Aluminum alloy material with high strength and optimized electrical conductivity and processing technology thereof

Abstract

The application relates to the alloy processing technical field and discloses an aluminum alloy material with high strength and optimized electric conductivity and a processing technology thereof, which comprises the following steps: plastic deformation is applied to an aluminum alloy component at low temperature to induce internal dislocation entanglement; the component is analyzed as a spatial grid, the deformation gradient and the projected cross-section parameter of a micro-element node are calculated to extract the dislocation density and the resistivity coefficient; the impedance correction coefficient is calculated based on the parameters and a non-uniform distribution pulse control data tensor is generated; an electric pulse sequence is input to the component according to the tensor to induce precipitation phase nucleation, and the component is controlled to be cooled to an intermediate temperature platform for heat preservation after the pulse is terminated; the application utilizes non-uniform electric pulses to offset macroscopic cross-section distortion, realizes targeted precipitation of a nano phase under the premise of maintaining a fine-grained skeleton, eliminates interface stress concentration through slow cooling, and breaks through the material science bottleneck of mutual repulsion of high strength and electric conductivity.

Description

Technical Field

[0001] This invention relates to the field of alloy processing technology, and in particular to a high-strength aluminum alloy material with optimized electrical conductivity and its processing technology. Background Technology

[0002] Currently, in the manufacturing of load-bearing base plates for new energy vehicles and fittings for ultra-high voltage transmission lines, aluminum-based alloys, represented by the Al-Mg-Si system, dominate due to their excellent comprehensive mechanical properties. Conventional strengthening methods in the industry rely on specific alloy composition design and solution treatment combined with artificial aging processes, or the superposition of cold rolling plastic deformation mechanisms. The core mechanism involves the extensive desolvation of solute atoms within the metal lattice, precipitating a strengthening phase. Simultaneously, the precipitated phase and a high-density dislocation network pin grain boundary slip surfaces, thereby improving the tensile strength of the alloy material. However, in applications where components need to withstand extreme mechanical loads and high-current grounding return flows simultaneously, aluminum-based alloys... The physical limitations of physical mutual exclusion at the crystallographic level are gradually becoming apparent. While lattice distortion forms the physical basis for improving strength, the solid solution atoms and dislocation intertwined regions remaining inside the lattice constitute strong scattering centers for electron transport. When electrons pass through these defective regions, they generate severe heat loss, leading to a significant decrease in the material's electrical conductivity. If the solid solution atoms are fully desoluble to purify the matrix by extending the aging holding time or increasing the heat treatment temperature, it will inevitably trigger the rapid recovery of high-density dislocations and the excessive coarsening of precipitated phases, causing an irreversible decrease in the alloy's yield strength. If the solution of increasing the cross-sectional area is used to compensate for the conductivity gap, it violates the original intention of lightweight structural design.

[0003] Conventional heat treatment processes rely on temperature gradients to drive phase transitions, failing to spatially decouple the nucleation thermodynamics of the precipitated phase from the crystal defect kinetics. Existing technologies suffer from the following inherent drawbacks: 1. Heat conduction inevitably induces disordered aggregation of solid-solution atoms along dislocation lines as the material traverses the transition temperature range. This uncontrolled natural aging prematurely depletes the material basis for phase transitions and disrupts the targeted nucleation mechanism of subsequent strengthening phases; 2. Conventional aging energy fields struggle to simultaneously suppress dislocation network recovery and accelerate the diffuse precipitation of solute atoms, resulting in incomplete matrix purification and persistently high electron scattering resistance; 3. For components with variable cross-section geometries, the uniform application of physical parameters ignores the impact of abrupt changes in solid thickness on energy flow. Distortion interference can easily cause local thermodynamic response imbalance and mechanical property deterioration. The macroscopic geometry of the component restricts the energy field distribution, and the control methods also have shortcomings. For example, Chinese invention patent application CN108018509A discloses a deformation heat treatment method to improve the mechanical properties of aluminum alloy rolled plates. It improves the strength and plasticity of the plate by combining deep cold rolling and warm rolling. Based on the logic of uniform deformation field and global thermal field control, it belongs to macroscopic process timing optimization. When facing irregularly shaped components with complex internal stress fields or dynamic current carrying requirements, this macroscopic control mode cannot identify and utilize the non-uniform impedance characteristics of the internal dislocation network of the material, and it is difficult to achieve targeted precipitation of solute atoms and deep purification of the matrix at the micron and nanoscale.

[0004] Therefore, the technical problem to be solved by this invention is how to break through the constraints of traditional thermodynamic timing without reducing strength, target and purify the metal matrix in the defect core area, and break the physical mutual exclusion constraint of the superposition of solid solution atoms and dislocation scattering resistance. Summary of the Invention

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a processing technology for high-strength aluminum alloy materials with optimized electrical conductivity, comprising the following steps: Step 101: Apply an extrusion operation with a cumulative true strain of not less than 4 to the aluminum alloy component to be processed at -190℃ to -150℃ to generate a pre-processed aluminum alloy component with dislocation entanglement inside. Step 102: Divide the solid model data of the initial-processed aluminum alloy component into a spatial tetrahedral mesh according to the Deloitte triangulation algorithm, extract the centroid of the mesh as a micro-element node, and calculate the deformation gradient parameter of the micro-element node. The deformation gradient parameter is defined as the equivalent tensor norm that characterizes the geometrically necessary dislocation evolution intensity at the micro-element node and the projection cross-sectional parameter of the line connecting adjacent micro-element nodes in the direction perpendicular to the main current. Step 103: Based on the deformation gradient parameter, extract the predicted dislocation density feature vector and residual resistivity mapping coefficient of the corresponding spatial location from the database. The database is constructed by combining the dislocation strengthening Taylor model with transmission electron microscopy observation data of no less than 10 typical deformed specimens. Step 104: Calculate the basic resistance parameter based on the projected cross-sectional parameter, multiply the magnitude of the predicted dislocation density eigenvector by the residual resistivity mapping coefficient to obtain the impedance correction term, add the basic resistance parameter and the impedance correction term to obtain the local impedance correction coefficient, and generate the transient pulse control data tensor based on the local impedance correction coefficient of all micro-element nodes. Step 105: Attach conductive electrode plates to both ends of the pre-processed aluminum alloy component, input an electrical pulse sequence based on the transient pulse control data tensor, and monitor the dynamic resistivity change during the injection process in real time through a high-frequency sampling circuit. Feed the real-time data back to the pre-trained machine learning proxy model to fine-tune the pulse amplitude and pulse width online to compensate for the energy fluctuations caused by solute atom nucleation. Maintain the injection for 5μs to 15μs when the surface temperature rise rate of the pre-processed aluminum alloy component reaches 80℃ / s to 120℃ / s. Step 106: Within 0.5s after the termination of the electrical pulse sequence injection, control the cooling rate to 2℃ / min to 5℃ / min, so that the initial processed aluminum alloy component is cooled to an intermediate temperature plateau of 120℃ to 160℃ and held for 2h to 4h.

[0006] Preferably, the extrusion operation that applies a cumulative true strain of not less than 4 includes: step 201: positioning the aluminum alloy component to be processed inside the equal channel corner extrusion die; step 202: performing 4 to 8 passes of continuous extrusion on the aluminum alloy component to be processed under the physical constraint of an extrusion channel angle of 90° to 120°.

[0007] Preferably, generating the transient pulse control data tensor includes: step 301, setting an initial pulse parameter vector for the micro-nodes; step 302, constructing a fitness function based on the local impedance correction coefficient and the potential difference between the micro-nodes; and step 303, solving for the target parameter that minimizes the fitness function as the transient pulse control data tensor.

[0008] Preferably, the calculation of the local impedance correction factor conforms to the mathematical equation: ,in, This is the local impedance correction factor. As a basic resistance parameter, The residual resistivity mapping coefficient, This is to estimate the magnitude of the dislocation density eigenvector.

[0009] Preferably, the heat preservation operation includes: step 501, collecting acoustic emission frequency data of the pre-processed aluminum alloy component; step 502, calculating the rate of change of the energy integral value of the acoustic emission frequency data as a relaxation rate index; step 503, when the relaxation rate index is lower than a preset threshold for ten minutes, triggering an interruption and performing air cooling operation to room temperature.

[0010] Preferably, the heat preservation operation further includes: step 601, applying an alternating magnetic field to the pre-processed aluminum alloy component; step 602, detecting the impedance phase angle parameter of the alternating eddy current induced by the alternating magnetic field; step 603, adjusting the driving frequency of the alternating magnetic field according to the impedance phase angle parameter, so that the impedance phase angle parameter is locked at a set extreme point.

[0011] Preferably, before step 101, the method includes: step 701, heating an aluminum alloy ingot with a total mass percentage of magnesium and silicon of 0.8% to 1.5% to perform a solution treatment at 530°C to 550°C; step 702, performing a quenching operation on the solution-treated aluminum alloy ingot, controlling the cooling rate to be greater than 100°C / s to generate the aluminum alloy component to be processed.

[0012] Preferably, after extracting the centroid of the mesh as micro-element nodes, the process includes: step 801, obtaining the normal vector of the micro-element node along the direction of the main current of the initially processed aluminum alloy component; step 802, calculating the projected profile area value of adjacent micro-element nodes on a plane perpendicular to the normal vector, as a projection cross-section parameter.

[0013] Preferably, the input electrical pulse sequence includes: step 901, extracting the peak current array and pulse width array of the transient pulse control data tensor; step 902, controlling the pulse waveform generation unit to output an asymmetric bipolar square wave current with amplitude matching the peak current array and duration matching the pulse width array, and controlling the cooling rate includes: step 1001, collecting the surface temperature and core temperature of the pre-processed aluminum alloy component to calculate the temperature difference; step 1002, adjusting the cooling fan speed in the cooling environment based on the temperature difference, controlling the temperature difference within 5°C, until it drops to an intermediate temperature plateau.

[0014] A high-strength aluminum alloy material with optimized electrical conductivity is disclosed. This material is prepared using a processing technique that optimizes the electrical conductivity of aluminum alloys. The aluminum alloy matrix contains nano-precipitates with an average particle size of 5 nm to 10 nm, distributed non-uniformly and locally within the dislocation entanglement core region. The lattice distortion rate in the solute-depleted region of the matrix is ​​reduced by more than 15% compared to conventional artificial aging. Furthermore, within a 10 μm × 10 μm observation domain, the interaction density between the nano-precipitates and high-density dislocations is not less than 5.0 × 10⁻⁶. 14 m -3 .

[0015] The beneficial effects of this invention are: 1. In the processing of aluminum alloy materials, a targeted thermodynamic intervention mechanism based on the extreme effect of micro-defect impedance is constructed. Conventional cryogenic deformation materials inevitably experience disordered agglomeration of solid solution atoms when heated through the critical temperature range. This invention applies a transient high-density pulsed current to the alloy matrix under the constraint of a lower temperature environment. Utilizing the intrinsic high impedance characteristics at the intersection of the high-density dislocation network inside the crystal, nanosecond-level local adiabatic Joule heating is excited around the dislocation core. This micro-region thermodynamic transition causes supersaturated solute atoms to nucleate in situ without causing macroscopic lattice temperature fluctuations. Subsequently, a high-purity solute depletion region is formed by evacuating the area around the dislocation. This spatially pre-emptive micro-purification mechanism cuts off the dynamic path of disordered agglomeration of free atoms to the dislocation channels during the heating stage, eliminating the ineffective loss of solid solution atoms caused by cross-temperature flow. This achieves the goal of clearing the electron transport barrier inside the metal lattice in advance while maintaining a high dislocation potential energy, breaking the intrinsic physical mutual exclusion between the crystallographic strengthening parameters and the electrodynamic conduction parameters for maintaining a high dislocation potential energy.

[0016] 2. Establish a synergistic phase transformation path of cryogenic plastic deformation and alternating pulsed magnetocaloric composite aging. Continuous plastic deformation in a cryogenic environment forcibly freezes the thermally activated diffusion ability of atoms, forming a high-density three-dimensional dislocation network at grain boundaries and within the grains to accumulate huge deformation potential energy. The introduced alternating pulsed magnetic field induces microscopic eddies in the conductive matrix and generates differentiated Lorentz forces on matrix atoms and solute atoms based on different magnetic susceptibility properties, directionally reducing the nucleation barrier of precipitated phases. Under the synergistic drive of non-thermodynamic magnetic field and basic thermal field, supersaturated solute atoms on dislocation lines attach to pre-made crystal nuclei and undergo extremely rapid and high-density dispersed nucleation. This highly dispersed nanoscale precipitated phase strongly pins the underlying dislocation network, blocking the coarsening and recovery of dislocations during artificial aging. Thus, while solidifying a high yield strength, it accelerates the consumption of free defect atoms inside the matrix to deeply purify the background lattice, enabling the processed alloy material to have low resistivity loss constitutive characteristics that can adapt to extreme current-carrying conditions.

[0017] 3. By setting a controlled nonlinear cooling gradient to reconstruct the micro-stress state at the phase boundary, the rapid cooling process after conventional aging heat treatment is prone to freezing high local elastic stress at the interface between the nanoscale strengthening phase and the metal matrix, increasing the sensitivity of the material to microcrack initiation under macroscopic loading. After the magnetocaloric co-nucleation stage is completed, a controlled slow cooling program is introduced to reduce the temperature to an intermediate plateau. This thermodynamic unloading window is used to release the micro-local elastic stress concentration caused by the high dislocation density at the phase interface. This micro-stress relief mechanism effectively reduces the tendency of the material to undergo brittle fracture at the grain boundary under alternating loads. Without losing the precipitation strengthening increment, the intrinsic plastic deformation margin of the metal matrix is ​​preserved, comprehensively improving the mechanical safety boundary of complex structural parts during forming and long-term service. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is the main flow chart of the high-strength and high-conductivity aluminum alloy composite processing technology of the present invention; Figure 2 This is a logic diagram for optimizing and generating transient pulse control data tensors in this invention. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Each embodiment of the present invention is experimentally verified based on a specific aluminum-magnesium-silicon (Al-Mg-Si) alloy composition ratio. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, an embodiment or embodiment referred to herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. An embodiment appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] This invention is described in detail with reference to the schematic diagrams. When describing the embodiments of this invention, for ease of explanation, the cross-sectional views of the device structure will be partially enlarged without adhering to the general scale. Moreover, the schematic diagrams are only examples and should not limit the scope of protection of this invention. In addition, in actual manufacturing, the three-dimensional spatial dimensions of length, width and depth should be included.

[0023] Furthermore, in the description of this invention, it should be noted that the terms such as "upper," "lower," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or component referred to has a specific orientation, or is constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0024] Unless otherwise explicitly specified and limited, the terms installation, connection, and linking in this invention should be interpreted broadly. For example, they can refer to fixed connection, detachable connection, or integrated connection; similarly, they can refer to mechanical connection, electrical connection, or direct connection, or indirect connection through an intermediate medium, or internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0025] A processing technology for a high-strength aluminum alloy material with optimized electrical conductivity includes the following steps: Step 101: Apply an extrusion operation with a cumulative true strain of not less than 4 to the aluminum alloy component to be processed at -190℃ to -150℃ to generate a pre-processed aluminum alloy component with dislocation entanglement inside. Step 102: Divide the solid model data of the initial-processed aluminum alloy component into a spatial tetrahedral mesh according to the Deloitte triangulation algorithm, extract the centroid of the mesh as a micro-element node, and calculate the deformation gradient parameter of the micro-element node. The deformation gradient parameter is defined as the equivalent tensor norm that characterizes the geometrically necessary dislocation evolution intensity at the micro-element node and the projection cross-sectional parameter of the line connecting adjacent micro-element nodes in the direction perpendicular to the main current. Step 103: Based on the deformation gradient parameter, extract the predicted dislocation density feature vector and residual resistivity mapping coefficient of the corresponding spatial location from the database. The database is constructed by combining the dislocation strengthening Taylor model with transmission electron microscopy observation data of no less than 10 typical deformed specimens. Step 104: Calculate the basic resistance parameter based on the projected cross-sectional parameter, multiply the magnitude of the predicted dislocation density eigenvector by the residual resistivity mapping coefficient to obtain the impedance correction term, add the basic resistance parameter and the impedance correction term to obtain the local impedance correction coefficient, and generate the transient pulse control data tensor based on the local impedance correction coefficient of all micro-element nodes. Step 105: Attach conductive electrode plates to both ends of the pre-processed aluminum alloy component, input an electrical pulse sequence based on the transient pulse control data tensor, and monitor the dynamic resistivity change during the injection process in real time through a high-frequency sampling circuit. Feed the real-time data back to the pre-trained machine learning proxy model to fine-tune the pulse amplitude and pulse width online to compensate for the energy fluctuations caused by solute atom nucleation. Maintain the injection for 5μs to 15μs when the surface temperature rise rate of the pre-processed aluminum alloy component reaches 80℃ / s to 120℃ / s. Step 106: Within 0.5s after the termination of the electrical pulse sequence injection, control the cooling rate to 2℃ / min to 5℃ / min, so that the initial processed aluminum alloy component is cooled to an intermediate temperature plateau of 120℃ to 160℃ and held for 2h to 4h.

[0026] Preferably, the extrusion operation that applies a cumulative true strain of not less than 4 includes: step 201: positioning the aluminum alloy component to be processed inside the equal channel corner extrusion die; step 202: performing 4 to 8 passes of continuous extrusion on the aluminum alloy component to be processed under the physical constraint of an extrusion channel angle of 90° to 120°.

[0027] Preferably, generating the transient pulse control data tensor includes: step 301, setting an initial pulse parameter vector for the micro-nodes; step 302, constructing a fitness function based on the local impedance correction coefficient and the potential difference between the micro-nodes; and step 303, solving for the target parameter that minimizes the fitness function as the transient pulse control data tensor.

[0028] Preferably, the calculation of the local impedance correction factor conforms to the mathematical equation: ,in, This is the local impedance correction factor. As a basic resistance parameter, The residual resistivity mapping coefficient, This is to estimate the magnitude of the dislocation density eigenvector.

[0029] Preferably, the heat preservation operation includes: step 501, collecting acoustic emission frequency data of the pre-processed aluminum alloy component; step 502, calculating the rate of change of the energy integral value of the acoustic emission frequency data as a relaxation rate index; step 503, when the relaxation rate index is lower than a preset threshold for ten minutes, triggering an interruption and performing air cooling operation to room temperature.

[0030] Preferably, the heat preservation operation further includes: step 601, applying an alternating magnetic field to the pre-processed aluminum alloy component; step 602, detecting the impedance phase angle parameter of the alternating eddy current induced by the alternating magnetic field; step 603, adjusting the driving frequency of the alternating magnetic field according to the impedance phase angle parameter, so that the impedance phase angle parameter is locked at a set extreme point.

[0031] Preferably, before step 101, the method includes: step 701, heating an aluminum alloy ingot with a total mass percentage of magnesium and silicon of 0.8% to 1.5% to perform a solution treatment at 530°C to 550°C; step 702, performing a quenching operation on the solution-treated aluminum alloy ingot, controlling the cooling rate to be greater than 100°C / s to generate the aluminum alloy component to be processed.

[0032] Preferably, after extracting the centroid of the mesh as micro-element nodes, the process includes: step 801, obtaining the normal vector of the micro-element node along the direction of the main current of the initially processed aluminum alloy component; step 802, calculating the projected profile area value of adjacent micro-element nodes on a plane perpendicular to the normal vector, as a projection cross-section parameter.

[0033] Preferably, the input electrical pulse sequence includes: step 901, extracting the peak current array and pulse width array of the transient pulse control data tensor; step 902, controlling the pulse waveform generation unit to output an asymmetric bipolar square wave current with amplitude matching the peak current array and duration matching the pulse width array, and controlling the cooling rate includes: step 1001, collecting the surface temperature and core temperature of the pre-processed aluminum alloy component to calculate the temperature difference; step 1002, adjusting the cooling fan speed in the cooling environment based on the temperature difference, controlling the temperature difference within 5°C, until it drops to an intermediate temperature plateau.

[0034] A high-strength aluminum alloy material with optimized electrical conductivity is disclosed. This material is prepared using a processing technique that optimizes the electrical conductivity of aluminum alloys. The aluminum alloy matrix contains nano-precipitates with an average particle size of 5 nm to 10 nm, distributed non-uniformly and locally within the dislocation entanglement core region. The lattice distortion rate in the solute-depleted region of the matrix is ​​reduced by more than 15% compared to conventional artificial aging. Furthermore, within a 10 μm × 10 μm observation domain, the interaction density between the nano-precipitates and high-density dislocations is not less than 5.0 × 10⁻⁶. 14 m -3 .

[0035] Example 1: In the continuous manufacturing process of fittings for ultra-high voltage overhead transmission lines, aluminum alloy materials need to withstand wind and snow mechanical loads and carry grounding return current. When the solution-treated aluminum alloy is placed in a high-temperature furnace for heat preservation and aging, the heat energy gradient transfer causes the supersaturated solution atoms in the metal matrix to randomly agglomerate along high-density dislocation lines when crossing the temperature transition zone, forming a low-coherence natural aging region. This process consumes solution atoms and intensifies electron scattering, resulting in a negative correlation between mechanical strength and electrical conductivity. The processing technology of this invention analyzes the continuous solid component into a spatial tetrahedral mesh and extracts micro-element nodes. Based on the deformation gradient parameters of the micro-element nodes, the estimated dislocation density feature vector and residual resistivity mapping coefficient of the corresponding spatial position are extracted from the pre-set material state database. The current field distortion generated by the solid geometry is converted into discrete time-series control data. Based on the internal impedance topology spectrum of the material, a non-uniform electric pulse sequence is issued to excite local transient thermal effects.

[0036] Aluminum alloy ingots with a combined magnesium and silicon content of 0.8% to 1.5% by mass are selected, heated to 530°C to 550°C for solution treatment, and quenched at a cooling rate greater than 100°C / s. The aluminum alloy component to be treated is positioned inside an equal-channel angular extrusion die and subjected to 4 to 8 consecutive extrusion passes at a temperature of -190°C to -150°C, under a solid constraint with an extrusion channel angle of 90° to 120°. An extrusion deformation with a cumulative true strain of not less than 4 is applied. The cryogenic environment suppresses atomic thermal activation diffusion and... A pre-machined aluminum alloy component with dislocation entanglements is generated at grain boundaries and within grains. The normal vector of each micro-node along the main current direction of the pre-machined aluminum alloy component is obtained. The projected profile area of ​​adjacent micro-nodes on a plane perpendicular to the normal vector is calculated as the projected cross-sectional parameter. Based on the projected cross-sectional parameter, the basic resistance parameter is calculated. The impedance correction term is obtained by multiplying the estimated dislocation density eigenvector magnitude by the residual resistivity mapping coefficient. The local impedance correction coefficient is obtained by adding the basic resistance parameter and the impedance correction term. The formula for calculating the local impedance correction coefficient is as follows: ,in, This is the local impedance correction factor. As a basic resistance parameter, The residual resistivity mapping coefficient, To predict the magnitude of the dislocation density eigenvector, a fitness function is constructed based on the local impedance correction coefficient and the potential difference between the micro-element nodes. The objective parameter that minimizes the fitness function is then used as the transient pulse control data tensor. Conductive electrode plates are attached to both ends of the pre-machined aluminum alloy component. An electrical pulse sequence is input based on the transient pulse control data tensor. The output amplitude of the pulse waveform generation unit matches the peak current array of the transient pulse control data tensor, and the duration matches the asymmetric bipolar square wave current of the pulse width array. The electrical pulse excites local Joule heating in the high dislocation density region. When the surface temperature rise rate of the pre-machined aluminum alloy component reaches 80℃ / s to 120℃ / s, the current injection is maintained for 5μs to 15μs. This causes the supersaturated solute atoms around the dislocation core to undergo in-situ nucleation and form a solute depletion region. The high-density dislocation network provides a high-impedance region to guide the current distribution. The local Joule heating effect causes the supersaturated solute atoms to form nanoscale precipitates based on the dislocation network.

[0037] Within 0.5 seconds after the termination of the electrical pulse sequence injection, the surface and core temperatures of the pre-processed aluminum alloy component are collected to calculate the temperature difference. Based on this temperature difference, the cooling fan speed is adjusted to ensure the temperature difference is less than or equal to 5°C, and the cooling rate is controlled at 2°C / min to 5°C / min. This cools the pre-processed aluminum alloy component to an intermediate temperature plateau of 120°C to 160°C and holds it at this temperature for 2 to 4 hours. During the holding period, an alternating magnetic field is applied to the pre-processed aluminum alloy component, and the impedance phase angle parameter of the alternating eddy current is detected. The alternating magnetic field is adjusted based on the impedance phase angle parameter. The driving frequency of the field keeps the impedance phase angle parameter at a set constant value. The difference in magnetic susceptibility is used to apply Lorentz force to the metal matrix atoms and solute atoms, reducing the nucleation barrier of the precipitated phase. This causes the supersaturated solute atoms to attach to the pre-made crystal nuclei, resulting in diffuse nucleation and pinning of the dislocation network to suppress dislocation recovery. Steps 601 to 603 of the alternating magnetic field intervention procedure use a complex impedance analyzer to monitor the real-time phase difference between the two ends of the induction coil and calculate the impedance phase angle parameter. The frequency adjustment range of the alternating magnetic field is set to 1kHz to 20kHz and the central magnetic induction intensity is controlled at 0. Between 5T and 1.5T, when a phase shift caused by matrix magnetic susceptibility drift due to solute atom precipitation is detected, the frequency synthesizer automatically scans and resets the driving frequency in 50Hz steps, relocking the impedance phase angle parameter to the set extreme point. Dynamic Lorentz force is used to drive solute atoms on dislocation lines to overcome the lattice diffusion barrier, causing supersaturated solute atoms to precipitate at a high density on an intermediate temperature plateau while maintaining a constant temperature. This preserves the dislocation strengthening effect and improves the material's conductivity by purifying the matrix. Acoustic emission frequency data is collected, and the energy product of the acoustic emission frequency data is calculated. The time derivative of the score is used as a relaxation rate index. When the relaxation rate index is lower than the preset threshold for 10 consecutive minutes, the heat preservation is stopped and the material is air-cooled to room temperature. The slow cooling process releases the local elastic stress at the phase interface. Through the above steps, a high-density dislocation network and nanoscale precipitates are formed inside the pre-processed aluminum alloy component. The large amount of solute atoms precipitates reduces the solid solubility and reduces the electron scattering centers inside the matrix. While maintaining the mechanical strength provided by the lattice defects, the electron conduction resistance rate is reduced, and the final aluminum alloy product material with specific tensile strength and electrical conductivity is obtained.

[0038] Example 2: Current manufacturing conditions for UHV overhead transmission line fittings require materials to possess both tensile strength and low impedance transmission characteristics. The test platform verifying the processing technology of this aluminum alloy material employs a cryogenic equal-channel angular extrusion press with a temperature control accuracy of ±0.1℃ and an electrical pulse generator with a sampling frequency of 100kHz. To simulate temperature fluctuations of ±5℃ and high-frequency electromagnetic interference in industrial settings, a Gaussian white noise sequence containing 50Hz power frequency harmonics is introduced as background noise when acquiring initial voltage data. The controller uses a moving average filtering algorithm with a time span of 10 cycles to extract the effective potential difference. The system corrects the basic resistance parameters based on the effective potential difference to eliminate baseline deviation caused by temperature drift. The setting of the electrical pulse duration is based on coordinating the phase transition activation energy of precipitate nucleation with the thermodynamic stability of the metal matrix. When the magnitude of the predicted dislocation density eigenvector approaches the upper limit of the extreme value, the high impedance caused by lattice defects increases the Joule heating rate. To avoid micro-region adiabatic shearing or grain boundary melting, the controller sets the pulse duration to the lower limit of the range of 5μs. When the magnitude is in the reference intermediate state, the system sets the electrical pulse duration to 10μs based on the coupling transfer function of the current field and the thermal field.

[0039] Four groups of samples were prepared from the same batch of aluminum alloy ingots with a combined magnesium and silicon mass percentage of 1.2%. Samples treated with conventional solution aging were designated as the first control group; samples exempt from the alternating magnetic field application step during the holding stage and with an electric pulse duration of 10 μs were designated as the second control group; samples with an electric pulse duration of 25 μs were designated as the third control group; and samples applying all the treatment steps of this invention and with an electric pulse duration of 10 μs were designated as the experimental group. During signal acquisition, an excitation current was input to both ends of the experimental group samples. The signal acquisition module recorded a transient voltage sequence with a 15% amplitude fluctuation. The smoothed basic resistance parameter was extracted using a moving average filtering algorithm and substituted into the calculation formula. Calculate the local impedance correction factor, where, This is the local impedance correction factor. As a basic resistance parameter, The residual resistivity mapping coefficient, To estimate the magnitude of the dislocation density eigenvector, the controller outputs an asymmetric bipolar square wave current based on the local impedance correction coefficient. The infrared thermometer measured the surface temperature rise rate of the test group to be 105.3℃ / s. The tensile strength of the first control group was 310.5MPa and the conductivity was 52.5%IACS, the tensile strength of the second control group was 355.2MPa and the conductivity was 57.2%IACS, the tensile strength of the third control group was 260.8MPa and the conductivity was 59.0%IACS, and the tensile strength of the test group was 415.6MPa and the conductivity was 60.5%IACS.

[0040] The test data comparison shows that the tensile strength of the third control group decreased after the electric pulse duration exceeded the upper limit of 15 μs. This trend corresponds to the static recovery process of the dislocation network caused by excessive transient heat injection, confirming that the electric pulse duration range of 5 μs to 15 μs is an effective operating range that balances dislocation retention and solute atom nucleation. Comparing the performance differences between the experimental group and the first and second control groups, the second control group, due to the lack of Lorentz force driven by the alternating magnetic field, had limited kinetic energy acquired by solute atoms, and the precipitated phase failed to fully pin the dislocation network. In contrast, under the combined effect of local Joule heating and alternating magnetic field, the supersaturated solute atoms in the experimental group underwent diffuse nucleation on the dislocation lines. The formation of the precipitated phase consumed the solute atoms in the solid solution matrix, reducing the scattering probability of electrons in the matrix. This process inhibited the recovery of the dislocation network and reduced electron transport obstacles, resulting in the final aluminum alloy material having higher tensile strength and electrical conductivity than the control group.

[0041] Example 3: In the manufacturing of fittings for ultra-high voltage overhead transmission lines, the components are constrained by asymmetric solid geometric topology. When the pre-processed aluminum alloy components are subjected to electric field injection, abrupt changes in the cross-section cause internal nonlinear current density distribution. Single-parameter electric pulses lead to insufficient adiabatic shear in high dislocation density regions or insufficient driving force for phase transformation in low-density regions. It is necessary to establish a mapping path that transforms solid geometric features and lattice defect states into discrete pulse execution commands. The three-dimensional geometric model data of the pre-processed aluminum alloy components is obtained. Based on the Delaunay triangulation algorithm, the three-dimensional geometric model data is discretized to generate a spatial tetrahedral mesh. The centroid of the mesh is extracted to form a set of micro-element nodes. The deformation gradient parameter of the micro-element nodes is extracted as a feature index value to access the pre-set material state database, matching and obtaining the corresponding estimated dislocation density feature vector. The magnitude of the estimated dislocation density feature vector is extracted, and the residual resistivity mapping coefficient is retrieved. The projected contour area value of adjacent micro-element nodes on the plane perpendicular to the main current normal vector is calculated as the projected cross-sectional parameter. Based on the projected cross-sectional parameter, the basic resistance parameter is calculated, and it is summed with the impedance correction term to generate a local impedance correction coefficient. The calculation formula is as follows: ,in, This is the local impedance correction factor. As a basic resistance parameter, The residual resistivity mapping coefficient, To predict the magnitude of the dislocation density eigenvector, an initial pulse parameter vector containing pulse amplitude and pulse width variables is set. A local Joule heating prediction model is established by combining the local impedance correction coefficient and the potential difference between micro-nodes. A fitness function is constructed that includes the mean square error between the predicted temperature rise rate of the micro-node and the center value of the target temperature rise rate interval. A penalty value is added to the micro-node with a predicted temperature rise rate exceeding 120℃ / s in the fitness function. The global optimization algorithm is run to iterate the initial pulse parameter vector until the output value of the fitness function converges. The pulse amplitude and pulse width set under the minimum condition are extracted to generate a transient pulse control data tensor. The transient pulse control data tensor is defined as a fourth-order discrete numerical matrix containing spatial micro-element coordinates, peak current array, pulse width array, and asymmetric bipolar waveform slope parameter. It serves as a discrete pulse execution instruction set, realizing the accurate mapping of the energy field to the dislocation topology at the nanoscale.

[0042] The pulse waveform generation unit outputs an asymmetric bipolar square wave current to the conductive electrode plates at both ends of the pre-machined aluminum alloy component based on the transient pulse control data tensor. The above steps compensate for the influence of the solid geometry on current transmission, enabling the electrical pulses in different impedance regions to generate a temperature rise rate of 80℃ / s to 120℃ / s. Supersaturated solute atoms complete dispersed nucleation within a thermal field maintained for 5μs to 15μs, reducing interference from solute atoms on electron transport. The transient pulse control data tensor is converted into a driving current by a central logic control unit with an operating frequency of not less than 100kHz. The pulse width modulation signal stream of the inverter bridge module inside the high-frequency pulse power supply is mapped to the control power switch tube on / off time and trigger frequency level sequence. The peak current array is read and converted into a voltage comparator reference voltage. The current waveform signal fed back by the bus current acquisition unit in real time is compared with the reference voltage. Based on the comparison result, the duty cycle output of the inverter circuit is adjusted in real time to keep the transient current density fluctuation range applied to both ends of the initial processed aluminum alloy component within the control preset value of 2%, so that the local Joule heat generation rate of the dislocation core in different micro-element node regions is controlled. Example 4: In the manufacturing process of fittings for ultra-high voltage overhead transmission lines, fluctuations in the initial microcrystalline state of different batches of aluminum alloy ingots cause drift in the pulse control data tensor calculation benchmark. Before processing batches of aluminum alloy components, the system initiates an offline data filling procedure for the material state database. The test unit selects standard aluminum alloy samples with deformation gradients and places them in a low-temperature equal-channel corner extrusion die to apply continuous extrusion. The system uses a transmission electron microscope to scan the sample cross-section to obtain microcrystalline lattice images, and simultaneously uses a four-terminal bridge array to synchronously acquire local resistance data of the corresponding observation nodes. When calibrating the residual resistivity mapping coefficient, it must be carried out at a standard ambient temperature of 20°C. This is achieved by subtracting the 2.42 μΩ·cm intrinsic impedance substrate generated by lattice thermal vibration from the total measured impedance. The residual value caused by defect scattering is obtained and divided by the corresponding estimated dislocation density eigenvector magnitude to determine the conversion coefficient. This quantitatively transforms the microscopic defect state into macroscopic electrical response parameters, establishing an accurate attribution physical model between defect scattering contribution and dislocation density. For the alternating magnetic field intervention process, the system calibrates the impedance phase angle parameter by comparing the real-time voltage phase difference between the induction coil and the reference standard sample. If a phase shift exceeding 1.5 degrees is detected, a frequency step search is initiated, and the driving frequency is fine-tuned in increments of 10 Hz. The data analysis module calculates the total length of dislocation lines per unit volume based on the microscopic lattice image and vectorizes it to generate an estimated dislocation density eigenvector. Simultaneously, the residual resistivity mapping coefficient is extracted from the phonon scattering substrate generated by lattice thermal vibrations in the local resistance data.

[0043] The storage controller uses the deformation gradient parameter as the feature index value and establishes a mapping relationship with the corresponding calculated estimated dislocation density eigenvector and residual resistivity mapping coefficient. This pre-set material state database uses a 64x64 discrete numerical matrix structure, dividing the deformation gradient into 64 mapping levels in 0.1 increments. Through transmission electron microscopy observation of 500 aluminum alloy samples with different degrees of cold deformation, the statistical average of dislocation lines measured at each deformation gradient level is used as the corresponding estimated dislocation density eigenvector magnitude. This ensures that the indexing process can be completed within 30 ns with only one table lookup operation. All related entries are directly written into the pre-set material state database, establishing a numerical correspondence matrix between microscopic defects and macroscopic impedance. Step 103 The pre-set material state database construction procedure selects aluminum alloy standard samples with chemical compositions consistent with the aluminum alloy components to be treated. Controlled deformation tests with a real strain gradient distribution from 0.5 to 8.0 are carried out on a cryogenic equal channel corner extrusion press. Images of dislocation structures in the stress core region of the standard sample are obtained using a transmission electron microscope with a working voltage of 200kV. The length of dislocation lines per unit volume is statistically analyzed using the truncation method, and the estimated dislocation density feature vector under the corresponding deformation gradient is quantified. The corresponding micro-area current-voltage characteristic data are collected using a four-probe tester. The intrinsic impedance is generated by stripping the lattice thermal vibration, and the residual resistivity mapping coefficient reflecting the scattering contribution of lattice defects is obtained. The discrete experimental data points are fitted by the least squares method to generate a three-dimensional mapping matrix and stored, which is used as the basis for extracting the numerical value of spatial location features. When the on-site manufacturing equipment is connected to the initial test batch, the system uses a contact acoustic emission sensor to collect the transient acoustic response signal of the pre-processed aluminum alloy component during the first electrical pulse cycle, triggering the pre-baseline calibration procedure. The feature extraction module applies a fast Fourier transform to the transient acoustic response signal to calculate the frequency domain envelope area characterizing the relaxation energy of the microstructure, and calculates the ratio difference between it and the preset reference threshold to generate impedance compensation weights. The controller reads the projected cross-sectional parameters and deformation gradient parameters of the micro-element nodes, and applies mathematical equations... Calculate the foundation local impedance correction factor, where, This is the local impedance correction factor. As a basic resistance parameter, The residual resistivity mapping coefficient, To estimate the magnitude of the dislocation density eigenvector, the controller multiplies the basic local impedance correction coefficient with the impedance compensation weight as the input variable of the objective function to iteratively generate a transient pulse control data tensor. The pulse waveform generation unit outputs an asymmetric bipolar square wave current accordingly. The aforementioned offline calibration and in-situ compensation procedures offset the physical property deviations between the original batches of materials, enabling the non-uniform electric pulse sequence to maintain stable local Joule heat generation accuracy in the complex impedance topology network.

[0044] Example 5: In the manufacturing process of fittings for ultra-high voltage overhead transmission lines, there is a physical lag in timing and energy transfer between the digital analysis commands of the control system and the hardware execution mechanism. Before mass deployment, the system needs to execute a parameter optimization procedure that includes spatial grid resolution calibration and feedforward cooling alignment. The test unit injects a test square wave current into the reference sample and extracts the heat conduction distance within a single pulse cycle. This distance is multiplied by a preset safety factor to convert it into a critical heat diffusion radius as the lower limit of the side length of the spatial tetrahedral grid. The system obtains the minimum switching time required to output pulse data of adjacent micro-element nodes, calculates the physical conduction time required for the current to cross the micro-element node under the current grid side length, and calculates the formula. Calculate the time margin index, among which This refers to the time margin indicator. For the time consumed by physical conduction, To minimize the switching time, when the time margin index is negative, the processor increases the grid side length by a constant step size and reconstructs the grid until the time margin index converges to a positive minimum. The side length parameter at this point is then extracted as the reference input to the Deloitte triangulation algorithm.

[0045] At the moment the electrical pulse sequence terminates, the control system calculates the basic heat dissipation flux required to maintain a cooling gradient of 2℃ / min to 5℃ / min based on the component mass and the targeted cooling rate, and issues the initial constant drive duty cycle of the fan accordingly. During the cooling process to the intermediate temperature plateau of 120℃ to 160℃, the system continuously collects the surface temperature and core temperature to calculate the temperature difference, inputs it into the integrator network to generate a dynamically adjusted duty cycle, and superimposes the dynamically adjusted duty cycle on the initial constant drive duty cycle to adjust the fan speed. The system controls the temperature difference to be within 5℃ until it reaches the intermediate temperature plateau, and maintains the cooling gradient through calculation. The initial constant drive duty cycle of the fan is issued based on the basic heat dissipation flux required for the gradient, and a dynamically adjusted duty cycle generated based on the integrator network is superimposed to suppress temperature regulation overshoot, so that the cooling trajectory accurately follows the preset thermodynamic evolution trajectory. The above offline optimization procedure establishes the effective physical boundary of the spatial tetrahedral mesh, avoiding hardware impulse response overshoot caused by excessive mesh division. The system establishes the heat dissipation base by establishing the initial constant drive duty cycle and superimposing the dynamically adjusted duty cycle for correction, suppressing temperature regulation overshoot in the early stage of transient thermal field switching, so that the pre-processed aluminum alloy component accurately approaches the preset thermodynamic evolution trajectory during the cooling stage.

[0046] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A processing technology for a high-strength aluminum alloy material with optimized electrical conductivity, characterized in that, Includes the following steps: Step 101: Apply an extrusion operation with a cumulative true strain of not less than 4 to the aluminum alloy component to be processed at -190℃ to -150℃ to generate a pre-processed aluminum alloy component with dislocation entanglement inside. Step 102: Divide the solid model data of the initial-processed aluminum alloy component into a spatial tetrahedral mesh according to the Deloitte triangulation algorithm, extract the centroid of the mesh as a micro-element node, and calculate the deformation gradient parameter of the micro-element node. The deformation gradient parameter is defined as the equivalent tensor norm that characterizes the geometrically necessary dislocation evolution intensity at the micro-element node and the projection cross-sectional parameter of the line connecting adjacent micro-element nodes in the direction perpendicular to the main current. Step 103: Based on the deformation gradient parameter, extract the predicted dislocation density feature vector and residual resistivity mapping coefficient of the corresponding spatial location from the database. The database is constructed by combining the dislocation strengthening Taylor model with transmission electron microscopy observation data of no less than 10 typical deformed specimens. Step 104: Calculate the basic resistance parameter based on the projected cross-sectional parameter, multiply the magnitude of the predicted dislocation density eigenvector by the residual resistivity mapping coefficient to obtain the impedance correction term, add the basic resistance parameter and the impedance correction term to obtain the local impedance correction coefficient, and generate the transient pulse control data tensor based on the local impedance correction coefficient of all micro-element nodes. Step 105: Attach conductive electrode plates to both ends of the pre-processed aluminum alloy component, input an electrical pulse sequence based on the transient pulse control data tensor, and monitor the dynamic resistivity change during the injection process in real time through a high-frequency sampling circuit. Feed the real-time data back to the pre-trained machine learning proxy model to fine-tune the pulse amplitude and pulse width online to compensate for the energy fluctuations caused by solute atom nucleation. Maintain the injection for 5μs to 15μs when the surface temperature rise rate of the pre-processed aluminum alloy component reaches 80℃ / s to 120℃ / s. Step 106: Within 0.5s after the termination of the electrical pulse sequence injection, control the cooling rate to 2℃ / min to 5℃ / min, so that the initial processed aluminum alloy component is cooled to an intermediate temperature plateau of 120℃ to 160℃ and held for 2h to 4h.

2. The processing technology for a high-strength aluminum alloy material with optimized electrical conductivity according to claim 1, characterized in that, The extrusion operation that applies a cumulative true strain of not less than 4 includes: Step 201: Positioning the aluminum alloy component to be processed inside the equal channel corner extrusion die; Step 202: Performing 4 to 8 passes of continuous extrusion on the aluminum alloy component to be processed under the physical constraint of an extrusion channel angle of 90° to 120°.

3. The processing technology for a high-strength aluminum alloy material with optimized electrical conductivity according to claim 1, characterized in that, The generation of transient pulse control data tensor includes: step 301, setting an initial pulse parameter vector for the micro-element node; step 302, constructing a fitness function based on the local impedance correction coefficient and the potential difference between the micro-element nodes; step 303, solving for the objective parameter that minimizes the fitness function as the transient pulse control data tensor.

4. The processing technology for a high-strength aluminum alloy material with optimized electrical conductivity according to claim 1, characterized in that, The calculation of the local impedance correction factor conforms to the mathematical equation: ,in, This is the local impedance correction factor. As a basic resistance parameter, The residual resistivity mapping coefficient, This is to estimate the magnitude of the dislocation density eigenvector.

5. The processing technology for a high-strength aluminum alloy material with optimized electrical conductivity according to claim 1, characterized in that, The heat preservation operation includes: step 501, collecting acoustic emission frequency data of the pre-processed aluminum alloy component; step 502, calculating the rate of change of the energy integral value of the acoustic emission frequency data as the relaxation rate index; step 503, when the relaxation rate index is lower than the preset threshold for ten minutes, triggering an interruption and performing air cooling operation to room temperature.

6. The processing technology for a high-strength aluminum alloy material with optimized electrical conductivity according to claim 1, characterized in that, The heat preservation operation also includes: step 601, applying an alternating magnetic field to the pre-processed aluminum alloy component; step 602, detecting the impedance phase angle parameter of the alternating eddy current induced by the alternating magnetic field; step 603, adjusting the driving frequency of the alternating magnetic field according to the impedance phase angle parameter, so that the impedance phase angle parameter is locked at the set extreme point.

7. The processing technology for a high-strength aluminum alloy material with optimized electrical conductivity according to claim 1, characterized in that, Before step 101, the process includes: step 701, heating an aluminum alloy ingot with a combined mass percentage of magnesium and silicon of 0.8% to 1.5% to perform a solution treatment at 530°C to 550°C; step 702, performing a quenching operation on the solution-treated aluminum alloy ingot, controlling the cooling rate to be greater than 100°C / s to generate the aluminum alloy component to be processed.

8. The processing technology for a high-strength aluminum alloy material with optimized electrical conductivity according to claim 1, characterized in that, After extracting the centroid of the mesh as micro-element nodes, the process includes: Step 801, obtaining the normal vector of the micro-element node along the direction of the main current of the pre-processed aluminum alloy component; Step 802, calculating the projected profile area value of adjacent micro-element nodes on the plane perpendicular to the normal vector, as the projection cross-section parameter.

9. The processing technology for a high-strength aluminum alloy material with optimized electrical conductivity according to claim 1, characterized in that, The input electrical pulse sequence includes: step 901, extracting the peak current array and pulse width array of the transient pulse control data tensor; step 902, controlling the pulse waveform generation unit to output an asymmetric bipolar square wave current with amplitude matching the peak current array and duration matching the pulse width array. The cooling rate control includes: step 1001, acquiring the surface temperature and core temperature of the pre-processed aluminum alloy component to calculate the temperature difference; step 1002, adjusting the cooling fan speed in the cooling environment based on the temperature difference, controlling the temperature difference within 5°C, until it drops to the intermediate temperature plateau.

10. A high-strength aluminum alloy material with optimized electrical conductivity, characterized in that, The aluminum alloy material is prepared by the processing technology of a high-strength aluminum alloy material with optimized electrical conductivity as described in any one of claims 1 to 9. The aluminum alloy material matrix contains nano-precipitates with an average particle size of 5 nm to 10 nm, which are non-uniformly and locally distributed in the dislocation entanglement core region. The lattice distortion rate in the solute-depleted region of the matrix is ​​reduced by more than 15% compared to conventional artificial aging, and within a 10 μm × 10 μm observation domain, the interaction point density between the nano-precipitates and high-density dislocations is not less than 5.0 × 10⁻⁶. 14 m -3 .

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