Molten pool numerical simulation prediction method and system in sensing part additive manufacturing process

Abstract

The invention provides a molten pool numerical simulation prediction method and system in a sensing part additive manufacturing process. The prediction method comprises the steps that a heat transfer physical field, a laminar flow physical field of a molten pool, a laser energy flow distribution equation, a laser moving path equation and a material adding model in the sensing part machining process are established; simulating a free surface of a molten pool, and establishing a convective heat transfer process, a surface recoil pressure field of the molten pool and a surface tension field of the molten pool in the machining process of the sensing part; and dividing grids, and solving a numerical simulation result in the machining process of the sensing part. According to the simulation prediction method, the machining process of the sensing part is simulated from the scale of the molten pool, the formation evolution rule of the molten pool can be obtained, and various physical factors such as solid-liquid phase change, severe convection heat transfer, recoil pressure, the Marangei effect and surface tension are fully considered; the real machining process of the additive manufacturing machining sensing part is comprehensively restored, and the accuracy of the simulation model is improved by establishing the material adding model.

Description

Technical Field

[0001] The present invention relates to the technical field of the machining of sensors for aero-engine tests, and particularly relates to a method and a system for numerically simulating and predicting a molten pool during the additive manufacturing process of a sensor. Background Art

[0002] During the development process of an aero-engine, in order to obtain the total temperature and total pressure data at different positions in the flow passage, sensors are usually used for measurement.

[0003] There are various types and different structures of sensors, and often new types of sensors need to be designed and machined according to different test requirements. The conventional machining method for sensors is to first machine the components and then assemble and weld them. This machining method has many processes and takes a long time, and the welding quality at the welds often needs to be inspected through fluorescence detection and metallographic analysis, which increases the production cycle of the sensors.

[0004] Currently, there is also a method of machining sensors by additive manufacturing. Additive manufacturing can machine special-shaped sensors, with fewer machining processes and no welds, so there is no need for fluorescence detection and metallographic analysis, which can greatly shorten the machining cycle and well meet the project requirements of the test system with a tight layout time and heavy tasks.

[0005] Due to the precise and complex structure of an aero-engine, with high pressure and fast air flow velocity in the flow passage, the strength of the sensor generally needs to meet the specified standards to avoid failure or fracture and affect the safe operation of the engine. However, the sensors machined by additive manufacturing often have poorer strength than those machined by traditional forging and welding. This is because additive manufacturing forms a molten pool by laser line scanning of molten metal powder, and after the molten pool solidifies, it is stacked layer by layer to form the sensor. In this process, there are defect areas where the metal powder is not completely melted or air bubbles are mixed in during the melting process, and these defects are the main factors affecting the strength of the sensors machined by additive manufacturing.

[0006] Based on this, the inventors of the present application have designed a method and a system for numerically simulating and predicting a molten pool during the additive manufacturing process of a sensor to overcome the above technical problems. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a method and a system for numerically simulating and predicting a molten pool during the additive manufacturing process of a sensor to overcome the problem in the prior art that there is no way to seek a way to reduce the machining defects of the sensor from the molten pool scale.

[0008] The present invention solves the above technical problem by the following technical solutions:

[0009] The present invention provides a method for numerically simulating and predicting a molten pool during the additive manufacturing process of a sensor, which is characterized by including:

[0010] Step 1: Establish the heat transfer physical field, laminar physical field of the molten pool, laser energy flux distribution equation, laser movement path equation, and material addition model during the sensing part processing;

[0011] Step 2: Simulate the free surface of the molten pool and establish the convective heat transfer process, recoil pressure field on the molten pool surface, and surface tension field of the molten pool during the sensing part processing;

[0012] Step 3: Divide the grid and solve the numerical simulation results during the sensing part processing.

[0013] According to an embodiment of the present invention, the heat transfer physical field during the sensing part processing is established through the heat transfer equation controlled by Fourier's law, and the heat transfer equation is as follows:

[0014]

[0015] where ρ represents the material density, C represents the heat capacity of the material, T represents the material temperature, represents the fluid flow velocity, λ represents the thermal conductivity of the material, and Q represents the energy input by the laser.

[0016] According to an embodiment of the present invention, the laminar physical field of the molten pool during the sensing part processing is established through the fluid Reynolds equation, and the fluid Reynolds equation is:

[0017]

[0018] where p represents the internal pressure of the fluid, I represents the unit matrix, μ represents the dynamic viscosity of the fluid, represents the acceleration due to gravity, γ represents the surface tension coefficient of the molten pool, κ represents the curvature at the interface between the molten pool and the external environment, represents the local interface unit normal vector of the molten pool and the external environment, represents the Marangoni coefficient.

[0019] According to an embodiment of the present invention, the laser energy flux distribution control equation is established through Gaussian distribution, and the control equation is as follows:

[0020]

[0021] where Q l represents the heat flux of the laser on the material surface, A represents the absorption rate of the material to the laser, P l represents the laser power, r 0 represents the focusing radius of the laser, r represents the distance from the target point to the laser focusing center point, and satisfies the following formula:

[0022]

[0023] Among them, (x, y) represents the planar coordinates of the target point, and (x 0 , y 0 ) represents the planar coordinates of the laser focusing center point, and v l represents the scanning rate of the laser.

[0024] According to an embodiment of the present invention, a material addition model is established using thermodynamics, and the model is as follows:

[0025]

[0026] Among them, Q p represents the heat flux absorbed by the added material, T represents the temperature of the molten pool surface, and T 0 represents the temperature of the added material, C 1 represents the solid heat capacity of the material, Q m represents the mass flux of the added material, T 1 represents the solidus temperature of the material, T 2 represents the liquidus temperature of the material, C 2 represents the liquid heat capacity of the material, L m represents the latent heat of fusion of the material, that is, the heat required for the material to complete the solid-liquid phase change, and C represents the heat capacity of the material when the temperature is in the range between the solidus and liquidus, which is represented by the following formula;

[0027]

[0028] Among them, ρ 1 represents the solid density of the material, ρ 2 represents the liquid density of the material, and θ represents the phase state of the material, which is represented by the following formula:

[0029]

[0030] α represents the latent heat distribution during the phase change process, which is represented by the following formula:

[0031]

[0032] According to an embodiment of the present invention, the convective heat transfer process during the machining of the sensing part is characterized by the convective heat transfer coefficient, and the convective heat transfer coefficient is:

[0033] H = Ae -lr + B;

[0034] Among them, A, B, and l are coefficients related to the material, and r represents the distance from the target point to the laser focusing center point.

[0035] According to an embodiment of the present invention, the recoil pressure of the recoil pressure field on the molten pool surface is:

[0036]

[0037] Among them, P re is the recoil pressure on the molten pool surface, and p 0 represents the ambient pressure, T represents the temperature of the molten pool surface, λ represents the latent heat of evaporation per atom, and K b represents the Boltzmann constant, and T v represents the evaporation temperature.

[0038] According to an embodiment of the present invention, the surface tension of the molten pool surface tension field is:

[0039]

[0040] Among them, σ is the surface tension of the molten pool, γ represents the surface tension coefficient, κ represents the curvature of the molten pool surface, represents the unit normal vector of the local surface, represents the Marangoni coefficient.

[0041] The present invention also provides a numerical simulation system for the molten pool in the sensing part additive manufacturing process. The feature is that the numerical simulation system for the molten pool in the sensing part additive manufacturing process adopts the above-mentioned numerical simulation prediction method for the molten pool in the sensing part additive manufacturing process. The simulation system includes:

[0042] A building module for building the heat transfer physical field, the laminar physical field of the molten pool, the laser energy flux distribution equation, the laser movement path equation, and the material addition model during the processing of the sensing part;

[0043] A simulation module for simulating the free surface of the molten pool and building the convective heat transfer process, the recoil pressure field on the molten pool surface, and the molten pool surface tension field during the processing of the sensing part;

[0044] A calculation module for dividing grids and solving the numerical simulation results during the processing of the sensing part.

[0045] The present invention also provides an electronic device, which is characterized by including: a processor and a memory. The memory stores a program or instruction that can run on the processor, and the program or instruction is executed by the processor to implement the numerical simulation prediction method for the molten pool in the sensing part additive manufacturing process according to any one of claims 1-8.

[0046] The present invention also provides a readable storage medium, which is characterized in that a program or instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, it implements the numerical simulation prediction method for the molten pool in the sensing part additive manufacturing process according to any one of claims 1-8.

[0047] The positive and progressive effects of the present invention are:

[0048] The present invention relates to a method and system for numerically simulating and predicting the molten pool in the additive manufacturing process of a sensor-receiving part, which simulates the processing process of the sensor-receiving part at the molten pool scale, is conducive to obtaining the formation and evolution law of the molten pool, and provides a favorable reference for further reducing the processing defects of the sensor-receiving part.

[0049] The simulation and prediction method of the present invention fully considers various physical factors such as solid-liquid phase change, strong convective heat transfer, recoil pressure, Marangoni effect, and surface tension, comprehensively restores the real processing process of additive manufacturing of the sensor-receiving part, and improves the accuracy of the simulation model by establishing a material addition model. Description of the Drawings

[0050] The above and other features, properties, and advantages of the present invention will become more apparent from the following description in conjunction with the drawings and embodiments, where:

[0051] Figure 1 is a processing model diagram of the sensor-receiving part processed by the LENS additive manufacturing process of the present invention;

[0052] Figure 2 is a flowchart of the numerical simulation and prediction method of the molten pool in the additive manufacturing process of the sensor-receiving part of the present invention;

[0053] Figure 3 is a schematic structural diagram of the electronic device of the present invention. Detailed Embodiments

[0054] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention is provided in conjunction with the drawings.

[0055] Embodiments of the present invention will now be described in detail with reference to the drawings. Preferred embodiments of the present invention will now be described in detail, and examples thereof are shown in the drawings. Whenever possible, the same reference numerals will be used throughout the drawings to represent the same or similar parts. In addition, although the terms used in the present invention are selected from well-known and commonly used terms, some of the terms mentioned in the specification of the present invention may be selected by the applicant according to his or her judgment, and their detailed meanings are described in the relevant parts of the present description. In addition, it is required to understand the present invention not only through the actual terms used, but also through the meaning implied by each term.

[0056] Referring to Figure 2 , the present invention provides a method for numerically simulating and predicting the molten pool in the additive manufacturing process of a sensor-receiving part, including:

[0057] Step 1: Establish a heat transfer physical field, a laminar physical field of the molten pool, a laser energy flux distribution equation, a laser movement path equation, and a material addition model during the processing of the sensor-receiving part.

[0058] Reference Figure 1 In the present invention, the sensing part is processed by using the LENS additive manufacturing process. During the processing, the laser beam 1 inputs energy, and the solid material 2 is continuously added. After melting, a molten pool 3 is formed. After the molten pool 3 solidifies, it is stacked layer by layer to form the sensing part.

[0059] In one embodiment, the present invention establishes a heat transfer physical field for the processing of the sensing part according to the heat transfer equation controlled by Fourier's law. The heat transfer equation is as follows:

[0060]

[0061] Among them, ρ represents the material density, C represents the heat capacity of the material, T represents the material temperature, represents the flow velocity of the fluid, λ represents the thermal conductivity of the material, and Q represents the energy input by the laser.

[0062] At the same time, a laminar flow physical field of the molten pool during the processing of the sensing part is established through the fluid Reynolds equation. The fluid Reynolds equation is:

[0063]

[0064] Among them, p represents the internal pressure of the fluid, I represents the unit matrix, μ represents the dynamic viscosity of the fluid, represents the acceleration due to gravity, γ represents the surface tension coefficient of the molten pool, κ represents the curvature at the interface between the molten pool and the external environment, represents the local interface unit normal vector of the molten pool and the external environment, represents the Marangoni coefficient.

[0065] A control equation for the laser energy flux distribution is also established through the Gaussian distribution. The control equation is as follows:

[0066]

[0067] Among them, Q l represents the heat flux of the laser on the material surface, A represents the absorption rate of the material to the laser, P l represents the laser power, r 0 represents the focusing radius of the laser, r represents the distance from the target point to the laser focusing center point, and satisfies the following formula:

[0068]

[0069] Among them, (x,y) represents the plane coordinates of the target point, (x 0 ,y 0 ) represents the plane coordinates of the laser focusing center point, v l represents the scanning rate of the laser.

[0070] Moreover, a material addition model is also established by using thermodynamics. The model is as follows:

[0071]

[0072] Among them, Q p represents the heat flux absorbed by the added material, T represents the temperature of the molten pool surface, T 0 represents the temperature of the added material, C 1 represents the solid heat capacity of the material, Q m represents the mass flux of the added material, T 1 represents the solidus temperature of the material, T 2 represents the liquidus temperature of the material, C 2 represents the liquid heat capacity of the material, L m represents the latent heat of fusion of the material, that is, the heat required for the material to complete the solid-liquid phase change, and C represents the heat capacity when the material temperature is in the range between the solidus and liquidus, which is expressed by the following formula;

[0073]

[0074] Among them, ρ 1 represents the solid density of the material, ρ 2 represents the liquid density of the material, and θ represents the phase state of the material, which is expressed by the following formula:

[0075]

[0076] α represents the latent heat distribution during the phase change process, which is expressed by the following formula:

[0077]

[0078] It should be noted that during the processing of the sensing part, the laser and the material nozzle are fixed together and move along a preset path, and its movement path equation is established as:

[0079] y = f(x).

[0080] In one embodiment, heat is absorbed during the material addition process. The material changes from a solid state to a solid-liquid coexistence state, and then from the transitional state of solid-liquid coexistence to a liquid state. According to this rule, a material addition model is established from a thermodynamic perspective as follows:

[0081]

[0082] Among them, Q p represents the heat flux absorbed by the added material, T represents the temperature of the molten pool surface, T 0 represents the temperature of the added material, C 1 represents the solid heat capacity of the material, Q m represents the mass flux of the added material, T 1Represents the solidus temperature of the material, T 2 Represents the liquidus temperature of the material, C 2 Represents the liquid heat capacity of the material, L m Represents the latent heat of fusion of the material, that is, the heat required for the material to complete the solid-liquid phase change. C represents the heat capacity when the material temperature is in the range between the solidus and liquidus, and is expressed by the following formula;

[0083]

[0084] Among them, ρ 1 Represents the solid density of the material, ρ 2 Represents the liquid density of the material. θ represents the phase state of the material and is expressed by the following formula:

[0085]

[0086] α represents the latent heat distribution during the phase change process and is expressed by the following formula:

[0087]

[0088] Step 2: Simulate the free surface of the molten pool, and establish the convective heat transfer process, the recoil pressure field on the molten pool surface, and the surface tension field during the machining process of the sensing part.

[0089] It should be noted that during the machining process of the sensing part, fluid-structure coupling of the material is involved, so the arbitrary Lagrangian-Euler method is used to simulate the free surface of the molten pool.

[0090] Furthermore, during the machining process of the sensing part, the high-temperature molten metal is easily oxidized. Usually, a protective gas is sprayed to avoid oxidation. During the spraying of the protective gas, there is a strong convective heat transfer process on the molten pool surface. The convective heat transfer coefficient is set according to the following formula:

[0091] H = Ae -lr + B;

[0092] Among them, A, B, and l are coefficients related to the material, and r represents the distance from the target point to the laser focusing center point.

[0093] For the laser irradiation process, when the laser irradiates the molten pool surface, the liquid metal material will evaporate, and the violently evaporating gas on the molten pool surface will exert a recoil pressure on the molten pool surface, which is described by the formula:

[0094]

[0095] Among them, P re Is the recoil pressure on the molten pool surface, p 0 Represents the ambient pressure, T represents the temperature of the molten pool surface, λ represents the evaporation latent heat of each atom, K brepresents the Boltzmann constant, T v represents the evaporation temperature.

[0096] Furthermore, the irradiation of the laser and the addition of the material continuously transfer heat and mass to the molten pool, causing the Marangoni effect to appear on the surface of the molten pool, thereby changing the surface tension of the molten pool. The surface tension is determined by the following formula:

[0097]

[0098] where σ is the surface tension of the molten pool, γ represents the surface tension coefficient, κ represents the surface curvature of the molten pool, represents the unit normal vector of the local surface, represents the Marangoni coefficient.

[0099] Step 3: Divide the grid and solve the numerical simulation results during the processing of the sensor part.

[0100] That is, divide the model grid into free tetrahedral grids and select an appropriate time step for solution. During the processing of the sensor part, information such as the surface topography of the molten pool, the temperature field distribution, the surface flow field distribution, and the surface tension distribution at any spatial coordinate and any moment can be obtained.

[0101] In summary, the method and system for numerical simulation prediction of the molten pool during the additive manufacturing process of the sensor part of the present invention simulate the processing process of the sensor part at the molten pool scale, which is beneficial to obtaining the formation and evolution law of the molten pool and provides a favorable reference for further reducing the processing defects of the sensor part.

[0102] The simulation prediction method of the present invention fully considers various physical factors such as solid-liquid phase change, strong convective heat transfer, recoil pressure, Marangoni effect, and surface tension, comprehensively restores the real processing process of additive manufacturing of the sensor part, and improves the accuracy of the simulation model by establishing a material addition model.

[0103] The present invention also proposes a numerical simulation system for the molten pool during the additive manufacturing process of the sensor part. The numerical simulation system for the molten pool during the additive manufacturing process of the sensor part adopts the above-mentioned method for numerical simulation prediction of the molten pool during the additive manufacturing process of the sensor part. The simulation system includes:

[0104] A building module, used to build the heat transfer physical field, the laminar physical field of the molten pool, the laser energy flux distribution equation, the laser moving path equation, and the material addition model during the processing of the sensor part;

[0105] A simulation module, used to simulate the free surface of the molten pool and establish the convective heat transfer process, the recoil pressure field on the surface of the molten pool, and the surface tension field during the processing of the sensor part;

[0106] A calculation module, used to divide the grid and solve the numerical simulation results during the processing of the sensor part.

[0107] With such a setting, the present invention takes into account various physical factors such as solid-liquid phase change, strong convective heat transfer, recoil pressure, Marangoni effect, and surface tension into the simulation method by using the establishment module and the simulation module, comprehensively restoring the real processing process of the additively manufactured sensing part, and increasing the accuracy of the simulation model.

[0108] Referring to Figure 3 , the present invention also provides an electronic device 900, including: a processor 901 and a memory 902. The memory 902 stores a program or instructions that can be run on the processor 901, and the program or instructions are executed by the processor 901 to perform the above-mentioned molten pool numerical simulation prediction method for the additive manufacturing process of the sensing part. When the program or instructions are executed by the processor 901, each process of the above-mentioned molten pool numerical simulation prediction method for the additive manufacturing process of the sensing part is implemented, and the same technical effects can be achieved. To avoid repetition, it will not be elaborated here.

[0109] The present invention also provides a readable storage medium, on which a program or instructions are stored. When the program or instructions are executed by a processor, the molten pool numerical simulation prediction method for the additive manufacturing process of the sensing part as described above is implemented. When the program or instructions are executed by the processor, each process of the above-mentioned molten pool numerical simulation prediction method for the additive manufacturing process of the sensing part is implemented, and the same technical effects can be achieved. To avoid repetition, it will not be elaborated here.

[0110] For those skilled in the art, the above invention disclosure is only an example and does not constitute a limitation to this application. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are proposed in this application, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this application.

[0111] Meanwhile, this application uses specific terms to describe the embodiments of this application. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

[0112] Some aspects of the present application may be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above-mentioned hardware or software may be referred to as a "data block", "module", "engine", "unit", "component" or "system". The processor may be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. In addition, aspects of the present application may be embodied as a computer product located on one or more computer-readable media, the product including computer-readable program code. For example, the computer-readable media may include, but is not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes...), optical disks (e.g., compact disk CD, digital versatile disk DVD...), smart cards, and flash memory devices (e.g., cards, sticks, key drives...).

[0113] The computer-readable media may include a propagated data signal having computer program code therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take many forms, including electromagnetic, optical, or the like, or suitable combinations thereof. The computer-readable media may be any computer-readable media other than a computer-readable storage media, which can communicate, propagate, or transport a program for use by being connected to an instruction execution system, apparatus, or device. The program code located on the computer-readable media may be propagated through any appropriate medium, including radio, cable, fiber optic cable, radio frequency signal, or similar media, or any combination of the above media.

[0114] Similarly, it should be noted that, in order to simplify the description disclosed in the present application and thus help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of the present application, sometimes multiple features are merged into one embodiment, drawing or description thereof. However, this disclosure method does not mean that the features required by the object of the present application are more than those mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above. In some embodiments, numbers describing components and attribute quantities are used. It should be understood that such numbers used for the description of the embodiments are modified by the modifiers "about", "approximate" or "substantially" in some examples. Unless otherwise stated, "about", "approximate" or "substantially" indicate that the number allows a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, and the approximate values can be changed according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining the general number of digits. Although the numerical ranges and parameters used in some embodiments of the present application to confirm the breadth of their scope are approximate values, in specific embodiments, such numerical settings are as precise as possible within the feasible range.

[0115] Although the present invention is disclosed above in preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of the present invention. Therefore, all modifications, equivalent changes and decorations made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for predicting the molten pool numerical simulation in the sensing part additive manufacturing process, characterized in that, it includes: Step 1: Establish the heat transfer physical field, laminar physical field of the molten pool, laser energy flux distribution equation, laser movement path equation, and material addition model during the processing of the sensing part; Step 2: Simulate the free surface of the molten pool, and establish the convective heat transfer process, recoil pressure field on the molten pool surface, and surface tension field on the molten pool surface during the processing of the sensing part; Step 3: Divide the grid and solve the numerical simulation results during the processing of the sensing part.

2. The method for predicting the molten pool numerical simulation in the sensing part additive manufacturing process according to claim 1, characterized in that, the heat transfer physical field during the processing of the sensing part is established through the heat transfer equation controlled by Fourier's law, and the heat transfer equation is as follows: Among them, ρ represents the material density, C represents the heat capacity of the material, T represents the material temperature, represents the flow rate of the fluid, λ represents the thermal conductivity of the material, and Q represents the energy input by the laser.

3. The method for predicting the molten pool numerical simulation in the sensing part additive manufacturing process according to claim 1, characterized in that, the laminar physical field of the molten pool during the processing of the sensing part is established through the fluid Reynolds equation, and the fluid Reynolds equation is: Among them, p represents the internal pressure of the fluid, I represents the unit matrix, μ represents the dynamic viscosity of the fluid, represents the acceleration due to gravity, γ represents the surface tension coefficient of the molten pool, κ represents the curvature at the interface between the molten pool and the external environment, represents the local interface unit normal vector between the molten pool and the external environment, represents the Marangoni coefficient.

4. The method for predicting the molten pool numerical simulation in the sensing part additive manufacturing process according to claim 1, characterized in that, the laser energy flux distribution control equation is established through Gaussian distribution, and the control equation is as follows: Among them, Q l represents the heat flux of the laser on the material surface, A represents the absorption rate of the material to the laser, P l represents the laser power, r 0 represents the focusing radius of the laser, r represents the distance from the target point to the laser focusing center point, and satisfies the following formula: Among them, (x, y) represents the planar coordinates of the target point, and (x 0 , y 0 ) represents the planar coordinates of the laser focusing center point, and v l represents the scanning rate of the laser.

5. The method for predicting the molten pool numerical simulation in the sensing part additive manufacturing process according to claim 1, characterized in that, a material addition model is established using thermodynamics, and the model is as follows: Among them, Q p represents the heat flux absorbed by the added material, T represents the temperature of the molten pool surface, T 0 represents the temperature of the added material, C 1 represents the solid heat capacity of the material ， Q m represents the mass flux of the added material, T 1 represents the solidus temperature of the material, T 2 represents the liquidus temperature of the material, C 2 represents the liquid heat capacity of the material, L m represents the latent heat of fusion of the material, that is, the heat required for the material to complete the solid-liquid phase change. C represents the heat capacity when the material temperature is in the range between the solidus and liquidus, and is represented by the following formula; where ρ 1 represents the solid density of the material, ρ 2 represents the liquid density of the material, and θ represents the phase state of the material, which is expressed by the following formula: α represents the latent heat distribution during the phase change process, which is represented by the following formula:

6. The method for predicting the molten pool numerical simulation in the sensing part additive manufacturing process according to claim 1, characterized in that, the convective heat transfer process during the processing of the sensing part is characterized by the convective heat transfer coefficient, and the convective heat transfer coefficient is: H = Ae -lr + B; where A, B, and l are coefficients related to the material, and r represents the distance from the target point to the laser focusing center point.

7. The method for predicting the molten pool numerical simulation in the sensing part additive manufacturing process according to claim 1, characterized in that, the recoil pressure of the recoil pressure field on the molten pool surface is: Among them, P re is the recoil pressure on the molten pool surface, p 0 represents the ambient pressure, T represents the temperature of the molten pool surface, λ represents the latent heat of evaporation per atom, K b represents the Boltzmann constant, and T v represents the evaporation temperature.

8. The method for predicting the molten pool numerical simulation in the sensing part additive manufacturing process according to claim 1, characterized in that, the surface tension of the surface tension field on the molten pool surface is: Among them, σ is the surface tension of the molten pool, γ represents the surface tension coefficient, κ represents the surface curvature of the molten pool, represents the unit normal vector of the local surface, represents the Marangoni coefficient.

9. A molten pool numerical simulation system for the sensing part additive manufacturing process, characterized in that, the molten pool numerical simulation system for the sensing part additive manufacturing process adopts the method for predicting the molten pool numerical simulation in the sensing part additive manufacturing process according to any one of claims 1-8, and the simulation system includes: A building module for establishing the heat transfer physical field, laminar physical field of the molten pool, laser energy flux distribution equation, laser movement path equation, and material addition model during the processing of the sensing part; A simulation module for simulating the free surface of the molten pool and establishing the convective heat transfer process, recoil pressure field on the molten pool surface, and surface tension field on the molten pool surface during the processing of the sensing part; A calculation module for dividing the grid and solving the numerical simulation results during the processing of the sensing part.

10. An electronic device, characterized in that, it includes: A processor and a memory, the memory storing programs or instructions that can run on the processor, and the programs or instructions are executed by the processor to implement the method for predicting the molten pool numerical simulation of the sensing part additive manufacturing process according to any one of claims 1-8.

11. A readable storage medium, characterized in that programs or instructions are stored on the readable storage medium, and when the programs or instructions are executed by a processor, the method for predicting the molten pool numerical simulation of the sensing part additive manufacturing process according to any one of claims 1-8 is implemented.

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