A method for predicting the diameter of pillars in additively manufactured lattice structures

Abstract

The present invention belongs to the field of additive manufacturing technology, and in particular, is a method for predicting the diameter of a strut in an additively manufactured lattice structure. The operating steps are as follows: S1. Selecting additive manufacturing process parameters to manufacture a lattice structure. This prediction method, by providing a method for rapidly predicting the diameter of a strut in an additively manufactured titanium alloy lattice structure, only requires statistically analyzing the diameters of the struts in the titanium alloy lattice structure manufactured under several different process parameters. This method can then, according to the method provided by the present invention, fit a relationship formula to achieve prediction of the diameter of the struts in the additively manufactured titanium alloy lattice structure under any process parameters. The proposed formula is only related to laser energy density and has the advantages of being simple in form and having fewer parameters. This formula can solve the problem of predicting the diameter of the struts in the lattice structure manufactured under different process parameters using the same material, especially for lattice structures manufactured using the same material.

Description

Technical Field

[0001] The present invention relates to the technical field of additive manufacturing, and in particular to a method for predicting the diameter of a pillar in an additively manufactured lattice structure. Background Art

[0002] Additive manufacturing, also known as 3D printing, is a new processing method that has rapidly developed in recent years. This technology utilizes the principle of discrete accumulation, melting and accumulating materials layer by layer to create solid parts. Compared to traditional material removal techniques, it is a "bottom-up" manufacturing method. It offers significant advantages, including high design freedom, high production efficiency, the ability to manufacture complex structures, and the ability to meet the needs of personalized small-batch production.

[0003] Lightweight design is a key requirement for equipment development in industries such as aerospace, automotive, and medical. It offers numerous advantages in reducing energy consumption, promoting environmental protection, and improving structural performance. In the aerospace sector, lightweighting can reduce aircraft fuel consumption, improve endurance, and maneuverability, making it a key technology for the future development of aerospace equipment.

[0004] In recent years, lattice structures have attracted widespread attention in the field of lightweight design due to their light weight and high strength. A lattice structure is an ordered structure composed of a series of unit cells with specific structural forms and pore shapes arranged periodically according to certain rules.

[0005] Due to the complex internal structure of the lattice structure, the traditional processing methods are no longer suitable. The traditional lattice structure preparation methods include investment casting, stamping and additive manufacturing. However, the investment casting method has problems such as a very complex process flow and high cost, and is prone to defects. The stamping method has problems such as the need to manufacture molds, high costs, and serious waste of metal sheets. The development and maturity of additive manufacturing technology has provided new ideas for the manufacture of lattice structures, allowing the lattice structure, which has many characteristics and advantages, to have a broader application prospect.

[0006] However, due to the raw material properties of metal lattice additive manufacturing powders and the limitations of the laser selective melting process, the dimensions of the resulting lattice structures deviate from the original design, which in turn affects the mechanical properties of the lattice structure. The additive manufacturing process parameters affect the diameter of the lattice struts, requiring extensive testing. Therefore, predicting the diameter of the lattice struts has always been a key and challenging aspect of additive manufacturing lattice structures.

[0007] In fact, there is a regular relationship between additive manufacturing process parameters and the diameter of the lattice structure pillars. Generally speaking, the diameter of the titanium alloy lattice structure pillars is related to the size of the molten pool. The size of the molten pool is determined by the energy density of the laser input into the molten pool, and the energy density is related to the process parameters. Therefore, starting from the energy density, by designing different process parameter experiments, the quantitative relationship between the process parameters and the diameter of the titanium alloy lattice structure pillars can be obtained. This allows the diameter of the titanium alloy lattice structure pillars under different process parameters to be predicted, thereby greatly reducing testing time and saving costs.

[0008] The present invention intends to provide a method for predicting the diameter of pillars in an additively manufactured titanium alloy lattice structure from the perspective of the molten pool input energy density. Summary of the Invention

[0009] Based on the existing technical problem of predicting the diameter of the pillars of the additive manufacturing lattice structure, the present invention proposes a method for predicting the diameter of the pillars of the additive manufacturing lattice structure.

[0010] The present invention proposes a method for predicting the diameter of a pillar in an additively manufactured lattice structure, and the operating steps are as follows:

[0011] S1. Selecting additive manufacturing process parameters to manufacture a lattice structure;

[0012] S11. Select TC4 titanium alloy as the manufacturing material and design the FCCZ lattice structure using CAD software, where the lattice structure is designed to have a diameter of 1 mm;

[0013] S12. Determine the range of additive manufacturing process parameters based on engineering requirements, including laser power P of 200W-600W, scanning speed V of 100mm / s-500mm / s, scanning spacing H of 80μm-200μm, and powder layer thickness T of 50μm-150μm;

[0014] S13. Using the orthogonal design method to select three representative process parameter combinations, the FCCZ lattice structure of titanium alloy was manufactured using the laser selective melting additive manufacturing technology with the selected process parameters;

[0015] S2. Calculate the laser energy density and measure the diameter of the lattice structure pillars:

[0016] S21, according to the formula Calculate the laser energy density E corresponding to different process parameter combinations, where P is the laser power in watts (W); V is the scanning speed in millimeters per second (mm / s); H is the scanning distance in millimeters (mm); and T is the powder layer thickness in millimeters (mm).

[0017] S22. Using CT scanning technology, scan the titanium alloy FCCZ lattice structure manufactured under different process parameters to obtain tomographic image data of its internal structure;

[0018] S23. Import the scan data into Avizo software for 3D visualization reconstruction. Using the software's image processing and measurement tools, select 10-20 pillars at different positions in each lattice structure for diameter measurement, and calculate the average value as the pillar diameter of the lattice structure.

[0019] S3. Fitting the relationship between laser energy density and the diameter of the pillars of the additively manufactured titanium alloy lattice structure:

[0020] S31, arranging the calculated laser energy density values ​​and the corresponding average values ​​of the diameters of the lattice structure pillars into a data file, with the first column being the laser energy density and the second column being the pillar diameters;

[0021] S32. Import the data file into Origin analysis software, select the linear regression fitting method, and fit the relationship between laser energy density and the pillar diameter of the additively manufactured titanium alloy FCCZ lattice structure according to the principle of least squares method. The formula D = 0.0083E + 0.6838, where D is the pillar diameter, E is the laser energy density, and a and b are coefficients to be determined. At the same time, obtain the fitting curve, fitting equation, and related statistical parameters;

[0022] S4. Prediction of the diameter of lattice structure pillars:

[0023] S41. For any new set of process parameters, first follow the formula Calculate its laser energy density E, and then substitute E into the prediction model formula D pred =0.0083E+0.6838, and the corresponding predicted value of the lattice structure pillar diameter D is obtained. pred ;

[0024] S42. Actual manufacture of a titanium alloy FCCZ lattice structure with new process parameters, and obtaining its actual pillar diameter D according to the above measurement method. actual ;

[0025] S43. Calculate relative error If the relative error is within the acceptable range determined according to specific engineering requirements, the prediction model is reliable; if the error is large, the process parameter range is readjusted.

[0026] Preferably, in S2, a temperature field distribution correction factor is introduced into the laser energy density calculation formula E, so that Where F(T) is the temperature field distribution function, which is used to compensate for the influence of the heat-affected zone on the actual laser energy density. T is the local temperature, T0 is the substrate temperature, T meltis the melting point of the titanium alloy, α is a parameter related to the temperature gradient, which indicates the rate of temperature drop when moving away from the center of the molten pool, β and γ are coefficients related to the change of thermal conductivity and specific heat capacity with temperature, which are used to adjust the degree of attenuation after the temperature exceeds the melting point, and σ is the standard deviation, which is used to control the width of the temperature distribution curve.

[0027] Preferably, in S23, artificial intelligence image recognition technology is used to automatically extract the pillar positions from the CT scan data and perform diameter measurement to reduce human errors.

[0028] Preferably, in said S32, in addition to linear regression fitting, nonlinear regression analysis is also used, including polynomial regression and exponential regression.

[0029] Preferably, in S41, an online monitoring system is established to monitor the laser energy density changes during the manufacturing process in real time, and dynamically adjust the prediction model parameters to ensure the accuracy of the prediction value.

[0030] Preferably, in S2, an adaptive algorithm is used to adjust the correction factor according to real-time data during the manufacturing process.

[0031] Preferably, in S2, the deformation behavior of the lattice structure during the molding process is predicted in combination with finite element analysis, and the design is optimized to reduce molding defects.

[0032] Preferably, in said S43, the relative error range of the prediction result is between 5% and 10%.

[0033] The beneficial effects of the present invention are:

[0034] By setting up a method for rapidly predicting the diameter of titanium alloy lattice structure struts in additive manufacturing, it is only necessary to count the diameters of titanium alloy lattice structure struts manufactured under a few different process parameters. The relationship formula can be fitted according to the method provided by the present invention to achieve the prediction of the diameter of titanium alloy lattice structure struts in additive manufacturing under any process parameters. The proposed formula is only related to the laser energy density and has the advantages of simple form and few parameters. In particular, for lattice structures manufactured with the same material under different process parameters, it can solve the problem of predicting the diameter of lattice structure struts of this type of material under different process parameters. The prediction through the formula can greatly save experimental materials, time and costs, improve efficiency, and is of great value for optimizing the diameter of lattice structure struts and high-quality and efficient manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of a method for predicting the diameter of a pillar in an additively manufactured lattice structure proposed in the present invention;

[0036] Figure 2This is a three-dimensional visualization reconstruction of CT data of a lattice structure for a method of predicting the diameter of a lattice structure pillar in additive manufacturing proposed by the present invention;

[0037] Figure 3 This is a schematic diagram of the relationship between laser energy density and lattice structure pillar diameter in a method for predicting the diameter of lattice structure pillars in additive manufacturing proposed by the present invention. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0039] Reference Figure 1-Figure 3 , a method for predicting the diameter of pillars in additively manufactured lattice structures, the operation steps are as follows:

[0040] S1. Selecting additive manufacturing process parameters to manufacture a lattice structure;

[0041] S11. Select TC4 titanium alloy as the manufacturing material and design the FCCZ lattice structure using CAD software, where the lattice structure is designed to have a diameter of 1 mm;

[0042] S12. Determine the range of additive manufacturing process parameters based on engineering requirements, including laser power P of 200W-600W, scanning speed V of 100mm / s-500mm / s, scanning spacing H of 80μm-200μm, and powder layer thickness T of 50μm-150μm;

[0043] When the laser power is less than 200W, the titanium alloy powder cannot be completely melted, resulting in poor formation of the lattice structure; when the laser power is greater than 600W, it will cause excessive melting and an excessively large heat-affected zone, affecting the accuracy and performance of the structure; S13. Use the orthogonal design method to select three groups of representative process parameter combinations, use laser selective melting additive manufacturing technology, and use the selected process parameters to manufacture titanium alloy FCCZ lattice structure. Combination 1: laser power P1=300W, scanning speed V1=200mm / s, scanning spacing H1=100μm, powder layer thickness T1=80μm; Combination 2: laser power P2=450W, scanning speed V2=300mm / s, scanning spacing H2=150μm, powder layer thickness T2=100μm; Combination 3: laser power P3=500W, scanning speed V3=400mm / s, scanning spacing H3=120μm, powder layer thickness T3=120μm. During the manufacturing process, strictly control the environmental conditions such as temperature, humidity and oxygen content; S2. Calculate the laser energy density and measure the diameter of the lattice structure pillars; S21. According to the formula Calculate the laser energy density E corresponding to different process parameter combinations, where P is the laser power in watts (W); V is the scanning speed in millimeters per second (mm / s); H is the scanning distance in millimeters (mm); and T is the powder layer thickness in millimeters (mm): For combination 1, For combination 2, For combination 2, S22. Using CT scanning technology, scan the titanium alloy FCCZ lattice structures manufactured under different process parameters to obtain tomographic image data of their internal structures, using a medical CT scanner with scanning parameters set to a voltage of 120 kV, a current of 200 mA, and a scanning resolution of 0.1 mm;

[0044] S23. Import the scan data into Avizo software for 3D visualization reconstruction. Using the software's image processing and measurement tools, select 10-20 pillars at different positions in each lattice structure for diameter measurement, and calculate the average value as the pillar diameter of the lattice structure.

[0045] For the lattice structure manufactured by combination 1, pillars at 15 positions were randomly selected for measurement, and the diameters were [0.8mm, 0.78mm, 0.82mm, 0.79mm, 0.81mm, 0.8mm, 0.77mm, 0.83mm, 0.81mm, 0.79mm, 0.8mm, 0.82mm, 0.78mm, 0.81mm, 0.79mm], and the average value was calculated as

[0046]

[0047] For the lattice structure manufactured by combination 2, 18 pillars were selected for measurement, and the diameters were [0.9mm, 0.88mm, 0.92mm, 0.89mm, 0.91mm, 0.9mm, 0.87mm, 0.93mm, 0.91mm, 0.89mm, 0.9mm, 0.92mm, 0.88mm, 0.91mm, 0.89mm, 0.9mm, 0.91mm, 0.92mm], and the average value was calculated as

[0048] For the lattice structure manufactured by combination three, 16 pillar positions were selected for measurement, and the diameters were [0.85mm, 0.83mm, 0.87mm, 0.84mm, 0.86mm, 0.85mm, 0.82mm, 0.88mm, 0.86mm, 0.84mm, 0.85mm, 0.87mm, 0.83mm, 0.86mm, 0.84mm, 0.85mm], and the average value was calculated as follows:

[0049]

[0050] S3. Fitting the relationship between laser energy density and the diameter of the pillars of the additively manufactured titanium alloy lattice structure:

[0051] S31. The calculated laser energy density values ​​and the corresponding average values ​​of the diameters of the lattice structure pillars are organized into a data file, with the first column representing the laser energy density and the second column representing the pillar diameters, as follows:

[0052] <![CDATA[Laser energy density E (W / mm 3 )]]> Pillar diameter D(mm) 18.75 0.8 10 0.9 8.68 0.85 ;

[0053] S32. Import the data file into the Origin analysis software, select the linear regression fitting method, and fit the relationship between laser energy density and the pillar diameter of the additively manufactured titanium alloy FCCZ lattice structure according to the least squares principle. The formula D = 0.0083E + 0.6838, where D is the pillar diameter and E is the laser energy density;

[0054] S4. Prediction of the diameter of lattice structure pillars:

[0055] S41. For any new set of process parameters, first follow the formula Calculate its laser energy density E, and then substitute E into the prediction model formula D pred =αE+b, and the corresponding predicted value of the lattice structure pillar diameter D is obtained. pred , the process parameters are laser power P4 = 400W, scanning speed V4 = 250mm / s, scanning spacing H4 = 120μm, powder layer thickness T4 = 100μm, then the laser energy density Substitute E4 into the quadratic polynomial regression equation D = -0.00003E 2 +0.014E+0.53 to get the predicted value of pillar diameter: D pred =-0.00003*13.33 2 +0.014*13.33+0.53≈0.716mm;

[0056] S42. Actual manufacture of a titanium alloy FCCZ lattice structure with new process parameters, and obtaining its actual pillar diameter D according to the above measurement method. actual The actual pillar diameter was obtained by CT scanning and measurement with Avizo software. actual =0.7mm;

[0057] S43. Calculate relative error If the relative error is within the acceptable range determined according to the specific engineering requirements, the prediction model is reliable; if the error is large, the process parameter range is readjusted and the relative error is calculated according to the above formula: In this embodiment, the relative error range of the prediction result is between 5% and 10%. The relative error of this prediction is 2.88%, which is within an acceptable range.

[0058] In this embodiment, in S2, the temperature field distribution correction factor is introduced into the laser energy density calculation formula E, so that Where F(T) is the temperature field distribution function, which is used to compensate for the influence of the heat-affected zone on the actual laser energy density. T is the local temperature, T0 is the substrate temperature, T melt is the melting point of the titanium alloy, α is a parameter related to the temperature gradient, which indicates the rate of temperature drop when moving away from the center of the molten pool, β and γ are coefficients related to the change of thermal conductivity and specific heat capacity with temperature, which are used to adjust the degree of attenuation after the temperature exceeds the melting point, and σ is the standard deviation, which is used to control the width of the temperature distribution curve.

[0059] Specifically, the substrate temperature T0 is 30°C, the melting point of the titanium alloy is T melt The temperature at different locations is 1720°C near the center of the melt pool, 1680°C at 1mm from the center, and 1650°C at 2mm from the center.

[0060] The temperature field distribution function value near the center of the molten pool:

[0061]

[0062] The temperature field distribution function value 1mm near the center of the molten pool:

[0063]

[0064] The temperature field distribution function value 2mm near the center of the molten pool:

[0065]

[0066] The value of the temperature field distribution function F(T) is calculated based on these data, and then the value in the laser energy density calculation formula is corrected.

[0067] In this embodiment, in S23, artificial intelligence image recognition technology is used to automatically extract the pillar position from the CT scan data and perform diameter measurement to reduce human error;

[0068] Specifically, the traditional manual measurement method was used to measure the pillar diameter of a lattice structure. 15 positions were selected, and the average diameter obtained was D1 = 0.85 mm. The measurement process took 30 minutes. Then, artificial intelligence image recognition technology was used to measure the same lattice structure, and the average diameter obtained was D2 = 0.83 mm. The measurement process only took 5 minutes. Comparing and, the difference between the two is within an acceptable range, and artificial intelligence image recognition technology has greatly improved the measurement efficiency.

[0069] In this embodiment, in S32, in addition to linear regression fitting, nonlinear regression analysis is also used, including polynomial regression and exponential regression;

[0070] Specifically, the laser energy densities are [30, 42, 48.5, 58, 67.8], and the corresponding pillar diameters are measured to be [0.93, 1.02, 1.06, 1.09, 1.22]. The linear regression equation is D = 0.0083E + 0.6838, and the coefficient of determination R 2 =0.839;

[0071] The polynomial regression fitting equation is D = -0.00003E 2 +0.014E+0.53, coefficient of determination R 2 =0.459;

[0072] The exponential regression fitting equation is D = 0.727e 0.0077E , coefficient of determination R 2 =-1.37. After the above three regression methods, the determination coefficient of linear regression is the highest and is more suitable for this group of data.

[0073] In this embodiment, in S41, an online monitoring system is established to monitor the laser energy density changes during the manufacturing process in real time, and dynamically adjust the prediction model parameters to ensure the accuracy of the prediction value.

[0074] Specifically, a set of process parameters were set for manufacturing, including laser power of 400W, scanning speed of 250mm / s, scanning spacing of 120μm, and powder layer thickness of 100μm. The online monitoring system detected in real time that the laser power had slight fluctuations during the manufacturing process, ranging from 400W to 410W, and the scanning speed dropped to 245mm / s due to equipment reasons. The system dynamically adjusted the prediction model parameters based on real-time data, and recalculated the laser energy density and pillar diameter prediction values. When the online monitoring system was not used, the predicted pillar diameter was 0.8mm, and the actual measured value was 0.78mm, with an error of 2.56%. After using the online monitoring system, the predicted pillar diameter was 0.79mm, with an error of 1.28%. After using the online monitoring system, the prediction error of the pillar diameter was significantly reduced, and the prediction accuracy was improved.

[0075] In this embodiment, in S2, an adaptive algorithm is used to adjust the correction factor according to real-time data during the manufacturing process.

[0076] Specifically, the initial process parameters were a laser power of 350W, a scanning speed of 200mm / s, a scanning pitch of 100μm, and a powder layer thickness of 80μm. The calculated laser energy density was E1. During the manufacturing process, the real-time monitoring system detected that the laser power rose to 360W due to equipment fluctuations, and the scanning speed dropped slightly to 195mm / s. The adaptive algorithm adjusted the parameters in the temperature field distribution correction factor based on this real-time data and recalculated the laser energy density to E2. A comparison of the two shows the dynamic adjustment effect of the adaptive algorithm on the laser energy density.

[0077] In this embodiment, in S2, the deformation behavior of the lattice structure during the molding process is predicted in combination with finite element analysis, and the design is optimized to reduce molding defects.

[0078] Specifically, a set of process parameters were set: laser power 450W, scanning speed 300mm / s, scanning spacing 150μm, and powder layer thickness 120μm. The simulation showed that the maximum deformation of the lattice structure during the forming process was 0.4mm, and the stress concentration area was mainly distributed at the connection between the pillars and the nodes. According to the analysis results, the connection method between the pillars and the nodes was improved through optimization design, and the transition radius was increased. Finite element analysis was performed again, and it was found that the maximum deformation was reduced to 0.25mm, and the stress concentration was also significantly improved.

[0079] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for predicting the diameter of pillars in additively manufactured lattice structures, characterized by: The steps are as follows: S1. Selecting additive manufacturing process parameters to manufacture a lattice structure; S11. Select TC4 titanium alloy as the manufacturing material and design the FCCZ lattice structure using CAD software, where the lattice structure is designed to have a diameter of 1 mm; S12. Determine the range of additive manufacturing process parameters based on engineering requirements, including laser power P of 200W-600W, scanning speed V of 100mm / s-500mm / s, scanning spacing H of 80μm-200μm, and powder layer thickness T of 50μm-150μm; S13. Using the orthogonal design method to select three representative process parameter combinations, the FCCZ lattice structure of titanium alloy was manufactured using the laser selective melting additive manufacturing technology with the selected process parameters; S2. Calculate the laser energy density and measure the diameter of the lattice structure pillars: S21, according to the formula Calculate the laser energy density E corresponding to different process parameter combinations, where P is the laser power in watts (W); V is the scanning speed in millimeters per second (mm / s); H is the scanning distance in millimeters (mm); and T is the powder layer thickness in millimeters (mm). S22. Using CT scanning technology, scan the titanium alloy FCCZ lattice structure manufactured under different process parameters to obtain tomographic image data of its internal structure; S23. Import the scan data into Avizo software for 3D visualization reconstruction. Using the software's image processing and measurement tools, select 10-20 pillars at different positions in each lattice structure for diameter measurement, and calculate the average value as the pillar diameter of the lattice structure. S3. Fitting the relationship between laser energy density and the diameter of the pillars of the additively manufactured titanium alloy lattice structure: S31, arranging the calculated laser energy density values ​​and the corresponding average values ​​of the diameters of the lattice structure pillars into a data file, with the first column being the laser energy density and the second column being the pillar diameters; S32. Import the data file into the Origin analysis software, select the linear regression fitting method, and fit the relationship formula between the laser energy density and the pillar diameter of the additively manufactured titanium alloy FCCZ lattice structure according to the principle of least squares. , where D is the pillar diameter and E is the laser energy density; S4. Prediction of the diameter of lattice structure pillars: S41. For any new set of process parameters, first follow the formula , calculate its laser energy density E, and then substitute E into the prediction model formula , and obtain the corresponding predicted value of the lattice structure pillar diameter ; S42. Actual manufacture of titanium alloy FCCZ lattice structure with new process parameters, and obtaining its actual pillar diameter according to the above measurement method ; S43. Calculate relative error If the relative error is within the acceptable range determined according to the specific engineering requirements, the prediction model is reliable; if the error is large, the process parameter range is readjusted. In S43, the relative error range of the prediction result is between 5% and 10%.

2. The method for predicting the diameter of a pillar in an additively manufactured lattice structure according to claim 1, wherein: In S2, the temperature field distribution correction factor is introduced into the laser energy density calculation formula E, so that , where F(T) is the temperature field distribution function, which is used to compensate for the influence of the heat-affected zone on the actual laser energy density. , T is the local temperature, T0 is the substrate temperature, T melt is the melting point of titanium alloy, It is a parameter related to the temperature gradient, which indicates the speed at which the temperature drops away from the center of the molten pool. and It is a coefficient related to the change of thermal conductivity and specific heat capacity with temperature, which is used to adjust the attenuation degree after the temperature exceeds the melting point. is the standard deviation, which is used to control the width of the temperature distribution curve.

3. The method for predicting the diameter of a pillar in an additively manufactured lattice structure according to claim 1, wherein: In S23, artificial intelligence image recognition technology is used to automatically extract the pillar position from the CT scan data and measure the diameter, thereby reducing human error.

4. The method for predicting the diameter of a pillar in an additively manufactured lattice structure according to claim 1, wherein: In the S32, in addition to the linear regression fitting, nonlinear regression analysis is also used, including polynomial regression and exponential regression.

5. The method for predicting the diameter of a pillar in an additively manufactured lattice structure according to claim 1, wherein: In S41, an online monitoring system is established to monitor the laser energy density changes during the manufacturing process in real time, and dynamically adjust the prediction model parameters to ensure the accuracy of the prediction value.

6. The method for predicting the diameter of a pillar in an additively manufactured lattice structure according to claim 1, wherein: In S2, an adaptive algorithm is used to adjust the correction factor according to real-time data during the manufacturing process.

7. The method for predicting the diameter of a pillar in an additively manufactured lattice structure according to claim 1, wherein: In S2, the deformation behavior of the lattice structure during the molding process is predicted by combining finite element analysis, and the design is optimized to reduce molding defects.

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