JP4729943B2 - Solidification analysis method for castings - Google Patents

Description

  The present invention relates to a solidification analysis method for a cast product, and more particularly to a solidification analysis method capable of accurately and quickly predicting the risk of occurrence of a leak path.

  Recently, solidification analysis by a solidification analysis simulation program has been widely performed in order to improve the yield of cast products. In the solidification analysis method disclosed in Patent Document 1 below, the generation position and the volume of a solidification defect called a shrinkage nest can be predicted in a short time in the solidification analysis.

  The operator then solidifies whether the predicted shrinkage can be a factor in forming a leak path between the processing site such as the liquid flow path formed in the casting and the surface of the casting. While looking at the display on which the analysis results are displayed, consider the processing site, the surface of the casting, the positional relationship between the shrinkage cavities, and the distance.

As a result, if there is a concern about the formation of a leak path, the solidification analysis is performed again by changing the casting conditions or the shape of the casting, and the leak path is determined based on the solidification analysis result. Repeat the examination.
JP-A-11-314152

  However, the conventional solidification analysis method does not consider the occurrence of seizure parts (parts where the temperature is particularly high with respect to the surroundings). Therefore, there is a problem that there is a limit and variation in the accuracy of predicting the occurrence of the leak path.

  In addition, since there is no quantitative indicator for the prediction of the occurrence of a leak path, it is not possible to objectively grasp the degree of risk of occurrence, and the occurrence of a leak path is a vague prediction by an operator. I can only judge.

  Furthermore, since there are so many occurrences of shrinkage cavities for one casting (for example, about 100 in the case of a cylinder), it takes a lot of time to predict the occurrence of leak paths for all of the occurrence points, Workability is not very good.

  The present invention has been made to solve such a conventional problem, and an object thereof is to provide a coagulation analysis method capable of accurately and quickly predicting the risk of occurrence of a leak path.

Coagulation analyzing method of casting according to the present invention for achieving the above object, casting when molten metal is poured into a mold to form the casting was opened mold shrinkage cavities and the mold that occurred until it congeals Both areas of seizure occurring in the mold are obtained by simulation, and the machining area on the inner surface of the fluid path formed in the casting by mold opening and the distance between the shrinkage nest area and seizure area determined by the simulation are calculated. Based on the calculated distances, the fluid flowing through the distribution path leaks from the processing site to the shrinkage site, from the processing site to the seizure site, and from the processing site to the processing site of another distribution channel. Calculate the occurrence risk.

According to the solidification analysis method for a cast product according to the present invention configured as described above, the distance between the processed portion of the inner surface of the fluid path formed in the cast product, the shrinkage nest generated in the cast product, and the seizing portion is determined. Based on the calculation, the risk of occurrence of a leak path that leaks from the processing site to the shrinkage nest, from the processing site to the seizure site, and from the processing site to the processing site of another distribution channel is calculated. Therefore, the accuracy of predicting the occurrence of a leak path is improved and the accuracy is stable.

  Since a quantitative occurrence risk can be obtained, it is possible to objectively determine how much a leak path is likely to occur.

  In addition, the risk of leak path generation is calculated by calculating the distance between the flow path, shrinkage nest, and burn-in part, and the calculation is performed at a high speed, so the prediction of leak path occurrence Can be performed in a short time.

  Hereinafter, the solidification analysis method for a cast product according to the present invention will be described in detail for the first and second embodiments.

[Embodiment 1]
FIG. 1 is a schematic configuration diagram of a coagulation analysis apparatus for performing the coagulation analysis method according to the present invention, FIG. 2 is a process diagram showing the procedure of the coagulation analysis method according to the present invention, and FIG. is there.

  As shown in FIG. 1, the coagulation analyzer 100 includes a coagulation analysis computer 110, a database 120, and a display 130. The solidification analysis computer 110 calculates the risk of occurrence of a leak path according to the solidification analysis program (simulation program) shown in FIG. The database 120 includes casting conditions such as hot water material, temperature, time from pouring to mold opening, mold opening, mold shape, cavity shape, and product information necessary for the solidification analysis computer 110 to execute the solidification analysis program. CAD data such as the final shape of the processed part is stored. The display 130 displays the leakage path occurrence risk calculated by the coagulation analysis computer 110 on the product.

  The coagulation analysis method according to the present invention is executed in the following procedure.

  First, the solidification analysis computer 110 takes out the casting conditions and CAD data stored in the database 120, performs solidification analysis, and obtains the site of the shrinkage cavity that has occurred until the molten metal poured into the product mold solidifies. . Note that a conventional method as disclosed in, for example, Japanese Patent Application Laid-Open No. 11-314152 is used as a method of obtaining the shrinkage nest by solidification analysis. When the result of the coagulation analysis is displayed on the display 130, for example, an image as shown in FIG. 3 is obtained. The image shown in FIG. 3 is a shrinkage distribution obtained when a cylinder is cast as a product. In the cylinder 200, dark portions 210A to 210E are portions where shrinkage cavities (hollow portions) are generated (S1).

  Next, the temperature distribution of the product when the mold is opened is obtained. In addition, the method of calculating | requiring this temperature distribution uses the general method used conventionally. When the result of obtaining the temperature distribution is displayed on the display 130, for example, an image as shown in FIG. 4 is obtained. The image shown in FIG. 4 is a temperature distribution when the cylinder of FIG. 3 is taken out of the mold. In the cylinder 200 and its peripheral part, the whiter the color, the higher the temperature. Therefore, it can be seen that portions 220A to 220C in FIG. 4 are very hot (S2).

  Then, the solidification analysis computer 110 performs an AND operation on the obtained temperature distribution of the product and a preset temperature as a seizure risk temperature (for example, 300 ° C.), and among the products at the time of mold opening, the product is higher than the seizure risk temperature. Extract the part exhibiting temperature. When this process is performed, only the portions 220A to 220C are extracted in FIG. Note that the seizing danger temperature is stored in the coagulation analysis computer 110, but since this temperature varies depending on the product, the optimum temperature is obtained by experiment (S3).

  The solidification analysis computer 110 groups the extracted burn-in portions to obtain a burn-in portion. As described above, the seizing portion is a portion exhibiting a temperature higher than the seizing risk temperature in the product when the mold is opened. Therefore, the burn-in portion to be extracted is either a point, a line, or a surface. If a burn-in part is extracted as a surface, the surface can be regarded as a burn-in part, but usually it is extracted as either a point, a line, or a surface, so it is necessary to recognize a continuous surface as a burn-in part. There is. For this purpose, a grouping process is performed. When this process is performed, as shown in FIG. 5, in a region surrounded by circles 230 </ b> A to 230 </ b> C, a portion that hits the surface of the cylinder is a burn-in portion that is a set of burn-in portions. The reason why the burn-in part is obtained in this manner is that if the burning is caused, the surface of the part causing the burning may be peeled off (S4).

  The above-described steps S1 to S4 constitute a stage for obtaining both the shrinkage nest and the burn-in part by simulation.

  Next, the solidification analysis computer 110 creates an analysis model by superimposing the seizure portion obtained in the above step on the cylinder shape based on the CAD data stored in the database 120. That is, this process is performed to reflect which part of the cylinder is the burn-in part in the cylinder shape. When this process is performed, as shown in FIG. 6, an analysis model is created in which the seizure parts 240A to 240C are superimposed on the surface of the cylinder (S4).

  Then, the solidification analysis computer 110 creates an analysis model by superimposing the machining site on the shape of the cylinder based on the CAD data stored in the database 120. That is, in this process, which part of the cylinder is scraped off by machining is reflected in the shape of the cylinder. When this processing is performed, an analysis model is created in which the processing parts 250A to 250I are superimposed on the cylinder 200 as shown in FIG. 7 (S5).

  The coagulation analysis computer 110 performs the following process on each of the shrinkage nests 210A to 210E obtained in step S1 to obtain a shrinkage nest virtual sphere. First, as shown in FIG. 8, the center of gravity of the shrinkage nest is obtained and the center of gravity is set as the center position P of the shrinkage nest. Next, the volume of the shrinkage nest is obtained, and the radius R of the sphere equivalent to this volume is obtained. The center position P of the phantom sphere 260 obtained in this way is overlapped with the center of gravity of the shrinkage nest 210B (see FIG. 3) that is predicted to actually occur, as shown in FIG. Is considered to be the part where the shrinkage nest has occurred. Although only the phantom sphere 260 for the shrinkage nest 210B is shown in FIG. 9, in reality, this phantom sphere is obtained for all other shrinkage nests (210A, 210C to 210E in FIG. 3), and the cylinder (S6).

  Next, the coagulation analysis computer 110 calculates the mutual distances of the processed part, shrinkage nest part, and seizure part obtained by the above processing. For example, when calculating the distance between the shrinkage nest part and the burn-in part, as shown in FIG. 10, the distances Y1 to Y4 from the surface of the virtual sphere 260 of the shrinkage nest to each burn-in part 220A to 220D are calculated. Although not shown in FIG. 10, similarly, the distance between the shrinkage nest site and the shrinkage nest site, the distance between the seizure site and the seizure site, the distance between the processing site and the shrinkage nest site, the distance between the processing site and the seizure site. Is also obtained (S7).

  The step of calculating the distance between the shrinkage nest part and the burn-in part is configured by the above steps S5 to S7.

  Then, the smallest and second smallest distances between the calculated shrinkage nest part and the burn-in part are extracted, and the smallest and second smallest among the calculated distances between the shrinkage nest part and the processed part Extract things and things. For example, in FIG. 10, the minimum distance (minimum value) Y1 and the second smallest distance (quasi-minimum value) Y3 among the distances between the shrinkage nest part and the burn-in part are obtained. Similarly, the minimum value and the quasi-minimum value of the distance between the shrinkage nest part and the machining part are obtained. Note that the minimum value and the quasi-minimum value are also obtained for the distance between the shrinkage nest site and the shrinkage nest site and the distance between the seizure site and the seizure site (S8).

  The coagulation analysis computer 110 further extracts the minimum value and the quasi-minimum value from the minimum value and the quasi-minimum value of the distances obtained in step S8, and calculates the sum thereof. For example, the minimum value of the burn-in part distance with respect to the focused shrinkage part is Y1 and the quasi-minimum value is Y3, and the minimum value of the processed part distance with respect to the focused shrinkage part is K1 and K2. If Y1 <K1 <Y3 <K2, then Y1 + K1 is calculated, and the sum is taken as the risk of occurrence of a leak path. Therefore, the risk of occurrence of a leak path indicates that the smaller the value, the higher the possibility that a leak path will be formed (S9).

  The step of calculating the risk of occurrence of the leakage path is configured by the above step S9.

  Finally, the coagulation analysis computer 110 predicts in which part the leak path occurs based on the obtained distance between each part and the risk of occurrence of the leak path, as shown in FIG. The route is displayed on the display 130. The leak path is displayed on the display 130 when the value of the risk of occurrence of the leak path is equal to or less than a preset value. Instead of displaying the leak path, the risk of occurrence of the leak path may be simply displayed on the product (S10).

  The processes from S8 to S10 described above are performed for all shrinkage nests, image sticking parts, and processed parts. Therefore, it can be easily understood that there is a possibility that a leak path is generated at a portion where the numerical value of the risk of occurrence of the leak path is small. For example, as shown in FIG. 12, the numerical value of the risk of occurrence of a leakage path in the machining site 250A (the inner peripheral surface of the oil passage cast portion is cut by a predetermined dimension in a later machining) and the shrinkage cavity 210A. If it is very small, there is a possibility that oil flowing through the oil passage may flow into the shrinkage nest part 210A, and furthermore, the shrinkage nest part 210A and the seizure part 240A (the surface may be peeled off due to seizure). If the numerical value of the risk of occurrence of a leak path at is very small, the oil that has flowed into the shrinkage nest 210A may leak out. Therefore, if the numerical value of the risk of occurrence of leak paths between the parts is traced, it is possible to predict the formation of a passage through which oil oozes out from the oil passage as shown in FIG.

  By performing the simulation as described above, the occurrence prediction of the leak path can be accurately performed, and the occurrence prediction can be quantified. Furthermore, since the occurrence of the leakage path is predicted by a computer, the workability is significantly improved as compared with the case where the operator examines it visually.

  In the above embodiment, the risk of occurrence of a leak path is predicted based simply on the distance. For example, when water is flowing in regions located at both ends of the leak path, the leak path The presence of should not be a problem and can be safely ignored. In the second embodiment, when the same quality fluid is flowing in the regions located at both ends of the leak path, the leak path is excluded from the calculation target of the occurrence risk and the prediction accuracy is improved. It is.

[Embodiment 2]
Since the coagulation analyzer according to the present embodiment is almost the same as the configuration of the coagulation analyzer according to the first embodiment, description of the configuration is omitted. However, the database 120 stores data related to the final shape of the fluid path and the type of fluid flowing through the fluid path, which is different from the case of the first embodiment.

  The solidification analysis computer 110 calculates the risk of occurrence of a leak path according to the following procedure in accordance with the solidification analysis program (simulation program) shown in FIG.

  The coagulation analysis method according to the present embodiment is executed according to the following procedure.

  First, the solidification analysis computer 110 shown in FIG. 1 takes out the casting conditions and CAD data stored in the database 120, performs solidification analysis, and shrinkage cavities generated until the molten metal poured into the product mold solidifies. Find the part. Note that, as in the first embodiment, the conventional method is used as a method for obtaining the shrinkage nest by solidification analysis. When the result of the coagulation analysis is displayed on the display 130, for example, an image as shown in FIG. 3 is obtained. The image shown in FIG. 3 is a shrinkage distribution obtained when a cylinder is cast as a product. In the cylinder 200, dark portions 210A to 210E are portions where shrinkage cavities (hollow portions) are generated (S11).

  Next, the temperature distribution of the product when the mold is opened is obtained. In addition, the method of calculating | requiring this temperature distribution uses the general method used conventionally. When the result of obtaining the temperature distribution is displayed on the display 130, for example, an image as shown in FIG. 4 is obtained. The image shown in FIG. 4 is a temperature distribution when the cylinder of FIG. 3 is taken out of the mold. In the cylinder 200 and its peripheral part, the whiter the color, the higher the temperature. Therefore, it can be seen that the portions 220A to 220C in FIG. 4 are very hot (S12).

  Then, the solidification analysis computer 110 performs an AND operation on the obtained temperature distribution of the product and a preset temperature as a seizure risk temperature (for example, 300 ° C.), and among the products at the time of mold opening, the product is higher than the seizure risk temperature. Extract the part exhibiting temperature. When this process is performed, only the portions 220A to 220C are extracted in FIG. In addition, although the seizing danger temperature is stored in the solidification analysis computer 110, since this temperature varies depending on the product, the optimum temperature is obtained by experiment (S13).

  The solidification analysis computer 110 groups the extracted burn-in portions to obtain a burn-in portion. As described above, the seizing portion is a portion exhibiting a temperature higher than the seizing risk temperature in the product when the mold is opened. Therefore, the burn-in portion to be extracted is either a point, a line, or a surface. If a burn-in part is extracted as a surface, the surface can be regarded as a burn-in part, but usually it is extracted as either a point, a line, or a surface, so it is necessary to recognize a continuous surface as a burn-in part. There is. For this purpose, a grouping process is performed. When this process is performed, as shown in FIG. 5, in a region surrounded by circles 230 </ b> A to 230 </ b> C, a portion that hits the surface of the cylinder is a burn-in portion that is a set of burn-in portions. Note that the reason for obtaining the burn-in portion in this way is that if baking has occurred, the surface of the portion causing baking may be peeled off. Next, the solidification analysis computer 110 creates an analysis model by superimposing the seizure portion obtained in the above step on the cylinder shape based on the CAD data stored in the database 120. That is, this process is performed to reflect which part of the cylinder is the burn-in part in the cylinder shape. When this processing is performed, as shown in FIG. 6, an analysis model is created in which the seizure parts 240A to 240C are superimposed on the surface of the cylinder (S14).

  Then, the solidification analysis computer 110 creates an analysis model by superimposing the machining site on the shape of the cylinder based on the CAD data stored in the database 120. That is, in this process, which part of the cylinder is scraped off by machining is reflected in the shape of the cylinder. When this process is performed, as shown in FIG. 7, an analysis model in which the processing parts 250A to 250I are overlaid on the cylinder 200 is created (S15).

  The coagulation analysis computer 110 performs the following process on each of the shrinkage nests 210A to 210E obtained in step S1 to obtain a shrinkage nest virtual sphere. First, as shown in FIG. 8, the center of gravity of the shrinkage nest is obtained and the center of gravity is set as the center position P of the shrinkage nest. Next, the volume of the shrinkage nest is obtained, and the radius R of the sphere equivalent to this volume is obtained. The center position P of the phantom sphere 260 obtained in this way is overlapped with the center of gravity of the shrinkage nest 210B (see FIG. 3) that is predicted to actually occur, as shown in FIG. Is considered to be the part where the shrinkage nest has occurred. Although only the phantom sphere 260 for the shrinkage nest 210B is shown in FIG. 9, in reality, this phantom sphere is obtained for all other shrinkage nests (210A, 210C to 210E in FIG. 3), and the cylinder (S16).

  Next, the coagulation analysis computer 110 calculates the mutual distances of the processed part, the shrinkage nest part, and the seized part obtained by the above processing. For example, when calculating the distance between the shrinkage nest part and the burn-in part, as shown in FIG. 10, the distances Y1 to Y4 from the surface of the virtual sphere 260 of the shrinkage nest to each burn-in part 220A to 220D are calculated. Although not shown in FIG. 10, similarly, the distance between the shrinkage nest site and the shrinkage nest site, the distance between the seizure site and the seizure site, the distance between the processing site and the shrinkage nest site, the distance between the processing site and the seizure site. Is also obtained (S17).

  Then, the smallest and second smallest distances between the calculated shrinkage nest part and the burn-in part are extracted, and the smallest and second smallest among the calculated distances between the shrinkage nest part and the processed part Extract things and things. For example, in FIG. 10, the minimum distance (minimum value) Y1 and the second smallest distance (quasi-minimum value) Y3 among the distances between the shrinkage nest part and the burn-in part are obtained. Similarly, the minimum value and the quasi-minimum value of the distance between the shrinkage nest part and the machining part are obtained. Note that the minimum value and the quasi-minimum value are also obtained for the distance between the shrinkage nest site and the shrinkage nest site and the distance between the seizure site and the seizure site (S18).

  Next, the coagulation analysis computer 110 calculates a leak path candidate based on the calculated distance between the shrinkage site and the burn-in site and the calculated distance between the shrinkage site and the processing site. That is, the leak path (see FIG. 9) is predicted based on the respective distances obtained in step S18. The solidification analysis computer 110 then determines what kind of regions are located at both ends of the leak path candidate based on the data stored in the database 120 regarding the final shape of the fluid path and the type of fluid flowing through the fluid path. The fluid flag is attached to each region according to the type of fluid. For example, as shown in FIG. 14, when leak path A and leak path B communicate with each other via a shrinkage nest, the area at one end of the leak path is area A and the area at the other end is area B. However, the fluid flag α is attached to the region A, and the fluid flag β is attached to the region B. As in the leak determination matrix shown in FIG. 15, if the fluid flags α and β are determined by the type of fluid, if air flows in the region A and water flows in the region B, the fluid flag in the region A is 0. , 1 is assigned to each of the fluid flags in the region B (S19).

  Then, the coagulation analysis computer 110 extracts the leak flag predicted in the step S19 and the fluid flags attached to the regions at both ends of all the leak path candidates (S20).

  Next, the coagulation analysis computer 110 applies the leak determination matrix shown in FIG. 15 to all leak path candidates, and determines whether each leak path candidate corresponds to a leak path. This determination is made by determining whether the regions located at both ends of each leak path candidate are regions where a homogeneous fluid flows or a region where different fluids flow, and each region has a fluid flag. Thus, this determination can be made based on the identity of the fluid flag. For example, if the fluid flags at both ends of a leak path candidate are 0, 0, 1, 1, 2 and 2, respectively, the leak path candidate is not determined to be a leak path as shown in FIG. (Because different fluids do not mix). Leak path candidates that have not been determined to be leak paths are excluded from the occurrence risk calculation target (S21).

  The coagulation analysis computer 110 further extracts the minimum value and the quasi-minimum value from among the minimum value and the quasi-minimum value of the distances obtained in the step S18 for all leak paths left in the step S21. The value is extracted and the sum is calculated. For example, the minimum value of the burn-in part distance with respect to the focused shrinkage part is Y1 and the quasi-minimum value is Y3, and the minimum value of the processed part distance with respect to the focused shrinkage part is K1 and K2. If Y1 <K1 <Y3 <K2, then Y1 + K1 is calculated, and the sum is taken as the risk of occurrence of a leak path. Therefore, the risk of occurrence of a leak path indicates that the smaller the value, the higher the possibility that a leak path will be formed (S22).

  Finally, the coagulation analysis computer 110 predicts in which part the leak path occurs based on the obtained distance between each part and the risk of occurrence of the leak path, as shown in FIG. The route is displayed on the display 130. The leak path is displayed on the display 130 when the value of the risk of occurrence of the leak path is equal to or less than a preset value. Instead of displaying the leak path, the risk of occurrence of the leak path may be simply displayed on the product (S23).

  In the above embodiment, the case where the risk of occurrence of the leak path is calculated simply based on the distance is shown. However, if the risk of occurrence of the leak path is calculated in consideration of not only the distance but also the shrinkage volume, the accuracy of the risk of occurrence is increased. Can be improved. That is, the occurrence risk may be calculated using the following regression equation.

If the occurrence risk is X, the minimum distance is Y1, and the shrinkage volume is V,
The occurrence risk X is expressed by the following formula.

X = α1 · Y1 + α2 · V + α3
The constants α1 to α3 of the regression equation are obtained by actually measuring the actually generated leak path. A more accurate constant can be obtained as more leak paths are actually measured. The constants α1 to α3 obtained by actual measurement are substituted into the above equation to obtain the occurrence risk. In the case of this equation, the greater the value, the higher the risk of leak occurrence.

  By performing the simulation as described above, the occurrence prediction of the leak path can be performed more accurately, and the occurrence prediction can be quantified. Furthermore, since leak path generation prediction is performed after eliminating leak path candidates that cannot be leak paths, the computer does not need to perform useless calculations, and the processing speed is improved.

  INDUSTRIAL APPLICABILITY The present invention is useful in the field of solidification analysis simulation because the risk of occurrence of a leak path can be predicted accurately and quickly.

It is a schematic block diagram of the coagulation analysis apparatus which implements the coagulation analysis method concerning this invention. It is process drawing which shows the procedure of the solidification analysis method which concerns on this invention. It is a figure which shows distribution of the shrinkage nest estimated by the coagulation analysis. It is a figure which shows the temperature distribution of a product when the mold predicted by the solidification analysis is opened. It is a figure which shows the seizing site | part calculated | required by the solidification analysis. It is a figure which shows the analysis model with which the burning | scorching site | part was piled up on the shape of the cylinder. It is a figure which shows the analysis model with which the process part was piled up on the shape of the cylinder. It is a figure which shows the virtual sphere calculated | required from the shrinkage nest. It is the figure which piled up and showed the processing part and the virtual sphere on the shape of a cylinder. It is a figure for demonstrating the calculation process of the distance of a shrinkage nest and a burning part. It is a figure which shows the generation | occurrence | production state of a leak path | route. It is a figure where it uses for description of the generation | occurrence | production state of a leak path | route. It is process drawing which shows the procedure of the other coagulation | solidification analysis method which concerns on this invention. It is a figure where it uses for description of the fluid flag attached | subjected to the area | region of the both ends of a leak path | route candidate. It is a figure which shows an example of a leak judgment matrix.

Explanation of symbols

100 Coagulation analysis computer,
110 database,
120 display,
200 cylinders,
210A-210E shrinkage nest site,
220A-220C The part which is showing the temperature more than the seizing dangerous temperature,
230A-230C A region where a burn-in portion exists,
240A-240C Burning part,
250A-250I processing part,
260 Virtual sphere.

Claims (7)

A step of obtaining by simulation a shrinkage nest generated until the molten metal poured into a mold forming a cast product solidifies and a seizure site occurring in the cast product when the mold is opened;
Calculating a processing portion of the inner surface of the fluid path formed in the casting by opening the mold, a distance between the shrinkage nest portion and the seizing portion determined by the simulation;
Based on the calculated distance, the fluid flowing through the flow path from the processing site to the shrinkage site, from the processing site to the seizing site, from the processing site to the processing site of another flow channel, a step of calculating the occurrence risk of leakage path leaking respectively,
A solidification analysis method for a cast product, comprising:   2. The solidification analysis method for a cast product according to claim 1, wherein the shrinkage nest part is represented as a virtual sphere obtained from a center position and a volume of the shrinkage nest. The solidification analysis of the cast product according to claim 1 or 2 , wherein the seizure part generated in the cast product is a set of portions exhibiting a temperature higher than a preset temperature as a seizure risk temperature. Method. The step of calculating the risk of occurrence includes
Extracting the smallest and second smallest distance between the calculated shrinkage nest and burn-in part and calculating the smallest and second smallest distance between the calculated nest and machining area Extracting the
Calculating the sum of the smallest and second smallest of the four extracted distances;
The solidification analysis method for a cast product according to any one of claims 1 to 3 , characterized by comprising: Further, the step of calculating the occurrence risk level includes:
Calculating a leak path candidate based on the calculated distance between the shrinkage nest part and the burn-in part and the calculated distance between the shrinkage nest part and the processed part;
Determining whether the regions located at both ends of each of the leak paths that are candidates for each leak path are areas where a homogeneous fluid flows or a heterogeneous fluid flows;
Excluding step the leakage path candidate from the calculation target of the generation degree of risk when regions located at both ends of the leakage path became the leak path candidate is a region through which homogeneous fluid,
The solidification analysis method for a cast product according to claim 4, comprising: After calculating the leak path candidate,
Including a step of attaching a fluid flag, which is a symbol determined by the type of fluid, to each region according to the type of fluid flowing in the region located at both ends of the leak route that is the calculated leak route,
The determination of whether the region located at both ends of the leakage path became the leakage path candidate is a region through which is the one heterogeneous fluid area through which homogeneous fluid that is performed on the basis of the identity of the fluid flag The solidification analysis method for a cast product according to claim 5. Computed generated solidification analyzing method of casting as claimed in any one of claims 1 to 6 for the risk superimposed on the casting and further comprising the step of displaying on a display.