Method and system for controlling metal deposition in a transparent pvd coating

Abstract

The application provides a light-transmitting PVD coating metal deposition control method and system, relates to the technical field of strategy optimization, and comprises the following steps: acquiring plastic part characteristic information of a plastic part sample, a target metal deposition effect, and the plastic part sample being a plastic part to be coated by light-transmitting PVD; acquiring light-transmitting PVD coating process information, including an initial light-transmitting PVD coating control scheme corresponding to the plastic part characteristic information; performing metal deposition characteristic detection to obtain a detected metal deposition effect; determining whether the target metal deposition effect is met; if not, performing optimization adjustment on the initial light-transmitting PVD coating control scheme to obtain an optimized light-transmitting PVD coating control scheme. The application solves the technical problem that in the prior art, light-transmitting PVD coating process control often relies on experience and rules to adjust parameters, ignores the requirements of different plastic parts on coating process parameters, and leads to the fact that the quality of a film layer cannot stably reach a target effect, thereby affecting product quality.

Description

Technical Field

[0001] This invention relates to the field of strategy optimization technology, specifically to a method and system for controlling metal deposition in transparent PVD coatings. Background Technology

[0002] Transparent PVD (Physical Vapor Deposition) coating technology is widely used for surface coating of materials such as plastics, glass, and metals. The transparent PVD coating process involves evaporating and depositing metal onto the substrate surface to form a thin film with excellent functionality and decorative properties. To achieve the ideal metal deposition effect, process parameters need to be precisely adjusted within a controlled range. However, because the deposition process is affected by multiple variables, process control presents certain challenges. Existing transparent PVD coating process control often relies on experience and rules for parameter adjustment. However, different plastic parts or substrates have significantly different requirements for process parameters, making it difficult to obtain the best metal deposition effect without precise adjustment. Furthermore, existing technologies for optimizing control parameters are relatively simple, mainly relying on trial and error to determine the optimal process parameters, resulting in low production efficiency and potentially a large number of scrap and defective products. Summary of the Invention

[0003] This application provides a method and system for controlling metal deposition in transparent PVD coating, aiming to solve the technical problem that the control of transparent PVD coating process in the prior art often relies on experience and rules to adjust parameters, ignoring the different requirements of different plastic parts for coating process parameters, resulting in the inability to achieve the target effect of film quality, and thus affecting product quality.

[0004] The first aspect disclosed in this application provides a method for controlling metal deposition in a transparent PVD coating. The method includes: acquiring plastic part feature information and a target metal deposition effect of a plastic part sample, wherein the plastic part sample is a plastic part that has undergone transparent PVD coating; acquiring transparent PVD coating process information, wherein the transparent PVD coating process information includes an initial transparent PVD coating control scheme corresponding to the plastic part feature information; performing metal deposition feature detection on the plastic part sample to obtain a detected metal deposition effect; determining whether the detected metal deposition effect meets the target metal deposition effect; if not, optimizing and adjusting the initial transparent PVD coating control scheme based on the target metal deposition effect to obtain an optimized transparent PVD coating control scheme.

[0005] The second aspect of this application discloses a control system for metal deposition in a transparent PVD coating. The system is used in the aforementioned method for controlling metal deposition in a transparent PVD coating. The system includes: a feature information acquisition module for acquiring feature information of a plastic part sample and a target metal deposition effect, wherein the plastic part sample is a plastic part that has undergone transparent PVD coating; a process information acquisition module for acquiring transparent PVD coating process information, wherein the transparent PVD coating process information includes an initial transparent PVD coating control scheme corresponding to the feature information of the plastic part; a feature detection module for performing metal deposition feature detection on the plastic part sample to obtain a detected metal deposition effect; an effect judgment module for judging whether the detected metal deposition effect meets the target metal deposition effect; and an optimization adjustment module for, if not meeting the target metal deposition effect, optimizing and adjusting the initial transparent PVD coating control scheme based on the target metal deposition effect to obtain an optimized transparent PVD coating control scheme.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects:

[0007] By acquiring the characteristic information of plastic parts and the target metal deposition effect from plastic part samples, precise input data is provided for subsequent process control. The characteristic information of plastic parts lays the foundation for formulating a reasonable initial transparent PVD coating control scheme, ensuring the targeting and effectiveness of the coating process. By acquiring an initial transparent PVD coating control scheme corresponding to the characteristic information of plastic parts, it is possible to ensure that the control scheme matches the actual production requirements. This matching relationship helps to reduce errors or deviations in the initial process scheme, improve the success rate of the initial control scheme, and provide a reasonable starting point for subsequent metal deposition optimization. By performing metal deposition characteristic detection on plastic part samples, the effect of the metal deposition layer can be comprehensively evaluated. The detection results can reveal the uniformity and surface quality of the deposition layer. This detection provides a direct basis for subsequent judgment on whether the coating control scheme needs to be adjusted, thereby providing support for further optimization. By comparing the detected metal deposition effect with the target metal deposition effect, it is possible to quickly determine whether the current process has achieved the expected results. If the detected effect does not meet the target requirements, process deficiencies can be identified in a timely manner, allowing for timely optimization and preventing unqualified products from entering the next process. This effectively ensures production quality and reduces resource waste during production. If the detected metal deposition effect does not meet the target metal deposition effect, the initial transparent PVD coating control scheme is optimized to obtain an optimized transparent PVD coating control scheme. This optimization process allows process parameters to be precisely adjusted according to the target deposition effect, thereby ensuring the quality stability of the coating process. This optimization method helps to obtain consistent and high-quality deposition effects under different production conditions, improves the flexibility and controllability of the production process, and ultimately achieves full-process strategy optimization, further improving production efficiency and product quality.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0009] Figure 1 This is a schematic flowchart of a method for controlling metal deposition in a transparent PVD coating provided in an embodiment of this application.

[0010] Figure 2 This is a schematic diagram of the control system structure for metal deposition in a transparent PVD coating provided in an embodiment of this application.

[0011] Explanation of reference numerals in the attached figures: Feature information acquisition module 10, process information acquisition module 20, feature detection module 30, effect judgment module 40, optimization adjustment module 50. Detailed Implementation

[0012] This application provides a method and system for controlling metal deposition in transparent PVD coating, which solves the technical problem that the control of transparent PVD coating process in the prior art often relies on experience and rules to adjust parameters, ignoring the different requirements of plastic parts for coating process parameters, resulting in the inability of the film quality to achieve the target effect, and thus affecting the product quality.

[0013] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0014] Example 1, as Figure 1 As shown in the embodiments of this application, a method for controlling metal deposition in a transparent PVD coating is provided, the method comprising:

[0015] Obtain the plastic part feature information and target metal deposition effect of the plastic part sample, wherein the plastic part sample is a plastic part that has completed transparent PVD coating.

[0016] Plastic part samples refer to plastic parts that have undergone the transparent PVD (Physical Vapor Deposition) coating process. PVD technology coats the surface of plastic parts with a thin metal film, typically to increase surface hardness, conductivity, or achieve specific optical effects. The characteristic information of the plastic part includes its dimensions, surface smoothness, shape, surface treatment, and substrate material, which can be obtained through relevant testing methods such as laser scanning, 3D imaging, and surface profile analysis. The target metal deposition effect includes characteristics such as the control of surface defects and light transmittance of the metal film, which are closely related to the functional requirements of the plastic part. The target metal deposition effect can be set according to the usage requirements of the plastic part.

[0017] Obtain transparent PVD coating process information, wherein the transparent PVD coating process information includes the initial transparent PVD coating control scheme corresponding to the feature information of the plastic part.

[0018] The core process information for the light-transmitting PVD coating process includes parameters such as coating equipment conditions, operating environment, coating materials, deposition time, temperature, pressure, and atmosphere. All of these are crucial to ensuring that the coating quality achieves the expected results. Based on the characteristics of the plastic parts, an initial light-transmitting PVD coating control scheme is set before the PVD coating process. This initial light-transmitting PVD coating control scheme is set based on historical data, experimental results, or theoretical analysis.

[0019] The plastic part sample was subjected to metal deposition feature detection to obtain the metal deposition effect.

[0020] Metal deposition characteristics were inspected on plastic parts samples. Specifically, scanning electron microscopy (SEM) was used to scan and analyze the surface morphology of the plastic parts samples in detail, analyzing the surface structure and morphology of the metal layer, identifying existing defects such as bubbles and cracks, and obtaining characteristic evaluation coefficients for surface defects. This helps to determine whether the metal deposition quality meets the requirements. A spectrometer was used to detect the light transmittance of the samples, measuring the light transmittance characteristics of the metal deposition layer. The light transmittance of the metal layer has a significant impact on the performance of the final product. Based on the obtained light transmittance data, a light transmittance characteristic evaluation coefficient was generated as another important indicator for evaluating the metal deposition effect. The surface defect characteristic evaluation coefficients and light transmittance characteristic evaluation coefficients obtained by SEM and spectrometer were integrated to form the final metal deposition effect assessment.

[0021] Determine whether the detected metal deposition effect meets the target metal deposition effect.

[0022] The target metal deposition effect is preset and determined according to the functional requirements of the plastic part, including the control of surface defects of the metal film and light transmittance. The obtained metal deposition effect is compared with the preset target metal deposition effect. The comparison process includes analyzing the evaluation coefficients of various aspects such as surface defects and light transmittance. If the target requirements are met, it means that the detected metal deposition effect meets the expectations and no adjustment is needed; if not, optimization and adjustment are required.

[0023] If the target metal deposition effect is not met, the initial transparent PVD coating control scheme is optimized and adjusted to obtain an optimized transparent PVD coating control scheme.

[0024] Based on the assessment results, identify which aspects of the metal deposition effect did not meet the target requirements, such as surface defects and insufficient light transmittance. Combine the target effect with the assessment results to pinpoint the discrepancies, for example, whether they were caused by factors such as temperature, pressure, or deposition rate during the deposition process. Based on the target metal deposition effect, optimize and adjust the initial light-transmitting PVD coating control scheme. Adjusted parameters include evaporation rate, substrate temperature, atmosphere pressure, deposition time, bias voltage, sputtering power, and film stress. Utilize optimization algorithms, such as genetic algorithms and particle swarm optimization, iteratively adjust the initial light-transmitting PVD coating control scheme to achieve the optimal metal deposition effect. During the adjustment process, use a metal deposition effect evaluation function to evaluate and compare each optimized scheme, ensuring that each adjustment brings the metal deposition effect closer to the target. After multiple optimization adjustments, obtain the final optimized light-transmitting PVD coating control scheme. This scheme can be applied in actual production to ensure that the metal deposition effect meets the requirements and improves the overall coating quality.

[0025] Furthermore, the method for detecting metal deposition characteristics of the plastic part sample to obtain the metal deposition effect includes:

[0026] The surface morphology of the plastic part sample is detected by scanning electron microscopy, and the surface morphology detection results include surface defect feature evaluation coefficients. The light transmittance of the plastic part sample is detected by spectrometer, and the light transmittance detection results include light transmittance feature evaluation coefficients. The surface defect feature evaluation coefficients and the light transmittance feature evaluation coefficients are integrated to obtain the detected metal deposition effect.

[0027] Scanning electron microscopy (SEM) creates high-resolution images by scanning the sample surface and utilizing the signals reflected from the electron beam. SEM can capture details such as surface microstructure, defects, and cracks, and is commonly used to inspect the surface quality of thin films. Specifically, the sample is placed in an SEM apparatus, and its surface is scanned by an electron beam in a vacuum environment. Analysis of the SEM images identifies defects on the surface of the metal layer, such as bubbles, cracks, and uneven particle distribution. Surface defect characteristic evaluation coefficients are used to describe the severity of defects on the metal deposition surface, and evaluation indicators include defect density, defect type, and defect size.

[0028] Spectrometers assess light transmittance by analyzing the wavelength and intensity of light reflected, transmitted, or scattered from a sample. For plastic parts with transparent PVD coatings, light transmittance is a crucial performance indicator, affecting the visual appearance of the final product. Specifically, the spectrometer is aimed at the plastic part sample after metal deposition, using a light source within a specific wavelength range. Light transmittance data is obtained by measuring the transmittance of light passing through the metal film layer. Light transmittance characteristic evaluation coefficients are used to quantify the light transmittance of the metal deposition layer, and evaluation indicators include transmittance, transmittance range, and optical transparency.

[0029] The integrated surface defect feature evaluation coefficient and light transmittance feature evaluation coefficient can be assigned different weights to each feature based on the influence of different parameters on the overall deposition effect. For example, if light transmittance is more critical than surface defects, then the light transmittance feature evaluation coefficient can be assigned a higher weight. The integration formula can use the weighted average method or multivariate analysis method. Through the integrated evaluation coefficients, a comprehensive detection of the metal deposition effect is generated. This result is used to guide subsequent process adjustments to ensure that the metal deposition effect achieves the predetermined target.

[0030] Furthermore, the method includes:

[0031] The surface morphology of the plastic part sample is detected by scanning electron microscopy, and a surface morphology detection dataset is generated. The surface morphology detection dataset includes defect morphology type, defect size, defect distribution, surface flatness, and surface smoothness. Based on the surface morphology detection dataset, the surface defect feature evaluation coefficient is output.

[0032] The surface of a plastic sample is scanned using a scanning electron microscope to acquire detailed image data. Specifically, the surface of the plastic sample is ensured to be clean, and all contaminants that may affect the measurement results are removed. Methods such as solvent cleaning and ultrasonic cleaning are typically used to clean the surface. Depending on the characteristics and requirements of the film, an appropriate electron beam energy, such as 5-10kV, is selected to ensure optimal image resolution. Through high-resolution scanning, an electron micrograph of the surface of the plastic sample is generated. The electron micrograph provides detailed morphology of the film, including information on surface defects, particles, microcracks, bubbles, etc. Image analysis software was used to process and analyze the acquired electron micrographs. Specifically, image recognition algorithms were used to identify various defect types, such as particles, bubbles, cracks, and pores. The dimensions of the defects, such as diameter and aspect ratio, were measured, and the size of each defect was recorded. This data helps to understand the uniformity and quality of the film. The distribution of defects on the film surface was analyzed. By statistically analyzing the spatial location of defects in the images, the distribution characteristics of defects were obtained, such as whether they were uniform or concentrated. Surface profile analysis methods were used to evaluate the smoothness of the film surface. For example, by measuring the height difference of the film surface, it was determined whether there were significant fluctuations in the surface. The smoothness was evaluated by quantifying the surface roughness of the film. Commonly used surface roughness parameters include Ra value (average roughness) and Rz value (maximum height difference). Finally, the electron micrographs and the above analysis results constituted a surface morphology detection dataset, which included defect morphology type, defect size, defect distribution, surface smoothness, and surface flatness.

[0033] Based on defect information extracted from electron micrographs, quantitative analysis is performed. For example, weights are assigned to each defect type according to defect size and distribution, generating scores for different defect types. For different types of defects, such as cracks and bubbles, severity weights are assigned based on their impact on film performance. Generally, cracks and pores have a greater impact, while bubbles and particles have a relatively smaller impact. For example, surface smoothness is quantified by calculating parameters such as the standard deviation of film surface height variation. Films with large height differences indicate uneven deposition, leading to unstable film performance. Roughness analysis is used to assess the smoothness of the film surface. Higher roughness values ​​usually indicate the presence of irregular surface morphology, which affects the adhesion and durability of the film.

[0034] By combining factors such as defect morphology, size, distribution, surface flatness, and smoothness, a comprehensive surface defect characteristic evaluation coefficient is generated through weighted summation or other statistical methods. This surface defect characteristic evaluation coefficient is output as a quantitative indicator, reflecting the quality of the film surface. The lower the value, the fewer the surface defects and the better the film quality.

[0035] Furthermore, the method includes:

[0036] A PVD coating record retrieval is performed to obtain multiple historical PVD coating control records. These records include multiple historical PVD coating control schemes corresponding to the feature information of multiple historical plastic parts. Based on the feature information of the plastic parts, the multiple historical plastic parts feature information are traversed, feature similarity is calculated, and the historical PVD coating control scheme corresponding to the feature information of the historical plastic parts with the highest feature similarity is extracted as the initial PVD coating control scheme.

[0037] By retrieving historical PVD coating records using a database or storage system, multiple historical PVD coating control records can be obtained, providing a reference for current metal deposition on plastic parts. Historical records provide past successful process parameters and results, which can serve as a basis for subsequent process optimization and adjustment. During the PVD coating process, each plastic part has corresponding characteristics, such as size, shape, and material type. This characteristic information can be obtained through physical measurement, 3D modeling, or other analytical techniques. Historical PVD coating control schemes include coating process parameters for each plastic part, such as evaporation rate, substrate temperature, atmosphere pressure, and deposition time. These parameters determine the quality and effect of metal deposition.

[0038] By calculating the similarity between the current plastic part feature information and the historical plastic part feature information, the most similar historical record is selected as the basis for the initial control scheme. Similar plastic part features usually correspond to similar coating control schemes, thereby improving the success rate and shortening the adjustment time. Similarity calculation methods, such as Euclidean distance, cosine similarity, Manhattan distance, etc., are used to calculate the similarity between the current plastic part feature information and each historical plastic part feature information. Based on the calculation results, the historical record that is most similar to the current plastic part feature information is selected. The record with the highest feature similarity usually indicates that its coating effect is most matched with the current requirements. Therefore, the corresponding historical coating control scheme can be used as the initial control scheme. The control scheme that is most similar to the current plastic part in the historical record is extracted as the current initial light-transmitting PVD coating control scheme. This scheme will serve as a starting point to help accelerate subsequent optimization and adjustment.

[0039] Furthermore, the method for optimizing the initial transparent PVD coating control scheme based on the target metal deposition effect includes:

[0040] Based on the multi-factor coating control parameters of transparent PVD coating, the multi-factor coating control interval of the multiple historical transparent PVD coating control schemes is analyzed to establish a multi-factor coating control interval; the multi-factor coating control interval is used as a spatial constraint condition to optimize and adjust the initial transparent PVD coating control scheme.

[0041] In the process of transparent PVD coating, key factors affecting metal deposition include multiple process parameters, such as evaporation rate, substrate temperature, atmosphere pressure, deposition time, bias voltage, sputtering power, and film stress. All these process parameters work together to determine the quality and effect of the coating. By analyzing multiple historical transparent PVD coating control schemes, the reasonable range of each parameter in historical coating records can be identified. Using this historical data, the working range of each process parameter can be determined. For example, through experimental data or regression analysis, the relationship between each parameter and the metal deposition effect can be established. For each control parameter, its influence range on the metal deposition effect can be obtained. By analyzing the ranges of each control parameter, the optimal working range of multiple control parameters can be obtained, and each parameter will have a corresponding value range. By combining these ranges of individual parameters with multi-dimensional data analysis, a multi-dimensional coating control range can be established to ensure that the metal deposition effect is optimized when various process parameters are combined within these ranges.

[0042] Using the multi-element coating control range as a spatial constraint means that in the subsequent optimization process, the values ​​of all process parameters must be adjusted within these preset ranges. The optimization objectives include maximizing the quality of the metal film, minimizing film defects, and improving the uniformity of the film. The optimization process aims to find a set of optimal process parameters within the control range so that the final coating effect reaches or approaches the target metal deposition effect. Numerical optimization methods, such as genetic algorithms, particle swarm optimization, simulated annealing, and gradient descent, are used for adjustment. These methods can find the optimal solution in multi-dimensional space while ensuring that the value of each control parameter is within a predetermined range. Through iterative optimization, the values ​​of each process parameter are continuously adjusted so that they gradually approach the optimal combination within the control range. In each iteration, the coating process is simulated or experimentally verified based on the current parameter settings, and the metal deposition effect evaluation index is calculated. After each iteration, the evaluation results of the current parameter combination are compared with the target metal deposition effect, and the parameters are adjusted again until an optimal transparent PVD coating control scheme that meets the target requirements is found.

[0043] Furthermore, the multi-element coating control parameters include evaporation rate parameters, substrate temperature parameters, atmosphere pressure parameters, deposition time parameters, bias voltage parameters, sputtering power parameters, and film stress parameters.

[0044] Evaporation rate refers to the mass or number of atoms of the metal evaporation source material evaporated per second. The evaporation rate directly affects the deposition speed and quality of the metal layer. An excessively high evaporation rate may cause metal particles to excessively collide with the substrate surface, forming a rough film and increasing defects in the film. An excessively low evaporation rate may result in a slow deposition rate, affecting production efficiency and potentially causing unevenness in the deposited layer. Substrate temperature refers to the surface temperature of the plastic substrate during the coating process. Temperature has a significant impact on the deposition process, diffusion behavior, and structure of the metal particles. Excessively high temperatures may result in a loose film structure or weak adhesion of the metal film, affecting the adhesion and hardness of the film. Excessively low temperatures may result in insufficient deposition of metal particles, forming a film with poor density and uneven structure. Atmosphere pressure refers to the gas pressure used in the PVD coating process. Atmosphere pressure affects the diffusion behavior of metal vapor during the deposition process. Under low pressure, metal vapor can freely deposit onto the substrate surface, forming a dense film. Under high pressure, metal vapor molecules will collide with gas molecules, affecting the deposition rate and the quality of the film. High pressure can lead to defects such as pores and voids in the metal film. Deposition time refers to the length of time the metal film is deposited on the substrate surface. Deposition time directly determines the film thickness; a longer deposition time results in a thicker metal layer, potentially increasing its strength, but excessive time can also lead to defects such as increased brittleness. Too short a deposition time can result in insufficient film thickness, affecting its functionality and stability. Bias voltage refers to the voltage applied between the substrate and the target during PVD deposition. This voltage controls the film's structure, quality, and adhesion. Increasing the bias voltage can alter the energy of metal particles during deposition, affecting the film's structure, for example, by promoting the rearrangement of metal atoms on the substrate surface to improve film density. Excessive bias voltage can lead to arc instability and metal sputtering, thus affecting film quality. Sputtering power refers to the power applied during sputtering deposition. The power of the sputtering target determines the generation rate and energy of the metal sputtering particles. Higher sputtering power results in a more intense sputtering process and produces higher-energy metal particles, which helps improve the quality and strength of the film. However, excessively high power may lead to overly intense sputtering, potentially causing damage to the substrate surface or unevenness in the film. Film stress refers to the mechanical stress that exists between the film and the substrate interface. This stress mainly originates from factors such as temperature changes during the deposition process and the expansion or contraction of the film. Films with lower internal stress usually have better adhesion and stability, preventing peeling or cracking. Films with higher internal stress may cause cracks, peeling, and other problems, especially when there is a significant difference in the coefficients of thermal expansion between the film and the substrate.

[0045] Furthermore, the method for optimizing the initial transparent PVD coating control scheme based on the target metal deposition effect includes:

[0046] Based on the target metal deposition effect, metal deposition effect evaluation indicators are extracted to obtain multiple metal deposition effect evaluation indicators; based on the multiple metal deposition effect evaluation indicators, function fitting is performed to generate a metal deposition effect evaluation function; within the multi-element coating control range, the parameters of the initial transparent PVD coating control scheme are iteratively optimized; after each iteration, with the target metal deposition effect as the objective, the metal deposition effect evaluation and parameter optimization are compared through the metal deposition effect evaluation function to determine the optimized transparent PVD coating control scheme.

[0047] Metal deposition performance evaluation metrics are multiple parameters used to quantify and assess the quality of metal deposition. Exemplary metal deposition performance evaluation metrics include: surface defects, used to assess the number or area of ​​defects such as blemishes, bubbles, and cracks on the film surface; film density, used to assess the compactness of the film and measure the uniformity of metal particle distribution on the substrate surface; film adhesion, used to assess the adhesion strength between the film and the substrate; optical transmittance, used to assess the light transmittance of the film, typically for applications with transparent or translucent substrates; and film stress, used to assess the stress state inside the film, as excessive stress may lead to film cracking or peeling.

[0048] Through experiments, simulations, or data analysis, the values ​​of the above metal deposition effect evaluation indicators are extracted. The extraction of evaluation indicators is usually based on experimental measurement results, such as using equipment such as laser rangefinders, scanning electron microscopes, and spectrometers to detect and obtain the specific values ​​of each parameter, thereby obtaining multiple metal deposition effect evaluation indicators, which are then used as the basis for subsequent optimization processes.

[0049] Mathematical modeling and fitting are performed based on multiple metal deposition effect evaluation indices. Specifically, the relationships between these indices are transformed into a mathematical formula, generating a metal deposition effect evaluation function. This function helps predict metal deposition effects given coating control parameters. For example, each metal deposition effect evaluation index is weighted according to specific application requirements, and a weighted sum is calculated based on the weighted summation results. This calculation process generates the metal deposition effect evaluation function. The generated metal deposition effect evaluation function can predict the effect of new parameter combinations based on known evaluation indices and control parameters.

[0050] The multi-dimensional coating control range represents the possible value range of each control parameter, such as the optimal operating range for evaporation rate, substrate temperature, and deposition time. These ranges provide constraints for parameter optimization, ensuring that all control parameters are adjusted within reasonable ranges. Through optimization algorithms, the parameters in the initial transparent PVD coating control scheme are continuously adjusted until an optimal parameter combination that maximizes the target metal deposition effect is found. In each iteration, by adjusting control parameters such as evaporation rate and substrate temperature, a metal deposition effect evaluation function is used to predict the metal deposition effect of the current parameter combination. The metal deposition effect evaluation function returns deposition effect indices under the current parameter settings. The optimization algorithm determines whether parameter adjustments are needed based on these indices, continuing the iteration until the optimal solution is found. Finally, an optimized transparent PVD coating control scheme is determined, which maximizes the target metal deposition effect, thereby ensuring the quality and uniformity of the coating.

[0051] Furthermore, before performing parameter iteration optimization on the initial transparent PVD coating control scheme, the method includes:

[0052] A PVD coating defect record retrieval process is performed to obtain multiple historical PVD coating defect records. Defect analysis is then conducted on these records to obtain multiple coating defect factors and corresponding coating control parameters. Based on the mapping relationship between these defect factors and control parameters, a coating defect and control parameter list is established. Guided by this list, the initial PVD coating control scheme is iteratively optimized.

[0053] A search was conducted to retrieve historical defect records for transparent PVD coatings. These records contain information on the types, quantities, and locations of film defects occurring under different process parameters. This information helps identify the causes of problems and provides a reference for current process adjustments. Existing historical coating defect data was used, or all relevant defect records were extracted from past production data, particularly the coating process parameters and defect types recorded. These records include defect information under different coating parameters, such as cracks, bubbles, uneven film layers, and peeling. Multiple historical transparent PVD coating defect records were obtained through this search.

[0054] Defect analysis was performed on multiple historical records of transparent PVD coating defects to identify the main coating defect factors affecting metal deposition quality. For example, excessively high or low temperatures can lead to poor or uneven film adhesion; changes in gas pressure can affect the movement path of metal particles, resulting in film defects such as bubbles or uneven deposition; excessively long or short deposition times can lead to excessively thick or thin films, affecting their performance; excessively high or low sputtering power can also result in poor or uneven film adhesion; and excessively fast evaporation rates can lead to excessively strong particle impaction, increasing film roughness. For each coating defect factor, the relationship between it and different coating control parameters was analyzed. Through multiple experiments and data analysis, it was determined which changes in coating control parameters might lead to which types of film defects. The defect analysis yielded multiple coating defect factors and their corresponding coating control parameters. The relationship between these factors and their corresponding coating control parameters will provide guidance for subsequent optimization.

[0055] A mapping relationship is established between each coating defect factor and its corresponding coating control parameter. This mapping helps to understand how different process parameters affect the quality and performance of the metal film, providing a clear direction for adjustment. By analyzing historical defect records, the relationship between each coating defect factor and coating control parameter is statistically analyzed. For example, regression analysis and correlation analysis are used to determine which coating control parameters have the greatest impact on specific coating defect factors. A mapping list between coating defects and control parameters is created, with each coating defect factor corresponding to one or more possible coating control parameters. Through this mapping relationship, a list of coating defects and control parameters is formed, providing a basis for subsequent parameter adjustments.

[0056] Based on a list of coating defects and control parameters, the initial transparent PVD coating control scheme is optimized through parameter iterative optimization. This process aims to avoid the occurrence of historical defects and improve film quality. Specifically, based on the mapping relationship between coating defects and control parameters, process parameters that may cause defects in the initial transparent PVD coating control scheme are identified and adjusted. For example, if a certain control parameter is related to film cracks, the parameter value can be adjusted based on the list. By iteratively adjusting the process parameters, the optimal control scheme is gradually approached. In each iteration, the metal deposition effect after the current adjustment is evaluated, and new defects are avoided through defect analysis. After multiple iterations, an optimal transparent PVD coating control scheme is determined.

[0057] Furthermore, the method includes:

[0058] Based on the optimized transparent PVD coating control scheme, sampling re-inspection is performed to obtain the re-inspected metal deposition effect; if the re-inspected metal deposition effect still does not meet the target metal deposition effect, then the optimized transparent PVD coating control scheme is optimized in feedback.

[0059] The purpose of sampling re-inspection is to verify the optimized transparent PVD coating control scheme and ensure that it can achieve the expected metal deposition effect in actual production. A certain number of samples are selected, coated according to the optimized transparent PVD coating control scheme, and then tested to confirm whether the optimized scheme can consistently maintain the expected coating quality. The metal deposition effect of the re-inspected samples is tested, including the surface quality and optical properties of the film, to obtain the re-inspected metal deposition effect.

[0060] The re-inspection of the metal deposition effect is compared with the target metal deposition effect to check for any deviations. If the re-inspection result meets the target requirements, the effectiveness of the optimization scheme can be confirmed; if the target effect is still not achieved, further feedback optimization is required. In actual production, although the initial control scheme has been optimized through historical data and experiments, the actual re-inspection results may still differ. These differences may be caused by various factors, such as changes in the production environment, fluctuations in equipment precision, or differences in raw materials. Analyze all problems and deficiencies found in the re-inspection to identify the reasons why the metal deposition effect does not meet the standard. For example, a certain parameter, such as substrate temperature, atmosphere pressure, sputtering power, may not function as expected, resulting in more surface defects in the film. Based on the analysis results, relevant process parameters are gradually adjusted. For example, if surface defects occur, the substrate temperature or atmosphere pressure needs to be adjusted. Optimization algorithms are continued to be used for feedback optimization within the range of control parameters. These algorithms can automatically adjust the combination of multiple parameters to find the most suitable process scheme. Feedback optimization is an iterative process. After each adjustment, a re-inspection is required, and the result is compared with the target effect. Optimization continues until the coating effect fully meets the target requirements.

[0061] In summary, the method for controlling metal deposition in transparent PVD coatings provided in this application has the following technical effects:

[0062] By acquiring the characteristic information of plastic parts and the target metal deposition effect from plastic part samples, precise input data is provided for subsequent process control. The characteristic information of plastic parts lays the foundation for formulating a reasonable initial transparent PVD coating control scheme, ensuring the targeting and effectiveness of the coating process. By acquiring an initial transparent PVD coating control scheme corresponding to the characteristic information of plastic parts, it is possible to ensure that the control scheme matches the actual production requirements. This matching relationship helps to reduce errors or deviations in the initial process scheme, improve the success rate of the initial control scheme, and provide a reasonable starting point for subsequent metal deposition optimization. By performing metal deposition characteristic detection on plastic part samples, the effect of the metal deposition layer can be comprehensively evaluated. The detection results can reveal the uniformity and surface quality of the deposition layer. This detection provides a direct basis for subsequent judgment on whether the coating control scheme needs to be adjusted. This provides support for further optimization. By comparing the detected metal deposition effect with the target metal deposition effect, it is possible to quickly determine whether the current process has achieved the expected results. If the detected effect does not meet the target requirements, the deficiencies in the process can be identified in time, allowing for timely optimization and preventing unqualified products from entering the next process. This effectively ensures production quality and reduces resource waste in the production process. If the detected metal deposition effect does not meet the target metal deposition effect, the initial transparent PVD coating control scheme is optimized to obtain an optimized transparent PVD coating control scheme. This optimization process allows the process parameters to be precisely adjusted according to the target deposition effect, thereby ensuring the quality stability of the coating process. This optimization method helps to obtain consistent and high-quality deposition effects under different production conditions, improving the flexibility and controllability of the production process.

[0063] Example 2, based on the same inventive concept as the method for controlling metal deposition in the transparent PVD coating in the foregoing examples, such as... Figure 2 As shown in the embodiments of this application, a control system for metal deposition in a transparent PVD coating is provided, the system comprising:

[0064] The feature information acquisition module 10 is used to acquire the feature information of the plastic part sample and the target metal deposition effect, wherein the plastic part sample is a plastic part that has completed a transparent PVD coating.

[0065] The process information acquisition module 20 is used to acquire transparent PVD coating process information, wherein the transparent PVD coating process information includes the initial transparent PVD coating control scheme corresponding to the feature information of the plastic part.

[0066] The feature detection module 30 is used to perform metal deposition feature detection on the plastic part sample to obtain the metal deposition detection effect.

[0067] The effect judgment module 40 is used to judge whether the detected metal deposition effect meets the target metal deposition effect.

[0068] The optimization adjustment module 50 is used to optimize the initial transparent PVD coating control scheme based on the target metal deposition effect if the target is not satisfied, so as to obtain an optimized transparent PVD coating control scheme.

[0069] Furthermore, the feature detection module 30 is used to perform the following operation steps:

[0070] The surface morphology of the plastic part sample is detected by scanning electron microscopy, and the surface morphology detection results include surface defect feature evaluation coefficients. The light transmittance of the plastic part sample is detected by spectrometer, and the light transmittance detection results include light transmittance feature evaluation coefficients. The surface defect feature evaluation coefficients and the light transmittance feature evaluation coefficients are integrated to obtain the detected metal deposition effect.

[0071] Furthermore, the feature detection module 30 is used to perform the following operation steps:

[0072] The surface morphology of the plastic part sample is detected by scanning electron microscopy, and a surface morphology detection dataset is generated. The surface morphology detection dataset includes defect morphology type, defect size, defect distribution, surface flatness, and surface smoothness. Based on the surface morphology detection dataset, the surface defect feature evaluation coefficient is output.

[0073] Furthermore, the process information acquisition module 20 is used to perform the following operation steps:

[0074] A PVD coating record retrieval is performed to obtain multiple historical PVD coating control records. These records include multiple historical PVD coating control schemes corresponding to the feature information of multiple historical plastic parts. Based on the feature information of the plastic parts, the multiple historical plastic parts feature information are traversed, feature similarity is calculated, and the historical PVD coating control scheme corresponding to the feature information of the historical plastic parts with the highest feature similarity is extracted as the initial PVD coating control scheme.

[0075] Furthermore, the optimization adjustment module 50 is used to perform the following operation steps:

[0076] Based on the multi-factor coating control parameters of transparent PVD coating, the multi-factor coating control interval of the multiple historical transparent PVD coating control schemes is analyzed to establish a multi-factor coating control interval; the multi-factor coating control interval is used as a spatial constraint condition to optimize and adjust the initial transparent PVD coating control scheme.

[0077] Furthermore, the multi-element coating control parameters include evaporation rate parameters, substrate temperature parameters, atmosphere pressure parameters, deposition time parameters, bias voltage parameters, sputtering power parameters, and film stress parameters.

[0078] Furthermore, the optimization adjustment module 50 is used to perform the following operation steps:

[0079] Based on the target metal deposition effect, metal deposition effect evaluation indicators are extracted to obtain multiple metal deposition effect evaluation indicators; based on the multiple metal deposition effect evaluation indicators, function fitting is performed to generate a metal deposition effect evaluation function; within the multi-element coating control range, the parameters of the initial transparent PVD coating control scheme are iteratively optimized; after each iteration, with the target metal deposition effect as the objective, the metal deposition effect evaluation and parameter optimization are compared through the metal deposition effect evaluation function to determine the optimized transparent PVD coating control scheme.

[0080] Furthermore, the optimization adjustment module 50 is used to perform the following operation steps:

[0081] A PVD coating defect record retrieval process is performed to obtain multiple historical PVD coating defect records. Defect analysis is then conducted on these records to obtain multiple coating defect factors and corresponding coating control parameters. Based on the mapping relationship between these defect factors and control parameters, a coating defect and control parameter list is established. Guided by this list, the initial PVD coating control scheme is iteratively optimized.

[0082] Furthermore, the system also includes a feedback optimization module for performing the following steps:

[0083] Based on the optimized transparent PVD coating control scheme, sampling re-inspection is performed to obtain the re-inspected metal deposition effect; if the re-inspected metal deposition effect still does not meet the target metal deposition effect, then the optimized transparent PVD coating control scheme is optimized in feedback.

[0084] Through the foregoing detailed description of the control method for metal deposition in transparent PVD coatings, those skilled in the art can clearly understand the control system for metal deposition in transparent PVD coatings in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to the method section.

[0085] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling metal deposition in transparent PVD coatings, characterized in that, The method includes: Obtain the plastic part feature information and target metal deposition effect of the plastic part sample, wherein the plastic part sample is a plastic part that has completed transparent PVD coating; Obtain transparent PVD coating process information, wherein the transparent PVD coating process information includes the initial transparent PVD coating control scheme corresponding to the feature information of the plastic part; The plastic part sample was subjected to metal deposition feature detection to obtain the metal deposition detection effect; Determine whether the detected metal deposition effect meets the target metal deposition effect; If the target metal deposition effect is not met, the initial transparent PVD coating control scheme is optimized and adjusted based on the target metal deposition effect to obtain an optimized transparent PVD coating control scheme. The method includes: A PVD coating record retrieval was performed to obtain multiple historical PVD coating control records, wherein the multiple historical PVD coating control records include multiple historical PVD coating control schemes corresponding to multiple historical plastic part feature information. Based on the plastic part feature information, the feature information of the multiple historical plastic parts is traversed, feature similarity is calculated, and the historical light-transmitting PVD coating control scheme corresponding to the historical plastic part feature information with the highest feature similarity is extracted as the initial light-transmitting PVD coating control scheme. The method for optimizing the initial transparent PVD coating control scheme based on the target metal deposition effect includes: Based on the multi-dimensional coating control parameters of transparent PVD coating, the multi-dimensional coating control range of the multiple historical transparent PVD coating control schemes is analyzed to establish a multi-dimensional coating control range. Using the multi-element coating control range as a spatial constraint, the initial light-transmitting PVD coating control scheme is optimized and adjusted. The multi-element coating control parameters include evaporation rate parameters, substrate temperature parameters, atmosphere pressure parameters, deposition time parameters, bias voltage parameters, sputtering power parameters, and film stress parameters. The method for optimizing the initial transparent PVD coating control scheme based on the target metal deposition effect includes: Based on the target metal deposition effect, metal deposition effect evaluation indicators are extracted to obtain multiple metal deposition effect evaluation indicators. Based on the aforementioned multiple metal deposition effect evaluation indicators, a function is fitted to generate a metal deposition effect evaluation function; Within the multi-element coating control range, the initial transparent PVD coating control scheme is iteratively optimized by parameter optimization. After each iteration, with the target metal deposition effect as the objective, the metal deposition effect is evaluated and the parameter optimization is compared by the metal deposition effect evaluation function to determine the optimized transparent PVD coating control scheme. Before performing parameter iteration optimization on the initial transparent PVD coating control scheme, the method includes: A search was conducted to retrieve historical records of defects in transparent PVD coatings, yielding multiple records of such defects. Defect analysis was performed on the multiple historical transparent PVD coating defect records to obtain multiple coating defect factors and multiple coating control parameters corresponding to the multiple coating defect factors; Based on the mapping relationship between the multiple coating defect factors and the multiple coating control parameters, a list of coating defects and control parameters is established. Guided by the list of coating defects and control parameters, the parameters of the initial transparent PVD coating control scheme are iteratively optimized. The method includes: Based on the optimized light-transmitting PVD coating control scheme, sampling re-inspection was conducted to obtain the re-inspection metal deposition effect; If the re-inspection of the metal deposition effect still does not meet the target metal deposition effect, then the feedback optimization of the optimized transparent PVD coating control scheme is performed.

2. The method for controlling metal deposition in a transparent PVD coating as described in claim 1, characterized in that, The method for detecting metal deposition characteristics of the plastic part sample to obtain the metal deposition effect includes: The surface morphology of the plastic part sample is detected by scanning electron microscopy to obtain the surface morphology detection results, wherein the surface morphology detection results include the surface defect feature evaluation coefficient. The transmittance of the plastic part sample is tested using a spectrometer to obtain transmittance test results, wherein the transmittance test results include transmittance characteristic evaluation coefficients; By integrating the surface defect feature evaluation coefficient and the light transmittance feature evaluation coefficient, the detected metal deposition effect is obtained.

3. The method for controlling metal deposition in a transparent PVD coating as described in claim 2, characterized in that, The method includes: The surface morphology of the plastic part sample is detected by scanning electron microscopy, and a surface morphology detection dataset is generated. The surface morphology detection dataset includes defect morphology type, defect size, defect distribution, surface flatness, and surface smoothness. Based on the surface morphology detection dataset, output the surface defect feature evaluation coefficients.

4. A control system for metal deposition in a transparent PVD coating, characterized in that, A system for controlling metal deposition in a transparent PVD coating according to any one of claims 1-3, the system comprising: The feature information acquisition module is used to acquire the feature information of the plastic part sample and the target metal deposition effect, wherein the plastic part sample is a plastic part that has completed the transparent PVD coating. The process information acquisition module is used to acquire transparent PVD coating process information, wherein the transparent PVD coating process information includes the initial transparent PVD coating control scheme corresponding to the feature information of the plastic part. The feature detection module is used to detect the metal deposition features of the plastic part sample to obtain the detection effect of metal deposition. The effect judgment module is used to determine whether the detected metal deposition effect meets the target metal deposition effect; The optimization adjustment module is used to optimize the initial transparent PVD coating control scheme based on the target metal deposition effect if the target is not satisfied, so as to obtain an optimized transparent PVD coating control scheme.

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