Method, device, medium and product for determining a reference value of a part manufactured by additive manufacturing

Abstract

This invention discloses a method, equipment, medium, and product for determining the reference values ​​of additively manufactured parts, relating to the field of additive manufacturing technology. The method includes: determining the target chemical composition of a target additive manufacturing material and analyzing the target chemical composition to obtain composition analysis results; determining target elements and their compositional characteristics; determining at least two reference additive manufacturing materials based on the compositional characteristics of the target elements; wherein the compositional gradient of the target elements in each reference additive manufacturing material is different; printing target parts based on each reference additive manufacturing material, testing the mechanical properties of each target part, and performing statistical analysis on each mechanical property to obtain the reference performance value of each target part. The solution of this invention can correlate the composition of additive manufacturing powder with the reference performance value of the part, more accurately determining the reference value of additively manufactured parts, and helping to improve the safety of additively manufactured parts.

Description

Technical Field

[0001] This invention relates to the field of additive manufacturing technology, and in particular to a method, equipment, medium, and product for determining the reference values ​​of additively manufactured parts. Background Technology

[0002] Additive manufacturing technology, as an advanced processing technology that revolutionizes traditional manufacturing processes, uses slicing software to slice parts layer by layer based on a 3D model, and interacts with manufacturing equipment via computer transmission. Currently, the mainstream manufacturing process is selective laser melting (SLM), whose main principle is that a laser interacts with a powder bed to achieve localized powder melting and solidification, forming layers one by one, and finally creating a complete part.

[0003] Currently, SLM (Selective Laser Melting) technology has been widely used in the aerospace field. In the context of civil aircraft manufacturing and the requirements of airworthiness and safety, reliable material performance data are needed to support the safety of the manufactured parts. As an important element, additive manufacturing powder raw materials play a key role in the strength verification of subsequent products by controlling their main components and developing benchmark properties.

[0004] Currently, additive manufacturing powders produced under fixed processes often have a composition concentrated in a relatively narrow range. This leads to the development of baseline values ​​for corresponding materials being concentrated in a relatively aggregated distribution range. Consequently, in engineering applications, it is impossible to further differentiate the application range of different powder compositions and corresponding performance parts based on the relationship between powder composition and performance according to the application conditions of the parts. At the same time, the relatively concentrated performance often results in a higher statistical baseline value, frequently causing acceptance failures and leading to an invisible increase in manufacturing costs.

[0005] How to correlate additive manufacturing powder composition with part performance benchmarks, and more accurately determine the benchmarks for additive manufacturing parts, thereby helping to improve the safety of additively manufactured parts, is a key research issue in the industry. Summary of the Invention

[0006] This invention provides a method, equipment, medium, and product for determining the reference values ​​of additively manufactured parts, thereby correlating the composition of additive manufacturing powder with the reference values ​​of part performance, more accurately determining the reference values ​​of additively manufactured parts, and helping to improve the safety of additively manufactured parts.

[0007] According to one aspect of the present invention, a method for determining the performance benchmark value of an additively manufactured part is provided, the method comprising:

[0008] The target chemical composition of the target additive manufacturing material is determined, and the target chemical composition is analyzed to obtain the composition analysis results;

[0009] Identify the target element and its component characteristics;

[0010] At least two reference additive manufacturing materials are determined based on the compositional characteristics of the target element; wherein the compositional gradient of the target element is different in each of the reference additive manufacturing materials.

[0011] Target parts are printed based on each of the aforementioned reference additive manufacturing materials, and the mechanical properties of each target part are tested. Statistical analysis is then performed on each of the mechanical properties to obtain the benchmark performance values ​​of each target part.

[0012] According to another aspect of the present invention, an apparatus for determining performance benchmark values ​​of additively manufactured parts is provided, the apparatus comprising:

[0013] The first determining module is used to determine the target chemical composition of the target additive manufacturing material and analyze the target chemical composition to obtain the composition analysis results;

[0014] The second determining module is used to determine the target element and the compositional characteristics of the target element;

[0015] The third determining module is used to determine at least two reference additive manufacturing materials based on the compositional characteristics of the target element; wherein the compositional gradient of the target element is different in each of the reference additive manufacturing materials;

[0016] The component performance benchmark value determination module is used to print target components based on each of the reference additive manufacturing materials, test the mechanical properties of each target component, and perform statistical analysis on each mechanical property to obtain the component performance benchmark value of each target component.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] At least one processor; and

[0019] A memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for determining the performance benchmark value of an additively manufactured part according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for determining the performance benchmark value of an additively manufactured part as described in any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method for determining the performance benchmark value of an additively manufactured part as described in any embodiment of the present invention.

[0023] The technical solution of this invention involves determining the target chemical composition of a target additive manufacturing material and analyzing the target chemical composition to obtain composition analysis results; determining target elements and their compositional characteristics; determining at least two reference additive manufacturing materials based on the compositional characteristics of the target elements; wherein the compositional gradient of the target elements in each of the reference additive manufacturing materials is different; printing target parts based on each of the reference additive manufacturing materials, testing the mechanical properties of each target part, and performing statistical analysis on each of the mechanical properties to obtain a performance benchmark value for each target part. This allows for the correlation between the additive manufacturing powder composition and the performance benchmark value, more accurately determining the benchmark value for additively manufactured parts, and providing assistance in improving the safety of additively manufactured parts.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a method for determining the performance benchmark value of an additively manufactured part according to Embodiment 1 of the present invention;

[0027] Figure 2 This is a flowchart of a method for determining the performance benchmark value of an additively manufactured part according to Embodiment 2 of the present invention;

[0028] Figure 3 This is a schematic diagram illustrating the mechanical properties of parts obtained from different reference additive manufacturing materials according to an embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of a device for determining the performance benchmark value of an additively manufactured part according to Embodiment 3 of the present invention;

[0030] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the method for determining the performance benchmark value of additively manufactured parts according to embodiments of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] Example 1

[0034] Figure 1 This is a flowchart illustrating a method for determining the performance benchmark value of an additively manufactured part according to Embodiment 1 of the present invention. This embodiment is applicable to situations where the composition of additively manufactured powder is correlated with the performance benchmark value of the part to determine the benchmark value of the additively manufactured part. This method can be executed by a device for determining the performance benchmark value of the additively manufactured part. This device can be implemented in hardware and / or software and can be configured in electronic devices such as computers, servers, or tablet computers. Figure 1 As shown, the method includes:

[0035] Step 110: Determine the target chemical composition of the target additive manufacturing material and analyze the target chemical composition to obtain the composition analysis results.

[0036] The target additive manufacturing material is any additive manufacturing powder. For example, the additive manufacturing powder can be metal powder, composite material powder, or ceramic powder, etc., and is not limited in this embodiment.

[0037] Optionally, in this embodiment, when it is determined that the target part needs to be obtained by additive manufacturing using the target additive manufacturing material, the chemical composition of the target additive manufacturing material can be analyzed. In this embodiment, the chemical composition of the target additive manufacturing material is referred to as the target chemical composition. Furthermore, after obtaining the target chemical composition, it can be analyzed to obtain the composition analysis results of the target chemical composition.

[0038] Optionally, determining the target chemical composition of the target additive manufacturing material and analyzing the target chemical composition to obtain the composition analysis results may include: analyzing the additive manufacturing material based on X-ray fluorescence spectroscopy to obtain the target chemical composition and the content of each component.

[0039] In an optional implementation of this embodiment, the additive manufacturing powder can be pressed into a sheet or placed in a special sample cup. Furthermore, the prepared sample can be placed in an XRF instrument and measured according to preset parameters (e.g., voltage, current, or measurement time, which are not limited in this embodiment) to obtain the target chemical composition and the content of each component.

[0040] Step 120: Determine the target element and the compositional characteristics of the target element.

[0041] Optionally, in this embodiment, after obtaining the compositional analysis results of the target additive manufacturing material, the target elements and their compositional characteristics can be further determined; wherein, the target elements are the chemical components in the target chemical composition that have a significant impact on the part.

[0042] Optionally, in this embodiment, determining the target element and the compositional characteristics of the target element may include: determining the target application field of the target part, determining the target performance index based on the target application field; determining each element associated with the target performance index, and determining each element as the target element; and determining the compositional characteristics of the target element based on the compositional analysis results.

[0043] The target performance indicators include at least one of the following: strength, hardness, toughness, corrosion resistance, and fatigue life;

[0044] In one optional implementation of this embodiment, after obtaining the composition analysis results of the target additive manufacturing material, the influence of each chemical component on the performance of the part can be further determined, thereby clarifying the main chemical components affecting the performance of the part and the component characteristics that change significantly during the printing process.

[0045] Optionally, in this embodiment, the target performance indicators of the target component can be determined by identifying the application field of the target component (e.g., aerospace, precision manufacturing, or marine). For example, if the target application field of the target component is aerospace, the target performance indicators can be high strength and high corrosion resistance, etc. Furthermore, each element associated with the target performance indicators can be identified, and each element can be identified as the target element.

[0046] In one example of this embodiment, the additive manufacturing material is Ti6Al4V, which contains elements such as Ti, Al, V, Fe, O, N, and C. Among them, the O element has a more significant effect on the tensile properties of the part.

[0047] Furthermore, apart from O, under the premise that the composition of the bar stock is solidified according to the standard, the proportion of related elements will not change significantly during the powder preparation process. At the same time, the O content will also change significantly due to the difference in the airtightness of the equipment during the printing process. Therefore, combining the element change patterns in the powder preparation and printing processes, the O content is defined as the main component characteristic.

[0048] Step 130: Determine at least two reference additive manufacturing materials based on the compositional characteristics of the target element.

[0049] The target element composition gradients of each of the reference additive manufacturing materials are different.

[0050] In this embodiment, the types of additive manufacturing materials can be 3, 5, or 6, etc., and are not limited thereto in this embodiment.

[0051] Optionally, in this embodiment, after determining the compositional characteristics of the target element, at least two additive manufacturing materials with different compositional gradients of the target element can be further determined based on the compositional characteristics of the target element. In this embodiment, these are referred to as reference additive manufacturing materials.

[0052] In one example of this embodiment, three batches of Ti6Al4V powder with different oxygen content gradients can be customized. Considering the increase in oxygen content during the powder reuse process, the oxygen content of the three batches of powder is correlated. For example, the oxygen content of the first batch of powder is C1~C2 ppm, the oxygen content of the second batch of powder is C3~C4 ppm, and the oxygen content of the third batch of powder is C5~C6 ppm. The number of times the cured powder is reused and the oxygen increase after reuse is ΔC, so C2+ΔC≈C3 and C4+ΔC≈C5.

[0053] Step 140: Print target parts based on each of the reference additive manufacturing materials, test the mechanical properties of each target part, and perform statistical analysis on each mechanical property to obtain the performance benchmark value of each target part.

[0054] The target component can be a tensile test bar or other standard components, and this embodiment does not limit it.

[0055] In this embodiment, after determining each reference additive manufacturing material, target parts can be printed based on each reference additive manufacturing material, and the mechanical properties of each printed part can be tested. Furthermore, statistical analysis can be performed on the mechanical properties of target parts printed with different reference additive manufacturing materials to obtain the performance benchmark value of the target parts.

[0056] In the above example, based on three batches of powder, tensile specimens can be printed according to the same specimen arrangement and printing parameters. N batches of test specimens can be printed using a reuse method. The total number of test bars formed by the three batches of powder is not less than 200, with at least 100 in each direction. The tensile properties are obtained by processing and testing according to ASTM E8 / E8M.

[0057] Furthermore, the tensile properties of each printed part can be summarized. For example, a normal distribution can be used to statistically analyze the data of all samples to obtain a performance benchmark value for the additive manufacturing material. The benchmark value is calculated according to the following method:

[0058]

[0059] Where: k 99 This is the one-sided tolerance limit coefficient, corresponding to at least the 99th percentile at a 95% confidence level for a normal distribution, corresponding to the A benchmark value T. 99 ;k 90 This is the one-sided tolerance limit coefficient, corresponding to at least the 99th percentile at a 95% confidence level for a normal distribution, and corresponding to the B benchmark value T. 90 .

[0060] The technical solution of this embodiment involves determining the target chemical composition of the target additive manufacturing material and analyzing the target chemical composition to obtain the composition analysis results; determining the target element and the composition characteristics of the target element; determining at least two reference additive manufacturing materials based on the composition characteristics of the target element; wherein the composition gradient of the target element is different in each of the reference additive manufacturing materials; printing target parts based on each of the reference additive manufacturing materials, testing the mechanical properties of each target part, and performing statistical analysis on each of the mechanical properties to obtain the part performance benchmark value of each target part. This allows for the correlation between the additive manufacturing powder composition and the part performance benchmark value, more accurately determining the part benchmark value of additive manufacturing, and helping to improve the safety of additively manufactured parts.

[0061] Example 2

[0062] Figure 2 This is a flowchart illustrating a method for determining the performance benchmark value of an additively manufactured part according to Embodiment 2 of the present invention. This embodiment is a further refinement of the above technical solution, and the technical solution in this embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the method includes:

[0063] Step 210: Determine the target chemical composition of the target additive manufacturing material and analyze the target chemical composition to obtain the composition analysis results.

[0064] Step 220: Determine the target element and the compositional characteristics of the target element.

[0065] Step 230: Within a preset gradient range, determine the different composition gradients of the target element; determine the additive manufacturing material based on each composition gradient to obtain each reference additive manufacturing material.

[0066] Optionally, in this embodiment, after determining the compositional characteristics of the target element, different compositional gradients of the target element can be further determined within a preset gradient range; additive manufacturing materials are determined based on each compositional gradient to obtain each reference additive manufacturing material, wherein the preset gradient range can be understood as the gradient range composed of the minimum and maximum compositional gradients of the target element.

[0067] Step 240: Print target parts based on each of the reference additive manufacturing materials, and test the mechanical properties of each target part.

[0068] Optionally, printing target parts based on each of the reference additive manufacturing materials and testing the mechanical properties of each target part may include: printing the target parts based on each of the reference additive manufacturing materials according to the same sample arrangement and printing parameters; and testing each target part based on a preset testing method to obtain the mechanical properties of each target part.

[0069] The test method is matched with the target performance index; the test method includes at least one of the following: strength test, hardness test, toughness test, corrosion resistance test, and fatigue life test; wherein, the number of target parts printed by each of the reference additive manufacturing materials is the same, and all the target parts are the same except for the printing material.

[0070] In an optional implementation of this embodiment, after determining each reference additive manufacturing material, the target part can be printed based on each reference additive manufacturing material according to the same sample arrangement and printing parameters; furthermore, the target parts can be tested based on the testing method to obtain the mechanical properties of each target part.

[0071] For example, Figure 3 This is a schematic diagram of the mechanical properties of parts obtained by different reference additive manufacturing materials according to an embodiment of the present invention. The three black dashed lines represent the properties of the target parts obtained by different additive manufacturing materials, the red solid lines represent the combined properties, and the two red vertical lines represent different reference values.

[0072] Step 250: Perform statistical analysis on each of the mechanical properties to obtain the benchmark values ​​of the performance of each target part.

[0073] Optionally, statistical analysis is performed on each of the mechanical properties to obtain the benchmark performance value of each target part. This may include: performing variance analysis on the mechanical property data of each target part to determine whether there are significant differences in the target performance of the target element under different composition gradients; if there are significant differences, determining the mean and standard deviation corresponding to each composition gradient; and determining the benchmark performance value of the part based on the mean and standard deviation of each component.

[0074] In one optional implementation of this embodiment, variance analysis can be performed on each mechanical property obtained from each test to determine whether there are significant differences in the target performance of the target element under different composition gradients; if there are significant differences, the mean and standard deviation corresponding to each composition gradient are determined; and the benchmark value of the part performance is determined based on each mean and standard deviation.

[0075] Optionally, in this embodiment, after statistically analyzing each of the mechanical properties to obtain the reference values ​​for the performance of each target part, the method may further include: verifying the reference values ​​for the performance of the part to determine the accuracy and reliability of the reference values ​​for the performance of the part.

[0076] To better understand the method for determining the performance benchmark values ​​of additively manufactured parts involved in this embodiment, a specific example is used below for illustration, which mainly includes the following:

[0077] Step 1: Conduct an analysis of the main chemical components of additive manufacturing powder raw materials and their impact on the performance of the manufactured parts. For example, taking additive manufacturing of Ti6Al4V as an example, its composition includes elements such as Ti, Al, V, Fe, O, N, and C, among which O has a more significant impact on the tensile properties of the manufactured parts.

[0078] Step 2: Identify the main chemical components that affect the performance of the part and the characteristics of components that change significantly during the printing process.

[0079] It is understandable that, apart from O, the proportion of related elements will not change significantly during the powder preparation process, provided that the composition of the bar stock is solidified according to the standard. At the same time, the O content will also change significantly during the printing process due to the difference in the airtightness of the equipment. Therefore, combining the element change patterns in the powder preparation and printing processes, the O content is defined as the main component characteristic.

[0080] Step 3: Customize three batches of additive manufacturing powder with different composition gradients around the main component.

[0081] Specifically, three batches of Ti6Al4V powder with different oxygen content gradients can be customized. Considering the increase in oxygen content during powder reuse, the oxygen content of the three batches of powder is correlated. For example, the oxygen content of the first batch of powder is C1~C2 ppm, the oxygen content of the second batch is C3~C4 ppm, and the oxygen content of the third batch is C5~C6 ppm. The number of times the cured powder is reused is T, and the oxygen increase after reuse is T*ΔC, so C2+T*ΔC≈C3, C4+T*ΔC≈C5.

[0082] Step 4: Print tensile test bars using powder from each batch and test the relevant mechanical properties.

[0083] In practice, tensile specimens can be printed based on three batches of powder, using the same specimen arrangement and printing parameters. N batches of test specimens can be printed using a reuse method. The total number of test bars formed from the three batches of powder is no less than 200, with at least 100 in each direction. The tensile properties are obtained by processing and testing according to ASTM E8 / E8M.

[0084] Step 5: Summarize the performance of the three batches and conduct statistical analysis to obtain the baseline value of the part performance.

[0085] In practical implementation, all tensile properties can be aggregated, and a normal distribution can be preferentially used to conduct statistical analysis of all sample data to obtain the performance benchmark value of the additive manufacturing material. The benchmark value is calculated according to the following method:

[0086]

[0087] Where: k 99 This is the one-sided tolerance limit coefficient, corresponding to at least the 99th percentile at a 95% confidence level for a normal distribution, corresponding to the A benchmark value T. 99 ;k 90 This is the one-sided tolerance limit coefficient, corresponding to at least the 99th percentile at a 95% confidence level for a normal distribution, and corresponding to the B benchmark value T. 90 .

[0088] The solution of this invention provides a performance benchmark value with statistical significance and composition range meaning. The verification process covers the performance of the parts corresponding to the key components in the composition range, and obtains a more reliable performance acceptance value based on composition changes. The solution of this invention plays a positive role in improving safety level and performance stability.

[0089] Example 3

[0090] Figure 4 This is a schematic diagram of a device for determining the performance benchmark value of an additively manufactured part according to Embodiment 3 of the present invention. Figure 4 As shown, the device includes: a first determining module 410, a second determining module 420, a third determining module 430, and a workpiece performance reference value determining module 440.

[0091] The first determining module 410 is used to determine the target chemical composition of the target additive manufacturing material and analyze the target chemical composition to obtain the composition analysis results.

[0092] The second determining module 420 is used to determine the target element and the composition characteristics of the target element;

[0093] The third determining module 430 is used to determine at least two reference additive manufacturing materials based on the compositional characteristics of the target element; wherein the compositional gradient of the target element is different in each of the reference additive manufacturing materials;

[0094] The part performance benchmark value determination module 440 is used to print target parts based on each of the reference additive manufacturing materials, test the mechanical properties of each target part, and perform statistical analysis on each mechanical property to obtain the part performance benchmark value of each target part.

[0095] In this embodiment, the first determining module determines the target chemical composition of the target additive manufacturing material and analyzes the target chemical composition to obtain the composition analysis results; the second determining module determines the target element and the composition characteristics of the target element; the third determining module determines at least two reference additive manufacturing materials based on the composition characteristics of the target element; wherein the composition gradient of the target element in each of the reference additive manufacturing materials is different; the part performance benchmark value determining module prints the target part based on each of the reference additive manufacturing materials, tests the mechanical properties of each target part, and performs statistical analysis on each of the mechanical properties to obtain the part performance benchmark value of each target part. This can correlate the additive manufacturing powder composition with the part performance benchmark value, more accurately determine the part benchmark value of additive manufacturing, and help improve the safety of additive manufacturing parts.

[0096] In an optional implementation of this embodiment, the first determining module 410 is specifically used for:

[0097] The additive manufacturing material was analyzed using X-ray fluorescence spectroscopy to obtain the target chemical composition and the content of each component.

[0098] In an optional implementation of this embodiment, the second determining module 420 is specifically used for:

[0099] The target application field of the target part is determined, and the target performance index is determined based on the target application field; the target performance index includes at least one of the following: strength, hardness, toughness, corrosion resistance, and fatigue life;

[0100] Identify each element associated with the target performance index, and define each element as the target element;

[0101] The compositional characteristics of the target element are determined based on the compositional analysis results.

[0102] In an optional implementation of this embodiment, the third determining module 430 is specifically used for:

[0103] Within a preset gradient range, determine the different component gradients of the target element;

[0104] Additive manufacturing materials are determined based on the respective composition gradients to obtain the respective reference additive manufacturing materials.

[0105] In an optional implementation of this embodiment, the part performance benchmark value determination module 440 is specifically used for:

[0106] Based on the aforementioned reference additive manufacturing materials, the target part is printed according to the same sample arrangement and printing parameters.

[0107] Based on a preset test method, each of the target parts is tested to obtain the mechanical properties of each target part; the test method is matched with the target performance index; the test method includes at least one of the following: strength test, hardness test, toughness test, corrosion resistance test, and fatigue life test;

[0108] The number of target parts printed by each of the reference additive manufacturing materials is the same, and the target parts are identical in all parameters except for the printing material.

[0109] In an optional implementation of this embodiment, the part performance benchmark value determination module 440 is further specifically used for:

[0110] A variance analysis was performed on the mechanical property data of each target part to determine whether there are significant differences in the target properties of the target elements under different composition gradients;

[0111] In cases where there are significant differences, determine the mean and standard deviation of each component gradient;

[0112] The benchmark values ​​for part performance are determined based on the average values ​​and standard deviations of each value.

[0113] In an optional implementation of this embodiment, the apparatus for determining the performance benchmark value of the additively manufactured part further includes: a verification module, used for:

[0114] The performance benchmark values ​​of the manufactured part are verified to determine their accuracy and reliability.

[0115] The apparatus for determining the performance benchmark value of additively manufactured parts provided in the embodiments of the present invention can execute the method for determining the performance benchmark value of additively manufactured parts provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0116] In the technical solutions of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of information related to additive manufacturing materials all comply with relevant laws and regulations and do not violate public order and good morals.

[0117] Example 4

[0118] Figure 5A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0119] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0120] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0121] Processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a method for determining the performance benchmark value of an additively manufactured part, which may include: determining the target chemical composition of a target additive manufacturing material and analyzing the target chemical composition to obtain a composition analysis result; determining a target element and the compositional characteristics of the target element; determining at least two reference additive manufacturing materials based on the compositional characteristics of the target element; wherein the compositional gradient of the target element is different in each of the reference additive manufacturing materials; printing a target part based on each of the reference additive manufacturing materials, testing the mechanical properties of each target part, and performing statistical analysis on each of the mechanical properties to obtain a performance benchmark value for each target part.

[0122] In some embodiments, the method for determining the performance benchmark value of an additively manufactured part can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining the performance benchmark value of an additively manufactured part described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for determining the performance benchmark value of an additively manufactured part by any other suitable means (e.g., by means of firmware).

[0123] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0124] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0125] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0126] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0127] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0128] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0129] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0130] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

[0131] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements a database detection method as provided in any embodiment of this application.

[0132] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0133] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0134] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for determining the performance benchmark value of an additively manufactured part, characterized in that, include: The target chemical composition of the target additive manufacturing material is determined, and the target chemical composition is analyzed to obtain the composition analysis results; The target application field of the target part is determined, and the target performance index is determined based on the target application field; the target performance index includes at least one of the following: strength, hardness, toughness, corrosion resistance, and fatigue life; Identify each element associated with the target performance index, and define each element as the target element; The compositional characteristics of the target element are determined based on the compositional analysis results; At least two reference additive manufacturing materials are determined based on the compositional characteristics of the target element; wherein the compositional gradient of the target element is different in each of the reference additive manufacturing materials. Based on the aforementioned reference additive manufacturing materials, the target part is printed according to the same sample arrangement and printing parameters. Based on a preset test method, each of the target parts is tested to obtain the mechanical properties of each target part; the test method is matched with the target performance index; the test method includes at least one of the following: strength test, hardness test, toughness test, corrosion resistance test, and fatigue life test; The number of target parts printed by each of the reference additive manufacturing materials is the same, and the target parts are identical in all parameters except for the printing material. A variance analysis was performed on the mechanical property data of each target part to determine whether there are significant differences in the target properties of the target elements under different composition gradients; In cases where there are significant differences, determine the mean and standard deviation of each component gradient; The benchmark values ​​for part performance are determined based on the average values ​​and standard deviations of each value.

2. The method for determining the performance benchmark value of an additively manufactured part according to claim 1, characterized in that, The process of determining the target chemical composition of the target additive manufacturing material and analyzing the target chemical composition to obtain the composition analysis results includes: The additive manufacturing material was analyzed using X-ray fluorescence spectroscopy to obtain the target chemical composition and the content of each component.

3. The method for determining the performance benchmark value of an additively manufactured part according to claim 1, characterized in that, The determination of at least two reference additive manufacturing materials based on the compositional characteristics of the target element includes: Within a preset gradient range, determine the different component gradients of the target element; Additive manufacturing materials are determined based on the respective composition gradients to obtain the respective reference additive manufacturing materials.

4. The method for determining the performance benchmark value of an additively manufactured part according to claim 1, characterized in that, After statistically analyzing each of the mechanical properties to obtain the benchmark values ​​for the properties of each target part, the process further includes: The performance benchmark values ​​of the manufactured part are verified to determine their accuracy and reliability.

5. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, which enables the at least one processor to perform the method for determining the performance benchmark value of the additively manufactured part according to any one of claims 1-4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining the performance benchmark value of an additively manufactured part according to any one of claims 1-4.

7. A computer program product comprising a computer program that, when executed by a processor, implements a method for determining the performance benchmark value of an additively manufactured part according to any one of claims 1-4.

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