Method and device for determining reliability of process parameters of shell

Abstract

The invention provides a shell process parameter reliability determination method and device, relates to the field of shell processes, and solves the problems of low reliability and large redundant mass caused by the fact that parameter randomness is not considered in existing traditional deterministic design and low calculation efficiency of a common probability reliability method. The method specifically comprises the following steps: acquiring process parameters of a plurality of shells, calculating an average value of the process parameters and a random parameter dispersion degree of a standard deviation, and determining a variable coefficient; based on the variable coefficient and the process parameters, determining a fiber strength exerting coefficient of the shell; determining fiber stresses of the plurality of shells based on the fiber strength exerting coefficients and the process parameters, and determining a mean value and a standard deviation of the fiber stresses; and determining the reliability of the process parameters based on the mean value and the standard deviation of the fiber stress and the fiber strength. The method is used for shell reliability analysis.

Description

Technical Field

[0001] The present application relates to the field of shell technology, and in particular to a method and device for determining the reliability of shell process parameters. Background Art

[0002] In the field of composite structural design, traditional methods, based on deterministic analysis, treat design variables such as material physical properties, structural geometry, and loads as deterministic values, without considering the random distribution characteristics of these factors. Approximations and assumptions in structural analysis and calculations are also ignored. Against this backdrop, technical challenges have become increasingly prominent. On the one hand, deterministic methods for structural analysis and design of composite shell process parameters fail to account for the impact of uncertainties on the structure, resulting in low reliability. On the other hand, the use of large safety factors to meet safety requirements increases the shell's redundant mass, making it impossible to achieve the goal of high-reliability lightweight design. Furthermore, common probabilistic reliability methods, such as the surface response method and the Monte Carlo method, suffer from high computational complexity and low efficiency, making it difficult to quickly determine the impact of shell process parameters on reliability. Therefore, the existing technology faces technical challenges: traditional deterministic design, which fails to account for parameter randomness, results in low reliability and high redundant mass, and common probabilistic reliability methods suffer from low computational efficiency. Summary of the Invention

[0003] The present application provides a method and device for determining the reliability of shell process parameters, which solves the technical problems of low reliability and large redundant mass caused by failure to consider parameter randomness in traditional deterministic design, as well as low calculation efficiency of common probabilistic reliability methods.

[0004] To achieve the above objectives, this application adopts the following technical solutions:

[0005] In a first aspect, a method for determining the reliability of a shell process parameter is provided, comprising:

[0006] The process parameters of multiple shells are obtained, including shell thickness, winding angle, radius, blasting pressure, and fiber strength during blasting.

[0007] Calculate the degree of random parameter dispersion of the mean value and standard deviation of the process parameters and determine the coefficient of variation.

[0008] The fiber strength utilization coefficient of the shell is determined based on the coefficient of variation and process parameters. The fiber strength utilization coefficient is used to characterize the proportion of the actual strength of the fiber that is effectively utilized.

[0009] Based on the fiber strength utilization coefficient and process parameters, the fiber stress of multiple shells is determined, and the mean and standard deviation of the fiber stress are determined; the fiber stress of multiple shells is used to characterize the stress borne by the fibers in the shells.

[0010] The reliability of the process parameters is determined based on the mean and standard deviation of fiber stress and fiber strength. The reliability represents the probability that the fiber stress is always less than or equal to the fiber strength under different pressures.

[0011] In combination with the first aspect above, in a possible implementation, obtaining process parameters of multiple shells, calculating the degree of random parameter dispersion of the mean value and standard deviation of the process parameters, and determining the coefficient of variation include:

[0012] By experimenting and measuring the process parameters of multiple shells, the shell thickness t, winding angle α, radius R, and blasting pressure P are obtained. b , fiber strength σ b A set of digital eigenvalues, the coefficient of variation of each parameter v t , v α , v R , Satisfies the following formula:

[0013]

[0014] Among them, μ t Represents the statistical mean of shell thickness, μ α Represents the statistical mean of the shell winding angle, μ R represents the statistical mean value of the shell radius, represents the statistical mean value of the shell burst pressure, represents the statistical mean value of shell fiber strength, σ t Represents the statistical standard deviation of shell thickness, σ α Represents the statistical standard deviation of the shell winding angle, σ R represents the statistical standard deviation of the shell radius, represents the statistical standard deviation of the shell burst pressure, Indicates the statistical standard deviation of shell fiber strength.

[0015] In conjunction with the first aspect above, in one possible implementation, determining the fiber strength utilization coefficient of the shell based on the coefficient of variation and the process parameters includes:

[0016] Determine the fiber strength when the pressure inside the shell increases to the burst pressure;

[0017] Based on the fiber strength, the structural composite parameters of the shell are determined; the structural composite parameters are the key intermediate variables for solving the fiber strength utilization coefficient;

[0018] The mean value of the structural composite parameter is determined, and the fiber strength utilization coefficient of the shell is determined based on the mean value of the structural composite parameter.

[0019] In combination with the first aspect above, in a possible implementation, the fiber strength exertion coefficient K αDetermined based on the following formula:

[0020] When the pressure inside the shell increases to the burst pressure, the fiber strength satisfies the following formula:

[0021]

[0022] The structural composite parameter ζ satisfies the following formula:

[0023]

[0024] The mean μ of the structural composite parameter ξ Satisfies the following formula:

[0025]

[0026] Among them, let cos 2 α=A,μ A is the statistical mean of the square of the cosine of the winding angle, v A is the coefficient of variation of the square of the winding angle cosine, and the mean μ of the structural composite parameter ζ The calculation formula is used to reversely calculate the fiber strength coefficient.

[0027] In combination with the first aspect above, in a possible implementation, the fiber stress S satisfies the following formula:

[0028]

[0029] Where P is the pressure inside the shell.

[0030] In combination with the first aspect above, in a possible implementation, the fiber stress is re-expressed using a random factor, and the fiber stress satisfies the following formula:

[0031]

[0032] in, A random factor representing the shell thickness, represents the random factor of the square of the shell winding angle cosine, represents a random factor for the shell radius, A random factor representing the shell pressure.

[0033] In combination with the first aspect above, in a possible implementation, the mean value μ of the fiber stress is s and standard deviation σ s Satisfies the following formula:

[0034]

[0035] In combination with the first aspect above, in one possible implementation, the method further includes:

[0036] Reliability is calculated based on the first-order second moment method and reliability index, reliability P r Satisfies the following formula:

[0037]

[0038] Among them, φ() is the cumulative distribution function of the standard normal distribution, and β is the reliability index.

[0039] Combined with the first aspect mentioned above, the reliability analysis under different pressures is completed by changing the coefficient of variation of each random parameter through control variables, including: giving each process parameter a set of the same coefficient of variation; changing the pressure to compare the degree of influence of different process parameters on reliability.

[0040] In a second aspect, a device for determining the reliability of shell process parameters is provided, comprising: a communication unit and a processing unit; the communication unit is used to obtain the process parameters of the shell; the processing unit is used to calculate the degree of random parameter dispersion of the mean value and standard deviation of the process parameters, and determine the coefficient of variation; based on the coefficient of variation and the process parameters, the fiber strength utilization coefficient of the shell is determined, and the fiber strength utilization coefficient is used to characterize the proportion of the actual strength of the fiber that is effectively utilized; based on the fiber strength utilization coefficient and the process parameters, the fiber stress of multiple shells is determined, and the mean and standard deviation of the fiber stress are determined; the fiber stress of multiple shells is used to characterize the stress size borne by the fibers in the shell; based on the mean and standard deviation of the fiber stress and the fiber strength, the reliability of the process parameters is determined; the reliability characterizes the probability that the fiber stress is always less than or equal to the fiber strength under different pressures.

[0041] In a third aspect, the present application provides a computer-readable storage medium, which stores instructions. When the instructions are run on a shell process parameter reliability determination device, the shell process parameter reliability determination device executes the method described in the first aspect and any possible implementation of the first aspect.

[0042] In a fourth aspect, the present application provides a device for determining the reliability of a casing process parameter, comprising: a processor and a storage medium; the storage medium comprising instructions, the processor being configured to execute the instructions to implement the method described in the first aspect and any possible implementation of the first aspect. The device for determining the reliability of a casing process parameter can be an electronic device or a chip within the electronic device.

[0043] In a fifth aspect, the present application provides a shell process parameter reliability determination system, comprising: a parameter measuring device, and an electronic device; wherein the parameter measuring device is used to obtain the process parameters of the shell and the electronic device is used to calculate the degree of random parameter dispersion of the mean value and standard deviation of the process parameters to determine the coefficient of variation; based on the coefficient of variation and the process parameters, the fiber strength utilization coefficient of the shell is determined, and the fiber strength utilization coefficient is used to characterize the proportion of the actual strength of the fiber being effectively utilized; based on the fiber strength utilization coefficient and the process parameters, the fiber stress of multiple shells is determined, and the mean and standard deviation of the fiber stress are determined; the fiber stress of multiple shells is used to characterize the stress size borne by the fibers in the shell; based on the mean and standard deviation of the fiber stress and the fiber strength, the reliability of the process parameters is determined; the reliability characterizes the probability that the fiber stress is always less than or equal to the fiber strength under different pressures.

[0044] In a sixth aspect, the present application provides a computer program product comprising instructions, which, when run on a shell process parameter reliability determination device, enables the shell process parameter reliability determination device to perform the method described in the first aspect and any possible implementation of the first aspect.

[0045] The present application provides a method and device for determining the reliability of shell process parameters. The shell process parameters are measured experimentally to determine the coefficient of variation. The coefficient of variation quantifies the discreteness of the parameters and converts randomness into a calculable indicator, avoiding the reliability misjudgment caused by assuming that the parameters are fixed values ​​in traditional designs. The fiber strength utilization coefficient of the shell is determined based on the coefficient of variation and the process parameters. The mean and standard deviation of the fiber stress of the shell are determined based on the fiber strength utilization coefficient and the process parameters. The reliability of the process parameters is determined based on the mean and standard deviation of the fiber stress and fiber strength. Compared with the multiple sampling calculations of the traditional Monte Carlo method, the present application only requires the mean and standard deviation for rapid solution. This method fully considers the random distribution characteristics of the parameters, improves the accuracy of reliability analysis, and effectively solves the technical problems of low reliability, large redundant mass, and low calculation efficiency of common probabilistic reliability methods in traditional deterministic design.

[0046] It should be understood that the description of technical features, technical solutions, beneficial effects or similar language in this application does not imply that all features and advantages can be realized in any single embodiment. On the contrary, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution or beneficial effect is included in at least one embodiment. Therefore, the description of a technical feature, technical solution or beneficial effect in this specification does not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions and beneficial effects described in the present embodiment can also be combined in any appropriate manner. Those skilled in the art will understand that the embodiment can be implemented without one or more specific technical features, technical solutions or beneficial effects of a specific embodiment. In other embodiments, additional technical features and beneficial effects can also be identified in specific embodiments that do not embody all embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A system architecture diagram of a shell process parameter reliability determination system provided in an embodiment of the present application;

[0048] Figure 2 A schematic flow chart of a method for determining the reliability of a shell process parameter provided in an embodiment of the present application;

[0049] Figure 3 A schematic flow chart of another method for determining the reliability of casing process parameters provided in an embodiment of the present application;

[0050] Figure 4 A schematic flow chart of another method for determining the reliability of casing process parameters provided in an embodiment of the present application;

[0051] Figure 5 A schematic structural diagram of a device for determining the reliability of a shell process parameter provided in an embodiment of the present application;

[0052] Figure 6 A schematic diagram of the hardware structure of a device for determining the reliability of shell process parameters provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In the description of this application, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "at least one" means one or more, and "a plurality" means two or more. Words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not limit them to be necessarily different.

[0054] It should be noted that, in this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described in this application as "exemplary" or "for example" should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0055] The analysis method of the impact of shell process parameters on reliability provided in the embodiment of the present application can be applied to Figure 1 In the shell process parameter reliability determination system shown, the system includes: a parameter measuring device 101 and an electronic device 102.

[0056] Among them, the parameter measuring device 101 is used to obtain the process parameters of the shell, including: thickness, winding angle, radius, blasting pressure, and fiber strength at blasting. The electronic device 102 is used to calculate the random parameter dispersion of the mean value and standard deviation of the process parameters to determine the coefficient of variation; based on the coefficient of variation and the process parameters, the fiber strength utilization coefficient of the shell is determined, and the fiber strength utilization coefficient is used to characterize the proportion of the actual strength of the fiber being effectively utilized; based on the fiber strength utilization coefficient and the process parameters, the fiber stress of multiple shells is determined, and the mean and standard deviation of the fiber stress are determined; the fiber stress of multiple shells is used to characterize the stress size borne by the fibers in the shell; based on the mean and standard deviation of the fiber stress and the fiber strength, the reliability of the process parameters is determined; the reliability characterizes the probability that the fiber stress is always less than or equal to the fiber strength under different pressures.

[0057] In order to solve the technical problems in the prior art that traditional deterministic design does not consider parameter randomness, resulting in low reliability and large redundant mass, and common probabilistic reliability methods have low calculation efficiency, an embodiment of the present application provides a method for determining the reliability of shell process parameters, the method comprising: obtaining process parameters of multiple shells, the process parameters including: shell thickness, winding angle, radius, blasting pressure, and fiber strength at blasting, calculating the degree of random parameter dispersion of the mean value and standard deviation of the process parameters, determining the coefficient of variation, and determining the fiber strength utilization coefficient of the shell based on the coefficient of variation and the process parameters. The fiber strength utilization coefficient is used to characterize the proportion of the actual strength of the fiber that is effectively utilized. Based on the fiber strength utilization coefficient and the process parameters, the fiber stress of multiple shells is determined, and the mean and standard deviation of the fiber stress are determined. The fiber stress of multiple shells is used to characterize the stress borne by the fibers in the shell. Based on the mean and standard deviation of the fiber stress and the fiber strength, the reliability of the process parameters is determined. The reliability characterizes the probability that the fiber stress is always less than or equal to the fiber strength under different pressures. Based on this, this application fully considers the random distribution characteristics of parameters, improves the accuracy of reliability analysis, and effectively solves the technical problems of low reliability, large redundant mass and low calculation efficiency of common probabilistic reliability methods in traditional deterministic design.

[0058] like Figure 2 As shown, the method for determining the reliability of the shell process parameters provided in the embodiment of the present application includes:

[0059] Step 201: The shell process parameter reliability determination device obtains the shell process parameters and determines the coefficient of variation.

[0060] The process parameters include: shell thickness, winding angle, radius, blasting pressure, and fiber strength during blasting.

[0061] The process parameters of the shell are obtained by experimentally measuring the geometric parameters and physical parameters of the process parameters of the shell to obtain the average value and standard deviation of each parameter. The coefficient of variation represents the degree of random parameter dispersion of the average value and standard deviation of the process parameters.

[0062] In an embodiment of the present application, different tools can be used to experiment and measure the thickness, winding angle, radius, blasting pressure, and fiber strength of the process parameters of winding-molded composite material shells of multiple different specifications and batches, obtain a set of digital features including the average value and standard deviation of each parameter, and calculate the coefficient of variation. This application does not make specific restrictions on this.

[0063] As an example, multiple experiments and measurements were conducted on the process parameters of 20 different specifications and batches of winding-molded composite shells. For example, the thickness of the container can be measured by evenly selecting no less than 5 measurement points in the axial and circumferential directions, and the average value can be taken to reduce local errors; the winding angle can be measured separately in different cross-sections at both ends and the middle of the container to avoid insufficient representativeness of a single position.

[0064] It should be noted that when statistical accuracy is high, the number of process parameters for the experimental shells can be increased to 30, 40, or even more. For preliminary feasibility analysis, a minimum experimental set of 5-10 samples can be selected. The specific number of samples does not constitute the sole limitation of this approach. Experimental subjects can include shells made with different fiber types, different resin systems, or different winding processes to cover the diverse application scenarios of wound composite shells.

[0065] Step 202 : The shell process parameter reliability determination device determines the fiber strength utilization coefficient of the shell based on the coefficient of variation and the process parameters, and determines the mean and standard deviation of the fiber stress.

[0066] Among them, the fiber strength utilization coefficient reflects the proportion of the actual fiber strength that is effectively utilized in the shell, and the fiber stress characterizes the stress size related to the process parameters of the composite shell that the fiber bears. Its mean reflects the average level of fiber stress, and the standard deviation reflects the degree of dispersion of fiber stress. The combination of the two is used to measure the distribution characteristics and fluctuation range of fiber stress under different working conditions.

[0067] It should be noted that the process of determining the fiber strength utilization coefficient in this scheme is to increase the pressure in the container to the explosion pressure, so that the fiber stress just reaches the fiber strength. The fiber strength utilization coefficient is calculated based on the grid theory, and the fiber stress is calculated in combination with the process parameters. Then, the mean and standard deviation of the fiber stress are calculated with the help of random factors.

[0068] Step 203: The shell process parameter reliability determination device determines the reliability of the process parameters based on the mean and standard deviation of the fiber stress and the fiber strength.

[0069] Among them, reliability represents the probability that the fiber stress is always less than or equal to the fiber strength. The reliability level of the shell process parameters meeting the strength requirements under random fluctuations is quantified by the mean and standard deviation of the fiber stress and fiber strength.

[0070] It should be noted that the reliability directly reflects the influence of the discrete degree of parameters such as shell thickness, winding angle, and blasting pressure on the structural reliability by quantifying the probability that the fiber stress does not exceed the strength.

[0071] As an example, if the reliability is 0.99, it means that under different pressure conditions, the probability that the fiber stress is less than or equal to the fiber strength is 99%, reflecting that the discreteness of the shell process parameters is small, and the impact of parameter randomness on the structural strength is low. The shell can meet the strength requirements in most cases and the structural reliability is high; if the reliability drops to 0.85, it means that the probability of the fiber stress exceeding the fiber strength rises to 15%. The large fluctuations in the process parameters may lead to the risk of insufficient strength of the shell under some working conditions. The process parameters need to be optimized to improve the reliability.

[0072] Based on the above technical solution, the present application provides a method for determining the reliability of shell process parameters. The shell process parameters are measured experimentally to determine the coefficient of variation. The coefficient of variation quantifies the discreteness of the parameters and converts randomness into a calculable indicator, avoiding the reliability misjudgment caused by assuming that the parameters are fixed values ​​in traditional design. The fiber strength utilization coefficient of the shell is determined based on the coefficient of variation and the process parameters. The mean and standard deviation of the fiber stress of the shell are determined based on the fiber strength utilization coefficient and the process parameters. The reliability of the process parameters is determined based on the mean and standard deviation of the fiber stress and fiber strength. Compared with the multiple sampling calculations of the traditional Monte Carlo method, the present application only needs the mean and standard deviation to quickly solve. This method fully considers the random distribution characteristics of the parameters, improves the accuracy of reliability analysis, and effectively solves the technical problems of low reliability, large redundant mass, and low calculation efficiency of common probabilistic reliability methods in traditional deterministic design.

[0073] In addition, in the embodiments of the present application, by introducing random factors to express parameters such as shell thickness, square of winding angle cosine, radius, pressure, etc. in the form of mean + random perturbation, the influence of the discrete characteristics of shell process parameters on fiber stress can be directly reflected.

[0074] In a possible implementation, combining the above Figure 2 ,like Figure 3 As shown, the process of determining the reliability of the shell process parameters in the above steps 201 to 203 can be specifically implemented by the following steps 301 to 306:

[0075] Step 301: The shell process parameter reliability determination device calculates the mean, standard deviation and coefficient of variation of each shell process parameter.

[0076] In some embodiments, a high-precision vernier caliper or micrometer is used to measure the shell thickness and radius, an optical goniometer or digital image analysis method is used to measure the winding angle, and a hydraulic burst test bench and accompanying data acquisition system are used to record the peak pressure at the time of shell rupture to obtain the burst pressure. This application does not limit the use of different measurement tools to obtain shell process parameters.

[0077] Optionally, by conducting experiments and measuring the process parameters of multiple shells, the shell thickness t, winding angle α, radius R, blasting pressure P b , fiber strength σ b A set of digital eigenvalues, the coefficient of variation of each parameter v t , v α , v R , Satisfies the following formula:

[0078]

[0079] Among them, μ t Represents the statistical mean of shell thickness, μ α Represents the statistical mean of the shell winding angle, μ R represents the statistical mean value of the shell radius, represents the statistical mean value of the shell burst pressure, represents the statistical mean value of shell fiber strength, σ t Represents the statistical standard deviation of shell thickness, σ α Represents the statistical standard deviation of the shell winding angle, σ R represents the statistical standard deviation of the shell radius, represents the statistical standard deviation of the shell burst pressure, Indicates the statistical standard deviation of shell fiber strength.

[0080] As an example, the measured mean and standard deviation of the shell radius are 255mm and 0.45mm, the mean and standard deviation of the shell thickness are 2.56mm and 0.044mm, the mean and standard deviation of the shell burst pressure are 15.77MPa and 1.03MPa, the mean and standard deviation of the shell winding angle are 32° and 0.26°, and the mean and standard deviation of the shell fiber strength are 2555.12MPa and 177.84MPa. Based on this, the coefficient of variation of each parameter can be calculated respectively: v R The value of v is 0.18%, t The value is 1.73%, The value of v is 6.54%, α The value of is 0.81%, The value is 6.96%.

[0081] Step 302: The shell process parameter reliability determination device determines the fiber strength utilization coefficient of the shell.

[0082] Optionally, determining the fiber strength utilization coefficient of the shell can be specifically achieved through the following process: the shell process parameter reliability determination device determines the fiber strength when the pressure inside the shell increases to the explosion pressure; based on the fiber strength, the structural composite parameters of the shell are determined; the structural composite parameters are the key intermediate variables for solving the fiber strength utilization coefficient; the mean of the structural composite parameters is determined, and the fiber strength utilization coefficient of the shell is determined based on the mean of the structural composite parameters.

[0083] As an example, when the pressure inside the shell increases to the burst pressure P b When the fiber stress σ b Just reaching the fiber strength limit, the shell is in a critical failure state. The mechanical equilibrium in this state is the key to determine K α Based on this, the fiber strength satisfies the following formula:

[0084]

[0085] It should be pointed out that K α It characterizes the ratio of the actual strength of the fiber in the shell to be effectively utilized, and the value range is usually between 0 and 1. α =1, indicating that the fiber strength is fully utilized; if K α <1, it means that the strength is not fully exerted due to factors such as winding angle and process fluctuations; if K α If the value is >1, it is usually due to abnormal experimental data or incorrect calculation of the coefficient of variation, and the data needs to be recalibrated. This parameter is directly related to the relationship between fiber stress and shell burst pressure, and it is the bridge between process parameter randomness and structural reliability.

[0086] As an example, the structural composite parameter of the shell is determined based on the fiber strength, and the structural composite parameter ξ satisfies the following formula:

[0087]

[0088] It should be pointed out that the structural composite parameter ξ is used to solve the fiber strength coefficient K α An intermediate parameter is introduced, obtained by combining the left and right sides of the fiber strength formula, to facilitate subsequent probability and statistical calculations, but has no actual physical meaning.

[0089] As an example, the mean value of the structural composite parameter is determined, and the fiber strength utilization coefficient of the shell is determined based on the mean value of the structural composite parameter. The mean value of the structural composite parameter μ ξ Satisfies the following formula:

[0090]

[0091] Among them, let cos 2 α=A,μ Ais the mean of the square cosine of the shell winding angle, v A is the coefficient of variation of the square of the shell winding angle cosine, according to the mean value μ of the structural composite parameters ξ The equation satisfied is: in which the mean and coefficient of variation except the fiber strength coefficient are obtained by measurement and calculation, which can be calculated based on the mean μ of the structural composite parameters. ξ The fiber strength coefficient K is calculated by the equation α .

[0092] As an example, according to the mean μ of the structural composite parameter ξ The equation that satisfies the requirement can be solved by using MATLAB after preprocessing the initial process parameters. The fiber strength coefficient K α The value of is 0.8454.

[0093] Step 303: The shell process parameter reliability determination device determines the fiber stress of the shell based on the fiber strength utilization coefficient and the shell process parameters.

[0094] As an example, the fiber stress S satisfies the following formula:

[0095]

[0096] Where P is the pressure inside the shell.

[0097] It should be noted that the fiber strength in step 302 is the critical value of fiber stress when the pressure reaches the burst pressure. At this point, the fiber stress has just reached the strength limit that the shell fibers can withstand. The fiber stress determined in this step based on the fiber strength utilization coefficient and the shell process parameters is calculated for the operating condition where the shell pressure is less than the burst pressure.

[0098] Based on the above steps, the actual stress conditions that the fiber bears can be accurately quantified under different pressure conditions, laying the foundation for analyzing the reliability of the shell process parameters by combining the probabilistic characteristics of fiber strength. This extends the structural reliability analysis from the critical explosion state to the condition of changing the pressure inside the shell, making the reliability assessment more in line with the dynamic pressure changes in actual use.

[0099] Step 304: The shell process parameter reliability determination device introduces a random factor to re-express the fiber stress.

[0100] Alternatively, the fiber stress is re-expressed using random factors, and the fiber stress satisfies the following formula:

[0101]

[0102] in, A random factor representing the shell thickness, represents the random factor of the square of the shell winding angle cosine, represents a random factor for the shell radius, A random factor representing the shell pressure.

[0103] As an example, the random factor The mean is 1, and the standard deviation is the coefficient of variation v of its corresponding parameter t , v A , v R , v P .

[0104] It's important to note that the random factor is a quantitative expression of the randomness of the shell's process parameters. During actual production and use, process parameters fluctuate due to manufacturing tolerances, environmental influences, and other factors, and are not fixed values. Introducing the random factor allows the mean and fluctuation characteristics of the parameters to be incorporated into the fiber stress calculation. This eliminates the fiber stress as a single, fixed value and instead represents a probabilistic description of the random distribution of the parameters.

[0105] Based on the above steps, the impact of random parameter fluctuations on the shell strength reliability can be more accurately considered when analyzing reliability, providing a basis for reliability calculation that is more in line with actual working conditions, and solving the problem of inaccurate reliability assessment caused by traditional deterministic analysis ignoring parameter fluctuation characteristics.

[0106] Step 305: The shell process parameter reliability determination device determines the mean and standard deviation of the fiber stress.

[0107] Alternatively, the mean fiber stress μ s and standard deviation σ s Satisfies the following formula:

[0108]

[0109] It should be pointed out that the process of solving the mean and standard deviation of fiber stress in this step is based on an algebraic synthesis method, which includes the desired operation and the second-order Taylor expansion at the mean, and this application does not make specific limitations on this.

[0110] Step 306: The housing process parameter reliability determination device calculates the housing reliability.

[0111] Optionally, the reliability is calculated based on the mean and standard deviation of fiber stress and fiber strength using the first-order second moment method and reliability index. The reliability P r Satisfies the following formula:

[0112]

[0113] Among them, φ() is the cumulative distribution function of the standard normal distribution, and β is the reliability index.

[0114] In the examples of this application, the first-order second-moment method refers to the commonly used parameter mean (first-order) and standard deviation (second-order) values ​​in reliability design, simplified by linearization. This application primarily considers the interference between fiber stress and fiber strength. The interference area between the two is the unsafe zone, so the mean and standard deviation of fiber strength and fiber stress must be determined. The mean and standard deviation of fiber strength are given by measuring fiber strength, while the mean and standard deviation of fiber stress are calculated by introducing a random factor.

[0115] Based on the above technical solution, the process parameters are determined experimentally and the coefficient of variation is calculated. Based on the coefficient of variation and the process parameters, the fiber strength utilization coefficient of the shell is determined. Then, random factors are introduced to determine the mean and standard deviation of the fiber stress. Finally, the reliability is calculated based on the second-order moment method and the reliability index, that is, the probability that the fiber stress does not exceed the fiber strength. This method not only breaks through the limitation of traditional deterministic design that does not consider parameter fluctuations, but also improves the calculation efficiency through probability statistics and algebraic synthesis methods, effectively solving the technical problems of low reliability, large redundant mass and low calculation efficiency of probabilistic methods in traditional design.

[0116] In one possible implementation, combining Figure 2 ,like Figure 4 As shown, after the above step 203, the shell process parameter reliability determination device can analyze the influence of different process parameters on reliability by comparing the control variables. This process can be specifically implemented by the following steps 401:

[0117] Step 401: The shell process parameter reliability determination device gives each random parameter a set of identical coefficients of variation by controlling variables, thereby changing the relationship between the pressure analysis reliability and the coefficients of variation of each process parameter.

[0118] As an example, by controlling the variables and assigning the same coefficient of variation (0.02, 0.04, 0.06, 0.08, and 0.1) to each random parameter, and comparing the reliability trends under different pressure conditions, the impact of fluctuations in each process parameter on structural reliability can be quantified. For example, when the coefficient of variation of the shell radius increases from 0.02 to 0.1, the reliability decreases by 10%, indicating that the shell radius has a significant impact on the reliability of the shell strength. Based on this, the process control accuracy of the shell radius parameter can be optimized to achieve a balanced design between reliability and lightweighting.

[0119] Based on the above technical solution, the control variable method can systematically quantify the impact of different parameter fluctuations on the shell strength reliability, accurately identify the key parameters sensitive to reliability among the shell process parameters, provide a clear guide for process optimization, and specifically improve the control accuracy of highly sensitive parameters. This method can achieve a quantitative balance between reliability and lightweighting.

[0120] The above mainly introduces the scheme of the embodiment of the present application from the perspective of device implementation. It can be understood that each device, for example, the shell process parameter reliability determination device, in order to realize the above functions, includes at least one of the hardware structure and software modules corresponding to the execution of each function. It should be easy for those skilled in the art to realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0121] In the embodiment of the present application, the shell process parameter reliability determination device can be divided into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0122] In the case of an integrated unit, Figure 5 A possible structural schematic diagram of the shell process parameter reliability determination device involved in the above embodiment (referred to as the shell process parameter reliability determination device 50) is shown. The shell process parameter reliability determination device 50 includes a processing unit 501 and a communication unit 502, and may also include a storage unit 503. Figure 5 The structural schematic diagram shown can be used to illustrate the structure of the housing process parameter reliability determination device involved in the above embodiment.

[0123] when Figure 5 The structural schematic diagram shown is used to illustrate the structure of the shell process parameter reliability determination device involved in the above embodiment. The processing unit 501 is used to control and manage the actions of the shell process parameter reliability determination device, the communication unit 502 is used for the shell process parameter reliability determination device to communicate with other devices, and the storage unit 503 is used to store the program code and data of the shell process parameter reliability determination device.

[0124] For example, the communication unit 502 is used to obtain the process parameters of the shell including: shell thickness, winding angle, radius, blasting pressure, and fiber strength during blasting;

[0125] Processing unit 501 is used to calculate the degree of random parameter dispersion of the mean value and standard deviation of the process parameters and determine the coefficient of variation; based on the coefficient of variation and the process parameters, determine the fiber strength utilization coefficient of the shell, and the fiber strength utilization coefficient is used to characterize the proportion of the actual strength of the fiber that is effectively utilized; based on the fiber strength utilization coefficient and the process parameters, determine the fiber stress of multiple shells, and determine the mean and standard deviation of the fiber stress; the fiber stress of multiple shells is used to characterize the stress borne by the fibers in the shell; based on the mean and standard deviation of the fiber stress and the fiber strength, determine the reliability of the process parameters; the reliability characterizes the probability that the fiber stress is always less than or equal to the fiber strength under different pressures.

[0126] In one possible implementation, the processing unit 501 is further configured to calculate the mean and standard deviation of the process parameters and determine the coefficient of variation. t , v α , v R , Satisfies the following formula:

[0127]

[0128] Among them, μ t Represents the statistical mean of shell thickness, μ α Represents the statistical mean of the shell winding angle, μ R represents the statistical mean value of the shell radius, represents the statistical mean value of the shell burst pressure, represents the statistical mean value of shell fiber strength, σ t Represents the statistical standard deviation of shell thickness, σ α Represents the statistical standard deviation of the shell winding angle, σ R represents the statistical standard deviation of the shell radius, represents the statistical standard deviation of the shell burst pressure, Indicates the statistical standard deviation of shell fiber strength.

[0129] In one possible implementation, the processing unit 501 is also used to determine the fiber strength utilization coefficient of the shell based on the coefficient of variation and process parameters, including: determining the fiber strength when the pressure inside the shell increases to the explosion pressure; determining the structural composite parameters of the shell based on the fiber strength; the structural composite parameters are key intermediate variables for solving the fiber strength utilization coefficient; determining the mean of the structural composite parameters, and determining the fiber strength utilization coefficient of the shell based on the mean of the structural composite parameters.

[0130] In a possible implementation, the processing unit 501 is further configured to calculate the fiber strength utilization coefficient, the fiber strength utilization coefficient K α Determined based on the following formula:

[0131] When the pressure inside the shell increases to the burst pressure, the fiber strength satisfies the following formula:

[0132]

[0133] The structural composite parameter ζ satisfies the following formula:

[0134]

[0135] The mean μ of the structural composite parameter ζ Satisfies the following formula:

[0136]

[0137] Among them, let cos 2 α=A,μ A is the statistical mean of the square of the cosine of the winding angle, v A is the coefficient of variation of the square of the winding angle cosine, and the mean μ of the structural composite parameter ζ The calculation formula is used to reversely calculate the fiber strength coefficient.

[0138] In one possible implementation, the processing unit 501 is further configured to calculate the fiber stress, where the fiber stress S satisfies the following formula:

[0139]

[0140] Where P is the pressure inside the shell.

[0141] In one possible implementation, the processing unit 501 is further configured to re-express the fiber stress using a random factor, and the fiber stress satisfies the following formula:

[0142]

[0143] in, A random factor representing the shell thickness, represents the random factor of the square of the shell winding angle cosine, represents a random factor for the shell radius, A random factor representing the shell pressure.

[0144] In a possible implementation, the processing unit 501 is further configured to calculate the mean and standard deviation of the fiber stress. The mean μ of the fiber stress is s and standard deviation σ s Satisfies the following formula:

[0145]

[0146]

[0147] In a possible implementation, the processing unit 501 is further configured to calculate the reliability under different pressures based on the first-order second moment method and the reliability index, the reliability P r Satisfies the following formula:

[0148]

[0149] Among them, φ() is the cumulative distribution function of the standard normal distribution, and β is the reliability index.

[0150] In one possible implementation, the processing unit 501 is further used to complete the reliability analysis under different pressures by changing the coefficient of variation of each random parameter through control variables, including: giving each process parameter a set of the same coefficient of variation; changing the pressure to compare the degree of influence of different process parameters on reliability.

[0151] Among them, the processing unit 501 can be a processor or a controller, and the communication unit 502 can be a communication interface, a transceiver, a transceiver, a transceiver circuit, a transceiver device, etc. Among them, the communication interface is a general term and can include one or more interfaces. The storage unit 503 can be a memory. When the shell process parameter reliability determination device 50 is a chip, the processing unit 501 can be a processor or a controller, and the communication unit 502 can be an input interface and / or output interface, a pin or a circuit, etc. The storage unit 503 can be a storage unit within the chip (for example, a register, a cache, etc.), or it can be a storage unit located outside the chip (for example, a read-only memory (ROM), a random access memory (RAM), etc.).

[0152] Among them, the communication unit can also be called a transceiver unit. The antenna and control circuit with transceiver functions in the shell process parameter reliability determination device 50 can be regarded as the communication unit 502 of the shell process parameter reliability determination device 50, and the processor with processing function can be regarded as the processing unit 501 of the shell process parameter reliability determination device 50. Optionally, the device used to implement the receiving function in the communication unit 502 can be regarded as a communication unit, and the communication unit is used to perform the receiving steps in the embodiment of the present application. The communication unit can be a receiver, a receiver, a receiving circuit, etc. The device used to implement the sending function in the communication unit 502 can be regarded as a sending unit, and the sending unit is used to perform the sending steps in the embodiment of the present application. The sending unit can be a transmitter, a transmitter, a sending circuit, etc.

[0153] Figure 5If the integrated units are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the various embodiments of the present application. The storage medium for storing computer software products includes various media that can store program codes, such as USB flash drives, mobile hard drives, read-only memories, random access memories, magnetic disks or optical disks.

[0154] Figure 5 A unit in a can also be called a module, for example, a processing unit can be called a processing module.

[0155] The embodiment of the present application also provides a hardware structure diagram of a shell process parameter reliability determination device (denoted as shell process parameter reliability determination device 60), see Figure 6 The shell process parameter reliability determination device 60 includes a processor 601 and, optionally, a memory 602 connected to the processor 601 .

[0156] In the first possible implementation, see Figure 6 The shell process parameter reliability determination device 60 further includes a transceiver 603. The processor 601, memory 602, and transceiver 603 are connected via a bus. The transceiver 603 is used to communicate with other devices or a communication network. Optionally, the transceiver 603 may include a transmitter and a receiver. The device used to implement the receiving function in the transceiver 603 can be considered a receiver, which is used to perform the receiving step in the embodiment of the present application. The device used to implement the transmitting function in the transceiver 603 can be considered a transmitter, which is used to perform the transmitting step in the embodiment of the present application.

[0157] Based on the first possible implementation, Figure 6 The structural schematic diagram shown can be used to illustrate the structure of the housing process parameter reliability determination device involved in the above embodiment.

[0158] in, Figure 6 The system chip in the housing process parameter reliability determination device may also be illustrated. In this case, the actions performed by the housing process parameter reliability determination device may be implemented by the system chip. The specific actions performed may be referred to above and will not be described in detail here.

[0159] During implementation, each step of the method provided in this embodiment can be completed by hardware integrated logic circuits in a processor or by software instructions. The steps of the method disclosed in the embodiments of this application can be directly implemented as execution by a hardware processor, or as a combination of hardware and software modules in a processor.

[0160] The processor in this application may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, and other types of computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform operations or processing. The processor may be a separate semiconductor chip, or it may be integrated into a semiconductor chip together with other circuits. For example, it may form an SoC (system on chip) with other circuits (such as a codec circuit, a hardware acceleration circuit, or various bus and interface circuits), or it may be integrated into an ASIC as a built-in processor of the ASIC. The ASIC with the integrated processor may be packaged separately or together with other circuits. In addition to the core for executing software instructions to perform operations or processing, the processor may further include necessary hardware accelerators, such as a field programmable gate array (FPGA), a PLD (programmable logic device), or a logic circuit that implements dedicated logic operations.

[0161] The memory in the embodiments of the present application may include at least one of the following types: read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or electrically erasable programmable read-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0162] An embodiment of the present application also provides a computer-readable storage medium, comprising instructions, which, when executed on a computer, enables the computer to execute any of the above methods.

[0163] An embodiment of the present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute any of the above methods.

[0164] An embodiment of the present application also provides a chip, which includes a processor and an interface circuit, the interface circuit is coupled to the processor, the processor is used to run a computer program or instruction to implement the above method, and the interface circuit is used to communicate with other modules outside the chip.

[0165] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more media integrated therewith. The available media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVDs), or semiconductor media (eg, solid state disks (SSDs)).

[0166] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0167] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely illustrative of the present application as defined by the appended claims and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the claims of the present application and their equivalents.

Claims

1. A method for determining the reliability of a shell process parameter, characterized in that: include: Acquiring process parameters of a plurality of shells, wherein the process parameters include: shell thickness, winding angle, radius, blasting pressure, and fiber strength during blasting; Calculate the degree of random parameter dispersion of the mean value and standard deviation of the process parameters to determine the coefficient of variation; Determining a fiber strength utilization coefficient of the shell based on the coefficient of variation and the process parameters, wherein the fiber strength utilization coefficient is used to represent a proportion of the actual strength of the fiber that is effectively utilized; Based on the fiber strength utilization coefficient and the process parameters, determining the fiber stress of the multiple shells, and determining the mean and standard deviation of the fiber stress; the fiber stress of the multiple shells is used to characterize the stress borne by the fibers in the shells; The reliability of the process parameters is determined based on the mean and standard deviation of the fiber stress and the fiber strength; the reliability represents the probability that the fiber stress is always less than or equal to the fiber strength under different pressures.

2. The method according to claim 1, wherein The step of obtaining process parameters of a plurality of shells, calculating the degree of random parameter dispersion of the mean value and standard deviation of the process parameters, and determining the coefficient of variation includes: The shell thickness t, winding angle α, radius R, blasting pressure P are obtained by experimenting and measuring the process parameters of the multiple shells. b , fiber strength σ b A set of digital eigenvalues, the coefficient of variation of each parameter v t , v α , v R , Satisfies the following formula: Among them, μ t represents the statistical mean value of the shell thickness, μ α represents the statistical mean value of the shell winding angle, μ R represents the statistical mean value of the shell radius, represents the statistical mean value of the shell bursting pressure, represents the statistical mean value of the shell fiber strength, σ t represents the statistical standard deviation of the shell thickness, σ α represents the statistical standard deviation of the shell winding angle, σ R represents the statistical standard deviation of the shell radius, represents the statistical standard deviation of the shell burst pressure, represents the statistical standard deviation of the shell fiber strength.

3. The method according to claim 2, characterized in that The determining of the fiber strength utilization coefficient of the shell based on the coefficient of variation and the process parameters includes: determining the fiber strength when the pressure in the shell increases to a bursting pressure; Based on the fiber strength, determining the structural composite parameters of the shell; the structural composite parameters are key intermediate variables for solving the fiber strength utilization coefficient; An average value of the structural composite parameter is determined, and a fiber strength utilization coefficient of the shell is determined based on the average value of the structural composite parameter.

4. The method according to claim 3, wherein The fiber strength exertion coefficient K α Determined based on the following formula: When the pressure in the shell increases to the bursting pressure, the fiber strength satisfies the following formula: The structural composite parameter ξ satisfies the following formula: The mean value μ of the structural composite parameter ξ Satisfies the following formula: Among them, let cos 2 α=A,μ A is the statistical mean value of the square of the cosine of the winding angle, v A is the coefficient of variation of the square of the cosine of the winding angle, and the mean μ of the structural composite parameter ξ The calculation formula is used to reversely calculate the fiber strength coefficient.

5. The method according to claim 4, wherein The fiber stress S satisfies the following formula: Where P is the pressure inside the shell.

6. The method according to claim 5, wherein The fiber stress is re-expressed using random factors and satisfies the following formula: in, A random factor representing the shell thickness, represents a random factor of the squared cosine of the shell winding angle, A random factor representing the shell radius, A random factor representing the shell pressure.

7. The method according to claim 6, wherein The mean value μ of the fiber stress s and standard deviation σ s Satisfies the following formula:

8. The method according to claim 7, wherein The method further comprises: The reliability is calculated based on the first-order second moment method and the reliability index. r Satisfies the following formula: Wherein, φ() is the cumulative distribution function of the standard normal distribution, and β is the reliability index.

9. The method according to claim 8, wherein By controlling the variables and changing the coefficient of variation of each random parameter, the reliability analysis under different pressures is completed, including: Giving each process parameter a set of identical coefficients of variation; Changing the pressure compares the impact of different process parameters on reliability.

10. A device for determining the reliability of a shell process parameter, characterized in that: The device includes: a communication unit and a processing unit; The communication unit is used to obtain the process parameters of the shell; The processing unit is used to calculate the degree of random parameter dispersion of the mean value and standard deviation of the process parameters to determine the coefficient of variation; based on the coefficient of variation and the process parameters, determine the fiber strength utilization coefficient of the shell, and the fiber strength utilization coefficient is used to characterize the proportion of the actual strength of the fiber that is effectively utilized; based on the fiber strength utilization coefficient and the process parameters, determine the fiber stress of the multiple shells, and determine the mean and standard deviation of the fiber stress; the fiber stress of the multiple shells is used to characterize the stress size borne by the fibers in the shells; based on the mean and standard deviation of the fiber stress and fiber strength, determine the reliability of the process parameters; the reliability characterizes the probability that the fiber stress is always less than or equal to the fiber strength under different pressures.

Images