A riser design method for investment castings based on reliability algorithm

Abstract

The present invention provides a method for designing risers for investment castings based on a reliability algorithm, comprising: determining the process parameters of actual casting production and the fluctuation range of the parameters; preliminarily designing the placement position and size optimization range of the riser through a modular method; performing integrated calculations to obtain defect conditions under different parameters; establishing a data model of casting riser size, process parameters, and defects; establishing a fluctuation parameter group based on the design value; establishing a relationship between the riser size design value and the casting reliability rate; optimizing the riser size with the goal of maximizing the actual casting yield, taking the casting reliability rate meeting the standard as a constraint condition, and obtaining an optimized riser size. The present invention avoids the use of past design methods that rely on experience, effectively and intelligently balances the process yield rate and the scrap rate, thereby improving the overall casting yield rate, shortening the development cycle, and improving production efficiency.

Description

Technical Field

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

[0002] Investment casting is a near-net-shape process with advantages such as high molding precision, high surface flatness, and no or minimal need for machining. However, investment casting has a long manufacturing cycle and a complex process flow, and defects such as shrinkage cavities, porosity, and inclusions often occur, seriously affecting 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 the occurrence of shrinkage cavities. Its design often adopts the "experience + trial and error" method, which produces a large amount of scrap, is highly random, and has poor reliability. At present, based on a large number of simulation results, the numerical relationship between riser size and defect occurrence is established through algorithms such as response surface and neural networks. The optimization target and constraints are obtained from the numerical relationship, and the optimal riser design can be obtained.

[0003] However, in actual casting production, process parameters cannot be controlled to a precise set of values, but rather to a series of random variations around set values. Using deterministic design methods and directly applying theoretically optimal solutions to actual production often reaches the limits of design constraints, potentially causing shrinkage defects to appear at the boundary between the casting and the gating system, leading to shrinkage defects under process fluctuations. Therefore, it is necessary to introduce reliability design algorithms that automatically balance improving reliability with reducing failure rates to maximize casting production efficiency and reduce design and production costs. Summary of the Invention

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

[0005] The purpose 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 for actual casting production and their fluctuation range;

[0008] Using the modular method, preliminarily design the placement position and size optimization range of the riser;

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

[0012] Establishing a relationship between the riser size design value and the casting reliability rate based on the fluctuation parameter group based on the design value;

[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 goal of maximizing the actual output rate of the casting, so as to obtain the optimized riser size.

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

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

[0016] Optionally, the integrated calculation is performed to obtain the defect conditions under different parameters, including: determining the experimental parameter range based on the fluctuation distribution model of the process parameters and dimensional parameters, using the Latin hypercube method to design the experiment, determining the experimental 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 experimental plan table.

[0017] Optionally, establishing a data model of the casting riser size, process parameters and defects based on 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 the casting riser size, process parameters and defects, and obtaining the relationship between parameter X and whether the casting is qualified based on the defect judgment criteria.

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

[0019]

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

[0021] Using the Latin supercube 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 experimental design;

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

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

[0026]

[0027] Optionally, based on the relationship between the riser size design value and the casting reliability, with the casting reliability reaching the standard as a constraint condition, optimization is performed with the goal of maximizing the actual casting yield, wherein the actual casting yield 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 present invention provides a method for designing a riser for investment castings based on a reliability algorithm. The method first determines the actual pouring parameters of the casting, establishes an approximate distribution model of process parameters and dimensional parameters based on fluctuations in actual production, determines the upper and lower limits of the riser size and the placement position based on the results of the modular method, determines the experimental design range based on the approximate distribution, and performs integrated calculations after the experimental 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 process yield rate, and finally obtains an optimized riser size design. The present invention avoids the use of past design methods that rely on experience 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 upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0034] Figure 1 Schematic diagram of the implementation process of a 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 will be described in detail below with reference to 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 skilled in the art, several variations and improvements can be made without departing from the scope of the present invention. These all fall within the scope of protection 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 castings based on a reliability algorithm comprises the following steps:

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

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

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

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

[0044] Step 5: Based on 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: Based on the fluctuation parameter group based on the design value, establish the relationship between the riser size design value and the casting reliability rate;

[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 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 for actual casting production include alloy type, shell material, pouring temperature, pouring speed, shell preheating temperature, shell-casting interface heat transfer coefficient, cooling method, etc. The parameter fluctuation range includes the fluctuation range of process parameters and dimensional parameters. The fluctuation range of each process parameter around the set value in batch production of castings is statistically analyzed, and the error of riser dimensional parameters in the wax pattern production process is statistically analyzed to establish a fluctuation distribution model (i.e., an approximate distribution model) for the process parameters and dimensional parameters.

[0048] In some embodiments, in step 2, because the modular method is a more conservative empirical formula design method, the modular 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 modular 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 three, an integrated calculation is performed based on the ranges of the process parameters and dimensional parameters determined in steps one and two. In some embodiments, the experimental parameter ranges are determined based on a fluctuation distribution model of the process parameters and dimensional parameters, and the Latin hypercube method is used for experimental design. A test plan table is determined, and an integrated calculation process is established. Multi-physics finite element software is called for calculation, and the riser dimensions and process parameters are automatically modified according to the test plan table. 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 parameters X, and takes 1 if it is qualified and 0 if it is unqualified.

[0054] In some embodiments, in step 5, the 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 experimental design;

[0055] According to the inverse cumulative distribution function x=ICDF in the fluctuation distribution model of process parameters and dimensional parameters i (p), find N groups with design value Xd=[xd i ] is the parameter of the fluctuation of 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. The determination of whether the casting is qualified in step 4 is applied to each group of fluctuated process parameters, and the average value is 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 weight of the casting including the gating system.

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

[0063] In a further embodiment, after obtaining the optimized riser size, the above 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 includes 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 mold shell preheat temperature is set to 900°C, and the cooling method is air cooling after pouring. The mean of the riser height is a set variable with a standard deviation of 2 mm; the mean of the riser diameter is a set variable with a standard deviation of 2 mm; the mean of the pouring temperature is 1500°C with a standard deviation of 10°C; the mean of the mold shell preheat temperature is 900°C with a mean of 20°C. All of these variables approximately follow a normal distribution.

[0066] Step 2: Based on the results of the modular method, place a riser in the area with the longest solidification time. The riser height 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 mold shell preheat 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 supercube method for experimental design, establish an integrated calculation process, call NX and ProCAST software, and establish the finite element model as follows: 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 feeding is successful and the casting is qualified, Q(X)=1. Otherwise, it means that the defect has entered the casting, the shrinkage feeding has failed, and the casting is unqualified, 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.

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

[0078] The above embodiment of the present invention first determines the actual pouring parameters of the casting; establishes an approximate distribution model of process parameters and dimensional parameters based on fluctuations in actual production; determines the upper limit of the riser size and placement position based on the calculation results of the modular method; determines the experimental design range based on the approximate distribution; adopts the Latin hypercube algorithm to perform experimental design and perform integrated calculations; 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 process yield rate, and finally obtains 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 improving reliability and reducing 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 specific embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various modifications or variations within the scope of the claims without affecting the essence of the present invention. The above preferred features may be used in any combination as long as they do not conflict 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 for actual casting production and their fluctuation range; Using the modular method, preliminarily design the placement position and size optimization range of the riser; 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; Establishing a fluctuation parameter group based on the design value according to the data model of the casting riser size, process parameters and defects and the fluctuation range of the parameters; Establishing a relationship between the riser size design value and the casting reliability rate based on the fluctuation parameter group based on the design value; According to 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 the goal of maximizing the actual casting yield, an optimized riser size is obtained; The method of establishing a data model of the riser size, process parameters and defects of the casting 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 the riser size, process parameters and defects of the casting, and obtaining a relationship between the parameter X and whether the casting is qualified according to the defect judgment standard; The step of establishing a fluctuation parameter group based on the design value includes: Using the Latin supercube 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 experimental design; According to the inverse cumulative distribution function x=ICDF in the fluctuation distribution model of the process parameters and size parameters i (p), find N groups with design value Xd=[xd i ] is the parameter of the fluctuation of the benchmark: Xf n (Xd)=[xf i,n ]=[xd i +ICDF i (p i,n )]; The establishment of the relationship between the riser size design value and the casting reliability rate includes: Establish a casting reliability function, where the design value Xd is the input and the reliability R is the output: According to the relationship between the riser size design value and the casting reliability rate, the optimization is performed with the casting reliability rate reaching the standard as the constraint condition and the goal of maximizing the actual casting yield rate. The actual casting yield rate is calculated as follows: W(Xd)=W0(Xd)*R(Xd) Among them, W0(Xd) is the casting process yield rate.

2. The method for designing a riser for investment castings based on a reliability algorithm according to claim 1, wherein: The method of determining the process parameters of actual casting production and the fluctuation range of the parameters includes: counting the fluctuation range of each process parameter around 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 the process parameters and size parameters.

3. The method for designing a riser for investment castings based on a reliability algorithm according to claim 1, wherein: The preliminary design of the placement position and size optimization range of the riser by the modular method includes: selecting the modular method design size as the upper limit of the riser size design domain, and selecting 1 / 2 of the modular method design size 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, wherein: The integrated calculation is performed to obtain defect conditions under different parameters, including: determining the experimental parameter range based on the fluctuation distribution model of the process parameters and dimensional parameters, using the Latin hypercube method to design the experiment, determining the experimental 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 experimental plan table.

5. The method for designing a riser for investment castings based on a reliability algorithm according to claim 1, wherein: The relationship between the parameter X and whether the casting is qualified is:

6. The method for designing a riser for investment castings based on a reliability algorithm according to any one of claims 1 to 5, wherein: 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, and verifying the reliability of the riser size result.

Images