Method, apparatus and electronic device for defect evaluation of additive manufacturing structure

Abstract

The present application relates to the technical field of fatigue life analysis, and discloses a defect evaluation method and device for an additive manufacturing structure and electronic equipment. The present application prepares a target test piece generated based on additive manufacturing material, detects defects of the target test piece, and obtains a defect detection result of the target test piece; performs fatigue testing on the target test piece, and obtains a stress and life curve of the target test piece; determines reliability parameters under different defect characteristics according to the stress and life curve of the target test piece; and determines the maximum allowable defect of the target test piece under different stresses at different confidence levels according to the reliability parameters under different defect characteristics and the stress and life curve of the target test piece. Therefore, the present application can provide support for the maximum allowable defect size of the additive manufacturing structure from the aspects of life and stress level under specific confidence conditions, and can further meet the requirement for the maximum allowable defect size of the additive manufacturing structure.

Description

Technical Field

[0001] This invention relates to the field of fatigue life analysis technology, and specifically to a defect assessment method, apparatus, and electronic equipment for additive manufacturing structures. Background Technology

[0002] Additive manufacturing creates solid parts by layering materials, enabling the formation of complex structures while reducing machining steps, shortening processing cycles, and lowering R&D and manufacturing costs. However, defects such as porosity and unmelted powder inevitably appear within the parts during additive manufacturing. These defects are difficult to completely eliminate through parameter optimization and post-heat treatment, and are one of the persistent problems affecting the reliability of metal additive manufacturing. These persistent manufacturing defects lead to decreased fatigue performance, thus impacting the lifespan of the additive manufacturing materials. In related technologies, there are various methods for assessing defects in additive manufacturing structures. Life prediction models predict fatigue life using machine learning, but this method relies on a large amount of experimental data. It cannot provide support for the maximum allowable defect size of additive manufacturing materials in terms of life and stress level under specific confidence conditions, nor can it meet the requirements for the maximum allowable defect size of additive manufacturing structures. Summary of the Invention

[0003] In view of this, the present invention provides a defect assessment method, apparatus and electronic device for additive manufacturing structures, in order to solve the problem that the defect assessment methods for additive manufacturing structures in related technologies cannot meet the requirements for the maximum permissible defect size of additive manufacturing structures.

[0004] In a first aspect, the present invention provides a method for defect assessment of additively manufactured structures, the method comprising: Prepare target test specimens based on additive manufacturing materials, and perform defect detection on the target test specimens to obtain the defect detection results of the target test specimens; Fatigue tests were performed on the target test specimen to obtain the stress-life curve of the target test specimen; Based on the stress-life curve of the target test piece, determine the reliability parameters under different defect characteristics; Based on the reliability parameters under different defect characteristics, the maximum permissible defect of the target test piece under different stresses at different confidence levels is determined by using the stress-life curve of the target test piece.

[0005] In some specific embodiments, a target test specimen based on additive manufacturing materials is prepared, and defect detection is performed on the target test specimen to obtain the defect detection results, including: Target test specimens based on additive manufacturing materials are generated using computer-aided design software; Computer-aided design software is used to determine the preset defect areas in the target test piece. The preset defect areas include: porosity defects, inclusion defects, crack defects and non-fusion defects inside the target test piece. The pre-defined defect area of ​​the target test piece is scanned using X-ray computed tomography software to obtain the three-dimensional defect features of the pre-defined defect area; The three-dimensional defect features of the preset defect area are used as the defect detection results of the target test piece.

[0006] In some specific implementations, fatigue testing is performed on the target test specimen to obtain its stress-life curve, including: Based on preset configuration parameters, fatigue testing software is used to perform fatigue testing on the target test piece under different test conditions to obtain the stress and life curves of the target test piece.

[0007] In some specific implementations, different defect characteristics include: stress defect characteristics, size defect characteristics, location defect characteristics, roundness defect characteristics, and orientation angle defect characteristics.

[0008] In some specific implementations, reliability parameters under different defect characteristics are determined based on the stress-life curve of the target test specimen, including: Obtain characteristics of stress defects, size defects, location defects, roundness defects, and orientation angle defects; Based on the characteristics of stress defects, size defects, location defects, roundness defects, and orientation angle defects, the dispersion lifetime data obtained from the test is statistically analyzed using the stress-life curves of the target test piece to obtain reliability parameters under different defect characteristics.

[0009] In some specific implementations, based on reliability parameters under different defect characteristics, the maximum permissible defect of the target test piece under different stresses at different confidence levels is determined through the stress-life curve of the target test piece, including: Given different confidence levels; Based on reliability parameters under different defect characteristics, stress and life curves of standard test specimens at different confidence levels, defect calculation equations for target test specimens are constructed. Based on the defect calculation equation of the target test piece, calculate the maximum permissible defect of the target test piece under different stresses.

[0010] In a second aspect, the present invention provides a defect assessment device for additively manufactured structures, the device comprising: The defect detection module is used to prepare target test pieces generated based on additive manufacturing materials, and to perform defect detection on the target test pieces to obtain the defect detection results of the target test pieces; The fatigue testing module is used to perform fatigue tests on the target test piece and obtain the stress-life curve of the target test piece. The reliability determination module is used to determine reliability parameters under different defect characteristics based on the stress-life curve of the target test piece. The defect determination module is used to determine the maximum permissible defect of the target test piece under different stresses at different confidence levels by using reliability parameters under different defect characteristics and stress-life curves of the target test piece.

[0011] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the defect assessment method for additive manufacturing structures described in the first aspect or any corresponding embodiment thereof.

[0012] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the defect assessment method for additive manufacturing structures described in the first aspect or any corresponding embodiment thereof.

[0013] Fifthly, the present invention provides a computer program product, characterized in that it includes computer instructions for causing a computer to execute the defect assessment method for additive manufacturing structures described in the first aspect or any corresponding embodiment thereof.

[0014] The technical solution of this invention has the following advantages: This invention discloses a method, apparatus, and electronic device for defect assessment of additive manufacturing structures. The invention involves preparing a target test specimen based on additive manufacturing materials and performing defect detection on the specimen to obtain the defect detection results; conducting fatigue testing on the specimen to obtain its stress-life curves; determining reliability parameters under different defect characteristics based on the stress-life curves; and determining the maximum permissible defect size of the target test specimen at different stresses under different confidence levels, using the stress-life curves based on the reliability parameters under different defect characteristics. Therefore, this invention can provide support for the maximum permissible defect size of additive manufacturing structures from aspects such as life and stress levels under specific confidence levels, thereby meeting the requirements for the maximum permissible defect size of additive manufacturing structures. Attached Figure Description

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a defect assessment method for additive manufacturing structures according to an embodiment of the present invention; Figure 2 This is a schematic diagram of defect detection of a target test piece according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the sphericity distribution of internal defects in the target test piece according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the diameter distribution of internal defects in the target test piece according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a defect assessment device for additive manufacturing structures according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] According to an embodiment of the present invention, a defect assessment method for additive manufacturing structures is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0019] This embodiment provides a defect assessment method for additive manufacturing structures, which can be used in electronic devices such as mobile phones, tablets, desktop computers, laptops, and servers. Figure 1 This is a flowchart of a method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Prepare a target test piece based on additive manufacturing materials, and perform defect detection on the target test piece to obtain the defect detection results of the target test piece.

[0020] Specifically, additive manufacturing materials include, but are not limited to: polymer materials, metallic materials, ceramic materials, composite materials, and other emerging and specialty materials. The target test piece is the object of this experiment.

[0021] In some specific embodiments, step S101 above, which involves preparing a target test piece based on additive manufacturing materials and performing defect detection on the target test piece to obtain the defect detection results, includes: Step a1: Use computer-aided design software to generate target test pieces based on additive manufacturing materials.

[0022] Specifically, computer-aided design (CAD) software is used to generate target test specimens based on additive manufacturing materials. These target test specimens are three-dimensional models generated by the CAD software.

[0023] Step a2: Use computer-aided design software to determine the preset defect areas in the target test piece. The preset defect areas include: porosity defects, inclusion defects, crack defects and non-fusion defects inside the target test piece.

[0024] Specifically, CAD software is used to determine the porosity defects, inclusion defects, crack defects, and lack of fusion defects inside the target test piece in the designed three-dimensional model of the target test piece.

[0025] Step a3: Use X-ray computed tomography software to scan the preset defect area of ​​the target test piece to obtain the three-dimensional defect features of the preset defect area.

[0026] Specifically, X-ray computed tomography (CT) software is used to scan the pre-defined defect area of ​​the target test piece to obtain the three-dimensional defect features of the pre-defined defect area. Taking gas-atomized TC4 titanium alloy powder as an example, such as... Figure 2 As shown, defect detection was performed on the target test piece. The pre-defined defect area of ​​the target test piece was scanned using X-ray computed tomography (CT) software to obtain the three-dimensional characteristics of the porosity defects in the TC4 titanium alloy powder. X-ray computed tomography is a common non-destructive three-dimensional imaging technique. Its greatest advantage is that it can reflect the internal spatial structure of the sample in situ without introducing artificial defects. This characteristic makes it an important software for studying internal defects in additive manufacturing materials.

[0027] Step a4: Use the three-dimensional defect features of the preset defect area as the defect detection result of the target test piece.

[0028] Specifically, the three-dimensional defect features of the preset defect area can be represented by irregular three-dimensional defect features, which can be represented by an equivalent diameter, that is, equivalent to the diameter of a sphere of the same volume, as expressed by the following formula: Equivalent diameter: ,in, V represents the equivalent diameter and the volume of the defect.

[0029] Furthermore, in the fatigue strength evaluation of defective materials, in order to quantitatively correlate the geometric characteristics of defects with the fatigue strength of the material, the morphological characteristics of defects can be represented by sphericity. Sphericity, also known as sphericity, is a geometric parameter commonly used to characterize the morphology of defects in cast alloys and welded joints. The greater the sphericity of a defect, the closer its morphology is to that of a standard sphere, with a sphericity of 1.

[0030] The sphericity of a defect is defined as the ratio of the surface area of ​​a standard sphere with the same volume as the defect to the surface area of ​​the defect. This can be expressed by the following formula: Sphericity: Where S represents sphericity, and V and A represent the volume and surface area of ​​the defect, respectively.

[0031] like Figure 3 The figure shows a schematic diagram of the sphericity distribution of internal defects in the target test piece. The dashed line represents the cumulative distribution frequency curve. It can be seen that only 10% of all defects have a sphericity lower than 0.75, while the remaining 90% of defects have a sphericity higher than 0.75. This also indicates that most defects in the TC4 alloy have a high sphericity, and the defect shapes are close to spherical and relatively regular.

[0032] like Figure 4 The figure shows a schematic diagram of the diameter distribution of internal defects in the target test specimen. After fitting, it can be seen that the equivalent diameter of the defects also follows a log-normal distribution. Furthermore, according to the cumulative frequency distribution diagram, approximately 90% of the internal defects in the specimen have an equivalent diameter less than 65 μm, indicating that most defects have a small equivalent diameter, with only a small number of defects having a larger size.

[0033] Step S102: Perform fatigue testing on the target test piece to obtain the stress-life curve of the target test piece.

[0034] In some specific embodiments, step S102 above, which involves performing fatigue testing on the target test specimen to obtain the stress-life curve of the target test specimen, includes: Based on preset configuration parameters, fatigue testing software is used to perform fatigue testing on the target test piece under different test conditions to obtain the stress and life curves of the target test piece.

[0035] Specifically, the preset configuration parameters include stress level test parameters and fatigue life parameters. The fatigue testing software can be ANSYS, which allows setting 4-8 stress gradients. This applies to the short to medium life range (e.g., 10...). 4 ~10 6 (Times). Long life region (near the fatigue limit): The fatigue limit is calculated by changing the stress level using the rise and fall method.

[0036] Different test conditions: The stress ratio (R), loading waveform (usually a sine wave), and test frequency are specified (e.g., for metallic parts, often in the range of 70-200 Hz). The test cycle can last 2-8 weeks or even longer. During fatigue testing, cyclic loading is applied to the target specimen until it fails (complete fracture or significant decrease in stiffness), and the number of cycles (N) at that stress level is recorded. After collecting all data points, the stress-life curve of the target specimen is obtained by fitting the data to a double logarithmic coordinate system (log S - log N).

[0037] Step S103: Determine the reliability parameters under different defect characteristics based on the stress-life curve of the target test piece.

[0038] In a specific example, different defect features include: stress defect features, size defect features, location defect features, roundness defect features, and orientation angle defect features.

[0039] In some specific embodiments, step S103 above, which determines reliability parameters under different defect characteristics based on the stress-life curve of the target test piece, includes: Step b1: Obtain stress defect features, size defect features, location defect features, roundness defect features, and orientation angle defect features.

[0040] Step b2: Based on the characteristics of stress defects, size defects, location defects, roundness defects, and orientation angle defects, statistical analysis is performed on the dispersion lifetime data obtained from the test using the stress-life curve of the target test piece to obtain reliability parameters under different defect characteristics.

[0041] Existing research shows that the fatigue life of target test specimens generated by additive manufacturing is jointly influenced by factors such as stress defect characteristics, dimensional defect characteristics, location defect characteristics, roundness defect characteristics, and orientation angle defect characteristics. Since the studies in this example focus on surface defects, the defect location has a negligible impact on fatigue performance; therefore, this parameter can be ignored. Consideration is given to using an effective stress that incorporates multiple factors. To correct for stress and avoid parameter values ​​of 0, all parameters other than the stress parameter are treated as dimensionless. The calculation method is expressed by the following formula:

[0042] definition:

[0043]

[0044] in For stress amplitude, Let α represent the effective defect area, C represent the defect roundness, and θ represent the defect orientation. The values ​​of α, β, γ, and λ are 0.17, 0.02, -0.1, and 9.5, respectively.

[0045] After normalization, the mean and standard deviation of roundness were 1.0157 and 0.2893, respectively, and the mean and standard deviation of orientation angle were 0.8405 and 0.3086, respectively. Roundness followed a normal distribution (goodness of fit 0.94). Assuming that the orientation angle followed a uniform distribution, the roundness and orientation angle corresponding to reliability parameters of 50%, 90%, and 95% were analyzed. The roundness and orientation angle under different reliability levels are shown in Table 2 below. Substituting into the formula, the dimensionless values ​​of the maximum acceptable critical defect area corresponding to 105 lifetime under different stress levels and reliability levels can be obtained, as shown in Tables 2 and 3.

[0046] Table 1. Roundness and Orientation Angle at Different Reliability Levels

[0047] Table 2 Maximum Acceptable Critical Defect Size

[0048] Table 3 Maximum acceptable stress amplitude

[0049] Step S104: Based on the reliability parameters under different defect characteristics, determine the maximum permissible defect of the target test piece under different stresses at different confidence levels by using the stress-life curve of the target test piece.

[0050] In some specific implementations, step S104 above, based on reliability parameters under different defect characteristics, determines the maximum permissible defect of the target test piece under different stresses at different confidence levels through the stress-life curve of the target test piece, including: Step c1, given different confidence levels.

[0051] Step c2: Based on the reliability parameters under different defect characteristics, different confidence levels, and the stress-life curves of the target test piece, construct the defect calculation equation for the target test piece.

[0052] Step c3: Calculate the maximum permissible defects of the target test piece under different stresses based on the defect calculation equation of the target test piece.

[0053] Specifically, it is assumed that the logarithmic fatigue life of the target test specimen follows a normal distribution under a specific stress level. For each defect feature of the target test specimen, the life value under a specified reliability parameter P and confidence level γ is calculated using the one-sided tolerance coefficient method. Finally, the CPSN curve under the defect size is fitted by regression analysis such as the least squares method, and the defect calculation equation of the target test specimen is constructed through this curve. Based on this, the defect propagation model is determined: the most commonly used is the Paris formula: da / dN=C(ΔK)^n, where da / dN is the crack propagation rate, ΔK is the stress intensity factor range, and C and n are material constants. The stress intensity factor range ΔK is calculated, which is related to the applied stress range Δσ, the defect size a, and the geometry of the component and the defect. The crack propagation life is calculated iteratively: starting from the initial defect size a0, the Paris formula is integrated (or numerically iterated) until the crack propagates to the critical size ac (determined by the fracture toughness of the material), thus obtaining the total life N corresponding to the initial defect.

[0054] For a given stress level S and a preset life N_design, find an initial defect size a such that the calculated life N is exactly equal to N_design. This a is the maximum permissible defect size under that condition.

[0055] The defect assessment method for additive manufacturing structures in this application involves preparing a target test piece based on additive manufacturing materials, performing defect detection on the target test piece to obtain the defect detection results, conducting fatigue testing on the target test piece to obtain its stress-life curve, determining reliability parameters under different defect characteristics based on the stress-life curve, and determining the maximum permissible defect size under different stresses at different confidence levels based on the reliability parameters under different defect characteristics and the stress-life curve of the target test piece. Therefore, this application can provide support for the maximum permissible defect size of additive manufacturing structures from aspects such as life and stress levels under specific confidence levels, thereby meeting the requirements for the maximum permissible defect size of additive manufacturing structures.

[0056] This embodiment also provides a defect assessment device for additive manufacturing structures, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0057] This embodiment provides a defect assessment device for additive manufacturing structures, such as... Figure 5 As shown, it includes: The defect detection module 501 is used to prepare the target test piece generated based on additive manufacturing materials, and to perform defect detection on the target test piece to obtain the defect detection results of the target test piece; The fatigue testing module 502 is used to perform fatigue testing on the target test piece and obtain the stress-life curve of the target test piece; The reliability determination module 503 is used to determine the reliability parameters under different defect characteristics based on the stress-life curve of the target test piece. The defect determination module 504 is used to determine the maximum permissible defect of the target test piece under different stresses at different confidence levels by using the stress and life curve of the target test piece based on the reliability parameters under different defect characteristics.

[0058] In some specific implementations, the defect detection module 501 is specifically used for: Target test specimens based on additive manufacturing materials are generated using computer-aided design software; Computer-aided design software is used to determine the preset defect areas in the target test piece. The preset defect areas include: porosity defects, inclusion defects, crack defects and non-fusion defects inside the target test piece. The pre-defined defect area of ​​the target test piece is scanned using X-ray computed tomography software to obtain the three-dimensional defect features of the pre-defined defect area; The three-dimensional defect features of the preset defect area are used as the defect detection results of the target test piece.

[0059] In some specific implementations, the fatigue testing module 502 is specifically used for: Based on preset configuration parameters, fatigue testing software is used to perform fatigue testing on the target test piece under different test conditions to obtain the stress and life curves of the target test piece.

[0060] In some specific implementations, different defect characteristics include: stress defect characteristics, size defect characteristics, location defect characteristics, roundness defect characteristics, and orientation angle defect characteristics.

[0061] In some specific implementations, the reliability determination module 503 is specifically used for: Obtain characteristics of stress defects, size defects, location defects, roundness defects, and orientation angle defects; Based on the characteristics of stress defects, size defects, location defects, roundness defects, and orientation angle defects, the dispersion lifetime data obtained from the test is statistically analyzed using the stress-life curves of the target test piece to obtain reliability parameters under different defect characteristics.

[0062] In some specific implementations, the defect determination module 504 is specifically used for: Given different confidence levels; Based on reliability parameters under different defect characteristics, stress and life curves of standard test specimens at different confidence levels, defect calculation equations for target test specimens are constructed. Based on the defect calculation equation of the target test piece, calculate the maximum permissible defect of the target test piece under different stresses.

[0063] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0064] In this embodiment, the defect assessment device for additive manufacturing structures is presented in the form of functional units. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0065] This invention also provides an electronic device having a defect assessment device for the additive manufacturing structure shown above.

[0066] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of an electronic device provided in an optional embodiment of the present invention, such as... Figure 6 As shown, the electronic device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6 Take a processor 10 as an example.

[0067] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0068] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0069] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0070] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0071] The electronic device also includes a communication interface 30 for communicating with other devices or communication networks.

[0072] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0073] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A defect assessment method for additively manufactured structures, characterized in that, The method includes: A target test specimen based on additive manufacturing materials is prepared, and the defect detection of the target test specimen is performed to obtain the defect detection results of the target test specimen; Fatigue tests were performed on the target test specimen to obtain the stress-life curve of the target test specimen; Based on the stress-life curve of the target test piece, determine the reliability parameters under different defect characteristics; Based on the reliability parameters under different defect characteristics, the maximum permissible defect of the target test piece under different stresses at different confidence levels is determined by the stress-life curve of the target test piece.

2. The method according to claim 1, characterized in that, Prepare a target test specimen based on additive manufacturing materials, and perform defect detection on the target test specimen to obtain the defect detection results of the target test specimen, including: Target test specimens based on additive manufacturing materials are generated using computer-aided design software; Computer-aided design software is used to determine a preset defect region in the target test piece. The preset defect region includes: porosity defects, inclusion defects, crack defects and non-fusion defects inside the target test piece. The predetermined defect area of ​​the target test piece is scanned using X-ray computed tomography software to obtain the three-dimensional defect features of the predetermined defect area; The three-dimensional defect features of the preset defect area are used as the defect detection results of the target test piece.

3. The method according to claim 1, characterized in that, Fatigue testing is performed on the target test specimen to obtain its stress-life curve, including: Based on preset configuration parameters, fatigue testing software is used to perform fatigue testing on the target test piece under different testing conditions to obtain the stress and life curves of the target test piece.

4. The method according to claim 1, characterized in that, The different defect characteristics include: stress defect characteristics, size defect characteristics, location defect characteristics, roundness defect characteristics, and orientation angle defect characteristics.

5. The method according to claim 4, characterized in that, Based on the stress-life curve of the target test specimen, reliability parameters under different defect characteristics are determined, including: Obtain characteristics of stress defects, size defects, location defects, roundness defects, and orientation angle defects; Based on the characteristics of stress defects, size defects, location defects, roundness defects, and orientation angle defects, the dispersion lifetime data obtained from the test is statistically analyzed using the stress-life curve of the target test piece to obtain reliability parameters under different defect characteristics.

6. The method according to claim 1, characterized in that, Based on the reliability parameters under different defect characteristics, and through the stress-life curve of the target test piece, the maximum permissible defects at different stresses of the target test piece under different confidence levels are determined, including: Given different confidence levels; Based on the reliability parameters under different defect characteristics, the different confidence levels, and the stress-life curves of the target test piece, a defect calculation equation for the target test piece is constructed. Based on the defect calculation equation of the target test piece, calculate the maximum permissible defect of the target test piece under different stresses.

7. A defect assessment device for additive manufacturing structures, characterized in that, The device includes: A defect detection module is used to prepare a target test piece generated based on additive manufacturing materials, and to perform defect detection on the target test piece to obtain the defect detection results of the target test piece; The fatigue testing module is used to perform fatigue testing on the target test piece and obtain the stress-life curve of the target test piece. The reliability determination module is used to determine reliability parameters under different defect characteristics based on the stress-life curve of the target test piece. The defect determination module is used to determine the maximum permissible defect of the target test piece under different stresses at different confidence levels based on the reliability parameters under different defect characteristics and the stress-life curve of the target test piece.

8. An electronic device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the defect assessment method for the additive manufacturing structure according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the defect assessment method for the additive manufacturing structure according to any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to perform the defect assessment method for additive manufacturing structures according to any one of claims 1 to 6.

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