A 3D printing data detection method

Abstract

The application discloses a kind of 3D printing data detection methods, it is related to 3D printing detection technical field, comprising the following steps: S1, the three-dimensional model to be printed is sliced and layered, obtains several two-dimensional cross section graphics;S2, to several two-dimensional cross section graphics is laser scanning printing, generates energy modulation term and molten pool dynamic modulation term, obtains the metal printing risk quantization value of each two-dimensional cross section graphic;S3, according to the metal printing risk quantization value of each two-dimensional cross section graphic, determines printing fault detection result.The application comprehensively considers laser energy density variation rate, powder layer thickness, laser effective area, molten pool solidification time and other key process parameters, the influence of energy input stability on unmelted risk is described by energy modulation term, the role of overmelting risk is reflected by molten pool dynamic modulation term, and the core influencing factors of printing quality are comprehensively covered, to provide more reliable basis for fault prediction.

Description

Technical Field

[0001] This invention relates to the field of 3D printing inspection technology, and more specifically to a 3D printing data inspection method. Background Technology

[0002] Metal 3D printing technology, with its core advantages such as near-net-shape forming and strong ability to manufacture complex structures, has been widely applied in key fields such as aerospace (e.g., engine blades), medical implants (e.g., titanium alloy prostheses), and high-end equipment (e.g., precision gears). Its core principle is to form parts by slicing and layering layers and then melting and stacking them with a laser beam. The stability of the printing quality directly determines the performance of the parts. For example, the porosity of aerospace parts needs to be controlled below 1%, and the density of medical implants needs to be ≥99.5%; otherwise, serious problems such as structural failure and decreased biocompatibility may occur.

[0003] However, significant bottlenecks still exist in the quality control and fault detection aspects of current metal 3D printing technology, specifically in the following two aspects: First, traditional detection methods are outdated, leading to resource waste due to post-printing scrapping: Current quality inspection of metal 3D printing largely relies on offline inspection after printing, such as metallographic microscopy to observe porosity, CT scan analysis of internal defects, and tensile tests to verify mechanical properties. These methods have limitations: defect location is vague; offline inspection can only determine the presence of defects, not accurately pinpoint the specific cross-sectional area where the defects are located. Second, the multi-parameter coupling effect is ignored: defects in metal 3D printing are the result of the coupling effect of multiple factors, including laser parameters, powder characteristics, and process conditions. Current monitoring often focuses on a single parameter (such as only looking at laser power), without establishing a quantitative correlation model between multiple parameters and defect risk, thus limiting the practicality of the monitoring results. Summary of the Invention

[0004] To address the above problems, this invention proposes a 3D printing data detection method.

[0005] The technical solution of the present invention is: a 3D printing data detection method comprising the following steps:

[0006] S1. Slice and layer the 3D model to be printed to obtain several 2D cross-sectional shapes;

[0007] S2. Perform laser scanning printing on several two-dimensional cross-sectional shapes to generate energy modulation terms and molten pool dynamic modulation terms, and obtain the metal printing risk quantification value of each two-dimensional cross-sectional shape;

[0008] S3. Determine the printing fault detection results based on the metal printing risk quantification value of each two-dimensional cross-sectional graphic.

[0009] Furthermore, S2 includes the following sub-steps:

[0010] S21. Based on the two-dimensional cross-sectional shape, generate the printing layer using the powder spreading operation, and determine the powder layer thickness of the printing layer;

[0011] S22. Perform laser scanning on the printed layer to determine the final effective area of ​​the laser;

[0012] S23. After completing the laser scanning, a molten pool is formed and solidified, and the solidification time of the molten pool is determined.

[0013] S24. Calculate the energy modulation term based on the powder layer thickness of the printed layer, the final effective area of ​​the laser, and the actual energy density of the laser scan;

[0014] S25. Calculate the dynamic modulation term of the molten pool based on the solidification time of the molten pool and the actual energy density of the laser scan;

[0015] S26. Add the energy modulation term and the molten pool dynamic modulation term to obtain the metal printing risk quantification value of the two-dimensional cross-sectional shape.

[0016] The beneficial effects of the above-mentioned further solutions are as follows: In this invention, powder spreading is the basic layer preparation step in metal 3D printing. Metal powder is evenly spread into the forming cylinder using a powder spreading device (such as a powder spreading roller and a scraper) to form the current printing layer. During powder spreading, parameters such as the stroke of the powder spreading device and the height of the scraper determine the thickness of each powder layer. For example, if the powder spreading thickness is set to 30 μm, the powder spreading device will spread a powder layer approximately 30 μm thick on the surface of the forming cylinder. The layer thickness directly determines the amount of powder that the laser needs to melt; thicker layers require more laser energy, while thinner layers require relatively less. The bulk density of the powder is determined by the original characteristics of the powder (such as particle size distribution and particle shape) and the powder spreading process. Powders with high sphericity and a reasonable particle size distribution have a higher bulk density after powder spreading; vibration and compaction operations during powder spreading also affect the bulk density. Bulk density reflects the compactness of the powder packing; the higher the density, the greater the interparticle thermal resistance that needs to be overcome during laser melting, and the more energy is required.

[0017] The effective area of ​​the laser is a key parameter for the laser energy acting on the powder layer. Its size affects the distribution of energy density. An inaccurate effective area will cause deviations in energy density calculation, resulting in insufficient or excessive melting. The solidification time of the molten pool reflects the speed at which the molten pool transforms from a liquid to a solid state. The solidification time is related to energy input, powder characteristics, etc. If solidification is too fast, gas may not have enough time to escape, forming pores. If it is too slow, it may lead to problems such as coarse grains.

[0018] Therefore, the energy modulation term integrates the powder layer thickness (determining the total energy required), the final effective area of ​​the laser (determining the energy distribution range), and the time-varying rate of change of laser energy density (reflecting energy stability), accurately characterizing the impact of energy factors on printing risks. The molten pool dynamic modulation term combines energy density, powder layer thickness, metal powder density (reflecting the quality characteristics of powder accumulation), and molten pool solidification time, comprehensively considering risk factors in the dynamic process of the molten pool. Finally, the two modulation terms are added together to integrate the risks from both energy and molten pool dynamics, resulting in a more comprehensive quantitative value for the printing risks of two-dimensional cross-sections.

[0019] The risk quantification process of 3D printing is broken down into several specific and key steps. Each step targets the core process of metal 3D printing (powder spreading, laser action, and molten pool solidification), transforming risk quantification from an abstract concept into an operable process.

[0020] Furthermore, S22 includes the following sub-steps:

[0021] S221. Perform laser scanning on the printed layer and determine the preliminary effective area based on the effective spot diameter of the laser scanning on the powder layer.

[0022] S222. Collect the distance between the powder layer and the ideal focal plane, as the defocusing amount;

[0023] S223. Determine the Rayleigh length during the laser scanning process;

[0024] S224. Use the ratio between the defocus amount and the Rayleigh length as the setting correction coefficient;

[0025] S225. The initial effective area is corrected using a set correction coefficient to obtain the final effective area of ​​the laser.

[0026] The beneficial effect of the above-mentioned further solution is that, in this invention, in S221, the preliminary effective area is calculated using the area formula of a circle. The optimal process state for metal 3D printing is that the powder bed surface of the current printing layer completely coincides with the ideal focal plane of the laser. In actual printing, due to factors such as powder spreading accuracy and equipment errors, the powder bed surface often deviates from the ideal focal plane (i.e., defocusing). In laser technology (especially in Gaussian beam applications involved in metal 3D printing), the Rayleigh length is a core parameter describing the slowness of the laser beam divergence from its narrowest point (beam waist), directly determining the range within which laser energy remains concentrated in space.

[0027] In metal 3D printing, lasers propagate as Gaussian beams. The characteristics of a Gaussian beam determine that its effective area varies with the defocusing amount (the distance between the powder layer and the ideal focal plane). First, the initial effective area is determined based on the effective spot radius, which is an approximation of the effective area under the ideal focal plane. Then, the defocusing amount is an unavoidable parameter in actual printing; defocusing causes the laser spot to diverge or converge, altering the effective area. The Rayleigh length is a crucial parameter of a Gaussian beam, reflecting the degree to which the beam diverges slowly from the beam waist (ideal focal plane). A longer Rayleigh length results in better beam collimation and less impact from defocusing. A correction coefficient is obtained by calculating the ratio of the defocusing amount to the Rayleigh length. This coefficient reflects the degree to which defocusing corrects the effective area. Finally, this correction coefficient is used to refine the initial effective area, making the calculated final effective laser area more consistent with the actual beam propagation in printing.

[0028] Taking into full account the physical characteristics of laser propagation (Gaussian beam, defocusing effect, Rayleigh length), the corrected final effective area of ​​the laser can more realistically reflect the energy range of the laser on the actual powder layer, providing more accurate basic parameters for subsequent calculations of energy modulation terms, etc.

[0029] Furthermore, in S225, the final effective area of ​​the laser... The expression is:

[0030] ;

[0031] in, Indicates the preliminary effective area. This indicates that a correction factor is set.

[0032] The beneficial effects of the above-mentioned further scheme are: In this invention, the preliminary effective area represents the effective area under the ideal focal plane; the setting correction coefficient is the ratio of the defocus amount to the Rayleigh length, which reflects the secondary correction relationship of the defocus amount on the effective area and conforms to the mathematical law of the change of the spot area of ​​the Gaussian beam with the defocus amount after defocusing.

[0033] Furthermore, in S24, the energy modulation term The expression is:

[0034] ;

[0035] in, This represents the first empirical coefficient. This indicates the actual energy density of the laser scan. Indicates the thickness of the powder layer. Indicates the final effective area of ​​the laser. Indicates duration, This indicates the absorption rate of the laser by the metal powder used for laser coating.

[0036] The beneficial effect of the above further scheme is that, in this invention, K1 is a first empirical coefficient with dimensions m. 3 ⋅s 3 / kg, obtained through experimental calibration by similarly changing the rate of change of energy density and measuring the risk of non-fusion. This is the rate of change of the actual energy density during laser scanning. Rapid changes in energy density lead to drastic fluctuations in the molten pool temperature, disrupting melting stability and becoming a significant factor in causing defects such as incomplete fusion. The powder layer thickness, the absorption rate of the metal powder to the laser, and the final effective area of ​​the laser beam together constitute the fundamental energy conditions for laser-powder interaction. (First empirical coefficient) Used to calibrate the deviation between theoretical calculations and actual processes, and through experimental calibration, the energy modulation term can more accurately reflect the energy-related printing risks in the actual process.

[0037] Furthermore, in S25, the molten pool dynamic modulation term The expression is:

[0038] ;

[0039] in, This represents the second empirical coefficient. This indicates the actual energy density of the laser scan. This indicates the bulk density of the metal powder used for powder coating. Indicates the thickness of the powder layer. This indicates the solidification time of the molten pool.

[0040] The beneficial effect of the above further scheme is that, in this invention, K2 is a second empirical coefficient with dimensions m. 4 ⋅s 3 / kg 2 This reflects the sensitivity of the powder melting-solidification process to over-melting under unit energy density. It is obtained through experimental calibration by fixing the energy modulation term, changing the energy density, and measuring the risk of over-melting. The actual energy density of the laser scan is the energy source for molten pool formation; the powder layer thickness, metal powder density, and molten pool solidification time reflect the material and time characteristics during molten pool formation and solidification. The combination of energy density and material-time characteristics can characterize the dynamic changes of the molten pool under energy input. If the energy does not match these characteristics, it may lead to defects such as over-melting and poor solidification. Second empirical coefficient Through experimental calibration, the dynamic modulation of the molten pool can more accurately reflect the dynamic printing risks related to the molten pool in the actual process.

[0041] Furthermore, in S3, the calculation of the first... The quantification value of metal printing risk for the first two-dimensional cross-sectional graphic and the first The ratio between the quantified risk values ​​of metal printing of each two-dimensional cross-sectional shape is used to calculate the first... The quantification value of metal printing risk for the first two-dimensional cross-sectional graphic and the first The ratio between the metal printing risk quantification values ​​of each two-dimensional cross-sectional graphic is used to determine the risk level. If either ratio exceeds a set threshold, then the risk level is determined to be... A printing error occurred with a two-dimensional cross-sectional graphic.

[0042] The beneficial effects of the above-mentioned further solution are as follows: In this invention, the interlayer bonding strength of metal 3D printing depends on the interaction of the molten pools of adjacent layers. An abnormal printing risk quantification value of a certain layer will affect the printing quality of adjacent layers, exhibiting interlayer correlation. By calculating the ratio of the metal printing risk quantification value of the i-th two-dimensional cross-section pattern to that of the (i-1)-th and (i+1)-th two-dimensional cross-section patterns, the relationship between the changes in risk quantification values ​​between adjacent layers can be reflected. If these ratios exceed a set threshold, it indicates that the risk change of the i-th cross-section and adjacent layers is abnormal, and there may be printing failures such as interlayer bonding defects.

[0043] The beneficial effects of this invention are:

[0044] (1) This invention follows the essence of metal 3D printing by layering. After slicing the three-dimensional model into layers, the energy modulation term and the dynamic modulation term of the molten pool are calculated for each two-dimensional cross-sectional shape. This achieves a fine risk decomposition from the three-dimensional whole to the two-dimensional local. Potential fault layers can be located in advance, avoiding the scrapping of the whole printing due to single-layer defects, and significantly improving the printing success rate.

[0045] (2) This invention comprehensively considers key process parameters such as laser energy density change rate, powder layer thickness, laser effective area, and molten pool solidification time. It characterizes the influence of energy input stability on the risk of non-fusion through energy modulation term and reflects the role of over-melting risk through molten pool dynamic modulation term. It comprehensively covers the core influencing factors of printing quality and provides a more reliable basis for fault prediction. Attached Figure Description

[0046] Figure 1 This is a flowchart of a 3D printing data detection method. Detailed Implementation

[0047] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0048] like Figure 1 As shown, the present invention provides a 3D printing data detection method, including the following steps:

[0049] S1. Slice and layer the 3D model to be printed to obtain several 2D cross-sectional shapes;

[0050] S2. Perform laser scanning printing on several two-dimensional cross-sectional shapes to generate energy modulation terms and molten pool dynamic modulation terms, and obtain the metal printing risk quantification value of each two-dimensional cross-sectional shape;

[0051] S3. Determine the printing fault detection results based on the metal printing risk quantification value of each two-dimensional cross-sectional graphic.

[0052] In this embodiment of the invention, S2 includes the following sub-steps:

[0053] S21. Based on the two-dimensional cross-sectional shape, generate the printing layer using the powder spreading operation, and determine the powder layer thickness of the printing layer;

[0054] S22. Perform laser scanning on the printed layer to determine the final effective area of ​​the laser;

[0055] S23. After completing the laser scanning, a molten pool is formed and solidified, and the solidification time of the molten pool is determined.

[0056] S24. Calculate the energy modulation term based on the powder layer thickness of the printed layer, the final effective area of ​​the laser, and the actual energy density of the laser scan;

[0057] S25. Calculate the dynamic modulation term of the molten pool based on the solidification time of the molten pool and the actual energy density of the laser scan;

[0058] S26. Add the energy modulation term and the molten pool dynamic modulation term to obtain the metal printing risk quantification value of the two-dimensional cross-sectional shape.

[0059] In this invention, powder spreading is the foundational layer preparation step in metal 3D printing. Metal powder is evenly spread into the forming cylinder using a powder spreading device (such as a powder spreading roller and a scraper) to form the current printing layer. During powder spreading, parameters such as the stroke of the powder spreading device and the height of the scraper determine the thickness of each powder layer. For example, setting the powder spreading thickness to 30 μm will result in a powder layer approximately 30 μm thick being spread on the surface of the forming cylinder. Layer thickness directly determines the amount of powder that the laser needs to melt; thicker layers require more laser energy, while thinner layers require relatively less. The bulk density of the powder is determined by its original characteristics (such as particle size distribution and particle shape) and the powder spreading process. Powders with high sphericity and a reasonable particle size distribution have a higher bulk density after spreading; vibration and compaction during powder spreading also affect the bulk density. Bulk density reflects the compactness of the powder packing; the higher the density, the greater the interparticle thermal resistance that needs to be overcome during laser melting, and the more energy is required.

[0060] The effective area of ​​the laser is a key parameter for the laser energy acting on the powder layer. Its size affects the distribution of energy density. An inaccurate effective area will cause deviations in energy density calculation, resulting in insufficient or excessive melting. The solidification time of the molten pool reflects the speed at which the molten pool transforms from a liquid to a solid state. The solidification time is related to energy input, powder characteristics, etc. If solidification is too fast, gas may not have enough time to escape, forming pores. If it is too slow, it may lead to problems such as coarse grains.

[0061] Therefore, the energy modulation term integrates the powder layer thickness (determining the total energy required), the final effective area of ​​the laser (determining the energy distribution range), and the time-varying rate of change of laser energy density (reflecting energy stability), accurately characterizing the impact of energy factors on printing risks. The molten pool dynamic modulation term combines energy density, powder layer thickness, metal powder density (reflecting the quality characteristics of powder accumulation), and molten pool solidification time, comprehensively considering risk factors in the dynamic process of the molten pool. Finally, the two modulation terms are added together to integrate the risks from both energy and molten pool dynamics, resulting in a more comprehensive quantitative value for the printing risks of two-dimensional cross-sections.

[0062] The risk quantification process of 3D printing is broken down into several specific and key steps. Each step targets the core process of metal 3D printing (powder spreading, laser action, and molten pool solidification), transforming risk quantification from an abstract concept into an operable process.

[0063] In this embodiment of the invention, S22 includes the following sub-steps:

[0064] S221. Perform laser scanning on the printed layer and determine the preliminary effective area based on the effective spot diameter of the laser scanning on the powder layer.

[0065] S222. Collect the distance between the powder layer and the ideal focal plane, as the defocusing amount;

[0066] S223. Determine the Rayleigh length during the laser scanning process;

[0067] S224. Use the ratio between the defocus amount and the Rayleigh length as the setting correction coefficient;

[0068] S225. The initial effective area is corrected using a set correction coefficient to obtain the final effective area of ​​the laser.

[0069] In this invention, in step S221, the preliminary effective area is calculated using the formula for the area of ​​a circle. The optimal process state for metal 3D printing is when the powder bed surface of the current printing layer completely coincides with the ideal focal plane of the laser. In actual printing, due to factors such as powder spreading accuracy and equipment errors, the powder bed surface often deviates from the ideal focal plane (i.e., defocusing). In laser technology (especially in Gaussian beam applications involved in metal 3D printing), the Rayleigh length is a core parameter describing the slowness of the laser beam divergence from its narrowest point (beam waist), directly determining the range within which laser energy remains concentrated in space.

[0070] In metal 3D printing, lasers propagate as Gaussian beams. The characteristics of a Gaussian beam determine that its effective area varies with the defocusing amount (the distance between the powder layer and the ideal focal plane). First, the initial effective area is determined based on the effective spot radius, which is an approximation of the effective area under the ideal focal plane. Then, the defocusing amount is an unavoidable parameter in actual printing; defocusing causes the laser spot to diverge or converge, altering the effective area. The Rayleigh length is a crucial parameter of a Gaussian beam, reflecting the degree to which the beam diverges slowly from the beam waist (ideal focal plane). A longer Rayleigh length results in better beam collimation and less impact from defocusing. A correction coefficient is obtained by calculating the ratio of the defocusing amount to the Rayleigh length. This coefficient reflects the degree to which defocusing corrects the effective area. Finally, this correction coefficient is used to refine the initial effective area, making the calculated final effective laser area more consistent with the actual beam propagation in printing.

[0071] Taking into full account the physical characteristics of laser propagation (Gaussian beam, defocusing effect, Rayleigh length), the corrected final effective area of ​​the laser can more realistically reflect the energy range of the laser on the actual powder layer, providing more accurate basic parameters for subsequent calculations of energy modulation terms, etc.

[0072] In this embodiment of the invention, in S225, the final effective area of ​​the laser... The expression is:

[0073] ;

[0074] in, Indicates the preliminary effective area. This indicates that a correction factor is set.

[0075] In this invention, the initial effective area represents the effective area under the ideal focal plane; the set correction coefficient is the ratio of the defocus amount to the Rayleigh length, which reflects the secondary correction relationship of the defocus amount on the effective area and conforms to the mathematical law of the change of the spot area of ​​the Gaussian beam with the defocus amount after defocusing.

[0076] In this embodiment of the invention, in S24, the energy modulation term The expression is:

[0077] ;

[0078] in, This represents the first empirical coefficient. This indicates the actual energy density of the laser scan. Indicates the thickness of the powder layer. Indicates the final effective area of ​​the laser. Indicates duration, This indicates the absorption rate of the laser by the metal powder used for laser coating.

[0079] In this invention, K1 is a first empirical coefficient with dimensions m. 3 ⋅s 3 / kg, obtained through experimental calibration by similarly changing the rate of change of energy density and measuring the risk of non-fusion. This is the rate of change of the actual energy density during laser scanning. Rapid changes in energy density lead to drastic fluctuations in the molten pool temperature, disrupting melting stability and becoming a significant factor in causing defects such as incomplete fusion. The powder layer thickness, the absorption rate of the metal powder to the laser, and the final effective area of ​​the laser beam together constitute the fundamental energy conditions for laser-powder interaction. (First empirical coefficient) Used to calibrate the deviation between theoretical calculations and actual processes, and through experimental calibration, the energy modulation term can more accurately reflect the energy-related printing risks in the actual process.

[0080] In this embodiment of the invention, in S25, the molten pool dynamic modulation term The expression is:

[0081] ;

[0082] in, This represents the second empirical coefficient. This indicates the actual energy density of the laser scan. This indicates the bulk density of the metal powder used for powder coating. Indicates the thickness of the powder layer. This indicates the solidification time of the molten pool.

[0083] In this invention, K2 is a second empirical coefficient with dimensions m. 4 ⋅s 3 / kg 2This reflects the sensitivity of the powder melting-solidification process to over-melting under unit energy density. It is obtained through experimental calibration by fixing the energy modulation term, changing the energy density, and measuring the risk of over-melting. The actual energy density of the laser scan is the energy source for molten pool formation; the powder layer thickness, metal powder density, and molten pool solidification time reflect the material and time characteristics during molten pool formation and solidification. The combination of energy density and material-time characteristics can characterize the dynamic changes of the molten pool under energy input. If the energy does not match these characteristics, it may lead to defects such as over-melting and poor solidification. Second empirical coefficient Through experimental calibration, the dynamic modulation of the molten pool can more accurately reflect the dynamic printing risks related to the molten pool in the actual process.

[0084] In this embodiment of the invention, in S3, the calculation of the first... The quantification value of metal printing risk for the first two-dimensional cross-sectional graphic and the first The ratio between the quantified risk values ​​of metal printing of each two-dimensional cross-sectional shape is used to calculate the first... The quantification value of metal printing risk for the first two-dimensional cross-sectional graphic and the first The ratio between the metal printing risk quantification values ​​of each two-dimensional cross-sectional graphic is used to determine the risk level. If either ratio exceeds a set threshold, then the risk level is determined to be... A printing error occurred with a two-dimensional cross-sectional graphic.

[0085] In this invention, the interlayer bonding strength in metal 3D printing depends on the interaction of the molten pools of adjacent layers. An abnormal printing risk quantification value in one layer can affect the printing quality of adjacent layers, exhibiting interlayer correlation. By calculating the ratio of the metal printing risk quantification value of the i-th two-dimensional cross-section to that of the (i-1)-th and (i+1)-th two-dimensional cross-sections, the relationship between the risk quantification values ​​of adjacent layers can be reflected. If these ratios exceed a set threshold, it indicates an abnormal change in the risk of the i-th cross-section and its adjacent layers, potentially indicating printing failures such as interlayer bonding defects.

[0086] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for detecting 3D printing data, characterized in that, Includes the following steps: S1. Slice and layer the 3D model to be printed to obtain several 2D cross-sectional shapes; S2. Perform laser scanning printing on several two-dimensional cross-sectional shapes to generate energy modulation terms and molten pool dynamic modulation terms, and obtain the metal printing risk quantification value of each two-dimensional cross-sectional shape; S3. Determine the printing failure detection results based on the metal printing risk quantification value of each two-dimensional cross-sectional graphic; S2 includes the following sub-steps: S21. Based on the two-dimensional cross-sectional shape, generate the printing layer using the powder spreading operation, and determine the powder layer thickness of the printing layer; S22. Perform laser scanning on the printed layer to determine the final effective area of ​​the laser; S23. After completing the laser scanning, a molten pool is formed and solidified, and the solidification time of the molten pool is determined. S24. Calculate the energy modulation term based on the powder layer thickness of the printed layer, the final effective area of ​​the laser, and the actual energy density of the laser scan; S25. Calculate the dynamic modulation term of the molten pool based on the solidification time of the molten pool and the actual energy density of the laser scan; S26. Add the energy modulation term and the molten pool dynamic modulation term to obtain the metal printing risk quantification value of the two-dimensional cross-sectional shape; S22 includes the following sub-steps: S221. Perform laser scanning on the printed layer and determine the preliminary effective area based on the effective spot diameter of the laser scanning on the powder layer. S222. Collect the distance between the powder layer and the ideal focal plane, as the defocusing amount; S223. Determine the Rayleigh length during the laser scanning process; S224. Use the ratio between the defocus amount and the Rayleigh length as the setting correction coefficient; S225. Correct the preliminary effective area using a set correction coefficient to obtain the final effective area of ​​the laser. In S225, the final effective area of ​​the laser The expression is: ； in, Indicates the preliminary effective area. This indicates that a correction factor is set; In S24, the energy modulation term The expression is: ； in, This represents the first empirical coefficient. This indicates the actual energy density of the laser scan. Indicates the thickness of the powder layer. Indicates the final effective area of ​​the laser. Indicates duration, This indicates the absorption rate of the laser by the metal powder used for laser coating. In S25, the molten pool dynamic modulation term The expression is: ； in, This represents the second empirical coefficient. This indicates the actual energy density of the laser scan. This indicates the bulk density of the metal powder used for powder coating. Indicates the thickness of the powder layer. Indicates the solidification time of the molten pool; In S3, the calculation of the first... The quantification value of metal printing risk for the first two-dimensional cross-sectional graphic and the first The ratio between the quantified risk values ​​of metal printing of each two-dimensional cross-sectional shape is used to calculate the first... The quantification value of metal printing risk for the first two-dimensional cross-sectional graphic and the first The ratio between the metal printing risk quantification values ​​of each two-dimensional cross-sectional graphic is used to determine the risk level. If either ratio exceeds a set threshold, then the risk level is determined to be... A printing error occurred with a two-dimensional cross-sectional graphic.

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