An automated plating qualification method and system

Abstract

The application relates to the field of electroplating detection, in particular to an automatic electroplating qualified detection method and system. The method comprises the following steps: acquiring plastic substrate information, determining the substrate complexity according to the plastic substrate information; acquiring real-time electroplating information, determining the surface abnormal information set of the plastic substrate in the electroplating process according to the real-time electroplating information; determining the associated parameters needing to be adjusted according to the surface abnormal information set; determining the real-time adjustment information set according to the surface abnormal information set, the associated parameters and the substrate complexity; and adjusting the real-time production process according to the real-time adjustment information set to obtain plastic product information. The application determines the electroplating associated parameters needing to be adjusted by analyzing the problems in the electroplating process, adjusts the electroplating production process according to the electroplating associated parameters, ensures the electroplating production quality of the plastic substrate product, and improves the electroplating qualified rate of the plastic electroplating product.

Description

Technical Field

[0001] This application relates to the field of electroplating inspection, and in particular to an automated method and system for inspecting the conformity of electroplating. Background Technology

[0002] With the rapid development of technology, electroplating technology has become increasingly mature. In the field of plastic electroplating, automating the monitoring of the electroplating process and adjusting and controlling the electroplating parameters can effectively improve the pass rate of plastic electroplated products.

[0003] However, in the process of automating the detection and control of the plastic electroplating process, the existing automated detection and control is difficult to adapt to the dynamically changing electroplating production process, resulting in the plastic electroplating products being unable to meet the actual production needs, thus affecting the pass rate of plastic electroplating products. Summary of the Invention

[0004] This application provides an automated method and system for detecting the quality of electroplating, in order to solve the above-mentioned problems.

[0005] In a first aspect, this application provides an automated method for detecting the conformity of electroplating, the method comprising: Obtain information about the plastic substrate and determine the complexity of the substrate based on that information. Obtain real-time electroplating information, and determine the set of surface anomaly information of the plastic substrate during the electroplating process based on the real-time electroplating information; Based on the surface anomaly information set, determine the relevant parameters that need to be adjusted; The real-time adjustment information set is determined based on the surface anomaly information set, the correlation parameters, and the complexity of the substrate; Based on the aforementioned real-time adjustment information set, the real-time production process is adjusted to obtain plastic product information.

[0006] This solution first accurately assesses the complexity of the plastic substrate by acquiring its information, laying the foundation for subsequent electroplating processes. Determining the complexity allows for more targeted adjustments at each stage of production. Next, real-time electroplating information ensures comprehensive monitoring of the electroplating status, enabling timely identification of anomalies on the plastic substrate surface. This collection of anomaly information provides clear guidance for potential problems in the production process, making subsequent parameter adjustments more scientific and rational. Based on the surface anomaly information set, the relevant parameters requiring adjustment can be identified; this step is crucial for precise adjustments, ensuring the effectiveness and relevance of the measures. Subsequently, combining surface anomaly information, relevant parameters, and substrate complexity, the generated real-time adjustment information set provides specific adjustment plans for the production line, enabling dynamic optimization of the entire production process. Finally, by implementing these adjustments, the quality of plastic products can be effectively improved, ensuring that the electroplating effect meets expected standards, ultimately obtaining compliant plastic product information, thereby improving production efficiency and product qualification rate.

[0007] Optionally, the plastic substrate information includes a plastic substrate image, and determining the substrate complexity based on the plastic substrate information includes: Based on the image of the plastic substrate, determine the shadow area, tilt angle, substrate volume, and surface roughness of the plastic substrate; The substrate complexity index is determined based on the shadow area, the tilt angle, the substrate volume, and the surface roughness, and is calculated using the following formula: ; in, This represents the complexity index of the substrate. This indicates the preset volume density weighting coefficient. This indicates the volume of the substrate. This represents the area of ​​the shadow. This represents the preset angle weighting coefficient. Indicates the tilt angle, This indicates the preset roughness weighting coefficient. This refers to the surface roughness; The complexity of the substrate is determined based on the substrate complexity index.

[0008] This solution first identifies key parameters such as shadow area, tilt angle, substrate volume, and surface roughness, which are crucial for assessing substrate complexity. Measuring the shadow area helps understand the impact of illumination on surface features; the tilt angle reflects the substrate's geometry; substrate volume is the basis for quantifying its three-dimensional characteristics; and surface roughness directly affects electroplating quality. By combining these parameters and using a pre-defined complexity index calculation formula, a comprehensive complexity index reflecting substrate complexity can be derived. This formula, through weighted calculation of volume, shadow area, tilt angle, and surface roughness, ensures the rationality and scientific validity of each factor in the overall assessment. Pre-set weighting coefficients further refine the relative importance of each factor in the complexity assessment. Ultimately, the determined substrate complexity index accurately reflects the substrate's complexity, providing data support for subsequent electroplating process adjustments, effectively guiding optimization decisions in the production process, and thereby improving product consistency and yield.

[0009] Optionally, the plastic substrate information further includes the substrate's coefficient of thermal expansion, and the real-time electroplating information includes electroplating solution temperature and current distribution information. The step of determining whether there are any abnormalities in the outline of the plastic substrate based on the real-time electroplating image and the plastic substrate information, and if there are abnormalities in the outline of the plastic substrate, then determining the outline abnormality information, including: Based on the real-time electroplating image and the information of the plastic substrate, it is determined whether there is an abnormality in the outline of the plastic substrate. If there is an abnormality in the outline of the plastic substrate, the abnormality information is determined. Based on the real-time electroplating image, it is determined whether there are bubbles in the plastic substrate. If there are bubbles in the plastic substrate, bubble abnormality information is determined. Based on the real-time electroplating image, it is determined whether there is an abnormal thickness change in the plastic substrate. If there is an abnormal thickness change in the plastic substrate, the thickness abnormality information is determined. Based on the contour anomaly information, the bubble anomaly information, and the thickness anomaly information, a surface anomaly information set is constructed; The real-time contour of the substrate is determined based on the real-time electroplating image; Obtain current distribution information, and determine the current distribution area based on the current distribution information; Based on the real-time profile of the substrate, the influence of the electroplating solution temperature and the current distribution area on the real-time profile of the substrate is determined, and calculated according to the following formula: ; in, Indicates the degree of influence. This indicates the preset thermal influence coefficient. This represents the coefficient of thermal expansion of the substrate. This indicates the temperature of the electroplating solution. This indicates the preset current influence coefficient. This represents the area of ​​the current distribution; Based on the degree of influence, the outline standard range of the plastic substrate is obtained, the real-time outline of the substrate is matched with the outline standard range, and it is determined whether the real-time outline of the substrate exceeds the outline standard range. If the real-time contour of the substrate exceeds the contour standard range, the real-time contour of the substrate is determined as contour anomaly information, and the contour anomaly information is included in the anomaly information set.

[0010] This solution combines real-time electroplating images with information about the plastic substrate to first determine if there are any anomalies in the substrate's contour. If an anomaly is detected, corresponding contour anomaly information is generated. Next, by analyzing the real-time electroplating images, it is determined whether there are bubbles. If bubbles are found, bubble anomaly information is recorded to prevent coating defects caused by bubbles. Simultaneously, thickness variation anomalies are also monitored; if thickness variation anomalies are found, corresponding thickness anomaly information is recorded. The contour, bubble, and thickness anomaly information are integrated to form a surface anomaly information set, providing a basis for subsequent adjustments. Based on the real-time electroplating images, after determining the real-time contour of the substrate, the influence of electroplating solution temperature and current distribution on the contour is analyzed using formulas. This step ensures precise control of electroplating conditions. After calculating a standard range using the degree of influence, the real-time contour is matched against it. If the real-time contour exceeds the standard range, it is considered contour anomaly information and further included in the anomaly information set. The effective execution of this series of steps enables real-time monitoring and adjustment of the electroplating process, ensuring that the quality of plastic products meets standards.

[0011] Optionally, the step of determining whether there are bubbles in the plastic substrate based on the real-time electroplating image, and if there are bubbles in the plastic substrate, then determining the bubble abnormality information, includes: The location of the bubbles is determined based on the real-time electroplating image; Based on the location of the bubbles, and according to the real-time electroplating image, it is determined whether the appearance of the bubbles is caused by the surface roughness of the plastic substrate. If the appearance of the bubbles is caused by the surface roughness of the substrate, then the surface roughness is determined as the bubble abnormality information; If the appearance of the bubble location is not caused by the surface roughness, then the current intensity is determined based on the electroplating information; The electroplating time is determined based on the real-time electroplating information; The bubble volume and bubble density are determined based on the electroplating time, the current distribution, and the current intensity. The bubble volume, bubble density, and bubble position are determined as the bubble anomaly information, and the bubble anomaly information is included in the surface anomaly information set.

[0012] This solution first identifies the location of bubbles through real-time electroplating image analysis, providing a foundation for subsequent problem diagnosis. Based on the bubble location, it further determines whether the bubble formation is related to the surface roughness of the plastic substrate. If it is confirmed that the bubble is caused by surface roughness, the corresponding roughness information is marked as bubble anomaly information. This process is crucial because surface characteristics directly affect electroplating quality and the likelihood of bubble formation. If the bubble location is not due to surface roughness, the current intensity needs to be determined based on real-time electroplating information to assess the energy supply during the electroplating process. Simultaneously, the electroplating time is further determined using real-time electroplating information, another key factor affecting bubble formation. Combining electroplating time, current distribution, and current intensity, the volume and density of the bubbles can be accurately calculated, providing a quantitative basis for judging the impact of bubbles on the final product. Finally, the volume, density, and location of the bubbles are incorporated into the bubble anomaly information, forming a comprehensive set of surface anomaly information. The effective execution of this series of steps ensures optimized management of the electroplating process, improving the consistency and reliability of product quality.

[0013] Optionally, based on the real-time electroplating image, determining whether the plastic substrate has an abnormal thickness change; if the plastic substrate has an abnormal thickness change, then determining the thickness abnormality information includes: Based on the real-time electroplating images, the electroplating area of ​​the substrate and the electroplating distance between the electrode and the substrate are determined; The electroplating area of ​​the substrate is divided to obtain several electroplating areas of the substrate. Based on the real-time electroplating information, the electrode position and current density are determined; Based on the electrode position, the coating adhesion is determined according to the electroplating distance and the current density; The plating thickness of each substrate plating area is obtained based on the coating adhesion and the plating areas of the substrates. Obtain the thickness error range, match the electroplating thickness with the thickness error range, and obtain the matching result; Based on the matching results, it is determined whether the electroplating thickness exceeds the thickness error range; If the electroplating thickness exceeds the thickness error range, the area exceeding the thickness error range is identified as a thickness abnormality area, the thickness abnormality area is identified as the thickness abnormality information, and the thickness abnormality information is included in the surface abnormality information set.

[0014] This solution first determines the electroplating area of ​​the substrate and the electroplating distance between the electrode and the substrate, providing crucial spatial positioning information for subsequent electroplating effect analysis. After segmenting the substrate electroplating area, it can be divided into several independent electroplating zones to more accurately evaluate the electroplating effect of each zone. Next, the electrode position and current density are determined based on real-time electroplating information, which is fundamental for evaluating the uniformity and effectiveness of the electroplating process. Based on the electrode position, electroplating distance, and current density, the adhesion of the coating can be calculated; this indicator directly affects the quality and durability of the coating. By analyzing the coating adhesion of each substrate electroplating zone, the specific electroplating thickness can be determined, further ensuring the consistency of the electroplating layer. The obtained thickness error range makes the inspection of the electroplating thickness operable, and it is matched with the thickness error range to determine whether the electroplating thickness meets the standard. If the electroplating thickness is found to exceed the error range, the relevant area is marked as a thickness anomaly area, and the thickness anomaly information is recorded and ultimately included in the anomaly information set. The implementation of the above series of steps ensures real-time monitoring and adjustment of the electroplating process, thereby improving the overall quality and reliability of the product.

[0015] Optionally, determining the correlation parameters that need to be adjusted based on the surface anomaly information set includes: The surface anomaly information is matched with the surface anomaly information set to obtain the anomaly information type; Based on the type of abnormal information, several parameter adjustment information is determined according to the real-time electroplating information and the complexity of the substrate, and the parameters of the electroplating production process are adjusted according to the several parameter adjustment information. Based on the type of abnormal information, determine whether the parameter adjustment causes other abnormal situations besides the type of abnormal information. If the parameter adjustment leads to other abnormal situations besides the abnormal information type, then based on the parameter adjustment information and the other abnormal situations, determine the parameter change magnitude, parameter change quantity, and parameter change type of the other abnormal situations; The degree of correlation of the parameters is determined based on the magnitude of the parameter changes, the number of parameter changes, and the type of parameter changes. The correlation parameters are determined based on the real-time electroplating information, the plastic substrate information, and the correlation degree of the parameters.

[0016] This solution effectively identifies the types of anomalies by matching surface anomaly information with a set of surface anomaly information, providing a basis for subsequent adjustments and optimizations. Based on these anomaly types, combined with real-time electroplating information and substrate complexity, targeted parameter adjustments can be formulated to improve deficiencies in the electroplating production process and thus optimize product quality. After implementing parameter adjustments, their effects need to be evaluated to determine whether they have caused other anomalies besides the identified types. If the parameter adjustments have indeed led to new anomalies, the magnitude, quantity, and type of parameter changes in these anomalies are further analyzed to understand the nature and severity of the new problems. Through in-depth analysis of parameter changes, the degree of correlation is determined, revealing the mutual influence between parameters. This systematic analysis method can more accurately identify and correct problems in the electroplating process, ensuring that each adjustment is based on empirical data, and ultimately accurately determining the relevant parameters to focus on based on real-time electroplating information, plastic substrate information, and parameter correlation. The execution of this series of steps ensures the scientific nature and effectiveness of the electroplating process, thereby improving production stability and product consistency.

[0017] Optionally, determining the real-time adjustment information set based on the surface anomaly information set, the correlation parameters, and the substrate complexity includes: If the surface anomaly information set includes the bubble anomaly information, then the bubble parameter association information is determined based on the association parameters and the degree of association of the parameters; Based on the complexity of the substrate, the temperature adjustment range and the stirring speed adjustment range are determined according to the bubble volume and the bubble position. Based on the temperature adjustment range, the bubble parameter association information, and the speed adjustment range, bubble adjustment information is determined and incorporated into the real-time adjustment information set.

[0018] This solution addresses the issue of bubble anomalies during electroplating. If the surface anomaly information includes bubble abnormalities, the correlation between these parameters and their degree of correlation can be analyzed to determine the bubble parameter correlation information. This step provides data support for bubble problem handling. Next, based on the complexity of the substrate and the volume and location of the bubbles, appropriate temperature and stirring speed adjustments can be determined. Proper adjustment of temperature and stirring speed is crucial for reducing bubble formation, effectively improving the fluidity and uniformity of the electroplating solution, thereby reducing bubble generation during the electroplating process. Combining the temperature adjustment range, bubble parameter correlation information, and speed adjustment range, further bubble adjustment information can be determined. This information provides guidance for actual operation, ensuring real-time response to bubble changes during adjustment. Finally, the bubble adjustment information is incorporated into the real-time adjustment information set to form a systematic adjustment plan for timely response and optimization during production. The implementation of this series of steps ensures precise control of the electroplating process, improves the quality and consistency of plastic products, and helps to better meet production requirements.

[0019] Optionally, determining the real-time adjustment information set based on the surface anomaly information, the associated parameters, and the substrate complexity includes: If the surface anomaly information set includes the thickness anomaly information, then based on the real-time electroplating information and the complexity of the substrate, it is determined whether the thickness anomaly information is generated by the real-time electroplating information; If the thickness anomaly information is generated by the real-time electroplating information, then the thickness anomaly type is determined based on the real-time electroplating information and the real-time electroplating image; Based on the associated parameters and the complexity of the substrate, the thickness parameter association information is determined; Based on the thickness anomaly type and the thickness parameter correlation information, determine the current adjustment information; Electroplating adjustment information is determined based on the bubble volume, the bubble density, and the bubble position; Based on the electroplating adjustment information and the current adjustment information, thickness adjustment information is determined and incorporated into the real-time adjustment information set.

[0020] This solution first requires determining whether the thickness anomaly is caused by the current electroplating conditions based on real-time electroplating information and the complexity of the substrate. If the anomaly is confirmed to be caused by real-time electroplating information, further analysis of the real-time electroplating information and images is conducted to determine the specific type of thickness anomaly. This step is crucial for understanding the nature of the thickness problem. Next, by analyzing the correlation parameters and the complexity of the substrate, thickness parameter correlation information can be obtained. This information helps to deeply understand the interrelationships between various factors in the electroplating process. Based on the type of thickness anomaly and the thickness parameter correlation information, corresponding current adjustment information is formulated to optimize the electroplating process. Simultaneously, based on the volume, density, and location of bubbles, electroplating adjustment information can be determined to prevent the bubble problem from affecting the thickness. Finally, the electroplating adjustment information and current adjustment information are combined to determine the thickness adjustment information, which is then incorporated into the real-time adjustment information set. The effective implementation of this series of steps not only ensures thickness control during the electroplating process but also improves the consistency of product quality, providing a scientific basis for subsequent production decisions.

[0021] Optionally, the contour anomaly information includes the electroplating area, electroplating structure, and plating thickness of the contour anomaly region. The step of determining real-time adjustment information based on the surface anomaly information, the associated parameters, and the substrate complexity includes: If the surface anomaly information set contains the contour anomaly information, then the contour parameter association information is determined based on the association parameter and the contour anomaly information; The electroplating time is determined based on the electroplating area, the electroplating structure, and the plating thickness. The degree of agitation of the electroplating solution is determined based on the shaded area, the electroplating time, and the tilt angle, and is calculated using the following formula: ; in, This indicates the degree of stirring. Indicates the adjustment constant. This represents the area of ​​the shadow. This indicates the preset area coefficient. This indicates the electroplating time. This indicates the preset electroplating time coefficient. Indicates the tilt angle; Based on the contour parameter association information, the complexity of the substrate, and the degree of agitation, contour adjustment information is determined and incorporated into the real-time adjustment information set.

[0022] This solution first requires determining the contour parameter correlation information based on the associated parameters and contour anomaly information. This step helps identify potential factors causing contour anomalies. Then, based on the electroplating area, electroplating structure, and coating thickness, the required electroplating time is accurately calculated to ensure the electroplating process can proceed sufficiently to achieve the desired coating effect. Next, the degree of agitation of the electroplating solution is calculated using the shaded area, electroplating time, and tilt angle, reflecting the impact of agitation on electroplating quality. A reasonable degree of agitation ensures the uniformity of the electroplating solution, thereby improving the adhesion and uniformity of the coating. Finally, based on the contour parameter correlation information, the complexity of the substrate, and the degree of agitation, contour adjustment information is comprehensively determined and incorporated into the real-time adjustment information set. This series of steps not only enables timely response to contour anomaly issues but also optimizes the electroplating process through scientific adjustment strategies, improving product quality and ensuring the final product meets design requirements.

[0023] Secondly, this application provides an automated electroplating qualification inspection system, the system comprising: The complexity determination module is used to acquire information about the plastic substrate and determine the complexity of the substrate based on the plastic substrate information. An anomaly information determination module is used to acquire real-time electroplating information and determine the set of surface anomaly information of the plastic substrate during the electroplating process based on the real-time electroplating information. The correlation parameter determination module is used to determine the correlation parameters that need to be adjusted based on the surface anomaly information set. The adjustment information determination module is used to determine the real-time adjustment information set based on the surface anomaly information set, the associated parameters, and the complexity of the substrate. The product information determination module is used to adjust the real-time production process based on the real-time adjustment information set to obtain plastic product information. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application; Figure 2 A flowchart illustrating an automated electroplating qualification inspection method provided in one embodiment of this application; Figure 3This is a schematic diagram of an automated electroplating qualification inspection system provided in one embodiment of this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0027] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0028] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0029] In the process of automating the detection and control of plastic electroplating, the existing automated detection and control is difficult to adapt to the complexity and dynamic changes of the substrate in the electroplating production process, which makes it difficult for the produced plastic electroplated products to meet the actual production needs, thus affecting the pass rate of plastic electroplated products.

[0030] Based on this, this application provides an automated electroplating quality inspection method and system. First, detailed information about the plastic substrate is collected to accurately assess its complexity, providing a basis for subsequent adjustments. Then, data from the electroplating process is captured in real time to quickly identify any anomalies on the plastic substrate surface, forming an anomaly information set, which helps to promptly identify potential problems. Next, based on this anomaly information, the electroplating-related parameters that need adjustment are precisely located to ensure the root cause of the problem is addressed. Taking into account surface anomalies, related parameters, and substrate complexity, a real-time adjustment information set is generated to guide immediate optimization during production. Finally, the production process is precisely controlled based on the adjustment information to ensure that the produced plastic products meet quality standards, achieving efficient and precise electroplating quality control.

[0031] Figure 1 This application provides an illustration of an application scenario. In the electroplating production of plastic substrates, the method provided in this application is applied to obtain and analyze abnormal information on the surface of the plastic substrate during the plastic electroplating production process, thereby determining a real-time adjustment information set related to the abnormal information, and adjusting the electroplating production process according to the real-time adjustment information set.

[0032] Specifically, the method provided in this application can be applied to any server. The server interacts with the electroplating production control center and electroplating production monitoring equipment. The electroplating production control center can be a system that controls and regulates the electroplating production process. The server obtains and analyzes the production monitoring information from the electroplating production monitoring equipment to obtain monitoring and analysis results. Based on the monitoring and analysis results, it determines real-time adjustment information and sends this information to the electroplating production control center. The server also obtains and analyzes electroplating images to identify anomalies. Based on these anomalies, it determines a set of real-time adjustment information for relevant electroplating parameters and sends this set to the electroplating production control center. Through real-time monitoring and analysis of the electroplating production process, anomalies in the electroplating production process can be accurately identified, and the electroplating production parameters that need to be adjusted can be precisely determined based on these anomalies, ensuring the production quality and pass rate of the electroplated products.

[0033] For specific implementation details, please refer to the following examples.

[0034] Figure 2 This is a flowchart illustrating an automated electroplating qualification inspection method according to an embodiment of this application. The method of this embodiment can be applied to the server in the above scenario. Figure 2 As shown, the method includes: S201. Obtain information about the plastic substrate and determine the complexity of the substrate based on the information.

[0035] Information about the plastic substrate can be related to the plastic substrate during the electroplating process. This information may include the volume, weight, density, and hardness of the plastic substrate.

[0036] The complexity of the substrate can be used to measure the level of complexity of the plastic substrate.

[0037] Specifically, in the electroplating process of plastic substrates, a higher degree of complexity in the plastic substrate indicates a more complex structure, which in turn presents challenges to the electroplating process. Therefore, before electroplating a plastic substrate, it is necessary to determine its complexity to provide a reference for adjusting subsequent electroplating production parameters.

[0038] Information about the plastic substrate is obtained from production monitoring equipment, and its volume, weight, density, and hardness are extracted from this information. Mathematical analysis methods are then used to analyze the volume, weight, density, and hardness of the plastic substrate to determine its complexity.

[0039] S202. Obtain real-time electroplating information and determine the set of surface abnormality information of the plastic substrate during the electroplating process based on the real-time electroplating information.

[0040] Real-time electroplating information can be information about the electroplating process during the real-time electroplating production of plastic substrates. Real-time electroplating information can include real-time electroplating images.

[0041] The surface anomaly information set can be a collection of abnormal information that appears on the surface of the plastic substrate during the real-time electroplating production process, affecting the electroplating quality of the plastic substrate.

[0042] Specifically, during the electroplating process of plastic substrates, the complexity of the substrate itself and the influence of electroplating parameters (such as current intensity and current density) on the electroplating process can lead to abnormal information appearing on the surface of the substrate. In this case, it is necessary to determine the surface abnormality information set based on this abnormal information to provide a reference for determining subsequent related parameters.

[0043] Real-time electroplating information is obtained from the electroplating production control center, and real-time electroplating images are extracted from this information. Image analysis technology is then used to analyze these images to identify anomalies on the surface of the plastic substrate. This anomaly information is then integrated to obtain a surface anomaly information set.

[0044] S203. Based on the surface anomaly information set, determine the correlation parameters that need to be adjusted.

[0045] The associated parameters can be production parameters that are associated with the anomaly information and are determined based on the surface anomaly information set.

[0046] Specifically, after determining the surface anomaly information set, it is necessary to determine the adjustment parameters for the electroplating production process based on the anomaly information on the plastic substrate surface. However, in determining the adjustment parameters, if only the anomaly information on the plastic substrate surface is used to determine a single adjustment parameter, it cannot be guaranteed that it will not affect other electroplating production parameters, thereby affecting the electroplating production status corresponding to other electroplating production parameters, and thus the determination of the adjustment parameters will not achieve the expected results. Therefore, it is necessary to determine the electroplating production parameters related to the anomaly information on the plastic substrate surface, providing a reference for the subsequent determination of real-time adjustment information.

[0047] Several surface anomaly information points are extracted from the surface anomaly information set determined in the above steps. Based on these anomaly information points, adjustment parameters corresponding to each anomaly information point are determined. Using the adjustment parameters corresponding to each anomaly information point determined in the above steps, the electroplating production process is simulated and predicted to obtain prediction results. Based on the prediction results, it is determined whether adjusting the adjustment parameters will cause the generation of other surface anomaly information points. If so, the adjustment parameters are determined as correlation parameters.

[0048] S204. Determine the real-time adjustment information set based on the surface anomaly information set, associated parameters, and substrate complexity.

[0049] The real-time adjustment information set can be the set of information determined before making real-time adjustments to the electroplating production process.

[0050] Specifically, after determining factors such as the surface anomaly information set, it is necessary to determine a real-time adjustment information set based on these factors in order to better and faster adjust the electroplating production process, thereby improving the pass rate of plastic electroplating products.

[0051] Based on the complexity of the substrate, the adjustment range of several surface anomaly information obtained in the above steps and the associated parameters corresponding to each surface anomaly information is determined, thereby obtaining real-time adjustment information of the associated parameters, and incorporating the real-time adjustment information into the real-time adjustment information set.

[0052] S205. Based on the real-time adjustment information set, adjust the real-time production process to obtain plastic product information.

[0053] Plastic product information can be the testing information of plastic products produced after adjusting the parameters of the electroplating production process.

[0054] Specifically, after determining the real-time adjustment information set, the real-time production process needs to be adjusted according to the real-time adjustment information in the real-time adjustment information set in order to obtain product information after the production process adjustment, thereby providing a basis for the qualified testing of plastic products.

[0055] Based on the real-time adjustment information set obtained from the above steps, the real-time production process is adjusted to obtain the adjusted plastic products. Product testing standards are obtained from the electroplating production control center, and the plastic products are tested according to these standards to obtain the product testing results, which are then used as the plastic product information.

[0056] The method provided in this embodiment firstly involves accurately assessing the complexity of the plastic substrate by acquiring its information, laying the foundation for subsequent electroplating processes. Determining the complexity allows for more targeted adjustments at each step of the production process. Next, acquiring real-time electroplating information ensures comprehensive monitoring of the electroplating status, enabling timely identification of anomalies on the plastic substrate surface. This collection of anomaly information provides clear guidance for potential problems in the production process, making subsequent parameter adjustments more scientific and reasonable. Based on the surface anomaly information set, the relevant parameters requiring adjustment can be identified; this step is crucial for achieving precise adjustments, ensuring the effectiveness and relevance of the adjustment measures. Subsequently, combining the surface anomaly information, relevant parameters, and substrate complexity, the generated real-time adjustment information set provides specific adjustment schemes for the production line, enabling dynamic optimization of the entire production process. Finally, by implementing these adjustments, the quality of the plastic products can be effectively improved, ensuring that the electroplating effect meets the expected standards, ultimately obtaining compliant plastic product information, thereby improving production efficiency and product qualification rate.

[0057] In some embodiments, the shadow area, tilt angle, substrate volume, and surface roughness of the plastic substrate are determined based on the image of the plastic substrate; the substrate complexity index is calculated according to formula (1) based on the shadow area, tilt angle, substrate volume, and surface roughness. (1); in, Indicates the complexity index of the substrate. This represents the preset density weighting coefficient. Indicates the volume of the substrate. Indicates the area of ​​the shaded region. This represents the preset angle weighting coefficient. Indicates the tilt angle. This indicates the preset roughness weighting coefficient. Indicates surface roughness; The complexity of the substrate is determined based on the substrate complexity index.

[0058] The preset density weighting coefficient can be a coefficient that represents the influence of the volume distribution density of the plastic substrate within the shaded area on the substrate complexity index.

[0059] The preset angle weighting coefficient can be a coefficient that represents the influence of the tilt angle of the plastic substrate on the substrate complexity index.

[0060] The preset roughness weighting coefficient can be a coefficient that represents the influence of the surface roughness of the plastic substrate on the substrate complexity index.

[0061] The image of the plastic substrate can be an image representing the plastic substrate during the electroplating production process.

[0062] The shaded area can be the area of ​​the shadow formed by the position of the plastic substrate on the electroplating hook during the electroplating production process.

[0063] The tilt angle can be the angle formed when the plastic substrate is tilted during the electroplating process using the electroplating hook.

[0064] The substrate complexity index can be a numerical value that measures the complexity of plastic substrates.

[0065] Specifically, during the electroplating process of plastic substrates, the complexity of the substrate affects the electroplating effect, and consequently, the yield rate of the electroplated products. Therefore, it is necessary to determine the complexity of the plastic substrate based on information from the electroplating process and the substrate itself, providing a reference for subsequent operations and ensuring their accuracy.

[0066] Image analysis algorithms are used to analyze images of plastic substrates to obtain the shadow area and tilt angle of the plastic substrates. The surface roughness of the plastic substrates is extracted from the plastic substrate information. Historical electroplating production information is obtained from the electroplating production control center, and historical substrate volume, historical substrate density, historical tilt angle, historical surface roughness, and historical substrate complexity are extracted from the historical electroplating production information. Linear regression algorithms are used to analyze historical substrate volume, historical substrate density, historical tilt angle, historical surface roughness, and historical substrate complexity to obtain preset volume density weight coefficients, preset angle weight coefficients, and preset roughness weight coefficients. Mathematical analysis methods are used to analyze preset volume density weight coefficients, substrate volume, shadow area, preset angle weight coefficients, tilt angle, preset roughness weight coefficients, and surface roughness. The substrate complexity index is calculated according to formula (1). Before determining the substrate complexity, a complexity index level corresponding to the complexity index is preset to obtain a preset index level. The preset index level obtained in the above steps is matched with the substrate complexity index to obtain the substrate complexity index level corresponding to the substrate complexity index. The substrate complexity index level is determined as the substrate complexity.

[0067] In formula (1), the relationship between volume and shaded area is: Volume This represents the space occupied by the substrate, while the shaded area... This reflects its projection onto a certain plane. The ratio It can be used to measure the relationship between the three-dimensionality of a substrate and its surface features. A higher [value / quality] is desirable. This indicates that the substrate is relatively "thick" in space or has a large volume.

[0068] The contribution of plastic substrate volume to complexity: This part is achieved through... This is used to adjust the impact of volume on complexity. Larger substrates tend to have more complex spatial structures, thus affecting the final level of complexity. .

[0069] The effect of tilt angle: tilt angle This describes the degree of tilt of the substrate surface relative to a reference plane. A larger tilt angle can result in more complex geometry and lighting effects, thus increasing the visual complexity of the substrate.

[0070] The contribution of tilt angle to complexity: through This part can adjust the effect of the tilt angle on the complexity of the substrate, reflecting the influence of different angles on illumination, reflection and overall shape complexity.

[0071] Surface roughness This reflects the roughness of the substrate surface. A rough surface typically leads to more light scattering, affecting light reflection and visual effects, thus increasing the complexity of the substrate. This section adjusts the impact of surface roughness on complexity, emphasizing the importance of surface properties for overall visual and structural complexity.

[0072] The method provided in this embodiment first determines key parameters such as shadow area, tilt angle, substrate volume, and surface roughness. These parameters are crucial for assessing the complexity of the substrate. Measuring the shadow area helps understand the impact of illumination on surface features; the tilt angle reflects the substrate's geometry; the substrate volume is the basis for quantifying its three-dimensional features; and surface roughness directly relates to electroplating quality. By combining these parameters and using a pre-defined complexity index calculation formula, a complexity index that comprehensively reflects the substrate's complexity can be derived. This formula uses a weighted calculation of volume, shadow area, tilt angle, and surface roughness to ensure the rationality and scientific validity of each factor in the overall assessment. Pre-set weighting coefficients further refine the relative importance of each factor in the complexity assessment. Ultimately, the determination of the substrate complexity index accurately reflects the substrate's complexity, providing data support for subsequent electroplating process adjustments, effectively guiding optimization decisions in the production process, and thereby improving product consistency and yield.

[0073] In some embodiments, based on the real-time electroplating image and plastic substrate information, it is determined whether there is an abnormality in the outline of the plastic substrate. If there is an abnormality in the outline of the plastic substrate, the outline abnormality information is determined. Based on the real-time electroplating image, it is determined whether there are bubbles in the plastic substrate. If there are bubbles in the plastic substrate, the bubble abnormality information is determined. Based on the real-time electroplating image, it is determined whether there is an abnormality in the thickness change of the plastic substrate. If there is an abnormality in the thickness change of the plastic substrate, the thickness abnormality information is determined. Based on the outline abnormality information, bubble abnormality information, and thickness abnormality information, a surface abnormality information set is constructed. Based on the real-time electroplating image, the real-time outline of the substrate is determined. Current distribution information is obtained, and based on the current distribution information, the current distribution area is determined. Based on the real-time outline of the substrate, the influence of the electroplating solution temperature and the current distribution area on the real-time outline of the substrate is calculated according to formula (2). (2); in, Indicates the degree of impact. This indicates the preset thermal influence coefficient. Indicates the coefficient of thermal expansion of the substrate. Indicates the temperature of the electroplating solution. This indicates the preset current influence coefficient. The current distribution area is represented; based on the degree of influence, the standard range of the plastic substrate contour is obtained, and the real-time contour of the substrate is matched with the standard range of the contour to determine whether the real-time contour of the substrate exceeds the standard range of the contour; if the real-time contour of the substrate exceeds the standard range of the contour, the real-time contour of the substrate is identified as contour anomaly information and the contour anomaly information is included in the anomaly information set.

[0074] Current distribution information can be information about how the current is distributed in the electroplating solution during the electroplating process on a plastic substrate. Current distribution information can include the area of ​​current distribution.

[0075] The current distribution area can be the area formed by the distribution of current in the electroplating solution during the electroplating process of plastic substrate.

[0076] The preset thermal influence coefficient can be a coefficient that represents the influence of the coefficient of thermal expansion of the plastic substrate on the degree of influence.

[0077] The preset current influence coefficient can be a coefficient that represents the influence of the current distribution area on the degree of influence during the electroplating process of plastic substrate.

[0078] Contour anomaly information can indicate that anomalies occur in the contour of the plastic substrate during the electroplating process.

[0079] Bubble anomaly information can indicate the appearance of bubbles on the surface of a plastic substrate during the electroplating process.

[0080] Thickness anomaly information can indicate that the coating thickness on the surface of the plastic substrate is abnormal during the electroplating process of the plastic substrate.

[0081] The real-time profile of the substrate can be the profile of the plastic substrate during the electroplating process.

[0082] Specifically, during the electroplating process of plastic substrates, surface roughness or the influence of electroplating parameters can cause air bubbles to appear on the substrate surface. The presence of these bubbles can alter the substrate's outline, and if this change becomes significant, it results in an abnormal outline. In such cases, it's necessary to combine electroplating information with factors specific to the plastic substrate to identify the abnormal outline and provide a basis for adjusting subsequent electroplating parameters.

[0083] The standard contour range of the plastic substrate is extracted from the plastic substrate information. A contour detection algorithm is used to detect the contour range of the plastic substrate during the electroplating process in the real-time electroplating image. The contour range of the plastic substrate during the electroplating process is matched with the standard contour range. If the contour range of the plastic substrate during the electroplating process is not within the standard contour range, it is determined that the contour of the plastic substrate is abnormal, and the contour range of the plastic substrate during the electroplating process is identified as contour abnormality information. A convolutional neural network is used to analyze the real-time electroplating image to obtain image analysis results. Based on the image analysis results, it is determined whether bubbles appear on the surface of the plastic substrate. If bubbles appear, it is determined that bubbles exist on the surface of the plastic substrate, and the presence of bubbles on the surface of the plastic substrate is identified as bubble abnormality information. The standard thickness range is extracted from the plastic substrate information. The thickness detection range is extracted from the image recognition results obtained in the above steps. The standard thickness range is matched with the thickness detection range to determine whether the thickness detection range is within the standard thickness range. If the thickness detection range is not within the standard thickness range, the thickness detection range is identified as thickness abnormality information. The contour anomaly information, bubble anomaly information, and thickness anomaly information obtained from the above steps are integrated to obtain a surface anomaly information set.

[0084] Edge detection algorithms are used to analyze real-time electroplating images to obtain the real-time contour of the substrate. Historical substrate thermal expansion coefficients, plating solution temperatures, current distribution information, and real-time contours are extracted from historical electroplating production information. The historical current distribution area is extracted from the historical current distribution information. Linear regression methods are used to analyze the historical substrate thermal expansion coefficients, historical current distribution areas, real-time contours, and historical plating solution temperatures to obtain preset thermal influence coefficients and current influence coefficients. Mathematical analysis methods are then used to analyze the preset thermal influence coefficients, substrate thermal expansion coefficients, plating solution temperatures, preset current influence coefficients, and current distribution areas to determine the degree of influence of the real-time substrate contour.

[0085] Before determining whether the real-time profile of the substrate exceeds the profile standard range, the degree of influence is calculated. This system can dynamically adjust the criteria for judging the real-time contour of the substrate based on characteristics such as the actual electroplating solution temperature and current distribution area. This adjustment avoids misjudging abnormalities in the contour of the plastic substrate due to normal contour changes during the electroplating process. Especially in electroplating environments where process conditions are easily variable, this method can significantly improve the accuracy of judgment.

[0086] If the real-time contour of the substrate is directly matched, normal contour changes caused by temperature and current during electroplating may be identified as abnormal. This false alarm leads to frequent and unnecessary anomaly detection, increasing the system load and potentially causing error accumulation while failing to accurately reflect actual problems in the electroplating process. Therefore, it is crucial to first determine the degree of influence. This allows for the identification of substrate contour anomalies after excluding normal fluctuations, reducing the probability of false alarms. This ensures that subsequent contour comparisons focus only on genuine abnormal changes, rather than natural contour deviations caused by electroplating conditions. Therefore, after determining the extent of the impact, it is necessary to further analyze real-time electroplating images to determine whether the real-time contour of the substrate exceeds the contour standard range.

[0087] In formula (2), the product is used. This allows for the quantification of shape changes in a substrate due to thermal expansion at a specific electroplating solution temperature. Furthermore, through... The weighting coefficients are adjusted to reflect the degree of influence of thermal expansion on the real-time profile of the substrate. A larger thermal influence coefficient indicates that the thermal effect has a more significant impact on the substrate profile under the current electroplating conditions. Current distribution. This reflects how the current is distributed on the substrate surface during electroplating. Uneven current distribution can lead to variations in the thickness of the electroplated layer, thus affecting the final profile and quality. Current Influence Coefficient The influence of current distribution on the real-time profile of the substrate was adjusted. Variations in current can cause the material to expand or contract in different regions, thus affecting the accuracy of the shape. A higher current influence coefficient indicates that the current distribution has a greater impact on the real-time profile.

[0088] The method provided in this embodiment combines real-time electroplating images and plastic substrate information to first determine if there are any abnormalities in the substrate's contour. If a contour abnormality is detected, corresponding contour abnormality information is generated. Next, by analyzing the real-time electroplating images, it is determined whether there are any bubbles. If bubbles are found, bubble abnormality information is recorded to prevent coating defects caused by bubbles. Simultaneously, thickness variation abnormalities are also monitored; if thickness variation abnormalities are found, thickness abnormality information is recorded accordingly. The contour, bubble, and thickness abnormality information are integrated to form a surface abnormality information set, providing a basis for subsequent adjustments. After determining the real-time contour of the substrate based on the real-time electroplating images, the influence of electroplating solution temperature and current distribution on the contour is analyzed using formulas. This step ensures precise control of electroplating conditions. After calculating a standard range using the degree of influence, the real-time contour is matched against it. If the real-time contour exceeds the standard range, it is considered contour abnormality information and further included in the abnormality information set. The effective execution of this series of steps enables real-time monitoring and adjustment of the electroplating process, ensuring that the quality of plastic products meets standards.

[0089] In some embodiments, the bubble location is determined based on a real-time electroplating image; based on the bubble location, it is determined whether the appearance of the bubble is caused by surface roughness of the plastic substrate; if the appearance of the bubble is caused by surface roughness of the substrate, the surface roughness is determined as bubble anomaly information; if the appearance of the bubble location is not caused by surface roughness, the current intensity is determined based on the electroplating information; the electroplating time is determined based on the real-time electroplating information; the bubble volume and bubble density are determined based on the electroplating time, current distribution, and current intensity; the bubble volume, bubble density, and bubble location are determined as bubble anomaly information, and the bubble anomaly information is included in the surface anomaly information set.

[0090] The location of the bubble can be the position of the bubble on the surface of the plastic substrate during the electroplating process.

[0091] Real-time electroplating images can be images of plastic substrates being electroplated in an electroplating solution.

[0092] Electroplating time can be the time required for the plastic substrate to be electroplated in the electroplating solution.

[0093] Current intensity can be the intensity of the effect of current on the plastic substrate during electroplating in the electroplating solution.

[0094] Specifically, during the electroplating process of plastic substrates, bubbles may appear on the substrate surface due to surface roughness or the influence of parameters such as current in the electroplating production process. The presence of bubbles on the plastic substrate surface affects the electroplating quality and consequently the pass rate of the electroplated products. Therefore, it is necessary to collect information on the presence of bubbles on the plastic substrate surface, identify the abnormal bubble information, and provide a reference for subsequent bubble handling.

[0095] Image analysis algorithms are used to analyze real-time electroplating images to determine bubble locations. Once bubble locations are identified, texture analysis techniques are applied to analyze the real-time electroplating images to determine the surface roughness of the plastic substrate during electroplating. The surface roughness range of the plastic substrate is obtained from the substrate information. The surface roughness during electroplating is matched against this range. If the surface roughness is outside this range, the appearance of bubbles is determined to be caused by surface roughness, and this surface roughness is identified as an anomaly. With the surface roughness considered an anomaly, the maximum value of the surface roughness range is extracted. This maximum value is subtracted from the surface roughness to obtain the subtraction value. The grinding intensity of the plastic substrate is obtained from the electroplating production control center. The subtraction value is divided by the grinding intensity to obtain the grinding time. The grinding time is sent to the electroplating production control center, which then grinds the surface of the plastic substrate according to the grinding time to ensure that the surface roughness of the plastic substrate is within the acceptable range after grinding. If the appearance of bubbles is not caused by the surface roughness of the plastic substrate, the current intensity is extracted from the electroplating information. The electroplating time is extracted from the real-time electroplating information. Mathematical analysis methods are used to analyze the electroplating time, current location, and current intensity to obtain the degree of bubble influence in the electroplating information. Based on the degree of bubble influence, image analysis algorithms are used to analyze the real-time electroplating image to obtain the bubble volume and bubble density. The bubble volume, bubble density, and bubble location are identified as bubble anomaly information and included in the surface anomaly information set.

[0096] The method provided in this embodiment first determines the location of bubbles through analysis of real-time electroplating images, laying the foundation for subsequent problem diagnosis. Based on the bubble location, it is further determined whether the bubble formation is related to the surface roughness of the plastic substrate. If it is confirmed that the bubble is caused by surface roughness, the corresponding roughness information is marked as bubble anomaly information. This process is crucial because surface characteristics directly affect electroplating quality and the likelihood of bubble formation. If the appearance of the bubble is not due to surface roughness, the current intensity needs to be determined based on real-time electroplating information to assess the energy supply during the electroplating process. Simultaneously, the electroplating time is further determined through real-time electroplating information, which is another key factor affecting bubble formation. Combining electroplating time, current distribution, and current intensity, the volume and density of the bubbles can be accurately calculated. These values ​​provide a quantitative basis for judging the impact of bubbles on the final product. Finally, the volume, density, and location of the bubbles are incorporated into the bubble anomaly information to form a comprehensive set of surface anomaly information. The effective execution of this series of steps ensures optimized management of the electroplating process and improves the consistency and reliability of product quality.

[0097] In some embodiments, the electroplating area of ​​the substrate and the electroplating distance between the electrode and the substrate are determined based on real-time electroplating images; the electroplating area of ​​the substrate is segmented to obtain several electroplating areas of the substrate; the electrode position and current density are determined based on real-time electroplating information; the coating adhesion is determined based on the electrode position, the electroplating distance, and the current density; the electroplating thickness of each electroplating area of ​​the substrate is obtained based on the coating adhesion and the several electroplating areas of the substrate; the thickness error range is obtained, and the electroplating thickness is matched with the thickness error range to obtain a matching result; based on the matching result, it is determined whether the electroplating thickness exceeds the thickness error range; if the electroplating thickness exceeds the thickness error range, the area exceeding the thickness error range is identified as a thickness abnormal area, the thickness abnormal area is identified as thickness abnormal information, and the thickness abnormal information is included in the surface abnormal information set.

[0098] Electroplating distance can be the relative distance between the electrode and the plastic substrate.

[0099] Electrode position can be the physical location of the electrode in the electroplating solution during the electroplating process of plastic substrate.

[0100] Current density can be defined as the concentration of current in the electroplating solution during the electroplating process of plastic substrates.

[0101] The thickness error range can be the range formed by the error between the coating thickness on the surface of the plastic substrate and the standard coating thickness.

[0102] Coating adhesion can be a numerical value representing the degree of adhesion of metal ions to the surface of a plastic substrate.

[0103] Electroplating thickness can refer to the thickness of the coating on the surface of the plastic substrate during the electroplating process.

[0104] Specifically, during the electroplating process of plastic substrates, the thickness of the plating layer on the substrate surface varies due to the influence of parameters such as current. When this variation reaches a certain point, an abnormality in the plating thickness occurs, affecting the electroplating quality. Therefore, it is necessary to identify the abnormalities in the plating thickness of the plastic substrate to provide a basis for subsequent handling of these issues.

[0105] The real-time electroplating image was analyzed using a target detection algorithm to obtain the substrate electroplating area, the plastic substrate position, and the electrode position. Given the plastic substrate position and electrode position, a computer vision algorithm was used to analyze these positions to obtain the electroplating distance between the electrode and the substrate. Based on the substrate electroplating area, an image segmentation algorithm was used to segment the real-time electroplating image to obtain several substrate electroplating areas. The current density was extracted from the real-time electroplating information. Historical electroplating distance, historical current intensity, historical surface roughness, historical current density, and historical coating adhesion were extracted from historical electroplating production information. A linear regression method was used to analyze these parameters to obtain empirical coefficients and the influence index of current intensity, electroplating distance, and current density on coating adhesion. Based on the electrode position, a mathematical analysis algorithm was used to analyze the empirical coefficients, influence index, electroplating distance, current intensity, surface roughness, and current density. The coating adhesion was calculated according to formula (3): (3); in, Indicates the adhesion of the coating. This represents an empirical coefficient (used to adjust for the effects of different materials and processes). Indicates current intensity. Indicates the electroplating distance. Indicates surface roughness. Indicates current density, This indicates the degree of influence.

[0106] Based on the coating adhesion obtained from the above steps, an image analysis algorithm is used to analyze the real-time electroplating image to obtain the electroplating thickness of each substrate electroplating area. The thickness error range is obtained from the plastic substrate information, and the electroplating thickness is matched with the thickness error range. If the electroplating thickness is outside the thickness error range, the area exceeding the thickness error range is identified as a thickness anomaly area. This thickness anomaly area is then identified as thickness anomaly information and included in the surface anomaly information set.

[0107] The method provided in this embodiment first determines the electroplating area of ​​the substrate and the electroplating distance between the electrode and the substrate, providing crucial spatial positioning information for subsequent electroplating effect analysis. After segmenting the substrate electroplating area, it can be divided into several independent electroplating areas to more accurately evaluate the electroplating effect of each area. Next, the electrode position and its current density are determined based on real-time electroplating information, which is the basis for evaluating the uniformity and effectiveness of the electroplating process. Based on the electrode position, electroplating distance, and current density, the adhesion of the coating can be calculated; this indicator directly affects the quality and durability of the coating. By analyzing the coating adhesion of each substrate electroplating area, the specific electroplating thickness can be obtained, further ensuring the consistency of the electroplating layer. The obtained thickness error range makes the inspection of the electroplating thickness operable, and it is matched with the thickness error range to determine whether the electroplating thickness meets the standard. If the electroplating thickness is found to exceed the error range, the relevant area is marked as a thickness anomaly area, and the thickness anomaly information is recorded and ultimately included in the anomaly information set. The implementation of the above series of steps ensures real-time monitoring and adjustment of the electroplating process, thereby improving the overall quality and reliability of the product.

[0108] In some embodiments, surface anomaly information is matched with a set of surface anomaly information to obtain anomaly information types; based on the anomaly information types, several parameter adjustment information is determined according to real-time electroplating information and substrate complexity, and the electroplating production process is adjusted according to these parameter adjustment information; based on the anomaly information types, it is determined whether the parameter adjustments have caused other anomalies besides the anomaly information types; if the parameter adjustments have caused other anomalies besides the anomaly information types, the parameter change magnitude, number of parameter changes, and parameter change types of the other anomalies are determined according to the several parameter adjustment information and other anomalies; the parameter correlation degree is determined according to the parameter change magnitude, number of parameter changes, and parameter change types; and the correlated parameters are determined according to the real-time electroplating information, plastic substrate information, and parameter correlation degree.

[0109] The anomaly information type can be the type of anomaly information appearing on the surface of the plastic substrate during the electroplating process. Anomaly information types can include bubble anomalies, thickness anomalies, and contour anomalies.

[0110] The parameter adjustment information can be information on adjusting several parameters of the electroplating process during the electroplating of plastic substrates.

[0111] The parameter change range can be the value corresponding to the change range of relevant parameters when other abnormal situations occur after the electroplating process has been adjusted based on several parameter adjustment information.

[0112] The number of parameter changes can be the number of changes in relevant parameters when other abnormal situations occur after the electroplating process has been adjusted based on several parameter adjustment information.

[0113] The parameter change type can be the type of change in the relevant parameters when other abnormal situations occur after the electroplating process has been adjusted based on several parameter adjustment information.

[0114] The degree of parameter correlation can be the numerical value corresponding to the probability of other abnormal situations occurring when the electroplating process is adjusted based on several parameter adjustment information.

[0115] Correlated parameters can be parameters that create a correlation between the type of abnormal information and other abnormal situations during the process of adjusting the electroplating process based on several parameter adjustment information.

[0116] Specifically, after identifying anomalies on the plastic substrate surface, the parameters of the electroplating process need to be adjusted accordingly. However, adjusting only a single parameter based on the anomalies on the plastic substrate surface cannot guarantee that other electroplating processes related to that single parameter will not experience anomalies, potentially leading to such anomalies. Therefore, it is necessary to consider the potential for other anomalies after adjusting a particular electroplating process parameter, and to determine the correlation between surface anomalies and other anomalies. This identification of related parameters provides an accurate basis for subsequent parameter adjustments in the electroplating process.

[0117] The surface anomaly information identified in the above steps is matched with the surface anomaly information set to obtain the type corresponding to the surface anomaly information. This type is then determined as the anomaly information type. Given the complexity of the substrate, the real-time electroplating information is adjusted according to the anomaly information type to obtain adjustment information for several parameters under the determined anomaly information type. The electroplating production process is then adjusted based on this parameter adjustment information. Before adjusting the electroplating production process according to the parameter adjustment information, an electrochemical simulation model is used to simulate and adjust the electroplating production process based on the parameter adjustment information, yielding simulation adjustment results. Based on the simulation adjustment results, it is determined whether adjusting the electroplating production process according to the parameter adjustment information will lead to other anomalies. If other anomalies occur, parameters under these other anomalies are extracted from the prediction results based on the parameter adjustment information and other anomalies. Parameters before the occurrence of other anomalies are extracted from the real-time electroplating information. The parameters under other anomalies are compared with the parameters before the occurrence of other anomalies to obtain comparison results. The comparison results extract the parameter change amplitude, the number of parameter changes, and the type of parameter change. The type of parameter change is then identified as the parameter change type. Based on the parameter change type, in determining the degree of parameter correlation, a larger parameter change amplitude and a larger parameter change number indicate a higher degree of correlation between the parameters; that is, there is a linear relationship between the degree of parameter correlation and the parameter change amplitude and number. Therefore, a linear regression algorithm is used to analyze the parameter change amplitude and number to obtain the degree of parameter correlation. Before determining the correlated parameters, a parameter correlation degree level is set, including high correlation, medium correlation, and low correlation. The parameter correlation degree is matched with the parameter correlation degree level to obtain the level corresponding to the current parameter correlation degree. Based on the level corresponding to the current parameter correlation degree and the plastic substrate information, the real-time electroplating information is adjusted to obtain electroplating parameters related to other anomalies. These electroplating parameters related to other anomalies are identified as correlated parameters.

[0118] By matching surface anomaly information with a set of surface anomaly information, the types of anomalies can be effectively identified, providing a basis for subsequent adjustments and optimizations. Based on these anomaly types, combined with real-time electroplating information and substrate complexity, targeted parameter adjustments can be formulated. These adjustments aim to improve deficiencies in the electroplating production process, thereby optimizing product quality. After implementing parameter adjustments, their effects need to be evaluated to determine whether they have caused other anomalies besides the identified types. If the parameter adjustments have indeed led to new anomalies, the magnitude, quantity, and type of parameter changes in these anomalies are further analyzed to understand the nature and severity of the new problems. Through in-depth analysis of parameter changes, the degree of correlation is determined, revealing the mutual influence between parameters. The above systematic analysis method can more accurately identify and correct problems in the electroplating process, ensuring that each adjustment is based on empirical data, and ultimately accurately determining the relevant parameters to be focused on based on real-time electroplating information, plastic substrate information, and parameter correlation. The execution of this series of steps ensures the scientific nature and effectiveness of the electroplating process, thereby improving production stability and product consistency.

[0119] In some embodiments, if the surface anomaly information set includes bubble anomaly information, then bubble parameter correlation information is determined based on correlation parameters and the degree of correlation between parameters; based on the complexity of the substrate, the temperature adjustment range and the stirring speed adjustment range are determined based on the bubble volume and bubble position; bubble adjustment information is determined based on the temperature adjustment range, bubble parameter correlation information and speed adjustment range, and the bubble adjustment information is incorporated into the real-time adjustment information set.

[0120] Bubble parameter correlation information can be parameter information that is determined when bubbles appear on the surface of a plastic substrate and is associated with other abnormal conditions.

[0121] Specifically, after bubbles appear on the surface of a plastic substrate, due to the complexity of the substrate, adjusting production parameters solely based on this phenomenon without considering its complexity cannot guarantee that other electroplating processes will remain normal after the adjustments. Therefore, it is necessary to determine the parameters associated with the bubble phenomenon in other abnormal situations when bubbles appear, based on relevant information about the bubble anomaly, to ensure more precise and effective adjustments to electroplating production parameters.

[0122] When the surface anomaly information set includes bubble anomaly information, based on the parameter correlation level determined in the above steps, high, medium, and low correlation levels are extracted from the parameter correlation level obtained in the above steps. The higher the parameter correlation level, the greater the promoting effect on the occurrence of other anomalies. When bubbles appear, the correlation parameters corresponding to the high and medium correlation levels are determined as bubble parameter correlation information. Mathematical analysis algorithms are used to analyze the bubble volume, bubble location, and substrate complexity to obtain the temperature value and stirring speed determined when bubbles appear. The temperature value and stirring speed before the bubbles appear are obtained from the real-time electroplating information. The temperature value and stirring speed before the bubbles appear are subtracted from the temperature value and stirring speed after the bubbles appear, respectively, to obtain the temperature subtraction result and stirring speed subtraction result. The temperature subtraction result and stirring speed subtraction result are determined as the temperature adjustment range and speed adjustment range. If the temperature subtraction result is positive, it indicates that the temperature value before the bubbles appear needs to be adjusted upward based on the temperature subtraction result. If the temperature subtraction result is negative, it indicates that the temperature value before the bubbles appeared needs to be lowered based on the subtraction result. If the stirring speed subtraction result is positive, it indicates that the stirring speed value before the bubbles appeared needs to be increased based on the subtraction result. If the stirring speed subtraction result is negative, it indicates that the stirring speed value before the bubbles appeared needs to be lowered based on the subtraction result. The bubble adjustment information is determined by combining the temperature adjustment range, bubble parameter correlation information, and speed adjustment range, and then incorporated into the real-time adjustment information set.

[0123] The method provided in this embodiment allows for the determination of bubble parameter correlation information during electroplating production if the surface anomaly information set includes bubble anomaly information. This step provides data support for handling bubble problems. Next, based on the complexity of the substrate and the volume and location of the bubbles, appropriate temperature and stirring speed adjustments can be determined. Reasonable adjustments to temperature and stirring speed are crucial for reducing bubble formation, effectively improving the fluidity and uniformity of the electroplating solution, thereby reducing bubble generation during the electroplating process. Combining the temperature adjustment range, bubble parameter correlation information, and speed adjustment range, bubble adjustment information can be further determined. This information provides guidance for actual operation, ensuring real-time response to bubble changes during adjustment. Finally, the bubble adjustment information is incorporated into the real-time adjustment information set to form a systematic adjustment plan for timely response and optimization during production. The implementation of this series of steps ensures precise control of the electroplating process, improves the quality and consistency of plastic products, and helps to better meet production requirements.

[0124] In some embodiments, if the surface anomaly information set includes thickness anomaly information, then based on the real-time electroplating information and the complexity of the substrate, it is determined whether the thickness anomaly information is generated by the real-time electroplating information; if the thickness anomaly information is generated by the real-time electroplating information, then based on the real-time electroplating information and the real-time electroplating image, the thickness anomaly type is determined; based on the associated parameters and the complexity of the substrate, the thickness parameter association information is determined; based on the thickness anomaly type and the thickness parameter association information, the current adjustment information is determined; based on the bubble volume, bubble density, and bubble position, the electroplating adjustment information is determined; based on the electroplating adjustment information and the current adjustment information, the thickness adjustment information is determined, and the thickness adjustment information is included in the real-time adjustment information set.

[0125] Thickness parameter correlation information can be parameter information that is determined to be related to other abnormal conditions when an abnormality occurs on the surface of a plastic substrate.

[0126] The thickness anomaly type can be the type of situation that causes abnormal thickness on the surface of the plastic substrate during the electroplating process.

[0127] Current adjustment information can be used to determine the adjustment of the electroplating current when an abnormal thickness of the coating appears on the surface of a plastic substrate.

[0128] Electroplating adjustment information can be information used to adjust the electroplating production process when an abnormal thickness of the coating appears on the surface of a plastic substrate.

[0129] Thickness adjustment information can be used to determine comprehensive adjustments to the electroplating production process when abnormal thickness of the coating appears on the surface of a plastic substrate.

[0130] Specifically, during the electroplating process of plastic substrates, electroplating the substrate in the presence of bubbles will result in a thicker plating layer in the bubble-filled areas than in the bubble-free areas. This will cause the plating thickness in the bubble-filled areas to fail to meet the standard plating thickness. In this case, it is necessary to comprehensively consider both the bubbles and the abnormal plating thickness to determine the appropriate parameter adjustments to ensure the pass rate of the electroplated plastic substrate products.

[0131] All possible types of coating anomalies that may occur on the surface of plastic substrates during electroplating are obtained from the electroplating production control center. These types are then integrated to obtain a preset set of thickness anomaly types. If thickness anomalies are present in this set, image analysis methods are used to analyze real-time electroplating images based on substrate complexity. The image analysis results are then used to determine if the thickness anomaly is caused by real-time electroplating information. If so, image analysis is used to analyze the real-time electroplating images to obtain the thickness anomaly information on the plastic substrate during electroplating. This thickness anomaly information is then matched with the preset set of thickness anomaly types to obtain the corresponding thickness anomaly types. Based on substrate complexity, when thickness anomalies occur, the correlation parameters corresponding to high and medium correlation levels are identified as thickness parameter correlation information. Based on the thickness anomaly type, current intensity adjustment range, current distribution adjustment information, and current density adjustment information are extracted from the thickness parameter correlation information. These three information are then identified as the current adjustment information. Based on bubble location, mathematical analysis methods are used to analyze bubble volume and density, determining the correlation between bubble volume / density and electroplating time. Before determining the electroplating adjustment information, a correlation level is established, categorized as high, medium, and low. The electroplating time adjustment range corresponding to a high correlation level and a medium correlation level are identified as the electroplating adjustment information. The electroplating adjustment information and current adjustment information are then identified as the thickness adjustment information and incorporated into the real-time adjustment information set.

[0132] The method provided in this embodiment first requires determining whether the thickness anomaly is caused by the current electroplating conditions based on real-time electroplating information and the complexity of the substrate. If it is confirmed that the thickness anomaly is caused by real-time electroplating information, the real-time electroplating information and images are further analyzed to determine the specific type of thickness anomaly. This step is crucial for understanding the nature of the thickness problem. Next, by analyzing the correlation parameters and the complexity of the substrate, thickness parameter correlation information can be obtained. This information helps to deeply understand the interrelationships between various factors in the electroplating process. Based on the type of thickness anomaly and the thickness parameter correlation information, corresponding current adjustment information is formulated to optimize the electroplating process. At the same time, based on the volume, density, and location of bubbles, electroplating adjustment information can be determined to prevent the bubble problem from affecting the thickness. Finally, the electroplating adjustment information and current adjustment information are combined to determine the thickness adjustment information, which is then incorporated into the real-time adjustment information set. The effective implementation of this series of steps not only ensures thickness control during the electroplating process but also improves the consistency of product quality, providing a scientific basis for subsequent production decisions.

[0133] In some embodiments, if the surface anomaly information set includes contour anomaly information, then the contour parameter association information is determined based on the association parameters and the contour anomaly information; the electroplating time is determined based on the electroplating area, electroplating structure, and plating thickness; and the degree of agitation of the electroplating solution is determined based on the shaded area, electroplating time, and tilt angle, according to formula (4). (4); in, Indicates the degree of mixing. Indicates the adjustment constant. Indicates the area of ​​the shaded region. This indicates the preset area coefficient. Indicates the electroplating time. This indicates the preset electroplating time coefficient. Indicates the tilt angle; based on the contour parameter association information, substrate complexity and agitation degree, determine the contour adjustment information and incorporate the contour adjustment information into the real-time adjustment information set.

[0134] The adjustment constant can be a numerical value used to adjust the proportional relationship between parameters in a formula.

[0135] The preset area coefficient can be a coefficient representing the influence of the shaded area on the degree of stirring.

[0136] The preset electroplating time coefficient can be a coefficient representing the influence of electroplating time on the degree of agitation.

[0137] Contour parameter association information can be parameter information that is associated with other abnormal conditions when contour abnormalities occur on the surface of a plastic substrate.

[0138] Electroplated structures can be the parts of a plastic substrate that require electroplating during the electroplating process.

[0139] Specifically, during the electroplating process of plastic substrates, abnormal thickness of the substrate often leads to abnormalities in its outline. Therefore, it is necessary to comprehensively consider both thickness and outline abnormalities to ensure that the adjustment information for electroplating parameters better reflects the actual electroplating production process and the occurrence of such abnormalities.

[0140] When the thickness of the plastic substrate surface is abnormal, the contour parameters corresponding to the high correlation level and the contour parameters corresponding to the medium correlation level are determined as contour parameter correlation information. The area of ​​the plastic substrate to be electroplated is extracted from the plastic substrate information, and the area of ​​the plastic substrate to be electroplated is determined as the electroplating structure. The electroplating area, electroplating structure and coating thickness are analyzed using mathematical analysis methods to obtain the electroplating time. The historical electroplating time, historical shadow area and historical stirring degree are extracted from the historical electroplating information. The historical electroplating time, historical shadow area and historical stirring degree are analyzed using linear regression methods to obtain the preset electroplating time coefficient and preset area coefficient. The shadow area, electroplating time, tilt angle, preset area coefficient and preset electroplating time coefficient are analyzed using mathematical analysis methods, and the stirring degree of the electroplating solution is calculated according to formula (4).

[0141] In formula (4), a larger shaded area usually means that the electroplating liquid flows more actively and at a faster rate, thus enhancing the stirring effect. The form of the formula can be used to assess the relative contribution of the shaded area to the degree of agitation, ensuring that the formula is applicable to different experimental settings or equipment configurations. This section can express how the shaded area affects the flow and agitation uniformity of the liquid under specific electroplating conditions.

[0142] Longer electroplating times typically mean more complete metal deposition and longer liquid agitation, thus contributing to improved coating uniformity. This allows for the assessment of the contribution of plating time to the degree of agitation, ensuring that the same formula is used for comparisons under different plating process conditions. This section effectively expresses how the degree of agitation changes during long-term plating, further optimizing the plating time to obtain the best coating quality.

[0143] The sine of the tilt angle This reflects the fluidity of the liquid in the tank. A larger tilt angle generally means that the liquid can flow better, thus promoting agitation. The form can capture the relationship between liquid flow and the degree of agitation, reflecting the nonlinear influence of the tilt angle on the flow state.

[0144] Based on the complexity of the substrate, the contour parameter correlation information and the degree of agitation of the electroplating solution are determined as contour adjustment information, and the contour adjustment information is incorporated into the real-time adjustment information set.

[0145] The method provided in this embodiment first requires determining the contour parameter correlation information based on the correlation parameters and contour anomaly information. This step helps identify potential factors causing contour anomalies. Then, based on the electroplating area, electroplating structure, and coating thickness, the required electroplating time is accurately calculated to ensure the electroplating process can proceed sufficiently to achieve the desired coating effect. Next, the degree of agitation of the electroplating solution is calculated using the shaded area, electroplating time, and tilt angle, reflecting the impact of agitation on electroplating quality. A reasonable degree of agitation ensures the uniformity of the electroplating solution, thereby improving the adhesion and uniformity of the coating. Finally, based on the contour parameter correlation information, the complexity of the substrate, and the degree of agitation, contour adjustment information is comprehensively determined and incorporated into the real-time adjustment information set. This series of steps not only enables timely response to contour anomaly issues but also optimizes the electroplating process through scientific adjustment strategies, improving product quality and ensuring the final product meets design requirements.

[0146] Figure 3 This is a schematic diagram of the structure of an automated electroplating qualification inspection system provided in one embodiment of this application, as shown below. Figure 3 As shown, the automated electroplating qualification inspection system 300 of this embodiment includes: a complexity determination module 301, an anomaly information determination module 302, a correlation parameter determination module 303, an adjustment information determination module 304, and a product information determination module 305.

[0147] The complexity determination module 301 is used to obtain plastic substrate information and determine the complexity of the substrate based on the plastic substrate information; The abnormal information determination module 302 is used to acquire real-time electroplating information and determine the set of surface abnormal information of the plastic substrate during the electroplating process based on the real-time electroplating information. The correlation parameter determination module 303 is used to determine the correlation parameters that need to be adjusted based on the surface anomaly information set. The adjustment information determination module 304 is used to determine the real-time adjustment information set based on the surface anomaly information set, the associated parameters, and the complexity of the substrate. The product information determination module 305 is used to adjust the real-time production process according to the real-time adjustment information set to obtain plastic product information.

[0148] Optionally, the complexity determination module 301 is specifically used for: Based on the image of the plastic substrate, determine the shadow area, tilt angle, substrate volume, and surface roughness of the plastic substrate; The substrate complexity index is determined based on the shadow area, the tilt angle, the substrate volume, and the surface roughness, and is calculated using the following formula: ; in, This represents the complexity index of the substrate. This indicates the preset volume density weighting coefficient. This indicates the volume of the substrate. This represents the area of ​​the shadow. This represents the preset angle weighting coefficient. Indicates the tilt angle, This indicates the preset roughness weighting coefficient. This refers to the surface roughness; The complexity of the substrate is determined based on the substrate complexity index.

[0149] Optionally, the anomaly information determination module 302 is specifically used for: Based on the real-time electroplating image and the information of the plastic substrate, it is determined whether there is an abnormality in the outline of the plastic substrate. If there is an abnormality in the outline of the plastic substrate, the abnormality information is determined. Based on the real-time electroplating image, it is determined whether there are bubbles in the plastic substrate. If there are bubbles in the plastic substrate, bubble abnormality information is determined. Based on the real-time electroplating image, it is determined whether there is an abnormal thickness change in the plastic substrate. If there is an abnormal thickness change in the plastic substrate, the thickness abnormality information is determined. Based on the contour anomaly information, the bubble anomaly information, and the thickness anomaly information, a surface anomaly information set is constructed; The real-time contour of the substrate is determined based on the real-time electroplating image; Obtain current distribution information, and determine the current distribution area based on the current distribution information; Based on the real-time profile of the substrate, the influence of the electroplating solution temperature and the current distribution area on the real-time profile of the substrate is determined, and calculated according to the following formula: ; in, Indicates the degree of influence. This indicates the preset thermal influence coefficient. This represents the coefficient of thermal expansion of the substrate. This indicates the temperature of the electroplating solution. This indicates the preset current influence coefficient. This represents the area of ​​the current distribution; Based on the degree of influence, the outline standard range of the plastic substrate is obtained, the real-time outline of the substrate is matched with the outline standard range, and it is determined whether the real-time outline of the substrate exceeds the outline standard range. If the real-time contour of the substrate exceeds the contour standard range, the real-time contour of the substrate is determined as contour anomaly information, and the contour anomaly information is included in the anomaly information set.

[0150] Optionally, the anomaly information determination module 302 is specifically used for: The location of the bubbles is determined based on the real-time electroplating image; Based on the location of the bubbles, and according to the real-time electroplating image, it is determined whether the appearance of the bubbles is caused by the surface roughness of the plastic substrate. If the appearance of the bubbles is caused by the surface roughness of the substrate, then the surface roughness is determined as the bubble abnormality information; If the appearance of the bubble location is not caused by the surface roughness, then the current intensity is determined based on the electroplating information; The electroplating time is determined based on the real-time electroplating information; The bubble volume and bubble density are determined based on the electroplating time, the current distribution, and the current intensity. The bubble volume, bubble density, and bubble position are determined as the bubble anomaly information, and the bubble anomaly information is included in the surface anomaly information set.

[0151] Optionally, the anomaly information determination module 302 is specifically used for: Based on the real-time electroplating images, the electroplating area of ​​the substrate and the electroplating distance between the electrode and the substrate are determined; The electroplating area of ​​the substrate is divided to obtain several electroplating areas of the substrate. Based on the real-time electroplating information, the electrode position and current density are determined; Based on the electrode position, the coating adhesion is determined according to the electroplating distance and the current density; The plating thickness of each substrate plating area is obtained based on the coating adhesion and the plating areas of the substrates. Obtain the thickness error range, match the electroplating thickness with the thickness error range, and obtain the matching result; Based on the matching results, it is determined whether the electroplating thickness exceeds the thickness error range; If the electroplating thickness exceeds the thickness error range, the area exceeding the thickness error range is identified as a thickness abnormality area, the thickness abnormality area is identified as the thickness abnormality information, and the thickness abnormality information is included in the surface abnormality information set.

[0152] Optionally, the association parameter determination module 303 is specifically used for: The surface anomaly information is matched with the surface anomaly information set to obtain the anomaly information type; Based on the type of abnormal information, several parameter adjustment information is determined according to the real-time electroplating information and the complexity of the substrate, and the parameters of the electroplating production process are adjusted according to the several parameter adjustment information. Based on the type of abnormal information, determine whether the parameter adjustment causes other abnormal situations besides the type of abnormal information. If the parameter adjustment leads to other abnormal situations besides the abnormal information type, then based on the parameter adjustment information and the other abnormal situations, determine the parameter change magnitude, parameter change quantity, and parameter change type of the other abnormal situations; The degree of correlation of the parameters is determined based on the magnitude of the parameter changes, the number of parameter changes, and the type of parameter changes. The correlation parameters are determined based on the real-time electroplating information, the plastic substrate information, and the correlation degree of the parameters.

[0153] Optionally, the adjustment information determining module 304 is specifically used for: If the surface anomaly information set includes the bubble anomaly information, then the bubble parameter association information is determined based on the association parameters and the degree of association of the parameters; Based on the complexity of the substrate, the temperature adjustment range and the stirring speed adjustment range are determined according to the bubble volume and the bubble position. Based on the temperature adjustment range, the bubble parameter association information, and the speed adjustment range, bubble adjustment information is determined and incorporated into the real-time adjustment information set.

[0154] Optionally, the adjustment information determining module 304 is specifically used for: If the surface anomaly information set includes the thickness anomaly information, then based on the real-time electroplating information and the complexity of the substrate, it is determined whether the thickness anomaly information is generated by the real-time electroplating information; If the thickness anomaly information is generated by the real-time electroplating information, then the thickness anomaly type is determined based on the real-time electroplating information and the real-time electroplating image; Based on the associated parameters and the complexity of the substrate, the thickness parameter association information is determined; Based on the thickness anomaly type and the thickness parameter correlation information, determine the current adjustment information; Electroplating adjustment information is determined based on the bubble volume, the bubble density, and the bubble position; Based on the electroplating adjustment information and the current adjustment information, thickness adjustment information is determined and incorporated into the real-time adjustment information set.

[0155] Optionally, the adjustment information determining module 304 is specifically used for: If the surface anomaly information set contains the contour anomaly information, then the contour parameter association information is determined based on the association parameter and the contour anomaly information; The electroplating time is determined based on the electroplating area, the electroplating structure, and the plating thickness. The degree of agitation of the electroplating solution is determined based on the shaded area, the electroplating time, and the tilt angle, and is calculated using the following formula: ; in, This indicates the degree of stirring. Indicates the adjustment constant. This represents the area of ​​the shadow. This indicates the preset area coefficient. This indicates the electroplating time. This indicates the preset electroplating time coefficient. Indicates the tilt angle; Based on the contour parameter association information, the complexity of the substrate, and the degree of agitation, contour adjustment information is determined and incorporated into the real-time adjustment information set.

[0156] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. An automated method for detecting the quality of electroplating, characterized in that, include: Obtain information about the plastic substrate and determine the complexity of the substrate based on that information. Obtain real-time electroplating information, and determine the set of surface anomaly information of the plastic substrate during the electroplating process based on the real-time electroplating information; Based on the surface anomaly information set, determine the relevant parameters that need to be adjusted; The real-time adjustment information set is determined based on the surface anomaly information set, the correlation parameters, and the complexity of the substrate; Based on the aforementioned real-time adjustment information set, the real-time production process is adjusted to obtain plastic product information.

2. The method according to claim 1, characterized in that, The plastic substrate information includes a plastic substrate image, and determining the substrate complexity based on the plastic substrate information includes: Based on the image of the plastic substrate, determine the shadow area, tilt angle, substrate volume, and surface roughness of the plastic substrate; The substrate complexity index is determined based on the shadow area, the tilt angle, the substrate volume, and the surface roughness, and is calculated using the following formula: ； in, This represents the complexity index of the substrate. This indicates the preset volume density weighting coefficient. This indicates the volume of the substrate. This represents the area of ​​the shadow. This represents the preset angle weighting coefficient. Indicates the tilt angle, This indicates the preset roughness weighting coefficient. This refers to the surface roughness; The complexity of the substrate is determined based on the substrate complexity index.

3. The method according to claim 2, characterized in that, The plastic substrate information also includes the substrate's coefficient of thermal expansion. The real-time electroplating information includes electroplating solution temperature and current distribution information. Based on the real-time electroplating image and the plastic substrate information, it is determined whether there are any abnormalities in the outline of the plastic substrate. If there are abnormalities in the outline of the plastic substrate, the outline abnormality information is determined, including: Based on the real-time electroplating image and the information of the plastic substrate, it is determined whether there is an abnormality in the outline of the plastic substrate. If there is an abnormality in the outline of the plastic substrate, the abnormality information is determined. Based on the real-time electroplating image, it is determined whether there are bubbles in the plastic substrate. If there are bubbles in the plastic substrate, bubble abnormality information is determined. Based on the real-time electroplating image, it is determined whether there is an abnormal thickness change in the plastic substrate. If there is an abnormal thickness change in the plastic substrate, the thickness abnormality information is determined. Based on the contour anomaly information, the bubble anomaly information, and the thickness anomaly information, a surface anomaly information set is constructed; The real-time contour of the substrate is determined based on the real-time electroplating image; Obtain current distribution information, and determine the current distribution area based on the current distribution information; Based on the real-time profile of the substrate, the influence of the electroplating solution temperature and the current distribution area on the real-time profile of the substrate is determined, and calculated according to the following formula: ； in, Indicates the degree of influence. This indicates the preset thermal influence coefficient. This represents the coefficient of thermal expansion of the substrate. This indicates the temperature of the electroplating solution. This indicates the preset current influence coefficient. This represents the area of ​​the current distribution; Based on the degree of influence, the outline standard range of the plastic substrate is obtained, the real-time outline of the substrate is matched with the outline standard range, and it is determined whether the real-time outline of the substrate exceeds the outline standard range. If the real-time contour of the substrate exceeds the contour standard range, the real-time contour of the substrate is determined as contour anomaly information, and the contour anomaly information is included in the anomaly information set.

4. The method according to claim 3, characterized in that, The step of determining whether there are air bubbles in the plastic substrate based on the real-time electroplating image, and if air bubbles are present in the plastic substrate, then identifying air bubble anomaly information, including: The location of the bubbles is determined based on the real-time electroplating image; Based on the location of the bubbles, and according to the real-time electroplating image, it is determined whether the appearance of the bubbles is caused by the surface roughness of the plastic substrate. If the appearance of the bubbles is caused by the surface roughness of the substrate, then the surface roughness is determined as the bubble abnormality information; If the appearance of the bubble location is not caused by the surface roughness, then the current intensity is determined based on the electroplating information; The electroplating time is determined based on the real-time electroplating information; The bubble volume and bubble density are determined based on the electroplating time, the current distribution, and the current intensity. The bubble volume, bubble density, and bubble position are determined as the bubble anomaly information, and the bubble anomaly information is included in the surface anomaly information set.

5. The method according to claim 4, characterized in that, The step involves determining whether there is an abnormal thickness change in the plastic substrate based on the real-time electroplating image. If the plastic substrate exhibits an abnormal thickness change, then the thickness anomaly information is determined, including: Based on the real-time electroplating images, the electroplating area of ​​the substrate and the electroplating distance between the electrode and the substrate are determined; The electroplating area of ​​the substrate is divided to obtain several electroplating areas of the substrate. Based on the real-time electroplating information, the electrode position and current density are determined; Based on the electrode position, the coating adhesion is determined according to the electroplating distance and the current density; The plating thickness of each substrate plating area is obtained based on the coating adhesion and the plating areas of the substrates. Obtain the thickness error range, match the electroplating thickness with the thickness error range, and obtain the matching result; Based on the matching results, it is determined whether the electroplating thickness exceeds the thickness error range; If the electroplating thickness exceeds the thickness error range, the area exceeding the thickness error range is identified as a thickness abnormality area, the thickness abnormality area is identified as the thickness abnormality information, and the thickness abnormality information is included in the surface abnormality information set.

6. The method according to claim 5, characterized in that, The step of determining the correlation parameters that need to be adjusted based on the surface anomaly information set includes: The surface anomaly information is matched with the surface anomaly information set to obtain the anomaly information type; Based on the type of abnormal information, several parameter adjustment information is determined according to the real-time electroplating information and the complexity of the substrate, and the parameters of the electroplating production process are adjusted according to the several parameter adjustment information. Based on the type of abnormal information, determine whether the parameter adjustment causes other abnormal situations besides the type of abnormal information. If the parameter adjustment leads to other abnormal situations besides the abnormal information type, then based on the parameter adjustment information and the other abnormal situations, determine the parameter change magnitude, parameter change quantity, and parameter change type of the other abnormal situations; The degree of correlation of the parameters is determined based on the magnitude of the parameter changes, the number of parameter changes, and the type of parameter changes. The correlation parameters are determined based on the real-time electroplating information, the plastic substrate information, and the correlation degree of the parameters.

7. The method according to claim 6, characterized in that, The step of determining the real-time adjustment information set based on the surface anomaly information set, the associated parameters, and the substrate complexity includes: If the surface anomaly information set includes the bubble anomaly information, then the bubble parameter association information is determined based on the association parameters and the degree of association of the parameters; Based on the complexity of the substrate, the temperature adjustment range and the stirring speed adjustment range are determined according to the bubble volume and the bubble position. Based on the temperature adjustment range, the bubble parameter association information, and the speed adjustment range, bubble adjustment information is determined and incorporated into the real-time adjustment information set.

8. The method according to claim 7, characterized in that, The step of determining the real-time adjustment information set based on the surface anomaly information, the associated parameters, and the substrate complexity includes: If the surface anomaly information set includes the thickness anomaly information, then based on the real-time electroplating information and the complexity of the substrate, it is determined whether the thickness anomaly information is generated by the real-time electroplating information; If the thickness anomaly information is generated by the real-time electroplating information, then the thickness anomaly type is determined based on the real-time electroplating information and the real-time electroplating image; Based on the associated parameters and the complexity of the substrate, the thickness parameter association information is determined; Based on the thickness anomaly type and the thickness parameter correlation information, determine the current adjustment information; Electroplating adjustment information is determined based on the bubble volume, the bubble density, and the bubble position; Based on the electroplating adjustment information and the current adjustment information, thickness adjustment information is determined and incorporated into the real-time adjustment information set.

9. The method according to claim 8, characterized in that, The contour anomaly information includes the electroplating area, electroplating structure, and coating thickness of the contour anomaly region. The step of determining real-time adjustment information based on the surface anomaly information, the associated parameters, and the substrate complexity includes: If the surface anomaly information set contains the contour anomaly information, then the contour parameter association information is determined based on the association parameter and the contour anomaly information; The electroplating time is determined based on the electroplating area, the electroplating structure, and the plating thickness. The degree of agitation of the electroplating solution is determined based on the shaded area, the electroplating time, and the tilt angle, and is calculated using the following formula: ； in, This indicates the degree of stirring. Indicates the adjustment constant. This represents the area of ​​the shadow. This indicates the preset area coefficient. This indicates the electroplating time. This indicates the preset electroplating time coefficient. Indicates the tilt angle; Based on the contour parameter association information, the complexity of the substrate, and the degree of agitation, contour adjustment information is determined and incorporated into the real-time adjustment information set.

10. An automated electroplating qualification inspection system, characterized in that, include: The complexity determination module is used to acquire information about the plastic substrate and determine the complexity of the substrate based on the plastic substrate information. An anomaly information determination module is used to acquire real-time electroplating information and determine the set of surface anomaly information of the plastic substrate during the electroplating process based on the real-time electroplating information. The correlation parameter determination module is used to determine the correlation parameters that need to be adjusted based on the surface anomaly information set. The adjustment information determination module is used to determine the real-time adjustment information set based on the surface anomaly information set, the associated parameters, and the complexity of the substrate. The product information determination module is used to adjust the real-time production process based on the real-time adjustment information set to obtain plastic product information.

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