Shell mold shell core casting boundary condition solving method based on multi-wall thickness temperature measurement model

Abstract

The application discloses a shell mold shell core casting boundary condition solving method based on a multi-wall thickness temperature measurement model and relates to the casting technical field, which comprises the following steps: acquiring a measured temperature curve and a measured shrinkage cavity volume; establishing a numerical simulation model of the multi-wall thickness temperature measurement model and setting an initial thermal physical property parameter combination; performing numerical simulation on a solidification process, outputting a simulated temperature curve of each temperature measurement point and a simulated shrinkage cavity volume; calculating temperature deviation values and defect deviation values; generating multiple sets of thermal physical property parameter combinations by using orthogonal experiment design, respectively executing the above steps, and obtaining temperature deviation values and defect deviation values corresponding to each set of parameter combinations; and verifying the optimal thermal physical property parameter combination by substituting it into the numerical simulation model, and if the verification result meets preset convergence conditions, outputting the optimal thermal physical property parameter combination. The method improves the accuracy of numerical simulation and provides reliable quantitative basis for process design and defect prevention.

Description

Technical Field

[0001] This invention relates to the field of casting technology, and more specifically to a method for solving boundary conditions in shell mold and core casting based on a multi-wall thickness temperature measurement model. Background Technology

[0002] Casting is a fundamental process in the equipment manufacturing industry. Among them, shell mold and shell core casting process has been rapidly promoted and applied in fields such as engineering machinery and aerospace due to its advantages such as high surface quality of castings, good dimensional accuracy and high production efficiency. Numerical simulation technology is an important means of optimizing casting process, and its prediction accuracy determines the reliability of casting quality control.

[0003] Existing technologies lack standardized thermophysical parameter testing models designed specifically for the characteristics of shell mold and core casting processes. Although traditional sand casting and investment casting have accumulated relatively mature simulation parameter systems, the molding materials, sand intake, and mortise materials in shell mold and core casting differ fundamentally from those in traditional processes. Furthermore, in actual production, the mortise methods and metal materials are diverse, and the heat transfer patterns between the casting and the mold change significantly under different process conditions. Current practices often use simple-shaped, single-wall-thickness samples for thermophysical property testing, which cannot reflect the differences in solidification sequence between castings of different wall thicknesses, nor does it incorporate key process design elements such as riser feeding and chilling into the testing system. As a result, the parameters obtained from the tests are difficult to characterize the actual heat transfer behavior of the casting under complex solidification conditions.

[0004] Regarding experimental data acquisition, existing technologies typically only collect temperature data of the casting body or only observe defects, failing to establish a direct correlation between temperature field evolution and defect formation. Temperature testing is often limited to the casting body or gating location, lacking a systematic temperature measurement layout for key hot spots such as riser roots and chill contact surfaces. This results in a lack of heat flow information at key heat transfer interfaces during solidification, making it impossible to provide complete data support for solving the multi-interface coupled heat transfer coefficient. At the same time, the thin-shell structure of the shell mold and core places higher demands on the installation and protection of thermocouples. Existing thermocouple protection methods cannot simultaneously ensure accurate positioning of temperature measurement points under thin-walled conditions and protection against the scouring of high-temperature molten metal, affecting the reliability and stability of measured data.

[0005] In the inverse method of thermophysical parameters, existing technologies usually adopt the method of independent testing of single parameters or adjustment of single factors one by one, failing to treat the casting, sand shell, chills and interface heat transfer coefficients as a coupled system for overall solution. This isolated testing method ignores the mutual coupling effect between parameters, and has a long experimental cycle and poor versatility. Moreover, the existing inverse process often only uses the fitting degree of temperature curve as the sole evaluation index, lacking effective verification of defect prediction results. As a result, although the parameters obtained by inverse calculation can match the measured data at the temperature field level, they cannot accurately reproduce the shrinkage porosity distribution characteristics of the actual casting, reducing the guiding value of simulation results for process design.

[0006] Therefore, how to design a method for solving the boundary conditions of shell mold and shell core casting based on a multi-wall thickness temperature measurement model, and accurately solve the combination of thermophysical parameters applicable to the actual production of shell mold and shell core casting, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] In view of this, the present invention provides a method for solving boundary conditions in shell mold and shell core casting based on a multi-wall thickness temperature measurement model, which solves the problem of low accuracy in numerical simulation of shell mold and shell core casting. By constructing a multi-wall thickness temperature measurement model, an experimental system is established to simultaneously conduct temperature field testing and defect analysis. A multi-parameter coupled inverse method combining orthogonal experimental design and range analysis is adopted to obtain thermophysical parameters that can accurately reflect the actual heat transfer characteristics and solidification law of shell mold and shell core casting, thereby improving the accuracy of numerical simulation and providing a reliable quantitative basis for process design and defect prevention.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A method for solving boundary conditions in shell mold and shell core casting based on a multi-wall-thickness temperature measurement model, wherein the multi-wall-thickness temperature measurement model includes a temperature measurement section and a defect comparison section, and the temperature measurement section is provided with multiple temperature measurement points; the method includes the following steps: S1. Obtain the measured temperature curves of the temperature measuring part at each temperature measuring point from the start of casting to complete cooling, and the measured shrinkage volume of the defect comparison part. S2. Establish a numerical simulation model of the multi-wall-thickness temperature measurement model, and set an initial combination of thermophysical parameters for the numerical simulation model; S3. Based on the numerical simulation model, perform numerical simulation of the solidification process and output the simulated temperature curves and simulated shrinkage volume of each temperature measurement point. S4. Calculate the temperature deviation between the simulated temperature curve and the measured temperature curve at each temperature measurement point, and calculate the defect deviation between the simulated shrinkage cavity volume and the measured shrinkage cavity volume. S5. Using each thermophysical parameter as a factor and setting multiple levels, orthogonal experimental design is used to generate multiple combinations of thermophysical parameters. Steps S3 to S4 are executed respectively to obtain the temperature deviation value and defect deviation value corresponding to each parameter combination. S6. Construct a response matrix using the temperature deviation value as the main evaluation index, determine the order of influence of each factor on the temperature deviation value and the optimal level through range analysis, verify the optimal level using the defect deviation value as a constraint, and comprehensively determine the optimal combination of thermophysical parameters. S7. Substitute the optimal combination of thermal property parameters into the numerical simulation model for verification. If the verification result meets the preset convergence condition, output the optimal combination of thermal property parameters; otherwise, adjust the level range of each factor and repeat steps S5 to S7.

[0010] Preferably, the multi-wall-thickness temperature measurement model has a symmetrical structure; the temperature measurement part and the defect comparison part have the same structure and are symmetrically arranged on both sides of the gating system; Both the temperature measuring section and the defect comparison section include multiple wall thickness zones with different wall thickness values; a riser is provided above the wall thickness zone with the largest wall thickness value, and a patch is provided at the bottom of the riser; and chills are provided on the side of some wall thickness zones.

[0011] Preferably, the temperature measuring points are set on the temperature measuring part, including: measuring points set at the center of each wall thickness area, the insertion depth of the measuring points being half of the corresponding wall thickness; measuring points set at the center of each chill side and extending into the cavity surface; and measuring points set at the patch.

[0012] Preferably, the wall thickness zones are sequentially 10mm, 20mm, 30mm, 40mm, and 50mm; the chills are disposed on the sides of the 30mm and 50mm wall thickness zones.

[0013] Preferably, in step S3, the numerical simulation of the solidification process is based on the heat conduction equation of the temperature field:

[0014] in, For material density, denoted as , where is the specific heat, T is the temperature, t is the time, k is the thermal conductivity, and Q is the latent heat release term of the phase change.

[0015] Preferably, in step S4, the temperature deviation value E is expressed as:

[0016] Where n is the total number of temperature measurement points, The moment the pouring begins. The moment of complete cooling, Let be the simulated temperature of the i-th temperature measuring point at time t. Let be the measured temperature of the i-th temperature measuring point at time t.

[0017] Preferably, in step S4, the defect deviation value D is represented as:

[0018] in, To simulate the shrinkage volume of shrinkage cavities, This is the actual measured volume of shrinkage cavity.

[0019] Preferably, in step S5, each thermophysical parameter is set to three levels—low, medium, and high—based on empirical value ranges. orthogonal arrays or Orthogonal arrays generate orthogonal experimental schemes, with each group of experiments corresponding to a set of thermophysical parameter combinations.

[0020] Preferably, in step S6, determining the order of influence and optimal level of each factor on the temperature deviation value through range analysis includes: For each factor in the response matrix, calculate the average temperature deviation value corresponding to the factor at each level, take the difference between the minimum and maximum values ​​of the average values ​​at each level as the range value of the factor, sort the factors according to the size of the range value, and determine the order of influence of each factor. For each factor, the level that minimizes the average temperature deviation is selected as the preferred level for that factor.

[0021] Preferably, in step S6, verifying the optimal level using the defect deviation value as a constraint includes: The defect deviation value corresponding to the optimal combination of each factor is compared with the preset defect deviation tolerance threshold. If the defect deviation value is less than the tolerance threshold, the optimal combination of factors passes the verification and is directly output as the optimal combination of thermophysical parameters.

[0022] As can be seen from the above technical solution, compared with the prior art, the technical solution of the present invention has the following beneficial effects: 1. This method simultaneously obtains the measured temperature curve and the measured shrinkage volume of the shrinkage cavity on the same multi-wall thickness temperature measurement model. In the process of parameter inversion, the response matrix is ​​constructed with temperature deviation as the main index for range analysis, and the optimization level is verified with defect deviation as a constraint. This ensures that the final determined optimal combination of thermophysical parameters can accurately reproduce the temperature change of the entire solidification process of the casting, avoiding the problem of defect prediction distortion caused by relying solely on temperature fitting.

[0023] 2. All thermophysical parameters are solved synchronously as a whole coupled system. The balanced dispersion characteristics of orthogonal experimental design are utilized to cover the main combinations of multiple parameters changing simultaneously with a finite number of numerical simulations. Then, the influence of each parameter on the temperature deviation is directly quantified through range analysis to determine the order of importance and the optimal level combination. Combined with defect deviation constraint verification and iterative verification mechanism, the solution results have accuracy and process adaptability in both temperature field and defect field. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0025] Figure 1 A flowchart illustrating a method for solving boundary conditions in shell mold and core casting based on a multi-wall thickness temperature measurement model, provided in this embodiment of the invention; Figure 2 This is a schematic diagram of the multi-wall-thickness temperature measurement model structure provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the temperature measurement point distribution of the multi-wall-thickness temperature measurement model provided in an embodiment of the present invention; In the diagram, 1 is the temperature measuring section, 2 is the defect comparison section, 3 is the gating system, 11 is the 10mm wall thickness area, 12 is the 20mm wall thickness area, 13 is the 30mm wall thickness area, 14 is the 40mm wall thickness area, 15 is the 50mm wall thickness area, 16 is the riser, 17 is the patch, 18 is the chill, 21 is the first temperature measuring point, 22 is the second temperature measuring point, 23 is the third temperature measuring point, 24 is the fourth temperature measuring point, 25 is the fifth temperature measuring point, 26 is the sixth temperature measuring point, and 27 is the seventh temperature measuring point. Detailed Implementation

[0026] 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, and 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.

[0027] like Figure 1 As shown, this embodiment provides a method for solving the boundary conditions of shell mold and shell core casting based on a multi-wall thickness temperature measurement model, including the following steps: S1. Obtain the measured temperature curves of the temperature measuring part at each temperature measuring point from the start of casting to complete cooling, and the measured shrinkage volume of the defect comparison part. S2. Establish a numerical simulation model of the multi-wall-thickness temperature measurement model, and set an initial combination of thermophysical parameters for the numerical simulation model; S3. Based on the numerical simulation model, perform numerical simulation of the solidification process and output the simulated temperature curves and simulated shrinkage volume of each temperature measurement point. S4. Calculate the temperature deviation between the simulated temperature curve and the measured temperature curve at each temperature measurement point, and calculate the defect deviation between the simulated shrinkage cavity volume and the measured shrinkage cavity volume. S5. Using each thermophysical parameter as a factor and setting multiple levels, orthogonal experimental design is used to generate multiple combinations of thermophysical parameters. Steps S3 to S4 are executed respectively to obtain the temperature deviation value and defect deviation value corresponding to each parameter combination. S6. Construct a response matrix using the temperature deviation value as the main evaluation index, determine the order of influence of each factor on the temperature deviation value and the optimal level through range analysis, verify the optimal level using the defect deviation value as a constraint, and comprehensively determine the optimal combination of thermophysical parameters. S7. Substitute the optimal combination of thermal property parameters into the numerical simulation model for verification. If the verification result meets the preset convergence condition, output the optimal combination of thermal property parameters; otherwise, adjust the level range of each factor and repeat steps S5 to S7.

[0028] This method simultaneously acquires two types of measured data—temperature curves and shrinkage volume—on the same multi-wall-thickness temperature measurement model. In the parameter inverse calculation, temperature deviation is used as the main index for range analysis to determine the primary and secondary influences and optimal levels of each parameter. Defect deviation is used as a constraint to verify the optimization results, ensuring that the final optimal combination of thermophysical parameters matches the measured results in both the temperature field and defect field dimensions. This effectively avoids the problem of defect prediction distortion caused by relying solely on temperature fitting. Furthermore, this scheme utilizes orthogonal experimental design to cover the multi-parameter coupling space with a finite number of simulations, and gradually narrows the parameter range with an iterative verification mechanism, making the solution process both computationally efficient and convergently reliable.

[0029] The following provides a more detailed explanation of each step in the above method and its related technical features. like Figure 2 As shown, the multi-wall-thickness temperature measurement model has a symmetrical structure; the temperature measurement part 1 and the defect comparison part 2 have the same structure and are symmetrically arranged on both sides of the gating 3; Both the temperature measuring unit 1 and the defect comparison unit 2 include multiple wall thickness zones with different wall thickness values; a riser 16 is provided above the wall thickness zone with the largest wall thickness value, and a pad 17 is provided at the bottom of the riser 16; and chills 18 are provided on the side of some wall thickness zones.

[0030] Furthermore, the temperature measuring points are set on the temperature measuring part, including: measuring points set at the center of each wall thickness area, the insertion depth of the measuring points being half of the corresponding wall thickness; measuring points set at the center of each chill side and extending into the cavity surface; and measuring points set at the patch.

[0031] Furthermore, there are 10mm wall thickness zone 11, 20mm wall thickness zone 12, 30mm wall thickness zone 13, 40mm wall thickness zone 14 and 50mm wall thickness zone 15; chills 18 are provided on the sides of 30mm wall thickness zone 13 and 50mm wall thickness zone 15.

[0032] like Figure 3 As shown, the specific distribution of temperature measuring points in this embodiment includes: a first temperature measuring point 21 is located at the center of the 10mm wall thickness area, with an insertion depth of 5mm; a second temperature measuring point 22 is located at the center of the 20mm wall thickness area, with an insertion depth of 10mm; a third temperature measuring point 23 is located at the center of the 30mm wall thickness area, with an insertion depth of 15mm; a fourth temperature measuring point 24 is located at the center of the 40mm wall thickness area, with an insertion depth of 20mm; a fifth temperature measuring point 25 is located at the center of the side of the chill in the 30mm wall thickness area, with an insertion depth of 5mm; a sixth temperature measuring point 26 is located at the center of the side of the chill in the 50mm wall thickness area, with an insertion depth of 5mm; and a seventh temperature measuring point 27 is located at the patch, with an insertion depth of 5mm.

[0033] Explanation of the working process of the multi-wall thickness temperature measurement model: During casting, molten steel enters through the gating system and splits into two, simultaneously being injected into the temperature measuring section and the defect comparison section. Since the structures on both sides are completely identical, the filling and solidification processes of the molten steel are the same on both sides. The temperature measuring section is used to collect temperature change data during the solidification process. The temperature measuring holes on its shell mold are equipped with thermocouples, which can continuously record the temperature signals of each measuring point after the start of casting. The defect comparison section does not have thermocouples installed during the casting process. The formation state of defects inside is not affected by the temperature measuring holes and thermocouples. After the casting cools, the casting on this side can be cut to truly reflect the formation of shrinkage cavities and porosity defects under this process condition.

[0034] The temperature measuring part and the defect comparison part each include five gradient wall thickness zones of 10mm to 50mm, the thin wall zone solidifies first and the thick wall zone solidifies later, so as to simulate the difference in solidification behavior under different wall thickness conditions; the riser arranged above the 50mm wall thickness zone is used to store high-temperature metal liquid to compensate for the solidification shrinkage of the thick wall zone, and the subsidy at the bottom of the riser is used to guide the feeding channel and facilitate subsequent riser cutting; the chills arranged at the side of the 30mm wall thickness zone and the 50mm wall thickness zone are used to accelerate the local solidification speed and form a temperature gradient of sequential solidification; the temperature measuring points are distributed at the center of each wall thickness zone (the depth of extension is one half of the corresponding wall thickness), the center of the chill side surface (the extension is to the surface of the cavity) and the subsidy, respectively, to collect temperature change data of the center of different wall thickness, the chill affected area and the riser feeding area, and to form a correspondence with the defect observation results of the defect comparison part, so as to provide data support for the inversion and verification of the thermophysical parameters.

[0035] The embodiment S1 acquires the measured temperature curve of the temperature measuring part at each temperature measuring point from pouring to complete cooling, and the measured shrinkage cavity volume of the defect comparison part; The measured temperature curve is acquired by: respectively installing each thermocouple at the corresponding temperature measuring point position of the temperature measuring part, continuously collecting the temperature signal of each temperature measuring point from the start of pouring to the complete cooling of the casting through the data acquisition system, and generating the data sequence of the temperature change of each temperature measuring point with time; Further, the measured shrinkage cavity volume is acquired by: cutting the defect comparison part along the preset cross section, image acquisition and defect area identification are performed on the cut cross section, the area of the defect area is counted, and the measured shrinkage cavity total volume is calculated by volume integration method combined with the cutting interval.

[0036] The embodiment S2 establishes a numerical simulation model of the multi-wall thickness temperature measuring model, and sets an initial thermophysical parameter combination for the numerical simulation model; The numerical simulation model of the multi-wall thickness temperature measuring model is established by: establishing a three-dimensional geometric model according to the geometric size of the multi-wall thickness temperature measuring model, performing mesh division on the three-dimensional geometric model, and generating a mesh file for finite element or finite difference solution; setting the solution region as the casting region and the sand shell region, setting the initial condition as the pouring temperature, and setting the boundary condition as the interface heat transfer condition of the casting and the sand shell, the heat transfer condition of the outer surface of the sand shell and the air, and the heat transfer condition of the exposed surface of the casting and the air; The specific method for setting the initial combination of thermal physical parameters for the numerical simulation model is: according to the material type and process conditions adopted by the multi-wall thickness temperature measurement model, an initial value within the experience value range is given to each thermal physical parameter to form an initial combination of thermal physical parameters; the thermal physical parameters include casting density, casting specific heat, casting thermal conductivity, casting viscosity, liquidus temperature, solidus temperature, phase change shrinkage, liquid shrinkage, phase change latent heat, sand shell density, sand shell specific heat, sand shell thermal conductivity, interfacial heat transfer coefficient of the casting and the sand shell, interfacial heat transfer coefficient of the sand shell and the air, and interfacial heat transfer coefficient of the casting and the air; wherein the initial combination of thermal physical parameters is used for the first numerical simulation calculation in step S3, and is also the basis for the parameter value range in the orthogonal experimental design in step S5.

[0037] In this embodiment S3, the solidification process is numerically simulated based on the numerical simulation model, and the simulated temperature curve and the simulated shrinkage cavity volume of each temperature measurement point are outputted. The solidification process numerical simulation is based on the temperature field heat conduction equation:

[0038] wherein, is the material density, is the specific heat, T is the temperature, t is the time, k is the thermal conductivity, and Q is the phase change latent heat release term.

[0039] This step directly correlates the thermal physical parameters with the temperature field and defect formation of the solidification process through numerical simulation, so that the simulation results can be directly compared with the measured data in step S1.

[0040] In this embodiment S4, the temperature deviation value between the simulated temperature curve and the measured temperature curve of each temperature measurement point is calculated, and the defect deviation value between the simulated shrinkage cavity volume and the measured shrinkage cavity volume is calculated. The temperature deviation value E is expressed as:

[0041] wherein, n is the total number of temperature measurement points, is the pouring start time, is the complete cooling time, is the simulated temperature of the i-th temperature measurement point at time t, is the measured temperature of the i-th temperature measurement point at time t.

[0042] Further, the defect deviation value D is expressed as:

[0043] wherein, is the simulated shrinkage cavity volume, is the measured shrinkage cavity volume.

[0044] It transforms the differences between simulation and actual measurement in the two dimensions of temperature field and defect field into a single quantifiable numerical index, providing a clear numerical basis for subsequent evaluation of parameter quality and range analysis.

[0045] In this embodiment, S5, using each thermophysical parameter as a factor and setting multiple levels, orthogonal experimental design is used to generate multiple sets of thermophysical parameter combinations. Steps S3 to S4 are executed respectively to obtain the temperature deviation value and defect deviation value corresponding to each set of parameter combinations. In this step, all the thermophysical properties to be inversely determined are given by S2. Based on the actual physical meaning of each parameter and engineering experience, a reasonable range of values ​​is defined for each parameter. Within this range, three representative values ​​of low, medium and high are uniformly selected as the three levels of the parameter. Subsequently, an orthogonal experimental design method was adopted, and it was selected when there were 13 parameters to be inversely determined. Orthogonal array (27 sets of experiments in total), selected when the number of parameters to be inversely determined is expanded to 15. The orthogonal array (a total of 81 sets of experiments) forms a complete combination of thermophysical parameters, where each set of experiments corresponds to a specific level of all parameters. Through the balanced and distributed characteristics of the orthogonal array, only a small number of experimental sets are needed to cover various combinations of multiple parameters changing simultaneously, reducing the workload of subsequent simulation calculations and ensuring the comparability of each parameter level in statistical analysis. It utilizes the balanced and distributed characteristics of orthogonal arrays to cover the main combinations of multiple parameters changing simultaneously with a limited number of simulations, thereby significantly reducing the computational workload while ensuring the comparability of statistical analysis.

[0046] In this embodiment S6, a response matrix is ​​constructed using the temperature deviation value as the main evaluation index. The order of influence of each factor on the temperature deviation value and the optimal level are determined by range analysis. The optimal level is verified by using the defect deviation value as a constraint condition. The optimal combination of thermal property parameters is determined comprehensively. The response matrix includes: row indices based on the experiment numbers of each group in the orthogonal experiment, column indices based on each thermophysical parameter and its level number, and the temperature deviation value corresponding to each group of experiments. Construct a response matrix R for matrix elements, where matrix elements This represents the temperature deviation value corresponding to the p-th factor in the q-th experiment group; Furthermore, for each factor p in the response matrix, the average value of the corresponding temperature deviation at each level l is calculated. The range of a factor is the difference between the maximum and minimum values ​​among the average values ​​of each level. :

[0047] range value The greater indicates that the factor p has more significant influence on the temperature deviation value. According to the range value, the factors are sorted to determine the primary and secondary order of the influence of each factor. For each factor, the level that minimizes the average temperature deviation value is selected as the preferred level of the factor.

[0048] Further, the specific way of verifying the preferred level by taking the defect deviation value as a constraint condition is: comparing the defect deviation value corresponding to the preferred level of each factor with the preset defect tolerance threshold value. If , the preferred level combination is determined as the optimal thermal physical property parameter combination by verification; if , the range value of each factor is adjusted in turn according to the order from large to small, and the defect deviation value is recalculated until the defect deviation value satisfies .

[0049] By range analysis, the primary and secondary order of the influence of each thermal physical property parameter on the temperature deviation value and the preferred level of each factor are determined, and the preferred level combination is verified by taking the defect deviation value as a constraint condition to comprehensively determine the optimal thermal physical property parameter combination. This step realizes the collaborative optimization of the temperature field fitting accuracy and the defect prediction accuracy, and ensures that the finally determined thermal physical property parameter combination has good accuracy in both temperature field and defect prediction.

[0050] In this embodiment S7, the optimal thermal physical property parameter combination is substituted into the numerical simulation model for verification. If the verification result satisfies the preset convergence condition, the optimal thermal physical property parameter combination is output; otherwise, the level range of each factor is adjusted and steps S5 to S7 are repeated.

[0051] Here, the preset convergence condition includes a temperature convergence condition and a defect convergence condition. The temperature convergence condition is that the average absolute deviation between the simulation temperature curve of each temperature measuring point obtained by verification simulation and the measured temperature curve is not more than a first threshold value. The defect convergence condition is that the relative deviation between the shrinkage cavity volume obtained by verification simulation and the measured shrinkage cavity volume is not more than a second threshold value.

[0052] Further, the specific way of adjusting the level range of each factor is: taking the value of each factor in the current optimal thermal physical property parameter combination as the center, narrowing the level range of each factor to one half to two-thirds of the original range, and resetting the low, medium and high levels before repeating steps S5 to S7.

[0053] ​​Further, the verified optimal thermal physical parameter combination is stored in the parameter library in association with corresponding process condition information, including raw sand type, binder type, box burying mode, coating type and pouring temperature range, for direct calling in numerical simulation under the same process condition.

[0054] The shell mold shell core casting boundary condition solving method based on the multi-wall thickness temperature measurement model in the embodiment can simultaneously obtain temperature field data and defect formation data in a single experiment by constructing a standardized temperature measurement model covering the main wall thickness range and typical process elements of shell mold shell core casting; taking the measured data as a benchmark, the method can solve a multi-parameter coupling system by combining numerical simulation and orthogonal experiment design, taking temperature deviation value as a main index and defect deviation value as a constraint condition, and forming a closed loop optimization through verification and iterative adjustment; the method can accurately solve the thermal physical parameter combination suitable for the actual production of shell mold shell core casting, and provide accurate boundary condition input for numerical simulation of shell mold shell core casting, thereby improving the simulation accuracy.

[0055] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the system disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0056] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for solving boundary conditions in shell mold and core casting based on a multi-wall thickness temperature measurement model, characterized in that, The multi-wall-thickness temperature measurement model includes a temperature measurement section and a defect comparison section, wherein the temperature measurement section is provided with multiple temperature measurement points; the method includes the following steps: S1. Obtain the measured temperature curves of the temperature measuring part at each temperature measuring point from the start of casting to complete cooling, and the measured shrinkage volume of the defect comparison part. S2. Establish a numerical simulation model of the multi-wall-thickness temperature measurement model, and set an initial combination of thermophysical parameters for the numerical simulation model; S3. Based on the numerical simulation model, perform numerical simulation of the solidification process and output the simulated temperature curves and simulated shrinkage volume of each temperature measurement point. S4. Calculate the temperature deviation between the simulated temperature curve and the measured temperature curve at each temperature measurement point, and calculate the defect deviation between the simulated shrinkage cavity volume and the measured shrinkage cavity volume. S5. Using each thermophysical parameter as a factor and setting multiple levels, orthogonal experimental design is used to generate multiple combinations of thermophysical parameters. Steps S3 to S4 are executed respectively to obtain the temperature deviation value and defect deviation value corresponding to each parameter combination. S6. Construct a response matrix using the temperature deviation value as the main evaluation index, determine the order of influence of each factor on the temperature deviation value and the optimal level through range analysis, verify the optimal level using the defect deviation value as a constraint, and comprehensively determine the optimal combination of thermophysical parameters. S7. Substitute the optimal combination of thermal property parameters into the numerical simulation model for verification. If the verification result meets the preset convergence condition, output the optimal combination of thermal property parameters; otherwise, adjust the level range of each factor and repeat steps S5 to S7.

2. The method for solving the boundary conditions of shell mold and shell core casting based on a multi-wall thickness temperature measurement model according to claim 1, characterized in that, The multi-wall-thickness temperature measurement model has a symmetrical structure; the temperature measurement part and the defect comparison part have the same structure and are symmetrically arranged on both sides of the gating. Both the temperature measuring section and the defect comparison section include multiple wall thickness zones with different wall thickness values; a riser is provided above the wall thickness zone with the largest wall thickness value, and a patch is provided at the bottom of the riser; and chills are provided on the side of some wall thickness zones.

3. The method for solving the boundary conditions of shell mold and shell core casting based on a multi-wall thickness temperature measurement model according to claim 1, characterized in that, The temperature measuring points are set on the temperature measuring part, including: measuring points set at the center of each wall thickness area, the insertion depth of the measuring points being half of the corresponding wall thickness; measuring points set at the center of each chill side and extending into the cavity surface; and measuring points set at the patch.

4. The method for solving the boundary conditions of shell mold and shell core casting based on a multi-wall thickness temperature measurement model according to claim 1, characterized in that, The wall thickness zones are sequentially 10mm, 20mm, 30mm, 40mm, and 50mm; the chills are disposed on the sides of the 30mm and 50mm wall thickness zones.

5. The method for solving the boundary conditions of shell mold and shell core casting based on a multi-wall thickness temperature measurement model according to claim 1, characterized in that, In S3, the numerical simulation of the solidification process is based on the heat conduction equation of the temperature field: in, For material density, denoted as , where is the specific heat, T is the temperature, t is the time, k is the thermal conductivity, and Q is the latent heat release term of the phase change.

6. The method for solving the boundary conditions of shell mold and shell core casting based on a multi-wall thickness temperature measurement model according to claim 1, characterized in that, In S4, the temperature deviation value E is expressed as: Where n is the total number of temperature measurement points, The moment the pouring begins. The moment of complete cooling, Let be the simulated temperature of the i-th temperature measuring point at time t. Let be the measured temperature of the i-th temperature measuring point at time t.

7. The method for solving the boundary conditions of shell mold and shell core casting based on a multi-wall thickness temperature measurement model according to claim 1, characterized in that, In S4, the defect deviation value D is represented as: in, To simulate the shrinkage volume of shrinkage cavities, This is the actual measured volume of shrinkage cavity.

8. The method for solving the boundary conditions of shell mold and shell core casting based on a multi-wall thickness temperature measurement model according to claim 1, characterized in that, In S5, each thermophysical parameter is set to three levels—low, medium, and high—based on empirical value ranges. orthogonal arrays or Orthogonal arrays generate orthogonal experimental schemes, with each group of experiments corresponding to a set of thermophysical parameter combinations.

9. The method for solving the boundary conditions of shell mold and shell core casting based on a multi-wall thickness temperature measurement model according to claim 1, characterized in that, In step S6, range analysis is used to determine the order of influence and optimal level of each factor on the temperature deviation value, including: For each factor in the response matrix, calculate the average temperature deviation value corresponding to the factor at each level, take the difference between the minimum and maximum values ​​of the average values ​​at each level as the range value of the factor, sort the factors according to the size of the range value, and determine the order of influence of each factor. For each factor, the level that minimizes the average temperature deviation is selected as the preferred level for that factor.

10. The method for solving the boundary conditions of shell mold and shell core casting based on a multi-wall thickness temperature measurement model according to claim 1, characterized in that, In step S6, the optimal level is verified using the defect deviation value as a constraint, including: The defect deviation value corresponding to the optimal combination of each factor is compared with the preset defect deviation tolerance threshold. If the defect deviation value is less than the tolerance threshold, the optimal combination of factors passes the verification and is directly output as the optimal combination of thermophysical parameters.

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