preprocessing; The morphological characteristics of the molten pool include: molten pool geometric parameters, including molten pool length Lp, width Wp, collapse amount Δh, molten pool lattice structure morphology, including rod diameter Di, surface roughness Ra, node ellipticity; molten pool temperature Based on multi-objective optimization and dynamic compensation, real-time closed-loop control of the melt pool state and structural morphology is achieved: Based on the real-time extracted molten pool morphological features, a multi-objective optimization function of morphological accuracy and process stability is constructed to optimize the process parameters: Constraints: Among them, D i is the measured diameter of the i-th rod, D0 is the target diameter, R a Surface roughness, E j is the ellipticity of the jth node, defined as Ej = D major / D minor , T max is the maximum temperature of the molten pool, is the maximum temperature gradient, v s is the scanning speed, R a Surface roughness, E j is the ellipticity of the jth node, defined as Ej = D major / D minor , T max is the maximum temperature of the molten pool, is the maximum temperature gradient, v s is the scanning speed; The multi-objective optimization function is solved to obtain the optimized process parameters, including laser power, arc current and scanning speed; For unsupported suspended structures, the compensation relationship between the molten pool drop and the arc current is determined, and the compensated arc current is obtained: The compensation relationship between the molten pool drop and the arc current is: Among them, ΔIa(t) is the real-time adjustment of the arc current, Δh(t) is the drop of the molten pool, that is, the deviation between the actual forming height and the target height, K p is the proportional gain coefficient, K d is the differential gain coefficient, K i is the integral gain coefficient; Based on feedforward-feedback composite control, the optimized process parameters are output according to the set control cycle to achieve dynamic control of the molten pool state and structural morphology: Where ΔPl(t) is the adjustment amount of laser power, e(t) is the diameter deviation, e(t) = D target -D measured .

Images