Method for predicting thermal stress distribution and defects of casting based on casting simulation
By constructing a casting data model and using numerical simulation software to predict the thermal stress distribution of castings, the problem of difficult to accurately predict the thermal stress concentration area of castings was solved, and the casting quality and production efficiency were improved.
Patent Information
- Application Number
- CN202510761260.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-09
- Publication Date
- 2025-10-17
AI Technical Summary
Existing technologies make it difficult to accurately predict the areas of thermal stress concentration in castings in the early stages of casting, which may lead to defects such as thermal cracking and deformation in the castings, increasing unnecessary costs and time consumption.
By constructing a casting data model, using numerical simulation software for meshing, setting material thermodynamic parameters and boundary conditions, calculating the temperature field and stress distribution, and using the Green-Eisen equation and stress correction formula, key areas of thermal stress concentration are identified.
It achieves accurate prediction of thermal stress concentration areas in the early stage of casting mold opening, reduces the occurrence of casting defects, and improves casting quality stability and production efficiency.
Smart Images
Figure CN120805401A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application relates to a method for predicting the thermal stress distribution and defects of a casting based on casting simulation, and belongs to the technical field of designing and preparing composite materials by casting. BACKGROUND
[0002] Computer simulation of the casting process, as a cutting-edge technology in the field of material science and manufacturing engineering, has made remarkable achievements worldwide after decades of vigorous development. The core of this technology lies in the accurate simulation and prediction of physical phenomena in the casting process using advanced computer algorithms and numerical analysis methods, thereby greatly improving the scientific nature and efficiency of casting production.
[0003] The traditional mode of casting production often relies on the experience and intuition of craftsmen, while the introduction of CAE technology marks the gradual transformation of the industry towards scientific theory guidance. Through CAE technology, process personnel can accurately predict various defects (such as shrinkage, porosity, hot cracking, deformation, etc.) that may occur in the casting before actual casting, and accordingly develop targeted preventive measures. This forward-looking management approach not only significantly improves the quality stability of the casting, but also greatly reduces the additional costs and time consumption caused by repeated trial molding and mold repair.
[0004] Thermal stress is an important factor that cannot be ignored in the casting process, and has a crucial impact on the quality and performance of the casting. During the solidification and cooling of the metal, due to the difference in cooling speed of each part of the casting, thermal stress will be generated inside. If this stress is not properly handled, it may cause various casting defects, such as hot cracking and deformation. Hot cracking is the most common defect caused by thermal stress, and when the internal thermal stress of the casting is too large, cracks may eventually form, severely affecting the mechanical properties and service life of the casting. At the same time, thermal stress may also cause the casting to deform, especially in thin-walled or complex-shaped castings, due to the uneven distribution of thermal stress, the casting may twist or warp, thereby affecting its assembly and use effect.
[0005] If all the thermal stress concentration areas analyzed by simulation and simulation are optimized and adjusted before actual pouring, it will undoubtedly increase unnecessary costs. Therefore, there is an urgent need for a method that can accurately predict the thermal stress concentration area of the casting based on simulation software before the casting is opened. SUMMARY
[0006] To solve or partially solve the problems in the related art, the application provides a method for predicting the thermal stress distribution and defects of a casting based on casting simulation, which predicts the thermal stress caused by the large heat transfer coefficient between different materials in the actual casting during preparation, thereby producing larger defects in the stress concentration area.
[0007] The application provides a method for predicting thermal stress distribution and defects of a casting based on casting simulation, comprising the following steps: (1) constructing a casting data model; (2) importing the casting data model into a numerical simulation software, constructing a triangular mesh through a surface mesh tool, and then constructing a tetrahedral mesh through a volume mesh tool; (3) setting thermodynamic parameters and boundary conditions of the casting material in the numerical simulation software (Procast, ansys, etc.); (4) selecting an initial temperature T1 and an end temperature Tn based on actual requirements of a casting process, n ≥ initial temperature / m, m is a multiple of 5, the maximum value of m is 100, n is a positive integer, and a series of temperature points T1, T2, …, Tn are determined in the interval at equal intervals; and based on the thermodynamic parameters and boundary conditions set in step (3), the temperature field distribution cloud diagram and the temperature change curve of the casting at each temperature point are extracted and generated through a temperature field calculation module of the simulation software; (5) based on the elastic modulus and the thermal expansion coefficient of the casting, the stress correction formula is obtained by correcting the to-be-determined coefficient according to the quantitative relationship of the Green-Eisen equation, and then the stress change is calculated through the stress correction formula according to the temperature field distribution cloud diagram and the temperature change curve of the lining at each temperature point, and the stress distribution cloud diagram and the stress change curve are obtained; (6) the defect volume is obtained according to the defect correction formula, and the thermal stress size generated in different regions of the casting is obtained according to the stress distribution cloud diagram, and the key region with concentrated thermal stress is identified as the defect distribution position.
[0008] The initial temperature is a pouring temperature.
[0009] Preferably, the casting data model comprises a geometric configuration of the casting.
[0010] Preferably, the thermodynamic parameters comprise a thermal expansion coefficient, an elastic modulus and a Poisson's ratio.
[0011] The thermal expansion coefficient can be calculated according to a preset pouring temperature, a set casting material composition and a prediction model.
[0012] The elastic modulus is mainly related to the binding force between atoms. When the alloy composition of the casting is determined, different objects that can be generated are obtained according to the composition, and then the elastic modulus is obtained, and the Young's modulus is obtained according to the Poisson's ratio obtained by experiment.
[0013] Preferably, the boundary conditions comprise a contact interface and heat exchange characteristics between the casting and the mold, a cooling temperature and cooling conditions.
[0014] Preferably, the initial temperature T1 is the pouring temperature, and the end temperature Tn is the temperature required at the end of solidification. According to the calculation step, the temperature range is divided into several nodes, and the nodes T1, T2,..., Tn are split, n is the pouring temperature / 5, and the stress change and defect formation reason at any time point in the casting process can be observed.
[0015] Preferably, the Green-Eisen equation quantitative relationship is , E is the elastic modulus, is the linear thermal expansion coefficient, is the molar mass of the material, and V is the cavity volume of the pouring mold.
[0016] Preferably, the stress correction formula is σ = K·E·α ·ΔT, K is a to-be-determined coefficient; σ is the thermal stress, Pa; E is the elastic modulus of the material, Pa; α is the linear thermal expansion coefficient of the material, 1 / ℃; and ΔT is the difference between the temperature T at a certain time and the initial temperature T0, ℃.
[0017] Preferably, the defect correction formula is V = K·σ 2 ; wherein V is the defect volume.
[0018] The technical scheme provided by the present application can include the following beneficial effects: the present application establishes a three-dimensional model of the casting, and then performs adaptive mesh division; material mechanics parameters such as thermal expansion coefficient, elastic modulus, Poisson's ratio, and heat exchange coefficient are set; an initial critical temperature threshold T1 and a termination temperature threshold Tn are defined, n threshold sequences T1, T2,..., Tn are generated in the interval according to a preset gradient, nonlinear static analysis is performed respectively, and n sets of temperature field distribution cloud diagrams and temperature change curves are output; the calculation results of the n sets of temperature points selected above are substituted into the stress calculation model, the conversion relationship between thermal stress and temperature is obtained according to σ = K·E·α ·ΔT, the stress distribution cloud diagram and the stress change curve are obtained through the temperature change curve and the temperature distribution cloud diagram, and the stress distribution cloud diagram and the stress change curve are obtained. 2 Each temperature field threshold is input into the simulation model as a failure criterion, the defect volume and position are calculated, and a scientific basis is provided for subsequent casting process optimization and casting quality control. BRIEF DESCRIPTION OF DRAWINGS
[0019] Figure 1 is a flowchart of the method for predicting actual casting shrinkage hole defects based on the casting simulation software.
[0020] Figure 2 is a temperature field distribution cloud diagram of a simulated lining plate in Example 1.
[0021] Figure 3 is a temperature field change curve of a certain point on the surface of the simulated lining plate in Example 1.
[0022] Figure 4 This is a cloud diagram of the stress field distribution of the liner obtained by simulation in Example 1. The left picture is a three-dimensional macroscopic picture, and the right picture is a bottom view.
[0023] Figure 5 This is the stress field change curve of a certain point on the liner surface obtained by simulation in Example 1.
[0024] Figure 6 This is a diagram of the key shrinkage defect locations obtained in Example 1.
[0025] Figure 7 This is a diagram of the locations of key shrinkage defects obtained in the experimental production of Example 1.
[0026] Figure 8 This is a temperature field distribution cloud diagram obtained at different filling stages when calculating the composite material obtained by simulation in Example 2.
[0027] Figure 9 This is the temperature field change curve of a certain point in the composite zone obtained by simulation in Example 2.
[0028] Figure 10 This is a cloud diagram of the stress distribution on the surface of the composite area when calculating the composite material obtained by simulation in Example 2.
[0029] Figure 11 This is the stress field change curve of a certain point in the composite zone obtained by simulation in Example 2.
[0030] Figure 12 This is a diagram of the key shrinkage cavity defect location obtained in Example 2. The left figure is a three-dimensional defect schematic diagram, and the right figure is a side view of the defect schematic diagram.
[0031] Figure 13 This is the scanning result of the solidification shrinkage cavity defect inside the actual casting in Example 2. DETAILED DESCRIPTION
[0032] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited to the contents described above.
[0033] Example 1 Taking a low-alloy liner (0.39C - 0.95Si - 0.6Mn - 4.5Cr - 0.8Mo – 92.76 Fe) developed by a company as an example, a method for predicting the thermal stress distribution and defects of actual castings based on casting simulation is described. The specific steps include: (1) Construct the lining data model: Use 3D drawing software to draw the lining into a 3D model at a 1:1 scale.
[0034] (2) The built lining data model is imported into the professional numerical simulation software Procast, and fine mesh partitioning operation is performed, and multi-polarization is carried out through the surface mesh and volume mesh tools. First, the mesh is drawn into the finest triangular mesh through the surface mesh tool, and the final mesh number is 456248. Then, the mesh is divided into tetrahedral mesh through the volume mesh tool, and the final mesh number is 48979166, so as to ensure that the mesh density is consistent with the accuracy requirements of the thermal stress analysis.
[0035] (3) The thermodynamic parameters and boundary conditions are set in the cast module of Procast. There is heat exchange between the lining casting and the sand mold, the heat exchange coefficient is set to 750 W / (m 2 ·℃), the elastic modulus is 202 GPa, the thermal expansion coefficient is 12.2×10 -6 / ℃, the Poisson's ratio is 0.28, and the riser and the runner are directly in contact with the air, and the cooling condition is set to air cooling. Accurate physical boundary is set for thermal stress analysis.
[0036] (4) Based on the actual requirements of the casting process, the pouring temperature is set to 1500℃ and the end temperature is room temperature 20℃, and 50℃ is an interval in this interval, a series of temperature points T1, T2,..., Tn (n=1500 / 50) are determined in equal intervals. Combined with the thermodynamic parameters and boundary conditions set in step (3), the temperature field distribution cloud diagram of the casting at each temperature point is calculated by the boundary element method iteration relationship in the cast module, as shown in Figure 2 , and the temperature change curve is shown in Figure 3 .
[0037] (5) According to the elastic modulus of the lining of 202 GPa and the linear thermal expansion coefficient of 12.2×10⁻ 6 / ℃, according to the quantitative relationship of Green-Eisen equation ( , E is the elastic modulus, is the linear thermal expansion coefficient, is the molar mass of the material, which is 55.69 g / mol, and V is the cavity volume of the pouring mold, which is 5.12*10 8 mm 3 ). The undetermined coefficient is corrected, and the undetermined coefficient k of the lining is 2.27×10 4 , and the final correction formula is σ = 2.27×10 4 *1.96*10 5 *1.2*10 -5 ΔT. Through the obtained calculation formula and the temperature field distribution cloud diagram and the temperature change curve at each temperature point, the thermal stress change is calculated, and the stress distribution cloud diagram is obtained as shown in Figure 4 , and the temperature change curve is shown inFigure 5 The stress change curve is shown.
[0038] (6) Assuming that the defect is a spherical hole and the material is in an elastic or plastic state, according to the energy or strain energy release rate theory, the defect volume and stress are in a power law relationship, and according to the obtained modified undetermined coefficient k = 2.27 × 10 4 , the defect correction formula is V = 2.27 × 10 4 σ 2 , V is the defect volume, and the thermal stress size generated in different regions of the casting is obtained according to the stress distribution cloud diagram, and the key region of thermal stress concentration is identified as the defect distribution position.
[0039] The simulation prediction of Example 1 can accurately predict the position of the defect, and plays a guiding role in actual production. Figure 6 It can be seen that a larger volume of shrinkage porosity defects will be generated at the center of the casting, so the center position is taken as the core position of the possible fracture failure of the backing plate, which is verified by experiments. Figure 7 It is found that cracks and fractures occur at the center of the backing plate, which is consistent with the simulation results, proving the accuracy of the simulation.
[0040] Example 2 A method for predicting the thermal stress distribution and defect prediction of an actual casting based on casting simulation is described by taking a ceramic particle reinforced steel-based composite material (steel-based material: 3C - 1Si - 1.6Mn- 23Cr – 1.3Mo - 70.1Fe) prepared by a preform as an example, which specifically includes the following steps.
[0041] S1: Build a data model, first use a three-dimensional drawing software to draw a three-dimensional model of the structure of the matrix and the preform according to a 1:1 scale.
[0042] S2: Import the built data model into the professional numerical simulation software Procast, and perform a fine mesh partitioning operation, and polarize by the surface mesh and volume mesh tools. First, draw the mesh into a triangular mesh as fine as possible by the surface mesh tool, and set the mesh size to 1 for the preform which is the main research position, and set the mesh size to 10 for other parts. The final number of meshes is 68797, and then divide the mesh into tetrahedral mesh by the volume mesh tool, and the final number of meshes is 687641, to ensure that the mesh density meets the accuracy requirements of thermal stress analysis.
[0043] S3: Set the thermodynamic parameters and boundary conditions in the cast module of Procast, and set the heat exchange between the casting and the sand mold, and set the heat exchange coefficient to 750 W / (m 2℃); the ceramic particle preform and the sand mold also have heat exchange, and the heat exchange coefficient is set to 750 W / (m 2 ℃); the ceramic particle preform and the high-chromium cast iron matrix have a large heat exchange coefficient, and the heat exchange coefficient is set to 1000 W / (m 2 ℃). The elastic modulus of the matrix is 303 GPa, the thermal expansion coefficient is 6.9*10 -6 / ℃, and the Poisson's ratio is 0.23; the elastic modulus of the preform is 105 GPa, the thermal expansion coefficient is 3.4*10 -6 / ℃, and the Poisson's ratio is 0.12; the riser and the gate are directly in contact with air, and the cooling condition is set to air cooling.
[0044] S4: Based on the actual requirements of the casting process, the pouring temperature is set to 1600°C and the end temperature is room temperature 20°C, and 50°C is an interval in this interval, a series of temperature points T1, T2, …, Tn (n=1600 / 50) are determined in an equal interval manner, the thermodynamic parameters and boundary conditions set in S3 are combined, and the boundary element method in the cast module is iteratively calculated to obtain the temperature field distribution cloud diagram of the casting at each temperature point as shown in Figure 8 , and the temperature change curve as shown in Figure 9 .
[0045] S5: The elastic modulus of the casting is 303 GPa, and the thermal expansion coefficient is 6.9*10⁻ 6 / ℃, according to the quantitative relationship of the Green-Eisen equation ( , E is the elastic modulus, is the linear thermal expansion coefficient, is the molar mass of the material, which is 53.98 g / mol, and V is the cavity volume of the pouring mold, which is 2.22*10 6 mm 3 ). The undetermined coefficient is corrected, and finally the undetermined coefficient k of the liner plate is 85.98, and the correction formula V=85.98*1.88*10 5 *1.2*10 -5 ΔT is obtained. The ceramic particles are WC particles, which are single components and do not need to be corrected. The thermal stress change is calculated based on the obtained calculation formula, the temperature field distribution cloud diagram at each temperature point, and the temperature change curve, and it is found that stress concentration occurs in the reaction zone of the two materials.
[0046] S6: Assuming that the defect is a spherical hole, the material is in an elastic or plastic state, and according to the energy or strain energy release rate theory, the defect volume and stress have a power law relationship, according to the obtained correction undetermined coefficient k=85.98, the defect correction formula is V=85.98σ2 V is the defect volume, the defect volume is obtained, and the thermal stress generated in different regions of the casting is obtained according to the stress distribution cloud map, and the key region of thermal stress concentration is identified as the defect distribution position.
[0047] The defects generated in the preparation of the composite material due to the larger stress are simulated and predicted by example 2, the accuracy of the finite element simulation is verified in combination with actual production, and the accuracy of the simulation is verified. Figure 6 It can be seen that the composite area at the center position will generate more intensive shrinkage defects, so the center position is taken as the main research position, the causes are researched, and the causes are verified through experiments, Figure 7 It is found through experiments that shrinkage and porosity defects appear at the center position, which is consistent with the simulation results, proving the accuracy of the simulation.
[0048] The above has described the embodiments of the present application, the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles, practical application or improvement of the technology in the market of the embodiments, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.
Claims
1. A method for predicting thermal stress distribution and defects of castings based on casting simulation, characterized by: The steps include: (1) Building a casting data model; (2) Import the casting data model into the numerical simulation software, construct a triangular mesh using the surface mesh tool, and then construct a tetrahedral mesh using the volume mesh tool; (3) Set the thermodynamic parameters and boundary conditions of the casting material in numerical simulation software (Procast, ANSYS, etc.); (4) Based on the actual requirements of the casting process, select the initial temperature T1 and the end temperature Tn, n ≥ initial temperature / m, m is a multiple of 5, the maximum value of m is 100, and n is a positive integer, and determine a series of temperature points T1, T2, ..., Tn at equal intervals within this interval; Based on the thermodynamic parameters and boundary conditions set in step (3), the temperature field calculation module of the simulation software is used to extract and generate the temperature field distribution cloud map and temperature change curve of the casting at each temperature point; (5) Based on the elastic modulus and thermal expansion coefficient of the casting, the undetermined coefficients are corrected according to the quantitative relationship of the Green-Eisen equation, and then the stress correction formula is obtained. Then, based on the temperature field distribution cloud map and temperature change curve at each temperature point of the liner, the thermal stress change is calculated through the stress correction formula to obtain the stress distribution cloud map and stress change curve; (6) According to the defect correction formula, the defect volume is obtained; and according to the stress distribution cloud map, the thermal stress generated in different areas of the casting is obtained, and the key areas where thermal stress is concentrated are identified as the defect distribution locations.
2. The method for predicting thermal stress distribution and defects of castings based on casting simulation according to claim 1, characterized in that: The casting data model includes the geometric configuration of the casting.
3. The method for predicting thermal stress distribution and defects of castings based on casting simulation according to claim 1, characterized in that: The thermodynamic parameters include thermal expansion coefficient, elastic modulus and Poisson's ratio; the boundary conditions include the contact interface between the casting and the mold and the heat exchange characteristics, cooling temperature and cooling conditions.
4. The method for predicting thermal stress distribution and defects of castings based on casting simulation according to claim 1, characterized in that: The initial temperature T1 is the pouring temperature, and the end temperature Tn is the temperature required for the molten metal to solidify.
5. The method for predicting thermal stress distribution and defects of castings based on casting simulation according to claim 1, characterized in that: The quantitative relationship of the Green-Issen equation is: , E is the elastic modulus, is the linear thermal expansion coefficient, is the molar mass of the material, and V is the cavity volume of the casting mold.
6. The method for predicting thermal stress distribution and defects of castings based on casting simulation according to claim 1, characterized in that: The stress correction formula is σ= K·E·α·ΔT, where K is the unknown coefficient; σ is the thermal stress, Pa; E is the elastic modulus of the material, Pa; α is the linear thermal expansion coefficient of the material, 1 / °C; and ΔT is the difference between the temperature T at a certain moment and the initial temperature T0, in °C.
7. The method for predicting thermal stress distribution and defects of castings based on casting simulation according to claim 1, characterized in that: The defect correction formula is V=K·σ 2 ; where V is the defect volume.