A die casting process simulation method based on cloud computing

Abstract

The present invention relates to a die-casting process simulation method based on cloud computing, which belongs to the technical field of die-casting process, and comprises the following steps: step one: according to the die-casting process, the die-casting process parameters are input into the simulation software to construct an equation for simulating molten metal filling; step two: the simulation software simulates the process of metal filling according to the equation, generates a result of filling rate changing with time, and uploads it to a cloud platform; step three: the cloud platform calculates the difference between the generated result and the actual filling process, judges whether the difference is greater than a threshold value, and when the judgment result is yes, calculates a critical solid phase fraction correction value, and executes step four, and when the judgment result is no, outputs the critical solid phase fraction; step four: the cloud platform sends the critical solid phase fraction correction value to the simulation software, and the simulation software corrects it according to the critical solid phase fraction, and then generates a new result of filling rate changing with time according to the corrected equation for simulating molten metal filling, and uploads it to the cloud platform, and returns to step three.

Description

Technical Field

[0001] The present invention belongs to the technical field of die-casting processes, and in particular relates to a die-casting process simulation method based on cloud computing. Background Art

[0002] As a near-net-shape forming technology, die casting uses pressure to drive the molten metal to fill and compensate for shrinkage smoothly, which can greatly improve the utilization rate of metal materials and production efficiency. With the increasing requirements for large-scale and rapid production of castings, the use of computer software to perform numerical simulation of the filling and solidification process of die casting production has become a major auxiliary means of mold design.

[0003] Generally, die-casting process simulation methods do not disclose a technical solution for simulating a simulation model. For this purpose, for example, Chinese patent CN107045571B discloses a die-casting simulation control method based on cloud computing, wherein the die-casting simulation control method comprises the following steps: the first step is parameter initialization, wherein the parameters include the initial temperature of the melt, the gate speed, and the material properties; the second step is to divide the calculation grid after determining the initial parameters, generate the calculation nodes, and then enter the execution link of the die-casting cloud solver; the third step is to transfer the parameters to the discrete equations of the die-casting cloud solver; the discrete equations are mathematical models expressed by partial differential equations of the continuity equation, the momentum equation, and the energy equation; the fourth step is to establish a discrete equation based on the mathematical model, and calculate the discrete boundary conditions, the discrete initial conditions, and the solver settings. The program continuously iterates to solve the partial differential equations based on the continuity equation, momentum equation and energy equation until the solution converges or the number of iterations is reached; the fifth step is to calculate the temperature field of the die-casting process through the energy conservation and momentum conservation fluid mechanics equations, and calculate the filling volume of the solution through the relationship between the spatial distribution / volume fraction of the melt and the time; however, although the above scheme improves the simulation process by setting the energy conservation and momentum conservation fluid mechanics equations and the related parameters in the equations, its accuracy is still limited, such as the random errors generated in each simulation process and the influence of the critical solid phase fraction of the molten metal on the simulation, and the guiding role of the actual die-casting production needs to be improved; for this reason, a die-casting process simulation method based on cloud computing with high filling simulation accuracy is needed. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides a die-casting process simulation method based on cloud computing, which has the characteristics of high filling simulation accuracy.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A die-casting process simulation method based on cloud computing includes the following steps:

[0007] Step 1: According to the die-casting process, the die-casting process parameters are input into the simulation software, and an equation for simulating molten metal filling is constructed in the simulation software;

[0008] Step 2: The simulation software simulates the metal filling process according to the constructed equation for simulating molten metal filling, generates the result of filling rate changing with time, and uploads the generated results corresponding to the die casting process parameters to the cloud platform;

[0009] Step 3: The cloud platform archives the generated results according to the die-casting process parameters, and compares them with the actual filling process consistent with the die-casting process parameters, calculates the difference between the simulation results and the actual filling process, and determines whether the difference is greater than the threshold. If the judgment result is yes, calculate the critical solid fraction correction value and execute step 4. If the judgment result is no, execute step 6.

[0010] Step 4: The cloud platform sends the critical solid fraction correction value to the simulation software. The simulation software adds the critical solid fraction correction value to the equation for simulating molten metal filling, corrects the equation, and then generates a new result of the filling rate changing with time based on the corrected equation for simulating molten metal filling, and uploads it to the cloud platform.

[0011] Step 5: Repeat steps 3 to 4;

[0012] Step 6: Output the critical solid fraction.

[0013] As a preferred technical solution of the present invention, the step three also includes: the operator uploads the critical solid fraction calculation value to the cloud platform in advance; the step four also includes: the cloud platform uses the average of the critical solid fraction correction value and the critical solid fraction calculation value as the critical solid fraction correction value, and the cloud platform sends the critical solid fraction correction value to the simulation software.

[0014] As a preferred technical solution of the present invention, the step five also includes: the cloud platform determines whether the number of times step three is executed is greater than or equal to three times. When the judgment result is yes, the cloud platform determines whether the difference between the critical solid phase fraction correction values ​​generated three times and the critical solid phase fraction calculated value becomes larger in sequence among the critical solid phase fraction correction values ​​generated three times most recently. When the judgment result is yes, the cloud platform deletes the critical solid phase fraction calculated value.

[0015] As a preferred technical solution of the present invention, the step six also includes: after the cloud platform outputs the critical solid phase fraction, the generated result is stored in the data storage module according to the die-casting process parameter archive data.

[0016] As a preferred technical solution of the present invention, the step one also includes: the operator uploads the random error threshold to the cloud platform; the step three also includes: the cloud platform compares the generated result with the actual filling process consistent with the die-casting process parameters, and finds the generated result corresponding to the die-casting process parameters closest to the current die-casting process parameters in the data storage module through comparison, and marks the found generation result as comparison data. The cloud platform determines whether the difference between the comparison data and the current generation result and the comparison data exceeds the random error threshold. When the judgment result is no, the cloud platform deletes the current generation result.

[0017] As a preferred technical solution of the present invention, the step one also includes: the operator uploads the random error threshold to the cloud platform; the step three also includes: the cloud platform compares the generated result with the actual filling process consistent with the die-casting process parameters, and finds the generation results corresponding to the three die-casting process parameters closest to the current die-casting process parameters in the data storage module through comparison, and calculates the comparison data based on the mean of several generation results. The cloud platform determines whether the difference between the comparison data and the current generation result and the comparison data exceeds the random error threshold. When the judgment result is no, the cloud platform deletes the current generation result.

[0018] As a preferred technical solution of the present invention, the step three also includes: finding the generation results A1, A2 and A3 corresponding to the three die-casting process parameters C1, C2 and C3 closest to the current die-casting process parameter C in the data storage module by comparison, and the cloud platform obtains the comparison data A by calculation according to the three generation results A1, A2 and A3;

[0019] Among them, A=(k1×A1+k2×A2+k3×A3) / (k1+k2+k3), |C1-C|>|C2-C|>|C3-C|, k1 <k2<k3。

[0020] As a preferred technical solution of the present invention, the step six further includes: after outputting the critical solid phase fraction, emitting an audible and visual prompt.

[0021] The beneficial effects of the present invention are:

[0022] (1) By cooperating with the simulation software and the cloud platform to continuously generate and correct the generated results in the simulation software, the generated results can be controllably close to the actual filling process, and the critical solid fraction is calculated based on the critical solid fraction correction value close to the process, so that the operator can simulate the influence of the critical solid fraction on die casting in the simulated filling experiment under the conditions of the mold shape and die casting process parameters of this simulation experiment, thereby improving the simulation accuracy;

[0023] (2) By pre-inputting the calculated value of the critical solid fraction in step 3 and sending the average of the corrected value of the critical solid fraction and the calculated value of the critical solid fraction to the simulation software in step 4, the number of repeated calculations in step 5 can be reduced, and the critical solid fraction generated in the end has a greater probability of being close to the true value;

[0024] (3) In step 5, the cloud platform determines whether the number of executions of step 3 is greater than or equal to three times, and checks whether the calculated value of the critical solid fraction has a negative impact on the calculation of the critical solid fraction when the judgment result is yes, thereby reducing the negative impact of the calculated value of the critical solid fraction with excessive deviation on the final calculation of the critical solid fraction;

[0025] (4) In step 3, the cloud platform determines the currently uploaded generation result, calculates the difference between the generation result corresponding to the die-casting process parameters closest to the current die-casting process parameters, and determines whether the difference exceeds the random error threshold. Then, when the random error threshold is exceeded, the current generation result is deleted, and the generation results with excessive errors are screened and discarded, thereby reducing the influence of the generation results that are too greatly affected by random errors on the calculation of the critical solid fraction.

[0026] (5) The comparison data A obtained by weighted average calculation of several generated results A1, A2 and A3 is less affected by random errors than a single generated result. When the difference between the generated result and the generated result is calculated to determine whether the difference exceeds the random error threshold, there is a greater probability that the generated result that is greatly affected by the error will be screened out. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0028] Figure 1 It is a schematic diagram of the steps of the present invention;

[0029] Figure 2 This is a schematic diagram of step five of the present invention. DETAILED DESCRIPTION

[0030] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0031] See also Figure 1-2 , a die-casting process simulation method based on cloud computing, comprising the following steps:

[0032] Step 1: According to the die-casting process, the die-casting process parameters are input into the simulation software, and an equation for simulating molten metal filling is constructed in the simulation software;

[0033] Specifically, in actual die-casting production, each die-casting mold may be operated under different die-casting process parameters. When each simulation experiment is performed, a certain set of die-casting process parameters of a certain mold is used for simulation. When the mold shape or die-casting process parameters change, it is recorded as another simulation experiment. Each simulation experiment corresponds to a generated result. The die-casting process parameters include initial temperature, gate speed, material flow parameters, mold gravity direction, and mold heat transfer coefficient.

[0034] Meanwhile, in this embodiment, the molten metal filling process is regarded as an unsteady viscous flow process and an unstable heat conduction process of an incompressible Newtonian fluid. Therefore, the equations for simulating the molten metal filling include mathematical models expressed by partial differential equations such as continuity equation, momentum equation and energy equation.

[0035] At the same time, before each simulation experiment, the operator first conducts an actual die-casting experiment on the die-casting equipment with the current die-casting process parameters. The operator uses a detection instrument to record the function image of the molten metal in the die-casting equipment as the filling rate changes over time, which is recorded as the actual filling process. The actual filling process is uploaded to the cloud platform, and the actual filling process of this simulation experiment is generated in the cloud platform;

[0036] After the equation is established, proceed to step 2;

[0037] Step 2: The simulation software simulates the metal filling process according to the constructed equation for simulating molten metal filling, generates the result of filling rate changing with time, and packages the generated result together with the die-casting process parameters input in step 1 of this simulation experiment and uploads it to the cloud platform;

[0038] Specifically, the simulation software is installed on a computer and is connected to the cloud platform through a computer network. The cloud platform is provided with a computing module, a communication module and a storage module. At the beginning of each simulation experiment, the simulation software synchronously sends a signal to the cloud platform that a new simulation experiment has started, prompting the cloud platform to start the current simulation experiment. During calculation, the simulation software calculates the temperature change of each point in the mold during the die-casting process through fluid mechanics equations such as conservation of energy and conservation of momentum, and simulates the relationship between the spatial distribution / volume fraction of the molten metal and the time, calculates the filling rate of the molten metal and the time change, and generates a function image of the filling rate of the molten metal and the time change. In this embodiment, the function image is the generated result. The simulation software uploads the function image together with the initial temperature, gate speed, material flow parameters, mold gravity direction and mold heat transfer coefficient to the cloud platform;

[0039] Since the influence of critical solid fraction in actual die casting production cannot be simulated by equations and simulation software representing ideal conditions, it is necessary to calculate the difference between actual results and simulation results and interpret the difference as the influence of critical solid fraction. To this end, step three needs to be performed;

[0040] Step 3: The cloud platform archives the generated results according to the die-casting process parameters. Each time the simulation experiment is archived, the cloud platform matches the die-casting process parameters with the generated results one by one, that is, each generated result corresponds to a set of die-casting process parameters, and each set of die-casting process parameters corresponds to a function image as the generated result:

[0041] After archiving is completed, the cloud platform uses its own computing module to compare the generated results with the actual filling process of this simulation experiment. According to the previous text, the die-casting process parameters corresponding to the actual filling process of this simulation experiment are consistent. After comparison, the cloud platform obtains the difference between the generated results and the actual filling process. At this time, the cloud platform determines whether this difference is greater than the threshold;

[0042] When the judgment result is yes, it means that the difference is an error caused by the equation for simulating molten metal filling in the simulation software not taking into account the critical solid phase fraction. Then, the cloud platform determines the correction direction of the equation for simulating molten metal filling, for example, whether the generated filling rate is too large or too small, and then calculates the critical solid phase fraction correction value for the correction. After the calculation is completed, step 4 is executed;

[0043] When the judgment result is no, it means that the difference is within the acceptable range, and step 6 is directly executed;

[0044] Step 4: The cloud platform sends the critical solid fraction correction value to the simulation software through the communication module. After receiving the critical solid fraction correction value, the simulation software adds the critical solid fraction correction value to the equation for simulating molten metal filling, and recalculates. According to the corrected equation for simulating molten metal filling, a new result of the filling rate changing with time is generated and uploaded to the cloud platform. After receiving the new function image uploaded by the simulation software, the cloud platform replaces the current function image with the latest received function image as the generated result.

[0045] After the replacement is completed, since the critical solid fraction correction value obtained by one calculation may not be able to output an accurate quantified critical solid fraction, sometimes multiple calculations are required to get closer to the actual situation. For this reason, step five needs to be performed;

[0046] Step 5: Repeat the judgment, calculation of critical solid fraction correction value and recalculation process of steps 3 and 4 until the cloud platform judges in step 3 that the difference between the generated result and the actual filling process is not greater than the threshold, that is, execute step 6;

[0047] In order to calculate the quantitative critical solid fraction and further quantify the influence of the critical solid fraction on the simulation results, the method further includes step 6: outputting the critical solid fraction, the cloud platform stores the initial function image and the function image obtained by the last calculation in this simulation experiment, the difference between which and the actual filling process is less than the threshold value, and then the cloud platform collects the critical solid fraction correction values ​​obtained by several calculations, and calculates the critical solid fraction according to the critical solid fraction correction values ​​to obtain the total critical solid fraction. At this time, the initial equation can directly obtain the final generated function image under the process parameters of the current simulation experiment after the correction of the critical solid fraction, and then the critical solid fraction is known;

[0048] By cooperating with the simulation software and the cloud platform to continuously generate and correct the generated results in the simulation software, the generated results can be controllably close to the actual filling process, and the critical solid phase fraction is calculated based on the critical solid phase fraction correction value of the close process. In this way, the operator can simulate the influence of the critical solid phase fraction on die-casting in the simulated filling experiment conducted under the conditions of the mold shape and die-casting process parameters of this simulation experiment, thereby improving the simulation accuracy.

[0049] Normally, operators can estimate the critical solid fraction of a certain experiment based on experience or calculation. At the same time, in some cases, the critical solid fraction correction value obtained by the first calculation of the cloud platform is far from the target value. At this time, it is necessary to iterate through step five for many times to generate the correct critical solid parameters. In order to improve the calculation accuracy and reduce the calculation time, step three also includes: the operator uploads the critical solid fraction calculation value to the cloud platform in advance; at the same time, step four also includes: the cloud platform uses the average of the critical solid fraction correction value and the critical solid fraction calculation value as the critical solid fraction correction value, and the cloud platform sends the critical solid fraction correction value obtained by the average calculation to the simulation software;

[0050] At this time, the correction value used to correct the equation for simulating molten metal filling each time is the average of the critical solid fraction correction value and the critical solid fraction calculation value. The average of the critical solid fraction correction value and the critical solid fraction calculation value has a greater probability of being close to the target value than a single critical solid fraction correction value. When repeated calculations are performed in step five, the target value of the critical solid fraction can be obtained with a lower number of calculations.

[0051] By pre-inputting the calculated value of the critical solid fraction in step three and sending the average of the corrected value of the critical solid fraction and the calculated value of the critical solid fraction to the simulation software in step four, the number of repeated calculations in step five can be reduced, and the critical solid fraction finally generated has a greater probability of being close to the true value.

[0052] In some cases, the critical solid fraction calculated by the operator is farther away from the true value than the critical solid fraction correction value obtained by the cloud platform, and both are greater than or less than the true value. In this case, the critical solid fraction calculated value plays a negative role in iteratively generating correct parameters. For example, the critical solid fraction calculated value is 0.5, the critical solid fraction correction value obtained by the first difference calculation is 0.8, and the true critical solid fraction is 1. At this time, when the calculated value is introduced into the iteration, compared with the direct use of the critical solid fraction correction value 0.8 obtained by the first difference calculation, 0.5 and 0. The mean value 0.7 of 8 is further away from the true value 1, and an additional number of iterations is required to use the simulation software to correct the correction value of the simulation equation, which increases the number of iterations and fails to achieve the purpose of reducing the number of cycles in step five. To avoid such a situation, step five also includes: the cloud platform determines whether the number of executions of step three is greater than or equal to three times. When the judgment result is yes, the cloud platform determines whether the difference between the critical solid fraction correction values ​​generated three times and the critical solid fraction calculation value in the most recently generated critical solid fraction correction values ​​is successively larger. When the judgment result is yes, the cloud platform deletes the critical solid fraction calculation value;

[0053] Specifically, the cloud platform records the critical solid fraction correction value generated by the calculation in each execution of step three. When it is determined that the number of executions of step three is greater than or equal to three times, it means that the calculation time is long, and there is a probability that the input critical solid fraction calculation value deviates greatly from the true value. At this time, the cloud platform determines whether the difference between the critical solid fraction correction value generated three times and the critical solid fraction calculation value increases successively. When it increases successively, it means that the critical solid fraction correction value gradually moves away from the critical solid fraction calculation value in the process of gradually approaching the true value, that is, the critical solid fraction correction value is closer to the true value than the average of the critical solid fraction calculation value and the critical solid fraction calculation value. At this time, the critical solid fraction calculation value can be deleted to increase the calculation speed;

[0054] After deletion, when executing step 4 for the third time, the cloud platform that deleted the critical solid fraction calculation value in this simulation experiment only transmits the calculated critical solid fraction correction value to the simulation software, thereby abandoning the critical solid fraction calculation value, thereby reducing the negative impact of the critical solid fraction calculation value with excessive deviation on the final critical solid fraction calculation;

[0055] By making the cloud platform determine in step five whether the number of executions of step three is greater than or equal to three times, and checking whether the calculated value of the critical solid fraction has a negative impact on the calculation of the critical solid fraction when the judgment result is yes, the negative impact of the calculated value of the critical solid fraction with excessive deviation on the final calculation of the critical solid fraction is reduced.

[0056] To facilitate subsequent operators to check a certain simulation experiment, step six also includes: after the cloud platform outputs the critical solid phase fraction, the generated results are stored in the data storage module according to the die-casting process parameter archive data. Specifically, the cloud platform groups the die-casting process parameters, generated results, critical solid phase fraction and several critical solid phase fraction correction values ​​of each simulation experiment into a group and stores them in the data storage module.

[0057] In the above calculation process, sometimes the simulation software itself will generate random errors during calculation, resulting in deviations in the generated results. When this part of the deviation is used to iteratively calculate the critical solid phase fraction in step five, the generated critical solid phase fraction will be generated based on the biased data, which may increase the generation time or reduce the accuracy of the final critical solid phase fraction. In order to reduce this part of the deviation, a data screening and discarding mechanism is introduced. In step one, the operator uploads the random error threshold to the cloud platform; step three also includes: the cloud platform compares the currently uploaded generation result with the actual filling process input in step three, and finds the generation result corresponding to the die-casting process parameters closest to the current die-casting process parameters in the data storage module through comparison, and marks the found generation result as comparison data. Subsequently, the cloud platform determines whether the difference between the comparison data and the current generation result and the comparison data exceeds the random error threshold;

[0058] When the judgment result is no, it means that the generated result is far from the theoretical value due to the influence of random errors, which makes the generated result of this calculation unreliable. The cloud platform deletes the current generated result and notifies the simulation software of the deletion action. After receiving the notification, the simulation software re-executes step 2 until the judgment result is yes;

[0059] In step three, the cloud platform determines the currently uploaded generation result, calculates the difference between the generation result corresponding to the die-casting process parameters closest to the current die-casting process parameters, and determines whether the difference exceeds the random error threshold. Then, the current generation result is deleted when the random error threshold is exceeded, and the generation results with excessive errors are screened and discarded, thereby reducing the influence of the generation results that are excessively affected by random errors on the calculation of the critical solid phase fraction.

[0060] Optionally, during actual operation, in order to avoid the situation where the die-casting process parameters used in the current simulation experiment are significantly different from any generated results in the data storage module, resulting in the loss of reference value for the generated results of the current simulation experiment, when the cloud platform is built, the cloud platform pre-inputs a process parameter difference threshold. Before the comparison, the cloud platform first determines one by one whether the difference between the process parameters in the data storage module and the current process parameters is less than the process parameter difference threshold. When the judgment result of a certain process parameter is yes, the cloud platform includes the generated result corresponding to the secondary process parameter in the selection range for random error threshold judgment, and then in step three, finds the generated result corresponding to the die-casting process parameter within the selection range that is closest to the current die-casting process parameter, and compares them. Specifically, the control module subtracts the die-casting process parameters within the selection range from the die-casting process parameters of the current simulation experiment, and finds the data with the smallest difference as the closest die-casting process parameter.

[0061] When judging whether a single generated result exceeds the random error threshold in step three, the generated result closest to the current die-casting process parameters in a single data storage module is also affected by random errors, and there is a probability that the generated result affected by random errors cannot be screened out. Therefore, in step three, the cloud platform compares the generated result with the actual filling process consistent with the die-casting process parameters, and finds the generated results corresponding to the three die-casting process parameters closest to the current die-casting process parameters in the data storage module through comparison, and calculates the comparison data based on the mean of several generated results. The cloud platform judges whether the difference between the comparison data and the current generation result and the comparison data exceeds the random error threshold;

[0062] Specifically, the die-casting process parameter of the current simulation experiment is C. The generation results A1, A2 and A3 corresponding to the three die-casting process parameters C1, C2 and C3 closest to the current die-casting process parameter C in the data storage module are found by comparison. The cloud platform calculates the comparison data A based on the three generation results A1, A2 and A3, where A=(k1×A1+k2×A2+k3×A3) / (k1+k2+k3), |C1-C|>|C2-C|>|C3-C|, k1 <k2<k3;

[0063] Since |C1-C|>|C2-C|>|C3-C|, the difference between C1 and the die-casting process parameters of the current simulation experiment is the largest, and the generated result A1 corresponding to C1 has the smallest reference value for the current simulation experiment. Therefore, accordingly, the weight k1 of A1 in the weighted average calculation is the smallest. Similarly, C3 is closest to the die-casting process parameters of the current simulation experiment, and the generated result A3 corresponding to C3 has the largest reference value for the current simulation experiment, and the weight k3 of A3 in the weighted average calculation is the largest;

[0064] After weighted average calculation, the comparison data A generated by A1, A2 and A3 is less affected by random errors than a single generated result. When used to perform difference calculation with the generated result and determine whether the difference exceeds the random error threshold, there is a greater probability that the generated result that is greatly affected by the error will be screened out.

[0065] In order to remind the operator after the calculation is completed, step six also includes: after outputting the critical solid phase fraction, issuing an audible and visual prompt.

[0066] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A die-casting process simulation method based on cloud computing, characterized in that: The following steps are involved: Step 1: According to the die-casting process, the die-casting process parameters are input into the simulation software, and an equation for simulating molten metal filling is constructed in the simulation software; Step 2: The simulation software simulates the metal filling process according to the constructed equation for simulating molten metal filling, generates the result of filling rate changing with time, and uploads the generated results corresponding to the die casting process parameters to the cloud platform; Step 3: The cloud platform archives the generated results according to the die-casting process parameters, and compares them with the actual filling process consistent with the die-casting process parameters, calculates the difference between the simulation results and the actual filling process, and determines whether the difference is greater than the threshold. If the judgment result is yes, calculate the critical solid fraction correction value and execute step 4. If the judgment result is no, execute step 6. Step 4: The cloud platform sends the critical solid fraction correction value to the simulation software. The simulation software adds the critical solid fraction correction value to the equation for simulating molten metal filling, corrects the equation, and then generates a new result of the filling rate changing with time based on the corrected equation for simulating molten metal filling, and uploads it to the cloud platform. Step 5: Repeat steps 3 to 4; Step 6: Output critical solid fraction; Step three also includes: the operator uploads the critical solid fraction calculation value to the cloud platform in advance; the step four also includes: the cloud platform uses the average of the critical solid fraction correction value and the critical solid fraction calculation value as the critical solid fraction correction value, and the cloud platform sends the critical solid fraction correction value to the simulation software; Step five also includes: the cloud platform determines whether the number of times step three is executed is greater than or equal to three times. When the judgment result is yes, the cloud platform determines whether the difference between the critical solid fraction correction values ​​generated three times and the critical solid fraction calculation value becomes larger in sequence among the critical solid fraction correction values ​​generated three times most recently. When the judgment result is yes, the cloud platform deletes the critical solid fraction calculation value.

2. The die-casting process simulation method based on cloud computing according to claim 1, characterized in that: The step six also includes: after the cloud platform outputs the critical solid phase fraction, the generated result is stored in the data storage module according to the die-casting process parameter archive data.

3. The die-casting process simulation method based on cloud computing according to claim 2, characterized in that: The step one also includes: the operator uploads the random error threshold to the cloud platform; the step three also includes: the cloud platform compares the generated result with the actual filling process consistent with the die-casting process parameters, and finds the generated result corresponding to the die-casting process parameters closest to the current die-casting process parameters in the data storage module through comparison, and marks the found generation result as comparison data. The cloud platform determines whether the difference between the comparison data and the current generation result and the comparison data exceeds the random error threshold. When the judgment result is no, the cloud platform deletes the current generation result.

4. The die-casting process simulation method based on cloud computing according to claim 2, characterized in that: The step one also includes: the operator uploads the random error threshold to the cloud platform; the step three also includes: the cloud platform compares the generated result with the actual filling process consistent with the die-casting process parameters, and finds the generation results corresponding to the three die-casting process parameters closest to the current die-casting process parameters in the data storage module through comparison, and calculates the comparison data based on the average of several generation results. The cloud platform determines whether the difference between the comparison data and the current generation result and the comparison data exceeds the random error threshold. When the judgment result is no, the cloud platform deletes the current generation result.

5. The die-casting process simulation method based on cloud computing according to claim 4, characterized in that: The step three also includes: finding the generation results A1, A2 and A3 corresponding to the three die-casting process parameters C1, C2 and C3 closest to the current die-casting process parameter C in the data storage module by comparison, and the cloud platform obtains the comparison data A by calculation according to the three generation results A1, A2 and A3; Among them, A=(k1×A1+k2×A2+k3×A3) / (k1+k2+k3), |C1-C|>|C2-C|>|C3-C|, k1 <k2<k3。 6. The die-casting process simulation method based on cloud computing according to claim 1, characterized in that: The step six also includes: after outputting the critical solid phase fraction, issuing an audible and visual prompt.

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