Fused mold precision casting riser design method based on reliability algorithm

Abstract

The invention provides an investment precision casting riser design method based on a reliability algorithm. The investment precision casting riser design method comprises the steps that process parameters of actual casting production and the fluctuation range of the parameters are determined; preliminarily designing a placement position and a size optimization range of a riser through a modulus method; performing integrated calculation to obtain defect conditions under different parameters; establishing a data model of casting riser size, process parameters and defects; establishing a fluctuation parameter group taking a design value as a reference; establishing the relationship between the riser size design value and the casting reliability; and optimization is carried out by taking the casting reliability reaching the standard as the constraint condition and taking the maximization of the actual casting yield as the target, and the optimized riser size is obtained. According to the method, a past design mode depending on experience is avoided as much as possible, and the process yield and rejection rate are effectively and intelligently balanced, so that the overall yield of castings is improved, the development cycle is shortened, and the production efficiency is improved.

Description

Technical Field

[0001] The invention relates to the technical field of investment casting, and in particular to a method for designing a riser of an investment precision casting based on a reliability algorithm. Background Art

[0002] Investment casting is a near-net-shape process with the advantages of high molding accuracy, high surface flatness, and no or less machining. However, the investment casting manufacturing cycle is long and the process flow is relatively complicated. Shrinkage cavities, shrinkage, inclusions and other defects often occur, which seriously affect the performance of the casting. Riser is a type of structure used to compensate for the volume shrinkage of the casting during solidification, and has the function of preventing shrinkage cavities. Its design often adopts the "experience + trial and error" method, which produces a large amount of waste, has high randomness and poor reliability. At present, based on a large number of simulation results, the numerical relationship between the riser size and the occurrence of defects is established through algorithms such as response surface and neural network. The optimization target and constraint conditions are obtained from the numerical relationship, and the optimal riser design can be obtained.

[0003] However, in the actual production of castings, process parameters cannot be controlled as a set of precise values, but a series of random variables around the set values. Using deterministic design methods and directly applying the theoretical optimal solution to actual production will usually reach the limit of design limitations, which is likely to cause shrinkage defects to appear at the boundary between the casting and the gating system, leading to the occurrence of shrinkage defects under process fluctuations. Therefore, it is necessary to introduce a reliability design algorithm to automatically balance improving reliability and reducing failure rates to maximize the efficiency of casting production and reduce design and production costs. Summary of the invention

[0004] In view of the defects in the prior art, the object of the present invention is to provide a method for designing a riser for investment castings based on a reliability algorithm.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] The present invention provides a method for designing a riser for investment castings based on a reliability algorithm, comprising:

[0007] Determine the process parameters of actual casting production and their fluctuation range;

[0008] The placement position and size optimization range of the riser are preliminarily designed through the modular method;

[0009] Perform integrated calculations to obtain defect conditions under different parameters;

[0010] According to the defect conditions under the different parameters, a data model of casting riser size, process parameters and defects is established;

[0011] According to the data model of the casting riser size, process parameters and defects and the fluctuation range of the parameters, a fluctuation parameter group based on the design value is established;

[0012] According to the fluctuation parameter group based on the design value, a relationship between the riser size design value and the casting reliability rate is established;

[0013] According to the relationship between the design value of the riser size and the reliability rate of the casting, the optimization is performed with the reliability rate of the casting reaching the standard as a constraint condition and the goal of maximizing the actual output rate of the casting to obtain the optimized riser size.

[0014] Optionally, determining the process parameters of actual casting production and the fluctuation range of their parameters includes: counting the fluctuation range of each process parameter near the set value in batch production of castings, counting the error of riser size parameters in the wax mold production process, and establishing a fluctuation distribution model of process parameters and size parameters.

[0015] Optionally, the preliminary design of the placement position and size optimization range of the riser by the modulus method includes: selecting the size designed by the modulus method as the upper limit of the riser size design domain, and selecting 1 / 2 of the size designed by the modulus method as the lower limit of the riser size design domain.

[0016] Optionally, the integrated calculation is performed to obtain defect conditions under different parameters, including: determining the experimental parameter range according to the fluctuation distribution model of the process parameters and dimensional parameters, using the Latin hypercube method to design the experiment, determining the experiment plan table, and establishing an integrated calculation process, calling multi-physics field finite element software for calculation, and automatically modifying the riser size and process parameters according to the experiment plan table.

[0017] Optionally, establishing a data model of casting riser size, process parameters and defects according to the defect conditions under the different parameters includes: using an approximate model to perform data analysis on the integrated calculation results to obtain a numerical relationship model of casting riser size, process parameters and defects, and obtaining a relationship between parameter X and whether the casting is qualified according to the defect judgment criteria.

[0018] Optionally, the relationship between the parameter X and whether the casting is qualified is:

[0019]

[0020] Optionally, the establishing of a fluctuation parameter group based on the design value includes:

[0021] Using the Latin hypercube method, establish an experimental design P with N groups. n =[p i,n ], the dimension I of the experimental design and the dimension I of the input fluctuation X The same, each dimension parameter value range is 0 to 1, where pi,n Indicates the value of the i-th dimension of the n-th group of experimental design;

[0022] According to the inverse cumulative distribution function x=ICDF in the fluctuation distribution model of the process parameters and the size parameters i (p), find N groups with design value Xd = [xd i ] is the parameter of the fluctuation based on the benchmark:

[0023] XF n (Xd) = [xf i，n ]=[xd i +ICDF i (p i，n )].

[0024] Optionally, establishing the relationship between the riser size design value and the casting reliability rate includes:

[0025] A casting reliability function is established, where the design value Xd is the input and the reliability R is the output:

[0026]

[0027] Optionally, according to the relationship between the riser size design value and the casting reliability rate, the casting reliability rate is taken as a constraint condition, and the optimization is performed with the goal of maximizing the actual casting yield rate, wherein the actual casting yield rate is calculated as follows:

[0028] W(Xd)=W0(Xd)*R(Xd)

[0029] Among them, W0(Xd) is the casting process yield rate.

[0030] Optionally, after obtaining the optimized riser size, the method further comprises: applying the optimized riser size design to actual production, and monitoring the actual production casting yield to verify the reliability of the riser size result.

[0031] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0032] The method for designing a riser for investment castings based on a reliability algorithm provided by the present invention first determines the actual pouring parameters of the castings, and establishes an approximate distribution model of process parameters and size parameters according to fluctuations in actual production; determines the upper and lower limits of the riser size and the placement position according to the calculation results of the modulus method; determines the test design range according to the approximate distribution, and performs integrated calculations after the test design; performs data analysis to obtain a numerical relationship model between the casting riser size and process parameters and defects; establishes a relationship between the riser size design value and the casting reliability rate, comprehensively considers the reliability rate and the process yield rate, and finally obtains an optimized riser size design. The present invention avoids the use of the design method that relies on experience in the past as much as possible, effectively and intelligently balances the process yield rate and the scrap rate, thereby improving the overall yield rate of castings, shortening the development cycle, and improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0034] Figure 1 It is a schematic diagram of the implementation process of the method for designing a riser for investment castings based on a reliability algorithm in one embodiment of the present invention;

[0035] Figure 2 A casting model used in one embodiment of the present invention;

[0036] Figure 3 A finite element model established in one embodiment of the present invention;

[0037] Figure 4 This is a result diagram of a simulation scheme designed in one embodiment of the present invention. DETAILED DESCRIPTION

[0038] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements may be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0039] Figure 1 The implementation process of the investment casting riser design method based on the reliability algorithm in one embodiment of the present invention is shown. Figure 1 , a method for designing a riser for investment casting based on reliability algorithm, comprising the following steps:

[0040] Step 1: Determine the process parameters of the actual production of castings and their fluctuation range;

[0041] Step 2: Preliminarily design the placement position and size optimization range of the riser by using the modular method;

[0042] Step 3: Perform integrated calculation to obtain defect conditions under different parameters;

[0043] Step 4: According to the defect conditions under different parameters, a data model of casting riser size, process parameters and defects is established;

[0044] Step 5: According to the data model of casting riser size, process parameters and defects and the fluctuation range of parameters, a fluctuation parameter group based on the design value is established;

[0045] Step 6: According to the fluctuation parameter group based on the design value, the relationship between the riser size design value and the casting reliability is established;

[0046] Step 7: Based on the relationship between the riser size design value and the casting reliability rate, with the casting reliability rate reaching the standard as a constraint condition and with the goal of maximizing the actual casting yield, optimization is performed to obtain the optimized riser size.

[0047] In some embodiments, in step 1, as an example, the process parameters of the actual production of castings include alloy type, shell material, pouring temperature, pouring speed, shell preheating temperature, shell-casting interface heat transfer coefficient, cooling method, etc. The fluctuation range of the parameters includes the fluctuation range of the process parameters and the size parameters, the fluctuation range of each process parameter near the set value in the batch production of castings is statistically calculated, the error of the riser size parameter in the wax pattern production process is statistically calculated, and the fluctuation distribution model (i.e., approximate distribution model) of the process parameters and the size parameters is established.

[0048] In some embodiments, in step 2, because the modulus method is a more conservative empirical formula design method, the modulus method design size is selected as the upper limit of the riser size design domain to ensure the quality of the casting; because loose defects generally occur, resulting in the inability to obtain qualified castings, 1 / 2 of the modulus method design size is selected as the lower limit of the riser size design domain, thereby ensuring that the optimal design is included in the upper and lower limits of the design domain.

[0049] In step 3, integrated calculation is performed based on the range of process parameters and dimensional parameters determined in step 1 and step 2. In some embodiments, the range of experimental parameters is determined according to the fluctuation distribution model of process parameters and dimensional parameters, the Latin hypercube method is used for experimental design, the experimental scheme table is determined, and an integrated calculation process is established, the multi-physics field finite element software is called for calculation, and the riser size and process parameters are automatically modified according to the experimental scheme table, and integrated calculation analysis is performed to derive the desired results.

[0050] In some embodiments, in step 4, an approximate model is used to perform data analysis on the integrated calculation results to obtain a numerical relationship model between the casting riser size and process parameters and defects, and based on the defect judgment criteria, the relationship between the parameter X and whether the casting is qualified is obtained.

[0051] Specifically, the relationship between parameter X and whether the casting is qualified is:

[0052]

[0053] Q(X) indicates whether the casting is qualified under the process parameter X, and takes 1 if it is qualified and 0 if it is unqualified.

[0054] In some embodiments, in step 5, a Latin hypercube method is used to establish an experimental design P with N groups. n =[p i,n ], the dimension I of the experimental design and the dimension I of the input fluctuation X The same, each dimension parameter value range is 0 to 1, where p i,n Indicates the value of the i-th dimension of the n-th group of experimental design;

[0055] According to the inverse cumulative distribution function x=ICDF in the fluctuation distribution model of process parameters and size parameters i (p), find N groups with design value Xd = [xd i ] is the parameter of the fluctuation based on the benchmark:

[0056] XF n (Xd) = [xf i，n ]=[xd i +ICDF i (p i，n )].

[0057] In some embodiments, in step 6, based on the design parameter Xd, N groups of fluctuated process parameters are obtained, and the judgment of whether the casting is qualified in step 4 is applied to each group of fluctuated process parameters, and the average value can be taken to obtain the qualified rate R(Xd) under the fluctuating process parameters. Specifically, a casting reliability function is established, in which the design value Xd is input and the reliability rate R is output:

[0058]

[0059] In some embodiments, in step seven, the actual casting yield W(Xd) can be obtained through R(Xd), which is the optimization target. The actual casting yield calculation formula is:

[0060] W(Xd)=W0(Xd)*R(Xd)

[0061] Among them, W0(Xd) is the casting process yield rate, which generally refers to the proportion of the part weight to the casting weight including the pouring system.

[0062] According to the casting reliability rate and casting actual yield rate function (casting actual yield rate calculation formula) obtained above, the casting reliability rate is taken as the constraint condition, and the optimization is carried out with the goal of maximizing the casting actual yield rate, and finally the optimized riser size design is obtained.

[0063] In a further embodiment, after obtaining the optimized riser size, the method further includes: applying the optimized riser size design to actual production, and monitoring the actual production casting yield to verify the reliability of the riser size result.

[0064] Continue to refer to Figure 1 In a specific embodiment, a method for designing a riser for investment castings based on a reliability algorithm comprises the following steps:

[0065] Step 1: Figure 2 As shown, the overall dimensions of the ring-in-ring feature casting are: diameter 300mm, height 100mm, wall thickness 5mm, outer ring outer diameter 300mm, inner diameter 290mm; inner ring outer diameter 126mm, inner diameter 120mm. The alloy is K4169 nickel-based high-temperature alloy, the shell material is mullite, the pouring temperature is set to 1500℃, and the pouring speed is 330cm 3 / s, the shell preheating temperature is set to 900℃, and the cooling method is air cooling after pouring. The mean of the riser height is a set variable with a standard deviation of 2mm; the mean of the riser diameter is a set variable with a standard deviation of 2mm; the mean of the pouring temperature is 1500℃ with a standard deviation of 10℃; the mean of the shell preheating temperature is 900℃ with a mean of 20℃. All the above variables approximately satisfy the normal distribution.

[0066] Step 2: According to the calculation results of the modulus method, a riser is placed in the area with the longest solidification time. The height of the riser is 50mm and the diameter is 40mm. The design lower limit is 25mm in height and 20mm in diameter. The alloy pouring temperature is set between 1450-1500℃, and the shell preheating temperature is set between 850-1000℃.

[0067] Step 3: Parameterize the riser size that needs to be optimized in NX software, record the script file for modifying the riser size parameters, and record the script file for the casting simulation process in ProCAST software. In the Isight integrated platform software, select the Latin hypercube method for experimental design, establish an integrated calculation process, call NX and ProCAST software, and establish a finite element model such as Figure 3 As shown in the table, the riser size and process parameters are automatically modified according to the test plan table, and numerical simulation analysis is performed. The results are shown in Table 1 and Figure 4 shown.

[0068] Table 1 Latin hypercube experimental design and results

[0069]

[0070]

[0071] Step 4: Analyze the results of numerical simulation to obtain the numerical relationship model between the casting riser size and process parameters and defects. According to the defect judgment criteria, the relationship between parameter X and whether the casting is qualified is obtained. Specifically,

[0072] The calculation formula for the distance between shrinkage and the upper surface of the casting is:

[0073] 332.68653-0.80234*A+3.33989*B-0.37611*C-0.22623*D+0.01468*A*B+0.00053*A*C+0.00027*A*D- 0.00054*B*C-0.00036*B*D+0.00020*C*D-0.00217*A^2-0.03694*B^2+6.69026E-05*C^2-3.77739E-05

[0074] Among them, if the distance is greater than 0, it means that the shrinkage compensation is successful, the casting is qualified, and Q(X)=1. Otherwise, the defect enters the casting, the shrinkage compensation fails, the casting is unqualified, and Q(X)=0.

[0075] Step 5: Based on the numerical relationship model between riser size, process parameters and defects and the fluctuation range of parameters, the Latin hypercube method is used to establish 1000 sets of fluctuation parameters based on the design values.

[0076] Step 6: Based on the fluctuation parameters obtained in step 5, establish the relationship between the riser size design value and the casting reliability rate.

[0077] Step 7: Optimize the casting reliability and casting process yield as the goal of maximizing the casting reliability and casting process yield, and obtain the optimized riser size: height is 29.1mm, diameter is 21.5mm, at this time the casting reliability is 99.8%, and the actual yield is 59.2%.

[0078] In the above embodiment of the present invention, the actual pouring parameters of the casting are first determined; according to the fluctuations in actual production, an approximate distribution model of process parameters and size parameters is established; the upper limit of the riser size and the placement position are determined according to the calculation results of the modulus method; the experimental design range is determined according to the approximate distribution; the Latin hypercube algorithm is used for experimental design and integrated calculation is performed; data analysis is performed to obtain a numerical relationship model between the casting riser size and process parameters and defects; the relationship between the riser size design value and the casting reliability rate is established, and the reliability rate and process yield rate are comprehensively considered to finally obtain the optimized riser size design. The above embodiment of the present invention reduces the design method that relies on experience in the past, effectively and intelligently balances the process yield rate and the scrap rate, and automatically balances the improvement of reliability and the reduction of failure rate to maximize the efficiency of casting production, thereby improving the overall yield rate of castings, shortening the development cycle, improving production efficiency, and reducing design and production costs.

[0079] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various modifications or variations within the scope of the claims, which does not affect the essence of the present invention. The above preferred features can be used in any combination without conflicting with each other.

Claims

1. A method for designing a riser for investment castings based on a reliability algorithm, characterized in that: include: Determine the process parameters of actual casting production and their fluctuation range; The placement position and size optimization range of the riser are preliminarily designed through the modular method; Perform integrated calculations to obtain defect conditions under different parameters; According to the defect conditions under the different parameters, a data model of casting riser size, process parameters and defects is established; According to the data model of the casting riser size, process parameters and defects and the fluctuation range of the parameters, a fluctuation parameter group based on the design value is established; According to the fluctuation parameter group based on the design value, a relationship between the riser size design value and the casting reliability rate is established; According to the relationship between the design value of the riser size and the reliability rate of the casting, the optimization is performed with the reliability rate of the casting reaching the standard as a constraint condition and the goal of maximizing the actual output rate of the casting to obtain the optimized riser size.

2. The method for designing a riser for investment castings based on a reliability algorithm according to claim 1, characterized in that: The method of determining the process parameters of actual casting production and their fluctuation range includes: counting the fluctuation range of each process parameter near the set value in batch production of castings, counting the error of riser size parameters in the wax mold production process, and establishing a fluctuation distribution model of process parameters and size parameters.

3. The method for designing a riser for investment castings based on a reliability algorithm according to claim 1, characterized in that: The method of preliminarily designing the placement position and size optimization range of the riser by the modulus method includes: selecting the design size by the modulus method as the upper limit of the riser size design domain, and selecting 1 / 2 of the design size by the modulus method as the lower limit of the riser size design domain.

4. The method for designing a riser for investment castings based on a reliability algorithm according to claim 2, characterized in that: The integrated calculation is performed to obtain defect conditions under different parameters, including: determining the experimental parameter range according to the fluctuation distribution model of the process parameters and size parameters, using the Latin hypercube method to design the experiment, determining the experiment plan table, and establishing an integrated calculation process, calling multi-physics field finite element software for calculation, and automatically modifying the riser size and process parameters according to the experiment plan table.

5. The method for designing a riser for investment castings based on a reliability algorithm according to claim 2, characterized in that: The method of establishing a data model of casting riser size, process parameters and defects according to the defect conditions under different parameters includes: using an approximate model to perform data analysis on the integrated calculation results to obtain a numerical relationship model between casting riser size, process parameters and defects, and obtaining a relationship between parameter X and whether the casting is qualified according to the defect judgment standard.

6. The method for designing a riser for investment castings based on a reliability algorithm according to claim 5, characterized in that: The relationship between the parameter X and whether the casting is qualified is:

7. The method for designing a riser for investment castings based on a reliability algorithm according to claim 6, characterized in that: The step of establishing a fluctuation parameter group based on the design value includes: Using the Latin hypercube method, establish an experimental design P with N groups. n =[p i,n ], the dimension I of the experimental design and the dimension I of the input fluctuation X The same, each dimension parameter value range is 0 to 1, where p i,n Indicates the value of the i-th dimension of the n-th group of experimental design; According to the inverse cumulative distribution function x=ICDF in the fluctuation distribution model of the process parameters and the size parameters i (p), find N groups with design value Xd = [xd i ] is the parameter of the fluctuation based on the benchmark: Xf n (Xd)=[xf i,n ]=[xd i +ICDF i (p i,n )]。 8. The method for designing a riser for investment castings based on a reliability algorithm according to claim 7, characterized in that: The establishing of the relationship between the riser size design value and the casting reliability rate includes: A casting reliability function is established, where the design value Xd is the input and the reliability R is the output:

9. The method for designing a riser for investment castings based on a reliability algorithm according to claim 8, characterized in that: According to the relationship between the riser size design value and the casting reliability rate, the casting reliability rate is taken as a constraint condition, and the optimization is carried out with the goal of maximizing the actual casting yield rate, wherein the calculation formula for the actual casting yield rate is: W(Xd)=W0(Xd)*R(Xd) Among them, W0(Xd) is the casting process yield rate.

10. The method for designing a riser for investment castings based on a reliability algorithm according to any one of claims 1 to 9, characterized in that: After obtaining the optimized riser size, the method further includes: applying the optimized riser size design to actual production, monitoring the actual production casting yield rate, and verifying the reliability of the riser size result.

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