Method for inhibiting plasma screening effect to improve femtosecond laser processing efficiency
By simulating the femtosecond laser processing process using multiphysics coupling theory and the DSMC method, and optimizing the plasma plume distribution, the problem of low processing efficiency caused by the plasma shielding effect was solved, thus improving the efficiency of femtosecond laser processing.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- YANGTZE DEITA GRADUATE SCHOOI OF BEIJING INST OF TECH (JIAXING)
- Filing Date
- 2023-09-13
- Publication Date
- 2026-06-09
AI Technical Summary
During femtosecond laser processing, the plasma shielding effect leads to a decrease in processing efficiency. Traditional air blowing treatment is difficult to effectively suppress the shielding effect of plasma plumes, especially in ultrafast laser processing.
By employing multiphysics coupling theory and combining it with the DSMC method to simulate the femtosecond laser-induced metal plasma plume ejection process, the plasma shielding effect on the laser is reduced and the processing efficiency is improved by optimizing the density and velocity distribution of the plume.
Accurate characterization of laser-material interaction and plasma formation optimizes plume distribution and significantly improves femtosecond laser processing efficiency.
Smart Images
Figure CN117381141B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a method for suppressing plasma shielding effects and improving femtosecond laser processing efficiency, belonging to the field of femtosecond laser micro-nano processing of metal materials technology. Background Technology
[0002] The interaction between femtosecond lasers and materials generates extremely high temperatures and pressures for a very short time, inducing phase transitions, liquefaction, evaporation, and plasma formation. This intense non-equilibrium process leads to material ejection and plume generation. Laser-induced plumes and ejection phenomena have a significant impact on the efficiency of femtosecond laser processing.
[0003] The ejected plasma plume significantly reduces the energy density of laser deposition onto the material surface; this phenomenon is known as the shielding effect. Air pumps are typically used to blow air during femtosecond laser processing to reduce the shielding effect of the plasma plume.
[0004] However, traditional air-blowing methods are suitable for continuous laser processing. For ultrafast laser processing, the plasma ejection timescale is in the picosecond to nanosecond range, with an extremely short duration. Moreover, the plasma velocity is supersonic, while the gas cylinder ejection velocity is in the subsonic range, making it difficult to disperse the plasma plume in such a short time. Therefore, it is necessary to consider how to suppress plasma ejection and reduce its shielding effect from the perspective of the plasma plume formation mechanism.
[0005] To study the plumes generated by the interaction between femtosecond lasers and materials, some studies have employed molecular dynamics (MD) or Monte Carlo (MC) methods for simulation. MD methods can describe microscopic processes at the atomic scale, but struggle to reach experimentally relevant temporal and spatial scales. MC methods, by neglecting microscopic processes, can achieve a larger temporal and spatial range, but are difficult to accurately describe complex physical behaviors such as material phase transitions. Summary of the Invention
[0006] To address the issue of plasma shielding effect reducing processing efficiency during femtosecond laser metal processing, this invention aims to provide a method for suppressing plasma shielding effect and improving femtosecond laser processing efficiency. This method is based on multiphysics coupling theory to solve the process of femtosecond laser-induced metal plasma plume eruption. Starting with the two-temperature equation, it simulates the thermal ablation and eruption process of the metal target. The DSMC method is used to simulate the interaction between the ejected metal plasma plume and the background gas, thereby accurately characterizing the laser-material interaction, material melting, and plasma formation, obtaining the plume distribution at experimentally relevant scales. By optimizing the density and velocity distribution of the plume, the shielding effect of the plasma plume on the laser is reduced, thus improving femtosecond laser processing efficiency.
[0007] The objective of this invention is achieved through the following technical solution.
[0008] This invention discloses a method for suppressing plasma shielding effects and improving femtosecond laser processing efficiency, comprising the following steps:
[0009] Step 1: Set up the computational domain, generate the mesh, and define boundary conditions; set the size of the simulation computational domain according to the experimental laser beam diameter, pulse time, and material size parameters; set the initial mesh to divide the flow field in the computational domain, with the mesh dimension Δx set to a value of...
[0010]
[0011] The mean free path of gas molecules within the grid. n is the number of gas molecules.
[0012] The boundary conditions are open boundary conditions, specular reflection boundary conditions, inflow boundary conditions, and eruption boundary conditions. During the simulation, two simulated particle number update modes are considered: simulated particles entering the computational domain on the inflow and eruption boundaries and simulated particles escaping the computational domain on the open boundaries. The specular reflection boundary condition also restricts the motion of the simulated particles.
[0013] Step 2: Initialize and generate background gas particles; set the physical properties of the gas particles, including particle mass, reference temperature, and reference diameter.
[0014] Based on physical constants and fundamental equations, the initial temperature distribution and gas density distribution are calculated. Then, a corresponding number of background gas particles are randomly placed within the calculation region according to the obtained probability density function, and the initial velocities of the gas particles are set to satisfy the Maxwell velocity distribution, as shown in equation (2).
[0015]
[0016] Wherein, the thermal velocity of gas molecules is c = (c1, c2, c3). For macroscopic speed, f c It is the probability that the velocity of a gas molecule is equal to c, and k is the Boltzmann constant;
[0017] In step one, 20 to 30 simulated molecules are arranged in each grid; the number of real gas molecules represented by each simulated molecule is the weighting factor F. num .
[0018] Step 3, Main Loop; The loop progresses according to the time step, and the time step... Where Δx1 and Δx2 are the grid length and width, and ξ 1max ξ represents the maximum velocity component of the simulated molecule along the long rectangular boundary within the grid;2max This represents the maximum velocity component of the simulated molecule along the rectangular wide boundary within the grid.
[0019] The electronic and lattice temperatures of the metallic material at the current time step are obtained by discretizing the time and solving the two-temperature equation using finite difference.
[0020]
[0021] Where the quantities with subscripts e and l are related to electrons and lattice, respectively; T represents temperature; K is thermal conductivity; C is heat capacity; G is electron-lattice coupling factor; S is volumetric laser heat source; ▽ is divergence operator;
[0022] When the lattice temperature reaches the boiling point, the material begins to erupt, at which point the saturated vapor pressure P of the metal vapor... v Obtained from the Clausius-Clapeyron equation:
[0023]
[0024] Among them, L v It is the latent heat of boiling, T v,0 It is p v,0 Boiling point at reference atmospheric pressure, T is the current temperature, m1 is the atomic mass of the metal, k B Boltzmann's constant;
[0025] The number of molecules evaporated per unit area from the evaporation source within the current time step is:
[0026]
[0027] in, v is the average thermal rate of molecular motion.
[0028] The DSMC algorithm is used to calculate particle collisions, change the motion state of each particle, record the physical quantities in each state in real time, and then obtain the macroscopic variation law of each physical quantity over time.
[0029] Step 4: Output and store the time evolution relationships of each physical quantity obtained from the simulation in Step 3 for later analysis.
[0030] As a preferred option, in step four, the time evolution results of each physical quantity are saved in VTK format and post-processed using ParaView software.
[0031] Step 5: Based on the time evolution results of the physical quantities obtained in Step 4, calculate the absorption rate and shielding efficiency of the plasma plume for laser energy. The absorption rate is calculated using the following formula:
[0032]
[0033] Where, m e ,n e e0 and e0 represent the electron mass, density, and charge, respectively. i and Z i These represent the ion number density and average degree of ionization. h is Planck's constant, ε0 is the vacuum permittivity, and λ is the wavelength of the laser.
[0034] Step Six: Optimize laser parameters. Repeat steps one through five until the laser parameters with the lowest shielding effect are found. Based on the optimized laser parameters, the plasma shielding effect is suppressed, improving the efficiency of femtosecond laser processing. The laser parameters include the number of laser pulses, sub-pulse interval, and power density.
[0035] Step 3, which involves calculating particle collisions based on the DSMC algorithm and changing the motion state of each particle, includes the following steps:
[0036] A. Particle motion update: Move each particle a predetermined distance based on its velocity and position;
[0037] B. Determine the boundary; compare the position of the moved particle with the boundary defined in step one to determine if it exceeds the boundary; if it exceeds the boundary, update the particle velocity and position according to the boundary conditions; if it does not exceed the boundary, continue particle collision.
[0038] C. Particle collision;
[0039] (1) Select collision pairs; In a grid, arbitrarily select two particles as collision pairs;
[0040] (2) Calculate the collision probability; Based on the relative velocity and the collision cross section, calculate the probability of the two particles colliding; If the probability is greater than a random number, a collision occurs; otherwise, no collision occurs.
[0041] (3) Update the velocity after the collision; the particle velocity after the collision is updated according to the conservation of momentum and energy;
[0042] D. Re-grid; re-divide the particles into the grid according to their new positions;
[0043] E. Repeat steps A to D until the particle collision calculation is complete.
[0044] Step 3 involves real-time recording of physical quantities in each state, including the microscopic states of temperature, density, and velocity.
[0045] The density ρ is calculated using the following formula:
[0046] ρ=(m*N) / V (6)
[0047] Where m is the particle mass, N is the number of particles in the region, and V is the region volume;
[0048] The macroscopic velocity u within the region is the average velocity of all particles within that region; it is calculated using the following formula:
[0049] u x =Σ(v x ) / N,u y =Σ(v y ) / N,u z =Σ(v z ) / N (7)
[0050] Where, Σ(v x ), Σ(v y ) and Σ(v z ) represent the sum of the velocity components in the x, y, and z directions of all particles in the region, respectively;
[0051] Macroscopic temperature T within the region t Calculated using the following formula:
[0052] T t = (2 / 3)*(∑(KE) / N)*(1 / k B (8)
[0053] Where ∑(KE) is the sum of the kinetic energies of all particles in the region. Under ideal gas conditions, pressure P = ρ * R * T t , where R is the gas constant.
[0054] The grid is divided and particle information is stored in the form of an octree.
[0055] Beneficial effects:
[0056] 1. This invention discloses a method for suppressing plasma shielding effect and improving femtosecond laser processing efficiency. It uses multiphysics coupling theory to solve the process of femtosecond laser-induced metal plasma plume ejection. Compared with traditional fluid dynamics calculation methods, it can accurately characterize the laser-material interaction, material melting and plasma formation, and obtain the plume distribution at experimentally relevant scales. Then, by optimizing the density and velocity distribution of the plume, the shielding effect of the plasma plume on the laser is reduced, thereby improving the femtosecond laser processing efficiency. Attached Figure Description
[0057] Figure 1 This is a flowchart of the simulation prediction method.
[0058] Figure 2 This is a schematic diagram of a hard sphere model simulating particle collisions.
[0059] Figure 3This invention establishes the Python program dsmc_laser_ablation framework.
[0060] Figure 4 It is the initial position distribution of 10,000 air particles in the calculation area.
[0061] Figure 5 This is a particle density distribution map in a femtosecond laser-induced copper plasma plume.
[0062] Figure 6 yes Figure 5 The result corresponds to the Octree grid distribution. Detailed Implementation
[0063] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0064] The interaction between femtosecond lasers and materials generates extremely high temperatures and pressures within a very short time, inducing phase transitions, liquefaction, evaporation, and plasma formation. This intense non-equilibrium process leads to material ejection and plume generation. Laser-induced plumes and ejection phenomena significantly impact the processing efficiency of femtosecond lasers. Therefore, computer simulations are needed to accurately describe and predict the formation process of plasma plumes in order to suppress plasma ejection and reduce the plasma shielding effect by addressing the formation mechanism of plasma plumes.
[0065] like Figure 1 As shown in the figure, this embodiment discloses a method for suppressing plasma shielding effects and improving femtosecond laser processing efficiency. The method involves: developing a Python package dsmc_laser_ablation based on a direct Monte Carlo simulation method (program framework as shown in the figure). Figure 3 As shown in the figure, based on the specified femtosecond laser parameters (i.e., laser wavelength of 800 nm, laser pulse width of 100 fs, and laser flux of 10–20 J / cm²), the formation process of a femtosecond laser-induced copper plasma plume is simulated on the surface of a bulk copper material. By optimizing the laser parameters, the distribution of the plasma plume is changed, reducing its shielding effect on the laser and improving processing efficiency. The specific prediction steps of this embodiment are as follows:
[0066] Step 1: Set up the computational domain and boundary conditions. Write a computational script file, calling the `input_parameters.py` and `grid.py` programs. Define the computational domain as [(-200e-6, 200e-6), (0, 500e-6), (-2.5e-6, 2.5e-6)]. Set relevant physical and simulation parameters, such as laser parameters, target material thermophysical properties, and output parameters. Define the y{min} boundary as the eruption boundary condition, x{min}, x{max}, and y{max} as open boundary conditions, and z{min} and z{max} as specular reflection boundary conditions. Use the Octree class to divide and store the initial mesh.
[0067] Step 2: Simulate initialization and generate background gas particles. Call the `initialization.py` program in the script file to generate initial background gas particles using the `Particle` class. The initial temperature is 300K, and the gas is an ideal gas. (Appendix) Figure 4 The image shows the initial position distribution of 10,000 air particles in the computational region. The particle parameters required for the simulation are shown below:
[0068]
[0069] Step 3, Main Loop. The `simulation_loop.py` program is called in the script file, using the `Solver` class to generate the solver. The `two_temperature_model` function in `laser_model.py` is called to solve for the electron and lattice temperatures of the copper target at the current moment, determining whether the lattice temperature has reached the boiling point. If it has, the material begins to erupt, using the `Particle` class to generate the erupted copper gas particles. The Solver time-steps `dt`, checking whether particles exceed the boundary and processing particle motion according to boundary conditions (elimination or bounce). The `collision_model.py` program is called to determine whether particles in the Octree-stored mesh collide. This process calls the `collision_probability` function to calculate the collision probability of particle pairs and the `post_collision` function to calculate the particle velocity after the collision. The `collision_probability` function uses the hard sphere (HS) model to calculate the collision cross-section. Collision probability P = 1 - exp(-σ T c r Δt / V), where c r Let V be the relative particle velocity, Δt be the time step, and V be the mesh volume. The collision model is attached. Figure 2As shown. After updating the particle velocity and position, the Octree class is used to re-divide and store the mesh. The Statistic class in the output_visualization.py program is called to update the Octree mesh and perform statistical calculations on the macroscopic physical quantities in the mesh.
[0070] Step 4: Outputting Results. In the script file, call the `output_visualization.py` program to determine if the output conditions are met (e.g., the number of iterations reaches the sampling interval). Then, call the `Write` class to write the results (including the Octree grid, macroscopic physical quantities, particle states, etc.) to a file, saving them as an image and a VTK file for post-processing using ParaView software. (Appendix) Figure 5 and 6 That is, the particle density distribution and the corresponding Octree mesh division results obtained after ParaView processing.
[0071] Step 5: Based on the time evolution results of the physical quantities obtained in Step 4, calculate the absorption rate and shielding efficiency of the plasma plume for laser energy. The absorption rate is calculated using the following formula:
[0072]
[0073] Where, m e ,n e e0 and e0 represent the electron mass, density, and charge, respectively. i and Z i These represent the ion number density and average degree of ionization. h is Planck's constant, ε0 is the vacuum permittivity, and λ is the wavelength of the laser.
[0074] Step Six: Optimize laser parameters. Repeat steps one through five until the laser parameters with the lowest shielding effect are found. Based on the optimized laser parameters, the plasma shielding effect is suppressed, improving the efficiency of femtosecond laser processing. The laser parameters include the number of laser pulses, sub-pulse interval, and power density.
[0075] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for suppressing plasma shielding effect and improving femtosecond laser processing efficiency, characterized in that: Includes the following steps, Step 1: Set up the computational domain, generate the mesh, and define boundary conditions; set the size of the computational domain according to the experimental laser beam diameter, pulse time, and material size parameters; set the initial mesh to divide the flow field in the computational domain, with the mesh dimensions... Values The mean free path of gas molecules within the grid. , The number of gas molecules; The boundary conditions are open boundary conditions, specular reflection boundary conditions, inflow boundary conditions, and eruption boundary conditions. During the simulation, two simulated particle number update modes are considered: simulated particles entering the computational domain on the inflow and eruption boundaries and simulated particles escaping the computational domain on the open boundaries. The specular reflection boundary condition also restricts the motion of the simulated particles. Step 2: Initialize and generate background gas particles; Set the physical properties of the background gas particles, including particle mass, reference temperature, and reference diameter; Based on physical constants and fundamental equations, the initial temperature distribution and gas density distribution are calculated. Then, a corresponding number of background gas particles are randomly placed in the computational domain according to the obtained probability density function, and the initial velocity of the background gas particles is set. The velocity of the background gas particles satisfies the Maxwell velocity distribution, as shown in equation (2). Among them, the thermal motion speed of gas molecules , For macroscopic speed, It is the probability that the gas molecule's velocity is equal to c. Boltzmann's constant; In step one, 20-30 simulated molecules are arranged in each grid; the number of real gas molecules represented by each simulated molecule is the weighting factor. ; Step 3, Main Loop; The loop progresses according to the time step, and the time step... ,in , For the grid length and width, This represents the maximum value of the velocity components of the simulated molecules along the long rectangular boundary within the grid. This represents the maximum value of the velocity component of the simulated molecule along the wide boundary of the rectangle within the grid. The electronic and lattice temperatures of the metallic material at the current time step are obtained by discretizing the time and solving the two-temperature equation using the finite difference method. Where the quantities with subscripts e and l are related to electrons and lattice, respectively; T represents temperature; K is thermal conductivity; C is heat capacity; G is electron-lattice coupling factor; and S is volumetric laser heat source. For divergence operators; When the lattice temperature reaches the boiling point, the material begins to erupt; at this point, the saturated vapor pressure of the metal vapor... Obtained from the Clausius-Clapeyron equation: in, It is the latent heat of boiling. yes The boiling point is referenced to atmospheric pressure, and T is the current temperature. It is the atomic mass of the metal. Boltzmann's constant; The number of molecules evaporated per unit area from the evaporation source within the current time step is: in, , It is the average thermal rate of molecular motion. ; The DSMC algorithm is used to calculate particle collisions, change the motion state of each particle, record the physical quantities in each state in real time, and then obtain the macroscopic variation law of each physical quantity over time. Step 4: Output and store the time evolution relationships of each physical quantity obtained from the simulation in Step 3 for later analysis; Step 5: Based on the time evolution results of the physical quantities obtained in Step 4, calculate the absorption rate and shielding efficiency of the plasma plume for laser energy; the absorption rate is calculated using the following formula: in, , and These are electron mass, density, and charge. and These are the number density and average degree of ionization, and ℎ is Planck's constant. is the vacuum permittivity, and is the wavelength of the laser. Step 6: Optimize laser parameters. Repeat steps 1 to 5 until the laser parameters with the lowest shielding effect are found. The optimized laser parameters are used to suppress the plasma shielding effect and improve the efficiency of femtosecond laser processing. The laser parameters include the number of laser pulses, the sub-pulse interval, and the power density.
2. The method for suppressing plasma shielding effect and improving femtosecond laser processing efficiency as described in claim 1, characterized in that: Step three describes the method for calculating particle collisions based on the DSMC algorithm and changing the motion state of each particle, which includes the following steps: A. Particle motion update; Based on the particle's velocity and position, move each particle a predetermined distance; B. Boundary judgment; Compare the moved particle position with the boundary defined in step one to determine if it exceeds the boundary; If it exceeds the boundary, update the particle velocity and position according to the boundary conditions; If it does not exceed the boundary, continue particle collision; C. Particle collision; (1) Select collision pair; In a grid, arbitrarily select two particles as a collision pair; (2) Calculate collision probability; Calculate the probability of two particles colliding based on their relative velocity and collision cross section; If the probability is greater than a random number, a collision occurs; otherwise, no collision occurs. (3) Update the velocity after the collision; the particle velocity after the collision is updated according to the conservation of momentum and energy; D. Re-divide the grid; the particles are re-divided into the grid according to their new positions; E. Repeat steps A to D until the particle collision calculation is complete.
3. The method for suppressing plasma shielding effect and improving femtosecond laser processing efficiency as described in claim 2, characterized in that: Step 3 involves real-time recording of physical quantities in each state, including the microscopic states of temperature, density, and velocity. The density Calculated using the following formula: in, It is the particle mass. It is the number of particles in the region. It is the volume of the region; Macro velocity within the region It is the average velocity of all particles in this region; it is calculated using the following formula: in, , and Representing all particles within the region , and The sum of the directional velocity components; Macroscopic temperature within the region Calculated using the following formula: in, It is the sum of the kinetic energies of all particles in the region. In the case of an ideal gas, the pressure ,in It is the gas constant.
4. A method for suppressing plasma shielding effect and improving femtosecond laser processing efficiency as described in any one of claims 1 to 3, characterized in that: In step four, the time evolution results of each physical quantity are saved in VTK format and post-processed using ParaView software.
5. A method for suppressing plasma shielding effect and improving femtosecond laser processing efficiency as described in any one of claims 1 to 3, characterized in that: The grid is divided and particle information is stored in the form of an octree.