Precision casting mold life prediction method based on digital twinning

Abstract

The invention relates to the technical field of digital twinning, and discloses a precision casting mold life prediction method based on digital twinning, which comprises a mold data acquisition module, a mold data processing module, a mold coefficient generation module, a prediction model establishment module and a mold life prediction module. Physical data, operation data and maintenance records are synchronously collected through the multi-source sensor, and full-life-cycle real-time monitoring is achieved; establishing comprehensive evaluation of four types of quantization coefficients, thermal fatigue coefficients, comprehensive mechanical coefficients, comprehensive wear coefficients and comprehensive maintenance coefficients to realize multi-field damage coupling analysis; the life prediction index unifies the multi-dimensional damage to be 0-1 standard, outputs a visual life percentage and sets a graded early warning threshold value, so that the prediction precision is improved; all coefficients are calculated through a standardized formula, a full-digital process from data acquisition to index generation to model prediction to decision support is constructed, and the detection requirements of new materials and complex structure molds are met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital twinning, more particularly to a precision casting mold life prediction method based on digital twinning. BACKGROUND

[0002] With the development of manufacturing industry towards intelligentization and digitization, the precision casting mold as a key production tool, its life directly affects the production efficiency and cost.

[0003] The traditional technology adopts empirical formula method or artificial detection method to predict the life of precision casting mold, wherein the empirical formula method establishes a statistical model based on historical data, inputs material hardness, load and other parameters to calculate the remaining life, and refers to the industry standard to quantify the damage degree through microscopic observation or 3D scanning; the artificial detection method regularly stops to check the mold state, including: surface damage such as tearing, sticking, collapsing, structural integrity such as small core bending, push rod fracture, size deviation such as using micrometer, profilometer detection combined with casting defects to infer the mold health state reversely. It has the following defects: Only through the stop checking to obtain data, it is impossible to capture the damage change under dynamic working condition; Isolated analysis of thermal fatigue or mechanical wear, ignoring the synergistic effect of multi-physical field; The experience of maintenance personnel directly affects the detection result, and the prediction accuracy is insufficient; Different enterprises adopt different evaluation standards, and it is also difficult to adapt to the detection needs of new materials and complex structure molds. Therefore, a method of real-time detection, multi-parameter coupling, accurate prediction and perfect evaluation system is needed to solve the above problems. SUMMARY

[0004] In order to overcome the above defects of the prior art, the present application provides a precision casting mold life prediction method based on digital twinning to solve the problems existing in the background art.

[0005] To achieve the above purpose, the present application provides the following technical scheme: a precision casting mold life prediction method based on digital twinning, comprising: S1, deploying multiple source sensors through a mold data acquisition module to monitor and collect physical data of the precision casting mold in real time, running data and maintenance data; S2, the original data collected are denoised, cleaned and time-aligned by a mold data processing module to generate thermal fatigue related indexes, mechanical load related indexes, mold wear related indexes and historical maintenance related indexes of the mold; S3, the related indexes obtained are mathematically modeled by a mold coefficient generation module to generate thermal fatigue coefficients, comprehensive mechanical coefficients, comprehensive wear coefficients and comprehensive maintenance coefficients; S4, the life prediction model is established by the prediction model establishment module to fuse each coefficient to construct a life prediction model to generate a life prediction index and convert it into a life percentage; S5, the life prediction index is set with a threshold value by the die life prediction module for graded early warning, and the maintenance decision is guided according to the grading result.

[0006] The present application collects die physical, operation and maintenance data in real time through multi-source sensors, quantifies thermal fatigue parameters such as temperature fluctuation and cooling rate, reflects mechanical damage in combination with static / dynamic load coefficients, evaluates the wear degree through weighted calculation of surface wear and crack propagation, and introduces maintenance frequency and component replacement coefficient to represent maintenance influence, generates thermal fatigue, mechanical load, wear and other indexes and models them into coefficients, unifies multi-physical field damage into a life prediction index, realizes closed-loop management from data collection to graded early warning, and provides digital evaluation basis for die health status.

[0007] Technical effects and advantages of the present application: 1. The present application synchronously collects physical data, operation data and maintenance records through multi-source sensors, realizes real-time monitoring in the whole life cycle, and eliminates the detection blind area; 2. The present application establishes four types of quantitative coefficients, integrates temperature fluctuation, cooling rate and thermal cycle number into thermal fatigue coefficient, comprehensively and weightedly fuses static load and dynamic load into comprehensive mechanical coefficient, dynamically weightedly calculates surface wear and crack propagation into comprehensive wear coefficient, and comprehensively evaluates maintenance frequency, component replacement and maintenance interval into comprehensive maintenance coefficient to realize multi-field damage coupling analysis; 3. The life prediction index of the present application unifies multi-dimensional damage into 0-1 standard, outputs intuitive life percentage and sets graded early warning threshold, directly relates to maintenance decision, and improves prediction accuracy; 4. All coefficients of the present application are calculated through standardized formula, and a full-digital process from data collection-index generation-model prediction-decision support is constructed to adapt to the detection needs of new materials and complex structure dies. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 The structure diagram of the present application.

[0009] Figure 2 The flowchart of the present application. DETAILED DESCRIPTION

[0010] The technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application, and in addition, the forms of each structure described in the following embodiments are only examples, and the automatic discharging device of the rotary furnace with self-cooling function involved in the present application is not limited to each structure described in the following embodiments, and all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the present application.

[0011] With reference to Figure 1 The present application provides a precision casting mold life prediction method based on digital twinning, comprising a mold data acquisition module, a mold data processing module, a mold coefficient generation module, a prediction model establishment module and a mold life prediction module.

[0012] With reference to Figure 2 The specific implementation steps of the present application include the following steps: S1, deploying multiple source sensors through the mold data acquisition module, real-time monitoring and collecting physical data, running data and maintenance data of the precision casting mold.

[0013] It needs to be specifically pointed out that the physical property data specifically includes: Geometric parameters: the CAD model data of the mold includes cavity size, parting surface structure, etc. Material properties: hardness, thermal expansion coefficient, fatigue limit of mold steel; Process parameters: pouring temperature, cooling rate, injection pressure and other casting process parameters. Real-time running state data includes: Mechanical load: collecting mold force, demolding resistance and vibration spectrum through stress sensors; Wear amount: 3D scanning or industrial camera records surface cracks and erosion depth. Maintenance data includes repair frequency, replacement component type and time stamp. S2, the original data collected are denoised, cleaned and time-aligned by the mold data processing module, to generate thermal fatigue related indexes, mechanical load related indexes, mold wear related indexes and historical maintenance related indexes.

[0014] It needs to be specifically pointed out that the thermal fatigue related indexes include temperature fluctuation coefficient and cooling rate coefficient.

[0015] The temperature fluctuation coefficient is specifically: ; Wherein T is the temperature fluctuation coefficient, reflecting the linear relationship between thermal fatigue damage and temperature fluctuation.

[0016] K is the material sensitivity coefficient, which represents the response strength of a specific mold steel to temperature fluctuations, such as H13 steel with a value between 0.01-0.03; T j is the pouring temperature, reflecting the thermal shock strength of molten metal on the mold, the higher the pouring temperature, the faster the thermal fatigue damage rate; Tb is the mold reference temperature, taking the mold preheating temperature as the reference benchmark for thermal fatigue damage; △Tc is the critical temperature difference threshold, which is the lower limit of the temperature difference for the material to occur thermal fatigue.

[0017] It needs to be explained that the temperature difference ratio (T j -T b ) / △T quantifies the amplitude of thermal stress, when the ratio >1, that is, when the temperature difference T j -T b exceeds the critical value △T c , the greater the temperature difference, the closer the exponential term to 0, the temperature fluctuation coefficient close to 1, the higher the thermal fatigue damage; T j =T b , the exponential term is 1, and the coefficient is 0, indicating no thermal fatigue accumulation.

[0018] The cooling rate coefficient is specifically: ; Among them, V is the cooling rate coefficient, reflecting the influence degree of cooling efficiency on the mold thermal fatigue life.

[0019] m is the material sensitivity constant, determined by fatigue test, reflecting the sensitivity of the material to the change of cooling rate, the greater the sensitivity, the value range is between 0.05-0.08; V a is the measured cooling rate; V b is the reference cooling rate, corresponding to the critical point of mold thermal fatigue, as the threshold value for judging cooling efficiency, which is calibrated by experiment.

[0020] It needs to be explained that no matter whether the cooling rate exceeds the reference value or is lower than the reference value, deviating from the reference value will lead to thermal stress accumulation. When the cooling rate is higher than the reference value, rapid cooling leads to high residual stress, and when the cooling rate is lower than the reference value, slow cooling causes organization coarsening; When the cooling rate is equal to the reference value, the exponential term is 1, and the coefficient is the smallest; The greater the difference between the cooling rate and the reference value, the closer the exponential term to 0, the closer the cooling rate coefficient to 1, and the more serious the thermal fatigue.

[0021] It needs to be specifically explained that the mechanical load related indexes include static load coefficient and dynamic load coefficient.

[0022] The static load coefficient calculation is specifically the clamping force divided by the mold pressure area and then divided by the yield strength of the mold material, reflecting the average stress level of the mold during casting; wherein the clamping force reflects the mechanical pressure that the mold bears during casting, directly affecting the closing accuracy of the cavity; the mold pressure area is the effective stress area region of the mold cavity in contact with the molten metal, which converts concentrated force into average distributed stress to avoid plastic deformation caused by local overload; the material yield strength is the plastic deformation column cutoff of the mold steel, serving as a safety margin benchmark to determine whether it enters the plastic deformation stage.

[0023] It needs to be explained that the above results are normalized, and the calculated value is compared with the preset safety threshold to obtain the normalized static load coefficient J. When J=0, it indicates that the mold is in an unloaded state without mechanical stress; when J approaches 1, it indicates that the local stress approaches the material yield strength, and further exceeding will cause permanent deformation.

[0024] The dynamic load coefficient calculation is specifically the n-th power of the ratio of the stress intensity factor amplitude and the material fracture toughness threshold, and then summed up, reflecting the cumulative life loss of the mold under periodic vibration and impact load; wherein the stress intensity factor amplitude reflects the local stress concentration degree of load fluctuation, directly affecting the crack initiation and development rate; the material fracture toughness threshold is the critical threshold, indicating the material's resistance to cracking, and exceeding this value will lead to rapid fracture; n is a material constant that controls the sensitivity of crack propagation rate to stress intensity factor amplitude, with a value range of 3-5.

[0025] It needs to be explained that the above results are normalized, and the normalized dynamic load coefficient D is obtained by logarithmic transformation, wherein , D a is the calculated value, and D max is the critical value; when D=0, it indicates that there is no alternating load action, only constant stress; when D approaches 1, it indicates that high-frequency vibration and impact load are significant, and the crack propagation rate increases.

[0026] It needs to be specified that the mold wear-related indicators include the surface wear coefficient and the crack propagation coefficient.

[0027] The surface wear coefficient calculation is specifically the product of the contact pressure and the relative sliding distance divided by the product of the mold material hardness and the initial contact area, and then multiplied by the material wear constant, reflecting the wear rate of the mold under sliding contact conditions; wherein the contact pressure is the load force per unit area, and an increase in pressure directly leads to an increase in wear; the sliding distance is the relative motion trajectory length of the mold and the metal, and the longer the cumulative sliding distance, the greater the wear; the material hardness is a property of the material itself, and the higher the hardness, the lower the wear coefficient; the initial contact area is the effective contact area between the metal and the mold, and the larger the unit area, the smaller the pressure; the wear constant is determined by the material properties, reflecting the chemical properties of the interface friction.

[0028] Need to explain, the calculation value is normalized, that is, the current calculation value minus the minimum value in the historical data, and the normalized surface wear coefficient B is obtained by subtracting the minimum value in the historical data from the critical failure threshold. The coefficient is 0, indicating no contact load or material hardness close to infinity; the larger the coefficient, the greater the wear, the shorter the mold life.

[0029] The crack propagation coefficient is specifically: ; Where L is the crack propagation coefficient, quantifying the crack propagation degree of the mold material under cyclic loading.

[0030] a0 and a t are the initial crack depth and the current crack depth, respectively, reflecting the cumulative damage degree of the crack; △K is the stress intensity factor amplitude, representing the strength of the stress field at the crack tip, and the larger the value, the faster the crack propagation rate, △K=K max -K min , wherein K max and K min are the maximum and minimum stress intensity factors, respectively; C is a material constant, obtained by experimental fitting, and n is an index, reflecting the sensitivity of the crack propagation coefficient to △K, with a value range of 2-4.

[0031] Need to explain, the crack propagation coefficient is normalized, the difference between the current value and the reference value is divided by the difference between the critical failure value and the reference value; when a0=at, it means no crack or stable crack, such as the initial value of a new mold close to 0; when the coefficient is close to 1, it means that the crack will soon lose stability and the mold life is shorter. Need to be explained in detail, the historical maintenance related indicators include the maintenance frequency coefficient and the component replacement coefficient.

[0032] The maintenance frequency coefficient is specifically: ; Where X is the maintenance frequency coefficient, reflecting the influence of each maintenance on the remaining life of the equipment.

[0033] λ is the attenuation coefficient, reflecting the influence of single maintenance on the mold life, with a value range of 0.15-0.3; N is the cumulative maintenance frequency, recording the mold maintenance frequency.

[0034] Need to explain, the exponential function is used to reflect the nonlinear relationship between maintenance frequency and performance degradation, and with the increase of maintenance frequency, the mold performance degradation is more serious, the coefficient value is larger, and the mold life is shorter.

[0035] The component replacement coefficient is specifically: ; Wherein G is the replacement coefficient of parts, reflecting the influence degree of different parts replacement on the mold life.

[0036] W i is the weight of the replaced parts, quantifying the importance of different parts, the core parts take the value range of 0.8-1, the key parts take the value range of 0.5-0.7, and the conventional parts take the value range of 0.2-0.4; g is the replacement mark, 0 represents not replaced, and 1 represents replaced.

[0037] It needs to be explained that the replacement of core parts has the greatest impact on the life, and the greater the coefficient value is, the shorter the mold life is when all core parts are replaced.

[0038] S3, the obtained related indexes are mathematically modeled by a mold coefficient generation module to generate a thermal fatigue coefficient, a comprehensive mechanical coefficient, a comprehensive wear coefficient and a comprehensive maintenance coefficient.

[0039] It needs to be specifically explained that the thermal fatigue coefficient is specifically: R=T*V*N; Wherein R is the thermal fatigue coefficient, quantifying the cumulative degree of damage of the mold under periodic thermal load.

[0040] T is the temperature fluctuation coefficient, reflecting the influence of the deviation of pouring temperature from the reference temperature on the thermal fatigue sensitivity of the material, the greater the temperature fluctuation coefficient is, the more serious the thermal fatigue damage is, and the shorter the mold life is.

[0041] V is the cooling rate coefficient, reflecting the contribution of the difference between the cooling rate and the reference rate to the accumulation of thermal stress, the greater the cooling rate coefficient value is, the more serious the thermal fatigue damage is, and the shorter the mold life is.

[0042] N is the thermal cycle number coefficient, reflecting the cumulative damage of thermal cycle number to fatigue life, the irreversible damage generated by each cycle is accumulated through the number of thermal cycles, and the more the number of cycles is, the more serious the thermal fatigue damage is.

[0043] It needs to be explained that the product relationship shows that the temperature fluctuation and the cooling rate jointly determine the damage degree of a single cycle, and the number of thermal cycles controls the total amount of damage, and the greater the thermal fatigue coefficient is, the greater the damage to the mold is, and the shorter the mold life is.

[0044] It needs to be specifically explained that the comprehensive mechanical coefficient is specifically: F=α*J+(1-α)*D; Wherein F is the comprehensive mechanical coefficient, quantifying the comprehensive influence of the mechanical load on the mold in work, reflecting the cumulative loss of the mold under mechanical stress, and the coefficient is 0, which represents that the mold has no any mechanical load; the greater the coefficient is, the faster the life loss of the mold under mechanical load is.

[0045] Need to explain, J is the static load coefficient, is the constant mechanical stress level of the mold, determine the plastic deformation risk of the mold under steady state working conditions, the larger the static load coefficient, the closer to the yield limit of the mold, the shorter the life; Alpha is the contribution weight of static load in comprehensive mechanical damage, the value range is between 0.4-0.6, but need to note that in injection mold, alpha value range can be increased to 0.8.

[0046] D is the dynamic load coefficient, reflects the cumulative damage of the mold under dynamic load, determines the damage propagation rate of the mold under vibration load, the larger the dynamic load coefficient, the greater the risk of crack propagation, the shorter the life of the mold; 1-Alpha is the contribution weight of dynamic load in comprehensive mechanical damage, the value range is between 0.4-0.6, but need to be in stamping die, 1-Alpha value can be increased to 0.7.

[0047] Need to be explained, the comprehensive wear coefficient is specifically: M=beta*B+(1-beta)*L; Where M is the comprehensive wear coefficient, which quantifies the wear degree of the mold by weighted calculation of surface wear and crack propagation, the higher the coefficient value, the more serious the wear, the shorter the remaining life.

[0048] Need to explain, B is the surface wear coefficient, which quantifies the wear rate and determines the material loss under unit contact pressure and sliding distance, the larger the surface wear coefficient, the more serious the wear, the shorter the remaining life of the mold.

[0049] Beta is the surface wear coefficient weight, specifically: ; T is the current number of use cycles, which determines the main stage of wear, including early wear and later crack dominated; T0 is the number of wear turning point cycles, which is calibrated by historical data; K is the decay rate coefficient, which controls the weight switching.

[0050] L is the crack propagation coefficient, which is the fatigue damage accumulation, which converts the stress intensity factor amplitude to crack depth increment, reflects the damage accumulation under load, embodies the effect of internal defects of the mold material on crack propagation, the larger the crack propagation coefficient, the more serious the crack, the shorter the life of the mold; 1-beta is the crack propagation coefficient weight.

[0051] Need to be explained, the comprehensive maintenance coefficient is specifically: ; Where W is the comprehensive maintenance coefficient, which evaluates the maintenance state of the precision casting mold, quantifies the comprehensive influence of repair behavior and component replacement on the life of the mold, the larger the coefficient value, the shorter the remaining life of the mold.

[0052] It needs to be explained that X is the maintenance frequency coefficient, which reflects the cumulative damage of maintenance frequency on the performance of the mold. The more the number of maintenance, the larger the coefficient, and the shorter the service life of the mold.

[0053] G is the component replacement coefficient, which reflects the influence of replacing different components on the service life of the mold. The core components are all replaced, the coefficient value is the largest, and the service life of the mold is shorter.

[0054] Z is the number of cycles since the last maintenance, Z a is the historical average maintenance interval. The ratio term reflects the deviation of the current maintenance cycle from the historical average level. If Z>Z a , it means that the current maintenance interval exceeds the historical average, indicating that the maintenance is delayed, and the coefficient value increases.

[0055] S4, the life prediction model is established by the prediction model establishment module to fuse each coefficient to construct a life prediction model to generate a life prediction index and convert it into a life percentage.

[0056] It needs to be specifically explained that the life prediction index is specifically: ; Where Z is the life prediction index, which reflects the health status of the mold by fusing multiple physical damages, 0 represents a new state, and 1 represents complete failure.

[0057] R is the thermal fatigue coefficient, which reflects the damage degree of the mold material under cold and hot cycles; since the failure of precision casting mold accounts for the highest proportion of thermal fatigue in the case, the highest weight is given, so the weight ω1 is in the range of 0.35-0.4.

[0058] F is the comprehensive mechanical coefficient, which quantifies the cumulative damage caused by alternating stress; mechanical load accounts for the second highest proportion of mold failure, so the value of weight ω2 is in the range of 0.2-0.25.

[0059] M is the comprehensive wear coefficient, which reflects the influence of the mold surface wear area and crack depth on the service life of the mold; wear accounts for the lowest proportion of mold failure, and the value of weight ω3 is in the range of 0.15-0.2.

[0060] W is the comprehensive maintenance coefficient, which quantifies the damage of maintenance frequency and component replacement on the service life of the mold; maintenance factors directly affect the evolution rate of other coefficients; the value of weight ω4 is in the range of 0.2-0.25.

[0061] The life percentage is specifically: S=(1-Z)*100%; Where S is the mold life percentage, which reflects the proportion of the current precision casting mold in the theoretical life, and the lower the percentage, the closer to the end of life.

[0062] S5, the life prediction index threshold is graded for early warning by the die life prediction module, and the maintenance decision is guided according to the grading result.

[0063] It should be specifically pointed out that the life prediction index threshold is graded, which is: When 0≤Z<z1, the remaining life of the die is >s1, and routine inspection is performed; When z1≤Z<z2, the remaining life of the die is between s2-s1, and the detection period is shortened; When z2≤Z<z3, the remaining life of the die is between s3-s2, spare parts are prepared, and the next maintenance is arranged; When z3≤Z<z4, the remaining life of the die is between s4-s3, and forced maintenance within 72 hours is performed; When z4≤Z≤1, the remaining life of the die is <s4, and immediate shutdown and replacement are performed; Wherein z1 is valued between 0.25-0.35, z2 is valued between 0.45-0.55, z3 is valued between 0.65-0.75, and z4 is valued between 0.85-0.95; Wherein s1 is valued between 65%-75%, s2 is valued between 45%-55%, s3 is valued between 25%-35%, and s4 is valued between 5%-15%.

[0064] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments of the present application can be realized by means of software or software combined with necessary general hardware platforms, and of course can also be realized by hardware functions; based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium, and includes a plurality of instructions for causing a computer device, such as but not limited to a personal computer, a server, or a network device, to execute all or part of the steps of the method described in any embodiment of the present application.

[0065] The above describes the exemplary embodiments of the present application, it should be understood that the above exemplary embodiments are not restrictive, but illustrative, the protection scope of the present application is not limited thereto; it should be understood that those skilled in the art can modify and vary the embodiments of the present application without departing from the spirit and scope of the present application, and these modifications and variations should be within the protection scope of the present application.

Claims

1. A method for predicting the service life of a precision casting mold based on digital twinning, characterized by, Specifically comprising: S1, deploying multiple source sensors through a mold data acquisition module, real-time monitoring and collecting physical data of precision casting mold, operation data and maintenance data; S2, through the mold data processing module, the original data collected are denoised, cleaned and time-aligned, to generate thermal fatigue related indexes, mechanical load related indexes, mold wear related indexes and historical maintenance related indexes; S3, through the mold coefficient generation module, the related indexes obtained are mathematically modeled to generate thermal fatigue coefficient, comprehensive mechanical coefficient, comprehensive wear coefficient and comprehensive maintenance coefficient; S4, through the prediction model establishment module, the life prediction model is constructed by fusing various coefficients to generate life prediction index and convert it into life percentage; S5, through the mold life prediction module, the threshold of the life prediction index is set to grade the warning, and the maintenance decision is guided according to the grading results.

2. The precision casting mold life prediction method based on digital twinning of claim 1, characterized in that: the thermal fatigue related indexes include temperature fluctuation coefficient and cooling rate coefficient; the mechanical load related indexes include static load coefficient and dynamic load coefficient; the mold wear related indexes include surface wear coefficient and crack propagation coefficient; the historical maintenance related indexes include repair frequency coefficient and component replacement coefficient.

3. The precision casting mold life prediction method based on digital twinning of claim 2, characterized in that: the temperature fluctuation coefficient is specifically: ； where T is the temperature fluctuation coefficient, K is the material sensitivity coefficient, T j is the pouring temperature, Tb is the mold reference temperature, and ATc is the critical temperature difference threshold value; the cooling rate coefficient is specifically: ； where V is the cooling rate coefficient, m is a material sensitive constant, V a is the measured cooling rate, V b is the reference cooling rate.

4. The precision casting mold life prediction method based on digital twinning of claim 2, characterized in that: the static load coefficient calculation is specifically the yield strength of the mold material divided by the mold pressure-bearing area and then divided by the mold material yield strength, reflecting the average stress level of the mold during the casting process; the dynamic load coefficient calculation is specifically the n-th power of the ratio of single stress intensity factor amplitude to material fracture toughness threshold, and then the sum is calculated, reflecting the cumulative life loss of the mold under periodic vibration and impact load.

5. The precision casting mold life prediction method based on digital twinning of claim 2, characterized in that: the surface wear coefficient calculation is specifically the product of contact pressure and relative sliding distance divided by the product of mold material hardness and initial contact area, and then multiplied by material wear constant, reflecting the wear rate of the mold under sliding contact conditions; the crack propagation coefficient is specifically: ； where L is the crack growth coefficient, a0and a t are the initial and current crack depths, respectively, and AKis the stress intensity factor amplitude, AK= K max -K min where K max and K min are the maximum and minimum stress intensity factors, respectively, and C is a material constant.

6. The precision casting mold life prediction method based on digital twinning of claim 2, characterized in that: the repair frequency coefficient is specifically: ； where X is the repair frequency coefficient, λ is the attenuation coefficient, and N is the cumulative repair frequency, which records the maintenance frequency of the mold using an exponential function to reflect the nonlinear relationship between repair frequency and performance degradation. With the increase of repair frequency, the performance degradation of the mold is more serious, the coefficient value is larger, and the mold life is shorter; the component replacement coefficient is specifically: ； Where G is the component replacement coefficient, w i is the weight of the replacement component, g is the replacement mark, 0 represents not replaced, and 1 represents replaced; the core component replacement has the greatest impact on the service life, the core component is completely replaced, the greater the coefficient value, the shorter the mold service life.

7. The precision casting mold life prediction method based on digital twinning of claim 1, characterized in that: the thermal fatigue coefficient is specifically: R=T*V*N; Wherein R is the thermal fatigue coefficient, quantifying the cumulative degree of damage of the mold under periodic thermal load; T is the temperature fluctuation coefficient, V is the cooling rate coefficient, and N is the thermal cycle number coefficient; The comprehensive mechanical coefficient is specifically: F=α*J+(1-α)*D; Wherein F is the comprehensive mechanical coefficient, quantifying the comprehensive influence of mechanical load on the precision casting mold in work; J is the static load coefficient, and D is the dynamic load coefficient; The comprehensive wear coefficient is specifically: M=β*B+(1-β)*L; Wherein M is the comprehensive wear coefficient, quantifying the wear degree of the mold by weighted calculation of surface wear and crack propagation; B is the surface wear coefficient, and β is the surface wear coefficient weight, which is specifically: ； t is the current service cycle number, determining the main stage of wear, including early wear and late crack dominated; t0 is the wear turning point cycle number, calibrated by historical data; k is the attenuation rate coefficient, controlling the weight switching; L is the crack propagation coefficient, and 1-β is the crack propagation coefficient weight; The comprehensive maintenance coefficient is specifically: ； Wherein W is the comprehensive maintenance coefficient, which evaluates the maintenance state of the precision casting mold; X is the maintenance frequency coefficient, G is the component replacement coefficient, Z is the number of periods since the last maintenance, and Z a is the historical average maintenance interval.

8. The precision casting mold life prediction method based on digital twinning according to claim 1, characterized in that: The life prediction index is specifically: ； Wherein Z is the life prediction index, reflecting the mold health status by fusing multiple physical damage, 0 indicating brand new state and 1 representing complete failure; R is the thermal fatigue coefficient, F is the comprehensive mechanical coefficient, M is the comprehensive wear coefficient, and W is the comprehensive maintenance coefficient; The life percentage is specifically: S=(1-Z)*100%; Wherein S is the mold life percentage, reflecting the proportional state of the current precision casting mold in the theoretical life, and the lower the percentage, the later the life enters the end period.

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