Manufacturing method of high-precision mold for casting complex cavity

Abstract

The invention relates to the technical field of mold manufacturing, and discloses a high-precision mold manufacturing method for complex cavity casting, which comprises the following steps: S1, based on a casting three-dimensional model, analyzing a definite stress area through thermal-mechanical coupling, generating a honeycomb frame through a bionic algorithm, and optimizing a parting surface, S2, manufacturing a mold base body through a metal additive, embedding a conformal cooling channel, and carrying out five-axis numerical control finish machining, and S3, carrying out five-axis numerical control finish machining. S4, positioning and assembling the mold blocks by using a laser tracker, and embedding a sensor array; and S5, cooperatively demolding by adopting pneumatic assistance and multidirectional hydraulic core pulling. The stress concentration area of the mold is determined through thermal-mechanical coupling finite element analysis, and a lightweight supporting frame of a honeycomb-like structure is generated based on a biological growth model or a topological optimization iterative algorithm, so that the weight of the mold is reduced, the bearing capacity of the stress concentration area is effectively enhanced, and the service life of the mold is prolonged. A stable mold foundation is provided for casting of a complex cavity, and the precision of the production process is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of mold manufacturing, and specifically to a high-precision mold manufacturing method for complex cavity casting. Background Art

[0002] In modern manufacturing, complex cavity castings are widely used in many key fields such as aerospace, automotive, and energy. For example, the blades of aeroengines, the complex internal cooling cavity structure of which is crucial for improving the thermal efficiency and performance of the engine; the cylinder block of an automotive engine, the complex cavity structures such as water jackets and oil channels inside it directly affect the cooling effect and lubrication performance of the engine. However, manufacturing such castings with complex cavity structures poses extremely high requirements on the accuracy and performance of the mold.

[0003] Currently, during high-precision mold manufacturing, conventional design methods often only rely on experience and simple mechanical analysis, and cannot accurately consider the influence of complex factors such as thermal-mechanical coupling during the casting process on the mold. This results in the mold being prone to premature failure in stress concentration areas during actual use, seriously affecting the casting accuracy of the mold. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a high-precision mold manufacturing method for complex cavity casting, which solves the problem of premature failure in stress concentration areas, seriously affecting the casting accuracy of the mold.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A high-precision mold manufacturing method for complex cavity casting, including the following steps:

[0006] S1, based on the three-dimensional model of the casting, determine the stress concentration area of the mold through thermal-mechanical coupling finite element analysis, generate a lightweight support frame with a honeycomb-like structure using a bionics algorithm, and automatically optimize the parting surface along the minimum curvature path of the cavity;

[0007] S2, use metal additive manufacturing technology to form the mold matrix, synchronously embed conformal cooling channels matching the cavity geometry in the support frame, and then perform micron-level subtractive finishing on the parting surface and the cavity surface through a five-axis CNC machine tool;

[0008] S3, deposit a physical vapor deposition coating on the machined cavity surface to form a hard wear-resistant layer with a thickness of 10 - 20 μm;

[0009] S4, use a laser tracker to perform six-degree-of-freedom positioning and assembly on the mold blocks, and bury a sensor array inside the mold to monitor the casting parameters in real time and dynamically adjust the process;

[0010] S5 adopts pneumatic assistance and multi-directional hydraulic core-pulling for collaborative demolding, and dynamically adjusts the demolding parameters according to the sensor data.

[0011] Preferably, in the S1, the bionic algorithm is based on a biological growth model or a topology optimization iteration algorithm. The size of the honeycomb structure unit generated is 0.5 - 3 mm, the porosity is 40% - 65%, and the ratio of the honeycomb wall thickness to the radius of curvature of the adjacent cavity is 1:5 to 1:10.

[0012] Preferably, in the S2, the cross-sectional shape of the conformal cooling channel is a gradually changing circle, ellipse or polygon. The channel diameter gradually changes from 2.5 - 4 mm at the inlet end to 1.0 - 1.8 mm at the end, and the distance between the center of the channel and the cavity surface is 1.2 - 1.8 times the maximum diameter of the channel.

[0013] Preferably, the physical vapor deposition coating is at least one of TiAlN, TiSiN or CrAlSiN. The coating thickness is 12 - 18 μm, the surface hardness ≥ 3000 HV, and the bonding strength between the coating and the substrate ≥ 50 N.

[0014] Preferably, in the S4, the sensor array includes a distributed optical fiber sensor and a micro piezoelectric sensor. The temperature monitoring accuracy ≤ ±1 °C, the pressure monitoring resolution ≤ 0.1 MPa, and the data feedback delay ≤ 50 ms.

[0015] Preferably, in the S5, the demolding includes the following steps:

[0016] Collaborative demolding mechanism control: The pneumatic-assisted demolding mechanism and the multi-directional hydraulic core-pulling mechanism act in coordination. The synchronous error between the core-pulling speed and the casting shrinkage rate ≤ 3%, the initial core-pulling angle is set to 0.8° - 1.2°, and it is adaptively adjusted based on the real-time shrinkage deformation amount, with the adjustment range ≤ ±0.2°, and the repeated positioning accuracy of the core-pulling stroke ≤ ±0.005 mm;

[0017] Preloading stress compensation: During the solidification stage of the casting, a reverse stress is preloaded through the multi-directional hydraulic core-pulling mechanism. The preloading force is 5% - 15% of the casting pressure, which is used to offset the shrinkage deformation;

[0018] Timing precise control: The collaborative action timing of the pneumatic assistance and the hydraulic core-pulling is controlled by a PLC, and the timing error ≤ 1 ms.

[0019] Preferably, the dynamic adjustment process in the S4 includes:

[0020] According to the real-time temperature gradient monitored by the optical fiber sensor, control the flow rate of the coolant in the conformal cooling channel. The flow rate adjustment range is 0.5 - 5 m / s, and the temperature fluctuation is suppressed within ±10 °C;

[0021] Based on the pressure distribution data of the micro piezoelectric sensor, adjust the loading curve of the casting press, and the pressure deviation compensation amount ≤ 0.5 MPa.

[0022] Preferably, in S4, the coating wear rate, the thermal fatigue crack propagation length, and the cooling channel blockage rate monitored by the sensor are used to predict the remaining life of the mold; when the remaining life is lower than the preset threshold, an automatic repair warning is triggered.

[0023] Preferably, the life prediction adopts a time series prediction model based on the LSTM neural network. The input parameters include: the coating thickness wear rate, the number of thermal cycles, and the change rate of the cooling channel pressure drop. The remaining life and the confidence interval are output. The model training data is sourced from the failure sample library of at least 1000 casting experiments.

[0024] Preferably, in S2, the metal additive manufacturing process includes the following steps:

[0025] Perform hot isostatic pressing on the formed mold substrate at a temperature of 1100 - 1250 °C, a pressure of 100 - 150 MPa, and a holding time of 2 - 4 hours;

[0026] After heat treatment, electrolytic polishing is used to reduce the surface roughness. The polishing depth is 10 - 30 μm, and the surface microcrack density is reduced to ≤ 5 cracks / cm 2 .

[0027] The present invention provides a high-precision mold manufacturing method for complex cavity casting. It has the following beneficial effects:

[0028] 1. The present invention determines the mold stress concentration area through thermo-mechanical coupling finite element analysis, and adopts a lightweight support frame with a honeycomb-like structure generated based on the biological growth model or the topology optimization iteration algorithm. While reducing the weight of the mold, it effectively enhances the bearing capacity of the stress concentration area, provides a stable mold foundation for complex cavity casting, and ensures the accuracy of the production process.

[0029] 2. By combining the metal additive manufacturing process with a five-axis CNC machine tool, the present invention can manufacture a mold substrate and cavity that meet the design requirements. At the same time, for the conformal cooling channels, the gradual cross-sectional shape, diameter change, and distance setting from the cavity surface achieve a good match with the complex cavity, solve the problem of uneven casting cooling caused by the mismatch between traditional cooling channels and complex cavities, significantly improve the forming accuracy and quality of the casting, and meet the high-precision requirements of complex cavity castings.

[0030] 3. The present invention preloads reverse stress through a pneumatic-hydraulic cooperative demoulding mechanism, with a casting pressure of 5%-15% and a synchronous error of core pulling speed ≤3%, reducing sticking die damage. Combining with the LSTM neural network prediction model, it warns the life threshold based on data such as coating wear rate and crack propagation, achieving a prediction error of the remaining die life ≤5%, with a longer service life, and solving the problem of rising costs caused by frequent replacement of high-value dies. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the method steps of the high-precision die manufacturing method for complex cavity casting of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0033] Please refer to the attached Figure 1 , the embodiment of the present invention provides a high-precision die manufacturing method for complex cavity casting, including the following steps:

[0034] S1. Based on the three-dimensional model of the casting, determine the die stress concentration area through thermo-mechanical coupling finite element analysis, generate a lightweight support frame with a honeycomb-like structure using a bionics algorithm, and automatically optimize the parting surface along the minimum curvature path of the cavity.

[0035] S2. Use the metal additive manufacturing process to form the die matrix, synchronously embed conformal cooling channels matching the cavity geometry in the support frame, and then perform micron-level subtractive finishing on the parting surface and the cavity surface through a five-axis CNC machine tool.

[0036] S3. Deposit a physical vapor deposition coating on the machined cavity surface to form a hard wear-resistant layer with a thickness of 10-20 μm.

[0037] S4. Use a laser tracker to perform six-degree-of-freedom positioning and assembly on the die blocks, and bury a sensor array inside the die to monitor the casting parameters in real time and dynamically adjust the process.

[0038] S5. Adopt pneumatic assistance and multi-directional hydraulic core pulling for cooperative demoulding, and dynamically adjust the demoulding parameters according to the sensor data.

[0039] In S1, the bionics algorithm is based on a biological growth model or a topology optimization iteration algorithm. The size of the generated honeycomb structure unit is 0.5-3 mm, the porosity is 40%-65%, and the ratio of the honeycomb wall thickness to the adjacent cavity curvature radius is 1:5 to 1:10.

[0040] In S2, the cross-sectional shape of the conformal cooling channels is a gradually changing circle, ellipse, or polygon. The channel diameter gradually changes from 2.5 - 4 mm at the inlet end to 1.0 - 1.8 mm at the end, and the distance from the center of the channel to the cavity surface is 1.2 - 1.8 times the maximum diameter of the channel.

[0041] The physical vapor deposition coating is at least one of TiAlN, TiSiN, or CrAlSiN. The coating thickness is 12 - 18 μm, the surface hardness ≥ 3000 HV, and the adhesion between the coating and the substrate ≥ 50 N.

[0042] In S4, the sensor array includes a distributed optical fiber sensor and a micro piezoelectric sensor. The temperature monitoring accuracy ≤ ±1 °C, the pressure monitoring resolution ≤ 0.1 MPa, and the data feedback delay ≤ 50 ms.

[0043] In S5, the demolding includes the following steps:

[0044] Coordinated demolding mechanism control: An air-assisted demolding mechanism and a multi-directional hydraulic core-pulling mechanism are used to act in coordination. The synchronous error between the core-pulling speed and the casting shrinkage rate ≤ 3%, the initial core-pulling angle is set to 0.8° - 1.2°, and it is adaptively adjusted based on the real-time shrinkage deformation amount, with the adjustment range ≤ ±0.2°, and the repeated positioning accuracy of the core-pulling stroke ≤ ±0.005 mm;

[0045] Preloading stress compensation: During the solidification stage of the casting, a reverse stress is preloaded through the multi-directional hydraulic core-pulling mechanism. The preloading force is 5% - 15% of the casting pressure, which is used to offset the shrinkage deformation;

[0046] Timing precise control: The coordinated action timing of the air-assisted and hydraulic core-pulling is controlled by a PLC, and the timing error ≤ 1 ms.

[0047] The dynamic adjustment process in S4 includes:

[0048] According to the real-time temperature gradient monitored by the optical fiber sensor, the flow rate of the coolant in the conformal cooling channels is controlled. The flow rate adjustment range is 0.5 - 5 m / s, and the temperature fluctuation is suppressed within ±10 °C;

[0049] Based on the pressure distribution data of the micro piezoelectric sensor, the loading curve of the casting press is adjusted, and the pressure deviation compensation amount ≤ 0.5 MPa.

[0050] The coating wear rate, thermal fatigue crack propagation length, and cooling channel blockage rate monitored by the sensors in S4 are used to predict the remaining life of the mold; when the remaining life is lower than the preset threshold, an automatic repair warning is triggered.

[0051] The life prediction adopts a time series prediction model based on the LSTM neural network. The input parameters include: the wear rate of the coating thickness, the number of thermal cycles, and the change rate of the pressure drop in the cooling channel. The output is the remaining life and the confidence interval. The model training data is sourced from a failure sample library of at least 1000 casting experiments.

[0052] In S2, the metal additive manufacturing process includes the following steps:

[0053] Perform hot isostatic pressing on the formed die substrate at a temperature of 1100 - 1250 °C, a pressure of 100 - 150 MPa, and a holding time of 2 - 4 hours;

[0054] After heat treatment, electrolytic polishing is used to reduce the surface roughness. The polishing depth is 10 - 30 μm, and the surface microcrack density is reduced to ≤5 cracks / cm 2 。

[0055] The following is an introduction in combination with specific embodiments:

[0056] Embodiment 1: A die manufacturing method based on a bionic structure and a composite process

[0057] Die design and manufacturing process:

[0058] Based on the three-dimensional model of the casting, the stress concentration area of the die is determined through thermo-mechanical coupling finite element analysis. A lightweight support frame with a honeycomb-like structure is generated using a bionics algorithm. The honeycomb cell size is 0.5 - 3 mm, the porosity is 40% - 65%, and the ratio of the honeycomb wall thickness to the radius of curvature of the adjacent cavity is 1:5 to 1:10. The parting surface is optimized along the minimum curvature path of the cavity.

[0059] Use metal additive manufacturing to form the die substrate, and simultaneously embed conformal cooling channels that match the cavity geometry within the support frame. The cross-section of the channels is a gradually changing circle / ellipse / polygon, with an inlet diameter of 2.5 - 4 mm and a terminal diameter of 1.0 - 1.8 mm. The distance from the center of the channels to the surface of the cavity is 1.2 - 1.8 times the maximum diameter; after forming, perform hot isostatic pressing on the die substrate at a temperature of 1100 - 1250 °C, a pressure of 100 - 150 MPa, and hold for 2 - 4 hours. Then, reduce the surface roughness through electrolytic polishing, with a polishing depth of 10 - 30 μm and a microcrack density of ≤5 cracks / cm 2 。

[0060] Deposit a physical vapor deposition coating on the surface of the finish-machined cavity. The material is TiAlN, TiSiN, or CrAlSiN, with a thickness of 12 - 18 μm, a surface hardness of ≥3000 HV, and a bonding strength of ≥50 N.

[0061] Demolding process:

[0062] Pneumatic assistance and multi-directional hydraulic core-pulling are used for cooperative demolding. The synchronous error between the core-pulling speed and the casting shrinkage rate is ≤ 3%. The initial setting of the core-pulling angle is 0.8° - 1.2°, and the adaptive adjustment range is ≤ ±0.2°. The stroke repeat positioning accuracy is ≤ ±0.005 mm. A preloaded reverse stress (5% - 15% of the casting pressure) is used to offset the shrinkage deformation, and the PLC control timing error is ≤ 1 ms

[0063] Example 2: A dynamic regulation method integrating intelligent monitoring and life prediction

[0064] Sensor deployment and real-time regulation:

[0065] Distributed fiber optic sensors with a temperature accuracy of ≤ ±1°C and miniature piezoelectric sensors with a pressure resolution of ≤ 0.1 MPa are buried inside the mold, and the data feedback delay is ≤ 50 ms

[0066] Dynamic process adjustment:

[0067] The flow rate of the conformal cooling channels is controlled according to the temperature gradient, 0.5 - 5 m / s, to suppress the temperature fluctuation ≤ ±10°C

[0068] The loading curve of the casting press is adjusted according to the pressure distribution, and the pressure deviation compensation is ≤ 0.5 MPa

[0069] Life prediction and maintenance warning:

[0070] Based on the coating wear rate, thermal fatigue crack propagation length, and cooling channel blockage rate monitored by sensors, an LSTM neural network model is used to predict the remaining life of the mold and output the confidence interval

[0071] The model training data comes from a failure sample library of at least 1000 casting experiments. When the remaining life is lower than the threshold, an automatic repair warning is triggered

[0072] Intelligent demolding optimization:

[0073] Based on the demolding process of Example 1, the core-pulling speed and angle are dynamically adjusted in combination with sensor data to achieve real-time matching of casting shrinkage - demolding actions

[0074] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents

Claims

1. A high-precision mold manufacturing method for complex cavity casting, characterized in that: It includes the following steps: S1. Based on the three-dimensional model of the casting, determine the stress concentration area of the mold through thermo-mechanical coupling finite element analysis, generate a lightweight support frame with a honeycomb-like structure using a bionic algorithm, and automatically optimize the parting surface along the minimum curvature path of the cavity; S2. Use the metal additive manufacturing process to form the mold matrix, synchronously embed conformal cooling channels geometrically matching the cavity in the support frame, and then perform micron-level subtractive finish machining on the parting surface and the cavity surface through a five-axis CNC machine tool; S3. Deposit a physical vapor deposition coating on the finish-machined cavity surface to form a hard wear-resistant layer with a thickness of 10 - 20 μm; S4. Use a laser tracker to perform six-degree-of-freedom positioning and assembly on the mold blocks, and bury a sensor array inside the mold to monitor the casting parameters in real time and dynamically adjust the process; S5. Use pneumatic assistance and multi-directional hydraulic core pulling to cooperate in demolding, and dynamically adjust the demolding parameters according to the sensor data.

2. The high-precision mold manufacturing method for complex cavity casting according to claim 1, wherein: In the above S1, the bionic algorithm is based on a biological growth model or a topology optimization iteration algorithm. The size of the honeycomb structure unit generated is 0.5 - 3 mm, the porosity is 40% - 65%, and the ratio of the honeycomb wall thickness to the curvature radius of the adjacent cavity is 1:5 to 1:

10.

3. The high-precision mold manufacturing method for complex cavity casting according to claim 1, characterized in that: In the above S2, the cross-sectional shape of the conformal cooling channel is a gradually changing circle, ellipse or polygon. The channel diameter gradually changes from 2.5 - 4 mm at the inlet end to 1.0 - 1.8 mm at the end, and the distance between the channel center and the cavity surface is 1.2 - 1.8 times the maximum diameter of the channel.

4. The high-precision mold manufacturing method for complex cavity casting according to claim 1, characterized in that: The physical vapor deposition coating is at least one of TiAlN, TiSiN or CrAlSiN. The coating thickness is 12 - 18 μm, the surface hardness ≥ 3000 HV, and the bonding force between the coating and the substrate ≥ 50 N.

5. The high-precision mold manufacturing method for complex cavity casting according to claim 1, characterized in that: In the above S4, the sensor array includes a distributed optical fiber sensor and a micro piezoelectric sensor. The temperature monitoring accuracy ≤ ±1 °C, the pressure monitoring resolution ≤ 0.1 MPa, and the data feedback delay ≤ 50 ms.

6. The high-precision mold manufacturing method for complex cavity casting according to claim 1, characterized in that: In the above S5, the demolding includes the following steps: Coordinated demolding mechanism control: Use a pneumatic assistance demolding mechanism and a multi-directional hydraulic core pulling mechanism to act cooperatively. The synchronous error between the core pulling speed and the casting shrinkage rate ≤ 3%, the initial setting of the core pulling angle is 0.8° - 1.2°, and it is adaptively adjusted based on the real-time shrinkage deformation amount, with the adjustment range ≤ ±0.2°, and the repeat positioning accuracy of the core pulling stroke ≤ ±0.005 mm; Preloading stress compensation: During the solidification stage of the casting, preload a reverse stress through the multi-directional hydraulic core pulling mechanism. The preloading force is 5% - 15% of the casting pressure, which is used to offset the shrinkage deformation; Timing precise control: The coordinated action timing of pneumatic assistance and hydraulic core pulling is controlled by a PLC, and the timing error ≤ 1 ms.

7. The high-precision mold manufacturing method for complex cavity casting according to claim 1, characterized in that: The dynamic adjustment process in the above S4 includes: According to the real-time temperature gradient monitored by the optical fiber sensor, control the flow rate of the coolant in the conformal cooling channel. The flow rate adjustment range is 0.5 - 5 m / s, and the temperature fluctuation is suppressed within ±10 °C; Based on the pressure distribution data of the micro piezoelectric sensor, adjust the loading curve of the casting press, and the pressure deviation compensation amount ≤ 0.5 MPa.

8. The high-precision mold manufacturing method for complex cavity casting according to claim 7, wherein: In S4, the coating wear rate, thermal fatigue crack propagation length, and cooling channel blockage rate monitored by the sensor are used to predict the remaining life of the mold. When the remaining life is lower than the preset threshold, an automatic repair warning is triggered.

9. The high-precision mold manufacturing method for complex cavity casting according to claim 8, characterized in that: The life prediction uses a time series prediction model based on the LSTM neural network. The input parameters include the coating thickness wear rate, number of thermal cycles, and cooling channel pressure drop change rate. The output is the remaining life and the confidence interval. The model training data is sourced from a failure sample library of at least 1000 casting experiments.

10. The high-precision mold manufacturing method for complex cavity casting according to claim 1, characterized in that: In S2, the metal additive manufacturing process includes the following steps: The formed mold substrate is subjected to hot isostatic pressing at a temperature of 1100 - 1250 °C, a pressure of 100 - 150 MPa, and a holding time of 2 - 4 hours. After heat treatment, electrolytic polishing is used to reduce the surface roughness. The polishing depth is 10 - 30 μm, and the surface microcrack density is reduced to ≤ 5 cracks / cm 2 .

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