A simulation optimization method for variable stress load spectrum under storage conditions of electromechanical products

Abstract

The present invention discloses a simulation optimization method for variable stress load spectrum under storage conditions of electromechanical products, which belongs to the field of accelerated test of constitutive models, and includes the following steps: S1, selecting Anand constitutive model as the accelerated test simulation model, and setting Anand constitutive model parameters; S2, performing virtual accelerated test of Anand constitutive model under temperature cycle load; S3, analyzing the position of the measured point using GXTL model; S4, based on the accelerated simulation degradation data obtained in step S2, optimizing the accelerated test stress spectrum of the measured point described in step S3. The present invention adopts the simulation optimization method for variable stress load spectrum under storage conditions of electromechanical products, utilizes the advantages of convenience, speed and accuracy of finite element simulation platform, combines the normal temperature variable stress accelerated life test scheme, and carries out accelerated life test design optimization based on simulation accelerated degradation data, which reduces the accelerated test cycle, and the optimization effect is more obvious after verification.

Description

Technical Field

[0001] The invention relates to the technical field of accelerated testing of constitutive models, and in particular to a simulation optimization method for a variable stress load spectrum under storage conditions of electromechanical products. Background Art

[0002] Most components of electromechanical products, especially fiber optic gyroscopes, are mechanical and electronic products, so their failure process can be explained from the perspective of structural and performance degradation. Under the continuous influence of environmental stress, most components of fiber optic gyroscopes will experience slow changes in varying degrees, such as parameter decline and material aging. When the degradation reaches a certain level, that is, the failure threshold, the electromechanical product will fail because it cannot meet the requirements of normal operation.

[0003] At the same time, since the various components that make up electromechanical products are made of different materials and structures, their respective performance degradation processes also show different characteristics, but under the combined influence, the performance indicators of electromechanical products will show a degradation trend.

[0004] For high-reliability, long-life products such as fiber optic gyroscopes, the performance degradation process is very slow under normal stress working environment, so it is difficult to obtain its degradation data. In response to the above problems, the existing technology generally accelerates the degradation of gyroscope performance through accelerated life tests without changing the product degradation trajectory and failure mechanism, and performs reliability assessment using accelerated degradation models (accelerated tests are to accelerate product failure by strengthening test conditions without changing the product failure mechanism, so as to obtain the necessary information for product reliability evaluation in a shorter time).

[0005] However, most of the existing accelerated test optimization methods are based on the analysis and processing of actual accelerated test data, so they need sufficient test data as a supporting condition. As a result, there are problems such as long optimization cycle, high time and economic costs, and difficulty in ensuring optimization efficiency. Summary of the invention

[0006] The present invention provides a variable stress load spectrum simulation optimization method under storage conditions of electromechanical products, which solves the problems of traditional accelerated test optimization methods such as long cycle, high time and economic costs, difficulty in ensuring efficiency, and difficulty in verifying optimization effects.

[0007] To achieve the above object, the present invention provides a method for simulating and optimizing a variable stress load spectrum under storage conditions of an electromechanical product, comprising the following steps:

[0008] S1. Select the Anand constitutive model as the accelerated test simulation model and set the Anand constitutive model parameters;

[0009] S2. Conduct virtual accelerated test of Anand constitutive model under temperature cycle load: set basic parameters of temperature cycle benchmark load spectrum, conduct accelerated test, and obtain accelerated simulation degradation data;

[0010] S3, using the GXTL model to analyze the location of the measured point;

[0011] S4. Based on the accelerated simulation degradation data obtained in step S2, the accelerated test stress spectrum optimization is performed on the measured point described in step S3.

[0012] Preferably, in step S1, the Anand constitutive model parameters are set as follows:

[0013] Initial value of deformation impedance s o =11.1, unit is MPa;

[0014] Activation energy / gas constant Q / R = 8178, in 1 / k;

[0015] Pre-exponential coefficient factor A = 28690, unit is 1 / sec;

[0016] Material constant ξ=4;

[0017] Strain hardening constant m = 0.22;

[0018] The strain rate sensitivity exponent h of stress o =13334, unit is MPa;

[0019] Deformation impedance saturation coefficient The unit is MPa;

[0020] The strain rate sensitivity index of the deformation impedance saturation value is n = 0.011;

[0021] Strain hardening exponent α=1.2.

[0022] Preferably, in step S2, the basic parameters of the temperature cycle benchmark load spectrum are set as follows: the insulation temperature is 10°C, the maximum temperature is T min 80℃, the lowest temperature is T max -10℃, temperature change rate v is 3℃ / min, insulation time is 1h, number of cycles at failure N f is 1000 times;

[0023] The accelerated simulation degradation data obtained include equivalent stress values ​​under temperature cycle stress loading.

[0024] Preferably, in step S3, the GXTL model is used to load the circuit board in the electromechanical product with a reference temperature cycle load to obtain the circuit board with the largest stress and strain value, and the point on the circuit board with the largest stress and strain value as the measured point.

[0025] Preferably, the electromechanical product is a fiber optic gyroscope.

[0026] Preferably, in step S4, the expected accelerated life N is first calculated based on the equivalent stress value obtained by the accelerated test and the SN curve of the material; the expected accelerated life N obtained under multiple set accelerated stress conditions is then brought into the modified Coffin-Manson model, and the coefficient fitting of the Coffin-Manson model is realized through the multivariate linear coefficient fitting algorithm; and then after the coefficient fitting of the modified Coffin-Manson model is completed, the optimal parameters are solved based on the optimization analysis so that the number of cycles at failure N f Minimum;

[0027] It specifically includes the following steps:

[0028] S41. Using linearization processing, the modified Coffin-Manson model formula is as follows:

[0029] lnN-lnG(T max )=lnA-mlnf-nlnΔT (1)

[0030] In the formula, G(T max ) is the Arrhenius equation, Where a is the material correlation coefficient, E a is the material activation energy, k is the Boltzmann constant; A, m, n are all formula coefficients; f is the temperature change frequency of the stress spectrum; ΔT is the temperature difference in the stress spectrum;

[0031] S42, substituting the acquired accelerated test data into the SN curve to solve for the expected accelerated life N;

[0032] S43, performing parameter fitting on the linearization formula using the expected accelerated life N obtained under multiple set accelerated stress conditions, and obtaining the fitting results as A=1.47e+08; m=0.3207; n=5.9224;

[0033] S44, using parameter variance as an accurate indicator of coefficient fitting, calculate the parameter variance R 2 , and determine the parameter variance R 2 Is it lower than the set threshold? If so, it is determined that the fitting meets the standard;

[0034] The formula after fitting reaches the standard is transformed into:

[0035] lnN-lnG(T max )=18.8088-0.3207lnf-5.9224lnΔT (2)

[0036] S45. Considering that the parameter f in formula (2) is related to the temperature difference ΔT, the temperature change rate v and the temperature change interval t0 in the stress spectrum, f is expanded into the following formula and substituted into the correction formula:

[0037]

[0038]

[0039] S46. According to the simulation results of the accelerated test, t0 is positively correlated with F(N), and v is negatively correlated with F(N), so it is simplified to solve the optimal ΔT to minimize F(N);

[0040] At this time, taking the partial derivative of F(N) yields:

[0041]

[0042] Among them, since the temperature change rate v ranges from 1℃ / min to 5℃ / min, the numerator is always negative and the denominator is always positive; at the same time, since F(N) decreases monotonically with respect to △T, the maximum value of △T within the limited range is taken as the optimal parameter, so that the number of cycles N at failure f Minimum.

[0043] Preferably, in step S4, the load spectrum optimization analysis of the model acceleration factor is considered, and the model acceleration factor calculation formula is as follows:

[0044]

[0045] In the formula, A F is the acceleration factor; N L N is the accelerated life under low stress load conditions; H is the accelerated life under high stress load conditions; ΔT H is the temperature difference under high stress load condition; ΔT L is the temperature difference under low stress load state; f L is the temperature change frequency under low stress load state; f H is the temperature change frequency under high stress load state; E a is the sample activation energy; k is the Boltzmann constant; T mL is the weighted average temperature under low stress load state; T mH is the weighted average temperature under high stress load state;

[0046] Linearize the above equation and get:

[0047]

[0048] Substituting the reference spectrum parameters as constant coefficients directly into formula (7), the linearized calculation formula of the model acceleration factor is transformed into:

[0049]

[0050] The partial derivative of formula (8) is calculated to obtain the following formula:

[0051]

[0052] According to the above formula, at ΔT L Determine the basis to obtain lnA F About ΔT H Monotonically increasing, so ΔT H Take the maximum value within the limited range as the optimal parameter, so that the acceleration factor A F maximum, thus obtaining the optimal accelerated stress spectrum.

[0053] Preferably, the reference spectrum parameters are set as follows: ΔT H =90℃, T mL =80℃, f L =12, T mL =T mH =80℃;

[0054] The optimization parameters are set as follows: ΔT H =90℃, v=5℃ / min, t0=30min.

[0055] The present invention has the following beneficial effects:

[0056] Taking advantage of the convenience, speed and accuracy of the finite element simulation platform, combined with the normal temperature variable stress accelerated life test scheme, the accelerated life test design optimization was carried out based on the simulation accelerated degradation data, which reduced the accelerated test cycle and the verified optimization effect was more obvious.

[0057] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flowchart of a variable stress load spectrum simulation optimization method under storage conditions of electromechanical products according to the present invention;

[0059] Figure 2 It is a reference acceleration load spectrum diagram of a variable stress load spectrum simulation optimization method under storage conditions of an electromechanical product described in the present invention;

[0060] Figure 3It is an equivalent stress diagram under temperature cycle stress loading of an accelerated test model of a variable stress load spectrum simulation optimization method under storage conditions of an electromechanical product described in the present invention;

[0061] Figure 4 It is an accelerated degradation trajectory diagram of a reference stress spectrum of a variable stress load spectrum simulation optimization method under storage conditions of an electromechanical product according to the present invention;

[0062] Figure 5 It is a simulation result diagram of equivalent plastic strain under temperature cycle stress loading of an accelerated test model of a variable stress load spectrum simulation optimization method under storage conditions of an electromechanical product described in the present invention;

[0063] Figure 6 It is a diagram of equivalent stress simulation results under temperature cycle stress loading of an accelerated test model of a variable stress load spectrum simulation optimization method under storage conditions of an electromechanical product described in the present invention;

[0064] Figure 7 A stress spectrum diagram of a verification example of the present invention with a changed storage temperature reference;

[0065] Figure 8 This is a diagram showing the accelerated results of changing the storage temperature reference in a verification example of the present invention;

[0066] Fig. 9 A stress spectrum diagram showing a change in the maximum temperature amplitude in a verification example of the present invention;

[0067] Fig.10 This is a diagram showing the acceleration results of changing the maximum temperature amplitude in a verification example of the present invention;

[0068] Fig.11 A stress spectrum diagram showing a change in the minimum temperature amplitude for a verification example of the present invention;

[0069] Fig.12 This is a diagram showing the acceleration results of changing the minimum temperature amplitude in the verification example of the present invention;

[0070] Fig.13 A stress spectrum diagram of a temperature difference in a stress spectrum of a verification example of the present invention;

[0071] Fig.14 This is a diagram showing the accelerated results of changing the temperature difference in the stress spectrum of the verification example of the present invention;

[0072] Fig.15 A stress spectrum diagram of a temperature change rate of a verification example of the present invention;

[0073] Fig.16 This is a diagram showing the accelerated results of changing the temperature variation rate in a verification example of the present invention. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical scheme and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions.

[0075] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products or devices.

[0076] Like reference numerals and letters denote similar items in the following drawings, and thus, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0077] The variable stress accelerated test can effectively simulate the temperature change process of the fiber optic gyroscope product storage link, realize the simulation of daily temperature change from maximum temperature to reference storage temperature to minimum temperature, and achieve acceleration on this basis.

[0078] like Figure 1-Figure 6 As shown, based on the above theory, the present invention is designed: a variable stress load spectrum simulation optimization method under storage conditions of electromechanical products, comprising the following steps:

[0079] S1. Select the Anand constitutive model as the accelerated test simulation model and set the Anand constitutive model parameters;

[0080] Preferably, in step S1, the Anand constitutive model parameters are set as follows:

[0081] Initial value of deformation impedance s o =11.1, unit is MPa;

[0082] Activation energy / gas constant Q / R = 8178, in 1 / k;

[0083] Pre-exponential coefficient factor A = 28690, unit is 1 / sec;

[0084] Material constant ξ=4;

[0085] Strain hardening constant m = 0.22;

[0086] The strain rate sensitivity exponent h of stress o =13334, unit is MPa;

[0087] Deformation impedance saturation coefficient The unit is MPa;

[0088] The strain rate sensitivity index of the deformation impedance saturation value is n = 0.011;

[0089] Strain hardening exponent α=1.2.

[0090] S2. Conduct virtual accelerated test of Anand constitutive model under temperature cycle load: set basic parameters of temperature cycle benchmark load spectrum, conduct accelerated test, and obtain accelerated simulation degradation data;

[0091] Preferably, in step S2, the basic parameters of the temperature cycle benchmark load spectrum are set as follows: the insulation temperature is 10°C, the maximum temperature is T min 80℃, the lowest temperature is T max -10℃, temperature change rate v is 3℃ / min, insulation time is 1h, number of cycles at failure N f is 1000 times;

[0092] The accelerated simulation degradation data obtained include equivalent stress values ​​under temperature cycle stress loading.

[0093] S3, using the GXTL model to analyze the location of the measured point;

[0094] Preferably, in step S3, the GXTL model is used to load the circuit board in the electromechanical product with a reference temperature cycle load to obtain the circuit board with the largest stress and strain value, and the point on the circuit board with the largest stress and strain value as the measured point.

[0095] Preferably, the electromechanical product is a fiber optic gyroscope. In this embodiment, the two circuit boards of the GXTL model are first loaded with a reference temperature cycle load at the same time. By comparison, it is found that the stress and strain values ​​generated by the solder joints of the optical path circuit board are greater and more prone to failure, which is the weak link of the model. Subsequent accelerated tests will be conducted on the circuit boards of the optical path. It can be seen that the weak link of the failure of the circuit board caused by temperature cycle stress is mainly concentrated on the solder joints between the electronic components and the circuit board. Therefore, the measured point is the solder joint position of the circuit board of the optical path.

[0096] S4. Based on the accelerated simulation degradation data obtained in step S2, the accelerated test stress spectrum optimization is performed on the measured point described in step S3.

[0097] Preferably, in step S4, the expected accelerated life N is first calculated based on the equivalent stress value obtained by the accelerated test and the SN curve of the material; the expected accelerated life N obtained under multiple set accelerated stress conditions is then brought into the modified Coffin-Manson model, and the coefficient fitting of the Coffin-Manson model is realized through the multivariate linear coefficient fitting algorithm; and then after the coefficient fitting of the modified Coffin-Manson model is completed, the optimal parameters are solved based on the optimization analysis so that the number of cycles at failure N f Minimum;

[0098] It specifically includes the following steps:

[0099] S41. Using linearization processing, the modified Coffin-Manson model formula is as follows:

[0100] lnN-lnG(T max )=lnA-mlnf-nlnΔT (1)

[0101] In the formula, G(T max ) is the Arrhenius equation, Where a is the material correlation coefficient, E a is the material activation energy, k is the Boltzmann constant; A, m, n are all formula coefficients; f is the temperature change frequency of the stress spectrum; ΔT is the temperature difference in the stress spectrum;

[0102] S42, substituting the acquired accelerated test data into the SN curve to solve for the expected accelerated life N;

[0103] The accelerated test data used in this embodiment are as follows:

[0104] Table 1 Accelerated test data

[0105]

[0106]

[0107] Table 2 Fitting results

[0108]

[0109] S43, performing parameter fitting on the linearization formula using the expected accelerated life N obtained under multiple set accelerated stress conditions, and obtaining the fitting results as A=1.47e+08; m=0.3207; n=5.9224;

[0110] S44, using parameter variance as an accurate indicator of coefficient fitting, calculate the parameter variance R 2 , the parameter variance R in this embodiment 2=0.965, and determine the parameter variance R 2 Is it lower than the set threshold? If so, it is determined that the fitting meets the standard;

[0111] The formula after fitting reaches the standard is transformed into:

[0112] lnN-lnG(T max )=18.8088-0.3207lnf-5.9224lnΔT (2)

[0113] S45. Considering that the parameter f in formula (2) is related to the temperature difference ΔT, the temperature change rate v and the temperature change interval t0 in the stress spectrum, f is expanded into the following formula and substituted into the correction formula:

[0114]

[0115]

[0116] S46. According to the simulation results of the accelerated test, t0 is positively correlated with F(N), and v is negatively correlated with F(N), so it is simplified to solve the optimal △T to minimize F(N);

[0117] At this time, taking the partial derivative of F(N) yields:

[0118]

[0119] Among them, since the temperature change rate v ranges from 1℃ / min to 5℃ / min, the numerator is always negative and the denominator is always positive; at the same time, since F(N) decreases monotonically with respect to △T, the maximum value of △T within the limited range is taken as the optimal parameter, so that the number of cycles N at failure f Minimum.

[0120] Preferably, in step S4, the load spectrum optimization analysis of the model acceleration factor is considered, and the model acceleration factor calculation formula is as follows:

[0121]

[0122] In the formula, A F is the acceleration factor N L N is the accelerated life under low stress load conditions; H is the accelerated life under high stress load conditions; ΔT H is the temperature difference under high stress load condition; ΔT L is the temperature difference under low stress load state; f L is the temperature change frequency under low stress load state; f H is the temperature change frequency under high stress load state; E a is the sample activation energy; k is the Boltzmann constant; TmL is the weighted average temperature under low stress load state; T mH is the weighted average temperature under high stress load state;

[0123] Linearize the above equation and get:

[0124]

[0125] Substituting the reference spectrum parameters as constant coefficients directly into formula (7), the linearized calculation formula of the model acceleration factor is transformed into:

[0126]

[0127] The partial derivative of formula (8) is calculated to obtain the following formula:

[0128]

[0129] According to the above formula, at ΔT L Determine the basis to obtain lnA F About ΔT H Monotonically increasing, so ΔT H Take the maximum value within the limited range as the optimal parameter, so that the acceleration factor A F maximum, thus obtaining the optimal accelerated stress spectrum.

[0130] Preferably, the reference spectrum parameters are set as follows: ΔT H =90℃, T mL =80℃, f L =12, T mL =T mH =80℃;

[0131] The optimization parameters are set as follows: ΔT H =90℃, v=5℃ / min, t0=30min.

[0132] Verification Example

[0133] The weak links of circuit board failure caused by temperature cycle stress are mainly concentrated on the solder joints between electronic components and circuit boards. In order to realize the fatigue analysis of solder joints under temperature cycle load, the Anand constitutive model is introduced (the Anand constitutive model is the most commonly used model to study the relationship between the stress and strain generated by the material. The time-related creep strain and the time-independent plastic strain generated by the material can be described by this model). The Anand constitutive model describes the inelastic flow process of the material through the deformation impedance s related to factors such as the grain size of the material.

[0134] In this verification example, the circuit board substrate of the acceleration model is made of FR-4 composite material, the electronic components are made of copper alloy material, and the solder joints are made of tin-lead alloy material.

[0135] In this verification example, under the variable stress test conditions, the temperature difference ΔT, temperature change rate ΔT / t, and temperature change amplitude of the stress spectrum of the temperature change stress in the test box are set: the maximum temperature T min -Minimum temperature T max , Amplitude duration: minimum time t min - Maximum time t max , number of temperature changes j;

[0136] Example 1: The test life T is predicted by using the variable stress load spectrum simulation optimization method under storage conditions of electromechanical products described in the present invention;

[0137] Comparative Example 1: Modified Norris-Landzberg model to calculate and predict the test life T: Set the temperature change of the temperature change stress ΔT = 90, the maximum temperature T mmax =353.15k.

[0138] N f =A·fL -m ΔT -n ·G(T max )

[0139] Where N f is the number of cycles to failure; fL is the cycle frequency; ΔT is the temperature range within a cycle; A is the model coefficient, and according to the regression fitting of relevant literature, A=1.6E8 is obtained; m is the cycle frequency index, usually m=1 / 3; n is the temperature range index, usually n=1.9; G(T max ) is the Arrhenius relation at the highest temperature of each cycle, G(T max )=exp(E a / kT max ), where E a is the activation energy of the material, and E is used for solder material a =0.61eV; k is the Boltzmann constant, k=8.62E-5.

[0140] Substitute the following formula to calculate the number of cycles to failure N f =\119383, convert the number of times into time:

[0141] T=N f ·Δt / (24·365)

[0142] Where T is the predicted test life in years; N fis the number of cycles to failure; Δt is the time of each cycle. For this project, Δt = 4h, and the final predicted test life is T = 27.26years;

[0143] Comparative Example 2: The plastic strain theory formula is used to predict the test life:

[0144]

[0145]

[0146] c=-0.442-6×10 -4 T m +1.74×10 -2 ln(1+fI)

[0147] Where, T f is the predicted life under the benchmark accelerated load spectrum; Δγ f is the plastic shear strain range, Δγ f = ε f is the fatigue coefficient, which is related to the material. For the solder joint material tin-lead alloy, the value is 0.325; Δε p is the plastic strain range, which can be obtained from the simulation model; c is the fatigue index, which is related to the material; T m is the average temperature of the thermal cycle, fI is the thermal cycle frequency, in cycles / day; and Δε p Select the maximum value of the plastic strain range in the first ten cycles;

[0148] Take Δε p =2.31E-3, we get According to the characteristics of the temperature load spectrum, the weighted average value is used and the calculation formula is as follows:

[0149]

[0150] Among them, T i For different temperature levels in each cycle, t i is the time corresponding to each temperature level, t0 is the time of one cycle; substitute it into the calculated T m =24.44℃; f=12 according to the load spectrum;

[0151] Substituting into the above formula, the number of cycles to failure under the benchmark accelerated load spectrum is calculated as N f =115976, convert the number of times into time, and get the predicted test life T=231952.8h=26.48years.

[0152] Take the optimized parameters △T = 90℃, v = 5℃ / min, t0 = 30min, substitute the optimized parameters into the Coffin-Manson model after parameter fitting correction, and obtain the optimal stress spectrum acceleration cycle number N f =73785 times; at the same time, the optimized parameters are substituted into the reference load spectrum parameter calculation to obtain the number of cycles N before the stress spectrum optimization f = 89378 times; Substitute the optimized parameters into the fatigue life prediction formula based on plastic strain to calculate the predicted number of cycles N f =116168 times.

[0153]

[0154] The following experiments were also conducted:

[0155] 1. Accelerated test to change the storage temperature benchmark:

[0156] Accelerated test with changing storage temperature benchmark: In the benchmark load spectrum, the storage temperature benchmark is set to 10℃, 25℃ and 40℃. Accelerated test simulation is performed based on the storage temperature benchmarks of 10℃, 25℃ and 40℃, respectively. The following results are obtained: Figure 7 and Figure 8 The accelerated degradation index of the model characterized by equivalent plastic strain is shown;

[0157] It can be seen that when the storage temperature benchmark increases, the equivalent plastic strain value of the accelerated model under the same number of cycles will also increase accordingly, and the degradation rate of the model using this as an indicator will also become faster accordingly.

[0158] 2. Accelerated test with changing maximum temperature amplitude:

[0159] In the benchmark load spectrum, the highest temperature amplitude is 80℃. This benchmark is changed to 60℃ and 70℃ respectively. On this basis, the accelerated test simulation is carried out, and the following results are obtained: Fig. 9 and Fig.10 The accelerated degradation index of the model characterized by equivalent plastic strain is shown;

[0160] It can be seen that when the maximum temperature amplitude increases, the equivalent plastic strain value of the accelerated model under the same number of cycles will also increase accordingly, and the degradation speed of the model using this as an indicator will also become faster accordingly.

[0161] 3. Accelerated test with changing minimum temperature amplitude:

[0162] In the benchmark load spectrum, the lowest temperature amplitude is -10℃. This benchmark is changed to 0℃, and the accelerated test simulation is carried out on this basis, and the following is obtained: Fig.11 and Fig.12 The accelerated degradation index of the model characterized by equivalent plastic strain is shown;

[0163] When the minimum temperature amplitude increases, the equivalent plastic strain value of the accelerated model under the same number of cycles will decrease accordingly, and the degradation rate of the model based on this index will also slow down accordingly.

[0164] 4. Accelerated test to change the temperature difference in stress spectrum:

[0165] In the benchmark load spectrum, the temperature difference in the stress spectrum is 1h. This benchmark is changed to 0.5h and 2h respectively. On this basis, the accelerated test simulation is carried out and the following results are obtained: Fig.13 and Fig.14 The accelerated degradation index of the model characterized by equivalent plastic strain is shown;

[0166] It can be seen that when the temperature difference in the stress spectrum increases, the equivalent plastic strain value of the accelerated model under the same number of cycles will decrease accordingly, and the degradation rate of the model using this as an indicator will also slow down accordingly.

[0167] 5. Accelerated test to change the temperature change rate:

[0168] In the benchmark load spectrum, the temperature change rate is 3℃ / min. This benchmark is changed to 1℃ / min and 5℃ / min respectively. On this basis, the accelerated test simulation is carried out and the following results are obtained: Fig.15 and Fig.16 The accelerated degradation index of the model characterized by equivalent plastic strain is shown;

[0169] It can be seen that when the temperature change rate increases, the equivalent plastic strain value of the accelerated model under the same number of cycles will increase accordingly, and the degradation speed of the model using this as an indicator will also become faster accordingly.

[0170] It can be seen that this embodiment can optimize the variable stress acceleration spectrum to seek the optimal acceleration effect under the constraints of test conditions.

[0171] Therefore, the present invention adopts the variable stress load spectrum simulation optimization method under the storage conditions of the above-mentioned electromechanical products, utilizes the convenience, speed and accuracy of the finite element simulation platform, combines the normal temperature variable stress accelerated life test scheme, and carries out accelerated life test design optimization based on the simulation accelerated degradation data, thereby reducing the accelerated test cycle, and it has been verified that the optimization effect is more obvious.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for simulating and optimizing the variable stress load spectrum under storage conditions of electromechanical products, characterized in that: The following steps are involved: S1. Select the Anand constitutive model as the accelerated test simulation model and set the Anand constitutive model parameters; In step S1, the Anand constitutive model parameters are set as follows: Initial value of deformation impedance , unit is MPa; Activation Energy / Gas Constant , the unit is 1 / k; Pre-exponential coefficient factor , the unit is 1 / sec; Material constants ; Strain hardening constant ; Strain rate sensitivity index of stress , unit is MPa; Deformation impedance saturation coefficient , unit is MPa; Strain rate sensitivity index of deformation impedance saturation value ; Strain hardening exponent ; S2. Conduct virtual accelerated test of Anand constitutive model under temperature cycle load: set basic parameters of temperature cycle benchmark load spectrum, conduct accelerated test, and obtain accelerated simulation degradation data; S3, using the GXTL model to analyze the location of the measured point; S4. Based on the accelerated simulation degradation data obtained in step S2, the accelerated test stress spectrum optimization is performed on the measured point described in step S3.

2. The method for simulating and optimizing the variable stress load spectrum under storage conditions of electromechanical products according to claim 1, characterized in that: In step S2, the basic parameters of the temperature cycle benchmark load spectrum are set as follows: the insulation temperature is 10°C, the maximum temperature The lowest temperature is 80℃ At -10℃, the temperature change rate The cycle number at failure is 3℃ / min, the holding time is 1h, and the is 1000 times; The accelerated simulation degradation data obtained include equivalent stress values ​​under temperature cycle stress loading.

3. The method for simulating and optimizing the variable stress load spectrum under storage conditions of electromechanical products according to claim 2, characterized in that: In step S3, the GXTL model is used to apply a reference temperature cycle load to the circuit board in the electromechanical product, and the circuit board with the largest stress and strain value, and the point on the circuit board with the largest stress and strain value are obtained as the measured point.

4. The method for simulating and optimizing the variable stress load spectrum under storage conditions of electromechanical products according to claim 3, characterized in that: The electromechanical product is a fiber optic gyroscope.

5. The method for simulating and optimizing the variable stress load spectrum under storage conditions of electromechanical products according to claim 4, characterized in that: In step S4, the expected accelerated life N is first calculated based on the equivalent stress value obtained from the accelerated test and the SN curve of the material; the expected accelerated life N obtained under multiple set accelerated stress conditions is then brought into the modified Coffin-Manson model, and the coefficient fitting of the Coffin-Manson model is achieved through the multivariate linear coefficient fitting algorithm; and after the coefficient fitting of the modified Coffin-Manson model is completed, the optimal parameters are solved based on the optimization analysis to reduce the number of cycles to failure. Minimum; It specifically includes the following steps: S41. Using linearization processing, the modified Coffin-Manson model formula is as follows: （1） In the formula, is the Arrhenius equation, ,in is the material activation energy, is the Boltzmann constant; is the temperature variation frequency of stress spectrum; is the temperature difference in the stress spectrum; S42, substituting the acquired accelerated test data into the SN curve to solve for the expected accelerated life N; S43, performing parameter fitting on the linearization formula using the expected accelerated life N obtained under multiple set accelerated stress conditions, and obtaining the fitting result: ; ; ; S44, using parameter variance as an accurate indicator of coefficient fitting, calculate the parameter variance , and determine the parameter variance Is it lower than the set threshold? If so, it is determined that the fitting meets the standard; The formula after fitting reaches the standard is transformed into: （2） S45, considering the parameters in formula (2) Temperature difference in stress spectrum , Temperature change rate and temperature change interval Related, will Expand it into the following formula and substitute it into the revised formula: （3） （4） S46, according to the simulation results of the accelerated test and Positive correlation, and Negative correlation, so it is simplified to solve the optimal Make Minimum; At this time, Taking partial derivatives we get: （5） Among them, due to the temperature change rate The value range is 1℃ / min-5℃ / min, so the numerator is always negative and the denominator is always positive; at the same time, about Monotonically decreasing, so take Take the maximum value within the limited range as the optimal parameter, so that the number of cycles at failure Minimum.

6. The method for simulating and optimizing the variable stress load spectrum under storage conditions of electromechanical products according to claim 5, characterized in that: In step S4, the load spectrum optimization analysis of the model acceleration factor is considered, and the model acceleration factor calculation formula is as follows: （6） In the formula, is the acceleration factor; It is the accelerated life under low stress load state; It is the accelerated life under high stress load state; is the temperature difference under high stress load condition; is the temperature difference under low stress load state; is the temperature change frequency under low stress load state; is the temperature change frequency under high stress load state; is the activation energy of the material; is the Boltzmann constant; is the weighted average temperature under low stress load state; is the weighted average temperature under high stress load state; Linearize the above equation and get: （7） Substituting the reference spectrum parameters as constant coefficients directly into formula (7), the linear calculation formula of the model acceleration factor is transformed into: （8） For formula (8), The partial derivative is calculated to obtain the following formula: （9） From the above formula, Based on the determination, about Monotonically increasing, so Take the maximum value within the limited range as the optimal parameter, so that the acceleration factor maximum, thus obtaining the optimal accelerated stress spectrum.

7. The method for simulating and optimizing the variable stress load spectrum under storage conditions of electromechanical products according to claim 6, characterized in that: The baseline spectrum parameters are set as follows: , =80℃, =12, = =80℃; The optimization parameters are set as follows: , , .

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