A high-throughput additive manufacturing process specimen design method and system

Abstract

The application aims to provide a high-flux additive manufacturing process sample design method and system, and belongs to the technical field of process manufacturing. The method comprises the following steps: designing a sample of additive manufacturing process parameter combinations; and obtaining an optimal process parameter combination according to the performance test result of the sample. The application can realize efficient testing of the quality of additive manufacturing workpieces under a plurality of additive manufacturing process parameter combinations, and obtain an optimal additive manufacturing process parameter combination.

Description

Technical Field

[0001] This invention relates to the field of process manufacturing technology, and in particular to a method for designing high-throughput additive manufacturing process samples. Background Technology

[0002] Additive manufacturing, also known as 3D printing, is a prototyping technology for the rapid and flexible production of solid parts. It has immense development potential and is a mainstream direction for future manufacturing. The technical challenge of additive manufacturing lies in controlling process parameters. A parameter-structure-property relationship chain exists, and the performance of the formed workpiece is highly sensitive to process parameters. Reasonable process parameters are essential to ensure successful workpiece forming and to guarantee that the microstructure, properties, dimensional accuracy, and defect control of the formed workpiece meet requirements. However, additive manufacturing involves numerous process parameters (taking selective laser melting as an example, including laser-related parameters, laser scanning-related parameters, metal powder-related parameters, temperature-related parameters, etc.), with a large range of parameter values, making it a typical multi-factor, multi-level problem. Furthermore, fluctuations in parameters significantly affect the stability of printed product quality. Using traditional trial-and-error methods to study additive manufacturing process parameters is inefficient, time-consuming, and costly. On the other hand, because the mechanism of additive manufacturing is not yet fully understood, numerical simulation methods have certain limitations in optimizing additive manufacturing processes. Summary of the Invention

[0003] The purpose of this invention is to provide a high-throughput additive manufacturing process sample design method, which can efficiently test the quality of additively manufactured workpieces under numerous combinations of additive manufacturing process parameters and obtain the optimal combination of additive manufacturing process parameters.

[0004] This invention proposes a method for designing high-throughput additive manufacturing process prototypes, comprising:

[0005] Design a sample with a combination of additive manufacturing process parameters;

[0006] Based on the performance test results of the samples, the optimal combination of process parameters is obtained.

[0007] The sample designed for additive manufacturing process parameter combinations includes:

[0008] Design a high-throughput parameter printing sample, and arrange test layers and standard layers alternately along the height direction on the sample, that is, one standard layer and one test layer, starting with the standard layer and the test layer is the S layer.

[0009] Each test layer is divided into orthogonally distributed p×q regions, i.e., p rows and q columns. The regions of the test layer are divided into two categories: test regions and standard regions. The test regions and standard regions are arranged alternately. Each test region is a set of process parameters that do not change.

[0010] The test layer specifically includes:

[0011] The test layer is designed according to the Taguchi experimental design method, selecting 3 to 20 factors affecting additive manufacturing, with each factor set to 2 to 3 factor levels. Based on the number of factors and factor levels, an appropriate orthogonal design table Ln(mF) orthogonal table is selected, where F is the number of factors, m is the number of factor levels, and n is the total number of schemes. This table is used as an inner orthogonal table, with n schemes.

[0012] The Lt(3F) orthogonal array is used as the outer orthogonal array, with t sets of schemes. Each scheme of the inner orthogonal array corresponds to an outer orthogonal array, with t sets of schemes, for a total of n×t sets of experimental schemes. The test layer is arranged according to the n×t sets of experimental schemes, that is, each scheme of each outer orthogonal array corresponds to a test area block. The arrangement order of the schemes in the test layer is that the inner layer is an outer orthogonal array in a loop, and the outer layer is an inner orthogonal array in a loop. The size of the horizontal sample is such that the number of test area blocks in each layer is greater than the number of schemes of an outer orthogonal array, that is, (p×q) / 2>t, so that one test layer can print all the schemes of an outer orthogonal array.

[0013] The standard layer specifically includes:

[0014] All standard and test layers use the same process parameters for their standard areas, and these process parameters are those that produce good molding quality.

[0015] Based on the performance test results of the samples, obtaining the optimal combination of process parameters specifically includes:

[0016] To obtain the microstructure and material properties of the sample;

[0017] To obtain the micromechanical properties of the sample;

[0018] Select the optimal combination of process parameters.

[0019] Obtaining the microstructure and material properties of the sample specifically includes:

[0020] After the printed sample is completed, it is cut layer by layer along the x, y, and z directions. The thickness of the cut layer is half the sum of the thickness of the standard layer and the test layer. High-throughput microstructure and material analysis are performed on each test area block to directly obtain a comparison chart of each process parameter.

[0021] Establish the correspondence between various process parameters and hole defects, and fill the results obtained, such as the secondary dendrite arm spacing and hole diameter, into the corresponding inner and outer orthogonal arrays of the Taguchi experimental design. Calculate the signal-to-noise ratio of each outer orthogonal array scheme, and then calculate the signal-to-noise ratio of the inner orthogonal array scheme. Select the process parameter scheme with the optimal angle.

[0022] Obtaining the micromechanical properties of the sample specifically includes:

[0023] After the sample is printed, it is cut layer by layer along the x, y, and z directions. The thickness of the cut layer is half the sum of the thickness of the standard layer and the test layer. The micromechanical properties of each test area block are tested one by one to obtain a comparison chart of the performance under various process parameters.

[0024] Establish the correspondence between various parameters and performance, directly fill the obtained performance values ​​into the corresponding inner and outer orthogonal arrays of the Taguchi experimental design, calculate the signal-to-noise ratio of each outer orthogonal array scheme, then calculate the signal-to-noise ratio of the inner orthogonal array scheme, and select the optimal process parameter scheme for that angle.

[0025] A high-throughput additive manufacturing process sample design system, the system comprising:

[0026] The design module is used to design samples with combinations of additive manufacturing process parameters;

[0027] The testing module is used to obtain the optimal combination of process parameters based on the performance test results of the sample.

[0028] A computer device includes a processor and a memory for storing processor-executable programs, wherein when the processor executes the programs stored in the memory, it implements the above-described method for designing and constructing high-throughput additive manufacturing process samples.

[0029] A storage medium, which is a computer-readable storage medium, stores a computer program. When the program is executed by a processor, the processor executes the computer program stored in the memory to implement the above-described method for designing and constructing high-throughput additive manufacturing process samples.

[0030] This invention enables additive manufacturing design with multiple combinations of process parameters in a single sample. Compared to existing technologies where only one set of process parameters is designed per sample, this invention significantly reduces the cost of testing process parameters. Traditional methods require printing many samples to test process parameter combinations, while this invention only requires printing one sample to test multiple combinations of process parameters. Furthermore, this invention can measure multiple combinations of process parameters simultaneously, achieving high-throughput testing. This is much more efficient than existing methods that measure one combination of process parameters at a time, allowing for the rapid identification of optimal additive manufacturing process parameters. This invention solves the problems of low efficiency, long cycle time, and high cost associated with traditional trial-and-error methods for studying additive manufacturing process parameter combinations. Attached Figure Description

[0031] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart of a high-throughput additive manufacturing process sample design method proposed in this invention;

[0034] Figure 2 This is a high-throughput additive manufacturing process optimized sample design diagram for a high-throughput additive manufacturing process sample design method proposed in this invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0036] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0037] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" and "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0038] This invention proposes a method for designing high-throughput additive manufacturing process prototypes, comprising:

[0039] S100, a sample of additive manufacturing process parameter combination;

[0040] S200: Based on the performance test results of the sample, obtain the optimal combination of process parameters.

[0041] This invention enables additive manufacturing design with multiple combinations of process parameters in a single sample. Compared to existing technologies where only one set of process parameters is designed per sample, this invention significantly reduces the cost of testing process parameters. Traditional methods require printing many samples to test process parameter combinations, while this invention only requires printing one sample to test multiple combinations of process parameters. Furthermore, this invention can measure multiple combinations of process parameters simultaneously, achieving high-throughput testing. This is much more efficient than existing methods that measure one combination of process parameters at a time, allowing for the rapid identification of optimal additive manufacturing process parameters. This invention solves the problems of low efficiency, long cycle time, and high cost associated with traditional trial-and-error methods for studying additive manufacturing process parameter combinations.

[0042] The sample of the S100 design additive manufacturing process parameter combination includes:

[0043] Design a high-throughput parameter printing sample, and arrange test layers and standard layers alternately along the height direction on the sample, that is, one standard layer and one test layer, starting with the standard layer and the test layer is the S layer.

[0044] Each test layer is divided into orthogonally distributed p×q regions, i.e., p rows and q columns. The regions of the test layer are divided into two categories: test regions and standard regions. The test regions and standard regions are arranged alternately. Each test region is a set of process parameters that do not change.

[0045] The thickness of the test layer selected in this invention is 2-10 mm, and the thickness of the standard layer is 1-3 mm. The horizontal dimension of the test area of ​​the test layer is (2-10 mm) × (2-10 mm), and the horizontal dimension of the standard area is (1-3 mm) × (1-3 mm). Figure 2 .

[0046] The high-throughput parameter printing sample is a printed sample containing multiple sets of process parameters, and multiple sets of process parameters can be tested simultaneously during the testing process, without the need to test each set of process parameters individually. The standard layer proposed in this invention is... Figure 2 The black area represents the test layer. Figure 2 The white area, with alternating test and standard layers, allows testers to easily identify the impact of process parameter combinations on additive manufacturing, quickly find the relationship between process parameter combinations and additive manufacturing, and further improve the process parameter combinations based on this relationship.

[0047] The test layer specifically includes:

[0048] The test layer is designed according to the Taguchi experimental design method, selecting 3 to 20 factors affecting additive manufacturing, with each factor set to 2 to 3 factor levels. Based on the number of factors and factor levels, an appropriate orthogonal design table Ln(mF) orthogonal table is selected, where F is the number of factors, m is the number of factor levels, and n is the total number of schemes. This table is used as an inner orthogonal table, with n schemes.

[0049] The Lt(3F) orthogonal array is used as the outer orthogonal array, with t sets of schemes. Each scheme of the inner orthogonal array corresponds to an outer orthogonal array, with t sets of schemes, for a total of n×t sets of experimental schemes. The test layer is arranged according to the n×t sets of experimental schemes, that is, each scheme of each outer orthogonal array corresponds to a test area block. The arrangement order of the schemes in the test layer is that the inner layer is an outer orthogonal array in a loop, and the outer layer is an inner orthogonal array in a loop. The size of the horizontal sample is such that the number of test area blocks in each layer is greater than the number of schemes of an outer orthogonal array, that is, (p×q) / 2>t, so that one test layer can print all the schemes of an outer orthogonal array.

[0050] The Taguchi experimental design method uses orthogonal arrays for experimental design. It is a low-cost, high-efficiency quality engineering method that emphasizes improving product quality not through inspection, but by achieving the best experimental results with the fewest possible trials. The Taguchi method is a novel quality management technique that, based on engineering technology and focused on economic benefits, opens up new horizons in quality management. Compared to traditional quality management, it has the following characteristics: it uses engineering methods to study product quality, treats product design as engineering design, considers the quality of product design as the quality of engineering design, and measures product quality by the economic losses it causes to society. This invention selects the Taguchi method to choose the most reasonable combination of process parameters in additive manufacturing.

[0051] The standard layer specifically includes:

[0052] All standard and test layers use the same process parameters for their standard areas, and these process parameters are those that produce good molding quality.

[0053] The standard layer uses parameters with good forming quality, which can be compared with the test layer. This allows the experimenters to discover the influence of process parameters on the microstructure, properties, and dimensions of the formed workpiece, and to find the effect of process parameter combinations on the workpiece. This makes it easier for the experimenters to control the process parameters to obtain the required formed workpiece.

[0054] Based on the performance test results of the samples, S200 obtains the optimal combination of process parameters, specifically including:

[0055] S201, to obtain the microstructure and material properties of the sample;

[0056] S202, to obtain the micromechanical properties of the sample;

[0057] S203, select the optimal combination of process parameters.

[0058] Obtaining the microstructure and material properties of the sample specifically includes:

[0059] After the printed sample is completed, it is cut layer by layer along the x, y, and z directions. The thickness of the cut layer is half the sum of the thickness of the standard layer and the test layer. High-throughput microstructure and material analysis are performed on each test area block to directly obtain a comparison chart of each process parameter.

[0060] Establish the correspondence between various process parameters and hole defects, and fill the results obtained, such as the secondary dendrite arm spacing and hole diameter, into the corresponding inner and outer orthogonal arrays of the Taguchi experimental design. Calculate the signal-to-noise ratio of each outer orthogonal array scheme, and then calculate the signal-to-noise ratio of the inner orthogonal array scheme. Select the process parameter scheme with the optimal angle.

[0061] Various analytical methods, such as optical metallography, scanning electron microscopy, transmission electron microscopy, energy dispersive spectroscopy, and XRD, can be used to analyze each test area. Alternatively, high-energy X-ray tomography reconstruction technology can be employed for overall three-dimensional analysis. This allows for direct comparison of the microstructures of each process parameter, quickly and intuitively establishing the correspondence between parameters, microstructures, and pore defects, thus enabling efficient and rapid judgment. Quantifiable results, such as secondary dendrite arm spacing and pore diameter, are directly entered into the corresponding inner and outer orthogonal arrays of the Taguchi experimental design. The signal-to-noise ratio (SNR) of each outer orthogonal array scheme is calculated, followed by the SNR of the inner orthogonal array scheme, thereby selecting the optimal process parameter scheme from this perspective.

[0062] Obtaining the micromechanical properties of the sample specifically includes:

[0063] After the sample is printed, it is cut layer by layer along the x, y, and z directions. The thickness of the cut layer is half the sum of the thickness of the standard layer and the test layer. The micromechanical properties of each test area block are tested one by one to obtain a comparison chart of the performance under various process parameters.

[0064] Establish the correspondence between various parameters and performance, directly fill the obtained performance values ​​into the corresponding inner and outer orthogonal arrays of the Taguchi experimental design, calculate the signal-to-noise ratio of each outer orthogonal array scheme, then calculate the signal-to-noise ratio of the inner orthogonal array scheme, and select the optimal process parameter scheme for that angle.

[0065] By performing micromechanical property tests on each test area, such as microhardness and elasticity, a comparison chart of performance under various process parameters is directly obtained. This invention can quickly and intuitively establish the correspondence between various parameters and performance, enabling experimental personnel to make efficient and rapid judgments. The obtained performance values ​​are directly filled into the inner and outer orthogonal arrays corresponding to the Taguchi experimental design, and the signal-to-noise ratio of each outer orthogonal array scheme is calculated. Then, the signal-to-noise ratio of the inner orthogonal array scheme is calculated, thereby selecting the optimal process parameter scheme from this perspective.

[0066] Selecting the optimal combination of process parameters specifically includes:

[0067] By combining the microstructure, defect, and performance results of the samples under various process parameters, the influence of each parameter was analyzed, and finally, the sample with the optimal process parameters under the comprehensive conditions was obtained.

[0068] This invention enables additive manufacturing design with multiple combinations of process parameters in a single sample. Compared to existing technologies where only one set of process parameters is designed per sample, this invention significantly reduces the cost of testing process parameters. Traditional methods require printing many samples to test process parameter combinations, while this invention only requires printing one sample to test multiple combinations of process parameters. Furthermore, this invention can measure multiple combinations of process parameters simultaneously, achieving high-throughput testing. This is much more efficient than existing methods that measure one combination of process parameters at a time, allowing for the rapid identification of optimal additive manufacturing process parameters. This invention solves the problems of low efficiency, long cycle time, and high cost associated with traditional trial-and-error methods for studying additive manufacturing process parameter combinations.

[0069] Example 2

[0070] A high-throughput additive manufacturing process sample design system, the system comprising:

[0071] The design module is used to design samples with combinations of additive manufacturing process parameters;

[0072] The testing module is used to obtain the optimal combination of process parameters based on the performance test results of the sample.

[0073] This invention enables additive manufacturing design with multiple combinations of process parameters in a single sample. Compared to existing technologies where only one set of process parameters is designed per sample, this invention significantly reduces the cost of testing process parameters. Traditional methods require printing many samples to test process parameter combinations, while this invention only requires printing one sample to test multiple combinations of process parameters. Furthermore, this invention can measure multiple combinations of process parameters simultaneously, achieving high-throughput testing. This is much more efficient than existing methods that measure one combination of process parameters at a time, allowing for the rapid identification of optimal additive manufacturing process parameters. This invention solves the problems of low efficiency, long cycle time, and high cost associated with traditional trial-and-error methods for studying additive manufacturing process parameter combinations.

[0074] Example 3

[0075] This embodiment provides a computer device, including a processor and a memory for storing processor-executable programs. When the processor executes the program stored in the memory, it implements the above-described method for designing and constructing high-throughput additive manufacturing process samples.

[0076] Example 4

[0077] This embodiment provides a storage medium, which is a computer-readable storage medium that stores a computer program. When the program is executed by a processor, the processor executes the computer program stored in the memory to implement the above-described method for designing and constructing high-throughput additive manufacturing process samples.

[0078] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for designing test specimens for a high-throughput additive manufacturing process, characterized in that, include: Design a sample with a combination of additive manufacturing process parameters; Based on the performance test results of the samples, obtain the optimal combination of process parameters; The samples containing the designed additive manufacturing process parameter combinations include: Design a high-throughput parameter printing sample, on which test layers and standard layers are arranged alternately along the height direction, i.e., one standard layer and one test layer, starting with the standard layer and the test layer is the s layer. Each test layer is divided into orthogonally distributed p×q regions, i.e., p rows and q columns. The regions of the test layer are divided into two categories: test regions and standard regions. The test regions and standard regions are arranged alternately. Each test region is a set of process parameters that do not change. The test layer specifically includes: The test layer is designed according to the Taguchi experimental design method, selecting 3 to 20 factors affecting additive manufacturing, with each factor set to 2 to 3 factor levels. Based on the number of factors and factor levels, an appropriate orthogonal design table Ln(mF) orthogonal table is selected, where F is the number of factors, m is the number of factor levels, and n is the total number of schemes. This table is used as an inner orthogonal table, with n schemes. The Lt(3F) orthogonal array is used as the outer orthogonal array, with t sets of schemes. Each scheme of the inner orthogonal array corresponds to an outer orthogonal array, with t sets of schemes, for a total of n×t sets of experimental schemes. The test layer is arranged according to the n×t sets of experimental schemes, that is, each scheme of each outer orthogonal array corresponds to a test area block. The arrangement order of the schemes in the test layer is that the inner layer is an outer orthogonal array in a loop, and the outer layer is an inner orthogonal array in a loop. The size of the horizontal sample is such that the number of test area blocks in each layer is greater than the number of schemes of an outer orthogonal array, that is, (p×q) / 2>t, so that one test layer can print all the schemes of an outer orthogonal array.

2. The high-throughput additive manufacturing process sample design method according to claim 1, characterized in that, The standard layer specifically includes: All standard and test layers use the same process parameters for their standard areas, and these process parameters are those that produce good molding quality.

3. The high-throughput additive manufacturing process sample design method according to claim 1, characterized in that, The process of obtaining the optimal combination of process parameters based on the performance test results of the sample specifically includes: To obtain the microstructure and material properties of the sample; To obtain the micromechanical properties of the sample; Select the optimal combination of process parameters.

4. The high-throughput additive manufacturing process sample design method according to claim 3, characterized in that, The acquisition of the microstructure and material properties of the sample specifically includes: After the sample is printed, it is cut layer by layer along the x, y, and z directions. The thickness of the cut layer is half the sum of the thickness of the standard layer and the test layer. High-throughput microstructure and material analysis are performed on each test area block to directly obtain a comparison chart of each process parameter. Establish the correspondence between various process parameters and hole defects, and fill the results—secondary dendrite arm spacing and hole diameter—into the corresponding inner and outer orthogonal arrays of the Taguchi experimental design. Calculate the signal-to-noise ratio of each outer orthogonal array scheme, and then calculate the signal-to-noise ratio of the inner orthogonal array scheme to select the optimal process parameter scheme.

5. The high-throughput additive manufacturing process sample design method according to claim 3, characterized in that, The specific methods for obtaining the micromechanical properties of the sample include: After the sample is printed, it is cut layer by layer along the x, y, and z directions. The thickness of the cut layer is half the sum of the thickness of the standard layer and the test layer. The micromechanical properties of each test area block are tested one by one to obtain a comparison chart of the performance under various process parameters. Establish the correspondence between various parameters and performance, directly fill the obtained performance values ​​into the corresponding inner and outer orthogonal arrays of the Taguchi experimental design, calculate the signal-to-noise ratio of each outer orthogonal array scheme, then calculate the signal-to-noise ratio of the inner orthogonal array scheme, and select the optimal process parameter scheme.

6. A high-throughput additive manufacturing process sample design system, used to implement the high-throughput additive manufacturing process sample design method as described in any one of claims 1 to 5, characterized in that, The system includes: The design module is used to design samples with combinations of additive manufacturing process parameters; The testing module is used to obtain the optimal combination of process parameters based on the performance test results of the sample.

7. A computer device, characterized in that, The computer device includes a processor and a memory for storing a processor-executable program, wherein when the processor executes the program stored in the memory, the computer device performs the method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, A stored program, which, when executed by a processor, performs the method according to any one of claims 1 to 5.

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