A surface nanocrystallization system for metal parts

Abstract

The application relates to the technical field of metal processing, and discloses a surface nanometer strengthening treatment system for metal pieces, which comprises a data acquisition module, an intelligent decision module, a process simulation module, a verification and optimization module and an instruction output module, extracts initial state data of the surface of the metal pieces, carries out nanometer strengthening decision analysis on the initial state data to obtain a nanometer strengthening process parameter sequence of the surface of the metal pieces, simulates an electrochemical deposition process on the nanometer strengthening process parameter sequence to obtain nanocrystalline layer growth prediction data of the surface of the metal pieces, carries out stability verification on the nanocrystalline layer growth prediction data, adjusts weight parameters in the nanometer strengthening decision analysis when the verification fails, obtains an optimized process parameter sequence of the surface of the metal pieces, encodes the optimized process parameter sequence to obtain nanometer strengthening treatment instructions, and can improve the process research and development efficiency and production implementation efficiency of the surface nanometer strengthening treatment of the metal pieces.

Description

Technical Field

[0001] This invention relates to the field of metal processing technology, and in particular to a surface nano-strengthening treatment system for metal parts. Background Technology

[0002] In the field of nano-strengthening treatment of metal parts, traditional technical solutions generally suffer from problems such as limited data acquisition dimensions and insufficient accuracy. Existing systems mostly only detect the surface roughness or single element composition of metal parts, failing to comprehensively integrate key information such as surface roughness distribution, elemental composition, and microscopic morphology characteristics. This results in incomplete initial state data of the metal parts, which cannot provide accurate data support for the subsequent formulation of nano-strengthening process parameters, and thus affects the adaptability of process parameters to the actual surface state of the metal parts.

[0003] Meanwhile, traditional process development lacks effective process simulation and dynamic optimization mechanisms. Existing technologies often determine nano-strengthening process parameters directly based on experience or simple formulas, failing to accurately simulate the electrochemical deposition process to predict the growth state of nanocrystalline layers, and also unable to perform real-time verification and parameter adjustment for the stability of crystal layer growth. This makes process parameters prone to exceeding safe ranges or failing to meet crystal layer quality requirements, necessitating corrections through multiple physical experiments. This not only prolongs the process development cycle but also increases production trial-and-error costs, severely restricting the process development and production implementation efficiency of nano-strengthening treatment for metal parts. Therefore, how to improve the process development and production implementation efficiency of nano-strengthening treatment for metal parts has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a surface nano-strengthening treatment system for metal parts to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a surface nano-strengthening treatment system for metal parts. The system includes a data acquisition module, an intelligent decision-making module, a process simulation module, a verification and optimization module, and an instruction output module, wherein: The data acquisition module is used to extract the surface roughness distribution, elemental composition and micromorphological features of the metal part to obtain the initial state data of the metal part surface. The intelligent decision-making module is used to perform nano-strengthening decision analysis on the initial state data to obtain a sequence of nano-strengthening process parameters for the surface of the metal part. The process simulation module is used to simulate the electrochemical deposition process of the nano-strengthening process parameter sequence to obtain the predicted data of nanocrystalline layer growth on the surface of the metal part. The verification and optimization module is used to verify the stability of the nanocrystalline layer growth prediction data. When the verification fails, the weight parameters in the nano-strengthening decision analysis are adjusted to obtain the optimized process parameter sequence for the surface of the metal part. The instruction output module is used to encode the optimized process parameter sequence to obtain nano-strengthening processing instructions.

[0006] In a preferred embodiment, the extraction of surface roughness distribution, elemental composition, and microstructure characteristics of the metal part to obtain initial state data of the metal part surface is specifically used for: Noise is filtered out from the multispectral image data of the metal part surface to obtain standard image data of the metal part surface; The standard image data is segmented to obtain multi-feature regions on the surface of the metal part; Roughness measurements are performed on multiple feature regions of the metal part surface to obtain roughness distribution data of the metal part surface; Energy dispersive spectroscopy (EDS) analysis was performed on the surface of the metal part to obtain the elemental composition data of the surface of the metal part. Image acquisition is performed on the surface of the metal part to obtain microscopic morphology image data of the surface of the metal part; By integrating the roughness distribution data, the elemental composition data, and the microstructure image data, the initial state data of the metal part surface is obtained.

[0007] In a preferred embodiment, the step of performing nano-strengthening decision analysis on the initial state data to obtain a sequence of nano-strengthening process parameters for the surface of the metal part is specifically used for: The roughness distribution data in the initial state data is evaluated for regional consistency to obtain the area to be strengthened on the surface of the metal part. Based on the elemental composition data, the surface elements of the metal part are matched with a preset nanomaterial deposition rule library to obtain candidate nanomaterials on the surface of the metal part. Based on the microscopic morphology image data, the surface defect features of the metal part are extracted; Based on the surface defect characteristics, the candidate nanomaterials are screened to obtain the target nanomaterial type on the surface of the metal part; By querying the roughness distribution data of the area to be strengthened on the surface of the metal part and the type of target nanomaterial on the surface of the metal part using a process parameter mapping table, preliminary nano-strengthening process parameters for the surface of the metal part are obtained. Logical verification is performed on the preliminary nano-strengthening process parameters to obtain the nano-strengthening process parameter sequence for the surface of the metal part.

[0008] In a preferred embodiment, the step of performing logical verification on the preliminary nano-strengthening process parameters to obtain the nano-strengthening process parameter sequence for the metal part surface is specifically used for: The completeness and logical consistency of the preliminary nano-reinforcement process parameters are checked. The preliminary nano-strengthening process parameters after inspection are compared with the pre-stored process safety range to obtain the abnormal parameters of the preliminary nano-strengthening process parameters. Based on predefined parameter optimization rules, abnormal parameters of the preliminary nano-strengthening process parameters are corrected to obtain the nano-strengthening process parameter sequence of the metal part surface.

[0009] In a preferred embodiment, the step of simulating the electrochemical deposition process of the nano-strengthening process parameter sequence to obtain predicted data for the growth of nanocrystalline layers on the surface of the metal part is specifically used for: The nano-strengthening process parameter sequence is extracted to obtain the deposition voltage, deposition current density, and deposition time parameters of the nano-strengthening process parameter sequence; Based on a pre-established deposition kinetics rule library, the deposition voltage, deposition current density, and deposition duration parameters are matched to obtain reference values ​​for the nucleation rate and growth rate of nanocrystalline layers on the surface of the metal part. The initial nucleation distribution of nanocrystals on the surface of the metal part was obtained by simulating the reference values ​​of nucleation rate and growth rate. Based on the initial nucleation distribution and the deposition duration parameters, the nanocrystal growth and coverage process on the surface of the metal part is simulated to obtain the nanocrystal layer growth simulation data on the surface of the metal part. The integrity of the nanocrystalline layer growth simulation data is verified to obtain the nanocrystalline layer growth prediction data on the surface of the metal part.

[0010] In a preferred embodiment, the formulas for calculating the nucleation rate and growth rate of the nanocrystalline layer on the surface of the metal part are as follows: The formula for calculating the nucleation rate of the nanocrystalline layer on the surface of the metal part is as follows: ; In the formula, The nucleation rate of the nanocrystalline layer on the surface of the metal part is given. This represents the initial nucleation rate of the nanocrystalline layer on the surface of the metal part. It is an exponential function. The critical nucleation work on the surface of the metal part is related to the deposition voltage of the nano-reinforcement process parameter sequence. The atomic diffusion activation energy is related to the deposition current density of the nano-reinforcement process parameter sequence on the surface of the metal part. The Boltzmann constant of the surface of the metal part is given. The absolute temperature of the surface of the metal part; The formula for calculating the growth rate of the nanocrystalline layer on the surface of the metal part is as follows: ; In the formula, The growth rate of the nanocrystalline layer on the surface of the metal part is given. The molar mass of the material deposited on the surface of the metal part. The deposition current density is the sequence of nano-reinforcement process parameters for the surface of the metal part. The number of electrons in the electrode reaction on the surface of the metal part. It is Faraday's constant. The density of the deposited layer on the surface of the metal part.

[0011] In a preferred embodiment, the step of extrapolating the growth and coverage process of nanocrystals on the surface of the metal part based on the initial nucleation distribution and the deposition duration parameters, to obtain simulated data on the growth of the nanocrystal layer on the surface of the metal part, is specifically used for: The properties of the initial nucleation distribution of nanocrystals on the surface of the metal part are extracted to obtain the initial spatial position and initial size distribution of the nanocrystals on the surface of the metal part. The total deposition time is divided based on the deposition duration parameter to obtain the time stages of the total deposition time on the surface of the metal part; During the total deposition time on the surface of the metal part, the radial growth process of nanocrystals on the surface of the metal part is simulated based on the grain growth kinetics rules, and the nanocrystal layer growth simulation data on the surface of the metal part is obtained.

[0012] In a preferred embodiment, the stability verification of the nanocrystalline layer growth prediction data, and the adjustment of the weight parameters in the nano-strengthening decision analysis when the verification fails, to obtain an optimized process parameter sequence for the metal part surface, is specifically used for: The predicted growth data of the nanocrystalline layer on the surface of the metal part is compared with the preset crystal quality stability threshold to obtain the growth state judgment result of the nanocrystalline layer on the surface of the metal part. When the growth state judgment result is not passed, the abnormal regions and abnormal patterns in the nanocrystalline layer growth prediction data are identified to obtain the weight parameters of the nano-strengthening process parameter sequence of the metal part surface in the nano-strengthening decision analysis process. Based on a predefined parameter adjustment strategy, the weight parameters are iteratively corrected to obtain the adjusted weight parameters. Based on the adjusted weight parameters, a new nano-strengthening decision analysis is performed on the initial state data of the metal part surface to obtain the optimized nano-strengthening process parameter sequence for the metal part surface.

[0013] In a preferred embodiment, the step of iteratively correcting the weight parameters based on a predefined parameter adjustment strategy to obtain adjusted weight parameters is specifically used for: The weight parameters of the nano-strengthening process parameter sequence on the surface of the metal part are extracted by attribute analysis to obtain the type and initial value of the weight parameters of the nano-strengthening process parameter sequence on the surface of the metal part. Based on the abnormal patterns in the predicted data of nanocrystalline layer growth on the surface of the metal part, the weight parameters of the nano-strengthening process parameter sequence on the surface of the metal part are prioritized to obtain the target parameters of the weight parameters of the nano-strengthening process parameter sequence on the surface of the metal part. Based on a predefined parameter adjustment strategy, the target parameters of the weight parameters of the nano-strengthening process parameter sequence for the surface of the metal part are incrementally corrected to obtain the intermediate weight parameters of the nano-strengthening process parameter sequence for the surface of the metal part. Based on the intermediate weight parameters, a nano-strengthening decision analysis is performed on the initial state data of the metal part surface to obtain the test process parameter sequence of the metal part surface. The predictive performance indicators of the test process parameter sequence are evaluated: When the test process parameter sequence of the metal part surface does not reach the preset optimization target, the incremental correction and nano-strengthening decision analysis are repeatedly executed to iteratively correct the target parameters of the weight parameters of the nano-strengthening process parameter sequence of the metal part surface. When the sequence of test process parameters for the metal part surface reaches the preset optimization target, the intermediate weight parameters of the sequence of nano-strengthening process parameters for the metal part surface are output as adjusted weight parameters.

[0014] In a preferred embodiment, encoding the optimized process parameter sequence to obtain nano-strengthening instructions is specifically used for: The optimized process parameter sequence is extracted to obtain the deposition control parameters, time control parameters, and material control parameters of the optimized process parameter sequence for the metal part surface. The deposition control parameters, time control parameters, and material control parameters are mapped and matched with predefined equipment instructions to obtain the basic control instructions for the surface of the metal part. The basic control instructions are logically sorted in chronological order to obtain a preliminary instruction sequence for the surface of the metal part. Perform a consistency check on the preliminary instruction sequence; Based on the standardized instruction format recognizable by the target nano-strengthening device, the preliminary instruction sequence after consistency detection is converted to obtain the nano-strengthening treatment instructions for the surface of the metal part.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Effectively improves the efficiency of process development and parameter optimization: The system accurately collects surface data of metal parts from multiple dimensions, and generates suitable initial process parameters by combining a preset rule base and logical verification. Then, it predicts the crystal growth state through electrochemical deposition process simulation, avoiding the blindness of traditional reliance on physical experiments and reducing trial and error costs. At the same time, the verification and optimization module can iteratively correct weight parameters in a targeted manner, quickly obtain a stable sequence of optimized process parameters, shorten the development cycle, and overcome the problems of low efficiency and insufficient accuracy in traditional process parameter formulation.

[0016] 2. Improved stability of processing quality and feasibility for production implementation: The data acquisition stage integrates key information such as roughness and elemental composition, providing comprehensive support for parameter decision-making and reducing crystal layer quality problems caused by incomplete data; the instruction output stage is coded according to the equipment's standardized format, ensuring that optimized parameters are accurately converted into executable instructions, connecting the decision-making and production stages. The overall system achieves closed-loop control from data acquisition to production execution, improving both the stability of nano-strengthening quality of metal parts and production implementation efficiency, making it suitable for industrial applications. Attached Figure Description

[0017] Figure 1 This is a system architecture diagram of a surface nano-strengthening treatment system for metal parts provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments belong to some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In practice, the server-side equipment deployed in a surface nano-strengthening treatment system for metal parts may consist of one or more devices. This surface nano-strengthening treatment system for metal parts can be implemented as: a business instance, a virtual machine, or a hardware device. For example, the surface nano-strengthening treatment system for metal parts can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, the surface nano-strengthening treatment system for metal parts can be understood as software deployed on a cloud node, used to provide a surface nano-strengthening treatment system for metal parts to various user terminals. Alternatively, the surface nano-strengthening treatment system for metal parts can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Alternatively, the surface nano-strengthening treatment system for metal parts can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide a surface nano-strengthening treatment system for metal parts to various user terminals.

[0020] In terms of implementation, a surface nano-strengthening treatment system for metal parts and a user terminal are mutually compatible. That is, if the surface nano-strengthening treatment system for metal parts is implemented as an application installed on a cloud service platform, then the user terminal is implemented as a client that establishes a communication connection with the application; or if the surface nano-strengthening treatment system for metal parts is implemented as a website, then the user terminal is implemented as a webpage; or if the surface nano-strengthening treatment system for metal parts is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0021] like Figure 1 The figure shown is a system architecture diagram of a surface nano-strengthening treatment system for metal parts provided in an embodiment of the present invention.

[0022] The surface nano-strengthening treatment system 100 for metal parts described in this invention can be located in a cloud server. In terms of implementation, it can be used as one or more service devices, as an application installed in the cloud, or developed as a website. Depending on the functions implemented, the surface nano-strengthening treatment system 100 for metal parts may include a data acquisition module 101, an intelligent decision-making module 102, a process simulation module 103, a verification and optimization module 104, and an instruction output module 105. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0023] In this embodiment of the invention, in a surface nano-strengthening system for metal parts, each of the above-mentioned modules can be implemented independently and can call other modules. Here, "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the surface nano-strengthening system for metal parts provided by this embodiment of the invention, without modifying the program code, the applicable scope of the system architecture can be adjusted by adding modules and directly calling them, achieving cluster-based horizontal expansion to quickly and flexibly expand the surface nano-strengthening system for metal parts. In practical applications, the above modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0024] The following describes, with reference to specific embodiments, each component and its specific workflow of a surface nano-strengthening treatment system for metal parts: The data acquisition module 101 is used to extract the surface roughness distribution, elemental composition, and microstructure characteristics of the metal part to obtain the initial state data of the metal part surface. The data acquisition module may include, but is not limited to, an optical microscope, scanning electron microscope, energy dispersive spectroscopy (EDS), roughness measuring instrument, and corresponding image acquisition card and data interface. The intelligent decision-making module, process simulation module, verification and optimization module, and instruction output module can be deployed on one or more servers or industrial control computers with processors and memory, and communicate with the data acquisition module and the target nano-strengthening device via a data bus or network.

[0025] In this embodiment of the invention, the extraction of the surface roughness distribution, elemental composition, and microstructure characteristics of the metal part to obtain initial state data of the metal part surface is specifically used for: Noise is filtered out from the multispectral image data of the metal part surface to obtain standard image data of the metal part surface; The standard image data is segmented to obtain multi-feature regions on the surface of the metal part; Roughness measurements are performed on multiple feature regions of the metal part surface to obtain roughness distribution data of the metal part surface; Energy dispersive spectroscopy (EDS) analysis was performed on the surface of the metal part to obtain the elemental composition data of the surface of the metal part. Image acquisition is performed on the surface of the metal part to obtain microscopic morphology image data of the surface of the metal part; By integrating the roughness distribution data, the elemental composition data, and the microstructure image data, the initial state data of the metal part surface is obtained.

[0026] Specifically, the multispectral image data of the metal part surface is imported into the preprocessing device, and noise is identified by pixel-by-pixel analysis. The noise is replaced with the gray-scale average of normal pixels in a 3×3 neighborhood to obtain standard image data.

[0027] Specifically, observe the grayscale difference of the standard image to determine the threshold, and divide the region pixel by pixel according to the threshold. For multi-grayscale feature regions, set multiple thresholds to obtain multi-feature regions.

[0028] Specifically, the roughness measuring instrument probe contacts the feature area, moves and records the displacement to generate a profile curve, measures all feature areas and summarizes the data to obtain roughness distribution data.

[0029] Specifically, an energy dispersive spectroscopy (EDS) analyzer probe is aimed at the surface of a metal part to excite atoms to produce characteristic X-rays. The signal is then processed to analyze the types and contents of elements, thus obtaining elemental composition data.

[0030] Specifically, the metal part is fixed on the stage of a high-power optical microscope, the focus and magnification are adjusted, and the image is converted and stored using an acquisition device to obtain microscopic morphology image data.

[0031] Specifically, a table containing columns such as "feature region number" is created, roughness and element data are filled in, and the microscopic image with the labeled number is associated to obtain the initial state data.

[0032] Furthermore, during noise filtering, if there is noise in the 3×3 neighborhood, the area is expanded to 5×5 to select normal pixels to ensure that the standard image has no obvious noise.

[0033] Furthermore, the gray-scale mean of the selected test points for region segmentation is used to determine the initial threshold, which is then fine-tuned to match the actual features of the segmented region.

[0034] Furthermore, the instrument is moved at a constant speed during roughness measurement, with a moving speed ≤1mm / s, to avoid displacement data deviation.

[0035] Furthermore, during energy dispersive spectroscopy analysis, the probe is kept 5–10 mm away from the metal part to prevent collisions and ensure X-ray reception.

[0036] Furthermore, the microscopic image acquisition is magnified 500-1000 times according to the structural fineness to ensure image clarity.

[0037] Furthermore, during data integration, feature regions are numbered sequentially, and missing data is supplemented to avoid data loss in the initial state.

[0038] In summary, from noise filtering of multispectral images to data integration, each step proceeds according to the core operation, resulting in standard images and other products, and ultimately obtaining accurate initial state data.

[0039] In summary, by adjusting the neighborhood, threshold, and other refined operations, the deviation problem is solved, the product quality at each stage is guaranteed, and the initial state data reflects the actual surface condition of the metal part.

[0040] In summary, the process progresses step by step from image processing to data integration, and streamlined operations ensure accurate results, providing reliable support for the surface analysis of metal parts.

[0041] The intelligent decision module 102 is used to perform nano-strengthening decision analysis on the initial state data to obtain the nano-strengthening process parameter sequence of the metal part surface; In this embodiment of the invention, the step of performing nano-strengthening decision analysis on the initial state data to obtain the nano-strengthening process parameter sequence for the surface of the metal part is specifically used for: The roughness distribution data in the initial state data is evaluated for regional consistency to obtain the area to be strengthened on the surface of the metal part. Based on the elemental composition data, the surface elements of the metal part are matched with a preset nanomaterial deposition rule library to obtain candidate nanomaterials on the surface of the metal part. Based on the microscopic morphology image data, the surface defect features of the metal part are extracted; Based on the surface defect characteristics, the candidate nanomaterials are screened to obtain the target nanomaterial type on the surface of the metal part; By querying the roughness distribution data of the area to be strengthened on the surface of the metal part and the type of target nanomaterial on the surface of the metal part using a process parameter mapping table, preliminary nano-strengthening process parameters for the surface of the metal part are obtained. Logical verification is performed on the preliminary nano-strengthening process parameters to obtain the nano-strengthening process parameter sequence for the surface of the metal part.

[0042] The logical verification of the preliminary nano-strengthening process parameters yields a sequence of nano-strengthening process parameters for the surface of the metal part, specifically used for: The completeness and logical consistency of the preliminary nano-reinforcement process parameters are checked. The preliminary nano-strengthening process parameters after inspection are compared with the pre-stored process safety range to obtain the abnormal parameters of the preliminary nano-strengthening process parameters. Based on predefined parameter optimization rules, abnormal parameters of the preliminary nano-strengthening process parameters are corrected to obtain the nano-strengthening process parameter sequence of the metal part surface.

[0043] Specifically, when evaluating the regional consistency of roughness distribution data, first arrange the roughness values ​​of all feature regions by location, set a reference range, compare and mark the regions to be inspected, merge continuous out-of-range regions, and obtain the regions to be strengthened.

[0044] Specifically, when matching the nanomaterial deposition rule library, the surface elements and contents of the metal parts are extracted, the rule library is retrieved for comparison, and the suitable materials are summarized to obtain candidate nanomaterials.

[0045] Specifically, when extracting surface defect features, the microscopic image is magnified, the defect area is identified, the defect information is recorded, and the surface defect features are integrated.

[0046] Specifically, when screening candidate nanomaterials, the material properties are defined, defect characteristics are matched to exclude unsuitable materials, and further screening is carried out as needed to obtain the target nanomaterial type.

[0047] Specifically, when querying the process parameter mapping table, the roughness of the area to be strengthened is sorted out, the target material is identified, the mapping table is retrieved to find the parameter combination, and the preliminary process parameters are obtained.

[0048] Specifically, when checking preliminary parameters, list the parameter items to verify their completeness, analyze their logical connections, mark defects, and record them.

[0049] Specifically, when comparing the process safety range, the safety range is retrieved, parameter values ​​are checked, and abnormal parameters are identified and summarized.

[0050] Specifically, when correcting abnormal parameters, the optimization rules are retrieved and corrected according to the rules. For example, if the temperature exceeds the upper limit, the upper limit value is changed. The corrected parameters are then arranged to obtain the sequence of nano-strengthening process parameters.

[0051] Furthermore, when setting the roughness reference range, the application scenario of the metal parts should be taken into account. For example, for metal parts used in high-precision equipment, the reference range should be set to a smaller roughness interval to ensure that the determination of the area to be strengthened is more in line with the actual use requirements.

[0052] Furthermore, when comparing the surface elements of metal parts with the rule library, the element content needs to be accurately checked. If the content of a certain element is close to the critical value of the matching range in the rule library, the element content needs to be measured repeatedly to confirm the accuracy of the data and avoid the screening deviation of candidate nanomaterials.

[0053] Furthermore, when magnifying microscopic morphology images, the magnification should be uniformly adjusted to 1000x to ensure consistent defect observation standards in different areas and avoid inaccurate defect feature extraction due to different magnification.

[0054] Furthermore, when determining the performance parameters of candidate nanomaterials, it is necessary to consult the official technical manuals of the nanomaterials to ensure that parameters such as filling capacity and adhesion strength are the latest measured data, so as to prevent the screening results from being affected by outdated parameters.

[0055] Furthermore, when organizing the roughness data of the area to be strengthened, it is necessary to calculate the average roughness of all feature points in each area to be strengthened, avoiding the direct use of the roughness value of a single feature point, and ensuring more accurate query of process parameters.

[0056] Furthermore, when listing the initial parameters, it is necessary to supplement specific parameters based on the type of target nanomaterial. For example, when using ceramic nanomaterials, a sintering temperature parameter should be added to ensure that the parameters are complete and without omissions.

[0057] Furthermore, when analyzing the logical correlation of parameters, a parameter correlation table needs to be established. For example, for every 10°C increase in deposition temperature, the deposition time should be shortened by 5 minutes. The preliminary parameters should be verified one by one according to this correlation table to ensure that the logic is flawless.

[0058] Furthermore, when retrieving the process safety range, it is necessary to confirm that the range matches the material of the metal part. For example, the upper limit of the deposition temperature safety for stainless steel parts is higher than that for aluminum alloy parts. Avoid using a general safety range to prevent misjudgment of abnormal parameters.

[0059] Furthermore, when correcting abnormal parameters, if the corrected parameters cause new logical problems in other parameters, the logical correlation check needs to be restarted and corrected again until all parameters are both within the safe range and logically consistent.

[0060] Furthermore, when arranging the corrected parameters, it is necessary to strictly follow the order of "deposition temperature → nanomaterial concentration → deposition pressure → deposition time" to ensure that the sequence of process parameters conforms to the actual operation process of nano-reinforcement.

[0061] Furthermore, after determining the area to be strengthened, physical markings need to be made on the surface of the metal part, such as using a special marker to circle the area to be strengthened, to facilitate precise positioning in subsequent nano-strengthening processes.

[0062] Furthermore, after compiling the candidate nanomaterials, a candidate material list needs to be created, indicating the manufacturer and batch of each material, to facilitate tracing the source of the material's performance during subsequent screening.

[0063] Furthermore, after extracting the surface defect features, the defect location information needs to be matched with the actual coordinates of the metal part. For example, the coordinate values ​​corresponding to the defects can be marked on the edge of the metal part to facilitate targeted processing in subsequent processes.

[0064] Furthermore, when screening target nanomaterials, if multiple compatible materials exist, the repair effect of each material can be verified through small-scale experiments, and the one with the best repair effect can be selected as the target nanomaterial.

[0065] Furthermore, after querying the process parameter mapping table, it is necessary to record the mapping table number corresponding to the parameter combination found, so as to facilitate tracing the source during subsequent parameter verification.

[0066] Furthermore, after marking parameter defects, the defect type, such as integrity defect or logical defect, needs to be associated with the corresponding parameter item and recorded, such as "deposition time missing - integrity defect", to facilitate subsequent targeted correction.

[0067] Furthermore, when summarizing abnormal parameters, they need to be categorized by parameter type, such as classifying abnormal parameters of temperature and abnormal parameters of pressure separately, so as to facilitate batch correction by category.

[0068] Furthermore, after correcting the abnormal parameters, the corrected parameters must be compared with the process safety range again to confirm that all parameters are within the safety range, so as to avoid the existence of abnormalities after correction.

[0069] Furthermore, after the process parameter sequence is formed, the parameter sequence table needs to be printed and signed by technical personnel to ensure the authority and traceability of the parameter sequence.

[0070] In summary, the entire process starts from the initial state data, determines the area to be strengthened through roughness assessment, screens candidate nanomaterials through element matching, extracts defect features through image analysis, determines the target material through defect adaptation, obtains preliminary parameters through parameter query, and verifies and corrects the parameters through multiple rounds, finally obtaining the sequence of nano-strengthening process parameters. Each step has a clear operation and product.

[0071] In summary, by refining operations such as standardizing image magnification, establishing parameter correlation tables, and physically marking areas to be enhanced, problems such as data deviation and inconsistent operations were solved, ensuring the accuracy and reliability of products at each stage.

[0072] In summary, the process is interconnected, progressing step by step from data processing to process parameter determination. The detailed operations of each step are all centered around obtaining the precise sequence of nano-strengthening process parameters, providing a clear and feasible implementation plan for nano-strengthening of metal parts surfaces.

[0073] The process simulation module 103 is used to simulate the electrochemical deposition process of the nano-strengthening process parameter sequence to obtain the predicted data of nanocrystalline layer growth on the surface of the metal part. In this embodiment of the invention, the electrochemical deposition process simulation of the nano-strengthening process parameter sequence to obtain predicted data for the growth of nanocrystalline layers on the surface of the metal part is specifically used for: The nano-strengthening process parameter sequence is extracted to obtain the deposition voltage, deposition current density, and deposition time parameters of the nano-strengthening process parameter sequence; Based on a pre-established deposition kinetics rule library, the deposition voltage, deposition current density, and deposition duration parameters are matched to obtain reference values ​​for the nucleation rate and growth rate of nanocrystalline layers on the surface of the metal part. The initial nucleation distribution of nanocrystals on the surface of the metal part was obtained by simulating the reference values ​​of nucleation rate and growth rate. Based on the initial nucleation distribution and the deposition duration parameters, the nanocrystal growth and coverage process on the surface of the metal part is simulated to obtain the nanocrystal layer growth simulation data on the surface of the metal part. The integrity of the nanocrystalline layer growth simulation data is verified to obtain the nanocrystalline layer growth prediction data on the surface of the metal part.

[0074] The formulas for calculating the nucleation rate and growth rate of the nanocrystalline layer on the surface of the metal part are as follows: The formula for calculating the nucleation rate of the nanocrystalline layer on the surface of the metal part is as follows: ; In the formula, The nucleation rate of the nanocrystalline layer on the surface of the metal part is given. This represents the initial nucleation rate of the nanocrystalline layer on the surface of the metal part. It is an exponential function. The critical nucleation work on the surface of the metal part is related to the deposition voltage of the nano-reinforcement process parameter sequence. The atomic diffusion activation energy is related to the deposition current density of the nano-reinforcement process parameter sequence on the surface of the metal part. The Boltzmann constant of the surface of the metal part is given. The absolute temperature of the surface of the metal part; The formula for calculating the growth rate of the nanocrystalline layer on the surface of the metal part is as follows: ; In the formula, The growth rate of the nanocrystalline layer on the surface of the metal part is given. The molar mass of the material deposited on the surface of the metal part. The deposition current density is the sequence of nano-reinforcement process parameters for the surface of the metal part. The number of electrons in the electrode reaction on the surface of the metal part. It is Faraday's constant. The density of the deposited layer on the surface of the metal part.

[0075] Based on the initial nucleation distribution and the deposition duration parameters, the growth and coverage process of nanocrystals on the surface of the metal part is simulated to obtain simulation data of nanocrystal layer growth on the surface of the metal part, specifically used for: The properties of the initial nucleation distribution of nanocrystals on the surface of the metal part are extracted to obtain the initial spatial position and initial size distribution of the nanocrystals on the surface of the metal part. The total deposition time is divided based on the deposition duration parameter to obtain the time stages of the total deposition time on the surface of the metal part; During the total deposition time on the surface of the metal part, the radial growth process of nanocrystals on the surface of the metal part is simulated based on the grain growth kinetics rules, and the nanocrystal layer growth simulation data on the surface of the metal part is obtained.

[0076] Specifically, three parameters—deposition voltage, deposition current density, and deposition time—are extracted from the nano-strengthening process parameter sequence. These parameters are the basic data for subsequent electrochemical deposition process simulation and are directly derived from the process parameter sequence itself. The extraction process involves accurately identifying and separating the specific numerical information of these three parameters from the sequence.

[0077] Specifically, the pre-established deposition kinetics rule base contains the correspondence between different deposition voltages, deposition current densities, and the nucleation rate and growth rate of nanocrystalline layers. These correspondences are deterministic associations derived from a large amount of experimental data and theoretical analysis. The extracted deposition voltage and deposition current density parameters are compared one by one with the contents of the rule base to find the completely matching entries, thereby determining the corresponding reference values ​​for the nucleation rate and growth rate of nanocrystalline layers. The deposition time parameter is not involved in the matching in this step, but is only retained as a basis for subsequent steps.

[0078] Specifically, based on the obtained nucleation rate and growth rate reference values, the initial formation process of nanocrystals on the surface of metal parts is simulated. By restoring the conditions and laws during nucleation, the position, quantity, and distribution of nanocrystals on the surface of metal parts at the initial formation are determined, and the initial nucleation distribution of nanocrystals on the surface of metal parts is finally obtained. This distribution accurately reflects the specific situation on the surface of metal parts when nanocrystals just begin to form.

[0079] Specifically, based on the determined initial nucleation distribution of nanocrystals and the extracted deposition time parameters, the process of nanocrystal growth on the surface of a metal part is gradually deduced according to the natural laws and characteristics of nanocrystal growth. Starting from the initial nucleation state, the nanocrystals grow continuously over time, come into contact with each other, and eventually cover the entire surface. During the deduction process, the time range specified by the deposition time is strictly followed, and finally, simulation data of nanocrystal layer growth on the surface of the metal part that can reflect this complete process is obtained.

[0080] Specifically, a comprehensive check is performed on the obtained nanocrystalline layer growth simulation data to confirm whether the data completely covers the entire process of nanocrystalline layer growth from nucleation to coverage, whether it contains all the necessary information points, and whether there is any missing data, logical contradiction, or content that does not conform to the actual situation. After such completeness verification, the simulation data that is confirmed to be correct is the nanocrystalline layer growth prediction data on the surface of the metal part.

[0081] Specifically, from the initial nucleation distribution of nanocrystals on the surface of the metal part, the specific position information of each nanocrystal on the surface of the metal part is extracted. This position information is accurate enough to clearly distinguish the spatial arrangement of different grains. At the same time, the size information of each initial nanocrystal is extracted. These size information together constitute the initial size distribution. Through this extraction process, the initial spatial position and initial size distribution of nanocrystals on the surface of the metal part are obtained.

[0082] Specifically, based on the total deposition time determined by the deposition duration parameter, the total deposition time is divided into multiple continuous and non-overlapping time periods at fixed time intervals. Each time period is a time stage. The division process ensures that the total deposition time is completely covered and that the duration of each time stage remains consistent. Through this division, the time stages of the total deposition time on the surface of the metal part are obtained.

[0083] Specifically, within each defined time stage, based on the grain growth kinetics rule, which clarifies the specific mode and law of radial growth of nanocrystals under different conditions, that is, nanocrystals grow uniformly in all directions around their own center as the origin, and the degree of growth corresponds to the time stage. According to this rule, the growth process of each nanocrystal on the surface of the metal part is simulated, and the size and distribution changes of the nanocrystals at the end of each time stage are recorded. The simulation results of all time stages are integrated to obtain the simulation data of nanocrystal layer growth on the surface of the metal part.

[0084] Specifically, for the nucleation rate formula, the inherent initial nucleation rate of the nanocrystalline layer on the metal surface is determined based on the inherent properties of the metal material itself; the critical nucleation work related to the deposition voltage in the nano-strengthening process parameter sequence on the metal surface is determined by analyzing the influence of the deposition voltage on the energy barrier during the nucleation process on the metal surface; the atomic diffusion activation energy related to the deposition current density in the nano-strengthening process parameter sequence on the metal surface is obtained based on the effect of the deposition current density on the energy required for atomic diffusion; the Boltzmann constant of the metal surface is a fundamental constant in physics and is directly adopted; the absolute temperature of the metal surface is obtained by measuring the surface temperature of the metal.

[0085] Specifically, for the growth rate formula, the molar mass of the deposited material on the metal surface is determined by the chemical composition of the deposited material; the deposition current density of the nano-strengthening process parameter sequence on the metal surface is extracted from the nano-strengthening process parameter sequence; the number of electrons in the electrode reaction on the metal surface is determined according to the chemical nature of the electrode reaction occurring on the metal surface; the Faraday constant is a fundamental constant in physics and is used directly; the density of the deposited layer on the metal surface is obtained by measuring the density of the deposited layer.

[0086] Specifically, the nucleation rate formula is used to calculate the nucleation rate of nanocrystalline layers on the surface of metal parts. It can reflect the number of nanocrystalline nuclei formed per unit time and per unit volume on the surface of metal parts under specific deposition voltage, deposition current density and other conditions, and help to understand the speed of the nucleation process.

[0087] Specifically, the growth rate formula is used to calculate the growth rate of the nanocrystalline layer on the surface of a metal part. It can be reflected in how quickly the thickness and other dimensions of the nanocrystalline layer increase over time under given deposition current density and other conditions, thus revealing the growth rate of the nanocrystalline layer.

[0088] Specifically, in the nucleation rate formula, when the deposition voltage changes and causes the critical nucleation work to decrease, the corresponding exponential part will increase. At the same time, when the deposition current density changes and causes the atomic diffusion activation energy to decrease, the corresponding exponential part will also increase, thus increasing the nucleation rate. If the temperature increases, both exponential parts will increase due to the increase in the relevant denominator, and the nucleation rate will also increase.

[0089] Specifically, in the growth rate formula, when the deposition current density increases, the molecular part increases, and the growth rate will increase accordingly; if the molar mass of the deposited material increases, or the number of electrons in the electrode reaction decreases, or the density of the deposition layer decreases, the growth rate will also increase.

[0090] Furthermore, when extracting the three parameters of deposition voltage, deposition current density, and deposition time, it is necessary to comprehensively review the sequence of nano-strengthening process parameters to ensure that no key parameter is missed, and the extraction of each parameter must accurately correspond to its position and value in the sequence.

[0091] Furthermore, in the process of establishing the sedimentation kinetics rule base, the collected experimental data covered a large number of samples from different metallic materials and different sedimentation environments, while the theoretical analysis combined knowledge from multiple disciplines such as electrochemistry and materials science to ensure the accuracy and universality of the correspondence in the rule base.

[0092] Furthermore, when simulating the initial formation process of nanocrystals, it is necessary to fully consider the microstructural characteristics of the metal surface, such as surface roughness and defect distribution, as these factors directly affect the initial nucleation location and number of nanocrystals.

[0093] Furthermore, when simulating the nanocrystal growth and covering process, it is necessary to track the growth status of each nanocrystal in real time, including its size changes and distance changes from surrounding grains. When grains come into contact with each other, the location and time of the contact point need to be recorded to accurately reflect the details of the covering process.

[0094] Furthermore, integrity verification not only checks the integrity of the data, but also verifies its rationality, such as whether the growth thickness of the nanocrystalline layer matches the deposition time, and whether the distribution of grain size conforms to the natural growth law.

[0095] Furthermore, when extracting the initial spatial position of nanocrystals, a high-precision positioning method is used to ensure that the position information of each crystallite can be accurately recorded. The extraction of the initial size distribution is achieved by measuring and statistically analyzing the diameter or volume of each crystallite.

[0096] Furthermore, when dividing the total deposition time into time stages, the time interval should be determined based on the growth rate of the nanocrystals. If the growth rate is fast, the time interval can be appropriately shortened to capture the growth process more precisely.

[0097] Furthermore, the grain growth kinetics rules were derived from the observation and summarization of a large number of nanocrystal growth processes, clarifying the rate and mode of radial grain growth under different temperatures, concentrations, and other conditions, providing a reliable basis for the simulation process.

[0098] Furthermore, when determining the inherent properties of the metal material to obtain the initial nucleation rate, a comprehensive analysis of the metal's chemical composition, crystal structure, etc., is required. These properties directly determine the ease and initial rate of nucleation of nanocrystalline layers on the surface of the metal part.

[0099] Furthermore, when analyzing the effect of deposition voltage on critical nucleation work, a quantitative relationship between the two is established by changing the deposition voltage and measuring the corresponding change in critical nucleation work, thereby accurately determining the critical nucleation work based on the deposition voltage.

[0100] Furthermore, when determining the atomic diffusion activation energy based on the deposition current density, the energy required for atomic diffusion under different deposition current densities is measured experimentally, and the variation law between the two is summarized, providing a method for extracting the atomic diffusion activation energy.

[0101] Furthermore, when determining the molar mass of the sediment, the molecular composition of the sediment is clarified through chemical analysis, and then the molar mass is calculated based on the atomic weight of each element.

[0102] Furthermore, when extracting the deposition current density, the value of the parameter is directly read from the nano-strengthening process parameter sequence to ensure the accuracy and completeness of the value.

[0103] Furthermore, when determining the number of electrons in the electrode reaction, the chemical equations of the electrode reactions occurring on the surface of the metal part are analyzed in depth, and the number of electrons is determined based on the electron transfer in the reaction.

[0104] Furthermore, in applying the nucleation rate formula, it is necessary to accurately substitute each parameter into the formula, and the calculated result can be directly used to evaluate the efficiency of the nucleation process under the current process parameters.

[0105] Furthermore, the growth rate formula is used to obtain the growth rate of the nanocrystalline layer by substituting relevant parameters, providing data support for predicting the final thickness and structure of the nanocrystalline layer.

[0106] Furthermore, when analyzing the trend of the nucleation rate formula, it is necessary to consider the impact of changes in each parameter on the overall result, clarify the sensitivity of each parameter, and provide direction for optimizing process parameters.

[0107] Furthermore, when studying the trend of the growth rate formula, the changes in growth rate were observed by changing different parameters, and the influence of parameter adjustment on growth rate was summarized to guide the actual deposition process.

[0108] In summary, the above process details the entire workflow from extracting key parameters from the nano-strengthening process parameter sequence, to determining reference values ​​for nucleation and growth rates through a deposition kinetics rule library, to simulating the initial nucleation and growth coverage process of nanocrystals, and finally obtaining predicted data for nanocrystalline layer growth. It also explains the source, significance, and trends of the parameters in the relevant formulas. The entire process revolves closely around the simulation of nanocrystalline layer growth on the surface of metal parts, with each step interconnected and logically clear, providing comprehensive and specific guidance for understanding and applying this nano-strengthening process.

[0109] The verification and optimization module 104 is used to verify the stability of the nanocrystalline layer growth prediction data. When the verification fails, the weight parameters in the nano-strengthening decision analysis are adjusted to obtain the optimized process parameter sequence for the surface of the metal part.

[0110] The nano-reinforcement decision analysis is based on a pre-defined decision model, which includes a series of weight parameters, such as the roughness weight W used to evaluate the reinforcement necessity of different regions. r Element matching weights W used to select nanomaterials e And the defect impact weight W used to determine process parameters. d wait.

[0111] In this embodiment of the invention, the stability verification of the nanocrystalline layer growth prediction data, and the adjustment of the weight parameters in the nano-strengthening decision analysis when the verification fails, to obtain the optimized process parameter sequence for the metal part surface, is specifically used for: The predicted growth data of the nanocrystalline layer on the surface of the metal part is compared with the preset crystal quality stability threshold to obtain the growth state judgment result of the nanocrystalline layer on the surface of the metal part. When the growth state judgment result is unsuccessful, abnormal regions and abnormal patterns in the nanocrystalline layer growth prediction data are identified to obtain the weight parameters of the nano-strengthening process parameter sequence of the metal part surface that cause deviations in the nano-strengthening decision analysis process; the abnormal patterns include uneven crystal layer thickness or excessive grain size, and the weight parameters refer to the parameters that affect the pattern in the decision analysis, such as roughness weight or material selection weight.

[0112] Based on a predefined parameter adjustment strategy, the weight parameters are iteratively corrected to obtain the adjusted weight parameters. Based on the adjusted weight parameters, a new nano-strengthening decision analysis is performed on the initial state data of the metal part surface to obtain the optimized nano-strengthening process parameter sequence for the metal part surface.

[0113] The predefined parameter adjustment strategy iteratively corrects the weight parameters to obtain adjusted weight parameters, specifically for: The weight parameters of the nano-strengthening process parameter sequence on the surface of the metal part are extracted by attribute analysis to obtain the type and initial value of the weight parameters of the nano-strengthening process parameter sequence on the surface of the metal part. Based on the abnormal patterns in the predicted data of nanocrystalline layer growth on the surface of the metal part, the weight parameters of the nano-strengthening process parameter sequence on the surface of the metal part are prioritized to obtain the target parameters of the weight parameters of the nano-strengthening process parameter sequence on the surface of the metal part. Based on predefined parameters, the target parameters of the weight parameters of the nano-strengthening process parameter sequence for the surface of the metal part are incrementally corrected to obtain the intermediate weight parameters of the nano-strengthening process parameter sequence for the surface of the metal part. Based on the intermediate weight parameters, a nano-strengthening decision analysis is performed on the initial state data of the metal part surface to obtain the test process parameter sequence of the metal part surface. The predictive performance indicators of the test process parameter sequence are evaluated: When the test process parameter sequence of the metal part surface does not reach the preset optimization target, the incremental correction and nano-strengthening decision analysis are repeatedly executed to iteratively correct the target parameters of the weight parameters of the nano-strengthening process parameter sequence of the metal part surface. When the sequence of test process parameters for the metal part surface reaches the preset optimization target, the intermediate weight parameters of the sequence of nano-strengthening process parameters for the metal part surface are output as adjusted weight parameters.

[0114] Specifically, when comparing the predicted data of nanocrystalline layer growth with the threshold of crystal layer quality stability, the predicted data of indicators such as thickness uniformity and the corresponding threshold of qualified range are retrieved first. Then, the data of each growth indicator is compared with the threshold one by one. If all indicators meet the standard, the result is judged as passing; otherwise, it is judged as failing, and the growth status judgment result is obtained.

[0115] Specifically, when the growth status judgment result is unsuccessful, abnormal areas where the growth index exceeds the range are marked on the visualization image. The index type corresponding to each abnormal area is recorded and the distribution pattern is analyzed to determine the abnormal pattern. Then, all weight parameters in the decision analysis are retrieved, and the weight parameters that cause the deviation are determined according to the correspondence between the abnormal pattern and the parameters.

[0116] Specifically, when extracting weight parameter attributes, the categories of basic attributes and special attributes are clearly defined, and the specific type of each parameter is determined by checking the identifier. For example, for roughness weight parameters, the initial values ​​are extracted from the original record file and organized to obtain the type and initial value of the weight parameters.

[0117] Specifically, when ranking weight parameters based on abnormal modes, the influence of abnormal modes on crystal layer quality is analyzed, the correlation between each parameter and the abnormal mode is determined and sorted from high to low, and the top 3 weight parameters are determined as target parameters.

[0118] Specifically, when incrementally correcting the target parameter, the predefined parameter adjustment increment value is retrieved, and the deviation direction between the current value of the parameter and the ideal value is determined. If it is lower, the value is increased by the increment value; if it is higher, the value is decreased. Each time, one target parameter is adjusted, and all the adjusted weight parameters are integrated to obtain the intermediate weight parameter.

[0119] Specifically, when making decisions based on intermediate weight parameters, the intermediate weight parameters are substituted into the process to reallocate weights. The analysis of regional assessment and material matching is completed according to the original steps. The intermediate weight parameters are used to make judgments and selections, generate new process parameters, and obtain the test process parameter sequence.

[0120] Specifically, when evaluating the predictive performance of the test process parameter sequence, predictive performance indicators, including thickness uniformity, are determined. The preset qualified thresholds for each indicator are retrieved and compared. If all indicators meet the standards, the optimization target is determined to be achieved; otherwise, it is considered not to have met the standards and the non-compliance is recorded.

[0121] Specifically, when the test process parameter sequence fails to meet the optimization target, the target parameters that need to be adjusted are identified based on the unmet indicators. The new intermediate weight parameters are then adjusted again using an incremental correction method. The new parameters are then used to re-analyze and generate a new test process parameter sequence, which is then evaluated. This process is repeated until the target is met.

[0122] Specifically, when the test process parameter sequence meets the standard, confirm its corresponding intermediate weight parameters, check the number of times the target parameters are adjusted and the final values, organize the parameter types and values ​​into a file and mark the adjustment basis, store it in the database and mark it as the adjusted weight parameters, and complete the output.

[0123] Specifically, when reanalyzing based on the adjusted weight parameters, the parameters are retrieved to ensure completeness and accuracy, and then applied to each stage of the decision analysis. For example, the priority of the areas to be strengthened is determined according to the adjusted weights, and new parameters that meet the optimization objectives are generated by operating the complete process, resulting in the optimized nano-strengthening process parameter sequence.

[0124] Furthermore, when retrieving the crystal layer quality stability threshold, it is necessary to confirm that the threshold matches the usage environment of the metal parts. For example, the threshold in a high-temperature environment should be stricter than that in a normal-temperature environment to ensure that the judgment results meet the actual needs.

[0125] Furthermore, when marking abnormal areas, a dedicated image marking tool is used to distinguish different types of abnormal areas with different colors. For example, red marks areas with uneven thickness, and blue marks areas with excessive grain size, which facilitates subsequent analysis.

[0126] Furthermore, when identifying abnormal patterns, the processing history of the metal parts is considered, such as whether they have undergone stamping, welding, or other processes. These processes may lead to specific abnormal patterns, thereby improving the accuracy of abnormal pattern identification.

[0127] Furthermore, when extracting the initial values ​​of the weight parameters, it is necessary to verify the creation time of the original record file to ensure that the latest record corresponding to this nano-enhanced decision analysis is used, so as to avoid numerical extraction errors.

[0128] Furthermore, when analyzing the impact of abnormal patterns, at least three senior technical personnel are invited to conduct independent assessments, and the consensus portion of the assessment results is taken as the final determination of the impact level to reduce subjective bias.

[0129] Furthermore, when determining the number of target parameters, if the anomaly pattern is complex and there are many influencing factors, the top 3 can be prioritized as target parameters; if the anomaly pattern is simple, only the top 2 can be selected to ensure targeted adjustments.

[0130] Furthermore, when setting parameter adjustment increment values, refer to historical adjustment data of similar metal parts. For sensitive weight parameters such as defect repair weight, the increment value is set to 0.05 to improve adjustment accuracy.

[0131] Furthermore, when redistributing weights by substituting intermediate weight parameters, a weight allocation comparison table is created to clearly show the changes in the weight ratio of each data point before and after the adjustment, making it easier to trace the impact of the adjustment.

[0132] Furthermore, when determining the predicted performance indicators, the corrosion resistance index of the nanocrystalline layer is added. This index is assessed by simulating the degree of damage to the crystalline layer under corrosive conditions, making the assessment more comprehensive.

[0133] Furthermore, when setting performance evaluation standards, a minimum qualified threshold and an ideal target threshold are set for each indicator. If the minimum threshold is not reached, it needs to be readjusted immediately, and if it is between the two, it can be further optimized as appropriate.

[0134] Furthermore, when confirming the target parameters for further adjustment, priority should be given to selecting weight parameters directly related to the unmet indicators. For example, if the corrosion resistance is not up to standard, priority should be given to adjusting the weight parameter of the adhesion strength of nanomaterials to improve the adjustment efficiency.

[0135] Furthermore, during the iterative correction process, after adjusting the target parameters three times, the process is paused and the adjustment trend is summarized. If it is found that the parameters continue to be adjusted in the same direction but still fail to meet the target, the correspondence between the abnormal patterns and the weight parameters needs to be re-evaluated.

[0136] Furthermore, when compiling the intermediate weight parameter files, the sequence of test process parameters and performance evaluation results for each adjustment are attached to form a complete adjustment file, which is convenient for subsequent review and analysis.

[0137] Furthermore, when storing the adjusted weight parameters, they are categorized and stored according to the model and specifications of the metal parts. Metal parts of the same model can be used for reference in subsequent analyses, thereby improving the parameter reuse rate.

[0138] Furthermore, when applying the adjusted weight parameters, checkpoints are set at each stage of the decision analysis to verify whether the correct adjusted parameters are used, thus avoiding operational errors.

[0139] Furthermore, after generating the optimized process parameter sequence, it is compared with the initial process parameter sequence to list the specific values ​​of parameter changes and the corresponding effects on crystal growth indicators, thus verifying the optimization effect.

[0140] In summary, the entire process, from comparing the predicted data of nanocrystalline layer growth with the threshold to obtain the judgment result, to identifying anomalies and determining the deviation weight parameters, to obtaining the adjusted weight parameters through attribute extraction, sorting, and incremental correction, and finally re-analyzing to obtain the optimized process parameter sequence, is clear in each step and yields clear results.

[0141] In summary, by refining operations such as color-marking abnormal areas, setting dual thresholds to evaluate performance, and creating adjustment files, problems such as identification bias and blind adjustments were solved, ensuring the accuracy and reliability of products at each stage.

[0142] In summary, the process, from determining the crystal growth state to optimizing process parameters, is interconnected. Through multiple rounds of iterative correction of weight parameters, a sequence of optimized process parameters that meets the requirements is finally obtained, providing precise parameter guidance for nano-strengthening of metal parts.

[0143] The instruction output module 105 is used to encode the optimized process parameter sequence to obtain the nano-strengthening treatment instruction.

[0144] In this embodiment of the invention, encoding the optimized process parameter sequence to obtain nano-strengthening processing instructions is specifically used for: The optimized process parameter sequence is extracted to obtain the deposition control parameters, time control parameters, and material control parameters of the optimized process parameter sequence for the metal part surface. The deposition control parameters, time control parameters, and material control parameters are mapped and matched with predefined equipment instructions to obtain the basic control instructions for the surface of the metal part. The basic control instructions are logically sorted in chronological order to obtain a preliminary instruction sequence for the surface of the metal part. Perform a consistency check on the preliminary instruction sequence; Based on the standardized instruction format recognizable by the target nano-strengthening device, the preliminary instruction sequence after consistency detection is converted to obtain the nano-strengthening treatment instructions for the surface of the metal part.

[0145] Specifically, when extracting parameters from the optimized process parameter sequence, first open the file that records all process parameters for nano-reinforcement by category, identify the parameters marked "deposition-related", including deposition temperature, pressure, rate, etc., and classify them to obtain deposition control parameters; identify the parameters marked "time-related", including total deposition time, stage allocation, holding time, etc., and classify them to obtain time control parameters; identify the parameters marked "material-related", including nanomaterial concentration, purity, mixing ratio, single spraying amount, etc., and classify them to obtain material control parameters.

[0146] Specifically, when mapping and matching the three types of control parameters with predefined device instructions, the instruction library of executable instructions and corresponding parameter type ranges stored in the storage device is first retrieved. The deposition control parameters are matched one by one with deposition-type instructions, the time control parameters are matched one by one with time-type instructions, and the material control parameters are matched one by one with material-type instructions. The basic control instructions are obtained by summarizing the successfully matched instructions.

[0147] Specifically, when logically sequencing the basic control instructions according to time sequence, the process execution flow is first clarified, namely, first preparing materials, then adjusting the deposition environment, then controlling the time, and finally post-processing. Based on this, the instructions are divided into material preparation group, deposition environment adjustment group, deposition time control group, and post-processing group. Within each group, the instructions are ordered according to "concentration first, then ratio" and "pressure first, then temperature". The instructions are then connected according to the group order to obtain the preliminary instruction sequence.

[0148] Specifically, when performing consistency checks on the initial instruction sequence, a testing standard including logic, parameter units, and instruction format is first established. The logic, units, and format are checked in sequence. If any problems are found, they are corrected according to the standard and then retested until they meet the standard.

[0149] Specifically, when converting the standardized instruction format of the target equipment, the format is first extracted from the equipment operation manual, the initial instruction elements are broken down one by one, the elements are combined according to the format to form a standardized instruction, and then the nano-strengthening treatment instruction is obtained by arranging them in the original order.

[0150] Furthermore, when opening the optimized process parameter sequence file, it is necessary to check the file version number to ensure that the latest optimized parameter file is used, and to avoid extracting incorrect parameters.

[0151] Furthermore, when categorizing deposition control parameters, a table is used to record the name, value, and corresponding process step of each parameter, such as "deposition temperature -200℃ - nanomaterial adhesion stage", to facilitate subsequent instruction matching.

[0152] Furthermore, before retrieving the equipment instruction library, it is necessary to confirm that the instruction library has been updated to a version that matches the target nano-strengthening equipment model to prevent incompatibility between instructions and equipment.

[0153] Furthermore, when matching instructions, if a certain control parameter does not have a directly corresponding instruction, it is necessary to contact the equipment manufacturer to supplement the customized instruction to ensure that all parameters can be matched with valid instructions.

[0154] Furthermore, when analyzing the execution process of the nano-strengthening process, referring to the historical process records of similar metal parts, if the metal parts are used in high-temperature conditions, a "high-temperature insulation" instruction can be added to the post-processing group to improve process adaptability.

[0155] Furthermore, after dividing the basic control instructions into groups, flowcharts are used to show the sequential relationships between each group and the instructions within each group, making it easier for technicians to visually check the sequencing logic.

[0156] Furthermore, when developing consistency testing standards, equipment operation engineers are invited to participate to ensure that the standards align with the actual operating requirements of the equipment. For example, the units of parameters must be consistent with the units displayed on the equipment control panel.

[0157] Furthermore, when correcting inconsistent instructions, the example instructions in the equipment operation manual should be consulted first to ensure that the corrected instructions comply with the equipment operation specifications.

[0158] Furthermore, after obtaining the target equipment's operation manual, highlight the format requirements for special instructions in the manual. For example, instructions involving high-voltage operations must be marked with safety symbols, and these must not be omitted during conversion.

[0159] Furthermore, after combining the standardized instructions, the recognizability of the instructions is tested in the equipment simulation operation software. If the equipment prompts an error during the simulation, the format is readjusted until the simulation passes.

[0160] Furthermore, when arranging the standardized instructions after conversion, a note of "execution interval - 5 seconds" is added between adjacent instructions to avoid the device from executing instructions continuously, which would cause excessive load.

[0161] Furthermore, when extracting material control parameters, the production batch number of the nanomaterial is additionally recorded. If problems occur in subsequent processes, the material quality can be traced, thus improving the completeness of parameter extraction.

[0162] Furthermore, when matching time control commands, the "deposition time allocation for each stage" command is split into multiple single-stage time commands, such as "first stage deposition time - 10 minutes" and "second stage deposition time - 20 minutes", to facilitate step-by-step execution by the equipment.

[0163] Furthermore, when checking the consistency of instruction logic, the focus should be on the matching of deposition temperature and holding time. For example, a longer holding time is required after high-temperature deposition to prevent logical conflicts from affecting the quality of the crystal layer.

[0164] Furthermore, when converting the instruction format, key parameters in the instruction are marked with colors, such as marking the parameter value "200℃" in red, to facilitate subsequent verification of whether the values ​​are correct.

[0165] In summary, the entire process begins with extracting three types of control parameters to optimize the process parameters. Through instruction mapping and matching, logical sorting, consistency detection, and standardized format conversion, each step revolves around obtaining accurate nano-strengthening instructions to ensure that the products at each stage meet the requirements.

[0166] In summary, by refining the operations such as verifying file versions, creating parameter tables, and simulating command testing, problems such as parameter extraction errors, command incompatibility, and disordered sorting were solved, ensuring the accuracy and executability of the final nano-enhancing processing commands.

[0167] In summary, the process is interconnected from parameter processing to instruction generation. Each step is refined in combination with equipment characteristics and process requirements. The final nano-strengthening instructions can be directly used on the target equipment, providing clear operational guidance for the smooth implementation of nano-strengthening of metal parts.

[0168] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0169] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0170] Finally, 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.

Claims

1. A surface nano-strengthening treatment system for metal parts, characterized in that, The system includes a data acquisition module, an intelligent decision-making module, a process simulation module, a verification and optimization module, and an instruction output module, wherein: The data acquisition module is used to extract the surface roughness distribution, elemental composition and micromorphological features of the metal part to obtain the initial state data of the metal part surface. The intelligent decision-making module is used to perform nano-strengthening decision analysis on the initial state data to obtain a sequence of nano-strengthening process parameters for the surface of the metal part. The process simulation module is used to simulate the electrochemical deposition process of the nano-strengthening process parameter sequence to obtain the predicted data of nanocrystalline layer growth on the surface of the metal part. The verification and optimization module is used to verify the stability of the nanocrystalline layer growth prediction data. When the verification fails, the weight parameters in the nano-strengthening decision analysis are adjusted to obtain the optimized process parameter sequence for the surface of the metal part. The instruction output module is used to encode the optimized process parameter sequence to obtain nano-strengthening processing instructions.

2. The surface nano-strengthening treatment system for metal parts as described in claim 1, characterized in that, The extraction of surface roughness distribution, elemental composition, and microstructure characteristics of the metal part to obtain initial state data of the metal part surface is specifically used for: Noise is filtered out from the multispectral image data of the metal part surface to obtain standard image data of the metal part surface; The standard image data is segmented to obtain multi-feature regions on the surface of the metal part; Roughness measurements are performed on multiple feature regions of the metal part surface to obtain roughness distribution data of the metal part surface; Energy dispersive spectroscopy (EDS) analysis was performed on the surface of the metal part to obtain the elemental composition data of the surface of the metal part. Image acquisition is performed on the surface of the metal part to obtain microscopic morphology image data of the surface of the metal part; By integrating the roughness distribution data, the elemental composition data, and the microstructure image data, the initial state data of the metal part surface is obtained.

3. The surface nano-strengthening treatment system for metal parts as described in claim 2, characterized in that, The nano-strengthening decision analysis of the initial state data yields a sequence of nano-strengthening process parameters for the surface of the metal part, specifically used for: The roughness distribution data in the initial state data is evaluated for regional consistency to obtain the area to be strengthened on the surface of the metal part. Based on the elemental composition data, the surface elements of the metal part are matched with a preset nanomaterial deposition rule library to obtain candidate nanomaterials on the surface of the metal part. Based on the microscopic morphology image data, the surface defect features of the metal part are extracted; Based on the surface defect characteristics, the candidate nanomaterials are screened to obtain the target nanomaterial type on the surface of the metal part; By querying the roughness distribution data of the area to be strengthened on the surface of the metal part and the type of target nanomaterial on the surface of the metal part using a process parameter mapping table, preliminary nano-strengthening process parameters for the surface of the metal part are obtained. Logical verification is performed on the preliminary nano-strengthening process parameters to obtain the nano-strengthening process parameter sequence for the surface of the metal part.

4. The surface nano-strengthening treatment system for metal parts as described in claim 3, characterized in that, The logical verification of the preliminary nano-strengthening process parameters yields a sequence of nano-strengthening process parameters for the surface of the metal part, specifically used for: The completeness and logical consistency of the preliminary nano-reinforcement process parameters are checked. The preliminary nano-strengthening process parameters after inspection are compared with the pre-stored process safety range to obtain the abnormal parameters of the preliminary nano-strengthening process parameters. Based on predefined parameter optimization rules, abnormal parameters of the preliminary nano-strengthening process parameters are corrected to obtain the nano-strengthening process parameter sequence of the metal part surface.

5. The surface nano-strengthening treatment system for metal parts as described in claim 1, characterized in that, The electrochemical deposition process simulation of the nano-strengthening process parameter sequence is used to obtain predicted data for the growth of nanocrystalline layers on the surface of the metal part, specifically for: The nano-strengthening process parameter sequence is extracted to obtain the deposition voltage, deposition current density, and deposition time parameters of the nano-strengthening process parameter sequence; Based on a pre-established deposition kinetics rule library, the deposition voltage, deposition current density, and deposition duration parameters are matched to obtain reference values ​​for the nucleation rate and growth rate of nanocrystalline layers on the surface of the metal part. The initial nucleation distribution of nanocrystals on the surface of the metal part was obtained by simulating the reference values ​​of nucleation rate and growth rate. Based on the initial nucleation distribution and the deposition duration parameters, the nanocrystal growth and coverage process on the surface of the metal part is simulated to obtain the nanocrystal layer growth simulation data on the surface of the metal part. The integrity of the nanocrystalline layer growth simulation data is verified to obtain the nanocrystalline layer growth prediction data on the surface of the metal part.

6. The surface nano-strengthening treatment system for metal parts as described in claim 5, characterized in that, The formulas for calculating the nucleation rate and growth rate of the nanocrystalline layer on the surface of the metal part are as follows: The formula for calculating the nucleation rate of the nanocrystalline layer on the surface of the metal part is as follows: ； In the formula, The nucleation rate of the nanocrystalline layer on the surface of the metal part. This represents the initial nucleation rate of the nanocrystalline layer on the surface of the metal part. It is an exponential function. The critical nucleation work on the surface of the metal part is related to the deposition voltage of the nano-reinforcement process parameter sequence. The atomic diffusion activation energy is related to the deposition current density of the nano-reinforcement process parameter sequence on the surface of the metal part. The Boltzmann constant of the surface of the metal part is given. The absolute temperature of the surface of the metal part; The formula for calculating the growth rate of the nanocrystalline layer on the surface of the metal part is as follows: ； In the formula, The growth rate of the nanocrystalline layer on the surface of the metal part is given. The molar mass of the material deposited on the surface of the metal part. The deposition current density is the sequence of nano-reinforcement process parameters for the surface of the metal part. The number of electrons in the electrode reaction on the surface of the metal part. It is Faraday's constant. The density of the deposited layer on the surface of the metal part.

7. The surface nano-strengthening treatment system for metal parts as described in claim 5, characterized in that, Based on the initial nucleation distribution and the deposition duration parameters, the growth and coverage process of nanocrystals on the surface of the metal part is simulated to obtain simulation data of nanocrystal layer growth on the surface of the metal part, specifically used for: The properties of the initial nucleation distribution of nanocrystals on the surface of the metal part are extracted to obtain the initial spatial position and initial size distribution of the nanocrystals on the surface of the metal part. The total deposition time is divided based on the deposition duration parameter to obtain the time stages of the total deposition time on the surface of the metal part; During the total deposition time on the surface of the metal part, the radial growth process of nanocrystals on the surface of the metal part is simulated based on the grain growth kinetics rules, and the nanocrystal layer growth simulation data on the surface of the metal part is obtained.

8. The surface nano-strengthening treatment system for metal parts as described in claim 1, characterized in that, The stability verification of the nanocrystalline layer growth prediction data is performed. If the verification fails, the weight parameters in the nano-strengthening decision analysis are adjusted to obtain the optimized process parameter sequence for the metal part surface, specifically used for: The predicted growth data of the nanocrystalline layer on the surface of the metal part is compared with the preset crystal quality stability threshold to obtain the growth state judgment result of the nanocrystalline layer on the surface of the metal part. When the growth state judgment result is not passed, the abnormal regions and abnormal patterns in the nanocrystalline layer growth prediction data are identified to obtain the weight parameters of the nano-strengthening process parameter sequence of the metal part surface in the nano-strengthening decision analysis process. Based on a predefined parameter adjustment strategy, the weight parameters are iteratively corrected to obtain the adjusted weight parameters. Based on the adjusted weight parameters, a new nano-strengthening decision analysis is performed on the initial state data of the metal part surface to obtain the optimized nano-strengthening process parameter sequence for the metal part surface.

9. The surface nano-strengthening treatment system for metal parts as described in claim 8, characterized in that, The predefined parameter adjustment strategy iteratively corrects the weight parameters to obtain adjusted weight parameters, specifically for: The weight parameters of the nano-strengthening process parameter sequence on the surface of the metal part are extracted by attribute analysis to obtain the type and initial value of the weight parameters of the nano-strengthening process parameter sequence on the surface of the metal part. Based on the abnormal patterns in the predicted data of nanocrystalline layer growth on the surface of the metal part, the weight parameters of the nano-strengthening process parameter sequence on the surface of the metal part are prioritized to obtain the target parameters of the weight parameters of the nano-strengthening process parameter sequence on the surface of the metal part. Based on a predefined parameter adjustment strategy, the target parameters of the weight parameters of the nano-strengthening process parameter sequence for the surface of the metal part are incrementally corrected to obtain the intermediate weight parameters of the nano-strengthening process parameter sequence for the surface of the metal part. Based on the intermediate weight parameters, a nano-strengthening decision analysis is performed on the initial state data of the metal part surface to obtain the test process parameter sequence of the metal part surface. The predictive performance indicators of the test process parameter sequence are evaluated: When the test process parameter sequence of the metal part surface does not reach the preset optimization target, the incremental correction and nano-strengthening decision analysis are repeatedly executed to iteratively correct the target parameters of the weight parameters of the nano-strengthening process parameter sequence of the metal part surface. When the sequence of test process parameters for the metal part surface reaches the preset optimization target, the intermediate weight parameters of the sequence of nano-strengthening process parameters for the metal part surface are output as adjusted weight parameters.

10. The surface nano-strengthening treatment system for metal parts as described in claim 1, characterized in that, The encoding of the optimized process parameter sequence to obtain nano-strengthening instructions is specifically used for: The optimized process parameter sequence is extracted to obtain the deposition control parameters, time control parameters, and material control parameters of the optimized process parameter sequence for the metal part surface. The deposition control parameters, time control parameters, and material control parameters are mapped and matched with predefined equipment instructions to obtain the basic control instructions for the surface of the metal part. The basic control instructions are logically sorted in chronological order to obtain a preliminary instruction sequence for the surface of the metal part. Perform a consistency check on the preliminary instruction sequence; Based on the standardized instruction format recognizable by the target nano-strengthening device, the preliminary instruction sequence after consistency detection is converted to obtain the nano-strengthening treatment instructions for the surface of the metal part.

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