Superconducting material comprehensive treatment and performance detection platform

Abstract

The invention discloses a superconducting material comprehensive treatment and performance detection platform, and relates to the technical field of superconducting material detection, and the superconducting material comprehensive treatment and performance detection platform comprises a sample numerical control pretreatment unit used for pretreating a superconducting material sample; the laser smooth cleaning unit is used for carrying out smooth cleaning on the surface of the superconducting material sample; the performance test output unit is used for testing resistance characteristics of the superconducting material sample under different temperature conditions and outputting test results; wherein the sample numerical control preprocessing unit comprises a master control module, an emergency control module, a monitoring module, a punching module, a shearing module and a bending module. The high-quality stable forming of the superconducting material sample is guaranteed, and the diversified and complicated precise bending requirements are met.

Description

Technical Field

[0001] This invention relates to the field of superconducting material testing technology, and more specifically, to a comprehensive processing and performance testing platform for superconducting materials. Background Technology

[0002] With the widespread application of superconducting materials in high-tech fields such as quantum computing, magnetic resonance imaging, and particle accelerators, the importance of their processing accuracy and performance testing is increasing. Superconducting materials typically possess extremely low resistivity and unique electromagnetic and thermodynamic properties, thus imposing stringent requirements on bending angles, surface finish, and dimensional accuracy. However, traditional processing methods often rely on manual experience or simple mechanical equipment, resulting in insufficient flexibility and limited control precision, making it difficult to meet current requirements for high consistency, mass production, and complex processes.

[0003] While existing bending equipment is available for metallic materials, it still falls short when dealing with the unique physical properties of superconducting materials (such as significant springback, high stress sensitivity, and cryogenic service environments). These devices lack modular adjustment mechanisms to flexibly adapt to samples of different sizes, and struggle to sense and dynamically control bending angles, loading paths, and forming states in real time. This can easily lead to forming deviations, uncontrolled springback, or microscopic damage, thus affecting the performance of the final device. Furthermore, existing performance testing methods are typically isolated and static evaluation processes, failing to be deeply coupled with the fabrication process and lacking data-driven closed-loop optimization capabilities. They cannot automatically adjust parameters based on the characteristics of different superconducting materials. Therefore, in practical applications, extensive trial-and-error experiments are often required for repeated corrections, which is not only inefficient but also significantly increases material waste and R&D costs.

[0004] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0005] In response to the problems in related technologies, this invention proposes a comprehensive processing and performance testing platform for superconducting materials to overcome the aforementioned technical problems existing in the current related technologies.

[0006] Therefore, the specific technical solution adopted by the present invention is as follows: A comprehensive processing and performance testing platform for superconducting materials, comprising: The sample CNC preprocessing unit is used to preprocess superconducting material samples; The laser smoothing and cleaning unit is used to smooth and clean the surface of superconducting material samples. The performance test output unit is used to test the resistance characteristics of superconducting material samples under different temperature conditions and output the test results. The sample CNC preprocessing unit includes a main control module, an emergency control module, a monitoring module, a punching module, a shearing module, and a bending module. The central control module is used to centrally coordinate the working sequence of each module and configure the operating parameters; The emergency control module is used to execute emergency response measures in abnormal situations; The monitoring module is used to monitor the operating status and key parameters of each module in real time and feed the monitoring information back to the central control module. The punching module is used for punching holes in superconducting material samples. The shearing module is used to shear and separate superconducting material samples; The bending module is used for CNC bending and shaping of superconducting material samples.

[0007] Furthermore, to provide a stable installation foundation and reliable operation conditions, and to ensure bending accuracy and operational stability, the bending module includes a bending housing. Several sliding grooves are provided on one side of the top of the bending housing, and bending sliding components are installed inside the sliding grooves. The bottom end of the bending sliding components cooperates with the drive component. An auxiliary component is provided on the other side of the top of the bending housing. A mold base is provided in the middle of the top of the bending housing. Several fixing holes are provided on the outer side of the mold base, and a bending mold is installed inside the mold base. The bending mold is fixed to the fixing holes by several fixing rods.

[0008] Furthermore, to improve the precise transmission and flexibility of the bending action, the bending sliding assembly includes a sliding rod disposed inside a sliding groove, a rotating disk disposed at the bottom end of the sliding rod, and a rotating shaft disposed at the bottom end of the rotating disk; a threaded groove is provided at the top end of the sliding rod, a threaded connector is disposed inside the threaded groove, a connecting post is provided at the top end of the sliding rod, a threaded hole is provided at the top end of the connecting post to mate with the threaded connector, and a rotating hole is provided at the top end of the threaded connector.

[0009] Furthermore, in order to provide reliable data support for angle control and precise positioning during bending operations, an arc-shaped scale is provided at the top of the bending housing, and the arc-shaped scale is located on one side of the sliding groove; an image sensor is provided at the top of the bending housing, and the image sensor is located on one side of the auxiliary components.

[0010] Furthermore, in order to provide precise and controllable driving force to the bending sliding component and ensure the accuracy and reliability of the bending action, the driving component includes a first bevel gear located at the bottom of the rotating shaft, and the first bevel gear meshes with a second bevel gear, with a servo motor located at one end of the second bevel gear.

[0011] Furthermore, to ensure that the superconducting material sample is firmly positioned without deviation, the auxiliary component includes a first fixing plate and a second fixing plate disposed on the other side of the top of the bent shell. A hydraulic rod is provided through one side of the second fixing plate, and a movable plate is provided on one side of the hydraulic rod. A limit slider is provided at the bottom of the movable plate. The auxiliary component also includes a limit groove formed on the other side of the top of the bent shell that cooperates with the limit slider.

[0012] Furthermore, the bending module includes: The initial parameter acquisition submodule is used to collect physical data of superconducting material samples and, based on a collaborative filtering algorithm of physical constraints, search a preset historical process database to obtain an initial bending parameter set that matches the superconducting material sample. The candidate bending strategy selection submodule is used to extract multi-scale feature data of the fixed superconducting material sample, construct a multi-objective optimization function in combination with the initial bending parameter set, generate a bending parameter optimization solution set using the finite element simulation algorithm, and select candidate bending strategies based on the bending parameter optimization solution set. The bending strategy generation submodule is used to combine the selected candidate bending strategies with the springback characteristics of the pre-acquired superconducting material samples to construct a nonlinear springback model, and use the nonlinear springback model to predict the springback angle error. Based on the springback angle error, the candidate bending strategies are dynamically adjusted to generate a bending strategy. The bending execution submodule is used to drive a servo motor using a model predictive control algorithm based on the generated bending strategy, so as to control the bending sliding component to perform bending operations on the superconducting material sample.

[0013] Furthermore, multi-scale feature data of the fixed superconducting material sample are extracted, and a multi-objective optimization function is constructed by combining it with the initial bending parameter set. A finite element simulation algorithm is then used to generate an optimized solution set of bending parameters. Candidate bending strategies are selected based on this optimized solution set, including: S1. Image data of the fixed superconducting material sample is acquired using an image sensor, and multi-scale feature data of the image data is extracted through a convolutional neural network. Combined with the initial bending parameter set, a multi-objective optimization function is constructed. S2. Based on the constructed multi-objective optimization function, the multi-physics coupled finite element simulation algorithm is used to simulate the bending effect under different combinations of bending parameters, and the simulation results are used as the input of the multi-objective optimization algorithm to iteratively generate the optimized solution set of bending parameters. S3. Based on the application scenarios and physical data of the superconducting material samples, a two-level screening mechanism is established, and the two-level screening mechanism is used to screen the optimized solution set of bending parameters to obtain candidate bending strategies.

[0014] Furthermore, based on the constructed multi-objective optimization function, a multi-physics coupled finite element simulation algorithm is used to simulate the bending effect under different combinations of bending parameters. The simulation results are then used as input to the multi-objective optimization algorithm to iteratively generate an optimized solution set for the bending parameters, including: S21. Based on the obtained initial bending parameter set, use the multiphysics coupled finite element simulation algorithm to perform parallel simulation of the bending process under different initial bending parameter combinations, and generate the corresponding simulation result dataset. S22. Construct a Kriging model based on the generated simulation result dataset, and use the Kriging model to calculate the Pareto front of the current bending optimization problem to obtain the optimized solution set of bending parameters; S23. Determine whether there is a feasible solution that satisfies the multi-objective optimization function in the initial bending parameter set. If there is, generate new initial bending parameters according to the fitness assignment criterion. If there is no, select the first new initial bending parameter according to the default probability criterion, calculate the multi-objective distance index function based on the first new initial bending parameter, and switch to the fitness assignment criterion to continue generating multiple new initial bending parameters. S24. Parallel call the multi-physics coupled finite element simulation algorithm to calculate the bending response value corresponding to the new initial bending parameters, and determine whether the preset termination condition is met. If it is met, output the optimized solution set of bending parameters. If it is not met, return to step S22 to continue iterative optimization until the termination condition is met and output the optimized solution set of bending parameters.

[0015] The beneficial effects of this invention are as follows: 1. This invention, through the structural design of a firmly fixed bending die and an adjustable connecting column, ensures high stability of the die during bending and enables rapid die replacement, flexibly adapting to bending requirements of different specifications. Simultaneously, the auxiliary components and bending sliding components work together to ensure precise positioning of the sample during the forming process, preventing slippage or misalignment. Furthermore, the precise coordination of the servo motor and drive components ensures uniform and controllable application of bending force to the material. Combined with an arc-shaped scale and high-precision speed adjustment function, it achieves real-time monitoring and precise control of the bending angle, ensuring not only high-quality and stable forming of superconducting material samples but also meeting diverse and complex precision bending needs.

[0016] 2. Through the collaborative work of multiple sub-modules, this invention enables the detection platform to achieve precise optimization of the entire bending process of superconducting material samples. It not only accurately selects the initial bending parameter set based on material physical properties and historical process data, but also combines multi-objective optimization and simulation to screen for the optimal candidate bending strategy, reducing the use of infeasible or inefficient parameters. Simultaneously, it relies on a nonlinear springback model to predict the springback angle error and dynamically corrects the bending strategy accordingly, achieving closed-loop adaptive optimization and improving forming accuracy. Furthermore, it employs a model predictive control algorithm to drive the servo motor, ensuring a highly controllable, timely, and precise bending process. This significantly improves the processing quality and batch consistency of superconducting material samples, reduces trial-and-error costs and material waste, and fully meets the requirements of complex and demanding precision manufacturing processes. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in 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.

[0018] Figure 1 This is a schematic diagram of the structure of a comprehensive processing and performance testing platform for superconducting materials according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a sample CNC preprocessing unit of a superconducting material integrated processing and performance testing platform according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a bending module of a superconducting material integrated processing and performance testing platform according to an embodiment of the present invention. Figure 4 This is one of the cross-sectional views of a bending module of a superconducting material integrated processing and performance testing platform according to an embodiment of the present invention. Figure 5 yes Figure 4 A magnified view of a section at point A in the middle; Figure 6 This is a second cross-sectional view of the bending module of a superconducting material integrated processing and performance testing platform according to an embodiment of the present invention. Figure 7 yes Figure 6 A magnified view of a section at point B in the middle; Figure 8 This is one of the schematic diagrams of a laser smoothing cleaning unit of a superconducting material integrated processing and performance testing platform according to an embodiment of the present invention; Figure 9 This is a second schematic diagram of a laser smoothing cleaning unit of a superconducting material integrated processing and performance testing platform according to an embodiment of the present invention; Figure 10 This is a schematic diagram of the performance test output unit of a superconducting material integrated processing and performance testing platform according to an embodiment of the present invention; Figure 11 This is a probe testing module of a superconducting material integrated processing and performance testing platform according to an embodiment of the present invention; Figure 12 This is a schematic diagram of the bending module of a superconducting material integrated processing and performance testing platform according to an embodiment of the present invention.

[0019] In the picture: 1. Sample CNC preprocessing unit; 101. Main control module; 102. Emergency control module; 103. Monitoring module; 104. Punching module; 105. Shearing module; 106. Bending module; 1061. Bending housing; 1062. Sliding groove; 1063. Bending sliding assembly; 10631. Sliding rod; 10632. Rotating disk; 10633. Rotating shaft; 10634. Threaded groove; 10635. Threaded connector; 10636. Connecting column; 10637. Threaded hole; 10638. Rotating hole; 1064. Drive assembly; 10641. First bevel gear; 10642. Second bevel gear; 10643. Servo motor; 1065. Auxiliary assembly; 10651. First fixing plate; 10652. Second fixing plate; 10653. Hydraulic rod; 10654. Moving plate; 0655, Limiting slider; 10656, Limiting groove; 1066, Mold base; 1067, Fixing hole; 1068, Bending mold; 1069, Fixing rod; 10610, Arc-shaped scale; 10611, Image sensor; 2. Laser smoothing cleaning unit; 201, Laser probe and guide rail module; 202, Cutting module; 203, Test module; 204, Laser control module; 205, Laser generation module; 206, Ventilation module; 207, Coolant circulation module; 3. Performance test output unit; 301, Test platform module; 302, Microresistance test module; 303, Display module; 304, Probe test module; 3041, Base frame; 3042, Test base; 3043, Probe station; 3044, Probe and probe holder; 3045, Adjusting arm; 3046, Electron microscope. Detailed Implementation

[0020] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention.

[0021] According to an embodiment of the present invention, a comprehensive processing and performance testing platform for superconducting materials is provided.

[0022] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figures 1-7 As shown, the superconducting material integrated processing and performance testing platform according to an embodiment of the present invention includes: Sample numerical control preprocessing unit 1 is used to preprocess superconducting material samples.

[0023] The sample numerical control preprocessing unit 1 includes a main control module 101, an emergency control module 102, a monitoring module 103, a punching module 104, a shearing module 105, and a bending module 106. The central control module 101 is used to centrally coordinate the working sequence of each module and configure the operating parameters; Emergency control module 102 is used to execute emergency response measures in abnormal situations; The monitoring module 103 is used to monitor the operating status and key parameters of each module in real time and to feed back the monitoring information to the main control module 101. Punching module 104 is used for punching holes in superconducting material samples; The shearing module 105 is used for shearing and separating superconducting material samples; The bending module 106 is used for CNC bending and forming of superconducting material samples.

[0024] In this optional embodiment, the bending module 106 includes a bending housing 1061. A plurality of sliding grooves 1062 are provided on one side of the top of the bending housing 1061. A bending sliding component 1063 is provided inside the sliding groove 1062, and the bottom end of the bending sliding component 1063 cooperates with the driving component 1064. An auxiliary component 1065 is provided on the other side of the top of the bending housing 1061. A mold base 1066 is provided in the middle of the top of the bending housing 1061. A plurality of fixing holes 1067 are provided on the outer side of the mold base 1066. A bending mold 1068 is provided inside the mold base 1066, and the bending mold 1068 is fixed to the fixing holes 1067 by a plurality of fixing rods 1069, thereby providing a stable installation foundation and reliable action execution conditions for bending operations, ensuring bending accuracy and operational stability.

[0025] In this optional embodiment, the bending sliding assembly 1063 includes a sliding rod 10631 disposed inside the sliding groove 1062, a rotating disk 10632 disposed at the bottom end of the sliding rod 10631, and a rotating shaft 10633 disposed at the bottom end of the rotating disk 10632; a threaded groove 10634 is provided at the top end of the sliding rod 10631, a threaded connector 10635 is disposed inside the threaded groove 10634, a connecting post 10636 is provided at the top end of the sliding rod 10631, a threaded hole 10637 is provided at the top end of the connecting post 10636 to cooperate with the threaded connector 10635, and a rotating hole 10638 is provided at the top end of the threaded connector 10635. Thus, the detachable design of the threaded connector 10635 adapts to different connecting posts 10636, improving the accurate transmission of bending action and the flexibility of use.

[0026] In this optional embodiment, an arc-shaped scale 10610 is provided at the top of the bending housing 1061, and the arc-shaped scale 10610 is located on one side of the sliding groove 1062; an image sensor 10611 is provided at the top of the bending housing 1061, and the image sensor 10611 is located on one side of the auxiliary component 1065, thereby providing reliable data support for angle control and precise positioning of the bending operation and ensuring bending accuracy.

[0027] In this optional embodiment, the drive assembly 1064 includes a first bevel gear 10641 disposed at the bottom end of the rotating shaft 10633, and the first bevel gear 10641 meshes with a second bevel gear 10642. A servo motor 10643 is disposed at one end of the second bevel gear 10642, thereby providing a precise and controllable driving force for the bending sliding assembly 1063, ensuring the accuracy and reliability of the bending action.

[0028] In this optional embodiment, the auxiliary component 1065 includes a first fixing plate 10651 and a second fixing plate 10652 disposed on the other side of the top of the bending housing 1061. A hydraulic rod 10653 is provided through one side of the second fixing plate 10652, and a moving plate 10654 is provided on one side of the hydraulic rod 10653. A limiting slider 10655 is provided at the bottom end of the moving plate 10654. The auxiliary component 1065 also includes a limiting groove 10656 formed on the other side of the top of the bending housing 1061 and cooperating with the limiting slider 10655, thereby ensuring that the superconducting material sample is firmly positioned without deviation and providing stable and reliable clamping support for bending operations.

[0029] The working principle of bending module 106 is as follows: 1. Based on the physical parameters of the superconducting material sample, select a suitable bending die 1068 and assemble it into the die base 1066. Insert several fixing rods 1069 into the fixing holes 1067 on the outside of the die base 1066 to firmly lock the bending die 1068, forming a fixed reference for the bending operation, avoiding displacement and deformation of the bending die 1068 during the bending process, and ensuring bending accuracy.

[0030] 2. Place the connecting post 10636 on the top of the sliding rod 10631, aligning the threaded hole 10637 of the connecting post with the threaded groove 10634 of the sliding rod. Insert the rotating rod into the rotating hole 10638 at the top of the threaded connector 10635, and rotate the threaded connector 10635 so that its top end is screwed out of the threaded groove 10634 and simultaneously screwed into the threaded hole 10637 of the connecting post 10636. This achieves a stable connection between the threaded groove 10634 and the threaded hole 10637, thereby achieving a detachable fixation between the connecting post 10636 and the sliding rod 10631. This not only facilitates the subsequent replacement of connecting posts 10636 of different specifications to adapt to various bending needs, but also allows for the use of different sliding rods 10631 to meet the bending requirements of different lengths.

[0031] 3. After assembly, place the superconducting material sample in the placement slot of the bending die 1068, so that both ends of the sample are respectively engaged with the auxiliary component 1065 and the bending sliding component 1063: (1) Start the hydraulic rod 10653 of the auxiliary component 1065 to drive the moving plate 10654 to move towards the first fixed plate 10651. The limiting slider 10655 at the bottom of the moving plate 10654 slides smoothly along the limiting groove 10656 at the top of the bending shell 1061, which plays a guiding and limiting role to prevent the moving plate from deviating. The moving plate 10654 and the first fixed plate 10651 work together to clamp one end of the sample to prevent the sample from sliding or misaligning during bending.

[0032] (2) Start the servo motor 10643. Its output rotational power drives the second bevel gear 10642 to rotate. Through the meshing transmission of the bevel gear, the power is transmitted to the first bevel gear 10641, which in turn drives the rotating shaft 10633 and the rotating disk 10632 to rotate synchronously. The rotational motion is converted into the directional movement of the sliding rod 10631 along the arc-shaped sliding groove 1062. The sliding rod 10631 then drives the connecting column 10636 to move along the arc-shaped trajectory. The connecting column 10636 applies a uniform bending force to the other end of the sample, so that the superconducting material sample undergoes plastic deformation along the contour of the bending mold 1068. During this process, the bending angle can be directly observed through the arc-shaped scale 10610 on one side of the sliding groove 1062. With the precise speed adjustment function of the servo motor 10643, the bending angle can be precisely controlled, and the bending and forming of the superconducting material sample can be completed.

[0033] In this alternative embodiment, such as Figure 12 As shown, the bending module 106 includes: Initial parameter acquisition submodule I is used to collect physical data of superconducting material samples and, based on a collaborative filtering algorithm of physical constraints, search a preset historical process database to obtain an initial bending parameter set that matches the superconducting material sample.

[0034] It should be further explained that the physical data of the superconducting material sample is collected, and a collaborative filtering algorithm based on physical constraints is used to search a pre-set historical process database to obtain the initial bending parameter set matching the superconducting material sample. Specifically, this includes: Physical data of the superconducting material is acquired in advance using high-precision sensors, instruments, or measuring equipment, including key parameters such as material type, thickness, elastic modulus, tensile strength, and ductility. The acquired physical data serves as input features and is fed into a physical constraint-based collaborative filtering algorithm. This algorithm searches for existing bending cases in a pre-set historical process database and identifies the most similar process record by comparing the physical characteristics of the current sample with those of historical samples. Specifically, the system calculates the similarity between the current sample and each historical sample in the database (e.g., using Euclidean distance, cosine similarity, etc.). If a historical sample is highly similar to the current sample in core parameters such as thickness and elastic modulus, its corresponding bending parameters (such as bending angle and speed) are recommended as initial reference values, thereby generating an initial bending parameter set suitable for the current superconducting material.

[0035] The candidate bending strategy selection submodule II is used to extract multi-scale feature data of the fixed superconducting material sample, construct a multi-objective optimization function in combination with the initial bending parameter set, and generate a bending parameter optimization solution set using the finite element simulation algorithm. Candidate bending strategies are selected based on the bending parameter optimization solution set.

[0036] In this optional embodiment, multi-scale feature data of the fixed superconducting material sample are extracted, and a multi-objective optimization function is constructed by combining it with the initial bending parameter set. A finite element simulation algorithm is then used to generate an optimized solution set of bending parameters. Candidate bending strategies are selected based on this optimized solution set, including: S1. Image data of the fixed superconducting material sample is acquired using image sensor 10611, and multi-scale feature data of the image data is extracted by convolutional neural network. Combined with the initial bending parameter set, a multi-objective optimization function is constructed.

[0037] It should be further explained that image data of the fixed superconducting material sample was acquired using the image sensor 10611, and multi-scale feature data of the image data was extracted through a convolutional neural network. Combined with the initial bending parameter set, a multi-objective optimization function was constructed, specifically including: The image sensor 10611 is used to perform high-precision imaging of the fixed superconducting material sample to obtain its surface features and geometric information, including key dimensions, texture distribution, and edge contours. The acquired image data is processed by a convolutional neural network (CNN) to automatically identify key visual features such as edges, corners, and textures. Through multi-layer convolution operations, multi-scale feature information of the image is extracted step by step, including local details (such as microcracks, surface defects, and local deformation areas) and global structure (such as overall flatness and contour symmetry). These features are of great guiding significance for predicting stress concentration, plastic deformation, and springback behavior during bending.

[0038] Based on the aforementioned image features and initial bending parameter set (e.g., bending angle 45°, pressure 100kN, die radius 5mm), a multi-objective optimization function is constructed. This function comprehensively considers multiple key indicators such as stress distribution, plastic deformation, and springback effect of the material during the bending process. Typical optimization objectives may include minimizing residual deformation, controlling springback angle error, avoiding the risk of local cracking, and balancing forming efficiency with material performance preservation. During the optimization process, if the image features extracted by the CNN show abnormal texture or potential weak areas (such as microcrack tendency) on the material surface, the system will dynamically adjust the optimization weights or constraints to guide the algorithm to prioritize avoiding high-risk parameter combinations. This achieves an adaptive balance among multiple conflicting objectives, ultimately generating a bending strategy that balances forming quality and process robustness.

[0039] S2. Based on the constructed multi-objective optimization function, the multi-physics coupled finite element simulation algorithm is used to simulate the bending effect under different combinations of bending parameters, and the simulation results are used as the input of the multi-objective optimization algorithm to iteratively generate the optimized solution set of bending parameters.

[0040] In this optional embodiment, based on the constructed multi-objective optimization function, a multi-physics coupled finite element simulation algorithm is used to simulate the bending effect under different combinations of bending parameters. The simulation results are used as input to the multi-objective optimization algorithm to iteratively generate an optimized solution set for the bending parameters, including: S21. Based on the obtained initial bending parameter set, use the multiphysics coupled finite element simulation algorithm to perform parallel simulation of the bending process under different initial bending parameter combinations, and generate the corresponding simulation result dataset. S22. Construct a Kriging model based on the generated simulation result dataset, and use the Kriging model to calculate the Pareto front of the current bending optimization problem to obtain the optimized solution set of bending parameters; S23. Determine whether there is a feasible solution that satisfies the multi-objective optimization function in the initial bending parameter set. If there is, generate new initial bending parameters according to the fitness assignment criterion. If there is no, select the first new initial bending parameter according to the default probability criterion, calculate the multi-objective distance index function based on the first new initial bending parameter, and switch to the fitness assignment criterion to continue generating multiple new initial bending parameters. S24. Parallel call the multi-physics coupled finite element simulation algorithm to calculate the bending response value corresponding to the new initial bending parameters, and determine whether the preset termination condition is met. If it is met, output the optimized solution set of bending parameters. If it is not met, return to step S22 to continue iterative optimization until the termination condition is met and output the optimized solution set of bending parameters.

[0041] It should be further explained that, based on the constructed multi-objective optimization function, a multi-physics coupled finite element simulation algorithm is used to simulate the bending effect under different combinations of bending parameters. The simulation results are then used as input to the multi-objective optimization algorithm to iteratively generate the optimized solution set of bending parameters, specifically including: 1. Based on initial bending parameters (such as bending angle 45°, die radius 5mm, loading rate 20mm / s), a finite element simulation algorithm supporting multiphysics coupling is invoked to simulate the material behavior during the actual bending process. This process considers the combined effects of multiple physical factors such as mechanical response, electromagnetic effects, and heat conduction to accurately predict deformation, residual stress distribution, strain status, and springback trend during the bending process. Each simulation task targets a specific set of parameters, and multiple tasks are executed simultaneously in a parallel computing environment to quickly generate a high-dimensional, high-resolution simulation dataset.

[0042] 2. Using the simulation result dataset as input variables and the corresponding response values ​​as target outputs, a Kriging model is trained to estimate bending performance without repeating simulations and to evaluate the effect of a large number of parameter combinations. Based on this model, the Pareto front of the current problem is calculated, and the optimal solution set that cannot be mutually improved among multiple optimization objectives (such as minimizing stress, angle error and springback) is identified.

[0043] 3. Check if there is a feasible solution in the existing parameter set that meets the requirements of the multi-objective optimization function; if so, generate new bending parameters for further exploration based on fitness allocation criteria (such as crowding distance or hypervolume contribution); if there is no suitable solution, select the solution with the lowest risk using the default probability criterion, and evaluate its proximity to the Pareto front using multi-objective distance indices (such as GD, IGD, etc.), and then return to the fitness allocation criterion to continue the search, ensuring a balance between the exploration and utilization of potential optimal solutions.

[0044] 4. All newly generated parameter sets are verified again using multiphysics coupled finite element simulation, and feedback data for each parameter set is collected before proceeding to the next round of iterative optimization. The optimization is terminated based on preset termination criteria, which may include the maximum number of iterations (e.g., 50 rounds), achieving a specific target accuracy (e.g., angle error less than 0.2°), or the convergence of the Pareto front (no significant change for several consecutive rounds). Once the termination condition is met, the final optimized bending parameter set is output; otherwise, the Kriging model prediction and iterative optimization continue until the target is achieved.

[0045] S3. Based on the application scenarios and physical data of the superconducting material samples, a two-level screening mechanism is established, and the two-level screening mechanism is used to screen the optimized solution set of bending parameters to obtain candidate bending strategies.

[0046] It should be further explained that, based on the application scenarios and physical data of the superconducting material samples, a two-level screening mechanism was established, and this mechanism was used to screen the optimized solution set of bending parameters to obtain candidate bending strategies, specifically including: 1. Superconducting materials are widely used in high-precision electronic devices, quantum computing components, and other scenarios with extremely high requirements for performance stability. When constructing a two-stage screening mechanism, it is necessary to set basic bending feasibility boundaries based on their physical parameters (such as thickness, elastic modulus, tensile strength, etc.). For example, if a superconducting material has a thickness of 0.5 mm, an elastic modulus of 200 GPa, and a tensile strength of 300 MPa, these parameters together determine the maximum stress it can withstand during bending, the allowable deformation range, and the expected springback behavior, which constitute the physical constraint basis for subsequent parameter screening.

[0047] 2. Within the aforementioned physical boundaries, further select bending parameter combinations that satisfy mechanical feasibility (such as bending angle, pressure, die radius, etc.), and evaluate the influence of each parameter on the material response through finite element simulation or analytical model, including stress distribution, plastic deformation, and springback angle. If a certain set of parameters causes the local stress of the material to exceed the tensile strength (e.g., the calculated stress reaches 350MPa > 300MPa at a 60° bending angle), or the springback exceeds the process tolerance, then the scheme is eliminated because it violates the physical constraints, ensuring that only safe and feasible candidate solutions are retained.

[0048] 3. In addition to meeting basic mechanical feasibility requirements, candidate parameters need to be screened a second time based on specific application needs. For example, in the field of quantum computing, the electrical properties and thermal stability of materials are easily affected by residual stress or microcracks. Therefore, bending strategies that minimize internal stress concentration and avoid surface damage should be prioritized. In the manufacturing of high-precision electronic devices, springback control and stress uniformity are of greater concern. The system can introduce multi-dimensional evaluation indicators (such as stress uniformity, thermo-mechanical coupling effect, springback error, etc.) to refine the ranking of the parameter set after the initial screening. For example, if a combination of parameters meets the strength requirements and has a springback angle error of only 0.1° and a highly uniform stress distribution, it is more likely to be selected as the final candidate bending strategy to meet the needs of high-reliability application scenarios.

[0049] The bending strategy generation submodule III is used to combine the selected candidate bending strategies with the springback characteristics of the pre-acquired superconducting material samples to construct a nonlinear springback model, and use the nonlinear springback model to predict the springback angle error. Based on the springback angle error, the candidate bending strategies are dynamically adjusted to generate the bending strategy.

[0050] It should be further explained that, by combining the selected candidate bending strategies with the springback characteristics of the pre-acquired superconducting material samples, a nonlinear springback model is constructed. This nonlinear springback model is then used to predict the springback angle error. Based on the springback angle error, the candidate bending strategies are dynamically adjusted, and the resulting bending strategies specifically include: 1. By combining the selected candidate bending strategies (such as bending angle, die radius, pressure, etc.) with the pre-acquired springback characteristics of superconducting materials (including thickness, elastic modulus, yield strength, and stress-strain response data obtained from history or simulation), a nonlinear springback model is constructed. This model establishes the mapping relationship between bending process parameters and springback angle error through nonlinear regression methods (such as deep neural networks, support vector machines, or multilayer polynomial regression). For example, for a superconducting material with a thickness of 0.5 mm and a bending pressure of 100 kN, the model can learn its springback behavior under different geometric and load conditions.

[0051] 2. Based on the constructed nonlinear springback model, springback error is predicted for the current candidate bending strategy. For example, when the initial strategy parameters (bending angle 45°, die radius 5mm) are input, the model outputs a predicted springback angle error of 0.5°, which serves as a key basis for subsequent strategy correction.

[0052] 3. If the predicted springback angle error exceeds the preset tolerance (e.g., target error ≤ 0.2°, actual prediction is 0.5°), the candidate bending parameters will be dynamically adjusted based on the gradient information of the nonlinear springback model or error sensitivity analysis. For example, the bending over-bending amount may be appropriately increased, the die radius may be optimized, or the loading pressure may be finely adjusted to compensate for the expected springback. This adjustment process forms a closed-loop feedback mechanism: each correction is based on the prediction error of the previous round, gradually approaching the target accuracy. Finally, a high-precision bending strategy after error compensation is output to ensure that the actual forming result meets the process requirements.

[0053] The bending execution submodule IV is used to drive the servo motor 10643 according to the generated bending strategy using a model predictive control algorithm, so as to control the bending sliding component 1063 to perform bending operations on the superconducting material sample.

[0054] It should be further explained that, based on the generated bending strategy, the model predictive control algorithm is used to drive the servo motor 10643 to control the bending sliding component 1063 to perform the bending operation on the superconducting material sample. Specifically, this includes: 1. Based on the generated bending strategy (including key parameters such as bending angle, die radius, bending pressure, and speed), actual processing is carried out to ensure that the superconducting material meets the predetermined geometric accuracy and mechanical performance requirements during the forming process. For example, if the final determined bending strategy is: bending angle 45°, die radius 5mm, bending pressure 100kN, and bending speed 20mm / s, the control system will use this as the instruction reference and adopt the model predictive control (MPC) algorithm to perform closed-loop control of the entire bending process.

[0055] 2. Under the MPC framework, bending strategy parameters are transformed into control inputs and combined with the physical properties of the material (such as elastic modulus and springback behavior) and real-time working conditions (such as current displacement, pressure, and die status) to construct a multi-physics coupled dynamic system model that includes servo motor dynamics and bending sliding component kinematics. This model can not only predict the deformation response of the material under the current control input, but also explicitly consider complex factors such as nonlinear springback effect, loading path dependence, and system dynamic delay, thereby achieving rolling prediction of system behavior over several future time steps.

[0056] 3. The servo motor 10643 precisely drives the bending sliding component 1063 to perform bending actions based on the optimal control signal calculated in real time by the MPC algorithm. Due to the high responsiveness and positioning accuracy of the servo system, the bending process can be advanced in micron-level steps and can dynamically respond to process deviations. For example, when online monitoring or model prediction shows that the springback trend exceeds the tolerance, the MPC will adjust the motor output in real time (such as correcting the loading speed) to actively compensate for potential errors, effectively avoid excessive deformation or uncontrolled springback, and ensure the geometric accuracy and process consistency of the final formed part.

[0057] Laser smoothing and cleaning unit 2 is used to smooth and clean the surface of superconducting material samples.

[0058] It should be added that, such as Figures 8-9 As shown, the laser smoothing cleaning unit 2 includes a laser probe and guide rail module 201, a cutting module 202, a testing module 203, a laser control module 204, a laser generating module 205, a ventilation module 206, and a coolant circulation module 207.

[0059] The laser probe and guide rail module 201 is responsible for precisely positioning the laser beam's location and direction, and controlling the laser probe's movement through the guide rail system to ensure the laser beam accurately covers the designated area on the material surface. The cutting module 202 adjusts the laser power and scanning speed according to the specific requirements of the cleaning task to efficiently remove surface impurities or irregular layers while avoiding excessive damage to the superconducting material. The testing module 203 monitors the cleaning process in real time through sensors and a feedback system, detecting whether the material surface reaches the required smoothness, and adjusting the cleaning parameters based on feedback to ensure the processing effect accurately meets expectations. To ensure the stability and effectiveness of the laser cleaning process, the laser control module 204 coordinates the work of other modules and adjusts the laser beam in real time. Parameters such as laser power, frequency, and irradiation time are adjusted to meet the cleaning needs of different material surfaces. The laser generation module 205 is responsible for generating a high-intensity laser beam, providing the necessary laser source for the entire cleaning process. Typically, the laser wavelength, power density, and focusing degree are finely adjusted according to the properties of the material. To ensure that the heat generated during the cleaning process does not affect the performance of the superconducting material, the ventilation module 206 provides effective airflow to disperse any hot air and dust that may be generated during the process, while maintaining a clean working environment. The coolant circulation module 207 effectively reduces the temperature of the laser-affected area through a liquid cooling system, preventing changes in the physical properties of the material due to local overheating and ensuring temperature control and stability during the cleaning process.

[0060] The performance test output unit 3 is used to test the resistance characteristics of superconducting material samples under different temperature conditions and output the test results.

[0061] It should be added that, such as Figures 10-11 As shown, the performance test output unit 3 includes a test stage module 301, a micro-resistance test module 302, a display module 303, and a probe test module 304; the probe test module 304 includes a base frame 3041, a test base 3042, a probe station 3043, a probe and probe holder 3044, an adjustment arm 3045, and an electron microscope 3046.

[0062] The test stage module 301 forms the foundation, constituting the support platform for the entire testing operation, used to fix and support the sample and testing equipment. After the sample is placed on the test stage, the probe testing module 304 is activated. Its core structure is stably supported by the base frame 3041, and the test base 3042 is used to accurately place the sample, ensuring that its position remains fixed during the test. The probe stage 3043 and the probe and probe holder 3044 work together to bring the probe to the test electrode point of the superconducting sample with minimal contact force through a micro-adjustment mechanism, effectively avoiding mechanical damage and contact errors, while ensuring the stability of the conductive path. To achieve high-precision positioning and observation, the adjusting arm 3045 provides multi-axis micro-displacement adjustment function, making the probe positioning accurate to the micrometer level. Even finer details are achieved through the accompanying electron microscope 3046, which provides real-time magnification to observe the contact state between the probe and the material surface, assisting in manual or automatic alignment. After probe positioning, the micro-resistance testing module 302 begins operation, applying a micro-current to the sample and detecting the micro-voltage drop using a four-probe method. This allows for highly sensitive measurement of the material's resistance, providing stable output even at the nano-ohm level, making it particularly suitable for near-zero resistance measurements in the superconducting state. The module dynamically monitors the resistance changes with temperature, obtaining key superconducting transition temperatures and resistance jump curves. Test data is output in real-time via the display module 303 in the form of graphs, data streams, and trend curves, facilitating researchers' observation of material performance evolution trends and enabling data recording and subsequent analysis.

[0063] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A comprehensive processing and performance testing platform for superconducting materials, characterized in that, include: A sample numerical control preprocessing unit (1) is used to preprocess superconducting material samples; Laser smoothing and cleaning unit (2) is used to smooth and clean the surface of superconducting material samples; The performance test output unit (3) is used to test the resistance characteristics of superconducting material samples under different temperature conditions and output the test results. The sample numerical control preprocessing unit (1) includes a main control module (101), an emergency control module (102), a monitoring module (103), a punching module (104), a shearing module (105), and a bending module (106). The central control module (101) is used to centrally coordinate the working sequence of each module and configure the operating parameters; The emergency control module (102) is used to execute emergency response measures in abnormal situations; The monitoring module (103) is used to monitor the operating status and key parameters of each module in real time and to feed back the monitoring information to the main control module (101). The punching module (104) is used to punch holes in the superconducting material sample; The shearing module (105) is used to shear and separate the superconducting material sample; The bending module (106) is used to perform CNC bending and forming of superconducting material samples.

2. The comprehensive processing and performance testing platform for superconducting materials according to claim 1, characterized in that, The bending module (106) includes a bending housing (1061), and a plurality of sliding grooves (1062) are provided on one side of the top end of the bending housing (1061). A bending sliding component (1063) is provided inside the sliding groove (1062), and the bottom end of the bending sliding component (1063) cooperates with the driving component (1064). An auxiliary component (1065) is provided on the other side of the top of the bent housing (1061). A mold base (1066) is provided at the top center of the bending housing (1061). A plurality of fixing holes (1067) are provided on the outer side of the mold base (1066). A bending mold (1068) is provided inside the mold base (1066), and the bending mold (1068) is fixed to the fixing holes (1067) by a plurality of fixing rods (1069).

3. The comprehensive processing and performance testing platform for superconducting materials according to claim 2, characterized in that, The bending sliding assembly (1063) includes a sliding rod (10631) disposed inside the sliding groove (1062), a rotating disk (10632) is disposed at the bottom end of the sliding rod (10631), and a rotating shaft (10633) is disposed at the bottom end of the rotating disk (10632). The sliding rod (10631) has a threaded groove (10634) at its top end, and a threaded connector (10635) is provided inside the threaded groove (10634). The sliding rod (10631) has a connecting post (10636) at its top end, and a threaded hole (10637) is provided at the top end of the connecting post (10636) to cooperate with the threaded connector (10635).

4. The comprehensive processing and performance testing platform for superconducting materials according to claim 3, characterized in that, The threaded connector (10635) has a rotating hole (10638) at its top end.

5. The comprehensive processing and performance testing platform for superconducting materials according to claim 2, characterized in that, The top of the bent housing (1061) is provided with an arc-shaped scale (10610), and the arc-shaped scale (10610) is located on one side of the sliding groove (1062); An image sensor (10611) is provided at the top of the bent housing (1061), and the image sensor (10611) is located on one side of the auxiliary component (1065).

6. The comprehensive processing and performance testing platform for superconducting materials according to claim 3, characterized in that, The drive assembly (1064) includes a first bevel gear (10641) disposed at the bottom end of the rotating shaft (10633), and the first bevel gear (10641) meshes with a second bevel gear (10642), and a servo motor (10643) is disposed at one end of the second bevel gear (10642).

7. The comprehensive processing and performance testing platform for superconducting materials according to claim 2, characterized in that, The auxiliary component (1065) includes a first fixing plate (10651) and a second fixing plate (10652) disposed on the other side of the top of the bent housing (1061). A hydraulic rod (10653) is provided through one side of the second fixing plate (10652), and a movable plate (10654) is provided on one side of the hydraulic rod (10653). A limit slider (10655) is provided at the bottom end of the movable plate (10654). The auxiliary component (1065) also includes a limiting groove (10656) formed on the other side of the top of the bent housing (1061) and cooperating with the limiting slider (10655).

8. The comprehensive processing and performance testing platform for superconducting materials according to claim 1, characterized in that, The bending module (106) includes: The initial parameter acquisition submodule is used to collect physical data of superconducting material samples and, based on a collaborative filtering algorithm of physical constraints, search a preset historical process database to obtain an initial bending parameter set that matches the superconducting material sample. The candidate bending strategy selection submodule is used to extract multi-scale feature data of the fixed superconducting material sample, construct a multi-objective optimization function in combination with the initial bending parameter set, generate a bending parameter optimization solution set using the finite element simulation algorithm, and select candidate bending strategies based on the bending parameter optimization solution set. The bending strategy generation submodule is used to combine the selected candidate bending strategies with the springback characteristics of the pre-acquired superconducting material samples to construct a nonlinear springback model, and use the nonlinear springback model to predict the springback angle error. Based on the springback angle error, the candidate bending strategies are dynamically adjusted to generate a bending strategy. The bending execution submodule is used to drive the servo motor (10643) using the model predictive control algorithm according to the generated bending strategy, so as to control the bending sliding component (1063) to perform bending operation on the superconducting material sample.

9. The comprehensive processing and performance testing platform for superconducting materials according to claim 8, characterized in that, The process involves extracting multi-scale feature data from the fixed superconducting material sample, constructing a multi-objective optimization function based on the initial bending parameter set, generating an optimized solution set of bending parameters using a finite element simulation algorithm, and selecting candidate bending strategies based on the optimized solution set of bending parameters. S1. Image data of the fixed superconducting material sample is acquired using an image sensor (10611), and multi-scale feature data of the image data is extracted by a convolutional neural network. Combined with the initial bending parameter set, a multi-objective optimization function is constructed. S2. Based on the constructed multi-objective optimization function, the multi-physics coupled finite element simulation algorithm is used to simulate the bending effect under different combinations of bending parameters, and the simulation results are used as the input of the multi-objective optimization algorithm to iteratively generate the optimized solution set of bending parameters. S3. Based on the application scenarios and physical data of the superconducting material samples, a two-level screening mechanism is established, and the two-level screening mechanism is used to screen the optimized solution set of bending parameters to obtain candidate bending strategies.

10. The comprehensive processing and performance testing platform for superconducting materials according to claim 9, characterized in that, The process involves using a multi-physics coupled finite element simulation algorithm based on the constructed multi-objective optimization function to simulate the bending effect under different combinations of bending parameters, and using the simulation results as input to the multi-objective optimization algorithm to iteratively generate an optimized solution set for the bending parameters, including: S21. Based on the obtained initial bending parameter set, use the multiphysics coupled finite element simulation algorithm to perform parallel simulation of the bending process under different initial bending parameter combinations, and generate the corresponding simulation result dataset. S22. Construct a Kriging model based on the generated simulation result dataset, and use the Kriging model to calculate the Pareto front of the current bending optimization problem to obtain the optimized solution set of bending parameters; S23. Determine whether there is a feasible solution that satisfies the multi-objective optimization function in the initial bending parameter set. If there is, generate new initial bending parameters according to the fitness assignment criterion. If there is no, select the first new initial bending parameter according to the default probability criterion, calculate the multi-objective distance index function based on the first new initial bending parameter, and switch to the fitness assignment criterion to continue generating multiple new initial bending parameters. S24. Parallel call the multi-physics coupled finite element simulation algorithm to calculate the bending response value corresponding to the new initial bending parameters, and determine whether the preset termination condition is met. If it is met, output the optimized solution set of bending parameters. If it is not met, return to step S22 to continue iterative optimization until the termination condition is met and output the optimized solution set of bending parameters.

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