Surface treatment method and system for forming engineering material

Abstract

The invention discloses a surface treatment method and system for a forming engineering material, and relates to the technical field of material surface treatment.The surface treatment method comprises the steps that material surface characteristics and surface quality standards of the forming engineering material are read; measuring the characteristic amplitude modulation of the material surface characteristics and the surface quality standard, and identifying the characteristic correlation; on the basis of feature amplitude modulation and feature correlation, in combination with an adaptive decision model, performing intra-layer independent analysis and inter-layer collaborative analysis, and optimizing a decision to determine a surface treatment scheme; configuring the intelligent central control system; with the exception handling features as indexes, industrial big data retrieval is performed, a regulation and control plan library is constructed, and an autonomous calibration rule based on control deviation is set; configuring the digital feedback device; and performing closed loop control on the surface treatment of the forming engineering material. The technical problem that in the prior art, real-time and accurate control over the material surface quality is difficult is solved, and the technical effect of improving the surface treatment efficiency and quality is achieved.

Description

Technical Field

[0001] The 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 molding engineering materials in various fields is becoming more and more extensive, and the requirements for material surface quality are becoming more and more stringent. Especially in high-end manufacturing fields such as automobiles, electronics, and aviation, the surface quality of molding engineering materials is directly related to the performance, life and market competitiveness of the products. Therefore, it is particularly important to develop efficient and precise surface treatment methods and systems for molding engineering materials.

[0003] However, traditional surface treatment methods for molding engineering materials often have problems such as low control accuracy, long processing cycle, and poor stability. These methods usually rely on manual experience and offline detection, and it is difficult to achieve real-time and accurate control of material surface quality. At the same time, with the increasing complexity and diversification of production processes, traditional surface treatment methods have been unable to meet the needs 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 it is difficult to carry out real-time and precise control of the surface quality of materials in the prior art.

[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: Read 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; measure the characteristic amplitude modulation of the material surface characteristics and the surface quality standard, and identify the characteristic correlation; based on the characteristic amplitude modulation and the characteristic correlation, combined with the adaptive decision model, perform independent analysis within the layer and collaborative analysis between layers, and make an optimal decision to determine the surface treatment plan, wherein the adaptive decision model is a three-layer fully connected parallel structure with lateral interaction; based on the surface treatment plan, configure the intelligent central control system, wherein the intelligent central control system and the equipment control system have interactive communication; use the abnormal processing feature as an index to perform industrial big data retrieval and build a control plan library, and set an autonomous calibration rule based on control deviation, wherein the control deviation is determined based on system error and exogenous error; based on the control plan library and the autonomous calibration rule, configure the digital feedback device; based on the configured intelligent central control system and the digital feedback device, perform closed-loop control of the surface treatment of the molding engineering material.

[0007] A second aspect of the present application provides a surface treatment system for a molded engineering material, the system comprising: A molding engineering material information reading module, the molding engineering material information reading module 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 appearance level; a characteristic amplitude modulation measurement module, the characteristic amplitude modulation measurement module measures the characteristic amplitude modulation of the material surface characteristics and the surface quality standard, and identifies the characteristic correlation; a surface treatment scheme determination module, the surface treatment scheme determination module is based on the characteristic amplitude modulation and the characteristic correlation, combined with an adaptive decision model, to perform independent analysis within the layer and collaborative analysis between layers, and determine the surface treatment scheme by 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; a control plan library construction module, wherein the control plan library construction module uses the abnormal processing feature 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, wherein the control deviation is determined based on system error and exogenous error; a digital feedback device configuration module, wherein the digital feedback device configuration module configures the digital feedback device based on the control plan library and the autonomous calibration rule; a closed-loop control module, wherein 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.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: 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 appearance level; measures the characteristic amplitude modulation of the material surface characteristics and the surface quality standards, and identifies the characteristic correlation; based on the characteristic amplitude modulation and the characteristic correlation, combined with the adaptive decision model, performs independent analysis within the layer and collaborative analysis between layers, and determines the surface treatment plan by optimal decision-making, 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 build 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 autonomous calibration rules; performs closed-loop control of the surface treatment of molding engineering materials based on the configured intelligent central control system and the digital feedback device. The present invention solves the technical problem that it is difficult to carry out real-time and precise control of material surface quality in the prior art. 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] 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.

[0010] Figure 1 A schematic flow chart of a surface treatment method for a molding engineering material provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a surface treatment system for a molding engineering material provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: molding engineering material information reading module 11, characteristic amplitude modulation measurement 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

[0012] The present application provides a surface treatment method and system for molding engineering materials, aiming to solve the technical problem that it is difficult for the existing technology to perform real-time and precise control of the surface quality of materials. 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.

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] 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 of their variations 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 that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0015] Embodiment 1 like Figure 1 As shown, the present application provides a surface treatment method for a molding engineering material, the method comprising: Step S100: 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; In the embodiment of the present application, the molding engineering material is a plastic material. Plastic materials have many different types and properties.

[0016] For plastic materials, when reading their surface features, their inherent physical and chemical properties are obtained, including but not limited to visually observable features such as gloss, roughness, texture, and microstructure and chemical composition detected by professional instruments.

[0017] Surface quality standards include three dimensions: mechanical, performance, and visual. In terms of mechanical aspects, there are indicators related to mechanical properties, such as hardness, wear resistance, and impact resistance; in terms of performance, there are physical and chemical properties such as weather resistance, corrosion resistance, and insulation; in terms of visual aspects, there are standards for appearance and visual effects such as color, gloss, and texture.

[0018] Step S200: measuring the characteristic amplitude modulation of the surface characteristics of the material and the surface quality standard, and identifying the characteristic correlation; 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, using a microscope to observe surface roughness, etc.

[0019] 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.

[0020] The characteristic amplitude modulation refers to the difference between the actual surface feature data of the material and the set value of the surface quality standard. It is achieved by calculating the deviation value, coefficient of variation or similarity index of each feature. For example, for the gloss feature, the difference or ratio between the actual gloss value and the standard gloss value is calculated to obtain the characteristic amplitude modulation of the gloss. Similarly, similar measurements are performed for other surface features.

[0021] After measuring the feature amplitude modulation, the correlation between the features is further analyzed. Feature correlation refers to the degree of mutual influence or association between different surface features. The correlation between features is calculated using statistical methods such as correlation coefficient, covariance matrix or principal component analysis.

[0022] The calculated correlation coefficient or analysis results can be used to identify which features have a strong correlation and which features are relatively independent.

[0023] Step S300: Based on the feature amplitude modulation and the feature correlation, combined with an adaptive decision model, an intra-layer independent analysis and an inter-layer collaborative analysis are performed to determine a surface treatment solution by optimal decision making. The adaptive decision model is a three-layer fully connected parallel structure with lateral interaction. In the embodiments of the present application, the characteristic modulation and characteristic correlation provide quantitative information about the difference between the material surface and the quality standard. This information is the basis for the decision model to perform independent analysis within the layer and collaborative analysis between layers. Independent analysis within the layer identifies the key influencing factors and potential optimization space within each layer based on the characteristic modulation and correlation within each layer, such as the mechanical level, performance level, and visual level. Collaborative analysis between layers ensures that the final surface treatment solution meets or exceeds the quality standard in all aspects based on the mutual influence and correlation between different levels.

[0024] Next, the adaptive decision model is introduced. The model is a three-layer fully connected parallel structure with lateral interactions. This structure enables the model to process information at multiple levels at the same time and interact and integrate information between different levels. Specifically, each layer of the model corresponds to a specific analysis level, namely mechanical, performance, and perception. The fully connected structure ensures that each layer can obtain the analysis results and feedback information of other layers. Lateral interactions allow the analysis results between different levels to influence each other, thereby reaching a more comprehensive and accurate decision.

[0025] During the model operation, the feature amplitude modulation and feature correlation data are first input, and then the model begins to perform independent analysis within the layer. Each layer calculates the optimal treatment solution for that layer based on its own characteristics and standards. Then, the model enters the inter-layer collaborative analysis stage. In this stage, the analysis results of different layers will be integrated and coordinated through lateral interactions to ensure that the final surface treatment solution can achieve balance and optimization in all aspects. Finally, the surface treatment solution determined by the optimal decision is output.

[0026] Step S400: configuring 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; In the embodiment of the present application, the intelligent central control system is configured accordingly according to the specific requirements of the surface treatment scheme, including setting various parameters, such as treatment time, temperature, pressure, etc., and selecting corresponding process steps and operation modes.

[0027] During the configuration process, the intelligent central control system interacts with the equipment control system. The equipment control system is responsible for directly controlling and processing the equipment, receiving instructions from the intelligent central control system, and controlling the operation of the equipment. The intelligent central control system communicates with the equipment control system in real time by sending instructions and receiving feedback.

[0028] Step S500: using the abnormal processing feature as an index, performing industrial big data retrieval and building a control plan library, and setting an autonomous calibration rule based on a control deviation, wherein the control deviation is determined based on a system error and an exogenous error; In the embodiment of the present application, the abnormal processing characteristics include various abnormal conditions that occur during the surface treatment process, such as temperature fluctuations, pressure abnormalities, equipment failures, etc. These characteristic data are collected and stored in a large database. When similar abnormal conditions occur in the actual production process, the abnormal processing characteristics are used as indexes to search in the large database to find similar cases and solutions.

[0029] Then, a control plan library is built based on the search results. The control plan library is a database that stores preset solutions for different abnormal situations. Through big data retrieval, the plan that matches the current abnormal situation is found, so that corresponding measures can be taken quickly. These plans include adjusting process parameters, replacing equipment, changing operating procedures, etc.

[0030] The control deviation is determined based on the system error and the external error. The system error is caused by the accuracy limitation or aging of the equipment itself, and the external error comes from the mutual influence between the equipment and the external environmental factors. The mutual influence between the equipment includes vibration, temperature, etc. The external environmental factors include humidity, temperature change, etc.

[0031] Based on these error data, autonomous calibration rules are set. Autonomous calibration rules can be formulated based on threshold judgment, trend analysis and other methods.

[0032] Step S600: configuring the digital feedback device based on the control plan library and the autonomous calibration rule; 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.

[0033] The control plan library contains countermeasures and strategies for different abnormal situations. Before configuring the digital feedback device, analyze the plan library to obtain the processing procedures and best practices for various abnormal situations.

[0034] The autonomous calibration rules are set based on the control deviation and are used to guide the digital feedback device to automatically adjust when the deviation is detected. According to the system characteristics and requirements, the appropriate autonomous calibration rules are determined. These rules include adjusting the amplitude, frequency or phase of the feedback signal to achieve precise control of the system.

[0035] Then, we start to configure the parameters of the digital feedback device, including setting the sampling rate, resolution, filtering method, etc. of the feedback signal. Next, we integrate the autonomous calibration function into the digital feedback device by writing or modifying the control algorithm. In the algorithm, the logic of the feedback signal is adjusted according to the preset rules to ensure the stability and reliability of the calibration process.

[0036] After the configuration is completed, the digital feedback device is tested and verified. This includes testing its response speed and accuracy in a simulated environment or in an actual system, as well as verifying the effectiveness of the self-calibration function. By continuously adjusting and optimizing parameters and rules, the digital feedback device is ensured to perform at its best in actual applications.

[0037] Through the above process, the configuration of the digital feedback device is completed.

[0038] Step S700: Based on the configured intelligent central control system and the digital feedback device, a closed-loop control of the surface treatment of the molding engineering material is performed.

[0039] 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. These data are transmitted to the intelligent central control system through the communication interface and displayed and monitored in real time.

[0040] After receiving the data transmitted by the digital feedback device, the intelligent central control system performs real-time processing and analysis, uses the preset algorithms and models to calculate the deviation between the current processing state and the set target, and evaluates whether the processing effect meets expectations. Based on the results of data processing and analysis, the intelligent central control system generates corresponding control instructions.

[0041] If the current processing state deviates from the set target, the control parameters are adjusted according to the deviation size and direction, and instructions are sent to the surface treatment equipment to correct the deviation and optimize the processing effect. After receiving the control instructions sent by the intelligent central control system, the surface treatment equipment performs corresponding operations, such as adjusting the processing speed, changing the temperature setting, etc. At the same time, the digital feedback device continues to collect data in the processing process in real time and feeds back the new data to the intelligent central control system.

[0042] The intelligent central control system processes, analyzes and generates control instructions again based on the new feedback data. This process is repeated continuously to form a closed-loop control.

[0043] Furthermore, step S300 in the method provided in the application embodiment also includes: 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; Taking the constraint coordinate system as a baseline, supervise the training of the safety control branch; 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.

[0044] 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.

[0045] After reading the safety standards, we can identify which elements are considered harmful and what the thresholds are for these elements. These thresholds are set based on comprehensive considerations of human health, environmental safety, and material performance.

[0046] Before determining the constraint coordinate system, the concept of element tuples is determined. In mathematics and computer science, a tuple is a finite sequence in which the elements at each position can belong to different sets or have different meanings. In this context, element tuples will be used to represent the content or concentration of different harmful elements.

[0047] Based on the threshold of harmful elements, a constraint coordinate system is constructed. Each axis of the constraint coordinate system represents a harmful element, and the value on the axis represents the content or concentration of the element. The boundary of the coordinate system is determined by the threshold of the element, forming a multi-dimensional constraint space. This space limits the allowable content range of harmful elements in the material surface treatment process.

[0048] Next, the constraint coordinate system is used as a baseline to supervise the training of the safety control branch. The safety control branch is used to ensure that the harmful element content of the material during the processing is always kept within the safe range. The safety control branch is trained using historical data or simulation data so that it can predict and adjust the processing parameters according to the current harmful element content to meet the safety standards.

[0049] Parallel decision branches are decision units that run simultaneously and independently of each other, and each of them is responsible for different decision tasks. In order to enhance the synergy and information sharing between parallel decision branches, a lateral interaction channel is established.

[0050] When establishing a lateral interaction channel, first formulate a unified interaction protocol and standard to ensure that each parallel decision-making branch can exchange information according to the same rules. The interaction protocol and standard include provisions on data format, communication frequency, information encryption, etc. Then, according to the interaction protocol, establish a communication link between each parallel decision-making branch through a local area network, a wide area network or a dedicated communication line. Through a data sharing platform or middleware technology, data sharing between each parallel decision-making branch is achieved.

[0051] When establishing the external interactive channel between the security control branch and each parallel decision-making branch, analyze the interactive requirements between the security control branch and each parallel decision-making branch, and determine the required interface type and quantity. These interfaces include data interface, control interface, and alarm interface. For each interface, design the corresponding interactive protocol and data stream format. Including data transmission mode, processing flow, and response mechanism, etc., to ensure accurate transmission and timely processing of information. According to the designed interactive protocol and data stream, realize the external interactive channel between the security control branch and each parallel decision-making branch.

[0052] By establishing the above-mentioned interactive channel, various parallel decision-making branches and security control branches are integrated into a unified decision-making model to form an adaptive decision-making model.

[0053] Furthermore, the parallel decision branches correspond to the mechanical level, the performance level and the perception level one by one, and the independent analysis within the level and the collaborative analysis between the levels are performed. The method further includes: 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, a lateral interaction and mutual influence analysis is performed 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 scheme is fitted with the one group of compensation schemes and the two groups of compensation schemes to determine the surface treatment scheme.

[0054] In an embodiment of the present application, branch mapping is performed based on feature 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, the performance level, and the perception 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 perception level features such as color, gloss, etc. will be mapped to the decision branch responsible for the perception level.

[0055] After completing the branch mapping, each decision branch independently performs intra-layer analysis. That is, each decision branch uses the corresponding algorithm and model to perform independent analysis and decision-making based on the feature set it is responsible for. Based on the results of the intra-layer analysis, each decision branch generates a single processing plan. These plans propose specific processing measures and parameter settings for the feature modulation and correlation at each level.

[0056] 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. According to the problem identification results, compensation strategies are automatically explored, such as adjusting processing parameters. Finally, the analysis results and strategy recommendations of each branch are combined to generate a set of compensation plans. When determining the two sets of compensation schemes, the constraint coordinate system provides clear restrictions on the element behavior and reaction during the precipitation process. These conditions include the type of precipitated elements, concentration range, precipitation rate, etc., which together constitute the boundary conditions of the precipitation process.

[0057] External interactive channels are used to share and interact with real-time data 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.

[0058] In order to find the global minimum, that is, the minimum concentration or the lowest precipitation rate of harmful elements in the precipitation process, the precipitation process is deeply analyzed based on the constraints of the constraint coordinate system and the data provided by the external interactive channel. In the process of in-depth analysis, the key factors affecting the precipitation process are identified, such as precipitation temperature, precipitation time, reactant ratio, etc. Then, these factors are adjusted and the impact of different operating conditions on the precipitation process is evaluated through simulation and other methods.

[0059] After many attempts and optimizations, the second set of compensation solutions was determined.

[0060] A single treatment scheme is based on a specific physical or chemical principle to modify and optimize the surface of a material. For example, electroplating is to cover a layer of metal film on the surface of the material to improve its corrosion resistance and decorativeness; electrolytic polishing uses electrochemical principles to remove fine burrs on the surface of the material and improve brightness. Each single treatment scheme has its applicable scenarios and limitations, so it is selected according to specific needs. Next, determine the role of one compensation scheme and two compensation schemes. The compensation scheme is designed to solve problems or deficiencies that may arise in the actual application of a single treatment scheme.

[0061] When fitting a single treatment plan and a compensation plan, the single treatment plan and the compensation plan should be able to complement each other and jointly improve the treatment effect. For example, if the single treatment plan is electroplating, the compensation plan proposes corresponding optimization measures for problems such as uneven coating or weak bonding that may occur during the electroplating process. When determining the surface treatment plan, based on its impact on the environment, give priority to those treatment methods and compensation plans that have less pollution to achieve green and sustainable surface treatment.

[0062] Finally, the surface treatment plan is determined by comprehensively evaluating various factors.

[0063] Further, based on the feature correlation, lateral interaction and mutual influence analysis is performed, and the method further includes: Traversing the feature correlation, performing upper and lower processing coverage analysis based on time series, and identifying the failure processing node; The failure processing nodes are traversed, compensation adjustments are made to the initial decision solutions of the parallel decision branches, and the group of compensation solutions is determined.

[0064] 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.

[0065] Next, we conduct a timing-based coverage analysis of upper and lower processing, and conduct a detailed timing analysis of each step in the processing flow to determine the sequence and dependency between them. At the same time, we analyze the coverage of the upper processing steps over the lower processing steps, that is, whether the upper processing can effectively correct or improve the problems that may exist in the lower processing.

[0066] In this process, if a subsequent step covers the defects of a previous step, for example, a subsequent step can correct a minor defect produced in the previous step, then the accuracy requirement of the previous step can be appropriately lowered, simplifying the processing flow and reducing ineffective work.

[0067] Based on the above analysis, the nodes that cause processing failure are identified. These failed nodes are caused by unreasonable arrangement between processing steps, feature misjudgment or conflict of processing goals.

[0068] After identifying the failed processing nodes, traverse them one by one, and analyze the cause and impact of each failed node, as well as its impact on the entire processing flow.

[0069] Based on the analysis of the failed processing nodes, the initial decision plan of the parallel decision branch is compensated and adjusted. The compensation adjustment includes adjusting the processing accuracy, simplifying the processing steps, and optimizing the processing sequence.

[0070] Adjust the processing accuracy to appropriately reduce the accuracy required for the parts in the previous steps to reduce unnecessary resources and time consumption.

[0071] Simplifying processing steps means removing unnecessary processing steps to make the processing flow more concise and efficient.

[0072] Optimizing the processing sequence is to rearrange the order of processing steps according to the timing relationship and the coverage of upper and lower processing to improve the processing effect.

[0073] After the above compensation adjustments, a set of compensation plans is determined.

[0074] Furthermore, step S400 in the method provided in the application embodiment further includes: Interactive equipment control system, the equipment control system is an independent control system for each processing equipment; Based on the surface treatment scheme, determining equipment coordination features, 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 coordination features and in combination with key coordination nodes, the equipment control system is integrated and configured to determine the system coordination strategy.

[0075] 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 coordinated operation and control of different processing equipment is achieved through the interactive equipment control system.

[0076] Equipment collaboration refers to the interaction between two or more relatively independent devices to jointly complete a certain instruction or task. Equipment collaboration characteristics include at least the timing collaboration dimension, the order collaboration dimension, and the collaboration collision dimension. The equipment collaboration characteristics are determined by analyzing the surface treatment solution.

[0077] The timing coordination dimension focuses on the time coordination of equipment during the surface treatment process. Surface treatment includes multiple steps, such as pretreatment, coating, curing, etc., and each step needs to 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 to avoid time delays or overlaps. Through precise timing control, the efficiency and consistency of surface treatment can be improved.

[0078] The order coordination dimension involves the operation sequence of the equipment in the surface treatment process. Different surface treatment steps require different equipment to complete, and the operation sequence between these equipment is crucial to the final treatment effect. Order coordination requires that the equipment can operate in a predetermined order to ensure that each step is carried out at the right time to avoid sequence errors or omissions. Through reasonable order arrangement, the smooth progress of the surface treatment process is ensured and the treatment quality is improved.

[0079] The collaborative collision dimension focuses on the conflicts or collisions that may occur during collaborative operation. Due to the spatial overlap or interaction between devices, if there is a lack of appropriate collaborative mechanisms, collisions or interference between devices will occur. The collaborative collision dimension requires that devices have functions such as obstacle avoidance and collision prevention to ensure safe and stable operation during collaborative operation.

[0080] Key coordination nodes are key links in equipment coordination and have a significant impact on the overall coordination effect. They are certain specific operation steps, data transmission points or equipment interaction interfaces, etc. After determining the key coordination nodes, the equipment control system is integrated and configured. This process includes connecting and integrating the control systems of various devices to achieve information sharing and command synchronization. During the configuration process, according to the equipment coordination characteristics and the requirements of key coordination nodes, ensure that the control system can support the coordination between devices.

[0081] Based on the equipment coordination features and key coordination nodes, when the equipment control system is integrated and configured, the control systems of various devices are connected and integrated to achieve information sharing and command synchronization. During the configuration process, according to the equipment coordination features and the requirements of key coordination nodes, ensure that the control system can support collaborative operations between devices.

[0082] Finally, the system coordination strategy is formulated based on the equipment coordination characteristics and the actual situation of key coordination nodes. The system coordination strategy includes the operation sequence, parameter adjustment, fault handling, etc. between devices.

[0083] Furthermore, the method further comprises: Identify the key coordination nodes, determine the surface treatment stages, and determine the stage-by-stage surface treatment quality; The surface treatment stage is mapped to the staged surface treatment quality, and staged quality control and resumption management of the surface treatment are carried out.

[0084] In the embodiment 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 an important impact on the final surface treatment quality. When identifying key coordination nodes, the surface treatment scheme 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, determine which steps or links are key coordination nodes.

[0085] Once the key synergy nodes are identified, the next step is to determine the surface treatment stages. The surface treatment stages are divided according to the logical sequence of the treatment process and the location of the key synergy nodes. Each stage contains a specific set of processing steps and operations designed to achieve a specific surface treatment effect.

[0086] While determining the surface treatment stage, the staged surface treatment quality of each stage is further determined, that is, the quantitative and qualitative evaluation of the treatment results. The evaluation methods include appearance inspection, performance testing, chemical analysis and other means. Through these evaluation methods, the staged surface treatment quality is determined.

[0087] When mapping each surface treatment stage with its corresponding staged surface treatment quality, it is achieved by establishing a data table, chart or visual interface, etc. Through the mapping relationship, the quality level of each stage, as well as the quality differences and change trends between different stages are clearly shown.

[0088] After the mapping is completed, the staged quality control of the surface treatment is carried out, including real-time monitoring and evaluation of the treatment effect at each stage to ensure that the quality is always above the predetermined standard. If the quality of a certain stage is found to have problems or deviates from expectations, adjustments and improvements are made by adjusting the treatment parameters, replacing equipment or improving the process.

[0089] At the same time, we will manage the resumption of work. When production is interrupted due to some reasons, such as equipment failure, material problems, etc., we will formulate an effective resumption plan to ensure that production can quickly return to normal.

[0090] Furthermore, the method further comprises: Read the feedback control records of a predetermined period and mine the pre-adjustment control points, where the pre-adjustment control points are determined based on the over-limit control points that meet the deviation tolerance interval and the frequent control points that meet the frequency threshold; Based on the pre-adjustment control point, tracing based on feedback control characteristics is performed to determine a compensation strategy; Based on the compensation strategy, the surface treatment solution is adjusted and responded.

[0091] In the embodiment of the present application, feedback control records about the surface treatment process in the previous period of time, such as one week, one month, etc., are retrieved and extracted through a database or file storage system. These records contain control parameters, timestamps, and possible effect evaluations.

[0092] When mining pre-adjustment control points, deviation tolerance interval analysis and frequency threshold analysis are performed. When performing deviation tolerance interval analysis, statistical analysis is performed on the control parameters in the feedback control records through threshold setting and parameter comparison to find control points that exceed the preset tolerance interval. When performing frequency threshold analysis, the frequency of occurrence of each control point is counted to determine which points reach the set frequency threshold.

[0093] When tracing the source based on the feedback control characteristics based on the pre-adjustment control point, the collected data is processed and analyzed using the data analysis algorithm. By comparing the changes in equipment parameters before and after the pre-adjustment control point occurs, the parameter items closely related to the pre-adjustment control point are found. In addition, the correlation between the parameters is analyzed to find out possible interactions or influences.

[0094] After determining the parameter items that are closely related to the pre-adjustment control point, further causal reasoning is performed. Based on process knowledge, historical data, and expert rules, it is inferred which equipment parameter adjustments may lead to the appearance of the pre-adjustment control point. This reasoning process can be rule-based or machine learning-based. For example, when causal reasoning is performed based on rules, a rule base is first established, which contains a series of rules related to the surface treatment process. These rules are determined based on process knowledge, expert experience, historical data, and actual operations. The rules include that if the spraying pressure is too high, the coating thickness will be uneven. If the baking temperature is too high or the time is too long, cracks will appear in the coating. When the pre-adjustment control point appears, the equipment parameter data related to the point is automatically collected. Then, this data is matched with the rules in the rule base. If the data meets the conditional part of a rule, the rule will be triggered. The triggered rules involve multiple parameters and conditions, so a causal chain analysis is performed to find the complete causal chain from parameter adjustment to the appearance of the pre-adjustment control point. By analyzing these chains, it is determined which parameter adjustments are the key factors that lead to the appearance of the pre-adjustment control point. Through causal chain analysis, the equipment parameters that have the greatest impact on the pre-adjustment control point are screened out. These parameters are associated with multiple rules in the rule base and are frequently triggered during the data matching process.

[0095] Based on the above analysis, a compensation strategy is formulated, that is, adjusting the equipment parameters identified as critical factors. The direction and magnitude of the adjustment are determined based on the recommendations in the rule base or the optimization algorithm based on historical data.

[0096] When adjusting the surface treatment scheme based on the compensation strategy, the compensation strategy is first analyzed to determine the adjustment of the equipment parameters. According to the analysis results of the compensation strategy, the original surface treatment scheme is adjusted as necessary. The adjustment includes equipment parameter adjustment. According to the compensation strategy, the parameter settings of related equipment are automatically adjusted, such as spraying pressure, baking temperature, conveying speed, etc. In addition to the equipment parameter adjustment, the operation process is optimized, such as adjusting the process sequence, adding or deleting certain steps, etc.

[0097] Finally, the adjusted surface treatment scheme is applied in the actual production process.

[0098] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects: 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 appearance level; measures the characteristic amplitude modulation of the material surface characteristics and the surface quality standards, and identifies the characteristic correlation; based on the characteristic amplitude modulation and the characteristic correlation, combined with the adaptive decision model, performs independent analysis within the layer and collaborative analysis between layers, and determines the surface treatment plan by optimal decision-making, 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 build 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 autonomous calibration rules; performs closed-loop control of the surface treatment of molding engineering materials based on the configured intelligent central control system and the digital feedback device. The present invention solves the technical problem that it is difficult to carry out real-time and precise control of material surface quality in the prior art. 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.

[0099] Embodiment 2 Based on the same inventive concept as the surface treatment method of a molding engineering material in the aforementioned embodiment, Figure 2 As shown, the present application provides a surface treatment system for molding engineering materials, and the system and method embodiments in the present application are based on the same inventive concept. The system includes: A molding engineering material information reading module 11, wherein the molding engineering material information reading module 11 reads 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; A characteristic amplitude modulation measurement module 12, the characteristic amplitude modulation measurement module 12 measures the characteristic amplitude modulation of the material surface feature and the surface quality standard, and identifies the characteristic correlation; A surface treatment scheme 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, and determines the surface treatment scheme by 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 14, wherein the intelligent central control system configuration module 14 configures 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 15, which uses the abnormal processing feature as an index to perform industrial big data retrieval and construct a control plan library, and sets an autonomous calibration rule based on a control deviation, wherein the control deviation is determined based on a system error and an exogenous error; A digital feedback device configuration module 16, wherein the digital feedback device configuration module 16 configures the digital feedback device based on the control plan library and the autonomous calibration rule; 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.

[0100] Furthermore, the system is also used to implement the following functions: 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; Taking the constraint coordinate system as a baseline, supervise the training of the safety control branch; 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.

[0101] Furthermore, the system is also used to implement the following functions: 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, a lateral interaction and mutual influence analysis is performed 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 scheme is fitted with the one group of compensation schemes and the two groups of compensation schemes to determine the surface treatment scheme.

[0102] Furthermore, the system is also used to implement the following functions: Traversing the feature correlation, performing upper and lower processing coverage analysis based on time series, and identifying the failure processing node; The failure processing nodes are traversed, compensation adjustments are made to the initial decision solutions of the parallel decision branches, and the group of compensation solutions is determined.

[0103] Furthermore, the system is also used to implement the following functions: Interactive equipment control system, the equipment control system is an independent control system for each processing equipment; Based on the surface treatment scheme, determining equipment coordination features, 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 coordination features and in combination with key coordination nodes, the equipment control system is integrated and configured to determine the system coordination strategy.

[0104] Furthermore, the system is also used to implement the following functions: Identify the key coordination nodes, determine the surface treatment stages, and determine the stage-by-stage surface treatment quality; The surface treatment stage is mapped to the staged surface treatment quality, and staged quality control and resumption management of the surface treatment are carried out.

[0105] Furthermore, the system is also used to implement the following functions: Read the feedback control records of a predetermined period and mine the pre-adjustment control points, where the pre-adjustment control points are determined based on the over-limit control points that meet the deviation tolerance interval and the frequent control points that meet the frequency threshold; Based on the pre-adjustment control point, tracing based on feedback control characteristics is performed to determine a compensation strategy; Based on the compensation strategy, the surface treatment solution is adjusted and responded.

[0106] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded 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 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.

[0107] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0108] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A surface treatment method for a molding engineering material, 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 the adaptive decision model, independent analysis within the layer and collaborative analysis between layers are performed to make an optimal decision and determine 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 features as indexes, industrial big data is retrieved and a control plan library is constructed, and autonomous calibration rules based on control deviations are set. The control deviations are determined based on system errors and exogenous errors. Based on the control plan library and the autonomous calibration rules, configuring the digital feedback device; 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, characterized in that 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; Taking the constraint coordinate system as a baseline, supervise the training of the safety control branch; A lateral interaction channel between parallel decision branches is established, and an external interaction channel between the security control branch and each parallel decision branch is established to generate the adaptive decision model.

3. The method according to claim 2, characterized in that The parallel decision branches correspond to the mechanical level, the performance level and the perception level one by one, and the independent analysis within the level and the collaborative analysis between the levels 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, a lateral interaction and mutual influence analysis is performed 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 scheme is fitted with the one group of compensation schemes and the two groups of compensation schemes to determine the surface treatment scheme.

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

5. The method according to claim 1, characterized in that The intelligent central control system interacts with the equipment control system, including: Interactive equipment control system, the equipment control system is an independent control system for each processing equipment; Based on the surface treatment scheme, determining equipment coordination features, 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 coordination features and in combination with key coordination nodes, the equipment control system is integrated and configured to determine the system coordination strategy.

6. The method according to claim 5, characterized in that Identify the key coordination nodes, determine the surface treatment stages, and determine the stage-by-stage surface treatment quality; The surface treatment stage is mapped to the staged surface treatment quality, and staged quality control and resumption management of the surface treatment are carried out.

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

8. A surface treatment system for molding engineering materials, characterized in that: The system comprises: A molding engineering material information reading module, wherein the molding engineering material information reading module reads 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; A characteristic amplitude modulation measurement module, the characteristic amplitude modulation measurement module measures the characteristic amplitude modulation of the surface feature of the material and the surface quality standard, and identifies the characteristic correlation; A surface treatment scheme 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, and determines the surface treatment scheme by 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, which configures 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 the abnormal processing feature 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, wherein the control deviation is determined based on system error and exogenous error; A digital feedback device configuration module, which configures the 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.

Images