A Simulation Optimization Method for Laser Processing Process Parameters on the Surface of a Liquid Metal Bearing

Abstract

The present invention belongs to the technical field of processing technology simulation and optimization, and specifically discloses a method for simulating and optimizing the process parameters of laser processing on the surface of a liquid metal bearing. By constructing a temperature field model of femtosecond laser ablation of liquid metal, single-factor simulation experiments on energy density, equivalent pulse number, and scanning times are respectively carried out under this model. Then, by measuring the ablation effect parameters and thermal influence parameters under the simulation experiments, the optimal energy density, equivalent pulse number, and scanning times are determined, realizing the optimization of the femtosecond laser processing technology, which is beneficial to ensuring the femtosecond laser processing effect. At the same time, after obtaining the optimal energy density, equivalent pulse number, and scanning times through simulation experiments, multi-variable experiments on environmental factors are added under these process conditions, thereby evaluating the ablation effect and ablation aging under different environmental conditions to determine the suitable environmental conditions, and ensuring the efficient exertion of the optimal process conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of processing technology simulation and optimization, and specifically discloses a method for simulating and optimizing the laser processing process parameters of the surface of a liquid metal bearing. Background Art

[0002] A liquid metal bearing is a bearing that uses liquid metal as a lubricant. Liquid metal has extremely high thermal conductivity and low viscosity characteristics, which can provide excellent lubrication effects during high-speed rotation, and at the same time effectively conduct heat to prevent the bearing from overheating. This type of bearing is particularly suitable for high-speed and high-load mechanical equipment, such as modern medical equipment like CT machines.

[0003] When using liquid metal as a lubricant in a bearing, it is necessary to form tiny grooves or holes on the bearing surface, which can better store and distribute the liquid metal lubricant, ensuring that the lubricant can evenly cover the entire contact surface during high-speed rotation, reducing friction. This process is the process of surface texture processing of the liquid metal bearing.

[0004] Traditional methods for processing the surface texture of liquid metal bearings include mechanical processing, chemical etching, electrical discharge machining, and long-pulse laser processing, etc. These methods have obvious disadvantages: First, the processing accuracy is insufficient, and it is difficult to achieve micron or nanometer-level fine textures. Especially on hard materials (such as molybdenum metal), the processing error is large, affecting the ability to lubricate and store liquid metal. In addition, the thermal effect is a major challenge. Laser and electrical discharge machining will form a heat-affected zone on the surface, resulting in material recasting, surface microcracks, and thermal damage, thereby reducing the friction performance and durability of the bearing. At the same time, chemical etching will cause environmental pollution and pose higher requirements for waste liquid treatment. In addition, traditional methods are also difficult to control the uniformity and consistency of complex textures, with low processing efficiency and are not suitable for large-area surface treatment. Summary of the Invention

[0005] In order to solve the problems of low processing accuracy, large processing error, and significant heat-affected zone in the prior art, here it is proposed to use femtosecond laser technology for high-precision processing of the surface texture of liquid metal bearings. Femtosecond lasers have an extremely short pulse width and can achieve fine processing at the micron or even nanometer level without generating a significant heat-affected zone. However, the processing effect of femtosecond laser processing is affected by various process parameters, including energy density, pulse frequency, and scanning speed, etc. Different combinations of process parameters will lead to significant differences in processing effects. Therefore, determining the optimal process parameters is crucial for ensuring processing quality. For this reason, the present invention proposes a method for simulating and optimizing the laser processing process parameters of the surface of a liquid metal bearing, aiming to evaluate the influence of different combinations of process parameters on the processing effect by constructing a simulation model and combining experimental data, so as to realize the optimization of the femtosecond laser processing process.

[0006] The object of the present invention can be achieved by the following technical solutions: A method for simulating and optimizing process parameters of laser processing on the surface of a liquid metal bearing, comprising the following steps: (1) Construct a two-temperature model for femtosecond laser processing of liquid metal, and predict the ablation threshold of the liquid metal through numerical simulation.

[0007] (2) Conduct a single-factor simulation experiment on the energy density based on the ablation threshold while keeping other process parameters unchanged, and measure the ablation effect parameters and thermal influence parameters at different energy densities after the experiment, where the ablation effect parameters include ablation performance parameters and ablation morphology parameters, to determine the optimal energy density.

[0008] (3) Conduct a single-factor simulation experiment on the equivalent pulse number while keeping other process parameters unchanged, and measure the ablation effect parameters and thermal influence parameters at different equivalent pulse numbers after the experiment, to determine the optimal equivalent pulse number.

[0009] (4) Conduct a single-factor simulation experiment on the number of scans while keeping other process parameters unchanged, and measure the ablation effect parameters and thermal influence parameters at different numbers of scans after the experiment, to determine the optimal number of scans.

[0010] (5) Conduct a multi-variable experiment on environmental factors under the process conditions of the optimal energy density, equivalent pulse number, and number of scans, evaluate the ablation effect and ablation aging under different environmental conditions, to determine the suitable environmental conditions.

[0011] The above technical solutions provided by the embodiments of the present application have the following advantages compared with the prior art: 1. By constructing a temperature field model for femtosecond laser ablation of liquid metal, the present invention conducts single-factor simulation experiments on the energy density, equivalent pulse number, and number of scans respectively under this model, and thus determines the optimal energy density, equivalent pulse number, and number of scans by measuring the ablation effect parameters and thermal influence parameters under the simulation experiment, realizing the optimization of the femtosecond laser processing process, which is beneficial to ensuring the femtosecond laser processing effect.

[0012] 2. After obtaining the optimal energy density, equivalent pulse number, and number of scans through simulation experiments, the present invention also adds a multi-variable experiment on environmental factors under these process conditions, and thus evaluates the ablation effect and ablation aging under different environmental conditions to determine the suitable environmental conditions, which can ensure the efficient performance of the optimal process conditions, help avoid process fluctuations caused by environmental changes, and improve the stability and reliability of the overall process. Description of the Drawings

[0013] The present invention is further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the following drawings without creative efforts.

[0014] Figure 1 This is the flow chart of the method implementation steps of the present invention.

[0015] Figure 2 This is the linear fitting diagram of the ablation threshold test experiment of femtosecond laser ablation of molybdenum metal in the present invention.

[0016] Figure 3 This is the processing optical path diagram of the single-factor process parameter test in the present invention. Detailed implementation manners

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] See Figure 1 As shown, the present invention proposes a method for simulating and optimizing the process parameters of laser processing on the surface of a liquid metal bearing, including the following steps: (1) Construct a two-temperature model for femtosecond laser processing of liquid metal, and predict the ablation threshold of the liquid metal through numerical simulation.

[0019] The application of constructing the two-temperature model for femtosecond laser processing of liquid metal is as follows: In the model, it is assumed that the electrons and the lattice of the liquid metal can be regarded as two independent thermal systems in a short time, and they exchange energy through phonon-electron coupling.

[0020] The core equations of the model include the electron temperature equation and the lattice temperature equation. The expression of the electron temperature equation is , in the model represents the electron heat capacity, , respectively represent the electron and lattice temperatures, represents the electron thermal conductivity, represents the electroacoustic coupling coefficient, represents the laser heat source term, represents the gradient operator.

[0021] The expression of the lattice temperature equation is , in the model represents the lattice heat capacity, represents the lattice thermal conductivity.

[0022] During the femtosecond laser ablation process, the laser heat source term can be expressed as , where in the formula represents the metal surface reflectivity, represents the energy density, represents the radiation depth, represents the distance in the depth direction, represents the natural constant.

[0023] It should be noted that during the femtosecond laser processing, electrons and lattices inside the material absorb and transfer energy at different time scales. Electrons rapidly absorb laser energy within the femtosecond time scale, while lattices require a longer time to respond to the energy transfer of electrons. Therefore, the traditional single-temperature model cannot accurately describe this rapid energy transfer process. The two-temperature model can more accurately describe the energy transfer process between femtosecond lasers and liquid metals by separately simulating the electron temperature. Due to the extremely short pulse width of femtosecond lasers, there is a significant time lag effect in the temperature changes of electrons and lattices. The two-temperature model can better capture this non-equilibrium heat conduction process, thereby more accurately predicting the ablation behavior of materials.

[0024] It should be noted again that the ablation threshold refers to the minimum laser energy density at which significant material removal begins. For liquid metals, the ablation threshold is a key parameter to ensure processing quality. If the laser energy is lower than the ablation threshold, no effective material removal will occur; while if the energy is too high, it may lead to excessive ablation, generating unnecessary heat-affected zones or material damage. Therefore, determining the ablation threshold under the two-temperature model can provide a scientific basis for the selection of process parameters, ensuring that the laser energy exactly reaches the critical point of material removal. This helps to achieve high-precision micro-nano structure processing, avoid processing defects caused by insufficient or excessive energy, and minimize the heat-affected zone while maintaining the integrity of the material's microstructure.

[0025] Further applied to the above scheme, the prediction of the ablation threshold of liquid metals is implemented as follows: Solve the two-temperature model by setting initial conditions and boundary conditions, where the initial condition is that the temperatures of electrons and lattices are both the ambient temperature before laser irradiation , that is , and the boundary condition is that the temperature distribution within the laser irradiation area can be described by the laser heat source term , and outside the laser irradiation area, the temperature remains the ambient temperature .

[0026] Gradually increase the laser energy density during the numerical solution of the two-temperature model , and monitor the change of the lattice temperature . When the lattice temperature reaches the melting temperature or the evaporation temperature of the liquid metal, the corresponding energy density is the ablation threshold , and the specific calculation formula is , where Represents a temperature distribution function that varies with time and spatial position.

[0027] It should be noted that the melting temperature of liquid metal refers to the temperature at which the liquid metal begins to undergo a phase change and enters the molten state.

[0028] The evaporation temperature of liquid metal refers to the temperature at which the material begins to directly transform from a solid or liquid state to a gaseous state, i.e., evaporation occurs.

[0029] In the above, when solving the two-temperature model, since the two-temperature model is a system of nonlinear partial differential equations, numerical methods (such as the finite difference method, the finite element method, Monte Carlo simulation, etc.) are usually required to solve it.

[0030] Specific numerical solution steps include: Discretization: Discretize time and space, and transform the continuous partial differential equation into a discrete algebraic equation.

[0031] Iterative solution: Gradually solve the changes in the electron temperature and the lattice temperature through an iterative algorithm (such as the Runge-Kutta method, the Euler method, etc.).

[0032] Convergence judgment: Set the convergence conditions to ensure the accuracy and stability of the numerical solution.

[0033] Furthermore, after predicting the ablation threshold of liquid metal, it can also be verified through experiments. The specific operations are as follows: Select the laser equipment indicators suitable for liquid metal processing, where the laser equipment indicators include laser wavelength, pulse width, repetition frequency, spot size, etc.

[0034] For example, the laser wavelength can be 515nm, 1030nm, or 266nm, depending on the absorption characteristics of the material.

[0035] The pulse width is specifically ensured to be in the femtosecond level (usually 100fs to 1ps) to achieve the "cold ablation" effect and reduce the heat affected zone.

[0036] The repetition frequency can select an appropriate pulse repetition frequency according to experimental requirements (such as 1kHz to 1MHz).

[0037] Spot size: Control the laser spot size (such as 10μm to 100μm) to ensure the uniformity of the ablation area.

[0038] Fix the liquid metal sample on the sample stage, and use the laser alignment to ensure that the laser beam is accurately focused on the sample surface, and adjust the laser equipment indicators to meet the selected values.

[0039] Control the energy density to start the first ablation experiment from below the predicted ablation threshold, and keep other process parameters unchanged. Then, measure the radius of the ablation area after the ablation experiment.

[0040] It should be added that the radius of the ablation area can be measured using an optical microscope or a scanning electron microscope.

[0041] In the example of the above operation, in the first ablation experiment starting from below the predicted ablation threshold, assuming that the predicted ablation threshold is 0.4 J / cm², it can start from 0.1 J / cm².

[0042] After the first ablation experiment, control the energy density to increase by a set step size for the next ablation experiment, and record the radius of the ablation area corresponding to each ablation experiment at the same time.

[0043] The set step size in the above can be 0.05 J / cm².

[0044] Plot the curve of the ablation radius versus the energy density for the energy density and the radius of the ablation area corresponding to each ablation experiment in a coordinate system with the energy density as the abscissa and the radius of the ablation area as the ordinate, and perform a linear fit on the plotted curve, and then read the slope from the fitted curve to calculate the experimental ablation threshold of the liquid metal.

[0045] It should be understood that the radial distribution of the laser energy density in the light spot can be expressed as , and according to this formula, it can be deduced that , where represents the ablation threshold of the liquid metal material, represents the ablation radius, represents the peak energy density, represents the distance from the center of the light spot, represents the radius of the light spot, represents the distance from the center of the light spot at which the laser energy density is located.

[0046] In the example of femtosecond laser ablation of molybdenum metal, fit the and linear relationship between them, as shown in Figure 2 .

[0047] Compare the experimental ablation threshold of the liquid metal with the predicted ablation threshold to calculate the proximity. Specifically, the difference between the experimental ablation threshold and the predicted ablation threshold can be taken as the absolute value and then divided by the experimental ablation threshold, and then the subtraction operation is performed between the value 1 and the division result to obtain the proximity. And compare the calculation result with the preset effective proximity. Exemplarily, the effective proximity is 0.8. If the proximity reaches the effective proximity, the experimental verification is successful; otherwise, the ablation threshold verification experiment is performed again.

[0048] In femtosecond laser processing, the factors that may affect the processing effect mainly include energy density, equivalent pulse number, and repeated scanning times. The influences of these three parameters on the experimental results are independent of each other. In the present invention, single-factor experiments are respectively carried out on the three parameters around. Specifically, the processing optical path diagram is as shown in Figure 3 shown.

[0049] (2) Under the condition of keeping other process parameters unchanged, a single-factor simulation experiment on energy density is carried out based on the ablation threshold, and after the experiment, the ablation effect parameters and thermal influence parameters at different energy densities are measured. Among them, the ablation effect parameters include ablation performance parameters and ablation morphology parameters to determine the optimal energy density.

[0050] In the preferred implementation of the above scheme, the single-factor simulation experiment on energy density based on the ablation threshold refers to the following process: The upper limit energy density is extracted from the curve of ablation radius changing with energy density, and thus the ablation threshold of liquid metal and the upper limit energy density constitute the energy density test interval.

[0051] The above-mentioned upper limit energy density refers to the energy density at which the ablation effect reaches the expected maximum value. Exemplarily, the extraction of the upper limit energy density can perform a linear fit on the rapid growth segment of the curve of ablation radius changing with energy density. The slope of the fitted straight line will gradually decrease until it approaches zero. The upper limit energy density usually appears near the point where the slope significantly decreases.

[0052] In another example, if the curve of ablation radius changing with energy density presents an S shape, the Sigmoid function can be used for fitting. The specific function expression is , where represents the maximum ablation radius, represents the growth rate, represents the upper limit energy density, represents the natural constant. Through the fitting parameter the upper limit energy density can be obtained.

[0053] It should be noted that the formed energy density test interval covers the entire energy density range from when the liquid metal starts to be effectively removed to the maximization of the ablation effect.

[0054] Several single-point energy densities are obtained by dividing within the energy density test interval according to a set step size, and each single-point energy density corresponds to an energy density experiment.

[0055] In each energy density experiment, ensure that the applied energy density conforms to the corresponding single-point energy density and keep other process parameters unchanged.

[0056] In a further preferred implementation of the above solution, the ablation effect parameters and thermal influence parameters are as described in the following measurement process: After the test, an ablation image is collected using a visual detection terminal, which can be an electron microscope, to ensure that the collected image can clearly show the edge contour and surface morphology of the ablation area. Edge detection is performed on the collected ablation image through image processing software, and the ablation depth and ablation width are extracted therefrom as ablation performance parameters.

[0057] Focus the lens of the visual detection terminal on the surface of the ablation area to obtain a high-resolution surface morphology image, and use non-contact measurement techniques such as a white light interferometer or an atomic force microscope to calculate the surface roughness as an ablation morphology parameter.

[0058] It should be added that the surface roughness mentioned above can be the arithmetic mean variance or the root mean square roughness, which is dimensioned. The dimension unit is usually a length unit, and its dimension reflects the height characteristics of the microscopic structure of the liquid metal material surface.

[0059] Ultrasonic waves are emitted into the ablation area through ultrasonic waves, and the thickness of the heat affected zone, the number of internal defects, and the residual stress are obtained as thermal influence parameters based on the intensity and propagation time of the reflected waves.

[0060] It should be added that the thickness of the heat affected zone can be calculated based on the propagation speed of ultrasonic waves in the material and the time difference of the reflected waves. This helps to evaluate the degree of heat diffusion during the laser processing.

[0061] Internal defects can be identified by analyzing the intensity change of the reflected waves to determine whether there are cracks, holes, or other microscopic defects in the heat affected zone, and the number of internal defects is counted. This helps to evaluate the integrity of the material and potential structural damage.

[0062] The residual stress can be evaluated by the phase change or the change in the propagation speed of ultrasonic waves in the heat affected zone. This helps to evaluate the mechanical properties of the material and possible stress concentration areas.

[0063] In a further preferred implementation of the above solution, determining the optimal energy density is as described in the following process: Compare the ablation performance parameters at different energy densities with the expected ablation performance parameters to calculate the ablation performance compliance at different energy densities. The specific calculation formula is , where represents the ablation performance compliance, , respectively represent the ablation depth and ablation width, , respectively represent the expected ablation depth and expected ablation width.

[0064] Substitute the ablation performance compliance and surface roughness at different energy densities into the formula to obtain the ablation effect coefficient at different energy densities , where represents the surface roughness, represents the allowable surface roughness

[0065] Use the expression to calculate the thermal influence coefficient at different energy densities for the thermal influence parameters at different energy densities , where , , respectively represent the thickness of the heat-affected zone, the number of internal defects in the heat-affected zone, and the residual stress, , , respectively represent the allowable thickness of the heat-affected zone, the allowable number of internal defects in the heat-affected zone, and the allowable residual stress, represents the natural constant

[0066] It should be noted that the above-mentioned expected ablation performance parameters, allowable surface roughness, allowable thickness of the heat-affected zone, allowable number of internal defects in the heat-affected zone, and allowable residual stress can be based on experimental design and material characteristics for reference

[0067] It should be explained that when calculating the ablation effect coefficient and the thermal influence coefficient, these values are amplified by a factor of 10 by multiplying the calculation results. This amplification process can avoid the problem that the curve performance is not obvious or difficult to interpret due to the too small original calculation results

[0068] Generate an ablation effect curve and a thermal influence curve for the ablation effect coefficient and the thermal influence coefficient at different energy densities in a coordinate system with the energy density as the horizontal axis and the ablation effect coefficient and the thermal influence coefficient as the vertical axes

[0069] Respectively capture the energy densities corresponding to the maximum ablation effect coefficient and the minimum thermal influence coefficient in the ablation effect curve and the thermal influence curve for comparison, and identify whether they are close. If they are close, take the average value of the two energy densities as the optimal energy density. If they are not close, judge whether there is an intersection point between the ablation effect curve and the thermal influence curve. If there is an intersection point, take the energy density corresponding to the intersection point in the curve as the optimal energy density. If there is no intersection point, respectively obtain the change rates of the ablation effect curve and the thermal influence curve, and then use the absolute value of the change rate as the weight factor for the ablation effect coefficient and the thermal influence coefficient. Thus, the ablation performance degrees at different energy densities are calculated by weighted averaging the ablation effect coefficient and the thermal influence coefficient at different energy densities with the weight factor, and then take the energy density corresponding to the maximum ablation performance degree as the optimal energy density

[0070] It should be added that the above-mentioned curve change rate refers to the slope of the curve at each energy density.

[0071] It should be further added that 10-minimum heat impact coefficient is used when performing weighted average calculation of ablation performance. This is because the smaller the heat impact coefficient, the better the ablation performance.

[0072] In the above, to identify whether the energy densities corresponding to the maximum ablation effect coefficient and the minimum heat influence coefficient are close, the difference between the energy densities corresponding to the maximum ablation effect coefficient and the minimum heat influence coefficient can be taken as the absolute value and then divided by the energy density corresponding to the maximum ablation effect coefficient, and then the value 1 is subtracted from the division result to obtain the proximity, and compared with the preset effective proximity. When the effective proximity is reached, it is identified as close, otherwise it is identified as not close.

[0073] It should be understood that when the energy densities corresponding to the maximum ablation effect coefficient and the minimum thermal influence coefficient are close, the average of the two can be directly taken as the optimal energy density. In this case, the energy density can minimize the thermal influence while ensuring the best ablation effect. When the two are not close, it is necessary to further analyze whether there is an intersection. If there is an intersection, the energy density corresponding to the intersection is used as the optimal energy density. The existence of the intersection means that at this energy density, the ablation effect and thermal influence have reached the best balance. When there is no intersection, the rate of change needs to be introduced as a weighting factor. This is because the rate of change reflects the degree of influence of energy density on the ablation effect and thermal influence.

[0074] (3) A single-factor simulation test of the equivalent pulse number is carried out while keeping other process parameters unchanged. After the test, the ablation effect parameters and heat influence parameters under different equivalent pulse numbers are measured to determine the optimal equivalent pulse number.

[0075] (4) A single-factor simulation test of the number of scans is performed while keeping other process parameters unchanged. After the test, the ablation effect parameters and heat influence parameters under different scan times are measured to determine the optimal scan times.

[0076] The above process of determining the optimal equivalent pulse number and the optimal scanning number is similar to the process of determining the optimal energy density.

[0077] It should be emphasized that the environmental conditions need to be consistent when conducting single-factor experiments on energy density, equivalent pulse number and scan number.

[0078] (5) Conduct multivariate tests on environmental factors under the process conditions of optimal energy density, equivalent pulse number, and scan number to evaluate the ablation effect and ablation time under different environmental conditions to determine the suitable environmental conditions.

[0079] Preferably, the implementation of the multi-variable test of environmental factors is as follows: Select the environmental factors to be tested and determine the test levels of each environmental factor.

[0080] Common environmental factors mentioned above include but are not limited to temperature, humidity, air pressure, etc. For each selected environmental factor, determine its test levels (i.e., different values). Usually, three levels (low, medium, high) are selected to capture non-linear effects. For example: Temperature: 20°C, 25°C, 30°C; Humidity: 30%, 50%, 70%; Air pressure: standard atmospheric pressure, 0.8 times the standard atmospheric pressure, 1.2 times the standard atmospheric pressure.

[0081] Select one test level from the test levels of each environmental factor in turn for combination to form several environmental level groups.

[0082] In the example where there are 3 environmental factors and each environmental factor has 3 levels, there will be a total of 27 environmental level groups.

[0083] During the environmental test, control the environmental conditions in the test to meet the environmental state in the corresponding environmental level group, and at the same time control the environmental test to be carried out under the process conditions of the optimal energy density, equivalent pulse number, and scanning times.

[0084] Further preferably, the evaluation of the ablation effect and ablation aging under different environmental conditions is implemented as follows: Measure the ablation effect parameters under different environmental level group tests, and calculate the ablation effect coefficients corresponding to different environmental level group tests from this.

[0085] Measure the ablation duration under different environmental level group tests and substitute it into the formula to obtain the ablation aging coefficients corresponding to different environmental level group tests , where in the formula represents the ablation duration, represents the sum of the ablation durations under all environmental level group tests.

[0086] Furthermore, the process of determining the adapted environmental conditions is as follows: Compare the ablation effect coefficients and ablation aging coefficients corresponding to different environmental level group tests, respectively extract the environmental level groups corresponding to the maximum ablation effect coefficient and ablation aging coefficient, analyze whether they are the same environmental level group. If they are the same environmental level group, then the environmental levels corresponding to this environmental level group are the adapted environmental conditions. If they are not the same environmental level group, then conduct a comprehensive evaluation of the ablation effect coefficients and ablation aging coefficients corresponding to different environmental level group tests, and then take the environmental levels corresponding to the environmental level group with the maximum comprehensive evaluation as the adapted environmental conditions.

[0087] It should be explained that when the maximum ablation effect coefficient and the minimum ablation aging coefficient correspond to the same environmental level group, then this environmental level group can be directly used as the adapted environmental condition. This indicates that under this condition, both the best ablation effect and the highest ablation efficiency can be obtained. If the maximum ablation effect coefficient and the minimum ablation aging coefficient correspond to different environmental level groups, it means that there is a trade-off between these two aspects. At this time, it is not possible to simply select one of the environmental conditions, but rather further comprehensive evaluation is required to find the optimal balance point. Specifically, for the comprehensive evaluation, weight factors for the ablation effect coefficient and the ablation aging coefficient can be set. If the ablation quality is crucial, a higher weight should be given to the ablation effect; if the production efficiency is the main concern, a higher weight should be given to the ablation aging.

[0088] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should fall within the protection scope of the present invention.

Claims

1. A simulation optimization method for laser processing process parameters on the surface of a liquid metal bearing, characterized in that, It includes the following steps: (1) Construct a two-temperature model for femtosecond laser processing of liquid metal, and predict the ablation threshold of liquid metal through numerical simulation; (2) Conduct a single-factor simulation experiment on energy density based on the ablation threshold while keeping other process parameters unchanged, and measure the ablation effect parameters and thermal influence parameters at different energy densities after the experiment, where the ablation effect parameters include ablation performance parameters and ablation morphology parameters, to determine the optimal energy density; The determination of the optimal energy density refers to the following process: Compare the ablation performance parameters at different energy densities with the expected ablation performance parameters to calculate the compliance of the ablation performance at different energy densities , , 、 represent the ablation depth and ablation width respectively, 、 represent the expected ablation depth and expected ablation width respectively; Substitute and surface roughness into the formula to obtain the ablation effect coefficient at different energy densities , where represents the allowable surface roughness; The thermal influence parameters at different energy densities are calculated using the expression to obtain the thermal influence coefficients at different energy densities , where , , represent the thickness of the heat affected zone, the number of internal defects, and the residual stress, respectively , , represent the allowable thickness of the heat affected zone, the allowable number of internal defects in the heat affected zone, and the allowable residual stress, respectively represents the natural constant; Put and respectively generate ablation effect curves and thermal influence curves in a coordinate system constructed with the energy density as the horizontal axis and , as the vertical axis; Respectively capture the energy density comparison corresponding to the maximum ablation effect coefficient and the minimum heat - affected coefficient in the ablation effect curve and the heat - affected curve, and identify whether they are close. If they are close, take the average value of the two energy densities as the optimal energy density. If they are not close, judge whether there is an intersection point between the ablation effect curve and the heat - affected curve. If there is an intersection point, take the energy density corresponding to the intersection point in the curve as the optimal energy density. If there is no intersection point, respectively obtain the change rates of the ablation effect curve and the heat - affected curve, and then use the absolute value of the change rate as and the weight factors of, and thus combine the and under different energy densities with the weight factors for weighted average calculation to obtain the ablation performance under different energy densities, and then take the energy density corresponding to the maximum ablation performance as the optimal energy density; (3) Conduct a single-factor simulation experiment on the equivalent pulse number while keeping other process parameters unchanged, and measure the ablation effect parameters and thermal influence parameters at different equivalent pulse numbers after the experiment, to determine the optimal equivalent pulse number; (4) Conduct a single-factor simulation experiment on the number of scans while keeping other process parameters unchanged, and measure the ablation effect parameters and thermal influence parameters at different numbers of scans after the experiment, to determine the optimal number of scans; (5) Conduct a multi-variable experiment on environmental factors under the process conditions of the optimal energy density, equivalent pulse number, and number of scans, and evaluate the ablation effect and ablation aging under different environmental conditions, to determine the suitable environmental conditions.

2. The simulation optimization method for laser processing process parameters on the surface of a liquid metal bearing according to claim 1, wherein: The construction of the two-temperature model for femtosecond laser processing of liquid metal refers to the following process: In the model, it is assumed that the electrons and lattice of the liquid metal are regarded as two independent thermal systems in a short time, and energy exchange occurs between them through phonon-electron coupling; The core equations of the model include the electron temperature equation and the lattice temperature equation. The expression of the electron temperature equation is In the model represents the electron heat capacity, , represent the electron and lattice temperatures respectively, represents the electron thermal conductivity, represents the acousto-electric coupling coefficient, represents the laser heat source term, represents the gradient operator; The expression of the lattice temperature equation is , in the model represents the lattice heat capacity, represents the lattice thermal conductivity; During the femtosecond laser ablation process, the laser heat source term is expressed as , where represents the reflectivity of the metal surface, represents the energy density, represents the radiation depth, represents the distance in the depth direction, represents the natural constant.

3. A simulation optimization method for laser processing process parameters on the surface of a liquid metal bearing according to claim 2, characterized in that: The implementation of predicting the ablation threshold of liquid metal is as follows: Solve the two-temperature model by setting the initial conditions and boundary conditions, where the initial conditions are that the temperatures of electrons and the lattice are both the ambient temperature before laser irradiation , namely ; The boundary condition is that the temperature distribution within the laser irradiation area is described by the laser heat source term while outside the laser irradiation area, the temperature remains at the ambient temperature ; Gradually increase the laser energy density in the numerical solution of the two-temperature model and monitor the change of the lattice temperature . When the lattice temperature reaches the melting temperature or evaporation temperature of the liquid metal, the corresponding energy density is the ablation threshold . Specifically, the calculation formula is , where represents a temperature distribution function that varies with time and spatial position.

4. A simulation optimization method for laser processing process parameters on the surface of a liquid metal bearing according to claim 1, characterized in that: After predicting the ablation threshold of liquid metal, it can also be verified by experiments. The specific operations are as follows: Select the laser device indicators suitable for liquid metal processing; Fix the liquid metal sample on the sample stage, and use the laser alignment to ensure that the laser beam is accurately focused on the sample surface, and adjust the laser device indicators to meet the selected values; Control the energy density to start the first ablation experiment from below the predicted ablation threshold, and keep other process parameters unchanged. Then measure the radius of the ablation area after the ablation experiment; After the first ablation experiment, control the energy density to increase by a set step size for the next ablation experiment, and record the radius of the ablation area corresponding to each ablation experiment at the same time; Plot the curve of the ablation radius versus the energy density for each ablation experiment in a coordinate system with the energy density as the abscissa and the radius of the ablation area as the ordinate, and perform a linear fit on the plotted curve. Then read the slope from the fitted curve to calculate the experimental ablation threshold of the liquid metal; Compare the experimental ablation threshold of the liquid metal with the predicted ablation threshold to calculate the proximity, and compare it with the preset effective proximity. If the proximity reaches the effective proximity, the experimental verification is successful; otherwise, re-conduct the ablation threshold verification experiment.

5. The simulation optimization method for laser processing process parameters on the surface of a liquid metal bearing according to claim 4, wherein: The single-factor simulation experiment on energy density based on the ablation threshold refers to the following process: Extract the upper limit energy density from the curve of the ablation radius versus the energy density, and thus form an energy density test interval with the ablation threshold and the upper limit energy density of the liquid metal; Within the energy density test range, a number of single-point energy densities are obtained by dividing according to a set step size, and each single-point energy density corresponds to an energy density test. In each energy density test, ensure that the applied energy density conforms to the corresponding single-point energy density and keep other process parameters unchanged.

6. The simulation optimization method for laser processing process parameters on the surface of a liquid metal bearing according to claim 1, characterized in that: The ablation effect parameters and thermal influence parameters are as described in the following measurement process: After the test, use a vision detection terminal to collect ablation images, and extract the ablation depth and ablation width from the images as ablation performance parameters. Focus the vision detection terminal on the surface topography image of the ablation area, and extract the surface roughness from it as the ablation topography parameter. Emit ultrasonic waves to the ablation area through ultrasonic waves, and obtain the thickness of the heat-affected zone, the number of internal defects, and the residual stress as thermal influence parameters according to the intensity and propagation time of the reflected waves.

7. A method for simulating and optimizing the process parameters of laser processing on the surface of a liquid metal bearing according to claim 1, characterized in that: The implementation of the multi-variable test of environmental factors is as follows: Select the environmental factors to be tested and determine the test levels of each environmental factor. Select one test level from the test levels of each environmental factor in turn to form several environmental level groups. When conducting the environmental test, control the environmental conditions in the test to meet the environmental state in the corresponding environmental level group.

8. The simulation optimization method for laser processing process parameters on the surface of a liquid metal bearing according to claim 7, characterized in that: The evaluation of the ablation effect and ablation aging under different environmental conditions is implemented as follows: Measure the ablation effect parameters under different environmental level group tests, and calculate the ablation effect coefficient therefrom. Measure the ablation duration under different environmental level group tests and substitute it into the formula to obtain the ablation aging coefficient corresponding to different environmental level group tests , where represents the ablation duration, represents the sum of the ablation durations under all environmental level group tests.

9. A simulation optimization method for laser processing process parameters on the surface of a liquid metal bearing according to claim 8, characterized in that: The determination of the adapted environmental conditions is as follows: Compare the ablation effect coefficients and ablation aging coefficients corresponding to different environmental level group tests, and respectively extract the environmental level groups corresponding to the maximum ablation effect coefficient and ablation aging coefficient, and analyze whether they are the same environmental level group. If they are the same environmental level group, then take the environmental level corresponding to this environmental level group as the adapted environmental conditions. Otherwise, conduct a comprehensive evaluation of the ablation effect coefficients and ablation aging coefficients corresponding to different environmental level group tests, and then take the environmental level corresponding to the environmental level group with the maximum comprehensive evaluation as the adapted environmental conditions.

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