A production process of copper-based alloy bimetallic synthetic material

Abstract

The present invention discloses a production process for a copper-based alloy bimetallic composite material, relating to the field of alloy materials. The production process comprises: selecting a copper-nickel alloy as a substrate, selecting a titanium alloy as a functional layer, optimizing the stress distribution of the bonding layer by gradient component distribution design; preparing a copper-nickel alloy liquid by vacuum induction melting, fixing a core rod by three-point support, controlling the temperature and flow rate for casting to form a joint crystallization; using a sandblasting process to clean the substrate surface, applying a Cu-Ti nano-coating by PVD technology to form a nano-scale convex-concave structure; constructing a mathematical model of the temperature field and the component diffusion field, optimizing the temperature distribution and diffusion behavior of the bonding area by simulation analysis; performing a gradient temperature-controlled annealing treatment to eliminate residual stress and promote diffusion; evaluating the bonding performance by combining shear strength and microhardness testing; and dividing the production process based on modular design. The present invention can be widely used in industrial fields requiring high temperature, high strength, and corrosion resistance.

Description

Technical Field

[0001] The present invention relates to the field of alloy materials, and in particular to a production process of a copper-based alloy bimetallic synthetic material. Background Art

[0002] With the increasing demand for high-performance materials in the industrial sector, bimetallic composites, which combine the advantages of different metals, have become an important research direction for structural and functional composite materials. Traditional copper-based alloys have excellent electrical conductivity and corrosion resistance, but their mechanical properties are poor under high temperature and high load conditions, making them difficult to meet the requirements of complex working conditions.

[0003] Existing processes for preparing copper-based alloy and titanium alloy bimetallic materials often rely on mechanical bonding or simple fusion welding. These methods often result in low bond strength, numerous interface defects, and uneven performance, limiting the widespread application of the materials. In particular, stress concentration and insufficient diffusion at the interface are key challenges in the current process. Furthermore, traditional processes lack precise control over the temperature field and composition diffusion in the bonding area, making it difficult to optimize microstructure and performance.

[0004] To address these challenges, the present invention provides a production process for copper-based alloy bimetallic composites. This process optimizes the metallurgical quality and microstructure of the bonding interface through vacuum melting, nano-coating surface treatment, liquid-solid bonding, and gradient annealing. Combined with mathematical modeling and simulation optimization, this technology achieves precise control of the temperature field and component diffusion within the bonding area. Furthermore, modular process design and intelligent production control significantly improve production efficiency and material performance stability. Summary of the Invention

[0005] In response to the above problems, the present invention provides a production process for a copper-based alloy bimetallic composite material to solve the problems of insufficient interface bonding strength, interface defects and stress concentration, and insufficient control of temperature field and component diffusion in the prior art.

[0006] To solve the above technical problems, the present invention provides the following technical solution: a production process of a copper-based alloy bimetallic composite material, comprising the following steps:

[0007] Step S1, selecting a copper-nickel alloy as a base material and adding the rare earth element cerium for material modification; selecting a titanium alloy as a functional layer and optimizing the stress distribution of the bonding layer through a gradient composition distribution design;

[0008] Wherein, step S1 further includes the following sub-steps:

[0009] S1-1, a copper-nickel alloy with a Ni content of 0.5%–30% is selected as the base material, and 0.05%–0.1% of the rare earth element cerium is added. The cerium element enhances the overall performance of the alloy by refining the grains and improving the surface activity, providing a base material for the subsequent bonding process;

[0010] S1-2, titanium alloy is selected as the functional layer, and a gradient material distribution design is adopted. By regulating the composition distribution of the bonding layer, the stress concentration of the bonding layer is reduced and the bonding strength is improved. The composition distribution of the bonding layer is optimized, as shown in the formula:

[0011]

[0012] in, To combine the components at a certain position in the region, is the content of substrate components, is the content of functional layer components, is the bonding layer thickness, is the gradient change index; the material simulation software ANSYS is used to simulate the stress distribution of the gradient structure to verify the reliability of the design.

[0013] Step S2, preparing a high-purity copper-nickel alloy liquid by vacuum induction melting, fixing a core rod with a three-point support structure, and performing pouring under conditions of controlled temperature and flow rate to form intergrowth crystals;

[0014] Wherein, in step S2, the following sub-steps are also included:

[0015] S2-1 uses a vacuum induction melting process to prepare copper-nickel alloy liquid. The melting environment is controlled below 10⁻²Pa to prevent the formation of oxides. The heating temperature is set at 1100-1150°C, that is, 100°C above the liquidus of the copper-nickel alloy. A real-time temperature control system is introduced, and the temperature fluctuation range is controlled within ±5°C using a high-precision temperature sensor. A spectrometer is used to dynamically detect the melt composition to ensure the uniformity of the alloy composition. During the melting process, inert gas is introduced to stir the melt to remove gas inclusions and oxides, thereby improving the purity of the melt.

[0016] S2-2 uses a three-point support structure, namely at both ends and in the middle, to fix the core rod. The core rod position is adjusted using high-precision measuring instruments to ensure that the error is controlled within ±0.1mm. A thermal expansion compensation device is added to the mold fixing structure to dynamically adjust the gap between the core rod and the mold to prevent stress concentration caused by thermal expansion. The support point distribution is optimized using the following formula, as shown in the formula:

[0017]

[0018] in, is the mandrel displacement, The impact force generated during the pouring process, is the support stiffness, is the current mold temperature, is the critical temperature of the mold;

[0019] The contact surface between the core rod and the mold is coated with a thin layer of ceramic heat-insulating coating to reduce the direct transfer of heat to the mold;

[0020] S2-3, the copper liquid temperature is heated to 100°C above the liquidus line, ranging from 1100-1150°C, so that the liquid can fully heat the surface of the core rod and reach the temperature of the liquid-solid two-phase region. A real-time temperature control system is used to control the casting temperature fluctuation range within ±5°C;

[0021] During the pouring process, the pouring speed is controlled at 1.5-2.5 m / s to avoid excessive impact of the liquid on the core rod. The pouring angle is maintained at 30°-45° to ensure that the copper liquid evenly covers the surface of the core rod. The flow rate is monitored by a sensor and adjusted to a stable flow rate to optimize pouring uniformity.

[0022] By utilizing the high temperature conditions in the liquid-solid bonding area, intergrowth crystals are formed on the core rod surface to achieve metallurgical bonding. The intergrowth crystal morphology is optimized by controlling the cooling rate, which is set at 5-10°C / min to prevent cracks or stress concentration. Before pouring, the copper liquid is heated to 100°C above the liquidus temperature. The optimal pouring temperature range is determined using the following formula, as shown in the formula:

[0023]

[0024] in, For the optimal pouring temperature, is the liquidus temperature of copper liquid, is the temperature compensation value;

[0025] The flow rate of the copper liquid is monitored in real time by the flow sensor, as shown in the formula:

[0026]

[0027] in, For traffic, is the pouring gate cross-sectional area, is the liquid flow rate.

[0028] Step S3, using a sandblasting process to clean the substrate surface to remove oxides and optimize the roughness; using PVD technology to apply a Cu-Ti nano-coating to form a nano-scale convex-concave structure to enhance the interface bonding quality;

[0029] Wherein, in step S3, the following sub-steps are also included:

[0030] S3-1, using sandblasting to clean the surface of the copper-nickel alloy substrate, remove the oxide layer and surface impurities, and enhance the metallurgical bonding strength during liquid-solid bonding by increasing the surface micro-roughness;

[0031] Environmentally friendly ceramic sand with a particle size of 50-100 μm was selected, the spray speed was controlled at 30 m / s, the spray angle was maintained at 45°, and the sandblasting time was 5-10 seconds. By optimizing the sandblasting parameters, the surface roughness was controlled at 0.2-0.5 μm to increase the effective contact area of ​​liquid-solid bonding. The following optimization formula was used to adjust the spray speed and angle, as shown in the formula:

[0032]

[0033] in, is the impact energy, is the mass of the ejected particles, is the injection velocity, The angle of the spray is 0.040°. After sandblasting, the metal surface should be immediately protected, such as covered with inert gas, to prevent secondary oxidation.

[0034] S3-2, using physical vapor deposition (PVD) technology, uniformly coats the surface of the substrate with a Cu-Ti alloy layer. The coating thickness is controlled at 2-5 μm, and the ratio of Cu to Ti in the coating is 80:20. The coating composition distribution is optimized, as shown in the formula:

[0035]

[0036] in, is the atomic diffusion flux, is the diffusion coefficient, is the concentration gradient of the chemical components at the coating interface;

[0037] By adjusting the deposition rate, a nanoscale convex-concave structure is formed on the coating surface with a particle diameter of 20-50nm, which enhances the actual contact area of ​​the bonding area and optimizes the surface roughness to 0.3-0.4μm. The coated core rod is stored in an inert gas environment to prevent oxidation and contamination of the coating.

[0038] Step S4, constructing a mathematical model of the temperature field and component diffusion field, performing simulation analysis using Matlab, and optimizing the temperature distribution and diffusion behavior of the bonding area;

[0039] Wherein, in step S4, the following sub-steps are also included:

[0040] S4-1, construct mathematical models of temperature field and component diffusion field to provide a basis for simulation calculation. The temperature field model is established based on the Fourier heat conduction equation, as shown in the formula:

[0041]

[0042] in, is the thermal conductivity of the material, is the internal heat source term, is the material density, is the specific heat capacity, is the temperature, is the radius, For time;

[0043] The component diffusion field is simulated based on Fick's second law, as shown in the formula:

[0044]

[0045] in, is the component concentration, is the diffusion coefficient, which varies with temperature;

[0046] The calculation area is divided into three parts: the core rod area, which is the heat transfer inside the solid metal; the copper liquid area, which is the convection and heat conduction of the liquid metal; and the interface area, which is the liquid-solid two-phase area of ​​the bonding layer.

[0047] Set initial conditions and boundary conditions in each area: For the initial condition, the temperature of the molten copper during pouring is the set value, and the temperature of the core rod is the ambient temperature; for the boundary condition, the heat transfer coefficient is considered in the contact area between the core rod surface and the molten copper. The boundary formula is as shown below:

[0048]

[0049] in, is the heat transfer coefficient, is the ambient temperature;

[0050] S4-2, use Matlab to perform numerical simulation of the temperature field in the liquid-solid bonding area, use the finite difference method to discretize the Fourier heat conduction equation, input the thermophysical parameters of the copper-nickel alloy and titanium alloy, including thermal conductivity, specific heat capacity, and density, use Matlab to perform numerical simulation of the temperature field, and combine the finite difference method to discretize the temperature field equation, as shown in the formula:

[0051]

[0052] in, The node temperature at the n+1th moment, is the temperature at the nth moment, is the time step, is the spatial step length;

[0053] Fick’s second law is used to simulate the diffusion behavior of Cu and Ti elements in the bonding layer, calculate the composition gradient of the bonding layer, and visualize the diffusion depth distribution, as shown in the following formula:

[0054]

[0055] in, is the composition gradient, is the change in element concentration, is the corresponding distance;

[0056] The temperature field simulation results are coupled with the component diffusion field to simulate the microstructural evolution of the bonding area. The component gradient and crystallization behavior of the bonding area are optimized through coupling analysis. Matlab is used to generate the temperature distribution map and component concentration gradient map of the bonding area. The results include the temperature change curve of the bonding layer and the diffusion depth distribution map.

[0057] Step S5, performing gradient temperature controlled annealing to eliminate residual stress and promote diffusion; evaluating the bonding performance by combining shear strength and microhardness tests, and observing the microstructure of the bonding area using microscopy;

[0058] Wherein, in step S5, the following sub-steps are also included:

[0059] S5-1, the annealing temperature of the bonding area is set at 800°C, and the holding time is kept at 800°C for 6 hours. The heating rate is controlled at 10°C / min to avoid stress concentration caused by excessive heating. The cooling rate is set at 5°C / min, and staged cooling is adopted to gradually release residual stress;

[0060] The bonding area is divided into a core bonding layer, a transition zone, and a substrate area, and different temperature control gradients are set for each area: 800°C for the core bonding layer, 750-770°C for the transition zone, and 700-750°C for the substrate area. The specific temperature control formula is as shown in the formula:

[0061]

[0062] in, is the temperature of the bonding layer from the core, is the core bonding layer temperature, is the temperature gradient, is the total thickness of the bonding area;

[0063] S5-2, using an electronic universal testing machine to measure the shear strength of the bonding layer, according to ASTM E8 standard: the composite material is made into a test sample, loaded at a constant rate and the maximum shear strength is recorded, and the average shear strength and standard deviation of the bonding layer are calculated by statistically analyzing multiple test results;

[0064] Use a Vickers microhardness tester to select multiple test points in the bonding area, test the hardness of the core area of ​​the bonding layer, the transition area and the main material respectively, and record the hardness distribution of each area. The hardness gradient formula is as shown in the formula:

[0065]

[0066] in, is the hardness of the bonding layer, is the core hardness of the bonding layer, is the hardness of the main material, is the thickness of the bonding layer;

[0067] Compare the test data with the temperature and diffusion field results of the S4 simulation calculation to analyze the source of errors. Optimize process parameters through error feedback to ensure that actual performance meets design requirements. Analyze the failure mode of the bonding area, i.e., ductile fracture or interface separation, by observing the fracture surface after the shear strength test and combining it with scanning electron microscopy.

[0068] S5-3, sample the bonding area and use mechanical grinding and electrolytic polishing to ensure that the surface finish of the sample meets the requirements of microscopic observation. According to the material composition, select appropriate chemical etching agents to clearly show the structural characteristics of the bonding layer;

[0069] Use an optical metallographic microscope to observe the macrostructure of the bonding area, including grain morphology and interface morphology, and analyze the uniformity of the bonding layer and transition zone and the presence of macroscopic defects, including pores and inclusions;

[0070] Observe the microstructure of the bonding layer, including grain boundary characteristics, bonding line morphology and defect distribution, and combine energy spectrum analysis to obtain the element distribution and composition concentration gradient of the bonding area, as shown in the formula:

[0071]

[0072] in, is the composition gradient, is the change in element concentration, is the corresponding distance.

[0073] Step S6: divide the production process based on modular design, combine sensor networks and machine learning algorithms to achieve real-time monitoring and optimization of process parameters.

[0074] Wherein, in step S6, the following sub-steps are also included:

[0075] S6-1, modular process design, divides the process into the following modules based on the functional requirements of the production process: smelting module, including copper alloy smelting and temperature control equipment; surface treatment module, responsible for cleaning and coating the core rod surface; bonding module, controlling the temperature, flow rate and environmental conditions during the liquid-solid bonding process; post-processing module, including annealing and performance testing;

[0076] The linkage relationship between modules is optimized through parameter transfer. For example, the temperature accuracy of the smelting module directly affects the pouring temperature range of the bonding module, and the roughness parameter of the surface treatment module is related to the metallurgical bonding strength of the bonding module, as shown in the formula:

[0077]

[0078] in, is the bonding strength, is the melting temperature, is the surface roughness;

[0079] S6-2, install high-precision sensors at key links of the production line, as follows:

[0080] Temperature sensor, monitoring the temperature changes of the melting module and the bonding module; flow rate sensor, monitoring the liquid flow rate during the pouring process; stress sensor, real-time monitoring of the thermal stress distribution in the bonding area;

[0081] The data sampling frequency is set to 1 second / time. The data collected by the sensors is uploaded to the central control system through the Industrial Internet of Things. A real-time parameter monitoring panel is established to display the temperature field, flow velocity distribution, and stress state during the production process. A machine learning algorithm is introduced to analyze the real-time data, predict parameter change trends, and adjust process parameters. The specific formula is shown as follows:

[0082]

[0083] in, is the optimized parameter, To monitor parameters in real time, is the target parameter.

[0084] Compared with the prior art, the present invention has the following beneficial effects:

[0085] The present invention realizes the metallurgical bonding of copper-based alloy and titanium alloy through liquid-solid bonding process and joint crystallization mechanism, significantly improving the interface bonding strength; and adopts nano-coating design and surface roughness optimization to enhance the interface bonding quality.

[0086] Through mathematical modeling and simulation analysis, the present invention accurately controls the temperature field distribution and component diffusion gradient in the bonding area, avoiding cracks and pore defects caused by temperature fluctuations in traditional processes; the gradient component distribution design effectively reduces interfacial stress concentration, making the microstructure of the bonding area more uniform and the material properties more stable.

[0087] The present invention solves the problem of poor interface bonding caused by oxides and impurities in traditional bonding processes through sandblasting cleaning and Cu-Ti nano-coating design; gradient annealing treatment eliminates residual stress in the bonding area and further enhances the reliability of the bonding layer.

[0088] Through modular process design, the present invention divides the production process into independent modules of smelting, surface treatment, bonding and post-processing, enabling rapid process adjustment and flexible combination; combined with an intelligent production control system, it realizes real-time monitoring and dynamic optimization of key parameters, reducing the scrap rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. It is understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0090] Figure 1 It is a production process flow chart of the present invention;

[0091] Figure 2 It is a material structure diagram of the present invention. DETAILED DESCRIPTION

[0092] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but is merely for selected embodiments of the present invention.

[0093] Please refer to Figure 1 This is a schematic diagram of a production process of a copper-based alloy bimetallic composite material provided by an embodiment of the present invention. Figure 2 This is a material structure diagram provided by an embodiment of the present invention, comprising the following steps:

[0094] Step S1, selecting a copper-nickel alloy as a base material and adding the rare earth element cerium for material modification; selecting a titanium alloy as a functional layer and optimizing the stress distribution of the bonding layer through a gradient composition distribution design;

[0095] Wherein, step S1 further includes the following sub-steps:

[0096] S1-1, using a copper-nickel alloy with a Ni content of 0.5%–30% as the base material, and adding 0.05%–0.1% of the rare earth element cerium. Cerium enhances the overall performance of the alloy by refining the grains and increasing surface activity, providing a base material for subsequent bonding processes;

[0097] It should be noted that, in an optional embodiment, the base copper-nickel alloy does not directly use ready-made alloy materials, but high-purity electrolytic copper and high-purity nickel ingots are selected as raw materials, wherein the purity of the electrolytic copper and the nickel ingots are not less than 99.9%, preferably 99.99%; the alloying operation is carried out by vacuum induction melting process, and a copper-nickel alloy with a Ni content of 0.5%-30% is prepared by self-proportioning; this method helps to reduce the cost of raw material procurement and improve the ability to control the impurity content. At the same time, the Ni content can be flexibly set according to application requirements to achieve better material performance regulation. 0.05%-0.1% of the rare earth element cerium is added in the later stage of smelting to further improve the grain structure and interface performance.

[0098] S1-2, titanium alloy is selected as the functional layer, and a gradient material distribution design is adopted. By regulating the composition distribution of the bonding layer, the stress concentration of the bonding layer is reduced and the bonding strength is improved. The composition distribution of the bonding layer is optimized, as shown in the formula:

[0099] in, To combine the components at a certain position in the region, is the content of substrate components, is the content of functional layer components, is the bonding layer thickness, is the gradient change index, and x is the distance in the thickness direction of the bonding layer. The material simulation software ANSYS is used to simulate the stress distribution of the gradient structure to verify the reliability of the design.

[0100] Step S2, preparing a high-purity copper-nickel alloy liquid by vacuum induction melting, fixing a core rod with a three-point support structure, and performing pouring under conditions of controlled temperature and flow rate to form intergrowth crystals;

[0101] Wherein, in step S2, the following sub-steps are also included:

[0102] S2-1 uses a vacuum induction melting process to prepare copper-nickel alloy liquid. The melting environment is controlled below 10⁻²Pa to prevent the formation of oxides. The heating temperature is set at 1100-1150°C, that is, 100°C above the liquidus of the copper-nickel alloy. A real-time temperature control system is introduced, and the temperature fluctuation range is controlled within ±5°C using a high-precision temperature sensor. A spectrometer is used to dynamically detect the melt composition to ensure the uniformity of the alloy composition. During the melting process, inert gas is introduced to stir the melt to remove gas inclusions and oxides, thereby improving the purity of the melt.

[0103] S2-2 uses a three-point support structure, namely at both ends and in the middle, to fix the core rod. The core rod position is adjusted using high-precision measuring instruments to ensure that the error is controlled within ±0.1mm. A thermal expansion compensation device is added to the mold fixing structure to dynamically adjust the gap between the core rod and the mold to prevent stress concentration caused by thermal expansion. The support point distribution is optimized using the following formula, as shown in the formula:

[0104]

[0105] in, is the mandrel displacement, The impact force generated during the pouring process, is the support stiffness, is the current mold temperature, is the critical temperature of the mold;

[0106] It should be noted that the core rod is a tooling component in the forming process and does not belong to the final material structure. The core rod adopts a pull-out design. After the copper-nickel alloy liquid is poured and before it is completely solidified, it can be smoothly pulled out through appropriate cooling control to prevent it from remaining inside the material; the core rod is mainly used to control the inner cavity shape and conduct heat. A thermal expansion compensation device is introduced into the mold structure to dynamically adjust the gap between the core rod and the mold to prevent stress concentration due to temperature difference; the position of the core rod is adjusted by high-precision measuring equipment, and the error is controlled within ±0.1mm. The surface of the core rod is coated with a ceramic thermal insulation coating to reduce the heat conduction rate and avoid adhesion. The core rod is not included in the final product structure.

[0107] S2-3, the copper liquid temperature is heated to 100°C above the liquidus line, ranging from 1100-1150°C, so that the liquid can fully heat the surface of the core rod and reach the temperature of the liquid-solid two-phase region. A real-time temperature control system is used to control the casting temperature fluctuation range within ±5°C;

[0108] During the pouring process, the pouring speed is controlled at 1.5-2.5 m / s to avoid excessive impact of the liquid on the core rod. The pouring angle is maintained at 30°-45° to ensure that the copper liquid evenly covers the surface of the core rod. The flow rate is monitored by a sensor and adjusted to a stable flow rate to optimize pouring uniformity.

[0109] By utilizing the high temperature conditions in the liquid-solid bonding area, intergrowth crystals are formed on the core rod surface to achieve metallurgical bonding. The intergrowth crystal morphology is optimized by controlling the cooling rate, which is set at 5-10°C / min to prevent cracks or stress concentration. Before pouring, the copper liquid is heated to 100°C above the liquidus temperature. The optimal pouring temperature range is determined using the following formula, as shown in the formula:

[0110]

[0111] in, For the optimal pouring temperature, is the liquidus temperature of copper liquid, is the temperature compensation value;

[0112] The flow rate of the copper liquid is monitored in real time by the flow sensor, as shown in the formula:

[0113]

[0114] in, For traffic, is the pouring gate cross-sectional area, is the liquid flow rate.

[0115] Step S3, using a sandblasting process to clean the substrate surface to remove oxides and optimize the roughness; using PVD technology to apply a Cu-Ti nano-coating to form a nano-scale convex-concave structure to enhance the interface bonding quality;

[0116] Wherein, in step S3, the following sub-steps are also included:

[0117] S3-1, using sandblasting to clean the surface of the copper-nickel alloy substrate, remove the oxide layer and surface impurities, and enhance the metallurgical bonding strength during liquid-solid bonding by increasing the surface micro-roughness;

[0118] Environmentally friendly ceramic sand with a particle size of 50-100 μm was selected, the spray speed was controlled at 30 m / s, the spray angle was maintained at 45°, and the sandblasting time was 5-10 seconds. By optimizing the sandblasting parameters, the surface roughness was controlled at 0.2-0.5 μm to increase the effective contact area of ​​liquid-solid bonding. The following optimization formula was used to adjust the spray speed and angle, as shown in the formula:

[0119]

[0120] in, is the impact energy, is the mass of the ejected particles, is the injection velocity, The angle of the spray is 0.040°. After sandblasting, the metal surface should be immediately protected, such as covered with inert gas, to prevent secondary oxidation.

[0121] S3-2, using physical vapor deposition (PVD) technology, uniformly coats the surface of the substrate with a Cu-Ti alloy layer. The coating thickness is controlled at 2-5 μm, and the ratio of Cu to Ti in the coating is 80:20. The coating composition distribution is optimized, as shown in the formula:

[0122]

[0123] in, is the atomic diffusion flux, is the diffusion coefficient, is the concentration gradient of the chemical components at the coating interface;

[0124] By adjusting the deposition rate, a nanoscale convex-concave structure is formed on the coating surface with a particle diameter of 20-50nm, which enhances the actual contact area of ​​the bonding area and optimizes the surface roughness to 0.3-0.4μm. The coated core rod is stored in an inert gas environment to prevent oxidation and contamination of the coating.

[0125] Step S4, constructing a mathematical model of the temperature field and component diffusion field, performing simulation analysis using Matlab, and optimizing the temperature distribution and diffusion behavior of the bonding area;

[0126] Wherein, in step S4, the following sub-steps are also included:

[0127] S4-1, construct mathematical models of temperature field and component diffusion field to provide a basis for simulation calculation. The temperature field model is established based on the Fourier heat conduction equation, as shown in the formula:

[0128]

[0129] in, is the thermal conductivity of the material, is the internal heat source term, is the material density, is the specific heat capacity, is the temperature, is the radius, For time;

[0130] The component diffusion field is simulated based on Fick's second law, as shown in the formula:

[0131]

[0132] in, is the component concentration, is the diffusion coefficient, which varies with temperature;

[0133] The calculation area is divided into three parts: the core rod area, which is the heat transfer inside the solid metal; the copper liquid area, which is the convection and heat conduction of the liquid metal; and the interface area, which is the liquid-solid two-phase area of ​​the bonding layer.

[0134] Set initial conditions and boundary conditions in each area: For the initial condition, the temperature of the molten copper during pouring is the set value, and the temperature of the core rod is the ambient temperature; for the boundary condition, the heat transfer coefficient is considered in the contact area between the core rod surface and the molten copper. The boundary formula is as shown below:

[0135]

[0136] in, is the heat transfer coefficient, is the ambient temperature;

[0137] S4-2, use Matlab to perform numerical simulation of the temperature field in the liquid-solid bonding area, use the finite difference method to discretize the Fourier heat conduction equation, input the thermophysical parameters of the copper-nickel alloy and titanium alloy, including thermal conductivity, specific heat capacity, and density, use Matlab to perform numerical simulation of the temperature field, and combine the finite difference method to discretize the temperature field equation, as shown in the formula:

[0138]

[0139] in, The node temperature at the n+1th moment, is the temperature at the nth moment, is the time step, is the spatial step length;

[0140] Fick’s second law is used to simulate the diffusion behavior of Cu and Ti elements in the bonding layer, calculate the composition gradient of the bonding layer, and visualize the diffusion depth distribution, as shown in the following formula:

[0141]

[0142] in, is the composition gradient, is the change in element concentration, is the corresponding distance;

[0143] The temperature field simulation results are coupled with the component diffusion field to simulate the microstructural evolution of the bonding area. The component gradient and crystallization behavior of the bonding area are optimized through coupling analysis. Matlab is used to generate the temperature distribution map and component concentration gradient map of the bonding area. The results include the temperature change curve of the bonding layer and the diffusion depth distribution map.

[0144] Step S5, performing gradient temperature controlled annealing to eliminate residual stress and promote diffusion; evaluating the bonding performance by combining shear strength and microhardness tests, and observing the microstructure of the bonding area using microscopy;

[0145] Wherein, in step S5, the following sub-steps are also included:

[0146] S5-1, the annealing temperature of the bonding area is set at 800°C, and the holding time is kept at 800°C for 6 hours. The heating rate is controlled at 10°C / min to avoid stress concentration caused by excessive heating. The cooling rate is set at 5°C / min, and staged cooling is adopted to gradually release residual stress;

[0147] The bonding area is divided into a core bonding layer, a transition zone, and a substrate area, and different temperature control gradients are set for each area: 800°C for the core bonding layer, 750-770°C for the transition zone, and 700-750°C for the substrate area. The specific temperature control formula is as shown in the formula:

[0148]

[0149] in, is the temperature of the bonding layer from the core, is the core bonding layer temperature, is the temperature gradient, is the total thickness of the bonding area;

[0150] S5-2, using an electronic universal testing machine to measure the shear strength of the bonding layer, according to ASTM E8 standard: the composite material is made into a test sample, loaded at a constant rate and the maximum shear strength is recorded, and the average shear strength and standard deviation of the bonding layer are calculated by statistically analyzing multiple test results;

[0151] Use a Vickers microhardness tester to select multiple test points in the bonding area, test the hardness of the core area of ​​the bonding layer, the transition area and the main material respectively, and record the hardness distribution of each area. The hardness gradient formula is as shown in the formula:

[0152]

[0153] in, is the hardness of the bonding layer, is the core hardness of the bonding layer, is the hardness of the main material, is the thickness of the bonding layer;

[0154] Compare the test data with the temperature and diffusion field results of the S4 simulation calculation to analyze the source of errors. Optimize process parameters through error feedback to ensure that actual performance meets design requirements. Analyze the failure mode of the bonding area, i.e., ductile fracture or interface separation, by observing the fracture surface after the shear strength test and combining it with scanning electron microscopy.

[0155] S5-3, sample the bonding area and use mechanical grinding and electrolytic polishing to ensure that the surface finish of the sample meets the requirements of microscopic observation. According to the material composition, select appropriate chemical etching agents to clearly show the structural characteristics of the bonding layer;

[0156] Use an optical metallographic microscope to observe the macrostructure of the bonding area, including grain morphology and interface morphology, and analyze the uniformity of the bonding layer and transition zone and the presence of macroscopic defects, including pores and inclusions;

[0157] Observe the microstructure of the bonding layer, including grain boundary characteristics, bonding line morphology and defect distribution, and combine energy spectrum analysis to obtain the element distribution and composition concentration gradient of the bonding area, as shown in the formula:

[0158]

[0159] in, is the composition gradient, is the change in element concentration, is the corresponding distance;

[0160] The nanoscale structure of the bonding area was observed, and the bonding mechanism and defect characteristics at the atomic scale, including dislocations and crystal mismatches, were analyzed. The microstructural observation results were compared with the temperature field and component diffusion field results of the simulation calculation to verify their rationality.

[0161] Step S6: divide the production process based on modular design, combine sensor networks and machine learning algorithms to achieve real-time monitoring and optimization of process parameters.

[0162] Wherein, in step S6, the following sub-steps are also included:

[0163] S6-1, modular process design, divides the process into the following modules based on the functional requirements of the production process: smelting module, including copper alloy smelting and temperature control equipment; surface treatment module, responsible for cleaning and coating the core rod surface; bonding module, controlling the temperature, flow rate and environmental conditions during the liquid-solid bonding process; post-processing module, including annealing and performance testing;

[0164] The linkage relationship between modules is optimized through parameter transfer. For example, the temperature accuracy of the smelting module directly affects the pouring temperature range of the bonding module, and the roughness parameter of the surface treatment module is related to the metallurgical bonding strength of the bonding module, as shown in the formula:

[0165]

[0166] in, is the bonding strength, is the melting temperature, is the surface roughness;

[0167] S6-2, install high-precision sensors at key links of the production line, as follows:

[0168] Temperature sensor, monitoring the temperature changes of the melting module and the bonding module; flow rate sensor, monitoring the liquid flow rate during the pouring process; stress sensor, real-time monitoring of the thermal stress distribution in the bonding area;

[0169] The data sampling frequency is set to 1 second / time. The data collected by the sensors is uploaded to the central control system through the Industrial Internet of Things. A real-time parameter monitoring panel is established to display the temperature field, flow velocity distribution, and stress state during the production process. A machine learning algorithm is introduced to analyze the real-time data, predict parameter change trends, and adjust process parameters. The specific formula is shown as follows:

[0170]

[0171] in, is the optimized parameter, To monitor parameters in real time, is the target parameter.

[0172] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A production process for a copper-based alloy bimetallic composite material, characterized in that: The following steps are involved: In step S1, a copper-nickel alloy with a Ni content of 0.5%–30% is selected as the substrate, and 0.05%–0.1% of the rare earth element cerium is added for material modification. A titanium alloy is selected as the functional layer. A composition gradient transition structure of the bonding layer between the substrate and the functional layer is designed to satisfy the following functional relationship: in, is the component at a certain position of the binding layer, is the content of substrate components, is the content of functional layer components, is the bonding layer thickness, is the gradient change index, is the distance in the thickness direction of the bonding layer; the material simulation software ANSYS is used to simulate the stress distribution of the gradient structure to verify the reliability of the design: Step S2, preparing a high-purity copper-nickel alloy liquid by vacuum induction melting, fixing a core rod with a three-point support structure, and performing pouring under conditions of controlled temperature and flow rate to form intergrowth crystals; Step S3, using a sandblasting process to clean the surface of the intergrowth crystal, remove oxides and control the surface roughness to 0.2-0.5 μm; using PVD technology to apply a Cu-Ti nano-coating to the functional layer to form a nano-scale convex-concave structure; A mathematical model of the temperature field and component diffusion field was constructed, and Matlab was used to perform numerical simulation of the temperature field. The finite difference method was used to discretize the Fourier heat conduction equation to generate a temperature distribution map and a component concentration gradient map of the bonding layer. The results included a temperature change curve and a diffusion depth distribution map of the bonding layer. Based on the above simulation results, the bonding layer was divided into a core bonding layer, a transition zone, and a substrate region. The annealing temperature of the core bonding layer was set at 800°C. Different temperature control gradients were set according to the annealing temperature of the core bonding layer: the transition zone temperature was controlled between 750–770°C, and the substrate region was controlled between 700–750°C. The temperature of each partition was maintained at the corresponding temperature for 6 hours, and the heating rate was controlled at 10°C / min to avoid stress concentration caused by excessive heating. The cooling rate was set at 5°C / min, and staged cooling was used to gradually release residual stress. The bonding performance was evaluated through shear strength and microhardness tests, and the microstructure of the bonding layer was observed using microscopy. The production process is divided based on modular design, and the sensor network and machine learning algorithm are combined to achieve real-time monitoring of the process parameters of steps S1, S2 and S3.

2. The production process of a copper-based alloy bimetallic composite material according to claim 1, characterized in that: Wherein, in step S2, the following sub-steps are also included: S2-1, vacuum induction melting process is used to prepare copper-nickel alloy liquid, and the melting environment is controlled at The heating temperature is set to 100°C above the liquidus of the copper-nickel alloy. A real-time temperature control system is introduced to control the temperature fluctuation range within ±5°C through a high-precision temperature sensor. A spectrum analyzer is used to dynamically detect the melt composition to ensure the uniformity of the alloy composition. Inert gas is introduced into the melt during the smelting process to stir the melt to remove gas inclusions and oxides and improve the purity of the melt. S2-2 uses a three-point support structure at both ends and in the middle to position and fix the core rod. The core rod position is adjusted using high-precision measuring instruments to ensure that the error is controlled within ±0.1mm. A thermal expansion compensation device is added to the mold fixing structure to dynamically adjust the gap between the core rod and the mold to prevent stress concentration caused by thermal expansion. The support point distribution is determined by the following formula, as shown in the formula: in, is the mandrel displacement, The impact force generated during the pouring process, is the support stiffness, is the current mold temperature, The critical temperature of the mold; a thin layer of ceramic thermal insulation coating is applied to the contact surface between the core rod and the mold to reduce the heat directly transferred to the mold.

3. The production process of a copper-based alloy bimetallic composite material according to claim 2, characterized in that: Wherein, in step S2, the following sub-steps are also included: During S2-3, the pouring speed is controlled at 1.5-2.5 m / s to prevent the liquid from causing excessive impact on the core rod. The pouring angle is maintained at 30°-45° to ensure that the copper-nickel alloy liquid evenly covers the core rod surface. The flow rate is monitored by a sensor to achieve a stable flow rate and improve pouring uniformity. By utilizing the high temperature conditions of the liquid-solid bonding layer, intergrowth crystals are formed on the surface of the core rod to achieve metallurgical bonding. The intergrowth crystal morphology is adjusted by cooling rate control, and the cooling rate is set to 5-10°C / min to prevent cracks or stress concentration. Before pouring, the copper-nickel alloy liquid is heated to 100°C above the liquidus temperature. The optimal pouring temperature range is determined using the following formula, as shown in the formula: in, For the optimal pouring temperature, is the liquidus temperature of the copper-nickel alloy liquid, is the temperature compensation value; The flow rate of the copper-nickel alloy liquid is monitored in real time by a flow sensor, as shown in the formula: in, For traffic, is the pouring gate cross-sectional area, is the liquid flow rate.

4. The production process of a copper-based alloy bimetallic composite material according to claim 1, characterized in that: Wherein, in step S3, the following sub-steps are also included: S3-1, using sandblasting to clean the surface of the copper-nickel alloy substrate, remove the oxide layer and surface impurities, and enhance the metallurgical bonding strength during liquid-solid bonding by increasing the surface micro-roughness; Select environmentally friendly ceramic sand with a particle size of 50-100μm, control the spray speed at 30m / s, maintain the spray angle at 45°, and the sandblasting time at 5-10 seconds. Use the following optimization formula to adjust the spray speed and angle, as shown in the formula: in, is the impact energy, is the mass of the ejected particles, is the injection velocity, is the spraying angle; after sandblasting, the metal surface should be immediately protected to prevent secondary oxidation; S3-2, using physical vapor deposition technology, uniformly coats the surface of the substrate with a Cu-Ti alloy layer. The coating thickness is controlled at 2-5 μm. The ratio of Cu to Ti in the coating is 80:

20. The coating composition distribution is regulated as shown in the formula: in, is the atomic diffusion flux, is the diffusion coefficient, is the concentration gradient of the chemical components at the coating interface; By adjusting the deposition rate, a nano-scale convex-concave structure is formed on the coating surface with a particle diameter of 20-50nm, which enhances the actual contact area of ​​the bonding layer. The surface roughness is adjusted to 0.3-0.4μm. The coated core rod is stored in an inert gas environment to prevent oxidation and contamination of the coating.

5. The production process of a copper-based alloy bimetallic composite material according to claim 4, characterized in that: Wherein, in step S3, the following sub-steps are also included: S3-3, the temperature control formula is as shown below: in, is the temperature of the bonding layer from the core, is the core bonding layer temperature, is the temperature gradient, is the total thickness of the bonding layer; In order to formulate the annealing system and predict the diffusion effect, a mathematical model of the temperature field and component diffusion field is constructed to provide a basis for simulation calculations. The temperature field model is established based on the Fourier heat conduction equation, as shown in the following formula: in, is the thermal conductivity of the material, is the internal heat source term, is the material density, is the specific heat capacity, is the temperature, is the radius, For time; The component diffusion field is simulated based on Fick's second law, as shown in the formula: in, is the component concentration, is the diffusion coefficient, which varies with temperature; The calculation area is divided into three parts: the core rod area, which is the heat transfer inside the solid metal; the copper-nickel alloy liquid area, which is the convection and heat conduction of the liquid metal; and the interface area, which is the liquid-solid two-phase area of ​​the bonding layer. Initial conditions and boundary conditions are set in each area: for the initial condition, the temperature of the copper-nickel alloy liquid during pouring is the set value, and the temperature of the core rod is the ambient temperature; for the boundary condition, the heat transfer coefficient is considered in the contact area between the core rod surface and the copper-nickel alloy liquid. The boundary formula is as shown below: in, is the heat transfer coefficient, is the ambient temperature; Matlab was used to numerically simulate the temperature field of the liquid-solid bonding layer. The finite difference method was used to discretize the Fourier heat conduction equation. The thermophysical parameters of the copper-nickel alloy and titanium alloy, namely thermal conductivity, specific heat capacity and density, were input. The specific formula is as follows: in, The node temperature at the n+1th moment, is the temperature at the nth moment, is the time step, is the spatial step length, is the thermal conductivity, is the specific heat capacity, is the density; Fick’s second law is used to simulate the diffusion behavior of Cu and Ti elements in the bonding layer, calculate the composition gradient of the bonding layer, and visualize the diffusion depth distribution, as shown in the following formula: in, is the composition gradient, is the change in element concentration, is the corresponding distance; The temperature field simulation results are coupled with the component diffusion field to simulate the microstructural evolution of the bonding layer.

6. The production process of a copper-based alloy bimetallic composite material according to claim 5, characterized in that: Wherein, in step S3, the following sub-steps are also included: S3-4, after the annealing treatment is completed, the shear strength of the bonding layer is measured using an electronic universal testing machine according to the ASTM E8 standard: the composite material is made into a test sample, the maximum shear strength is recorded at a constant rate, and the average shear strength and standard deviation of the bonding layer are calculated by statistically analyzing the test results for multiple times; Use a Vickers microhardness tester to select multiple test points in the bonding layer, test the hardness of the core area, transition area and main material of the bonding layer respectively, and record the hardness distribution of each area. The hardness gradient formula is as shown in the formula: in, is the hardness of the bonding layer, is the core hardness of the bonding layer, is the hardness of the main material, is the thickness of the bonding layer; Compare the test data with the temperature and diffusion field results of the simulation calculations to analyze the source of errors. Adjust the process parameters through error feedback to ensure that the actual performance meets the design requirements. Analyze the failure mode of the bonding layer through fracture observation after the shear strength test and scanning electron microscopy, namely ductile fracture or interface separation. The bonding layer is sampled and mechanical grinding and electrolytic polishing processes are used to ensure that the surface finish of the sample meets the requirements of microscopic observation. According to the material composition, appropriate chemical etching agents are selected to clearly show the organizational characteristics of the bonding layer; Use an optical metallographic microscope to observe the macrostructure of the bonding layer, including grain morphology and interface morphology, and analyze the uniformity of the bonding layer and transition zone and the presence of macroscopic defects, including pores and inclusions; Observe the microstructure of the bonding layer, including grain boundary characteristics, bonding line morphology and defect distribution, and combine energy spectrum analysis to obtain the element distribution and composition concentration gradient of the bonding layer, as shown in the formula: in, is the composition gradient, is the change in element concentration, is the corresponding distance.

7. The production process of a copper-based alloy bimetallic composite material according to claim 1, characterized in that: According to the functional requirements of the production process, the process is divided into the following modules: smelting module, including copper alloy smelting and temperature control equipment; surface treatment module, responsible for the cleaning and coating of the core rod surface; bonding module, controlling the temperature, flow rate and environmental conditions during the liquid-solid bonding process; post-processing module, including annealing and performance testing; The linkage relationship between modules is clarified through parameter transfer. The roughness parameter of the surface treatment module is related to the metallurgical bonding strength of the bonding module, as shown in the formula: in, is the bonding strength, is the melting temperature, is the surface roughness; Install high-precision sensors at key points of the production line, as follows: Temperature sensor, monitoring the temperature changes of the melting module and the bonding module; flow rate sensor, monitoring the liquid flow rate during the pouring process; stress sensor, real-time monitoring of the thermal stress distribution of the bonding layer; The data sampling frequency is set to 1 second / time. The data collected by the sensors is uploaded to the central control system through the Industrial Internet of Things. A real-time parameter monitoring panel is established to display the temperature field, flow velocity distribution, and stress state during the production process. A machine learning algorithm is introduced to analyze the real-time data, predict parameter change trends, and adjust process parameters. The specific formula is shown as follows: in, is the optimized parameter, To monitor parameters in real time, is the target parameter.

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