Surface treatment method and system for molding engineering materials

Abstract

The present invention discloses a surface treatment method and system for molding engineering materials, which relates to the field of material surface treatment technology, including: reading the material surface characteristics and surface quality standards of the molding engineering materials; measuring the characteristic amplitude modulation of the material surface characteristics and the surface quality standards, and identifying the characteristic correlation; based on the characteristic amplitude modulation and characteristic correlation, combined with an adaptive decision model, performing independent analysis within the layer and collaborative analysis between layers, and determining the optimal decision to determine the surface treatment plan; configuring an intelligent central control system; using abnormal processing characteristics as an index, performing industrial big data retrieval and building a control plan library, and setting autonomous calibration rules based on control deviations; configuring a digital feedback device; and performing closed-loop control of the surface treatment of molding engineering materials. The present invention solves the technical problem of the existing technology that it is difficult to perform real-time and precise control of material surface quality, thereby improving surface treatment efficiency and quality technical effects.

Description

Technical Field

[0001] The present invention relates to the technical field of material surface treatment, and in particular to a surface treatment method and system for molding engineering materials. Background Art

[0002] With the rapid development of modern industry, the application of molded engineering materials is becoming increasingly widespread across various fields, and the requirements for material surface quality are becoming increasingly stringent. Especially in high-end manufacturing fields such as automobiles, electronics, and aviation, the surface quality of molded engineering materials is directly related to product performance, lifespan, and market competitiveness. Therefore, the development of efficient and precise surface treatment methods and systems for molded engineering materials is particularly important.

[0003] However, traditional surface treatment methods for molding engineering materials often suffer from low control accuracy, long processing cycles, and poor stability. These methods often rely on manual experience and offline testing, making it difficult to achieve real-time, precise control of material surface quality. Furthermore, with the increasing complexity and diversification of production processes, traditional surface treatment methods are no longer able to meet the demands of modern industrial production. Summary of the Invention

[0004] The present application provides a surface treatment method and system for forming engineering materials, which are used to solve the technical problem that the existing technology is difficult to control the surface quality of materials in real time and accurately.

[0005] In view of the above problems, the present application provides a surface treatment method and system for molding engineering materials.

[0006] In a first aspect of the present application, a surface treatment method for a molded engineering material is provided, the method comprising:

[0007] The method comprises reading the material surface characteristics and surface quality standards of a molding engineering material, wherein the molding engineering material is a plastic material, and the surface quality standards include mechanical, performance, and visual aspects; measuring the characteristic amplitude modulation of the material surface characteristics and the surface quality standards, and identifying characteristic correlations; performing independent analysis within a layer and collaborative analysis between layers based on the characteristic amplitude modulation and the characteristic correlations, in combination with an adaptive decision model, to determine a surface treatment solution through optimal decision-making, wherein the adaptive decision model is a three-layer fully connected parallel structure with lateral interaction; configuring an intelligent central control system based on the surface treatment solution, wherein the intelligent central control system interacts with the equipment control system; performing industrial big data retrieval and constructing a control plan library using abnormal processing characteristics as an index, and setting autonomous calibration rules based on control deviation, wherein the control deviation is determined based on system error and exogenous error; configuring a digital feedback device based on the control plan library and the autonomous calibration rules; and performing closed-loop control of the surface treatment of the molding engineering material based on the configured intelligent central control system and the digital feedback device.

[0008] A second aspect of the present application provides a surface treatment system for formed engineering materials, the system comprising:

[0009] A molding engineering material information reading module, which reads the material surface characteristics and surface quality standards of the molding engineering materials, wherein the molding engineering materials are plastic materials, and the surface quality standards include mechanical level, performance level and visual level; a characteristic amplitude modulation measurement module, which measures the characteristic amplitude modulation of the material surface characteristics and the surface quality standard, and identifies the characteristic correlation; a surface treatment solution determination module, which conducts independent analysis within the layer and collaborative analysis between layers based on the characteristic amplitude modulation and the characteristic correlation, combined with an adaptive decision model, to determine the surface treatment solution through optimal decision-making, and the adaptive decision model is a three-layer fully connected parallel structure with lateral interaction; an intelligent central control system configuration module The intelligent central control system configuration module configures the intelligent central control system based on the surface treatment solution, wherein the intelligent central control system has interactive communication with the equipment control system; the control plan library construction module, the control plan library construction module uses the abnormal processing characteristics as an index to perform industrial big data retrieval and construct a control plan library, and sets an autonomous calibration rule based on control deviation, and the control deviation is determined based on the system error and the exogenous error; the digital feedback device configuration module, the digital feedback device configuration module configures the digital feedback device based on the control plan library and the autonomous calibration rule; the closed-loop control module, the closed-loop control module performs closed-loop control of the surface treatment of the molding engineering material based on the configured intelligent central control system and the digital feedback device.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The present application reads the material surface characteristics and surface quality standards of molding engineering materials, wherein the molding engineering materials are plastic materials, and the surface quality standards include mechanical level, performance level and visual level; measures the characteristic amplitude modulation of the material surface characteristics and surface quality standards, and identifies the characteristic correlation; based on the characteristic amplitude modulation and the said characteristic correlation, combined with the adaptive decision model, performs independent analysis within the layer and collaborative analysis between layers, and makes the best decision to determine the surface treatment plan, and the adaptive decision model is a three-layer fully connected parallel structure with lateral interaction; based on the surface treatment plan, configures the intelligent central control system, wherein the intelligent central control system and the equipment control system have interactive communication; uses the abnormal processing feature as an index to perform industrial big data retrieval and construct a control plan library, and sets autonomous calibration rules based on control deviation, and the control deviation is determined based on system error and exogenous error; configures the digital feedback device based on the control plan library and the said autonomous calibration rules; based on the configured intelligent central control system and the digital feedback device, performs closed-loop control of the surface treatment of molding engineering materials. The present invention solves the technical problem that the existing technology is difficult to control the surface quality of materials in real time and accurately. By real-time monitoring of key parameters of the material surface, such as roughness and glossiness, and using an adaptive decision-making model for precise control, the surface treatment efficiency and quality technical effects are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic flow chart of a surface treatment method for a molding engineering material provided in an embodiment of the present application;

[0014] Figure 2 A schematic structural diagram of a surface treatment system for a molding engineering material provided in an embodiment of the present application.

[0015] Explanation of the accompanying symbols: molding engineering material information reading module 11, characteristic amplitude modulation module 12, surface treatment plan determination module 13, intelligent central control system configuration module 14, control plan library construction module 15, digital feedback device configuration module 16, closed loop control module 17. DETAILED DESCRIPTION

[0016] This application provides a surface treatment method and system for formed engineering materials to solve the technical problem that the existing technology is difficult to control the surface quality of materials in real time and accurately. By real-time monitoring of key parameters of the material surface, such as roughness, glossiness, etc., and using an adaptive decision-making model for precise control, the surface treatment efficiency and quality technical effects are improved.

[0017] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] Example 1

[0020] like Figure 1 As shown, the present application provides a surface treatment method for a molded engineering material, the method comprising:

[0021] Step S100: Reading the surface characteristics and surface quality standards of the molding engineering material, wherein the molding engineering material is a plastic material, and the surface quality standards include mechanical aspects, performance aspects, and visual aspects;

[0022] In the embodiment of the present application, the molding engineering material is a plastic material. Plastic materials have many different types and properties.

[0023] For plastic materials, when reading their surface features, we can obtain their inherent physical and chemical properties, including but not limited to visually observable features such as gloss, roughness, and texture, as well as microstructure and chemical composition detected by professional instruments.

[0024] Surface quality standards encompass three dimensions: mechanical, performance, and visual. Mechanically, these include indicators related to mechanical properties, such as hardness, wear resistance, and impact resistance. Performance-wise, these include physical and chemical properties, such as weather resistance, corrosion resistance, and insulation. Visually, these include standards for appearance and visual effects, such as color, gloss, and texture.

[0025] Step S200: measuring the characteristic amplitude modulation of the surface characteristics of the material and the surface quality standard, and identifying the characteristic correlation;

[0026] In the embodiments of the present application, actual surface feature data of the plastic material are collected by professional testing equipment and instruments, such as using a gloss meter to measure gloss, and using a microscope to observe surface roughness.

[0027] The collected actual surface feature data is preprocessed, including data cleaning, standardization or normalization, to eliminate the impact of dimensional differences and outliers and ensure the comparability and accuracy of the data.

[0028] Characteristic modulation measures the difference between a material's actual surface characteristics and the surface quality standard. This is achieved by calculating the deviation, coefficient of variation, or similarity index for each characteristic. For example, for gloss, the difference or ratio between the actual gloss value and the standard gloss value is calculated to obtain the characteristic modulation of gloss. Similar measurements are performed for other surface characteristics.

[0029] After measuring feature amplitude modulation, we further analyze the correlation between features. Feature correlation refers to the degree of mutual influence or association between different surface features. We use statistical methods such as correlation coefficients, covariance matrices, or principal component analysis to calculate the correlation between features.

[0030] The calculated correlation coefficient or analysis results can be used to identify which features have strong correlations and which features are relatively independent.

[0031] Step S300: Based on the feature amplitude modulation and the feature correlation, combined with an adaptive decision model, independent analysis within the layer and collaborative analysis between layers are performed to determine the optimal decision for the surface treatment solution. The adaptive decision model is a three-layer fully connected parallel structure with lateral interaction.

[0032] In the embodiments of the present application, feature modulation and feature correlation provide quantitative information about the differences between the material surface and the quality standard. This information forms the basis for the decision-making model to perform independent analysis within each layer and collaborative analysis between layers. Independent analysis within each layer, such as the feature modulation and correlation within each layer, identifies key influencing factors and potential optimization space within each layer. Collaborative analysis between layers, based on the mutual influence and correlation between different layers, ensures that the final surface treatment solution meets or exceeds the quality standard in all aspects.

[0033] Next, we introduce an adaptive decision-making model, which consists of a three-layer, fully connected parallel architecture with lateral interactions. This architecture enables the model to simultaneously process information from multiple layers and to interact and integrate information between them. Specifically, each layer of the model corresponds to a specific analysis level: mechanical, performance, and visual. The fully connected architecture ensures that each layer can access the analysis results and feedback from other layers. Lateral interactions allow the analysis results from different layers to influence each other, resulting in more comprehensive and accurate decisions.

[0034] During the model run, feature amplitude modulation and feature correlation data are first input. The model then begins independent layer analysis. Each layer calculates the optimal treatment solution based on its own characteristics and standards. Next, the model enters the inter-layer collaborative analysis phase. During this phase, the analysis results from different layers are integrated and coordinated through lateral interaction to ensure that the final surface treatment solution is balanced and optimized in all aspects. Finally, the surface treatment solution determined by the optimal decision is output.

[0035] Step S400: configuring an intelligent central control system based on the surface treatment solution, wherein the intelligent central control system interacts and communicates with the equipment control system;

[0036] In the embodiment of the present application, the intelligent central control system is configured accordingly according to the specific requirements of the surface treatment solution, including setting various parameters such as treatment time, temperature, pressure, etc., and selecting the corresponding process steps and operation modes.

[0037] During the configuration process, the intelligent central control system interacts with the device control system. The device control system is responsible for directly controlling and processing the device, receiving instructions from the intelligent central control system and controlling the device's operation. The intelligent central control system communicates with the device control system in real time by sending instructions and receiving feedback.

[0038] Step S500: Using the abnormal processing feature as an index, perform industrial big data retrieval and build a control plan library, and set an autonomous calibration rule based on the control deviation, where the control deviation is determined based on the system error and the exogenous error;

[0039] In the embodiments of this application, the exception handling signature includes various abnormalities that occur during the surface treatment process, such as temperature fluctuations, pressure anomalies, and equipment failures. This signature data is collected and stored in a large database. When similar abnormalities occur during actual production, the exception handling signature is used as an index to search the large database for similar cases and solutions.

[0040] Based on the search results, a control plan library is constructed. This library contains pre-defined solutions for various abnormal situations. By searching big data, a plan matching the current abnormal situation is found, allowing for rapid implementation of appropriate measures. These plans include adjusting process parameters, replacing equipment, and modifying operating procedures.

[0041] Control deviation is determined based on systematic error and exogenous error. Systematic error is caused by factors such as device accuracy limitations or aging, while exogenous error stems from interactions between devices and external environmental factors. Interactions between devices include vibration and temperature, while external environmental factors include humidity and temperature fluctuations.

[0042] Based on these error data, autonomous calibration rules are set. Autonomous calibration rules can be formulated based on methods such as threshold judgment and trend analysis.

[0043] Step S600: configuring the digital feedback device based on the control plan library and the autonomous calibration rule;

[0044] In the embodiment of the present application, the digital feedback device is used to monitor and control the output in the system, and generates a feedback signal by comparing the actual output with the expected output to adjust the system state.

[0045] The control plan library contains response measures and strategies for different abnormal situations. Before configuring the digital feedback device, analyze the plan library to obtain the handling procedures and best practices for various abnormal situations.

[0046] Self-calibration rules are defined based on control deviations and guide the digital feedback controller to automatically adjust when deviations are detected. Determine appropriate self-calibration rules based on system characteristics and requirements. These rules can adjust parameters such as the amplitude, frequency, or phase of the feedback signal to achieve precise control of the system.

[0047] Next, configure the parameters of the digital feedback controller, including setting the sampling rate, resolution, and filtering method for the feedback signal. Next, integrate the autonomous calibration function into the digital feedback controller by writing or modifying the control algorithm. In the algorithm, the feedback signal logic is adjusted according to pre-set rules to ensure the stability and reliability of the calibration process.

[0048] After configuration is complete, the digital feedback controller is tested and verified. This includes testing its response speed and accuracy in a simulated environment or in a real system, as well as verifying the effectiveness of its self-calibration function. By continuously adjusting and optimizing parameters and rules, the digital feedback controller is ensured to achieve optimal performance in the actual application.

[0049] Through the above process, the configuration of the digital feedback device is completed.

[0050] Step S700: Based on the configured intelligent central control system and the digital feedback device, closed-loop control of the surface treatment of the molding engineering material is performed.

[0051] In the embodiment of the present application, the digital feedback device collects data in real time during the surface treatment process of the molding engineering material, such as surface temperature, processing speed, material thickness, etc. This data is transmitted to the intelligent central control system through the communication interface and displayed and monitored in real time.

[0052] After receiving data from the digital feedback device, the intelligent central control system performs real-time processing and analysis. Using pre-set algorithms and models, it calculates the deviation between the current processing status and the set target and assesses whether the processing effect meets expectations. Based on the results of data processing and analysis, the intelligent central control system generates corresponding control instructions.

[0053] If the current processing status deviates from the set target, control parameters are adjusted based on the magnitude and direction of the deviation, and instructions are sent to the surface treatment equipment to correct the deviation and optimize the treatment effect. Upon receiving the control instructions from the intelligent central control system, the surface treatment equipment performs the corresponding operation, such as adjusting the processing speed or changing the temperature setting. Simultaneously, the digital feedback device continues to collect data from the processing process in real time and feeds the new data back to the intelligent central control system.

[0054] The intelligent central control system processes and analyzes the new feedback data and generates control instructions again. This process repeats continuously, forming a closed-loop control.

[0055] Furthermore, step S300 in the method provided in the application embodiment further includes:

[0056] Reading the safety standard of the surface quality standard, determining a constraint coordinate system based on a harmful element threshold, wherein the constraint coordinate system takes the element multi-element group as a coordinate axis;

[0057] Supervise the training of the safety control branch using the constraint coordinate system as a baseline;

[0058] A lateral interaction channel is established between the parallel decision branches, and an external interaction channel is established between the security control branch and each parallel decision branch to generate the adaptive decision model.

[0059] In the embodiment of the present application, the safety standard of the surface quality standard is first read to obtain the regulations related to the content of harmful elements.

[0060] After reading the safety standards, we can identify which elements are considered hazardous and what the thresholds are for these elements. These thresholds are set based on a comprehensive consideration of human health, environmental safety, and material performance.

[0061] Before defining the constraint coordinate system, we need to define the concept of an element tuple. In mathematics and computer science, a tuple is a finite sequence where each element in a position can belong to a different set or have a different meaning. In this context, an element tuple will be used to represent the content or concentration of different harmful elements.

[0062] Based on the thresholds for harmful elements, a constraint coordinate system is constructed. Each axis of the constraint coordinate system represents a harmful element, and the values on the axes indicate the content or concentration of that element. The boundaries of the coordinate system are determined by the element thresholds, forming a multidimensional constraint space. This space defines the allowable content range of harmful elements during material surface treatment.

[0063] Next, the constrained coordinate system is used as a baseline for supervised training of the safety control branch. This branch ensures that the hazardous element content of the material during processing remains within safe limits. The safety control branch is trained using historical or simulated data, enabling it to predict and adjust processing parameters based on the current hazardous element content to achieve safety standards.

[0064] Parallel decision branches are independent decision-making units that run simultaneously and are responsible for different decision-making tasks. To enhance the coordination and information sharing between parallel decision branches, a lateral interaction channel is established.

[0065] When establishing a lateral interaction channel, first establish unified interaction protocols and standards to ensure that all parallel decision-making branches can exchange information according to the same rules. These protocols and standards include provisions for data formats, communication frequencies, and information encryption. Then, based on the interaction protocols, communication links are established between the parallel decision-making branches via a local area network, wide area network, or dedicated communication lines. Data sharing between the parallel decision-making branches is achieved through a data sharing platform or middleware technology.

[0066] When establishing external interaction channels between the security control branch and each parallel decision-making branch, analyze the interaction requirements between the two branches and determine the required interface types and number. These interfaces include data interfaces, control interfaces, and alarm interfaces. For each interface, design the corresponding interaction protocol and data flow format. This includes data transmission methods, processing procedures, and response mechanisms to ensure accurate information transmission and timely processing. Based on the designed interaction protocols and data flows, implement the external interaction channels between the security control branch and each parallel decision-making branch.

[0067] By establishing the above-mentioned interactive channel, the parallel decision-making branches and security control branches are integrated into a unified decision-making model to form an adaptive decision-making model.

[0068] Furthermore, the parallel decision branches correspond one-to-one to the mechanical level, the performance level, and the visual level, and the independent analysis within the level and the collaborative analysis between the levels are performed. The method further includes:

[0069] Based on the feature amplitude modulation and the feature correlation, branch mapping is performed based on the attribution level, a target decision branch is matched to make a processing decision, and a single processing solution is determined;

[0070] In combination with the lateral interaction channel, performing lateral interaction and mutual influence analysis based on the feature correlation to determine a set of compensation schemes;

[0071] Combined with the external interactive channel, with the constraint coordinate system as the limit and the global minimum as the optimal direction, the decision on weakening and precipitation of harmful elements is made to determine two groups of compensation schemes;

[0072] The single treatment solution is fitted with the one group of compensation solutions and the two groups of compensation solutions to determine the surface treatment solution.

[0073] In an embodiment of the present application, branch mapping is performed based on feature amplitude modulation and feature correlation. Specifically, each feature or feature combination is matched with a corresponding decision branch. Since the parallel decision branches correspond one-to-one to the mechanical level, performance level, and visual level, the appropriate decision branch is selected according to the level to which the feature belongs. For example, mechanical level features such as hardness, toughness, etc. will be mapped to the decision branch responsible for the mechanical level, performance level features such as wear resistance, corrosion resistance, etc. will be mapped to the decision branch responsible for the performance level, and visual level features such as color, gloss, etc. will be mapped to the decision branch responsible for the visual level.

[0074] After completing branch mapping, each decision branch independently performs intra-layer analysis. Specifically, each decision branch uses the corresponding algorithm and model to independently analyze and make decisions based on the set of features it is responsible for. Based on the results of the intra-layer analysis, each decision branch generates a specific processing solution. These solutions address the feature modulation and correlation at each layer, proposing specific processing measures and parameter settings.

[0075] Through the lateral interactive channel, the feature correlation analysis results are automatically shared with other decision branches to ensure that each branch can obtain the latest data and analysis results. Based on the shared data and feature correlation analysis results, mutual influence analysis is automatically performed to identify the potential influence relationship between different treatment measures and form an influence chain. According to the mutual influence analysis results, existing problems or conflict points are automatically identified, such as mechanical performance degradation caused by performance improvement. Based on the problem identification results, compensation strategies are automatically explored, such as adjusting treatment parameters. Finally, the analysis results and strategy recommendations of each branch are combined to generate a set of compensation plans.

[0076] When determining the two sets of compensation schemes, the constraint coordinate system provides clear restrictions on the element behavior and reactions during the precipitation process. These conditions include the type of precipitating elements, concentration range, precipitation rate, etc., which together constitute the boundary conditions of the precipitation process.

[0077] External interactive channels are used for real-time data sharing and interaction to ensure that various data of the precipitation process can be obtained in real time, such as concentration changes of precipitated elements, precipitation rate, temperature distribution, etc.

[0078] To find the global minimum—the minimum concentration or precipitation rate of harmful elements during the precipitation process—we conducted an in-depth analysis of the precipitation process based on the constraints of the constrained coordinate system and combined it with data provided by an external interactive channel. This in-depth analysis identified key factors influencing the precipitation process, such as precipitation temperature, precipitation time, and reactant ratio. These factors were then adjusted, and the effects of different operating conditions on the precipitation process were evaluated through simulation and other methods.

[0079] After many attempts and optimizations, the second set of compensation plans was determined.

[0080] Individual treatments modify and optimize the material surface based on specific physical or chemical principles. For example, electroplating coats the surface of a material with a thin metal film to improve its corrosion resistance and decorative properties, while electropolishing utilizes electrochemical principles to remove fine burrs and enhance brightness. Each individual treatment has its applicable scenarios and limitations, so the selection is based on specific needs. Next, the role of the first and second compensation solutions is determined. Compensation solutions are designed to address potential issues or shortcomings that may arise with individual treatments during actual application.

[0081] When combining individual treatment solutions with compensation solutions, they should complement each other and enhance the overall treatment effect. For example, if the individual treatment is electroplating, the compensation solution should address potential issues such as uneven coating or weak adhesion during the electroplating process by providing optimization measures. When determining surface treatment solutions, prioritize those with minimal pollution based on their environmental impact to achieve green and sustainable surface treatment.

[0082] Finally, the surface treatment plan is determined by comprehensively evaluating various factors.

[0083] Furthermore, based on the feature correlation, lateral interaction and mutual influence analysis is performed, and the method further includes:

[0084] Traversing the feature correlation, performing time-series-based upper and lower processing coverage analysis, and identifying failed processing nodes;

[0085] The failure processing nodes are traversed, compensation adjustments are made to the initial decision solutions of the parallel decision branches, and the set of compensation solutions is determined.

[0086] In the embodiment of the present application, the correlation between the various features is first comprehensively analyzed, including evaluating the direct and indirect effects between the features to determine how these effects change as the processing flow progresses.

[0087] Next, we conduct a timing-based coverage analysis of both upper and lower processing steps. This involves a detailed timing analysis of each step in the process flow to determine their sequence and dependencies. We also analyze the coverage of lower-level processing steps by the upper-level processing steps to determine whether the upper-level processing can effectively correct or improve potential issues in the lower-level processing.

[0088] In this process, if a subsequent step covers the defects of a previous step, for example, a subsequent step can correct a minor defect generated in the previous step, then the accuracy requirement of the previous step can be appropriately lowered, simplifying the processing flow and reducing ineffective work.

[0089] Based on the above analysis, nodes that lead to processing failure are identified. These failure nodes are caused by unreasonable arrangement between processing steps, feature misjudgment, or conflicting processing goals.

[0090] After identifying the failed processing nodes, we traverse them one by one, analyzing the cause and impact of each failed node, as well as the extent of its impact on the entire processing flow.

[0091] Based on the analysis of failed processing nodes, compensation adjustments are made to the initial decision plans of the parallel decision branches. Compensation adjustments include adjusting processing accuracy, simplifying processing steps, and optimizing processing sequence.

[0092] Adjust the processing accuracy to appropriately reduce the accuracy required for the parts in the previous steps to reduce unnecessary resources and time consumption.

[0093] Simplifying processing steps means removing unnecessary processing steps to make the processing flow more concise and efficient.

[0094] Optimizing the processing sequence is to rearrange the order of processing steps according to the timing relationship and the coverage of upper and lower level processing to improve the processing effect.

[0095] After the above compensation adjustments, a set of compensation plans are determined.

[0096] Furthermore, step S400 in the method provided in the application embodiment further includes:

[0097] Interactive equipment control system, the equipment control system is an independent control system for each processing equipment;

[0098] Determining equipment coordination features based on the surface treatment solution, wherein the equipment coordination features at least include a timing coordination dimension, an order coordination dimension, and a coordination collision dimension;

[0099] Based on the equipment collaboration characteristics and combined with key collaboration nodes, the equipment control system is integrated and configured to determine the system collaboration strategy.

[0100] In the embodiment of the present application, each processing equipment, such as a grinding machine, a burr removal machine, a coating equipment, a laser etching equipment, etc., has an independent control system. The interactive equipment control system is used to achieve coordinated operation and control of different processing equipment.

[0101] Device collaboration refers to the interaction between two or more relatively independent devices to jointly complete a command or task. Device collaboration characteristics include at least timing collaboration, sequence collaboration, and collision collaboration. Device collaboration characteristics are determined by analyzing surface treatment solutions.

[0102] Timing coordination focuses on the timing coordination of equipment during the surface treatment process. Surface treatment involves multiple steps, such as pretreatment, coating, and curing, each of which must be completed within a specific time window. Timing coordination requires precise coordination between equipment to ensure that each step is carried out in a predetermined time sequence, avoiding delays or overlaps. Through precise timing control, the efficiency and consistency of surface treatment can be improved.

[0103] Sequential coordination concerns the order in which equipment is used within the surface treatment process. Different surface treatment steps require different equipment, and the order in which these equipment are operated is crucial to the final treatment outcome. Sequential coordination requires that equipment be able to operate in a predetermined sequence, ensuring that each step is performed at the correct time and avoiding sequence errors or omissions. This rational sequence ensures a smooth surface treatment process and improves treatment quality.

[0104] The collaborative collision dimension focuses on potential conflicts or collisions that may occur during collaborative operation. Due to spatial overlap or interaction between devices, the lack of appropriate coordination mechanisms can lead to collisions or interference. This dimension requires devices to possess obstacle avoidance and collision avoidance capabilities to ensure safe and stable operation during collaborative operation.

[0105] Key collaboration nodes are crucial links in equipment collaboration and significantly impact the overall collaborative effect. These nodes include specific operational steps, data transmission points, or device interfaces. After identifying these nodes, the equipment control systems are integrated and configured. This process involves connecting and integrating the control systems of each device to enable information sharing and command synchronization. During configuration, the control system must be configured to support collaborative operation based on the device collaboration characteristics and the requirements of these nodes.

[0106] When configuring an integrated device control system based on device collaboration characteristics and key collaboration nodes, the control systems of each device are connected and integrated to enable information sharing and command synchronization. During the configuration process, the device collaboration characteristics and key collaboration nodes are considered to ensure that the control system can support collaborative operations between devices.

[0107] Finally, a system collaboration strategy is formulated based on the equipment collaboration characteristics and the actual situation of key collaboration nodes. The system collaboration strategy includes the operation sequence, parameter adjustment, fault handling, etc. between devices.

[0108] Furthermore, the method further comprises:

[0109] Identify the key coordination nodes, determine the surface treatment stages, and determine the stage-by-stage surface treatment quality;

[0110] Map the surface treatment stage to the staged surface treatment quality, and perform staged quality control and resumption management of the surface treatment.

[0111] In the embodiments of the present application, identifying key coordination nodes is a comprehensive process. Key coordination nodes involve interactions between multiple devices or systems, or steps that have a significant impact on the final surface treatment quality. When identifying key coordination nodes, the surface treatment solution is analyzed to determine the logical relationship, timing relationship, and potential mutual influence between each step. At the same time, based on factors such as equipment performance, material characteristics, and process requirements, it is determined which steps or links are key coordination nodes.

[0112] Once the key synergy points are identified, the next step is to define the surface treatment stages. These stages are categorized based on the logical sequence of the treatment process and the location of the key synergy points. Each stage contains a specific set of processing steps and operations designed to achieve a specific surface treatment result.

[0113] While determining the surface treatment stages, we further determine the stage-by-stage surface treatment quality of each stage, i.e., quantitatively and qualitatively evaluate the treatment results. Evaluation methods include various means such as visual inspection, performance testing, and chemical analysis. Through these evaluation methods, the stage-by-stage surface treatment quality is determined.

[0114] When mapping each surface treatment stage to its corresponding staged surface treatment quality, this can be achieved by establishing a data table, chart, or visual interface. Through the mapping relationship, the quality level of each stage, as well as the quality differences and change trends between different stages, can be clearly expressed.

[0115] After mapping is complete, stage-by-stage quality control of the surface treatment is performed. This involves real-time monitoring and evaluation of the treatment results at each stage to ensure that quality consistently exceeds the predetermined standards. If quality issues or deviations from expectations are detected at any stage, adjustments and improvements are made by adjusting treatment parameters, replacing equipment, or refining the process.

[0116] At the same time, work resumption management is carried out. When production is interrupted due to some reasons, such as equipment failure or material problems, an effective work resumption plan is formulated to ensure that production can quickly return to normal.

[0117] Furthermore, the method further comprises:

[0118] Reading feedback control records of a predetermined period to mine pre-adjustment control points, wherein the pre-adjustment control points are determined based on over-limit control points that meet the deviation tolerance interval and frequent control points that meet the frequency threshold;

[0119] Based on the pre-adjustment control point, tracing is performed based on the feedback control characteristics to determine the compensation strategy;

[0120] Based on the compensation strategy, the surface treatment plan is adjusted and responded.

[0121] In an embodiment of the present application, feedback control records of the surface treatment process within a previous period of time, such as a week or a month, are retrieved and extracted through a database or file storage system. These records contain control parameters, timestamps, and possible effect evaluations.

[0122] When mining pre-adjusted control points, deviation tolerance interval analysis and frequency threshold analysis are performed. Deviation tolerance interval analysis uses threshold settings and parameter comparisons to statistically analyze the control parameters in the feedback control records to identify control points that exceed the preset tolerance interval. Frequency threshold analysis counts the frequency of each control point to determine which points meet the set frequency threshold.

[0123] When tracing back to the source of feedback control features based on pre-set control points, data analysis algorithms are used to process and analyze the collected data. By comparing changes in equipment parameters before and after the pre-set control point, parameters closely related to the pre-set control point are identified. Furthermore, correlations between parameters are analyzed to identify possible interactions or influences.

[0124] After identifying the parameters closely related to the pre-set control point, causal reasoning is further performed. Based on process knowledge, historical data, and expert rules, it is inferred which equipment parameter adjustments may lead to the occurrence of the pre-set control point. This reasoning process can be rule-based or machine learning-based. For example, when using rule-based causal reasoning, a rule base is first established, containing a series of rules related to the surface treatment process. These rules are based on process knowledge, expert experience, historical data, and actual operations. Rules include: excessive spray pressure leads to uneven coating thickness; excessive baking temperature or time leads to cracks in the coating. When a pre-set control point occurs, relevant equipment parameter data is automatically collected. This data is then matched against the rules in the rule base. If the data meets the conditions of a rule, the rule is triggered. Triggered rules involve multiple parameters and conditions, so causal chain analysis is performed to identify the complete chain of causal relationships from parameter adjustment to the occurrence of the pre-set control point. By analyzing these chains, the parameter adjustments that are the key factors leading to the occurrence of the pre-set control point are determined. Through causal chain analysis, the equipment parameters that have the greatest impact on the pre-set control point are screened. These parameters are associated with multiple rules in the rule base and are frequently triggered during the data matching process.

[0125] Based on this analysis, a compensation strategy is developed, which involves adjusting the equipment parameters identified as critical factors. The direction and magnitude of the adjustment are determined based on recommendations in the rule base or an optimization algorithm based on historical data.

[0126] When adjusting the surface treatment solution based on the compensation strategy, the compensation strategy is first analyzed to determine adjustments to equipment parameters. Based on the analysis of the compensation strategy, necessary adjustments are made to the original surface treatment solution. These adjustments include equipment parameter adjustments. Based on the compensation strategy, relevant equipment parameter settings, such as spray pressure, baking temperature, and conveyor speed, are automatically adjusted. In addition to adjusting equipment parameters, operational processes are optimized, such as adjusting the process sequence and adding or removing steps.

[0127] Finally, the adjusted surface treatment scheme is applied in the actual production process.

[0128] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0129] The present application reads the material surface characteristics and surface quality standards of molding engineering materials, wherein the molding engineering materials are plastic materials, and the surface quality standards include mechanical level, performance level and visual level; measures the characteristic amplitude modulation of the material surface characteristics and surface quality standards, and identifies the characteristic correlation; based on the characteristic amplitude modulation and the said characteristic correlation, combined with the adaptive decision model, performs independent analysis within the layer and collaborative analysis between layers, and makes the best decision to determine the surface treatment plan, and the adaptive decision model is a three-layer fully connected parallel structure with lateral interaction; based on the surface treatment plan, configures the intelligent central control system, wherein the intelligent central control system and the equipment control system have interactive communication; uses the abnormal processing feature as an index to perform industrial big data retrieval and construct a control plan library, and sets autonomous calibration rules based on control deviation, and the control deviation is determined based on system error and exogenous error; configures the digital feedback device based on the control plan library and the said autonomous calibration rules; based on the configured intelligent central control system and the digital feedback device, performs closed-loop control of the surface treatment of molding engineering materials. The present invention solves the technical problem that the existing technology is difficult to control the surface quality of materials in real time and accurately. By real-time monitoring of key parameters of the material surface, such as roughness and glossiness, and using an adaptive decision-making model for precise control, the surface treatment efficiency and quality technical effects are improved.

[0130] Example 2

[0131] Based on the same inventive concept as the surface treatment method of a molding engineering material in the above embodiment, Figure 2 As shown, the present application provides a surface treatment system for forming engineering materials. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0132] A molding engineering material information reading module 11 is configured to read the surface characteristics and surface quality standards of the molding engineering material. The molding engineering material is a plastic material, and the surface quality standards include mechanical, performance, and visual aspects.

[0133] a characteristic amplitude modulation measurement module 12, which measures the characteristic amplitude modulation of the material surface feature and the surface quality standard and identifies feature correlation;

[0134] A surface treatment solution determination module 13, which performs intra-layer independent analysis and inter-layer collaborative analysis based on the feature amplitude modulation and the feature correlation in combination with an adaptive decision model to determine the optimal surface treatment solution. The adaptive decision model is a three-layer fully connected parallel structure with lateral interaction.

[0135] An intelligent central control system configuration module 14, which configures the intelligent central control system based on the surface treatment solution, wherein the intelligent central control system interacts with the equipment control system;

[0136] A control plan library construction module 15, which uses the abnormal processing characteristics as an index to search industrial big data and construct a control plan library, and sets an autonomous calibration rule based on the control deviation, wherein the control deviation is determined based on the system error and the exogenous error;

[0137] A digital feedback device configuration module 16, configured to configure a digital feedback device based on the control plan library and the autonomous calibration rule;

[0138] The closed-loop control module 17 performs closed-loop control of the surface treatment of the molding engineering material based on the configured intelligent central control system and the digital feedback device.

[0139] Furthermore, the system is also used to implement the following functions:

[0140] Reading the safety standard of the surface quality standard, determining a constraint coordinate system based on a harmful element threshold, wherein the constraint coordinate system takes the element multi-element group as a coordinate axis;

[0141] Supervise the training of the safety control branch using the constraint coordinate system as a baseline;

[0142] A lateral interaction channel is established between the parallel decision branches, and an external interaction channel is established between the security control branch and each parallel decision branch to generate the adaptive decision model.

[0143] Furthermore, the system is also used to implement the following functions:

[0144] Based on the feature amplitude modulation and the feature correlation, branch mapping is performed based on the attribution level, a target decision branch is matched to make a processing decision, and a single processing solution is determined;

[0145] In combination with the lateral interaction channel, performing lateral interaction and mutual influence analysis based on the feature correlation to determine a set of compensation schemes;

[0146] Combined with the external interactive channel, with the constraint coordinate system as the limit and the global minimum as the optimal direction, the decision on weakening and precipitation of harmful elements is made to determine two groups of compensation schemes;

[0147] The single treatment solution is fitted with the one group of compensation solutions and the two groups of compensation solutions to determine the surface treatment solution.

[0148] Furthermore, the system is also used to implement the following functions:

[0149] Traversing the feature correlation, performing time-series-based upper and lower processing coverage analysis, and identifying failed processing nodes;

[0150] The failure processing nodes are traversed, compensation adjustments are made to the initial decision solutions of the parallel decision branches, and the set of compensation solutions is determined.

[0151] Furthermore, the system is also used to implement the following functions:

[0152] Interactive equipment control system, the equipment control system is an independent control system for each processing equipment;

[0153] Determining equipment coordination features based on the surface treatment solution, wherein the equipment coordination features at least include a timing coordination dimension, an order coordination dimension, and a coordination collision dimension;

[0154] Based on the equipment collaboration characteristics and combined with key collaboration nodes, the equipment control system is integrated and configured to determine the system collaboration strategy.

[0155] Furthermore, the system is also used to implement the following functions:

[0156] Identify the key coordination nodes, determine the surface treatment stages, and determine the stage-by-stage surface treatment quality;

[0157] Map the surface treatment stage to the staged surface treatment quality, and perform staged quality control and resumption management of the surface treatment.

[0158] Furthermore, the system is also used to implement the following functions:

[0159] Reading feedback control records of a predetermined period to mine pre-adjustment control points, wherein the pre-adjustment control points are determined based on over-limit control points that meet the deviation tolerance interval and frequent control points that meet the frequency threshold;

[0160] Based on the pre-adjustment control point, tracing is performed based on the feedback control characteristics to determine the compensation strategy;

[0161] Based on the compensation strategy, the surface treatment plan is adjusted and responded.

[0162] It should be noted that the above-mentioned order of the embodiments of the present application is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. The above description is of specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0163] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0164] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A surface treatment method for forming engineering materials, characterized in that: The method comprises: Reading the material surface characteristics and surface quality standards of the molding engineering material, wherein the molding engineering material is a plastic material, and the surface quality standards include mechanical level, performance level and appearance level; measuring characteristic amplitude modulation of the surface characteristics of the material and the surface quality standard, and identifying characteristic correlation; Based on the feature amplitude modulation and the feature correlation, combined with an adaptive decision model, independent analysis within the layer and collaborative analysis between layers are performed to determine the optimal decision for the surface treatment solution. The adaptive decision model is a three-layer fully connected parallel structure with lateral interaction. Based on the surface treatment solution, an intelligent central control system is configured, wherein the intelligent central control system interacts and communicates with the equipment control system; Using abnormal processing characteristics as an index, industrial big data is searched and a control plan library is constructed. Autonomous calibration rules based on control deviations are set. The control deviations are determined based on system errors and exogenous errors. Configuring a digital feedback device based on the control plan library and the autonomous calibration rules; Based on the configured intelligent central control system and the digital feedback device, closed-loop control of the surface treatment of the molding engineering material is performed.

2. The method according to claim 1, wherein Build an adaptive decision model, including: Reading the safety standard of the surface quality standard, determining a constraint coordinate system based on a harmful element threshold, wherein the constraint coordinate system takes the element multi-element group as a coordinate axis; Supervise the training of the safety control branch using the constraint coordinate system as a baseline; A lateral interaction channel is established between the parallel decision branches, and an external interaction channel is established between the security control branch and each parallel decision branch to generate the adaptive decision model.

3. The method according to claim 2, wherein The parallel decision branches correspond one-to-one to the mechanical level, the performance level, and the visual level. The independent analysis within each level and the collaborative analysis between each level include: Based on the feature amplitude modulation and the feature correlation, branch mapping is performed based on the attribution level, a target decision branch is matched to make a processing decision, and a single processing solution is determined; In combination with the lateral interaction channel, performing lateral interaction and mutual influence analysis based on the feature correlation to determine a set of compensation schemes; Combined with the external interactive channel, with the constraint coordinate system as the limit and the global minimum as the optimal direction, the decision on weakening and precipitation of harmful elements is made to determine two groups of compensation schemes; The single treatment solution is fitted with the one group of compensation solutions and the two groups of compensation solutions to determine the surface treatment solution.

4. The method according to claim 3, wherein Perform lateral interaction and mutual influence analysis based on the feature correlation, including: Traversing the feature correlation, performing time-series-based upper and lower processing coverage analysis, and identifying failed processing nodes; The failure processing nodes are traversed, compensation adjustments are made to the initial decision solutions of the parallel decision branches, and the set of compensation solutions is determined.

5. The method according to claim 1, wherein The intelligent central control system and the equipment control system have interactive communication, including: Interactive equipment control system, the equipment control system is an independent control system for each processing equipment; Determining equipment coordination features based on the surface treatment solution, wherein the equipment coordination features at least include a timing coordination dimension, an order coordination dimension, and a coordination collision dimension; Based on the equipment collaboration characteristics and combined with key collaboration nodes, the equipment control system is integrated and configured to determine the system collaboration strategy.

6. The method according to claim 5, wherein Identify the key coordination nodes, determine the surface treatment stages, and determine the stage-by-stage surface treatment quality; Map the surface treatment stage to the staged surface treatment quality, and perform staged quality control and resumption management of the surface treatment.

7. The method according to claim 1, wherein The method further comprises: Reading feedback control records of a predetermined period to mine pre-adjustment control points, wherein the pre-adjustment control points are determined based on over-limit control points that meet the deviation tolerance interval and frequent control points that meet the frequency threshold; Based on the pre-adjustment control point, tracing is performed based on the feedback control characteristics to determine the compensation strategy; Based on the compensation strategy, the surface treatment plan is adjusted and responded.

8. A surface treatment system for forming engineering materials, characterized in that: The system comprises: A molding engineering material information reading module, which reads the material surface characteristics and surface quality standards of the molding engineering material. The molding engineering material is a plastic material, and the surface quality standards include mechanical aspects, performance aspects, and visual aspects. a characteristic amplitude modulation measurement module, the characteristic amplitude modulation measurement module measuring the characteristic amplitude modulation of the surface feature of the material and the surface quality standard, and identifying the characteristic correlation; a surface treatment solution determination module, which performs intra-layer independent analysis and inter-layer collaborative analysis based on the feature amplitude modulation and the feature correlation in combination with an adaptive decision model to determine the surface treatment solution through optimal decision-making. The adaptive decision model is a three-layer fully connected parallel structure with lateral interaction; An intelligent central control system configuration module, configured to configure the intelligent central control system based on the surface treatment solution, wherein the intelligent central control system interacts and communicates with the equipment control system; A control plan library construction module, which uses abnormal processing features as an index to search industrial big data and construct a control plan library, and sets autonomous calibration rules based on control deviations, where the control deviations are determined based on system errors and exogenous errors; a digital feedback device configuration module, configured to configure a digital feedback device based on the control plan library and the autonomous calibration rule; A closed-loop control module performs closed-loop control of the surface treatment of the molding engineering material based on the configured intelligent central control system and the digital feedback device.

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