Additive manufacturing process optimization method based on crack propagation numerical simulation

Abstract

The invention discloses an additive manufacturing process optimization method based on crack propagation numerical simulation, and relates to the technical field of additive manufacturing, and the method comprises the following steps: preparing additive manufacturing test pieces of different process parameter combinations; carrying out a tensile test and an impact toughness test; judging whether the results of the tensile test and the impact toughness test meet the technical requirements or not, and if not, changing the technological parameters and repeating the previous steps; establishing an impact toughness numerical simulation model, and performing numerical simulation calculation; judging whether the impact absorption energy calculated through numerical simulation is consistent with the test impact absorption energy or not, and if not, changing the material attribute and repeating the previous steps; establishing a crack propagation numerical simulation model; calculating the service lives of test pieces with different process parameters, and determining a process parameter combination with the optimal service life; according to the method, life evaluation of different process parameter combination forming parts is achieved, effective optimization selection of additive manufacturing process parameter combinations is achieved, and therefore guarantee is provided for prolonging the life of additive manufacturing parts.

Description

Technical Field

[0001] This invention relates to the field of additive manufacturing technology, and specifically to an additive manufacturing process optimization method based on numerical simulation of crack propagation. Background Technology

[0002] Additive manufacturing technology is highly favored in fields such as nuclear industry, aerospace, and medical devices due to its advantages such as high design freedom, high material utilization, and ability to manufacture complex-shaped parts. However, microscopic defects in additively manufactured parts can often become crack initiation points, and the rate of crack propagation largely determines the lifespan of the part.

[0003] Additive manufacturing process parameters have a significant impact on the mechanical properties and crack propagation characteristics of components. However, in the current process development, only tensile properties and impact toughness tests are generally performed on the test pieces. After obtaining the test results, it is only verified whether the technical requirements are met. It is impossible to evaluate the component life and select the optimal process parameters based on the evaluation results.

[0004] For example, patent document CN110188420A provides a numerical simulation-based method for predicting the propagation of hot cracks in casting. This method can accurately predict the initiation and propagation of hot cracks during the casting process, providing a basis for optimizing casting processes and improving hot crack defects. However, it focuses on the casting process rather than the initiation and propagation of cracks under service conditions, and therefore cannot evaluate the lifespan of components.

[0005] Patent document CN113378432B discloses a numerical simulation method for crack propagation on pitted pits in RPV pipes based on extended finite element method. It considers the influence of pit morphology and size on crack initiation location and does not add pre-fabricated cracks in the simulation. This method simulates the entire process of damage accumulation and cracking around pitted pits under approximate RPV pipe operating conditions, effectively and accurately predicting the service life of RPV pipes. However, it targets crack propagation initiated by pitted pits rather than additive manufacturing defects, and the material mechanical parameters are fixed values ​​that cannot be changed with process parameters, thus failing to achieve process optimization.

[0006] Patent document CN120373018A discloses a numerical simulation method for different pre-crack propagation in welded joints of pressure steel pipes. By clarifying the relationship between welding residual stress and initial cracks, this numerical simulation method can provide a scientific basis for the structural design and welding process optimization of pipelines. However, it focuses on the influence of residual stress on crack propagation, focuses on the welding process rather than the additive manufacturing process, does not consider the influence of the process on material properties, and does not utilize tensile test and impact toughness test data, thus failing to achieve process optimization for additive manufacturing components. Summary of the Invention

[0007] This invention addresses the technical problems of existing technologies, which, while capable of predicting crack propagation and assessing lifespan based on numerical simulation, fail to fully utilize tensile and impact toughness test data, thus hindering the optimization of additive manufacturing processes. The aim is to provide a method for optimizing additive manufacturing processes based on numerical simulation of crack propagation. This method involves preparing additive manufacturing test specimens with different process parameters, conducting tensile and impact toughness tests, performing impact toughness numerical simulation, and performing crack propagation numerical simulation to calculate the lifespan of test specimens with different process parameters. The optimal process parameters are then selected, enabling lifespan evaluation of components formed by different combinations of process parameters. By quantifying the merits of process parameter combinations through numerical simulation of crack propagation, the method achieves effective optimization of additive manufacturing process parameter combinations, thereby ensuring improved lifespan of additively manufactured components.

[0008] This invention is achieved through the following technical solution:

[0009] An additive manufacturing process optimization method based on numerical simulation of crack propagation includes the following steps:

[0010] (1) Preparation of additive manufacturing test pieces with different combinations of process parameters;

[0011] (2) Conduct tensile tests and impact toughness tests;

[0012] (3) Determine whether the results of the tensile test and impact toughness test meet the technical requirements. If they do not meet the requirements, change the process parameters and repeat steps (1)-(3).

[0013] (4) Establish a numerical simulation model for impact toughness and perform numerical simulation calculations;

[0014] (5) Determine whether the impact absorption energy calculated by numerical simulation is consistent with the impact absorption energy of the test. If they are inconsistent, change the material properties and repeat steps (4)-(5).

[0015] (6) Establish a numerical simulation model for crack propagation;

[0016] (7) Calculate the life of test specimens with different process parameters and determine the optimal combination of process parameters for life.

[0017] This invention prepares additive manufacturing test pieces with different process parameters, conducts tensile and impact toughness tests, performs numerical simulations of impact toughness and crack propagation, and calculates the lifespan of test pieces with different process parameters. Then, it selects the process parameters with the optimal lifespan, realizing the lifespan evaluation of parts formed by different combinations of process parameters. By using numerical simulation of crack propagation, it quantifies the advantages and disadvantages of process parameter combinations, achieving effective optimization of additive manufacturing process parameter combinations, thereby providing a guarantee for improving the lifespan of additive manufacturing parts.

[0018] Furthermore, the different process parameter combinations mentioned in step (1) include laser power, scanning speed, and solution heat treatment regime. Laser power and scanning speed directly affect the morphology and stability of the molten pool, thereby determining the density, microstructure, and mechanical properties of the part. The laser power and scanning speed are selected through initial screening and then combined with the solution heat treatment regime to prepare test pieces. The solution heat treatment regime includes the solution heat treatment temperature, holding time, and cooling method.

[0019] Furthermore, the preparation of additive manufacturing test pieces with different combinations of process parameters in step (1) specifically includes:

[0020] Raw material preparation

[0021] 3D model design;

[0022] Test specimens were prepared by setting different laser powers and scanning speeds, and test specimens with a density of not less than 99.5% were selected to obtain the corresponding laser power and scanning speed.

[0023] The selected laser power and scanning speed were then combined with different solution heat treatment regimes to prepare test pieces.

[0024] Among them, the density test was conducted on samples with different combinations of process parameters using the drainage method for screening.

[0025] Furthermore, the tensile test was performed in the X, Y, and Z printing directions at room temperature, 350°C, and 555°C, respectively.

[0026] Furthermore, the impact toughness test is conducted in the X, Y, and Z printing directions at room temperature.

[0027] Furthermore, the impact toughness numerical simulation model refers to the numerical simulation model of the Charpy pendulum impact test process.

[0028] Furthermore, in the numerical simulation of impact toughness, the material properties are described using the JC constitutive model and the failure model.

[0029] Furthermore, the JC constitutive model expression is:

[0030] ,

[0031] in:

[0032] d1 is the initial yield stress;

[0033] d2 is the strain hardening coefficient;

[0034] n is the strain hardening exponent;

[0035] d3 is the strain rate strengthening coefficient;

[0036] m is the temperature softening index.

[0037] Furthermore, the failure model expression is as follows:

[0038] ,

[0039] in:

[0040] D1 is the baseline failure strain;

[0041] D2 is the stress triaxiality sensitivity coefficient;

[0042] D3 is the stress triaxiality sensitivity coefficient;

[0043] D4 is the strain rate sensitivity coefficient;

[0044] D5 is the temperature sensitivity coefficient.

[0045] Furthermore, the calculation of the test specimen life under different process parameters refers to: calculating the crack propagation amount under a single cycle of different combinations of process parameters.

[0046] Furthermore, the optimal combination of process parameters for lifespan refers to the combination of process parameters that minimizes crack propagation.

[0047] Furthermore, between steps (3) and (4) there is also an anisotropy evaluation. If the anisotropy of the test piece does not exceed 5%, the next step is carried out. If it exceeds 5%, the process parameters are readjusted.

[0048] Specifically, the anisotropy coefficient of the sample is calculated based on the Hill48 yield criterion. , , Evaluate whether the anisotropy is significant; if it exceeds 5%, readjust the process parameters.

[0049] Yield strengths in different directions and at different temperatures are extracted from tensile test data obtained from tensile tests, and anisotropy coefficients at different temperatures are obtained. , , The value is used to determine whether it exceeds 5%.

[0050] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0051] This invention prepares additive manufacturing test pieces with different process parameters, conducts tensile and impact toughness tests, performs numerical simulations of impact toughness and crack propagation, and calculates the lifespan of test pieces with different process parameters. Then, it selects the process parameters with the optimal lifespan, realizing the lifespan evaluation of parts formed by different combinations of process parameters. By using numerical simulation of crack propagation, it quantifies the advantages and disadvantages of process parameter combinations, achieving effective optimization of additive manufacturing process parameter combinations, thereby providing a guarantee for improving the lifespan of additive manufacturing parts. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart of the present invention; Figure 2 This is a diagram of the numerical simulation model of the present invention; Figure 3 This is a diagram of the crack propagation model in thin plates according to the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0054] Throughout this specification, references to "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "an embodiment," "an example," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0055] It should be noted that the terms "comprising" or "may include" used in the various embodiments of this application indicate the presence of the claimed function, operation, or element, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in the various embodiments of this application, the terms "comprising," "having," and their cognates are intended only to indicate a specific feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or adding one or more combinations of the foregoing.

[0056] It should be understood that terms such as "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0057] Example 1

[0058] An additive manufacturing process optimization method based on numerical simulation of crack propagation includes the following steps:

[0059] Part 1: Preparation of additive manufacturing test pieces with different combinations of process parameters.

[0060] Step 1: Raw material preparation

[0061] Prepare metal powder that meets the requirements, and ensure that its composition and physical properties meet the requirements.

[0062] Step 2: 3D Model Design and Data Preparation

[0063] Create a 3D digital model of the test specimen, segment the model into a series of extremely thin 2D layers, and generate code to control the printer path.

[0064] Step 3: Set and execute specific process parameters

[0065] Setting key parameters, including laser power and scanning speed, directly affects the morphology and stability of the molten pool, which in turn determines the density, microstructure, and mechanical properties of the part.

[0066] An orthogonal experimental design was adopted to design and number combinations of laser power and scanning speed. Samples were prepared and the density of sample blocks with different process parameters was screened by the water displacement method. The density of the test pieces was required to be not less than 99.5%.

[0067] The process parameters that passed the initial screening were combined with different solution heat treatment temperatures, and test pieces were prepared.

[0068] Part Two: Conducting tensile and impact toughness tests.

[0069] The sampling methods and standards for tensile and impact toughness tests are shown in Table 1.

[0070] Table 1. Inspection Items

[0071]

[0072] Part Three: Determine whether the results of the tensile test and impact toughness test meet the technical requirements. If they do not, change the process parameters and repeat Part One and Part Two.

[0073] Select a combination of process parameters that meets the technical requirements, compile the test data, and if there are too few combinations of process parameters that meet the technical requirements, change the process parameters and re-prepare the test pieces and carry out tensile and impact toughness tests.

[0074] Part 4: Anisotropy Evaluation (This step is optional).

[0075] The anisotropy coefficient of the sample was calculated based on the Hill 48 yield criterion. , , Evaluate whether the anisotropy is significant; if it exceeds 5%, readjust the process parameters.

[0076] Yield strengths in different directions and at different temperatures were extracted from the tensile test data obtained in Part II, yielding values ​​at different temperatures. , , The value is used to determine whether it exceeds 5%.

[0077] Part 5: Numerical simulation of impact toughness.

[0078] Step 1: Model Building

[0079] A numerical simulation model of the Charpy pendulum impact test process was established, such as... Figure 1 As shown.

[0080] Impact velocity: Calibration value of the test equipment;

[0081] Pendulum edge chamfer: standard value;

[0082] Specimen geometry: Standard V-notch specimen;

[0083] Simplified: Set the pendulum and anvil as rigid bodies.

[0084] Step 2: Material Property Description

[0085] Material properties are described using the JC constitutive model and failure model.

[0086] The JC constitutive model expression is:

[0087]

[0088] in:

[0089] d1 is the initial yield stress: taken as the average room temperature tensile yield strength;

[0090] d2 is the strain hardening coefficient: fitting of the plastic segment of the room temperature stress-strain curve;

[0091] n is the strain hardening exponent: obtained in the same way as d2;

[0092] d3 is the strain rate strengthening coefficient: since no strain rate-related tests were conducted, it is set to 0;

[0093] m is the temperature softening index, obtained by fitting data at different temperatures.

[0094] Each parameter is determined based on the engineering stress-strain curve.

[0095] The failure model expression is:

[0096]

[0097] in:

[0098] D1 is the baseline failure strain;

[0099] D2 is the stress triaxiality sensitivity coefficient multiplier term;

[0100] D3 is the stress triaxiality sensitivity coefficient exponential term;

[0101] D4 is the strain rate sensitivity coefficient: the strain rate remains constant in the Charpy pendulum impact test, so it is taken as 0;

[0102] D5 is the temperature sensitivity coefficient: the Charpy pendulum impact test was conducted at room temperature, and it was set to 0.

[0103] right , , Values ​​were taken separately and numerical simulations were performed to obtain the impact absorbed energy (plastic absorbed energy + frictional dissipation energy). It was then determined whether the numerically simulated impact absorbed energy matched the experimental impact absorbed energy; if they did not match, the values ​​were adjusted. , , The values ​​are taken until they are consistent, and if the deviation is within ±5%, this set is adopted. , , Values.

[0104] Part 6: Optimal selection of process parameter combinations based on numerical simulation of crack propagation.

[0105] Step 1: Model Building

[0106] Geometric dimensions: thin plate;

[0107] Initial crack pre-setting;

[0108] Load: Applying a tensile load;

[0109] Boundary conditions: One end is fixed.

[0110] The material yield criterion, constitutive model, and failure model adopt the values ​​from the preceding part.

[0111] The Maxpe damage model was adopted, with the maximum principal strain taken as the room temperature tensile reference failure strain, i.e., D1; the fracture energy was taken as the plastic absorption energy per unit area (obtained by plastic absorption energy / sample cross-sectional area).

[0112] The established crack propagation model for thin plates is as follows: Figure 3 As shown.

[0113] Step 2: Calculation of crack propagation

[0114] Calculate the crack propagation amount under different process parameters in a single cycle, and select the process parameter combination with the smallest crack propagation amount as the optimal process.

[0115] This invention prepares additive manufacturing test pieces with different process parameters, conducts tensile and impact toughness tests, performs numerical simulations of impact toughness and crack propagation, and calculates the lifespan of test pieces with different process parameters. Then, it selects the process parameters with the optimal lifespan, realizing the lifespan evaluation of parts formed by different combinations of process parameters. By using numerical simulation of crack propagation, it quantifies the advantages and disadvantages of process parameter combinations, achieving effective optimization of additive manufacturing process parameter combinations, thereby providing a guarantee for improving the lifespan of additive manufacturing parts.

[0116] Those skilled in the art will understand that some of the steps in implementing the above facts and methods can be accomplished by a program instructing related hardware, and the program involved or the program described can be stored in a computer-readable storage medium.

[0117] The computer-readable storage medium is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor; these instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps in the above embodiments. Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] Application Example 1

[0119] An additive manufacturing process optimization method based on numerical simulation of crack propagation includes the following steps:

[0120] Part 1: Preparation of additive manufacturing test pieces with different combinations of process parameters.

[0121] Step 1: Raw material preparation

[0122] Prepare metal powder that meets the requirements, and ensure that its composition and physical properties meet the requirements.

[0123] Step 2: 3D Model Design and Data Preparation

[0124] Create a 3D digital model of the test specimen, segment the model into a series of extremely thin 2D layers, and generate code to control the printer path.

[0125] Step 3: Set and execute specific process parameters

[0126] This embodiment takes the additive manufacturing of 316H stainless steel as an example. A 5×5 full factorial orthogonal experimental design was adopted, with 25 combinations of laser power (185-245W) and scanning speed (730-1130mm / s), corresponding to numbers 1-25. The specific parameters are shown in Table 2. Samples were prepared and the density of sample blocks with different process parameters was screened by the water displacement method. The density of the test pieces was required to be not less than 99.5%.

[0127] Table 2. Density Detection of 316H Stainless Steel Deposited Samples

[0128]

[0129] The process parameters that passed the initial screening were combined with different solution heat treatment regimes of 1070℃ / 2h / AC and 1120℃ / 2h / AC, as shown in Table 3, and test pieces were prepared.

[0130] Table 3. Types of Process Parameter Combinations

[0131]

[0132] Part Two: Conducting tensile and impact toughness tests.

[0133] The test items, sampling methods, and standards are shown in Table 1.

[0134] Table 1. Inspection Items

[0135]

[0136] The tensile properties were tested at different solution treatment temperatures. For example, the test results of samples with process parameters of 215W and 830mm / s are shown in Table 4. As can be seen from the results in Table 4, the properties under both solution heat treatment regimes can meet the technical requirements.

[0137] Table 4. Tensile test data

[0138]

[0139] The impact toughness test results of the sample with process parameters of 215W, 830mm / s, and 1120℃ / 2h / AC are shown in Table 5.

[0140] Table 5. Room temperature shock test data

[0141]

[0142] Part Three: Determine whether the results of the tensile test and impact toughness test meet the technical requirements. If they do not, change the process parameters and repeat Part One and Part Two.

[0143] In this application example, the test results of all 8 sets of process parameters (not all are listed) meet the technical requirements, and there is no need to re-prepare the test pieces.

[0144] Part Four: Anisotropy Evaluation

[0145] The anisotropy coefficient of the sample was calculated based on the Hill 48 yield criterion. , , Evaluate whether the anisotropy is significant. If it exceeds 5%, readjust the process parameters, including:

[0146] The yield strength in different directions and at different temperatures was extracted from the tensile test data obtained in Part II. For example, the yield strength in different directions for process parameter combinations of 215W, 830mm / s, and 1120℃ / 2h / AC is shown in Table 6.

[0147] Table 6. Yield strength data in different directions

[0148]

[0149] Take the yield strength in the X direction as That is, at different temperatures of 277, 182, and 155.5 MPa, the pressure at different temperatures is... , , As shown in Table 7, all values ​​are greater than 0.95, meaning the anisotropy does not exceed 5%.

[0150] Table 7. Anisotropy Coefficient Data

[0151]

[0152] Part 5: Numerical simulation of impact toughness.

[0153] Step 1: Model Building

[0154] A numerical simulation model of the Charpy pendulum impact test process was established, such as... Figure 1 As shown.

[0155] Impact velocity: Test equipment calibration value—5.5 m / s;

[0156] Pendulum edge chamfer: Standard value – 8mm;

[0157] Specimen geometry: Standard V-notch specimen;

[0158] Simplified: Set the pendulum and anvil as rigid bodies.

[0159] Step 2: Material Property Description

[0160] Material properties are described using the JC constitutive model and failure model.

[0161] The JC constitutive model expression is:

[0162]

[0163] in:

[0164] d1 is the initial yield stress: taken as the average room temperature tensile yield strength;

[0165] d2 is the strain hardening coefficient: fitting of the plastic segment of the room temperature stress-strain curve;

[0166] n is the strain hardening exponent: obtained in the same way as d2;

[0167] d3 is the strain rate strengthening coefficient: since no strain rate-related tests were conducted, it is set to 0;

[0168] m is the temperature softening index, obtained by fitting data at different temperatures.

[0169] Each parameter was determined based on the engineering stress-strain curves at room temperature, 350℃, and 550℃ in different directions, and the results are shown in Table 8.

[0170] Table 8. Values ​​of d1, d2, n, and m corresponding to different combinations of process parameters

[0171]

[0172] The failure model expression is:

[0173]

[0174] in:

[0175] D1 is the baseline failure strain; for stainless steel, the value range is 0-0.4.

[0176] D2 is the stress triaxiality sensitivity coefficient multiplier; for stainless steel, the value range is 0.26-0.94.

[0177] D3 is the stress triaxiality sensitivity coefficient index; for stainless steel materials, the value range is 3.6-9.5.

[0178] D4 is the strain rate sensitivity coefficient: the strain rate remains constant in the Charpy pendulum impact test, so it is taken as 0;

[0179] D5 is the temperature sensitivity coefficient: the Charpy pendulum impact test was conducted at room temperature, and it was set to 0.

[0180] right , , Values ​​were taken separately and numerical simulations were performed to obtain the impact absorbed energy (plastic absorbed energy + frictional dissipation energy). It was then determined whether the numerically simulated impact absorbed energy matched the experimental impact absorbed energy; if they did not match, the values ​​were adjusted. , , The values ​​are taken until they are consistent, and if the deviation is within ±5%, this set is adopted. , , The values ​​were selected, and the calculation results are shown in Table 9.

[0181] Table 9. Corresponding to different combinations of process parameters , , Value

[0182]

[0183] Part 6: Optimal selection of process parameter combinations based on numerical simulation of crack propagation.

[0184] Step 1: Model Building

[0185] Geometric dimensions: 100mm*50mm*3mm (flexible);

[0186] Initial crack: center of the thinnest plate – length 20mm;

[0187] Load: A tensile load of 10 MPa is applied to the upper end;

[0188] Boundary condition: Fixed support at the lower end.

[0189] The constitutive model and failure model use values ​​from Part 5.

[0190] The Maxpe damage model was adopted, with the maximum principal strain taken as the room temperature tensile reference failure strain, i.e., D1; the fracture energy was taken as the plastic absorption energy per unit area (obtained from the plastic absorption energy / sample cross-sectional area in step 3 of Part 5).

[0191] The established crack propagation model for thin plates is as follows: Figure 3 As shown.

[0192] Step 2: Calculation of crack propagation

[0193] The crack propagation rate under different process parameters in a single cycle was calculated, and the results are shown in Table 10. The process parameter combination with the smallest crack propagation rate was selected as the optimal process. As can be seen from the data in Table 10, the minimum crack propagation rate is 0.55 × 10⁻⁶. -5 In this application example, the optimal process parameter combination is 215W, 930mm / s, and 1120℃ / 2h / AC.

[0194] Table 10. Crack propagation amount corresponding to different process parameters

[0195] Finally, it should be noted that the above specific embodiments are only used to describe the purpose, technical solution, and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation of the present invention and is not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the foregoing specific embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions or improvements can be made to some or all of the technical features. These modifications, equivalent substitutions, and improvements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for optimizing additive manufacturing processes based on numerical simulation of crack propagation, characterized in that, Includes the following steps: (1) Preparation of additive manufacturing test pieces with different combinations of process parameters; (2) Conduct tensile tests and impact toughness tests; (3) Determine whether the results of the tensile test and impact toughness test meet the technical requirements. If they do not meet the requirements, change the process parameters and repeat steps (1)-(3). (4) Establish a numerical simulation model for impact toughness and perform numerical simulation calculations; (5) Determine whether the impact absorption energy calculated by numerical simulation is consistent with the impact absorption energy of the test. If they are inconsistent, change the material properties and repeat steps (4)-(5). (6) Establish a numerical simulation model for crack propagation; (7) Calculate the life of test specimens with different process parameters and determine the optimal combination of process parameters for life.

2. The additive manufacturing process optimization method based on numerical simulation of crack propagation according to claim 1, characterized in that, The different combinations of process parameters mentioned in step (1) include laser power, scanning speed and solution heat treatment regime.

3. The additive manufacturing process optimization method based on numerical simulation of crack propagation according to claim 1, characterized in that, The preparation of additive manufacturing test pieces with different combinations of process parameters in step (1) specifically includes: Raw material preparation 3D model design; Test specimens were prepared by setting different laser powers and scanning speeds, and test specimens with a density of not less than 99.5% were selected to obtain the corresponding laser power and scanning speed. The selected laser power and scanning speed were then combined with different solution heat treatment regimes to prepare test pieces.

4. The additive manufacturing process optimization method based on numerical simulation of crack propagation according to claim 1, characterized in that, The tensile test was performed in the X, Y, and Z printing directions at room temperature, 350°C, and 555°C, respectively.

5. The additive manufacturing process optimization method based on numerical simulation of crack propagation according to claim 1, characterized in that, The impact toughness test was conducted in the X, Y, and Z printing directions at room temperature.

6. The additive manufacturing process optimization method based on numerical simulation of crack propagation according to claim 1, characterized in that, The impact toughness numerical simulation model refers to the numerical simulation model of the Charpy pendulum impact test process.

7. The additive manufacturing process optimization method based on numerical simulation of crack propagation according to claim 6, characterized in that, In the numerical simulation of impact toughness, material properties are described using the JC constitutive model and the failure model.

8. The additive manufacturing process optimization method based on numerical simulation of crack propagation according to claim 7, characterized in that, The JC constitutive model expression is: ， in: d1 is the initial yield stress; d2 is the strain hardening coefficient; n is the strain hardening exponent; d3 is the strain rate strengthening coefficient; m is the temperature softening index.

9. The additive manufacturing process optimization method based on numerical simulation of crack propagation according to claim 7, characterized in that, The failure model expression is: ， in: D1 is the baseline failure strain; D2 is the stress triaxiality sensitivity coefficient multiplier term; D3 is the stress triaxiality sensitivity coefficient exponential term; D4 is the strain rate sensitivity coefficient; D5 is the temperature sensitivity coefficient.

10. The additive manufacturing process optimization method based on numerical simulation of crack propagation according to claim 1, characterized in that, The calculation of the test specimen life under different process parameters refers to the calculation of the crack propagation amount under a single cycle under different combinations of process parameters.

11. The additive manufacturing process optimization method based on numerical simulation of crack propagation according to claim 10, characterized in that, The optimal combination of process parameters for lifespan refers to the combination of process parameters that minimizes crack propagation.

12. The additive manufacturing process optimization method based on numerical simulation of crack propagation according to any one of claims 1-11, characterized in that, Anisotropy evaluation is also included between steps (3) and (4). If the anisotropy of the test piece does not exceed 5%, proceed to the next step. If it exceeds 5%, readjust the process parameters.

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