A method for integrated control of strength, stiffness and randomness of 3D printed structural parts

Abstract

The present invention belongs to the field of additive manufacturing technology, and specifically discloses a method for integrated control of the strength, stiffness and randomness of 3D printed structural parts, comprising the following steps: S1, selecting printing materials according to mechanical requirements; S2, determining printing parameters; S3, determining the range of printing parameters; S4, determining the selected values of printing parameters; S5, combining printing parameters through orthogonal experiments; S6, mechanical testing; S7, multivariate regression model fitting; S8, structural part force analysis; S9, structural part regional division; S10, assigning printing parameter combinations; S11, 3D printing. The present invention adopts the above-mentioned method for integrated control of the strength, stiffness and randomness of 3D printed structural parts, and takes the regional integrated optimization of the strength, stiffness and randomness of the target component as the goal, divides the components by tension, compression, strength, stiffness and randomness, and assigns different printing parameters, so that the components meet the strength requirements while having higher stiffness and lower randomness.

Description

Technical Field

[0001] The present invention relates to the field of additive manufacturing technology, and in particular to a method for integrated control of strength, stiffness and randomness of 3D printed structural parts. Background Art

[0002] Melt extrusion is a widely used technology in additive manufacturing. It involves feeding thermoplastic filaments to a heating element, melting them within a specific temperature range. The filaments are then extruded through a nozzle, cooled, and bonded to the print plate, forming a stacked structure layer by layer. Throughout the 3D printing process, the mechanical properties of the filaments undergo significant changes, which in turn affect the mechanical performance of the printed product.

[0003] During the melt extrusion printing process, various machine parameters such as nozzle temperature, base speed, extrusion layer height, trace width and fill rate can also be adjusted according to needs. Different printing parameter settings for the same material have a great impact on the mechanical properties of the printed components. The strength of printed components produced by different printing equipment is also different, making it difficult to obtain printed products with stable performance.

[0004] In the prior art, improving the performance of printed components mainly focuses on improving strength. For example, the invention patent application with publication number CN117172070A discloses a method for optimizing component 3D printing parameters based on mechanical guidance. Through orthogonal experiments and signal-to-noise ratio analysis, it takes into account the optimization control of printing parameters for strength, but fails to consider the situation of stiffness, nor to ensure that the randomness of printed components is minimized, making it difficult to ensure the performance stability of 3D printed products. Only some areas of the printed components have large stress and strain, and some areas still have large strength redundancy. While ensuring that the strength of 3D printed structural parts meets the requirements, the strength redundancy area can be given parameters with higher stiffness and lower randomness, ensuring strength while improving the stiffness and stability of the structure.

[0005] Therefore, it is necessary to provide an integrated control method for the strength, stiffness and randomness of 3D printed structural parts to solve the above problems. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for integrated control of the strength, stiffness and randomness of 3D-printed structural parts. Based on the actual force type composition and printing principle, the method takes the regional integration of the strength, stiffness and randomness of the target component as the optimization goal, divides the components according to tension and compression, strength, stiffness and randomness, and assigns different printing parameters, so that the components meet the strength requirements while having higher stiffness and lower randomness.

[0007] To achieve the above objectives, the present invention provides a method for integrated control of strength, stiffness, and randomness of 3D printed structural parts, comprising the following steps:

[0008] S0. Build material property library;

[0009] S1. Select 3D printing materials based on mechanical requirements;

[0010] S2. Determine printing parameters that affect mechanical properties;

[0011] S3. Determine the printing parameter range: Determine the maximum and minimum values of each printing parameter from the molding perspective to obtain the printing parameter range;

[0012] S4. Determine the selected values of printing parameters: select 3-5 values of the same number at equal intervals for each printing parameter;

[0013] S5. Orthogonal experiment combination printing parameters: obtaining a printing parameter combination based on the orthogonal experiment design, wherein the printing parameters are factors in the orthogonal experiment, and the printing parameter values are levels in the orthogonal experiment;

[0014] S6. Mechanical test: Use the printing parameter combination obtained in S5 to print the tension standard specimen and the compression standard specimen respectively, and conduct mechanical tests to obtain mechanical test data;

[0015] S7, multivariate regression model fitting: obtain the regression matrix of strength, stiffness, randomness and printing parameters;

[0016] S8. Structural component stress analysis: Perform finite element analysis on structural components to obtain stress and strain predictions for each part;

[0017] S9. Structural component area division: Structural component areas are divided according to the dominant relationship between tension and compression state, stiffness and strength randomness;

[0018] S10, assigning printing parameter combinations: assigning printing parameter combinations according to the structural part area division;

[0019] S11. 3D printing of structural parts: combining slicing and printing according to the given printing parameters.

[0020] Preferably, in S0, the material property library includes actual 3D printing materials and the strength, stiffness and elastic modulus of the 3D printing materials.

[0021] Preferably, in S2, the printing parameters include one or more of layer height, line width, nozzle temperature and printing speed.

[0022] Preferably, in S3, the printing parameter range is determined by the molding effect of the target structural part through trial printing.

[0023] Preferably, in S4, the number of selected values of the printing parameters is n , the range of printing parameter selection values is as follows:

[0024] ;

[0025] in, The minimum value among the selected values for the print parameters, The maximum value among the selected values of the print parameters, The number of values selected for the print parameters, Select values for printing parameters No. A selected value, =1,2,…,max.

[0026] Preferably, in S6, the tensile standard specimen and the compressive standard specimen are one of ASTM, ISO or GB, and the mechanical test data include one or more of tensile strength, tensile stiffness, tensile elastic modulus, compressive strength, compressive stiffness and compressive elastic modulus.

[0027] Preferably, in S7, the data of the multivariate regression model fitting is derived from S6 to finally obtain the regression matrix of each printing parameter A, B, C, ..., and strength Sr, stiffness Si and randomness R:

[0028] ;

[0029] in, is a vector of printing parameters, is the regression coefficient matrix, is the vector of mechanical properties, is the error vector.

[0030] Preferably, in S8, the stress analysis of the structural parts adopts finite element method, and priority is given to ensuring that the strength of the structural parts meets the requirements. The data adopted by the finite element method are respectively derived from the mechanical test data of the tensile quasi-specimen with the highest tensile strength and the compressive standard specimen with the highest compressive strength in S6.

[0031] Preferably, in S9, the strength of the material obtained in S5 , stiffness , randomness The stress and strain prediction results obtained from S8 are divided into regions as follows:

[0032] Randomness-dominated region: The region in the target structural part where the finite element stress is less than 30% of the strength is the randomness-dominated region;

[0033] Strength-dominated region: When the finite element stress of the region in the target structural part is ≥ 70% of the strength, it is considered as the strength-dominated region;

[0034] Stiffness-dominated region: When the finite element stress of the region in the target structural part is less than 70% and ≥30% of the strength, it is the stiffness-dominated region;

[0035] According to the positive and negative stress values of the S8 finite element analysis, the target structural member is first divided into tension and compression zones. The zone types include tensile strength zone, compressive strength zone, tensile stiffness zone, compressive stiffness zone, tensile random zone, and compressive random zone.

[0036] The strength was quantified by taking the average value of the peak value of the mechanical test curve;

[0037] The quantification method of stiffness is to select the average value of the tangent of the elastic segment of the mechanical test curve;

[0038] Randomness comes from strength and stiffness respectively, and the quantitative formula is as follows:

[0039] ;

[0040] in, is the standard deviation of the mechanical test data, is the mean value of the mechanical test data.

[0041] Preferably, S10 specifically includes:

[0042] Tensile strength zone: the printing parameter combination that gives the highest printable tensile strength;

[0043] Compressive strength zone: gives the highest printable compressive strength printing parameter combination;

[0044] Tensile stiffness zone: the printing parameter combination that gives the highest printable tensile stiffness and a tensile strength not less than the regional stress;

[0045] Compressive stiffness zone: a printing parameter combination that gives the highest printable compressive stiffness and a compressive strength not less than the regional stress;

[0046] Tensile random zone: the printing parameter combination that gives the lowest printable tensile randomness and a tensile strength not lower than the regional stress;

[0047] Compressive random zone: This is the printing parameter combination that gives the lowest printable compressive randomness and a compressive strength not lower than the regional stress.

[0048] Therefore, the present invention adopts the above-mentioned integrated control method for strength, stiffness and randomness of 3D printed structural parts, and the beneficial effects are as follows:

[0049] The present invention takes into account the actual force type composition and printing principles, and according to actual needs, takes the regional integration of the strength, stiffness and randomness of the target component as the optimization goal, and integrates the control of the strength, stiffness and randomness of the structural parts. The components are divided by tension and compression, strength, stiffness and randomness to determine the required manufacturing parameters of the components under specific target force conditions, control the performance and randomness of the structural parts, and make the components meet the strength requirements while having higher stiffness and lower randomness.

[0050] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of an embodiment of a method for integrated control of strength, stiffness and randomness of 3D printed structural parts according to the present invention;

[0052] Figure 2 are test results of a standard tensile specimen of an embodiment of a method for integrated control of strength, stiffness, and randomness of a 3D-printed structural component of the present invention, wherein (a) is the tensile strength, and (b) is the tensile elastic modulus;

[0053] Figure 3 are test results of a standard compression test piece of an embodiment of a method for integrated control of strength, stiffness, and randomness of a 3D-printed structural component of the present invention, wherein (a) is the compressive strength, and (b) is the compressive elastic modulus;

[0054] Figure 4 This is a component tension and compression distribution diagram of an embodiment of a method for integrated control of strength, stiffness and randomness of a 3D printed structural component of the present invention;

[0055] Figure 5 This is a component area division diagram of an embodiment of a method for integrated control of strength, stiffness and randomness of a 3D printed structural component of the present invention;

[0056] Figure 6 This is the printing parameter optimization result of an embodiment of a method for integrated control of strength, stiffness and randomness of 3D printed structural parts of the present invention. DETAILED DESCRIPTION

[0057] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0058] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0059] Example 1

[0060] like Figure 1As shown, a method for integrated control of the strength, stiffness, and randomness of 3D-printed structural parts is provided, including material and structural levels. At the material level, a 3D-printed material parameter combination prediction method based on multivariate regression is used to establish the relationship between the material's strength, stiffness, randomness, and 3D-printed parameter combination. At the structural level, a 3D-printed parameter combination allocation method for structural parts is used based on force analysis and mechanical requirements. This method uses finite element analysis results of structural parts to divide the structural parts into regions according to the tension-compression state and the dominant relationship between strength, stiffness, and randomness, and assign different printing parameters. S0 and S1 are preparation methods; S2-S7 are the specific steps of the material-level method; and S8-S11 are the specific steps of the structural-level method, as follows:

[0061] S0. Build a material property library: Enter existing 3D printing materials and their strength, stiffness, and elastic modulus into the material property library. First, select a roughly matching material from the existing 3D printing material property data. The material properties in the material property library are provided directly by the manufacturer or accumulated through preliminary testing. They serve only as reference values to facilitate material selection in S1.

[0062] S1. Select 3D printing materials based on mechanical requirements: Select appropriate 3D printing materials based on the strength, stiffness and other mechanical requirements of the printed structural parts.

[0063] S2. Determine the printing parameters that have the greatest impact on mechanical properties: Determine the printing parameters that have the greatest impact on the properties of the 3D printing material. In this embodiment, the printing parameters that have the greatest impact on the mechanical properties of FDM-type 3D printing are generally layer height, line width, nozzle temperature, and printing speed.

[0064] S3. Determine the printing parameter range: Determine the maximum and minimum values of each printing parameter from the molding perspective to obtain the printing parameter range. In this embodiment, the printing parameter range is determined based on the molding effect of the target structural part during trial printing.

[0065] S4, determine the selected values of printing parameters: each printing parameter is equally spaced and selected with the same number of 3-5 values. In this embodiment, the range of the selected values of the printing parameters is determined by S3, and the number of selected values of each printing parameter is the same. n , then the range of printing parameter selection values is:

[0066] ;

[0067] Where: The minimum value among the selected values for the printing parameters; The maximum value among the selected values for the printing parameters; The number of values selected for the print parameters; Print parameters No. Selected values ( =1,2,…,max).

[0068] S5. Orthogonal experiment combination printing parameters: obtain a printing parameter combination based on the orthogonal experiment design. In this embodiment, in the orthogonal experiment combination printing parameters, the printing parameters are factors in the orthogonal experiment, and the selected printing parameter values are levels in the orthogonal experiment.

[0069] S6. Mechanical Testing: Using the printing parameter combination obtained in S5, a tensile quasi-specimen and a compressive standard specimen are printed, and mechanical testing is performed to obtain mechanical test data. In this embodiment, the tensile quasi-specimen and compressive standard specimen printed in the mechanical testing are based on one of ASTM, ISO, or GB standards, with the specific specification determined by the material used.

[0070] S7, multivariate regression model fitting: Obtain a regression matrix of strength, stiffness, randomness, and printing parameters. In this embodiment, the data for multivariate regression model fitting comes from S6, and the final regression matrix of each printing parameter (A, B, C, ...) and strength (Sr), stiffness (Si), and randomness (R) is obtained:

[0071] ;

[0072] In the formula is a vector of printing parameters;

[0073] is the regression coefficient matrix;

[0074] is the vector of mechanical properties;

[0075] is the error vector.

[0076] S8. Stress analysis of structural parts: Conduct finite element analysis on structural parts to obtain stress and strain predictions for each part, giving priority to ensuring that the strength of the structural parts meets the requirements.

[0077] S9. Structural component area division: The component area is divided according to the dominant relationship between tension and compression state, stiffness and strength randomness. According to the positive and negative stress values of the finite element analysis in S8, the target structural component is first divided into a tensile zone and a compressive zone. Therefore, the data used by the finite element are respectively from the mechanical test data of the specimens with the highest tensile strength and compressive strength in S6. Combined with the dominant relationship between strength, stiffness and randomness of the target structural component, the structural component is divided into six types of areas: tensile strength zone, compressive strength zone, tensile stiffness zone, compressive stiffness zone, tensile random zone, and compressive random zone. The method for determining the dominant relationship between strength, stiffness and randomness of a certain area is as follows: According to the strength of the material obtained in S5 , stiffness , randomness The data and analysis results of the finite element analysis in S8 are as follows:

[0078] Randomness Dominant Region: The finite element stress of the region in the target structure <Intensity 30% is the randomness-dominated area.

[0079] Strength Dominant Region: The finite element stress of the region in the target structure ≥Intensity When the intensity is 70%, it is the intensity-dominated area.

[0080] Stiffness Dominant Region: The stress of the finite element in the region of the target structure <Intensity When the stiffness is 70% and ≥30%, it is the stiffness-dominated area.

[0081] The strength is quantified by taking the average value of the peak value of the mechanical test curve.

[0082] The method for quantifying stiffness is to select the average value of the tangent of the elastic segment of the mechanical test curve.

[0083] Randomness comes from strength and stiffness respectively, and the quantitative formula is as follows:

[0084] ;

[0085] in, is the standard deviation of the mechanical test data, is the mean value of the mechanical test data.

[0086] S10, Printing Parameter Combination Assignment: Assign printing parameter combinations based on the structural component area division. Based on the multivariate regression model built in S7 to calculate printing parameters, S10's printing parameter combination assignment divides the component into six areas:

[0087] Tensile Strength Zone: The printing parameter combination that gives the highest printable tensile strength.

[0088] Compressive Strength Zone: The printing parameter combination that gives the highest compressive strength to the printable product.

[0089] Tensile stiffness zone: The printing parameter combination that gives the highest printable tensile stiffness and a tensile strength not less than the regional stress.

[0090] Compressive stiffness zone: The printing parameter combination that gives the highest printable compressive stiffness and a compressive strength not less than the regional stress.

[0091] Tensile random zone: The printing parameter combination that gives the lowest printable tensile randomness and a tensile strength not lower than the regional stress.

[0092] Compressive random zone: This is the printing parameter combination that gives the lowest printable compressive randomness and a compressive strength not lower than the regional stress.

[0093] S11. 3D printing of structural parts: combining slicing and printing according to the given printing parameters.

[0094] Example 2

[0095] Using the method of Example 1, based on the materials in the 3D printing material property library, polylactic acid (PLA) was selected as the research object according to the requirements. Given the characteristics of PLA material, the printing parameters that have the greatest impact on its mechanical properties are layer height, nozzle temperature, and printing speed.

[0096] The nozzle temperature range was determined based on the PLA material's molding temperature of 195°C-215°C. The printing speed range was determined to be 50mm / s-70mm / s based on the printer's speed. The layer height range was 0.20mm-0.30mm, considering both molding quality and efficiency.

[0097] For each printing parameter, three values of the same number are selected at equal intervals, such as printing speed (A) 50mm / s ( )、60mm / s( )、70mm / s( ), nozzle temperature (B) 195℃ ( )、205℃( ) and 215℃ ( ), layer height (C) 0.20mm ( )、0.25mm( ) and 0.30mm ( ). Then the orthogonal test combination is three factors and three levels L9(3 3 ), as shown in Table 1.

[0098] Table 1 Orthogonal test combinations

[0099] ;

[0100] ASTM D638-2014 and ASTM D695 were used to print the tensile standard specimens and the compressive standard specimens respectively under the obtained printing parameter combinations, and mechanical tests were carried out respectively. The mechanical test data results are shown in the figure below. Figure 2 and Figure 3 shown.

[0101] The multivariate regression model is used to fit the mechanical test results to obtain the regression matrix of strength, stiffness, randomness and printing parameters. The data for the multivariate regression model fitting comes from the final regression matrix of each printing parameter (A, B, C) and strength (Sr), stiffness (Si) and randomness (R):

[0102] ;

[0103] Where: is a vector of printing parameters; is the regression coefficient matrix; is the vector of mechanical properties; is the error vector.

[0104] In this embodiment, the stretched regression coefficient matrix :

[0105] [4.29397545e-02 1.48807179e+01 -3.25070769e-02 2.08546476e-011.11938109e+01 4.17655046e-02 -3.16225765e+01 -1.58276433e+03 -1.20941617e+01]

[0106] In this embodiment, the stretched error vector for:

[0107] [-2.86651579e+03 2.51574537e+02 3.10228331e+05 -3.01455481e+033.43447006e+02 3.26057907e+05 -3.16612799e+03 3.05141643e+02 3.41881555e+05 -2.86930386e+03 4.65276729e+02 3.10542340e+05 -3.01332282e+03 6.76075130e+023.26369077e+05 -3.16151760e+03 5.04512933e+02 3.42195300e+05 -2.86697360e+036.94308519e+02 3.10858565e+05 -3.01916986e+03 4.11895155e+02 3.26687647e+05 -3.16336292e+03 7.04407557e+02 3.42515430e+05]

[0108] In this embodiment, the compressed regression coefficient matrix :

[0109] [-4.64348004e-02 2.01499077e+00 -2.04965338e-02 3.65098479e-011.06812636e+01 7.68021500e-03 -6.80488257e+01 -1.80281682e+03 4.59824888e+00]

[0110] In this embodiment, the compressed error vector for:

[0111] [-3.31968508e+02 -7.31208609e+01 3.54951902e+05 -3.53746846e+02 -1.99196714e+02 3.72980893e+05 -3.77118703e+02 -6.19804567e+02 3.91008036e+05-3.40450048e+02 -3.82504614e+02 3.55632128e+05 -3.50780767e+02 -4.86399648e+02 3.73660420e+05 -3.75103549e+02 -4.05932002e+02 3.91690166e+05 -3.36550290e+02 -2.20666381e+02 3.56312461e+05 -3.59953387e+02 -4.96588234e+023.74341443e+05 -3.70712553e+02 -2.51867936e+02 3.92368986e+05]

[0112] First, if Figure 4 As shown, finite element analysis is performed on the component to obtain stress and strain predictions for each part. According to the positive and negative stress values in the finite element analysis, the target structural component is first divided into a tensile zone (purple) and a compressive zone (gray).

[0113] Then, Figure 2 and Figure 3 The mechanical test data of the specimens with the highest tensile strength (tensile strength 40.41 MPa, tensile elastic modulus 3125 MPa) and the highest compressive strength (compressive strength 59.50 MPa, compressive elastic modulus 2070 MPa) were assigned to the tension zone and the compression zone, respectively.

[0114] Then, if Figure 5As shown in the figure, the component area is divided according to the dominant relationship between the tensile and compressive states and the randomness of the stiffness and strength. The printing parameters are calculated based on the multivariate regression model and the printing parameter combinations are assigned. The component is divided into 6 areas, of which the orange is the tensile strength area ( ≥Tensile strength 70% of the total), the printing parameters are: T6: printing speed 60mm / s, nozzle temperature 215℃, printing layer height 0.25mm; pink is the compressive strength area ( ≥Compression strength 70% of the total), the printing parameters are: C5: printing speed 60mm / s, nozzle temperature 205℃, printing layer height 0.20mm. The green is the tensile stiffness area ( <Tensile strength 70% and ≥30%), the printing parameters are: T9: printing speed 70mm / s, nozzle temperature 215℃, printing layer height 0.20mm; red is the compressive stiffness area ( <Compression strength 70% and ≥30%), the printing parameters are: C1: printing speed 50mm / s, nozzle temperature 195℃, printing layer height 0.20mm; blue is the tensile random area ( <Tensile strength 30% of the total area), the printing parameters are: T8: printing speed 70mm / s, nozzle temperature 205℃, printing layer height 0.30mm; yellow is the random area under pressure ( <Compression strength The printing parameters are: C4: printing speed 60mm / s, nozzle temperature 195℃, and printing layer height 0.30mm.

[0115] Slice and print according to the assigned printing parameter combination. A 3D printing slicing algorithm is used to convert the analyzed control printing parameter combination into specific instructions for slicing and printing. Except for the print speed, nozzle temperature, and layer height, which are modified according to requirements, all other settings remain the same to obtain the 3D printed structure.

[0116] Experimental testing

[0117] The 3D printed structural parts obtained in Example 2 were subjected to compression tests, and the test results are shown in FIG. Figure 6 shown.

[0118] The first group corresponds to printing parameter T6, that is, the entire structure is built using printing parameter T6. Similarly, the second group corresponds to printing parameter C5, the third group corresponds to printing parameter T9, the fourth group corresponds to printing parameter C1, the fifth group corresponds to printing parameter T8, the sixth group corresponds to printing parameter C4, and the seventh group corresponds to printing parameter C5. Figure 5 The partitions are assigned corresponding printing parameters.

[0119] Depend on Figure 6 It can be seen that the first and second groups respectively use the parameters with the best tensile and compressive strength, and the corresponding lattice structures have a larger ultimate bearing capacity, but smaller stiffness and greater randomness; the third and fourth groups respectively use the printing parameters with the best tensile and compressive stiffness, and the corresponding lattice structures have a larger stiffness, but smaller strength and greater randomness; the fifth and sixth groups respectively use the printing parameters with the smallest tensile and compressive randomness, and the corresponding lattice structures have the smallest randomness, but smaller strength and smaller stiffness; the seventh group is based on Figure 6 Optimal printing parameters are used for each partition. This combination of printing parameters maximizes the lattice structure's strength and stiffness, while minimizing randomness. The tensile zone of the entire structure is larger than the compressive zone, and the performance of the optimal printing parameters for tension is slightly higher than that for compression.

[0120] Therefore, the present invention adopts the above-mentioned integrated control method for strength, stiffness and randomness of 3D printed structural parts. By taking the regional integrated optimization of the strength, stiffness and randomness of the target component as the goal, the components are divided by tension, compression, strength, stiffness and randomness, and different printing parameters are assigned to make the components meet the strength requirements while having higher stiffness and lower randomness.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for integrated control of strength, stiffness and randomness of 3D printed structural parts, characterized in that: The following steps are involved: S0. Build material property library; S1. Select 3D printing materials based on mechanical requirements; S2. Determine printing parameters that affect mechanical properties; S3. Determine the printing parameter range: Determine the maximum and minimum values of each printing parameter from the molding perspective to obtain the printing parameter range; S4. Determine the selected values of printing parameters: select 3-5 values of the same number at equal intervals for each printing parameter; S5. Orthogonal experiment combination printing parameters: obtaining a printing parameter combination based on the orthogonal experiment design, wherein the printing parameters are factors in the orthogonal experiment, and the printing parameter values are levels in the orthogonal experiment; S6. Mechanical test: Use the printing parameter combination obtained in S5 to print the tension standard specimen and the compression standard specimen respectively, and conduct mechanical tests to obtain mechanical test data; S7, multivariate regression model fitting: obtain the regression matrix of strength, stiffness, randomness and printing parameters; S8. Structural component stress analysis: Perform finite element analysis on structural components to obtain stress and strain predictions for each part; S9. Structural component area division: Structural component areas are divided according to the dominant relationship between tension and compression state, stiffness and strength randomness; S10, assigning printing parameter combinations: assigning printing parameter combinations according to the structural part area division; S11, 3D printing of structural parts: combining slicing and printing according to the given printing parameters; In S9, the strength of the material obtained in S5 , stiffness , randomness The stress and strain prediction results obtained from S8 are divided into regions as follows: Randomness-dominated region: The region in the target structural part where the finite element stress is less than 30% of the strength is the randomness-dominated region; Strength-dominated region: When the finite element stress of the region in the target structural part is ≥ 70% of the strength, it is considered as the strength-dominated region; Stiffness-dominated region: When the finite element stress of the region in the target structural part is less than 70% and ≥30% of the strength, it is the stiffness-dominated region; According to the positive and negative stress values of the S8 finite element analysis, the target structural member is first divided into tension and compression zones. The zone types include tensile strength zone, compressive strength zone, tensile stiffness zone, compressive stiffness zone, tensile random zone, and compressive random zone. The strength was quantified by taking the average value of the peak value of the mechanical test curve; The quantification method of stiffness is to select the average value of the tangent of the elastic segment of the mechanical test curve; Randomness comes from strength and stiffness respectively, and the quantitative formula is as follows: ； in, is the standard deviation of the mechanical test data, is the mean value of the mechanical test data.

2. A method for integrated control of strength, stiffness and randomness of 3D printed structural parts according to claim 1, characterized in that: In S0, the material property library includes actual 3D printing materials and the strength, stiffness and elastic modulus of the 3D printing materials.

3. The method for integrated control of strength, stiffness and randomness of 3D printed structural parts according to claim 1, characterized in that: In S2, the printing parameters include one or more of layer height, line width, nozzle temperature and printing speed.

4. A method for integrated control of strength, stiffness and randomness of 3D printed structural parts according to claim 1, characterized in that: In S3, the printing parameter range is determined by the molding effect of the target structural part through trial printing.

5. The method for integrated control of strength, stiffness and randomness of 3D printed structural parts according to claim 1, characterized in that: In S4, the number of selected values of the printing parameters is , the range of printing parameter selection values is as follows: ； in, The minimum value among the selected values for the print parameters, The maximum value among the selected values of the print parameters, The number of values selected for the print parameters, Select values for printing parameters No. A selected value, .

6. A method for integrated control of strength, stiffness and randomness of 3D printed structural parts according to claim 1, characterized in that: In S6, the tensile standard specimen and the compressive standard specimen are one of ASTM, ISO or GB, and the mechanical test data include one or more of tensile strength, tensile stiffness, tensile elastic modulus, compressive strength, compressive stiffness and compressive elastic modulus.

7. The method for integrated control of strength, stiffness and randomness of 3D printed structural parts according to claim 1, characterized in that: In S7, the data of the multivariate regression model fitting comes from S6, and the regression matrix of each printing parameter A, B, C, ..., and strength Sr, stiffness Si and randomness R is finally obtained: ； in, is a vector of printing parameters, is the regression coefficient matrix, is the vector of mechanical properties, is the error vector.

8. The method for integrated control of strength, stiffness and randomness of 3D printed structural parts according to claim 1, characterized in that: In S8, the stress analysis of the structural parts adopts finite element method, and priority is given to ensuring that the strength of the structural parts meets the requirements. The data used by the finite element method are respectively from the mechanical test data of the tensile quasi-specimen with the highest tensile strength and the compressive standard specimen with the highest compressive strength in S6.

9. The method for integrated control of strength, stiffness and randomness of 3D printed structural parts according to claim 1, characterized in that: S10 is specifically: Tensile strength zone: the printing parameter combination that gives the highest printable tensile strength; Compressive strength zone: gives the highest printable compressive strength printing parameter combination; Tensile stiffness zone: the printing parameter combination that gives the highest printable tensile stiffness and a tensile strength not less than the regional stress; Compressive stiffness zone: a printing parameter combination that gives the highest printable compressive stiffness and a compressive strength not less than the regional stress; Tensile random zone: the printing parameter combination that gives the lowest printable tensile randomness and a tensile strength not lower than the regional stress; Compressive random zone: This is the printing parameter combination that gives the lowest printable compressive randomness and a compressive strength not lower than the regional stress.

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