Method for predicting mechanical properties of castings and designing casting molds

Abstract

A method of predicting casting mechanical properties through the flow distance of an aluminum alloy melt, a method of designing a casting mold for integral die casting, and an aluminum alloy casting manufactured from the casting mold so designed are disclosed. By means of the method, under the given technological parameter window, especially under the given injection speed, the mechanical performance of the integrally-cast large casting can be predicted, regulated and controlled, the automation degree of automobile production is improved, and the design and performance of integrally-cast products are optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of mega casting. Specifically, it relates to a method for predicting the mechanical properties of large-scale mega-castings and optimizing product design. Through the method of the present invention, under a given process parameter window, especially at a given injection speed, the mechanical properties of large-scale castings in mega casting can be predicted and regulated, improving the automation level of automobile production and optimizing the design and performance of mega-cast products. Background Art

[0002] Mega casting uses a large die-casting machine to directly cast large aluminum castings in one go, eliminating the steps of assembling and welding multiple components in traditional processes, and having advantages such as simplifying the production process and reducing the body weight, thus receiving extensive attention and development.

[0003] However, the mega casting process also faces many challenges. For example, the influence of the die-casting process on the casting properties is not clear, and the casting properties are unpredictable. Therefore, further research on the properties of large-scale aluminum alloy castings in mega casting is needed. Summary of the Invention

[0004] The inventors have found that in the mega casting process, the mechanical properties of large castings can be quickly and conveniently predicted by the flow distance of the molten aluminum alloy. Based on this, the present invention provides a method for predicting the mechanical properties of castings by the flow distance of the aluminum alloy melt, enabling the mechanical properties of large castings in mega casting to be predictable and controllable under a given process parameter window, especially at a given injection speed, improving the automation level of automobile production and optimizing the design and performance of mega-cast products. The present invention also provides a method for optimizing the design of mega-cast products by designing a mold for mega casting, including predicting the mechanical property parameters of the castings at each sampling point; and changing the mold structure design (especially the runner setting) at the corresponding position in the mold according to the difference between the predicted value and the expected value of the mechanical property parameters, thereby changing the flow distance of the aluminum alloy melt at the sampling point so that the castings have the expected value of the mechanical property parameters at the sampling point. Thus, according to the requirements for automobile performance, the mold structure can be quickly and conveniently customized, designed, and adjusted to optimize and correct the design and performance of mega-cast products.

[0005] According to one aspect of the present invention, there is provided a method for predicting the mechanical properties of castings by the flow distance of the aluminum alloy melt, characterized by comprising the following steps:

[0006] Step a) manufacturing a casting by directly casting an aluminum alloy melt through a mold in mega casting,

[0007] Step b) selecting a plurality of sampling points on the casting,

[0008] Step c) Calculate the flow distance of the aluminum alloy melt at each sampling point. The flow distance refers to the flowing distance of the aluminum alloy melt along the mold runner from the injection chamber outlet to the casting sampling point during the integrated die-casting process.

[0009] Step d) Test the mechanical property parameters of the castings at each sampling point. The mechanical property parameters are one or more selected from the following: ultimate tensile strength, yield strength, elongation at break, and Brinell hardness.

[0010] Step e) According to the flow distance obtained from step c) and the mechanical property parameters obtained from step d) at each sampling point, fit a function between the mechanical property parameters and the flow distance through multiple regression, and

[0011] Step f) Based on the flow distance and the function between the mechanical property parameters and the flow distance, predict the mechanical properties of other castings manufactured through the mold at each sampling point. The other castings are from different manufacturing batches than the castings in step a).

[0012] In one embodiment of the present invention, the function between the mechanical property parameters and the flow distance fitted in step e) is one or more of the following formulas:

[0013] i) Formula 1: UTS = a1 - a2*FD + a3*FD 2 ,

[0014] where UTS represents the ultimate tensile strength, unit MPa; FD represents the flow distance of the aluminum alloy melt, unit m;

[0015] ii) Formula 2: YS = b1 + b2*HIS - b3*FD + b4*FD 2 ,

[0016] where YS represents the yield strength, unit MPa; HIS represents the average high-speed injection velocity of the indenter, unit m / s; FD represents the flow distance of the aluminum alloy melt, unit m;

[0017] iii) Formula 3: EI = c1 - c2*FD,

[0018] where EI represents the elongation at break, unit %; FD represents the flow distance of the aluminum alloy melt, unit m;

[0019] iv) Formula 4: H = d1 - d2*FD + d3*FD 2 ,

[0020] where H represents the Brinell hardness, unit HBW; FD represents the flow distance of the aluminum alloy melt, unit m;

[0021] Among them, a1, a2, a3, b1, b2, c1, c2, d1, d2, and d3 are coefficients.

[0022] In one embodiment of the present invention, FD is 0.8 - 2 m.

[0023] In one embodiment of the present invention, HIS is greater than 5.7 to 7 m / s.

[0024] In one embodiment of the present invention, in step b), 4 - 15 sampling points are selected, preferably 5 - 10 sampling points, and more preferably 6 - 8 sampling points.

[0025] In one embodiment of the present invention, in step b), a mold flow analysis software is used to select the sampling points.

[0026] In one embodiment of the present invention, in step c), a mold flow analysis software is used to calculate the flow distance of the aluminum alloy melt at each sampling point.

[0027] In one embodiment of the present invention, in step d), a universal testing machine is used to test the mechanical property parameters of the castings at each sampling point.

[0028] In one embodiment of the present invention, in step e), Minitab software is used for fitting.

[0029] In one embodiment of the present invention, in step e), two, three, or four of Formula 1, Formula 2, Formula 3, and Formula 4 are fitted,

[0030] Preferably, Formula 1, Formula 2, Formula 3, and Formula 4 are fitted.

[0031] In another embodiment of the present invention, a method for designing a mold for integrated die - casting is provided, which is characterized by including the following steps: According to the method of predicting the mechanical properties of castings through the flow distance of aluminum alloy melt of the present invention, based on the flow distance and the function between the mechanical property parameters and the flow distance, predict the mechanical property parameters of the castings at each sampling point; and

[0032] According to the difference between the predicted value and the expected value of the mechanical property parameters and the function between the mechanical property parameters and the flow distance, change the structural settings at the corresponding positions of the mold.

[0033] In one embodiment of the present invention, the change of the structural settings at the corresponding positions of the mold is to change the runner settings at the corresponding positions of the mold,

[0034] Preferably, the runner settings of the mold are changed in the following ways: providing one or more obstacles, bends, and / or flow - guiding means.

[0035] In one embodiment of the present invention, the aluminum alloy is an aluminum-silicon alloy;

[0036] Preferably, based on the total weight of the aluminum alloy, the aluminum alloy comprises:

[0037] 7.6 - 8.5 wt% of silicon,

[0038] 0 - 0.15 wt% of iron,

[0039] 0.5 - 0.6 wt% of manganese,

[0040] 0.2 - 0.25 wt% of magnesium,

[0041] 0.07 - 0.15 wt% of titanium,

[0042] 0.018 - 0.022 wt% of strontium, and

[0043] the balance being aluminum.

[0044] In another embodiment of the present invention, an aluminum alloy casting is provided, which is manufactured by an integrated die-casting mold designed according to the method of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a three-dimensional schematic view of an integrated die-cast automotive rear floor.

[0046] Figure 2 is a schematic view of the punch moving in the pressure chamber.

[0047] Figure 3 shows a multiple regression modeling report of the ultimate tensile strength of an integrated die-cast casting.

[0048] Figure 4 shows a multiple regression modeling report of the yield strength of an integrated die-cast casting.

[0049] Figure 5 shows a multiple regression modeling report of the elongation at break of an integrated die-cast casting.

[0050] Figure 6 shows a multiple regression modeling report of the Brinell hardness of an integrated die-cast casting.

[0051] Figure 7 shows the metallographic structure diagram of an integrated die-cast casting. The left figure (I) and the right figure (II) respectively represent the microstructures at magnifications of 500 times and 1000 times.

[0052] Figure 8It is a matrix diagram of the ultimate tensile strength UTS (MPa), yield strength (MPa), elongation at break EI (%), Brinell hardness (HBW), and secondary dendrite arm spacing SDAS (μm) of an integrated die-cast casting respectively versus the flow distance FD.

[0053] Figure 9 It is a method flow chart for predicting the mechanical properties of a casting according to an embodiment of the present invention.

[0054] Figure 10 It is a method flow chart for designing a mold for integrated die casting according to an embodiment of the present invention. Detailed implementation mode

[0055] Figure 1 It is a three-dimensional schematic diagram of an integrated die-cast automotive rear floor.

[0056] During the integrated die-casting process, aluminum alloy is pre-melted in a furnace, and the molten aluminum alloy is injected into the pressure chamber. As an embodiment of the present invention, as Figure 2 shown, the pressure chamber 20 is provided with a single punch 10, and the outlet B point of the pressure chamber 20 is connected to the mold. The punch 10 is connected to a hydraulic system. The punch 10 pushes the molten aluminum alloy to move from the inlet A point to the outlet B point in the pressure chamber 20 along the direction of the arrow. The molten aluminum alloy leaves the outlet B point of the pressure chamber 20 at a certain speed, spreads through the mold runner and fills the cavity on the mold surface. The mold runner is relatively flat, and the spreading thickness of the molten aluminum alloy in the mold runner is about 3 - 4 mm. After the molten aluminum alloy is cooled and solidified, it is demolded to obtain an integrated die-cast casting. The distance from point A to point B is called the stroke. Along the order from point A to point B, the entire stroke is divided into a first stage, a second stage, and a third stage. During the manufacturing Figure 1 shown of the automotive rear floor, the first stage and the second stage are the low-speed injection stages, and the average speed of the low-speed injection is about 0.18 to less than about 0.38 m / s. The third stage is the high-speed injection stage, and the average speed of the high-speed injection is greater than about 5.7 to about 7 m / s. The injection speed refers to the distance that the punch 10 moves in the pressure chamber 20 per unit time. In other embodiments of the present invention, the injection speeds of the first stage, the second stage, and the third stage can be the same or different and are independently selected from: low-speed injection speed (L (LIS), with an average of about 0.18 to less than about 0.38 m / s), nominal injection speed (N, with an average of about 0.38 to about 5.7 m / s), high-speed injection speed (H (HIS), with an average of greater than about 5.7 to about 7 m / s).

[0057] Figure 9 It is a method flow chart for predicting the mechanical properties of a casting according to an embodiment of the present invention. As Figure 9As shown, in an embodiment of the present invention, a method for predicting the mechanical properties of a casting by the flow distance of an aluminum alloy melt is provided, which is characterized by including the following steps:

[0058] Step a) Manufacturing a casting by integrally die-casting an aluminum alloy melt through a mold,

[0059] Step b) Selecting a plurality of sampling points on the casting,

[0060] Step c) Calculating the flow distance of the aluminum alloy melt at each sampling point, where the flow distance refers to the flowing distance of the aluminum alloy melt along the mold runner from the outlet of the pressure chamber to the sampling point of the casting during the integrally die-casting process,

[0061] Step d) Testing the mechanical property parameters of the casting at each sampling point, where the mechanical property parameters are one or more selected from the following: ultimate tensile strength, yield strength, fracture elongation rate, and Brinell hardness,

[0062] Step e) According to the flow distance obtained from step c) and the mechanical property parameters obtained from step d) at each sampling point, fitting a function between the mechanical property parameters and the flow distance through multiple regression, and

[0063] Step f) Predicting the mechanical properties of other castings manufactured through the mold at each sampling point based on the flow distance and the function between the mechanical property parameters and the flow distance, where the other castings are from different manufacturing batches than the casting in step a).

[0064] In an embodiment of the present invention, the function between the mechanical property parameters and the flow distance fitted in step e) is one or more of the following formulas:

[0065] i) Formula 1: UTS = a1 - a2*FD + a3*FD 2 ,

[0066] where UTS represents the ultimate tensile strength, in MPa; FD represents the flow distance of the aluminum alloy melt, in m;

[0067] ii) Formula 2: YS = b1 + b2*HIS - b3*FD + b4*FD 2 ,

[0068] where YS represents the yield strength, in MPa; HIS represents the average high-speed injection velocity of the indenter, in m / s; FD represents the flow distance of the aluminum alloy melt, in m;

[0069] iii) Formula 3: EI = c1 - c2*FD,

[0070] Wherein, EI represents the elongation at break, with the unit of %; FD represents the flow distance of the aluminum alloy melt, with the unit of m;

[0071] iv) Formula 4: H = d1 - d2 * FD + d3 * FD 2 ,

[0072] Wherein, H represents the Brinell hardness, with the unit of HBW; FD represents the flow distance of the aluminum alloy melt, with the unit of m;

[0073] Wherein, a1, a2, a3, b1, b2, c1, c2, d1, d2 and d3 are coefficients.

[0074] As a preferred embodiment of the present invention, the coefficients a1, a2, a3, b1, b2, c1, c2, d1, d2 and d3 are determined through specific experiments and / or simulations.

[0075] In the context of the present invention, a numerical value modified by the word "about" means that the modified numerical value can fluctuate by 10% up and down. For example, about 2 meters represents a range of 2 ± 0.2 meters. According to Formulas 1 to 4, the accuracy rate of the calculated predicted value is 95%, and an error not exceeding 5% belongs to the acceptable error range of the predicted value.

[0076] According to the present invention, steps a), b), c), d), e) and f) are carried out in sequence.

[0077] In an embodiment of the present invention, using the same mold, during the die-casting process of 8 batches of castings, the injection speed of the plunger in the first stage, second stage and third stage is adjusted according to Table 1 below. On the 8 batches of castings obtained, 6 - 8 points are selected from each batch, and a total of about 50 - 60 sampling points are selected. By using the prediction method according to the present invention, the relationship curves of the ultimate tensile strength, yield strength, elongation at break and Brinell hardness of the castings with the flow distance of the aluminum alloy melt are fitted and are respectively shown in Figure 3 , Figure 4 , Figure 5 and Figure 6 .

[0078] Table 1 Die-casting process parameters of castings in different batches

[0079]

[0080] Remark: The stroke refers to the distance between the plunger and the inlet of the pressure chamber. L represents the low-speed injection speed, with an average of about 0.18 to less than about 0.38 m / s; N represents the nominal injection speed, with an average of about 0.38 to about 5.7 m / s; H represents the high-speed injection speed, with an average of greater than about 5.7 to about 7 m / s.

[0081] Figure 3Shows the multiple regression modeling report of the ultimate tensile strength. The following steps are carried out in sequence: Step a) Manufacture the Figure 1 automobile rear floor shown; Step b) Select 6-8 sampling points on the castings from 8 batches shown in Table 1 via Flow 3D software; Step c) Calculate the flow distance FD of the aluminum alloy melt at each sampling point via Flow 3D software; Step d) Test the ultimate tensile strength UTS at each sampling point via a universal testing machine; and Step e) According to the flow distance FD obtained from Step c) and the ultimate tensile strength UTS obtained from Step d) at each sampling point, via Minitab software, perform multiple regression fitting to obtain Formula 1: UTS = 358.7 - 166.8 * FD + 46.7 * FD 2 , where UTS represents the ultimate tensile strength, with the unit of MPa; FD represents the flow distance of the aluminum alloy melt, and FD is about 0.8 - 2 m. In Step e), in addition to inputting the two parameters of the ultimate tensile strength UTS and the flow distance FD into Minitab software, two other parameters HIS and LIS are also input, where HIS represents the average high-speed injection velocity of the punch, HIS is greater than about 5.7 to about 7 m / s, and LIS represents the average low-speed injection velocity of the punch, LIS is about 0.18 to less than about 0.38 m / s. After multiple regression screening and fitting by Minitab software, it is first found that the correlation between the ultimate tensile strength UTS and the average high-speed injection velocity HIS and the average low-speed injection velocity LIS is not high, and it is correlated with the flow distance FD and the square of the flow distance FD 2 both. Then, Minitab software fits the curve of the ultimate tensile strength UTS with the flow distance FD and the square of the flow distance FD 2 to obtain Formula 1: UTS = 358.7 - 166.8 * FD + 46.7 * FD 2 . It can be seen from the Figure 3 fitting curve that when the flow distance FD varies within the range of about 0.8 - 2 m, the ultimate tensile strength UTS of the casting decreases as the flow distance FD increases.

[0082] Figure 4 Shows the multiple regression modeling report of the yield strength. The following steps are carried out in sequence: Step a) Manufacture the Figure 1The shown rear floor of the vehicle; Step b) Select 6 - 8 sampling points on each of the castings from the 8 batches shown in Table 1 via Flow 3D software; Step c) Calculate the flow distance FD of the aluminum alloy melt at each sampling point via Flow 3D software; Step d) Test the yield strength YS at each sampling point via a universal testing machine; and Step e) According to the flow distance FD obtained from Step c) and the yield strength YS obtained from Step d) at each sampling point, via Minitab software, fit the formula 2 by multiple regression: YS = 125.3 + 4.7 * HIS - 40.1 * FD + 11.19 * FD 2 , where YS represents the yield strength, with the unit of MPa; HIS represents the average high-speed injection velocity of the punch, and HIS is greater than about 5.7 to about 7 m / s; FD represents the flow distance of the aluminum alloy melt, and FD is about 0.8 - 2 m. In Step e), in addition to inputting the two parameters of the yield strength YS and the flow distance FD into Minitab software, two other parameters HIS and LIS are also input, where HIS represents the average high-speed injection velocity of the punch, HIS is greater than about 5.7 to about 7 m / s, and LIS represents the average low-speed injection velocity of the punch, and LIS is about 0.18 to less than about 0.38 m / s. After multiple regression screening and fitting by Minitab software, it is first found that the correlation between the yield strength YS and LIS is not high, and it is correlated with the average high-speed injection velocity HIS, the flow distance FD, and the square of the flow distance FD 2 are all correlated. Then, Minitab software fits the curve of the yield strength YS with the average high-speed injection velocity HIS, the flow distance FD, and the square of the flow distance FD 2 to obtain the formula 2: YS = 125.3 + 4.7 * HIS - 40.1 * FD + 11.19 * FD 2 . In Figure 4 , when analyzing the "increasing influence of X variables", as shown by the length of the black rectangle, compared with the influence of the flow distance FD, the influence of the average high-speed injection velocity HIS on the yield strength YS of the casting is not obvious.

[0083] Figure 5 shows the multiple regression modeling report of the elongation at break. The following steps are carried out in sequence: Step a) Manufacture by die-casting aluminum alloy melt integrally Figure 1The automotive rear floor shown; Step b) Select 6 - 8 sampling points on each of the castings from the 8 batches shown in Table 1 via Flow 3D software; Step c) Calculate the flow distance FD of the aluminum alloy melt at each sampling point via Flow 3D software; Step d) Test the elongation at break EI at each sampling point via a universal testing machine; and Step e) According to the flow distance FD obtained from Step c) and the elongation at break EI obtained from Step d) at each sampling point, via Minitab software, fit the formula 3 by multiple regression: EI = 10.38 - 3.412 * FD, where EI represents the elongation at break, in %; FD represents the flow distance of the aluminum alloy melt, and FD is about 0.8 - 2 m. In Step e), in addition to inputting the two parameters of elongation at break EI and flow distance FD into Minitab software, two other parameters HIS and LIS are also input, where HIS represents the average high-speed injection velocity of the plunger, HIS is greater than about 5.7 to about 7 m / s, and LIS represents the average low-speed injection velocity of the plunger, LIS is about 0.18 to less than about 0.38 m / s. After multiple regression screening and fitting by Minitab software, it is first found that the correlation between the elongation at break EI and the average high-speed injection velocity HIS and the average low-speed injection velocity LIS is not high, and it is correlated with the flow distance FD. Then, Minitab software fits the curve of the elongation at break EI and the flow distance FD to obtain the formula 3: EI = 10.38 - 3.412 * FD. From Figure 5 the fitted curve, it can be seen that when the flow distance FD varies within the range of about 0.8 - 2 m, the elongation at break EI of the casting shows a linear decreasing trend as the flow distance FD increases.

[0084] Figure 6 shows the multiple regression modeling report of Brinell hardness. The following steps are carried out in sequence: Step a) Manufacture the Figure 1 automotive rear floor shown by die-casting aluminum alloy melt; Step b) Select 6 - 8 sampling points on each of the castings from the 8 batches shown in Table 1 via Flow 3D software; Step c) Calculate the flow distance FD of the aluminum alloy melt at each sampling point via Flow 3D software; Step d) Test the Brinell hardness H at each sampling point via a universal testing machine; and Step e) According to the flow distance obtained from Step c) and the Brinell hardness H obtained from Step d) at each sampling point, via Minitab software, fit the formula 4 by multiple regression: H = 95.26 - 24.36 * FD + 7.66 * FD 2, where H represents the Brinell hardness, with the unit of HBW; FD represents the flow distance of the aluminum alloy melt, and FD is approximately 0.8 - 2 m. In step e), in addition to inputting the two parameters of Brinell hardness H and FD into the Minitab software, two other parameters HIS and LIS are also input. Among them, HIS represents the average high-speed injection velocity of the indenter, and HIS is greater than approximately 5.7 to approximately 7 m / s. LIS represents the average low-speed injection velocity of the indenter, and LIS is approximately 0.18 to less than approximately 0.38 m / s. After the Minitab software performs multiple regression screening and fitting, it is first found that the correlation between the Brinell hardness H and the average high-speed injection velocity HIS and the average low-speed injection velocity LIS is not high, and it is correlated with the flow distance FD and the square of the flow distance FD 2 Then, the Minitab software fits the curve of the Brinell hardness H with the flow distance FD and the square of the flow distance FD 2 to obtain Formula 4: H = 95.26 - 24.36 * FD + 7.66 * FD 2 . From Figure 6 the fitted curve, it can be seen that when the flow distance FD varies within the range of approximately 0.8 - 1.6 m, the Brinell hardness H decreases as the flow distance FD increases; when the flow distance FD varies within the range of approximately 1.6 - 2 m, the Brinell hardness H increases as the flow distance FD increases.

[0085] During the process of performing multiple regression fitting as Figures 3 to 6 , the inventor found that the relationships between physical property parameters, such as ultimate tensile strength, yield strength, elongation at break, and Brinell hardness, and different injection velocities are discrete, which means there is no obvious and intuitive connection between these physical property parameters and the injection velocity. The inventor also found that compared with the injection velocity, physical property parameters, such as ultimate tensile strength, yield strength, elongation at break, and Brinell hardness, are more sensitive to the influence of the flow distance. When the flow distance varies within the range of approximately 0.8 - 2 m, as the flow distance increases, the ultimate tensile strength, yield strength, and elongation at break all show a decreasing trend. When the flow distance varies within the range of approximately 0.8 - 1.6 m, the Brinell hardness decreases as the flow distance FD increases; when the flow distance varies within the range of approximately 1.6 - 2 m, the Brinell hardness increases as the flow distance FD increases.

[0086] As a preferred embodiment of the present invention, in step b), when selecting the sampling points, the selected casting samples are flat and are formed by the aluminum alloy melt along the same injection direction. Selecting the casting samples according to such rules avoids uneven panels and interference from different injection directions to the mechanical properties tested in the subsequent step d), and ultimately affects the accuracy of the fitting formula in step e).

[0087] As a preferred embodiment of the present invention, in step a), different batches of castings are prepared by changing the speed of the punch in the three stages of the stroke. In step b), casting samples from different batches are selected. For each batch of castings, 4 - 15 sampling points are selected, preferably 5 - 10 sampling points, and more preferably 6 - 8 sampling points. There are no specific requirements for sampling, as long as the sampling is evenly distributed within the flow distance range and complex structure positions are avoided. The more batches the casting samples come from, and the more points are selected on the casting samples, the higher the accuracy of the fitting formula and the more reliable the prediction result of the casting performance.

[0088] Figure 10 is a flowchart of a method for designing a die-casting mold for integrated die-casting according to an embodiment of the present invention. As Figure 10 shown, in another embodiment of the present invention, a method for designing a die-casting mold for integrated die-casting is provided, which is characterized by including the following steps:

[0089] Predict the mechanical property parameters of the castings at each sampling point according to the method of predicting the mechanical properties of aluminum alloy castings through the flow distance of the aluminum alloy melt according to the present invention; and

[0090] According to the difference between the predicted value and the expected value of the mechanical property parameters, change the structural settings at the corresponding positions of the mold. Thus, the flow distance of the aluminum alloy melt at the sampling point can be changed, so that the castings manufactured via the mold have the expected value of the mechanical property parameters at the sampling point, realizing the correction and optimization of the mechanical properties of the product.

[0091] In another embodiment of the present invention, an aluminum alloy casting is provided, which is manufactured by an integrated die-casting mold designed according to the method of the present invention. The mold designed according to the present invention has its structure changed and adjusted, changing the flow distance of the aluminum alloy melt at the sampling point, so that the castings manufactured via the mold have the expected value of the mechanical property parameters at the sampling point, and the resulting product has corrected and optimized mechanical properties.

[0092] As a preferred embodiment of the present invention, by providing one or more local settings such as obstacles, bends, and / or flow guiding means, such as ribs, stiffeners, and / or bosses, to change the structure of the mold, especially the runner settings, thereby affecting the flow path of the aluminum alloy melt in the mold, and further changing the mechanical properties of the castings manufactured via the mold.

[0093] In addition, the local thickness of the product manufactured via the mold can be changed by altering the runner depth or cavity depth of the mold, thereby optimizing the product performance at that location. As mentioned above, the mold runner is relatively flat, and the spreading thickness of the aluminum alloy melt in the mold runner is approximately 3 - 4 mm, and the thickness of the casting manufactured via this mold is also approximately 3 - 4 mm. Considering that the casting thickness is extremely thin relative to the length (about 0.8 - 2 m) and width (greater than 0 - about 2 m) of the casting, in mold design, it is preferably to mainly adjust the flow path of the aluminum alloy melt in the runner, and the runner depth or cavity depth can be adjusted or not adjusted.

[0094] Figure 7 It is a metallographic structure diagram of the casting at a sample point N2 selected at a position where the flow distance of the integrated die-cast casting is about 1 - 1.2 m according to an embodiment of the present invention. The left figure (I) and the right figure (II) respectively represent the microstructures at magnification ratios of 500 times and 1000 times. The letter a represents the α-aluminum phase, the letter b represents the eutectic silicon structure, the letter c represents intermetallic compounds (e.g., Fe-Mn, Mg-Si), and the letter d represents casting defects (e.g., pores and oxide inclusions). The larger the secondary dendrite arm spacing, the coarser the dendritic structure, indicating that the microstructure of the casting is rougher and the mechanical properties of the casting are poorer.

[0095] As shown in Table 2 below, in addition to Figure 7 the sample point N2 shown in, three other sample points N1, N3, and N4 were also selected at different flow distances of the same casting. The secondary dendrite arm spacing SDAS (μm) was measured for these four sample points and recorded in Table 2 below.

[0096] Table 2

[0097]

[0098] As can be seen from Table 2 above, when the flow distance varies within the range of 0.8 - 2 m, the average secondary dendrite arm spacing SDAS increases with the increase of the flow distance FD, which is consistent with Figures 3 to 6 the conclusion that as the flow distance FD increases, the casting becomes rougher and the mechanical properties deteriorate.

[0099] Figure 8 It is a fitting curve of the ultimate tensile strength UTS (MPa), yield strength (MPa), elongation at break EI (%), Brinell hardness H (HBW), and secondary dendrite arm spacing SDAS (μm) of six sample points taken at different flow distances of the same casting according to an embodiment of the present invention, with each of these four parameters versus the flow distance FD.

[0100] As Figure 8As shown, as the flow distance increases from 0.8 m to 2 m, the secondary dendrite arm spacing increases. Correspondingly, the ultimate tensile strength UTS (MPa), yield strength (MPa), elongation at break EI (%), and hardness (HEW) all show a trend of deterioration as the flow distance increases.

[0101] As a preferred embodiment of the present invention, the aluminum alloy is an aluminum-silicon alloy.

[0102] As a preferred embodiment of the present invention, based on the total weight of the aluminum alloy, the aluminum alloy comprises:

[0103] about 7.6 - about 8.5 wt% of silicon,

[0104] 0 - about 0.15 wt% of iron,

[0105] about 0.5 - about 0.6 wt% of manganese,

[0106] about 0.2 - about 0.25 wt% of magnesium,

[0107] about 0.07 - about 0.15 wt% of titanium,

[0108] about 0.018 - about 0.022 wt% of strontium, and

[0109] the balance being aluminum.

[0110] Such an aluminum alloy can be both heat-treatment-free and have high strength and high toughness, and is suitable for the casting mechanical property prediction method and mold design method according to the present invention to provide large-sized integral castings with large volume and thin walls.

[0111] Although not explicitly listed, according to the method of the present invention, the aluminum alloy may also contain other impurities, which are inevitably present due to the source or preparation process of the aluminum alloy material, or impurities are deliberately doped into the aluminum alloy to improve the casting performance, as long as the effects of the present invention are not affected.

[0112] According to the method of the present invention, compared with other heat-treatment-free or non-heat-treatable aluminum-silicon alloys commonly used in integral die-casting, the types of elements that need to be deliberately added are reduced, especially rare earth elements do not need to be deliberately added.

[0113] As a preferred embodiment of the present invention, the melting temperature of the aluminum alloy is about 740 °C.

[0114] As a preferred embodiment of the present invention, the drossing agent is a potassium-based sodium-free drossing agent. Based on the weight of the aluminum alloy, the dosage of the drossing agent can be about 0.5 wt%.

[0115] As a preferred embodiment of the present invention, the aluminum alloy is degassed in nitrogen for about 15 minutes.

[0116] As a preferred embodiment of the present invention, the aluminum alloy is modified with strontium, for example, adding AlSr10 with a density of 2.64 to the aluminum alloy for strontium modification.

[0117] The method for predicting the mechanical properties of castings and the method for designing casting molds according to the present invention are applicable to automobile bodies, including automobile floors, such as the front compartment, the rear floor, and the middle floor.

[0118] Although specific embodiments of the present invention are disclosed in detail herein, they are merely examples given for the purpose of explanation and should not be considered as limiting the scope of the present invention defined in the claims. Various substitutions, changes, and modifications can be conceived without departing from the spirit and scope of the present invention defined in the claims.

Claims

1. A method for predicting the mechanical properties of a casting by the flow distance of an aluminum alloy melt, characterized in that It includes the following steps: Step a) manufacturing a casting by integrally die-casting an aluminum alloy melt through a mold; Step b) selecting a plurality of sampling points on the casting; Step c) calculating the flow distance of the aluminum alloy melt at each sampling point, where the flow distance refers to the flowing distance of the aluminum alloy melt along the mold runner from the outlet of the pressure chamber to the sampling point of the casting during the integrally die-casting process; Step d) testing the mechanical property parameters of the casting at each sampling point, where the mechanical property parameters are one or more selected from the following: ultimate tensile strength, yield strength, elongation at break, and Brinell hardness; Step e) fitting a function between the mechanical property parameters and the flow distance obtained from step c) and the mechanical property parameters obtained from step d) through multiple regression; and Step f) predicting the mechanical properties of other castings manufactured through the mold at each sampling point based on the flow distance and the function between the mechanical property parameters and the flow distance, where the other castings are from different manufacturing batches than the casting in step a).

2. The method according to claim 1, wherein the function between the mechanical property parameters and the flow distance fitted in step e) is one or more of the following formulas: i) Formula 1: UTS = a1 - a2*FD + a3*FD 2 , where UTS represents the ultimate tensile strength, in MPa; FD represents the flow distance of the aluminum alloy melt, in m; ii) Formula 2: YS = b1 + b2 * HIS - b3 * FD + b4 * FD 2 , where YS represents the yield strength, in MPa; HIS represents the average high-speed injection velocity of the indenter, in m / s; FD represents the flow distance of the aluminum alloy melt, in m; iii) Formula 3: EI = c1 - c2 * FD, where EI represents the elongation at break, in %; FD represents the flow distance of the aluminum alloy melt, in m; iv) Formula 4: H = d1 - d2 * FD + d3 * FD 2 , where H represents the Brinell hardness, in HBW; FD represents the flow distance of the aluminum alloy melt, in m; Among them, a1, a2, a3, b1, b2, c1, c2, d1, d2, and d3 are coefficients.

3. The method according to claim 2, wherein FD is 0.8 - 2 m.

4. The method according to any one of claims 2 - 3, wherein HIS is greater than 5.7 to 7 m / s.

5. The method according to any one of claims 1 - 4, wherein in step b), 4 - 15 sampling points are selected, preferably 5 - 10 sampling points, more preferably 6 - 8 sampling points.

6. The method according to any one of claims 1 - 5, wherein in step b), a mold flow analysis software is used to select the sampling points.

7. The method according to any one of claims 1 - 6, wherein in step c), a mold flow analysis software is used to calculate the flow distance of the aluminum alloy melt at each sampling point.

8. The method according to any one of claims 1 - 7, wherein in step d), a universal testing machine is used to test the mechanical property parameters of the casting at each sampling point.

9. The method according to any one of claims 1 - 8, wherein in step e), Minitab software is used for fitting.

10. The method according to any one of claims 2 - 9, wherein in step e), 2, 3, or 4 of Formula 1, Formula 2, Formula 3, and Formula 4 are fitted. Preferably, formulas 1, 2, 3, and 4 are fitted out.

11. A method for designing a mold for integrated die casting, characterized in that, It includes the following steps: According to the method described in any one of claims 1-10, based on the flow distance and the function between the mechanical property parameters and the flow distance, predict the mechanical property parameters of the casting at each sampling point; and According to the difference between the predicted value and the expected value of the mechanical property parameters and the function between the mechanical property parameters and the flow distance, change the structural settings at the corresponding positions of the mold.

12. According to the method described in claim 11, wherein changing the structural settings at the corresponding positions of the mold is to change the runner settings at the corresponding positions of the mold, Preferably, the runner settings of the mold are changed in the following manner: providing one or more obstacles, bends, and / or flow guiding means.

13. According to the method described in any one of claims 1-12, wherein the aluminum alloy is an aluminum-silicon alloy; Preferably, based on the total weight of the aluminum alloy, the aluminum alloy contains: 7.6-8.5 wt% of silicon, 0-0.15 wt% of iron, 0.5-0.6 wt% of manganese, 0.2-0.25 wt% of magnesium, 0.07-0.15 wt% of titanium, 0.018-0.022 wt% of strontium, and the balance being aluminum.

14. An aluminum alloy casting manufactured by an integrated die-casting mold designed according to the method described in any one of claims 11-13.

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