Casting process design method and system based on simulation analysis

Abstract

The application belongs to the technical field of casting process optimization, and provides a casting process design method and system based on simulation analysis. The method comprises the following steps: determining a design scheme of a riser and a side riser based on successful casting cases, and constructing a simulation model of a casting process based on the design scheme; extracting fluctuation parameters in the equipment operation process based on recent casting records, performing correlation analysis on the fluctuation parameters to determine a set of shrinkage correlation parameters, analyzing the coupling relationship between each shrinkage correlation parameter in the set of shrinkage correlation parameters to obtain a coupling relationship set, and generating fluctuation sequence data simulating the actual production state based on the set of shrinkage correlation parameters and the coupling relationship set; inputting the fluctuation sequence data and preset inner chill parameters into the simulation model to drive the simulation model to perform casting process simulation, and then obtaining optimal solidification parameters. The application overcomes the defects of traditional simulation based on idealized state of equipment, thereby effectively reducing shrinkage defects and improving the compactness of casting organization.

Description

Technical Field

[0001] This invention relates to the field of casting process optimization technology, and more specifically, to a casting process design method and system based on simulation analysis. Background Technology

[0002] In the foundry industry, the quality of castings directly determines their reliability and service life in subsequent industrial applications. Insufficient feeding leading to shrinkage porosity remains a core challenge restricting the production of high-quality castings. Especially for castings with complex structures and large dimensions, traditional casting processes rely on experience-based riser layouts and internal chill designs, often making it difficult to achieve balanced solidification across all parts of the casting. This easily leads to problems such as insufficient feeding at the top and loose microstructure at the bottom, which can even result in the casting being scrapped, significantly increasing production costs.

[0003] With the introduction of computer simulation technology, casting process design has entered a digital optimization stage. Existing technologies use three-dimensional models of castings to simulate and analyze temperature and flow fields during the casting process, attempting to predict defects and optimize process parameters in advance. However, current simulation technologies are generally based on assumptions of ideal equipment operation: for example, assuming a completely unobstructed gating system, cooling equipment always maintaining its rated power, and uniform mold temperature distribution without deviation. This idealized simulation model differs significantly from the actual production environment—in actual production, factors such as equipment wear, material property fluctuations, and changes in ambient temperature can cause process parameters to deviate from theoretical values, drastically reducing the match between simulation results and the actual casting process, resulting in insufficient accuracy.

[0004] Therefore, how to further consider the actual condition of the equipment in the existing simulation analysis technology, and thus effectively reduce or avoid shrinkage defects in the through-hole area, has become a technical problem that the current casting industry urgently needs to solve. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a casting process design method and system based on simulation analysis.

[0006] This invention provides a casting process design method based on simulation analysis, comprising the following steps: determining the design scheme of risers and side risers based on successful casting cases, including the size, position, and riser neck parameters of risers and side risers; constructing a simulation model of the casting process based on the design scheme, including a three-dimensional model of the casting, a gating system model, and a cooling system model; extracting fluctuation parameters during equipment operation based on recent casting records, including gating system resistance fluctuation values, cooling system power fluctuation values, and mold temperature distribution deviation values; performing correlation analysis on the fluctuation parameters to determine the shrinkage porosity correlation parameter set, analyzing the coupling relationship between each shrinkage porosity correlation parameter in the shrinkage porosity correlation parameter set to obtain a coupling relationship set, generating fluctuation sequence data simulating the actual production state based on the shrinkage porosity correlation parameter set and the coupling relationship set; inputting the fluctuation sequence data and preset internal chill parameters into the simulation model to drive the simulation model to simulate the casting process, thereby obtaining the optimal solidification parameters, including temperature gradient, solidification rate, pouring temperature, and pouring speed.

[0007] This invention also provides a casting process design system based on simulation analysis. The system includes: a scheme and modeling unit, which determines the design scheme of risers and side risers based on successful casting cases, including the size, position, and riser neck parameters of risers and side risers; constructs a simulation model of the casting process based on the design scheme, including a three-dimensional model of the casting, a gating system model, and a cooling system model; a fluctuation generation unit, which extracts fluctuation parameters during equipment operation based on recent casting records, including the fluctuation value of the gating system resistance, the fluctuation value of the cooling system power, and the deviation value of the mold temperature distribution; performs correlation analysis on the fluctuation parameters to determine the set of shrinkage porosity correlation parameters, analyzes the coupling relationship between each shrinkage porosity correlation parameter in the set of shrinkage porosity correlation parameters to obtain a set of coupling relationships, and generates fluctuation sequence data simulating the actual production state based on the set of shrinkage porosity correlation parameters and the set of coupling relationships; and a simulation unit, which inputs the fluctuation sequence data and preset internal chill parameters into the simulation model, drives the simulation model to simulate the casting process, and then obtains the optimal solidification parameters, including temperature gradient, solidification rate, pouring temperature, and pouring speed.

[0008] This invention, on the one hand, utilizes successful cases to ensure the initial reliability of the feeding system, and on the other hand, improves the simulation accuracy through real fluctuation data, overcoming the shortcomings of traditional simulation based on the ideal state of the equipment. Thus, it can effectively reduce shrinkage defects, improve the density of the casting structure, and reduce production costs. Attached Figure Description

[0009] Figure 1 This is a schematic flowchart of a casting process design method based on simulation analysis disclosed in an embodiment of the present invention.

[0010] Figure 2 This is a schematic diagram of the structure of the simulation model disclosed in the embodiments of the present invention.

[0011] Figure 3 This is a schematic diagram of a casting process design system based on simulation analysis disclosed in an embodiment of the present invention. Detailed Implementation

[0012] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0015] like Figure 1 As shown, this invention discloses a casting process design method based on simulation analysis, including the following steps: 100, determining the design scheme of risers and side risers based on successful casting cases, including the size, position and riser neck parameters of risers and side risers; constructing a simulation model of the casting process based on the design scheme, including a three-dimensional model of the casting, a gating system model and a cooling system model.

[0016] Specifically, key design parameters for risers and side risers are extracted from successful casting cases. These cases contain effective feeding data for similar casting structures. By analyzing the matching relationship between the casting and the riser in these successful cases, the riser's dimensional parameters (such as height, diameter, and volume), location parameters (relative spatial position to the casting's hot spots to ensure the riser is located in the final solidification region), and riser neck parameters (cross-sectional shape, length, and area, serving as the channel for feeding the casting) are determined. These parameters directly determine the molten metal supply capacity and feeding path unobstructedness of the feeding system, which is fundamental to ensuring the density of the casting. It can be understood that the design schemes for risers and side risers in these successful casting cases can be derived from simulation systems or from designers' design experience; this invention does not specifically limit this.

[0017] A simulation model of the casting process is constructed based on the above design scheme. For example... Figure 2As shown, the simulation model includes a 3D model of the casting, a gating system model, and a cooling system model. The constructed 3D model accurately reproduces the geometry of the casting, especially thick sections and areas prone to shrinkage porosity such as through-holes. The gating system model simulates the filling path of molten metal from the gate to the mold cavity, including the dimensions and layout of the sprue, runner, and ingate. The cooling system model reflects factors affecting solidification rate, such as the location of the cooling device and the type of cooling medium.

[0018] 200. Based on recent casting records, extract fluctuation parameters during equipment operation, including gating system resistance fluctuation values, cooling system power fluctuation values, and mold temperature distribution deviation values; perform correlation analysis on the fluctuation parameters to determine the shrinkage porosity correlation parameter set, analyze the coupling relationship between each shrinkage porosity correlation parameter in the shrinkage porosity correlation parameter set to obtain a coupling relationship set, and generate fluctuation sequence data simulating the actual production state based on the shrinkage porosity correlation parameter set and the coupling relationship set.

[0019] Specifically, recent (e.g., the last six months) casting records are retrieved from the production database, including historical data collected by equipment sensors and abnormal records in the production logs. From these, key fluctuation parameters related to process stability are identified, including at least the gating system resistance fluctuation value, cooling system power fluctuation value, and mold temperature distribution deviation value. Fluctuations in these parameters directly affect critical process conditions such as the molten metal filling speed and solidification time, and are significant contributing factors to shrinkage defects. Specifically, the gating system resistance fluctuation value reflects changes in the flowability of the gating channel due to impurities and wear; the cooling system power fluctuation value reflects the stability of cooling capacity, such as fluctuations in water pump flow rate and cooling fan speed; and the mold temperature distribution deviation value represents the difference between the actual temperature and the standard value caused by mold wear, uneven preheating, etc.

[0020] Using statistical analysis and machine learning methods, the correlation between the aforementioned fluctuation parameters and the incidence of shrinkage porosity defects is calculated. Irrelevant or weakly correlated parameters are removed, and parameters that have a significant impact on shrinkage porosity are retained to form a set of shrinkage porosity correlation parameters. For example, if data analysis shows that the correlation coefficient between the fluctuation of the gating system resistance and shrinkage porosity defects is greater than 0.7, then it is included in the set of correlation parameters.

[0021] Meanwhile, in actual production, fluctuating parameters do not act independently. For example, fluctuations in cooling system power can lead to deviations in mold temperature distribution, while changes in mold temperature can affect the fluidity of molten metal, thereby exacerbating fluctuations in the gating system resistance. By using methods such as causal analysis and cross-validation, the interaction patterns between parameters (such as linear correlation and nonlinear coupling) are identified, forming a set of coupling relationships, thereby determining the transmission path of parameter fluctuations.

[0022] Thus, fluctuation sequence data is generated based on the set of loosening correlation parameters and coupling relationships. According to the actual fluctuation range of the correlation parameters (such as the historical maximum and minimum values ​​of resistance fluctuations), and combined with the parameter influence patterns in the coupling relationship set (such as a 2°C increase in temperature deviation for every 5% increase in power fluctuation), fluctuation sequence data containing the dynamic change characteristics of the parameters is constructed using stochastic process simulation methods. This obtained fluctuation sequence data can simulate the fluctuation process of parameters over time in actual production, making subsequent simulation analysis closer to real working conditions and avoiding simulation distortion caused by idealized assumptions.

[0023] 300. The fluctuation sequence data and preset internal chill parameters are input into the simulation model to drive the simulation model to simulate the casting process, thereby obtaining the optimal solidification parameters, including temperature gradient, solidification rate, pouring temperature and pouring speed.

[0024] Specifically, the fluctuation sequence data generated in step 200 and the preset internal chill parameters are input into the simulation model. The internal chill parameters are preset based on the location of the hot spot in the casting, including material (such as low-carbon steel, copper alloy, or other high thermal conductivity materials), dimensions (length and diameter matching the hot spot volume), and distribution density (ensuring uniform heat dissipation in the hot spot area). Their function is to control the location of the hot spot by locally accelerating solidification and assisting riser feeding. The fluctuation sequence data and internal chill parameters are used together as input to the simulation model, enabling the model to simultaneously consider the combined effects of equipment fluctuations and process control measures.

[0025] The simulation model, based on input parameters, simulates the entire process of molten metal from filling, solidification to feeding, calculating key information such as temperature field distribution (reflecting the temperature gradient of different parts of the casting), solidification progress (obtaining the solidification rate), and molten metal flow state at different times. Through dynamic simulation, it can be determined whether there are areas of insufficient feeding inside the casting under the influence of fluctuating parameters, and the effect of internal chills and risers on suppressing shrinkage defects.

[0026] Furthermore, based on simulation output data such as temperature field and solidification rate, the risk of shrinkage porosity under different process conditions is assessed. For example, the time difference in solidification time is used to determine whether feeding is timely, and the temperature gradient distribution is used to determine whether sequential solidification is achieved. Through simulation comparison of multiple parameter combinations, the solidification parameters that minimize shrinkage porosity defects are optimized, specifically including temperature gradient, solidification rate, pouring temperature, and pouring speed. Among these, the optimal temperature gradient ensures that the casting solidifies sequentially from the end furthest from the riser to the riser end; the optimal solidification rate controls the overall solidification time, avoiding excessive solidification time that leads to coarse grains; the optimal pouring temperature can be reduced to a reasonable range while ensuring mold filling, reducing the tendency for shrinkage porosity; and the optimal pouring speed ensures smooth mold filling, avoiding defects such as air entrapment and cold shuts. Subsequent actual casting production based on these solidification parameters can improve the density of the casting microstructure.

[0027] This invention, on the one hand, utilizes successful cases to ensure the initial reliability of the feeding system, and on the other hand, improves the simulation accuracy through real fluctuation data, overcoming the shortcomings of traditional simulation based on the ideal state of the equipment. Thus, it can effectively reduce shrinkage defects, improve the density of the casting structure, and reduce production costs.

[0028] As an example, the method of determining the design scheme of risers and side risers based on successful casting cases includes: 101, selecting cases from the successful casting case library that have a structural similarity higher than the similarity threshold with the casting to be designed, and extracting the original design parameters of risers and side risers in the cases, including the ratio coefficient of riser volume to casting hot spot volume, the relative coordinate offset between riser center and hot spot center, and the matching ratio of riser neck cross-sectional dimensions to hot spot thickness.

[0029] Specifically, cases with a structural similarity higher than a preset similarity threshold (e.g., 70%) to the casting to be designed are selected from a successful casting case library. This structural similarity is achieved through 3D model comparison, focusing on matching key features such as the overall geometry of the casting, wall thickness distribution, and the location of through holes, ensuring that the selected cases are comparable to the casting to be designed in terms of feeding requirements.

[0030] Extract the original design parameters of risers and side risers from the successfully matched cases. These parameters directly reflect the adaptation rules of the feeding system and hot spot in the case. Specifically, they include: (1) the ratio coefficient of riser volume to casting hot spot volume: reflecting the matching relationship between riser molten metal storage and hot spot feeding requirements. For example, a ratio coefficient of 1.2 indicates that the riser volume is 1.2 times the hot spot volume, ensuring that the riser has enough molten metal to compensate for the solidification shrinkage of the hot spot.

[0031] (2) Relative coordinate offset between riser center and hot spot center: reflects the spatial correspondence between riser and hot spot (such as three-dimensional coordinate deviation value), ensuring that riser is located directly above hot spot or in optimal shrinkage position, and shortening shrinkage path.

[0032] (3) The ratio of riser neck cross-sectional dimensions to hot spot thickness: This characterizes the compatibility between the riser neck channel size and the hot spot heat dissipation and solidification rate. For example, a ratio of 0.8 indicates that the riser neck diameter is 0.8 times the hot spot thickness, ensuring that the riser neck can both provide smooth feeding and solidify later than the hot spot.

[0033] 102. Calculate the hot spot distribution characteristics based on the three-dimensional model of the casting to be designed, including the hot spot location coordinates, hot spot volume and hot spot thickness.

[0034] Specifically, based on the three-dimensional model of the casting to be designed, the hot spot distribution characteristics are calculated using the hot spot analysis function of the casting simulation software. Specifically, it includes: (1) Hot spot location coordinates: the coordinates of the last solidified area in the casting (such as the three-dimensional coordinates of the center point of the thick part) are determined by temperature field simulation. These areas are high-incidence areas of shrinkage defects and need to be fed through risers; (2) Hot spot volume: the spatial volume of the hot spot area is calculated, which reflects the total amount of molten metal required for the solidification of the area and is the core basis for determining the riser volume; (3) Hot spot thickness: the maximum wall thickness of the hot spot area is measured, which affects the solidification rate of the area (the thicker the wall, the slower the solidification) and determines the cross-sectional size requirement of the riser neck (a thick hot spot requires a larger riser neck to ensure the duration of feeding).

[0035] It is understood that the aforementioned casting simulation software may be any of MAGMAsoft, ProCAST, AnyCasting, etc., and this invention does not specifically limit it.

[0036] 103. Based on the hot spot distribution characteristics, the original design parameters are scaled and corrected, specifically as follows: the riser volume parameters are adjusted according to the ratio of the hot spot volume of the casting to be designed to the hot spot volume of the case casting, combined with the proportional coefficient; the position parameters of the riser and side riser are corrected according to the deviation between the hot spot position coordinates of the casting to be designed and the hot spot position coordinates of the case casting, combined with the relative coordinate offset; the riser neck cross-sectional dimension parameters are corrected according to the difference between the hot spot thickness of the casting to be designed and the hot spot thickness of the case casting, combined with the adaptation ratio, to form a riser and side riser design scheme adapted to the casting to be designed.

[0037] Specifically, this step achieves precise optimization of the feeding scheme by dynamically adjusting historical parameters to adapt to the specific needs of the casting to be designed. The specific correction logic is as follows: (1) Adjustment of riser volume parameters: Based on the ratio coefficient between the riser volume and the hot spot volume of the casting in the case casting, the ratio of the hot spot volume of the casting to be designed to that of the case casting is scaled. For example, if the ratio coefficient of the case casting is 1.2, and the hot spot volume of the casting to be designed is 1.5 times that of the case casting, then the adjusted riser volume = the hot spot volume of the casting to be designed × 1.2 = (the hot spot volume of the case casting × 1.5) × 1.2 = the riser volume of the case casting × 1.5 (because the riser volume of the case casting = the hot spot volume of the case casting × 1.2), ensuring that the riser volume matches the feeding requirements of the casting to be designed.

[0038] (2) Position parameter correction: Based on the deviation between the hot spot position coordinates of the casting to be designed and the hot spot position coordinates of the case casting, the spatial position of the riser is adjusted in combination with the relative coordinate offset. For example, if the center of the riser in the case casting is located 50mm directly above the center of the hot spot, while the hot spot of the casting to be designed is offset by 20mm along the X-axis, then the corrected riser position is offset by 20mm along the X-axis to maintain the optimal feeding space relationship between the riser and the hot spot.

[0039] (3) Correction of riser neck cross-sectional dimensions: Based on the matching ratio of the riser neck cross-sectional dimensions to the hot spot thickness in the case casting, adjustments are made in conjunction with the difference in hot spot thickness between the casting to be designed and the case casting. For example, if the matching ratio of the case casting is 0.8 (riser neck diameter = hot spot thickness × 0.8), and the hot spot thickness of the casting to be designed is 1.2 times that of the case casting, then the corrected riser neck diameter = hot spot thickness to be designed × 0.8, ensuring that the riser neck channel size matches the hot spot solidification rate and avoiding feeding interruption.

[0040] This embodiment selects highly similar successful cases, extracts key matching parameters between risers and hot spots, and makes precise corrections based on the hot spot characteristics of the casting to be designed, forming a more adaptable feeding scheme. This approach, while relying on the reliability of successful experience, avoids the matching deviations caused by direct application, ensuring that the riser volume, position, and riser neck dimensions accurately match the hot spot of the casting to be designed, thus improving the rationality of the initial design of the feeding system.

[0041] As an example, the correlation analysis of the fluctuation parameters to determine the set of shrinkage-related parameters includes: 201, using the Pearson coefficient analysis method to calculate the linear correlation between each fluctuation parameter and the shrinkage defect, and screening out the fluctuation parameters with correlation coefficients greater than the correlation threshold as candidate parameters.

[0042] Specifically, fluctuation parameters include process parameters that may change during casting, such as pouring temperature, riser volume, cooling rate, and alloy composition ratio; shrinkage porosity defects are characterized by their characteristic indicators such as defect volume, number density, and distribution area. Pearson coefficient analysis was used to calculate the linear correlation coefficient between each fluctuation parameter and the characteristics of shrinkage porosity defects. This coefficient reflects the strength of the linear dependence between variables, and its value ranges from [-1, 1]. A larger absolute value indicates a more significant correlation.

[0043] Fluctuation parameters with absolute values ​​of correlation coefficients greater than the correlation threshold (such as 0.5 or 0.6, which can be adjusted according to process accuracy requirements) are selected as candidate parameters. For example, if the correlation coefficient between riser volume deviation and shrinkage defect volume is 0.72, which is greater than the correlation threshold of 0.6, it is included in the candidate set; while the correlation coefficient between casting time fluctuation and shrinkage defect is 0.23, which is less than the correlation threshold, it is directly excluded.

[0044] In this way, parameters with weak correlations can be eliminated, reducing the computational load of subsequent analysis, while retaining potential key influencing factors.

[0045] 202. Perform a significance test on each candidate parameter to verify the statistical reliability of the association between the candidate parameters and the shrinkage defect characteristics, and retain each candidate parameter that passes the test.

[0046] Specifically, for each candidate parameter selected in step 201, a significance test method (such as t-test, F-test, or p-value analysis) is used for verification. For example, the p-value test is used as an example: assuming that a candidate parameter has "no real association" with the shrinkage defect (null hypothesis), the probability that this hypothesis is true (i.e., p-value) is obtained by calculating the statistic of the sample data (such as t-value). If the p-value is less than the preset significance level (such as 0.05), the null hypothesis is rejected, and it is considered that the association between the candidate parameter and the shrinkage defect is "not accidental" and has statistical reliability, and is retained; if the p-value is greater than 0.05, it indicates that the association may be caused by random factors, and the candidate parameter needs to be removed.

[0047] For example, if the candidate parameter "thermal section thickness deviation" has a P-value of 0.03 (<0.05), then its association with shrinkage defects is confirmed to be reliable; while the P-value of "sand mold humidity fluctuation" is 0.12 (>0.05), so it is determined to be an unreliable association and is removed.

[0048] This step ensures that subsequent analyses are based only on real and stable relationships, avoiding invalid parameters from interfering with the optimization direction.

[0049] 203. The importance of each candidate parameter after testing is evaluated, and the candidate parameters that have a significant impact on shrinkage defects are extracted to form a set of shrinkage-related parameters.

[0050] Specifically, for each candidate parameter retained after significance testing, the importance of the random forest algorithm is evaluated. The process is as follows: using the candidate parameters as input features and the features of the contraction defect (such as defect volume and occurrence rate) as the output target, a random forest model composed of multiple decision trees is trained. The random forest model generates multiple sets of training data through bootstrap sampling. Each tree grows independently based on a randomly selected subset of features. Finally, the prediction stability is improved through multi-tree voting or average output.

[0051] After the random forest model is trained, the importance of each parameter is quantified by calculating the reduction in feature impurity: when a candidate parameter is used for decision tree node splitting, the higher its importance score is if it can minimize node impurity (such as the Gini coefficient or entropy value), indicating a greater contribution of the candidate parameter to the prediction of shrinkage defects. For example, riser neck cross-sectional size deviation is used as a key splitting feature in multiple trees, and its importance score is significantly higher than other candidate parameters, indicating that its impact on shrinkage defects is more significant.

[0052] Next, an importance threshold is set according to process requirements (e.g., selecting parameters with scores in the top 80%), and parameters with scores higher than the importance threshold are extracted to form a set of shrinkage porosity-related parameters. For example, if the importance scores of hot spot volume deviation, pouring temperature fluctuation, and riser volume deviation are among the highest and meet the importance threshold requirements, then these three parameters are included in the shrinkage porosity-related parameter set.

[0053] This embodiment can eliminate fluctuation parameters with weak or spurious correlations, thereby focusing on the core influencing factors and effectively improving the efficiency of shrinkage defects improvement.

[0054] As an example, the analysis of the coupling relationship between each loosening correlation parameter in the loosening correlation parameter set to obtain the coupling relationship set includes: 204, using partial correlation analysis to calculate the partial correlation coefficient between any two loosening correlation parameters in the loosening correlation parameter set, and calculating the mutual information entropy between any two loosening correlation parameters.

[0055] Specifically, for any two parameters in the set of shrinkage-related parameters, the following two types of calculations are carried out simultaneously: (1) Calculation of partial correlation coefficient: Using the partial correlation analysis method, under the premise of controlling the influence of other parameters, the partial correlation coefficient between the two related parameters is calculated, which is used to characterize the linear correlation strength between the two shrinkage-related parameters. This partial correlation coefficient eliminates the interference of third-party parameters and only reflects the direct linear coupling relationship between the two shrinkage-related parameters. The value range is [-1,1]. The larger the absolute value, the more significant the linear coupling. For example, the partial correlation coefficient between the hot spot volume deviation and the riser neck size deviation is 0.68, indicating that there is still a strong linear correlation between the two after excluding the influence of other parameters.

[0056] (2) Mutual information entropy calculation: Mutual information entropy is used to quantify the degree of nonlinear coupling between two loosely related parameters. Mutual information entropy is based on information theory and can capture non-obvious dependencies between variables (such as quadratic curves, piecewise function relationships, etc.). The larger the value, the tighter the nonlinear coupling. For example, the mutual information entropy value of cooling rate fluctuation and alloy composition deviation is high, indicating that there is a significant nonlinear interaction between the two, which cannot be fully characterized by linear methods.

[0057] By combining the two methods mentioned above, both linear correlations and nonlinear interactions are covered, thus enabling a comprehensive quantification of the coupling relationship between loose correlation parameters.

[0058] 205. Based on the partial correlation coefficient and the mutual information entropy, a dual coupling strength threshold is set, and the loose correlation parameter pairs that satisfy the dual coupling strength threshold are selected to form a set of coupling relationships characterizing the interaction between parameters; wherein, the dual coupling strength threshold includes a partial correlation coefficient threshold and a mutual information entropy threshold.

[0059] Specifically, by combining the partial correlation coefficient and mutual information entropy results obtained in step 204, a dual coupling strength threshold is set, including a partial correlation coefficient threshold (e.g., absolute value ≥ 0.5) and a mutual information entropy threshold (e.g., ≥ 0.3; the specific values ​​can be adjusted according to the parameter distribution characteristics). The partial correlation coefficient threshold reflects linear coupling, while the mutual information entropy threshold reflects nonlinear coupling.

[0060] When the partial correlation coefficient or mutual information entropy of any two shrinkage porosity correlation parameters meets the corresponding threshold, a significant coupling relationship is determined, and the shrinkage porosity correlation parameter pair is included in the coupling relationship set. For example, the partial correlation coefficient between riser volume deviation and hot spot volume deviation is 0.62 (≥0.5), so it is included in the coupling relationship set; the mutual information entropy between casting temperature fluctuation and cooling rate fluctuation is 0.35 (≥0.3), so it is included in the coupling relationship set; while both the alloy composition deviation and riser neck size deviation are below the threshold, so they are not included.

[0061] This embodiment calculates the linear coupling strength between parameters using partial correlation analysis and quantifies the degree of nonlinear interaction using mutual information entropy, achieving a comprehensive characterization of the coupling relationship. Combined with dual threshold screening, it accurately captures parameter pairs that satisfy significant linear or nonlinear coupling, forming a set of coupling relationships. This setup eliminates interference from third-party parameters, covers non-explicit dependencies, clarifies key interactions between parameters, and avoids failures caused by ignoring coupling effects when optimizing a single parameter. It provides a reliable basis for the collaborative optimization of casting process parameters and improves the accuracy of shrinkage porosity defect control.

[0062] As an example, the step of generating fluctuation sequence data simulating actual production status based on the set of shrinkage correlation parameters and the set of coupling relationships includes: 206, determining the actual fluctuation range of each shrinkage correlation parameter based on the set of shrinkage correlation parameters and historical production data, and extracting parameter distribution characteristics based on the actual fluctuation range.

[0063] Specifically, for each shrinkage porosity-related parameter in the set of shrinkage porosity-related parameters, its actual process fluctuation range is determined by historical production records, process specifications and on-site test data. For example, the fluctuation range of the casting temperature is ±5℃ and the riser volume deviation range is ±3%.

[0064] Meanwhile, based on the historical production data of the casting equipment to be used in this casting, the distribution characteristics of each shrinkage porosity-related parameter are extracted, including the probability distribution type (such as normal distribution, skewed distribution), mean, standard deviation and other statistical attributes. For example, the hot spot volume deviation is normally distributed in the historical data, with a mean of 0, a standard deviation of 0.8 mm³, and a fluctuation range of [-2 mm³, 2 mm³].

[0065] This step ensures that the subsequently generated fluctuation data conforms to the natural fluctuation characteristics of shrinkage-related parameters in actual production, thus avoiding deviation from real process conditions.

[0066] 207. Combining parameter distribution characteristics and correlation constraint models, Monte Carlo simulation is used to generate multiple sets of fluctuation sequence data, wherein each set of fluctuation sequence data contains the dynamic fluctuation values ​​of each loose correlation parameter; wherein, the correlation constraint model is constructed based on the interaction law of parameter pairs in the coupling relationship set, and is used to characterize the cooperative change characteristics of parameter fluctuations.

[0067] Specifically, a correlation constraint model is constructed based on the interaction laws of parameter pairs in the coupling relationship set. Examples of these interaction laws include: linear coupling between riser volume deviation and hot spot volume deviation; and nonlinear coordinated change between cooling rate fluctuation and pouring temperature fluctuation. This correlation constraint model can characterize the coordinated change characteristics of parameter fluctuations through mathematical formulas or logical rules. For example, when the riser volume deviation increases by 1%, the hot spot volume deviation changes synchronously with a coupling coefficient of 0.6.

[0068] Next, combining the parameter distribution characteristics (such as mean, standard deviation, and distribution type) obtained in step 206 with the constructed correlation constraint model, random sampling is performed using the Monte Carlo simulation method. Specifically, within the fluctuation range of each parameter, initial values ​​are randomly generated according to their distribution characteristics. At the same time, the numerical relationships between parameters are adjusted through the correlation constraint model to ensure that parameter fluctuations conform to the coupling law. Finally, multiple sets of fluctuation sequence data are generated. Each set of fluctuation sequence data contains the dynamic fluctuation values ​​of each parameter over time or batch. For example, in a set of fluctuation sequence data, the casting temperature fluctuates successively by 2℃, -3℃, and 1℃, and the corresponding riser volume deviation fluctuates synchronously by 1%, -2%, and 0.5%.

[0069] 208. Verify the degree of consistency between each fluctuation sequence data and the actual production fluctuation pattern, and retain the fluctuation sequence data that meets the preset error threshold.

[0070] Specifically, for each set of fluctuation sequence data generated in step 207, the degree of consistency with the actual production fluctuation pattern is verified from the following two dimensions: (1) Single parameter consistency: compare the mean, standard deviation, fluctuation range of each parameter in the simulated fluctuation sequence data with the differences in historical actual data; (2) Coupling relationship consistency: verify whether the coordinated change trend of parameter pairs in the simulated fluctuation sequence data (such as linear correlation, nonlinear dependency relationship) is consistent with the interaction pattern in actual production, for example, by calculating the correlation coefficient deviation between simulated data and actual data for quantification.

[0071] Set a preset error threshold, such as single parameter statistical feature deviation ≤10% and coupling relationship deviation ≤15%. Retain fluctuation sequence data that meet the above preset error threshold requirements for all indicators, and remove fluctuation sequence data that deviate from the actual pattern.

[0072] The resulting fluctuation sequence data can accurately simulate the dynamic fluctuations and coordinated changes of parameters in actual production, providing input data that closely resembles real working conditions for subsequent casting process simulation analysis.

[0073] As an example, the preset error threshold is determined in the following way: the size parameters, arrangement density and material properties of the internal chill are obtained from the internal chill parameters; the difficulty of shrinkage porosity in the shrinkage porosity-prone area of ​​the casting three-dimensional model is quantitatively evaluated, a difficulty level coefficient is generated, and the corresponding basic error threshold range is determined based on the difficulty level coefficient.

[0074] By combining the actual fluctuation deviation data under each difficulty level coefficient in historical production data with the allowable tolerance requirements for casting quality, the basic error threshold range is calibrated to determine the final values ​​of the single-parameter statistical characteristic deviation threshold and the coupling relationship deviation threshold.

[0075] The control precision of shrinkage porosity defects in castings is closely related to the parameters of the internal chill. Different internal chill configurations (size, density, material) lead to significant differences in the difficulty of shrinkage porosity formation. If a fixed preset error threshold is used, it is easy to encounter problems such as insufficient control of high-risk areas or excessive constraint of low-risk areas. Therefore, this invention sets up a method based on internal chill parameters to quantify the difficulty of shrinkage porosity formation, and then dynamically adjusts the preset error threshold to achieve a precise match between the threshold and the actual shrinkage porosity control requirements.

[0076] Specifically, key parameters are extracted from the internal chill parameters, including dimensional parameters (such as length, diameter, and cross-sectional shape), arrangement density (such as quantity per unit volume and spacing), and material properties (such as thermal conductivity and melting point). Among these, dimensional parameters reflect the heat dissipation coverage of the internal chill, arrangement density reflects the heat dissipation uniformity of the internal chill, and material properties determine the heat dissipation efficiency of the internal chill.

[0077] Based on these key characteristic parameters, a quantitative assessment is conducted on areas prone to shrinkage porosity in the three-dimensional model of the casting (such as hot spots and abrupt changes in wall thickness). Specifically, the probability of shrinkage porosity formation is calculated by simulating heat dissipation capacity, and the assessment results are converted into a difficulty level coefficient (e.g., level 1-5, with higher values ​​indicating more difficult-to-control shrinkage porosity). For example, the area corresponding to the small-sized, low-density, and low-thermal-conductivity internal chill has a difficulty level coefficient of 4, indicating a high risk of shrinkage porosity.

[0078] Then, the threshold range is dynamically adjusted to ensure that the fluctuation simulation adapts to different risk scenarios. Specifically, the basic error threshold range is set based on the difficulty level coefficient. Specifically, higher difficulty levels (such as level 4-5) correspond to stricter thresholds, such as single parameter deviation ≤8% and coupling deviation ≤12%, to strengthen fluctuation control in high-risk areas; lower difficulty levels (such as level 1-2) correspond to relatively lenient thresholds, such as single parameter deviation ≤12% and coupling deviation ≤18%. This setting can balance calculation accuracy and calculation efficiency.

[0079] Next, statistical deviations of actual fluctuations at each difficulty level are extracted from historical production data. For example, the historical average deviation of a single parameter in the level 4 region is 7%, and this is matched with the allowable tolerance requirements for casting quality, such as a tolerance of ≤5% for shrinkage porosity defects in critical areas. Fine-tuning is then performed on the basic range. Finally, the specific values ​​of the single-parameter statistical characteristic deviation threshold and the coupling relationship deviation threshold are determined to ensure that the thresholds not only conform to the actual production fluctuation patterns but also meet the shrinkage porosity control accuracy requirements of different risk areas.

[0080] As an example, the method of driving the simulation model to simulate the casting process and obtain the optimal solidification parameters includes: 301, inputting the fluctuation sequence data into the simulation model, setting simulation boundary conditions, driving the simulation model to simulate the casting solidification process under different fluctuation sequence data, and outputting the solidification parameters and shrinkage defect prediction results corresponding to each fluctuation sequence data.

[0081] Specifically, the fluctuation sequence data (including the dynamic fluctuation values ​​of each shrinkage porosity-related parameter) filtered in step 208 is input into the casting process simulation model, and simulation boundary conditions are set simultaneously, including fixed process conditions such as initial mold temperature (e.g., 200℃), environmental heat dissipation coefficient (e.g., 15W / (m²・K)), and gating system structural parameters.

[0082] The simulation model drives the simulation of the entire process of casting from pouring to complete solidification for each set of fluctuation sequence data. It focuses on recording solidification parameters, such as solidification time in each region, temperature gradient distribution, solid fraction change curve over time, and peak cooling rate. It also predicts the characteristics of shrinkage defects (such as defect location, volume, and quantity) in the corresponding sequence using built-in shrinkage criterion (such as the Niyama criterion). For example, in a certain set of fluctuation sequence data, "pouring temperature fluctuation +3℃" and "riser volume deviation +2%", the simulation output shows that the corresponding solidification time is extended by 20s, and the predicted shrinkage volume in the hot spot region is 1.2mm³.

[0083] By simulating multiple sets of fluctuation sequence data, the correspondence between the solidification process and shrinkage defects under different parameter fluctuation scenarios can be obtained.

[0084] 302. With minimizing shrinkage defects as the optimization objective, a multi-objective optimization algorithm is used to optimize the solidification parameters, select the solidification parameter combination with the best prediction result of shrinkage defects, and determine the optimal solidification parameters through process feasibility verification.

[0085] Specifically, the core optimization objective is to minimize shrinkage defects (e.g., shrinkage volume ≤ 0.5 mm³, number of defects ≤ 2). The solidification parameters output in step 301 are used as optimization variables, and multi-objective optimization algorithms (such as NSGA-II or particle swarm optimization) are employed to optimize the parameters. Through iterative calculations, the algorithm searches for the optimal combination of parameters within the feasible range of solidification parameters that yields the best prediction results for shrinkage defects, such as a parameter range where the temperature gradient is ≥ 5℃ / mm and the cooling rate is ≥ 2℃ / s.

[0086] For example, among the candidate combinations selected through optimization, the one with "solidification time 180s + temperature gradient 6℃ / mm" has the smallest shrinkage volume, which is 0.3mm³.

[0087] Subsequently, the feasibility of the candidate optimal parameter combinations is verified. Specifically, it is evaluated whether any set of solidification parameters matches the capabilities of existing production equipment (e.g., whether the cooling system can achieve the target cooling rate), whether it meets production efficiency requirements (e.g., whether the solidification time is within a reasonable period), and whether it complies with material performance constraints (e.g., avoiding casting cracks due to excessively rapid cooling). The parameter combinations that pass the verification are ultimately retained and determined as the optimal solidification parameters, ensuring that they can effectively control shrinkage defects and are feasible in actual production.

[0088] like Figure 3As shown, this embodiment of the invention also provides a casting process design system 100 based on simulation analysis, including: a scheme and modeling unit 1001, which determines the design scheme of risers and side risers based on successful casting cases, including the size, position and riser neck parameters of risers and side risers; and constructs a simulation model of the casting process based on the design scheme, including a three-dimensional model of the casting, a gating system model and a cooling system model.

[0089] The fluctuation generation unit 1002 extracts fluctuation parameters from the equipment operation process based on recent casting records, including the fluctuation value of the gating system resistance, the fluctuation value of the cooling system power, and the deviation value of the mold temperature distribution; performs correlation analysis on the fluctuation parameters to determine the set of shrinkage porosity correlation parameters, analyzes the coupling relationship between each shrinkage porosity correlation parameter in the set of shrinkage porosity correlation parameters to obtain a set of coupling relationships, and generates fluctuation sequence data simulating the actual production state based on the set of shrinkage porosity correlation parameters and the set of coupling relationships.

[0090] The simulation unit 1003 inputs the fluctuation sequence data and preset internal chill parameters into the simulation model, drives the simulation model to simulate the casting process, and then obtains the optimal solidification parameters, including temperature gradient, solidification rate, pouring temperature and pouring speed.

[0091] As an example, the scheme and modeling unit 1001 are specifically implemented as follows: Select cases from the successful casting case library that have a structural similarity to the casting to be designed that is higher than the similarity threshold, and extract the original design parameters of the riser and side riser in the case, including the ratio coefficient of riser volume to casting hot spot volume, the relative coordinate offset between riser center and hot spot center, and the matching ratio of riser neck cross-sectional dimensions to hot spot thickness.

[0092] The original design parameters are scaled and corrected based on the hot spot distribution characteristics. Specifically, the riser volume parameters are adjusted according to the ratio of the hot spot volume of the casting to be designed to that of the case casting, combined with the proportional coefficient. The position parameters of the riser and side riser are corrected according to the deviation between the hot spot position coordinates of the casting to be designed and that of the case casting, combined with the relative coordinate offset. The riser neck cross-sectional dimension parameters are corrected according to the difference between the hot spot thickness of the casting to be designed and that of the case casting, combined with the fit ratio, to form a riser and side riser design scheme that fits the casting to be designed.

[0093] As an example, the fluctuation generation unit 1002 specifically implements the following: Pearson coefficient analysis is used to calculate the linear correlation between each fluctuation parameter and the shrinkage defect; fluctuation parameters with correlation coefficients greater than a correlation threshold are selected as candidate parameters; significance testing is performed on each candidate parameter to verify the statistical reliability of the association between the candidate parameters and the shrinkage defect characteristics; candidate parameters that pass the test are retained; importance assessment is performed on each tested candidate parameter, and candidate parameters that have a significant impact on the shrinkage defect are extracted to form a shrinkage-related parameter set.

[0094] As an example, the fluctuation generation unit 1002 further implements the following: calculating the linear correlation between each fluctuation parameter and the shrinkage defect using the Pearson coefficient analysis method, and selecting fluctuation parameters with correlation coefficients greater than the correlation threshold as candidate parameters; performing a significance test on each candidate parameter to verify the statistical reliability of the association between the candidate parameters and the shrinkage defect characteristics, and retaining each candidate parameter that passes the test; and evaluating the importance of each candidate parameter that has passed the test, extracting candidate parameters that have a significant impact on the shrinkage defect, and forming a set of shrinkage-related parameters.

[0095] As an example, the fluctuation generation unit 1002 further implements: calculating the partial correlation coefficient between any two loosely correlated parameters in the loosely correlated parameter set using a partial correlation analysis method, and calculating the mutual information entropy between any two loosely correlated parameters; setting a dual coupling strength threshold based on the partial correlation coefficient and the mutual information entropy, and selecting loosely correlated parameter pairs that satisfy the dual coupling strength threshold to form a coupling relationship set characterizing the interaction between parameters; wherein, the dual coupling strength threshold includes a partial correlation coefficient threshold and a mutual information entropy threshold.

[0096] As an example, the fluctuation generation unit 1002 further implements the following: determining the actual fluctuation range of each shrinkage-related parameter based on the shrinkage-related parameter set and historical production data; extracting parameter distribution characteristics based on the actual fluctuation range; combining the parameter distribution characteristics with the correlation constraint model, generating multiple sets of fluctuation sequence data using the Monte Carlo simulation method, wherein each set of fluctuation sequence data contains the dynamic fluctuation value of each shrinkage-related parameter; wherein the correlation constraint model is constructed based on the interaction law of parameter pairs in the coupling relationship set, and is used to characterize the cooperative change characteristics of parameter fluctuations; verifying the consistency between each fluctuation sequence data and the actual production fluctuation law, and retaining the fluctuation sequence data that meets the preset error threshold.

[0097] As an example, the preset error threshold is determined as follows: the size parameters, arrangement density, and material properties of the internal chill are parsed from the internal chill parameters; the difficulty of shrinkage porosity generation in the shrinkage porosity-prone areas of the casting 3D model is quantitatively evaluated to generate a difficulty level coefficient; the corresponding basic error threshold range is determined based on the difficulty level coefficient; the basic error threshold range is calibrated by combining the actual fluctuation deviation data under each difficulty level coefficient in historical production data with the casting quality tolerance requirements, and the final values ​​of the single-parameter statistical characteristic deviation threshold and the coupling relationship deviation threshold are determined.

[0098] As an example, simulation unit 1003 specifically implements the following: inputting the fluctuation sequence data into the simulation model, setting simulation boundary conditions, driving the simulation model to simulate the solidification process of castings under different fluctuation sequence data, and outputting the solidification parameters and shrinkage defect prediction results corresponding to each fluctuation sequence data; taking the minimization of shrinkage defects as the optimization objective, using a multi-objective optimization algorithm to optimize the solidification parameters, selecting the solidification parameter combination with the optimal shrinkage defect prediction results, and determining the optimal solidification parameters through process feasibility verification.

[0099] Although the invention has been specifically shown and described with reference to preferred embodiments, those skilled in the art will understand that various modifications in form and detail may be made without departing from the spirit and scope of the invention. Accordingly, the disclosed invention should be considered merely illustrative and limited only by the scope specified in the appended claims.

Claims

1. A casting process design method based on simulation analysis, characterized in that, Includes the following steps: Based on successful casting cases, the design schemes for risers and side risers are determined, including the size, location, and riser neck parameters of risers and side risers; based on the design scheme, a simulation model of the casting process is constructed, including a three-dimensional model of the casting, a gating system model, and a cooling system model; based on recent casting records, fluctuation parameters during equipment operation are extracted, including the fluctuation values ​​of gating system resistance, cooling system power, and mold temperature distribution deviation. Correlation analysis is performed on the fluctuation parameters to determine the set of shrinkage correlation parameters. The coupling relationship between each shrinkage correlation parameter in the set of shrinkage correlation parameters is analyzed to obtain the set of coupling relationships. Based on the set of shrinkage correlation parameters and the set of coupling relationships, fluctuation sequence data simulating the actual production state is generated. The fluctuation sequence data and preset internal chill parameters are input into the simulation model to drive the simulation model to simulate the casting process, thereby obtaining the optimal solidification parameters, including temperature gradient, solidification rate, pouring temperature and pouring speed. Correlation analysis is performed on the fluctuation parameters to determine the set of loosening correlation parameters, including: The linear correlation between each fluctuation parameter and shrinkage defect was calculated using the Pearson coefficient analysis method. Fluctuation parameters with correlation coefficients greater than the correlation threshold were selected as candidate parameters. Significance tests were performed on each candidate parameter to verify the statistical reliability of the association between the candidate parameter and the shrinkage defect characteristics. Candidate parameters that passed the test were retained. The importance of each candidate parameter that passed the test was evaluated, and candidate parameters that have a significant impact on shrinkage defect were extracted to form a set of shrinkage correlation parameters. Fluctuation sequence data simulating actual production conditions is generated based on the loosening correlation parameter set and coupling relationship set, including: Based on the set of shrinkage-related parameters and historical production data, the actual fluctuation range of each shrinkage-related parameter is determined, and the parameter distribution characteristics are extracted based on the actual fluctuation range. Combining the parameter distribution characteristics with the correlation constraint model, a Monte Carlo simulation method is used to generate multiple sets of fluctuation sequence data, where each set of fluctuation sequence data contains the dynamic fluctuation value of each shrinkage-related parameter. The correlation constraint model is constructed based on the interaction law of parameter pairs in the coupling relationship set and is used to characterize the coordinated change characteristics of parameter fluctuations. The degree of consistency between each fluctuation sequence data and the actual production fluctuation law is verified, and the fluctuation sequence data that meets the preset error threshold is retained.

2. The casting process design method based on simulation analysis according to claim 1, characterized in that: Based on successful casting cases, the design schemes for risers and side risers were determined, including: Cases with a structural similarity higher than the similarity threshold of the casting to be designed are selected from the successful casting case database. The original design parameters of the risers and side risers in the cases are extracted, including the ratio coefficient of riser volume to casting hot spot volume, the relative coordinate offset between the riser center and the hot spot center, and the fit ratio between the riser neck cross-sectional dimensions and the hot spot thickness. The original design parameters are scaled and corrected according to the hot spot distribution characteristics. Specifically, the riser volume parameters are adjusted according to the ratio of the hot spot volume of the casting to be designed to the hot spot volume of the case casting, combined with the ratio coefficient. The position parameters of the riser and side risers are corrected according to the deviation between the hot spot position coordinates of the casting to be designed and the hot spot position coordinates of the case casting, combined with the relative coordinate offset. The riser neck cross-sectional dimension parameters are corrected according to the difference between the hot spot thickness of the casting to be designed and the hot spot thickness of the case casting, combined with the fit ratio, to form a riser and side riser design scheme adapted to the casting to be designed.

3. The casting process design method based on simulation analysis according to claim 1, characterized in that: Analyze the coupling relationships among the various shrinkage correlation parameters in the shrinkage correlation parameter set to obtain the coupling relationship set, including: The partial correlation coefficient between any two loose association parameters in the loose association parameter set is calculated using the partial correlation analysis method, and the mutual information entropy between any two loose association parameters is also calculated. Based on the partial correlation coefficient and the mutual information entropy, a dual coupling strength threshold is set, and loose association parameter pairs that satisfy the dual coupling strength threshold are selected to form a coupling relationship set characterizing the interaction between parameters. The dual coupling strength threshold includes a partial correlation coefficient threshold and a mutual information entropy threshold.

4. The casting process design method based on simulation analysis according to claim 1, characterized in that: The preset error threshold is determined in the following manner: The dimensional parameters, arrangement density, and material properties of the internal chill are analyzed from the internal chill parameters. The difficulty of shrinkage porosity in areas prone to shrinkage porosity in the three-dimensional model of the casting is quantitatively evaluated, and a difficulty level coefficient is generated. Based on the difficulty level coefficient, the corresponding basic error threshold range is determined. Combining the actual fluctuation deviation data under each difficulty level coefficient in historical production data with the casting quality tolerance requirements, the basic error threshold range is calibrated to determine the final values ​​of the single-parameter statistical characteristic deviation threshold and the coupling relationship deviation threshold.

5. The casting process design method based on simulation analysis according to claim 1, characterized in that: The simulation model is driven to simulate the casting process, thereby obtaining the optimal solidification parameters, including: The fluctuation sequence data is input into the simulation model, simulation boundary conditions are set, and the simulation model is driven to simulate the solidification process of castings under different fluctuation sequence data. The solidification parameters and shrinkage defect prediction results corresponding to each fluctuation sequence data are output. Taking the minimization of shrinkage defects as the optimization objective, a multi-objective optimization algorithm is used to optimize the solidification parameters, and the optimal solidification parameter combination with the best shrinkage defect prediction results is selected. The optimal solidification parameters are determined through process feasibility verification.

6. A casting process design system based on simulation analysis, characterized in that, The system includes: The design and modeling unit determines the design schemes for risers and side risers based on successful casting cases, including the size, location, and riser neck parameters of risers and side risers; based on the design scheme, it constructs a simulation model of the casting process, including a 3D model of the casting, a gating system model, and a cooling system model. The fluctuation generation unit extracts fluctuation parameters from the equipment operation process based on recent casting records, including fluctuation values ​​of gating system resistance, cooling system power, and mold temperature distribution deviation. It performs correlation analysis on the fluctuation parameters to determine the set of shrinkage porosity correlation parameters, analyzes the coupling relationship between each shrinkage porosity correlation parameter in the set of shrinkage porosity correlation parameters to obtain a set of coupling relationships, and generates fluctuation sequence data simulating the actual production state based on the set of shrinkage porosity correlation parameters and the set of coupling relationships. The simulation unit inputs the fluctuation sequence data and preset internal chill parameters into the simulation model, drives the simulation model to simulate the casting process, and then obtains the optimal solidification parameters, including temperature gradient, solidification rate, pouring temperature and pouring speed. The wave generation unit is specifically implemented as follows: The linear correlation between each fluctuation parameter and shrinkage defect was calculated using the Pearson coefficient analysis method. Fluctuation parameters with correlation coefficients greater than the correlation threshold were selected as candidate parameters. Significance tests were performed on each candidate parameter to verify the statistical reliability of the association between the candidate parameter and the shrinkage defect characteristics. Candidate parameters that passed the test were retained. The importance of each candidate parameter that passed the test was evaluated, and candidate parameters that have a significant impact on shrinkage defect were extracted to form a set of shrinkage correlation parameters. The fluctuation generation unit further implements the following: determining the actual fluctuation range of each shrinkage-related parameter based on the shrinkage-related parameter set and historical production data; extracting parameter distribution characteristics based on the actual fluctuation range; combining the parameter distribution characteristics with the correlation constraint model, generating multiple sets of fluctuation sequence data using the Monte Carlo simulation method, wherein each set of fluctuation sequence data contains the dynamic fluctuation value of each shrinkage-related parameter; wherein the correlation constraint model is constructed based on the interaction law of parameter pairs in the coupling relationship set, and is used to characterize the cooperative change characteristics of parameter fluctuations; verifying the degree of consistency between each fluctuation sequence data and the actual production fluctuation law, and retaining the fluctuation sequence data that meets the preset error threshold.

7. The casting process design system based on simulation analysis according to claim 6, characterized in that: The specific implementation of the aforementioned scheme and modeling unit is as follows: Cases with a structural similarity higher than the similarity threshold of the casting to be designed are selected from the successful casting case library. The original design parameters of the riser and side riser in the case are extracted, including the ratio coefficient of riser volume to casting hot spot volume, the relative coordinate offset between riser center and hot spot center, and the fit ratio of riser neck cross-sectional dimensions to hot spot thickness. The original design parameters are scaled and corrected according to the hot spot distribution characteristics. Specifically, the riser volume parameters are adjusted according to the ratio of the hot spot volume of the casting to be designed to the hot spot volume of the case casting, combined with the ratio coefficient. Based on the deviation between the hot spot position coordinates of the casting to be designed and the hot spot position coordinates of the case casting, the position parameters of the riser and side riser are corrected in combination with the relative coordinate offset; based on the difference between the hot spot thickness of the casting to be designed and the hot spot thickness of the case casting, the riser neck cross-sectional dimension parameters are corrected in combination with the fit ratio, thus forming a riser and side riser design scheme that is adapted to the casting to be designed.

Images