A heat treatment-free aluminum alloy die-casting process optimization method

Abstract

The present invention discloses a method for optimizing a heat-treatment-free aluminum alloy die-casting process, comprising the following steps: Step 1: Obtaining the thermophysical property parameters of the heat-treatment-free aluminum alloy material used for the die-cast part to be optimized; Step 2: Determining the optimizable variables of the die-cast part to be optimized; Step 3: Grouping the optimizable variables according to the similarity of their influence on the die-cast part to be optimized, and constructing one or more optimizable variable groups; Step 4: Determining corresponding reference sample groups based on the optimizable variable groups; Step 5: Screening the optimizable variable group with the greatest influence as the optimal variable group based on the reference sample groups, and determining the optimal process for the part to be optimized based on the optimal variable group and the corresponding reference sample group. The present invention has the characteristics of shortening the design cycle and improving flexibility and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of aluminum alloy die-casting, and more particularly to a method for optimizing a heat treatment-free aluminum alloy die-casting process. Background Art

[0002] The integrated die-casting process has become a disruptive technology in the automotive industry. Compared to traditional processes, one-step forming offers inherent advantages in reducing manufacturing costs. In 2020, Tesla debuted an integrated die-cast rear floorpan on the Model Y. In 2023, Huawei's M9 featured the world's largest integrated die-cast body. Common challenges faced by integrated die-cast parts in production applications include part deformation and surface blistering caused by heat treatment, as well as the high transportation costs of oversized die-cast parts.

[0003] Heat treatment-free die-casting aluminum alloy is a new type of die-casting material developed on the basis of traditional die-casting aluminum alloy. It can directly obtain good comprehensive performance without heat treatment, effectively avoiding the adverse effects of heat treatment on part performance.

[0004] In recent years, due to the increasingly higher requirements for stability and aesthetics of aluminum alloy die-castings, there has been less research on die-castings with complex shapes and uneven wall thicknesses, and they are difficult to form, and are prone to defects such as cold shut, insufficient pouring, shrinkage cavities and porosity.

[0005] The optimized design of the die-casting process can greatly reduce the impact of casting defects on part quality, which is directly related to part quality, production efficiency and cost control.

[0006] However, the traditional aluminum alloy die-casting process optimization design lacks unified automated tool support, making it difficult to ensure the consistency and flexibility of process design, resulting in low design efficiency.

[0007] In addition, the trial and error method is currently a widely used optimization method, which causes a great waste of resources in terms of manpower costs and equipment costs. Summary of the Invention

[0008] The purpose of the present invention is to design and develop a heat treatment-free aluminum alloy die-casting process optimization method, determine the optimization direction of shrinkage defects, combine orthogonal experiments, save costs, and ensure flexibility and accuracy.

[0009] The technical solution provided by the present invention is:

[0010] A method for optimizing a heat treatment-free aluminum alloy die-casting process comprises the following steps:

[0011] Step 1: Obtain the thermal physical property parameters of the heat-treatment-free aluminum alloy material used for the die-cast parts to be optimized;

[0012] Step 2: determining the variables that can be optimized based on reducing the shrinkage and porosity defects of the die-cast part to be optimized;

[0013] Wherein, the optimizable variables are casting temperature, initial mold temperature and injection speed;

[0014] Step 3: Group the optimizable variables according to the similarity of their influence on the die-cast parts to be optimized, and construct one or more optimizable variable groups;

[0015] Step 4: Determine the corresponding reference sample group based on the optimizable variable group through orthogonal experiments;

[0016] Step 5: Screening out the optimizable variable group with the greatest influence as the optimal variable group based on the reference sample group, and determining the optimal process of the part to be optimized based on the optimal variable group and the corresponding reference sample group;

[0017] Among them, the optimal process of the part to be optimized is:

[0018] The pouring temperature was 680°C, the initial mold temperature was 200°C, and the injection speed was 5 m / s.

[0019] Preferably, the thermophysical parameters include density, viscosity, latent heat, liquidus, solidus, thermal conductivity and specific heat capacity.

[0020] Preferably, the optimizable variables further include: cooling method and / or casting wall thickness.

[0021] Preferably, the influencing principles of the initial mold temperature, pouring temperature and injection speed are all to improve the filling capacity of the molten metal and reduce the shrinkage and shrinkage cavity defects.

[0022] Preferably, the reference sample group is a set consisting of a plurality of reference samples corresponding to a plurality of optimizable variables included in the optimizable variable group.

[0023] Preferably, the plurality of reference samples corresponding to the pouring temperature include: 640°C, 660°C and 680°C.

[0024] Preferably, the multiple reference samples corresponding to the initial mold temperature include: 120°C, 160°C and 200°C.

[0025] Preferably, the plurality of reference samples corresponding to the injection speed include: 1 m / s, 3 m / s and 5 m / s.

[0026] Preferably, the influence of the reference sample group is determined by principal component analysis or range analysis.

[0027] Preferably, the optimal process of the part to be optimized needs to meet the thermal physical property parameters of the heat-treatment-free aluminum alloy material used.

[0028] The beneficial effects of the present invention are:

[0029] The present invention designs and develops a heat-treatment-free aluminum alloy die-casting process optimization method. Based on an objective analysis of the molding defect problems of the parts to be optimized, by obtaining optimizable variables, the optimization direction of the die-casting design scheme can be determined in advance, avoiding the manpower and cost burden caused by multiple trial and error and mold opening, and greatly saving the design cycle of heat-treatment-free die-casting. The optimizable variables are grouped according to the actual defect problems, and combined with orthogonal experiments, multiple optimizable variable groups and reference sample groups are obtained. By determining the influence of each optimizable variable group on the casting quality, the most preferred variable group can be determined. This method can ensure high flexibility and accuracy when dealing with design situations with many influencing factors and complex processes, and has good process universal applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a schematic flow chart of the process optimization method for heat-treatment-free aluminum alloy die-casting according to the present invention.

[0031] Figure 2 This is a schematic diagram of a crankcase cover product before the die-casting process is optimized in the embodiment of the present invention.

[0032] Figure 3 This is a schematic diagram of a crankcase cover product after the die-casting process is optimized in the embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention will be further described below in detail with reference to the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0034] like Figure 1 As shown, the present invention provides a method for optimizing a heat treatment-free aluminum alloy die-casting process, comprising the following steps:

[0035] Step 1 S1, obtaining the thermal physical property parameters of the heat-treatment-free aluminum alloy material used for the die-cast part to be optimized;

[0036] The thermophysical parameters include density, viscosity, latent heat, liquidus, solidus, thermal conductivity and specific heat capacity;

[0037] The thermal physical property parameters can be obtained through thermodynamic calculations or related experimental operations;

[0038] Step 2 S2: determining the optimizable variables of the die-cast part to be optimized;

[0039] Among them, the optimizable variables refer to the key variables that directly affect the quality of castings during the design process and die-casting molding process of heat-treatment-free die-cast parts (such as cooling method, casting wall thickness, initial mold temperature, pouring temperature, injection speed, etc.), and can be adjusted during the die-casting molding design process of heat-treatment-free die-cast parts. Obtaining optimizable variables helps to determine the optimization direction of the process, reduce the number of trial and error and mold repairs, and improve optimization efficiency.

[0040] Step 3 S3: based on the similarity of the principles of how the optimizable variables affect casting quality, group the optimizable variables to construct one or more optimizable variable groups;

[0041] The optimizable variable group refers to a collection of multiple optimizable variables that have a direct impact on a certain defect in the die-casting process of heat-treatment-free die-cast aluminum alloy, that is, each optimizable variable group is composed of multiple optimizable variables with similar principles, wherein the defect can be all molding defects that occur in the molding process, including but not limited to shrinkage cavities and porosity, insufficient pouring, cold shut, etc.

[0042] Step 4 S4: obtaining a reference sample group corresponding to the optimizable variable group according to an orthogonal experiment;

[0043] Among them, the reference sample group is a collection of multiple reference samples corresponding to multiple optimizable variables contained in the optimizable variable group. Based on the defects occurring in the molding process and the selected optimizable variables, it is confirmed how the optimizable variables affect the molding defects and determine their scope of influence.

[0044] Step 5 S5: Perform mathematical statistical analysis on each optimizable variable group;

[0045] Wherein, the mathematical statistics analysis is used to determine the influence of the optimizable variable group on the casting quality of the part to be optimized;

[0046] Step 6 S6: Filter out the optimizable variable group with the strongest influence as the optimal variable group, and determine the best optimized design of the part to be optimized based on the optimal variable group;

[0047] Among them, based on multiple optimizable variables and corresponding reference samples, the interaction between the optimizable variables and the casting quality is obtained. Principal component analysis, range analysis and other methods can be used to confirm whether there is a strong correlation between the optimizable variables and the casting quality. Similarly, the optimizable variable group with a strong correlation with the casting quality can be screened out.

[0048] The best optimization design obtained in step six must meet the objective reality of the thermophysical parameters described in step one. Example

[0049] The die-casting process of a company's heat-treatment-free die-cast aluminum alloy crankcase cover was optimized to reduce shrinkage defects during molding.

[0050] S1. The thermal physical properties of the heat-treatment-free aluminum alloy material used for the die-cast parts to be optimized are shown in Table 1:

[0051]

[0052] S2. The optimizable variables of the die-cast part to be optimized are:

[0053] Pouring temperature (T): affects the fluidity, cooling rate and possible hot crack formation of the alloy;

[0054] Initial mold temperature (T0): affects the surface quality of the casting, the fluidity of the molten metal and the uniformity of solidification;

[0055] Injection speed (V): affects the removal of gas inside the cavity, the fluidity of the molten metal and the density of the casting;

[0056] S3. Confirm the impact principle:

[0057] In order to reduce shrinkage defects for the purpose of optimization, the residual melt modulus is used as an indicator to measure the casting quality.

[0058] The pouring temperature (T) refers to the temperature of the molten metal when it enters the mold cavity from the pressure chamber to fill it. It is negatively correlated with the residual melt modulus. The principle is that a lower pouring temperature leads to a lower overall temperature of the molten aluminum and poor flow. The initial mold temperature (T0) refers to the temperature of the mold surface where the melt contacts. It is negatively correlated with the residual melt modulus. The principle is that the lower mold temperature causes the filling molten metal to cool rapidly and its fluidity is weakened. The injection speed (V) refers to the movement speed of the injection punch in the pressure chamber to push the molten metal. It is negatively correlated with the residual melt modulus. The principle is that a high injection speed gives the molten aluminum greater kinetic energy, thereby improving the filling quality.

[0059] Constructing an optimizable variable group: Based on the analysis of the influence principle, the optimizable variables pouring temperature (T), initial mold temperature (T0), and injection speed (V) all reduce the occurrence of shrinkage defects by improving the filling capacity of the molten metal. They have similar influence principles and therefore form an optimizable variable group.

[0060] S4. Combined with the orthogonal experiment, the reference sample group corresponding to the optimizable variable group is shown in Table 2:

[0061]

[0062] S5. Mathematical and statistical analysis: Based on historical data, the relationship between parameter samples and residual melt modulus is analyzed. Range analysis is used to determine the impact of the optimizable variables on the residual melt modulus, thereby determining the correlation between the optimal variables and casting quality. The range analysis is shown in Table 3:

[0063]

[0064] The simulation test results were analyzed using the range analysis method to compare the effects of pouring temperature, initial mold temperature and injection speed on the residual melt modulus of the crankcase cover die casting, as shown in Table 3, where K i1 , K i2 , K i3 (i=A, B, C) represents the total number of residual melt moduli at different levels of the same factor; k i1 、k i2 、k i3 (i=A, B, C) represent the average values ​​of the residual melt modulus at different levels of the same factor, and the range represents the difference between the maximum and minimum average values.

[0065] S6. According to the calculation results, the ranges of the three factors are 0.5508, 0.3301, and 0.1447, respectively. It can be inferred that the degree of influence on the residual melt modulus of the casting is: A>B>C. The above analysis shows that the order of influence on the residual melt modulus of the casting is pouring temperature, initial mold temperature, and injection speed, from large to small. The minimum level corresponding to the average value of the residual melt modulus in each factor is taken as the optimal process parameter combination: A3, B3, C3-Optimization. The optimizeable variable combination is pouring temperature 680℃, initial mold temperature 200℃, and injection speed 5m / s.

[0066] like Figure 2 、 Figure 3 The figure shows a comparison of the crankcase cover products before and after the die-casting process is optimized. It can be seen that the defects are greatly reduced.

[0067] The present invention designs and develops a heat-treatment-free aluminum alloy die-casting process optimization method. Based on an objective analysis of the molding defect problems of the parts to be optimized, by obtaining optimizable variables, the optimization direction of the die-casting design scheme can be determined in advance, avoiding the manpower and cost burden caused by multiple trial and error and mold opening, and greatly shortening the design cycle of heat-treatment-free die-casting. The optimizable variables are grouped according to the actual defect problems to obtain multiple optimizable variable groups and reference sample groups. By determining the influence of each optimizable variable group on the casting quality, the most preferred variable group can be determined. This method can ensure high flexibility and accuracy when dealing with design situations with many influencing factors and complex processes, and has good process universal applicability.

[0068] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A method for optimizing heat treatment-free aluminum alloy die-casting process, characterized in that: The steps include: Step 1: Obtain the thermal physical property parameters of the heat-treatment-free aluminum alloy material used for the die-cast parts to be optimized; Step 2: determining the variables that can be optimized based on reducing the shrinkage and porosity defects of the die-cast part to be optimized; Wherein, the optimizable variables are casting temperature, initial mold temperature and injection speed; Step 3: Group the optimizable variables according to the similarity of their influence on the die-cast parts to be optimized, and construct one or more optimizable variable groups; Step 4: Determine the corresponding reference sample group based on the optimizable variable group through orthogonal experiments; Step 5: Screening out the optimizable variable group with the greatest influence as the optimal variable group based on the reference sample group, and determining the optimal process of the die-casting part to be optimized based on the optimal variable group and the corresponding reference sample group; Among them, the optimal process for the die-casting part to be optimized is: The pouring temperature was 680°C, the initial mold temperature was 200°C, and the injection speed was 5 m / s.

2. The heat treatment-free aluminum alloy die-casting process optimization method according to claim 1, characterized in that: The thermophysical parameters include density, viscosity, latent heat, liquidus, solidus, thermal conductivity and specific heat capacity.

3. The heat treatment-free aluminum alloy die-casting process optimization method according to claim 2, characterized in that: The optimizable variables also include: cooling method and / or casting wall thickness.

4. The heat treatment-free aluminum alloy die-casting process optimization method according to claim 3, characterized in that: The influencing principles of the initial mold temperature, pouring temperature and injection speed are all to improve the metal liquid filling ability and reduce the shrinkage and shrinkage cavity defects.

5. The heat treatment-free aluminum alloy die-casting process optimization method according to claim 4, characterized in that: The reference sample group is a set consisting of multiple reference samples corresponding to multiple optimizable variables included in the optimizable variable group.

6. The heat treatment-free aluminum alloy die-casting process optimization method according to claim 5, characterized in that: The plurality of reference samples corresponding to the pouring temperature include: 640°C, 660°C and 680°C.

7. The heat treatment-free aluminum alloy die-casting process optimization method according to claim 6, characterized in that: The reference samples corresponding to the initial mold temperature include: 120°C, 160°C and 200°C.

8. The heat treatment-free aluminum alloy die-casting process optimization method according to claim 7, characterized in that: The multiple reference samples corresponding to the injection speed include: 1 m / s, 3 m / s and 5 m / s.

9. The heat treatment-free aluminum alloy die-casting process optimization method according to claim 8, characterized in that: The influence of the reference sample group is determined by principal component analysis or range analysis.

10. The heat treatment-free aluminum alloy die-casting process optimization method according to claim 9, characterized in that: The optimal process for the die-cast part to be optimized must meet the thermophysical property parameters of the heat-treatment-free aluminum alloy material used.

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