Optical fiber product production line process strategy intelligent generation method and system

Abstract

The application discloses a production line process strategy intelligent generation method and system based on an optical fiber product, relates to the technical field of optical fiber products, and has the following steps: obtaining product base material characteristics of a target optical fiber production product, obtaining comprehensive surface treatment parameters according to surface treatment requirement parameters of the target optical fiber product, and obtaining base material quality proportion of the target optical fiber product and surface roughness of a target plastic spraying area; processing the comprehensive surface treatment parameters, the base material quality proportion and the surface roughness of the target plastic spraying area to obtain initial plastic spraying process parameters of the target optical fiber product, and statistically obtaining historical process adjustment characteristics and reference process comparison characteristics of a historical spraying quality qualified area in historical production data; and the effect is to ensure product quality and improve production line operation efficiency.

Description

Technical Field

[0001] This invention relates to the field of optical fiber product technology, and more specifically, to a method and system for intelligent generation of production line process strategies based on optical fiber products. Background Technology

[0002] In the field of optical fiber product manufacturing, powder coating processes have a significant impact on product protection and appearance quality. Traditional production line process strategies rely on manual experience, neglecting to consider factors such as the characteristics of the product substrate and environmental changes. For example, the surface treatment requirements of different optical fiber products vary greatly. Inaccurate control of the substrate material ratio and surface roughness parameters can easily lead to poor paint adhesion after powder coating, resulting in quality problems such as peeling and unevenness. Processes designed for standard production environments are prone to failure when faced with actual environmental changes. For instance, high humidity environments may slow paint drying and cause sagging, and traditional processes struggle to dynamically respond to these environmental changes, thus affecting production quality. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a method and system for intelligent generation of production line process strategies based on optical fiber products.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for intelligently generating production line process strategies based on optical fiber products, which includes the following steps:

[0006] Obtain the characteristics of the substrate material of the target optical fiber product, and obtain comprehensive surface treatment parameters by statistically analyzing the surface treatment requirements of the target optical fiber product based on the substrate material characteristics. Calculate the substrate material ratio and surface roughness of the target powder coating area of ​​the target optical fiber product.

[0007] The initial powder coating process parameters of the target optical fiber product are obtained by processing the comprehensive surface treatment parameters, the proportion of substrate material and the surface roughness of the target powder coating area. The historical process adjustment characteristics and the reference process comparison characteristics of the historical powder coating quality qualified areas are statistically analyzed in historical production data.

[0008] The environmental variation characteristics between the current production environment and the standard production environment of the target optical fiber product are statistically analyzed. The environmental variation characteristics are matched with the reference process comparison characteristics to obtain the process adaptation prediction area under the current environment. Based on the process adaptation prediction area and the paint adhesion performance parameters within the process adaptation prediction area, the powder coating process correction parameters under the current environment are obtained.

[0009] The parameters for powder coating process modification are processed and analyzed to generate the first process strategy and the second process strategy.

[0010] Preferably, the powder coating process correction parameters are processed and analyzed to generate a first process strategy and a second process strategy, specifically including the following steps:

[0011] If the deviation between the initial powder coating process parameters and the standard process parameters is greater than or equal to the preset first process warning threshold, then the remaining adjustment nodes excluding the environmentally disturbed adjustment nodes will be activated. The initial powder coating process parameters will be formulated based on the paint utilization rate and powder coating uniformity of other process steps to obtain the first process strategy.

[0012] If the deviation between the initial powder coating process parameters and the standard process parameters is greater than or equal to the preset second process warning threshold, and the deviation between the initial powder coating process parameters and the standard process parameters is less than the first process warning threshold, and the first process warning threshold is greater than the second process warning threshold, then the adjustment control of each adjustment node in all process adjustment nodes is performed in a hierarchical sequence, and the second process strategy is obtained by formulating the initial powder coating process parameters according to the powder coating process correction parameters.

[0013] Preferably, the comprehensive surface treatment parameters are obtained by statistically analyzing the surface treatment requirements of the target optical fiber product based on the characteristics of the product substrate, specifically including the following steps:

[0014] The required pretreatment time for substrate surface pretreatment is calculated based on the characteristics of the product substrate.

[0015] Develop a substrate processing trend feature map for the target optical fiber product based on the characteristics of the product substrate and the required preprocessing time.

[0016] Based on the maximum surface defect value in the product substrate characteristics, select a portion of the pretreatment stage to be tested from the required pretreatment time;

[0017] The actual surface characteristics of the target optical fiber product after the test processing period are detected based on the test processing period.

[0018] The comprehensive surface treatment parameters are output by comparing the actual surface features with the surface trend features of the corresponding treatment period in the substrate treatment trend feature map.

[0019] Preferably, the comprehensive surface treatment parameters are output by comparing the actual surface features with the surface trend features of the corresponding treatment period in the substrate treatment trend feature map. This specifically includes the following steps:

[0020] If the actual surface characteristics are consistent with the surface trend characteristics of the time period to be tested in the substrate processing trend characteristic map, then the comprehensive surface processing parameters of the target optical fiber product are calculated based on the substrate processing trend characteristic map and the comprehensive surface processing parameters are output.

[0021] Preferably, predicting the initial powder coating process parameters of the target optical fiber product based on comprehensive surface treatment parameters specifically includes the following steps:

[0022] The material proportion values ​​are obtained by statistically analyzing the proportion of the substrate material in the target optical fiber product.

[0023] The target roughness value is obtained by statistically analyzing the surface roughness of the target powder coating area based on the substrate processing trend feature map;

[0024] Multiplying the target roughness value and the material ratio value yields the basic influence coefficient of the target optical fiber product substrate on paint adhesion;

[0025] The initial powder coating process parameters of the target optical fiber product are obtained by weighted calculation of the comprehensive surface treatment parameters and the basic influence coefficient.

[0026] Preferably, the comparison of historical process adjustment characteristics and historical powder coating quality qualified areas in historical production data with reference process characteristics includes the following steps:

[0027] Obtain historical production data of the target optical fiber product under different production batches;

[0028] Extract historical process adjustment features from historical production data under the same substrate characteristics;

[0029] Extract historical powder coating areas that met the powder coating quality requirements under the historical process adjustment characteristics from historical production data;

[0030] The historical process adjustment features and historical powder coating quality qualified areas are combined to form reference process comparison features.

[0031] Preferably, the powder coating process correction parameters for the current environment are obtained based on the process adaptation prediction region and the paint adhesion performance parameters belonging to the process adaptation prediction region, specifically including the following steps:

[0032] The pre-treated adhesion parameters are obtained by detecting the paint adhesion performance parameters within the predicted area of ​​the detection process adaptation.

[0033] The target powder coating area is obtained by statistically analyzing the area of ​​the target powder coating region.

[0034] The treatment parameters per unit area are obtained by calculating the ratio between the comprehensive surface treatment parameters and the target powder coating area.

[0035] The comprehensive process adaptation parameters are obtained by multiplying the unit area processing parameters and the pretreatment adhesion parameters.

[0036] The pre-processed adhesion parameters and the target roughness value are multiplied to obtain the correction influence coefficient of paint adhesion under the current environment;

[0037] The powder coating process correction parameters for the current environment are obtained by calculating the difference between the comprehensive process adaptation parameters and the correction influence coefficient.

[0038] Preferably, the initial powder coating process parameters are formulated based on the paint utilization rate and powder coating uniformity of other process steps to obtain the first process strategy, which specifically includes the following steps:

[0039] The auxiliary utilization rate value is obtained by statistically analyzing the paint utilization rate in other process steps;

[0040] Obtain the uniformity of powder coating at each stage of the process in historical periods;

[0041] The auxiliary test utilization rate value and powder coating uniformity are integrated into a process optimization factor.

[0042] The factor weight ratio is obtained by calculating the ratio of each factor in the statistical process optimization factors to the comprehensive optimization factors.

[0043] Obtain the maximum process adjustment range value for each process step, and multiply the maximum process adjustment range value by the factor weight ratio to obtain the actual adjustment range for each process step;

[0044] The required adjustment deviation is calculated by taking the difference between the initial powder coating process parameters and the standard process parameters and the first process warning threshold.

[0045] Based on the adjustment and control conditions of the first process and the actual adjustment range, the required adjustment deviation is allocated and processed before the first process strategy is output.

[0046] Preferably, the adjustment nodes in all process adjustment nodes are subject to hierarchical timing control, and the initial powder coating process parameters are formulated based on the powder coating process correction parameters to obtain the second process strategy, specifically including the following steps:

[0047] Obtain the initial adjustment capacity of environmentally sensitive adjustment nodes;

[0048] The actual adjustment capacity is obtained by subtracting the initial adjustment capacity from the powder coating process correction parameters.

[0049] Hierarchical timing adjustment control is implemented for each adjustment node in all process adjustment nodes;

[0050] If the required adjustment deviation is greater than the actual adjustment capacity, a second process strategy is output after coordinating and processing a stable environment selected from other process steps.

[0051] A production line process strategy intelligent generation system based on optical fiber products, characterized in that it includes:

[0052] Acquisition Module: Acquires the substrate characteristics of the target optical fiber product, calculates the surface treatment requirements parameters of the target optical fiber product based on the substrate characteristics to obtain comprehensive surface treatment parameters, and calculates the substrate material ratio and surface roughness of the target powder-coated area of ​​the target optical fiber product.

[0053] The statistics module processes the comprehensive surface treatment parameters, substrate material ratio, and surface roughness of the target powder coating area to obtain the initial powder coating process parameters of the target optical fiber product. It also statistically analyzes the historical process adjustment characteristics and the reference process comparison characteristics of historical powder coating quality qualified areas in historical production data.

[0054] Matching module: Statistically analyzes the environmental variation characteristics between the current production environment and the standard production environment of the target optical fiber product, matches the environmental variation characteristics with the reference process comparison characteristics to obtain the process adaptation prediction area under the current environment, and obtains the powder coating process correction parameters under the current environment based on the process adaptation prediction area and the paint adhesion performance parameters belonging to the process adaptation prediction area.

[0055] Generation module: Processes and analyzes the powder coating process correction parameters to generate the first process strategy and the second process strategy.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] This invention obtains the characteristics of the product substrate and statistically analyzes surface treatment requirements, material proportions, and powder coating area roughness to match the powder coating process requirements of the fiber optic product's inherent properties. The powder-coated coating bonds more tightly to the substrate, improving product protection and appearance quality, and avoiding problems such as coating peeling and blistering due to process and substrate mismatch. Statistical analysis of process adjustment characteristics and quality-compliant area reference process comparison characteristics from historical production data effectively leverages historical production experience. This reduces process exploration time, lowers trial-and-error costs, and improves production efficiency and quality stability. Considering the changing characteristics of the current production environment and standard environment, matching the process adaptation prediction area and correcting the powder coating process parameters can address the impact of environmental fluctuations on powder coating quality. It solves problems such as coating sagging and poor drying that easily occur with traditional fixed processes when the environment changes. Based on the powder coating process correction parameters, different process strategies are generated, flexibly adapting to actual production conditions while ensuring quality, thus guaranteeing product quality and improving production line efficiency. Attached Figure Description

[0058] Figure 1 This is a schematic diagram illustrating the steps of the intelligent generation method for production line process strategies based on optical fiber products proposed in this invention.

[0059] Figure 2 This is a schematic diagram of the module of the intelligent generation system for production line process strategies based on optical fiber products proposed in this invention. Detailed Implementation

[0060] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0061] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0063] Reference Figures 1-2 As shown.

[0064] The embodiments further illustrate the intelligent generation method and system for production line process strategies based on optical fiber products proposed in this invention.

[0065] A method for intelligently generating production line process strategies based on optical fiber products, which includes the following steps:

[0066] Obtain the characteristics of the substrate material of the target optical fiber product, and obtain comprehensive surface treatment parameters by statistically analyzing the surface treatment requirements of the target optical fiber product based on the substrate material characteristics. Calculate the substrate material ratio and surface roughness of the target powder coating area of ​​the target optical fiber product.

[0067] The initial powder coating process parameters of the target optical fiber product are obtained by processing the comprehensive surface treatment parameters, the proportion of substrate material and the surface roughness of the target powder coating area. The historical process adjustment characteristics and the reference process comparison characteristics of the historical powder coating quality qualified areas are statistically analyzed in historical production data.

[0068] The environmental variation characteristics between the current production environment and the standard production environment of the target optical fiber product are statistically analyzed. The environmental variation characteristics are matched with the reference process comparison characteristics to obtain the process adaptation prediction area under the current environment. Based on the process adaptation prediction area and the paint adhesion performance parameters within the process adaptation prediction area, the powder coating process correction parameters under the current environment are obtained.

[0069] The parameters for powder coating process modification are processed and analyzed to generate the first process strategy and the second process strategy.

[0070] First, surface treatment requirements are statistically analyzed based on the characteristics of the product substrate. Because the material composition and initial surface condition of different fiber optic product substrates vary, this directly affects the surface treatment process and standards. By determining the substrate characteristics, the requirements for cleaning, polishing, and pretreatment of the surface treatment are identified, thus integrating them to form comprehensive surface treatment parameters. This clarifies the specific requirements and target state of fiber optic surface treatment. Simultaneously, the proportion of different substrate materials and the surface roughness of the target powder-coated area are statistically analyzed. The proportion of different materials in the substrate affects the compatibility of subsequent processes, and surface roughness reflects the degree of microscopic unevenness on the surface, thereby affecting the paint adhesion effect.

[0071] The system integrates surface treatment parameters, substrate material ratio, and surface roughness for calculation. Considering the influence coefficient of material ratio on paint adhesion and the weight of surface roughness on powder coating uniformity, it adapts the powder coating process parameters to the basic state of the optical fiber product, thereby obtaining the initial powder coating process parameters and clarifying the ideal powder coating process execution standard.

[0072] Historical production experience and real-time environmental variables are incorporated to optimize process parameters. Historical process adjustment features and reference process comparison features corresponding to historical powder coating quality-compliant areas are extracted. Historical process adjustment features represent changes made to the process in past production due to quality and efficiency requirements, while reference process comparison features represent the combination of process parameters and adaptation conditions for qualified products. Environmental variation characteristics between the current production environment and the standard environment, such as temperature and humidity fluctuations and cleanliness differences, are monitored and matched with reference process comparison features to locate the process adaptation prediction area under the current environment. The process steps and parameter ranges adapted to the current environment are predicted, and the initial process parameters are corrected based on the paint adhesion performance parameters of this area, thus obtaining the powder coating process correction parameters. The paint adhesion performance parameters are indicators of the impact of the environment on the adhesion between the paint and the optical fiber surface.

[0073] Differentiated process strategies are output based on the degree of parameter deviation. If the initial powder coating process parameters deviate from the standard process parameters to the first process warning threshold, the remaining adjustment nodes, excluding those susceptible to environmental disturbances (i.e., process nodes that are slightly affected by environmental changes, have significant impacts, and are prone to adjustment failures), are activated. The first process strategy is formulated in conjunction with the paint utilization rate and powder coating uniformity of other process nodes. If the deviation is within the second process warning threshold range, all process adjustment nodes are subject to hierarchical and sequential control. That is, adjustments are made in stages according to the importance and impact of the process nodes. The second process strategy is generated by optimizing the initial parameters in conjunction with the powder coating process correction parameters.

[0074] The powder coating process correction parameters are processed and analyzed to generate a first process strategy and a second process strategy, specifically including the following steps:

[0075] If the deviation between the initial powder coating process parameters and the standard process parameters is greater than or equal to the preset first process warning threshold, then the remaining adjustment nodes excluding the environmentally disturbed adjustment nodes will be activated. The initial powder coating process parameters will be formulated based on the paint utilization rate and powder coating uniformity of other process steps to obtain the first process strategy.

[0076] If the deviation between the initial powder coating process parameters and the standard process parameters is greater than or equal to the preset second process warning threshold, and the deviation between the initial powder coating process parameters and the standard process parameters is less than the first process warning threshold, and the first process warning threshold is greater than the second process warning threshold, then the adjustment control of each adjustment node in all process adjustment nodes is performed in a hierarchical sequence, and the second process strategy is obtained by formulating the initial powder coating process parameters according to the powder coating process correction parameters.

[0077] If the deviation between the initial powder coating process parameters and the standard process parameters reaches or exceeds the first process warning threshold, the first process strategy generation process will be initiated. The initial powder coating process parameters are derived based on the basic state of the optical fiber product and the ideal environment. The standard process parameters are the industry-set optimal powder coating parameter benchmark. The first process warning threshold is a preset critical value for serious deviation, indicating that conventional small-scale adjustments are insufficient to meet quality requirements.

[0078] All remaining process adjustment nodes, excluding those susceptible to environmental disturbances, are activated. These process adjustment nodes refer to the process steps in the powder coating process that are being adjusted, such as spray pressure, spray distance, and drying temperature. Environmental disturbances include the tendency for frequent adjustments to spray atomization parameters in high humidity environments, which can lead to paint agglomeration. A primary process strategy is developed for the initial powder coating process parameters based on the paint utilization rate and powder coating uniformity of other process steps. Paint utilization rate reflects the ratio of actual paint usage to standard usage, while powder coating uniformity refers to the evenness of paint distribution on the fiber optic surface after powder coating. The analysis focuses on how to optimize parameters to improve efficiency in areas with low paint utilization and how to adjust processes to eliminate deviations in areas with poor powder coating uniformity.

[0079] If the deviation between the initial powder coating process parameters and the standard process parameters is greater than or equal to the second process warning threshold, and the deviation between the initial powder coating process parameters and the standard process parameters is less than the first process warning threshold, then the second process strategy generation process is triggered.

[0080] All process adjustment nodes are subject to hierarchical and sequential adjustment control. Hierarchical timing means that adjustment nodes are divided into different levels based on the importance of each process step to the powder coating quality, the ease of adjustment, and their correlation with other processes, and each adjustment node is adjusted sequentially. Simultaneously, powder coating process correction parameters are introduced to optimize the initial powder coating process parameters. These correction parameters are the basis for process adjustments after incorporating historical experience and environmental adaptation. By adjusting nodes in a hierarchical and orderly manner and integrating correction parameters to guide the optimization direction, the initial parameter deviations are gradually corrected to generate a second process strategy.

[0081] Based on the characteristics of the product substrate, the surface treatment requirements of the target optical fiber product are statistically analyzed to obtain comprehensive surface treatment parameters, which specifically include the following steps:

[0082] The required pretreatment time for substrate surface pretreatment is calculated based on the characteristics of the product substrate.

[0083] Develop a substrate processing trend feature map for the target optical fiber product based on the characteristics of the product substrate and the required preprocessing time.

[0084] Based on the maximum surface defect value in the product substrate characteristics, select a portion of the pretreatment stage to be tested from the required pretreatment time;

[0085] The actual surface characteristics of the target optical fiber product after the test processing period are detected based on the test processing period.

[0086] The comprehensive surface treatment parameters are output by comparing the actual surface features with the surface trend features of the corresponding treatment period in the substrate treatment trend feature map.

[0087] The required time for surface pretreatment of the substrate is calculated based on the characteristics of the product substrate. For example, some substrates are dense, while others are relatively porous. Dense substrates require a longer time for cleaning and polishing during surface pretreatment, thus determining the required time for surface pretreatment for that substrate.

[0088] By combining the characteristics of the product substrate with the required pretreatment time, a substrate processing trend feature map for the target optical fiber product is developed. For example, if the pretreatment of a certain type of optical fiber substrate requires 10 hours, the roughness and cleanliness of the substrate surface will change regularly as the processing progresses. The surface features corresponding to each time point are analyzed to form a trend map similar to time-surface features, which can be used to predict the state of the substrate surface during the processing.

[0089] Find the maximum surface defect value from the product substrate characteristics, and select some of the required pre-treatment durations as the test treatment periods to be measured. Suppose the pre-treatment duration is 10 hours. The maximum surface defect value reflects the most prominent area of the substrate surface. If the treatment in a certain period is not good, problems are likely to occur in subsequent powder coating. Then select this critical period as the test period to detect the treatment effect.

[0090] Detect the actual surface characteristics of the target optical fiber product after being treated for the test treatment period according to the test treatment period. For example, select the 3rd - 5th hours as the test period, and actually monitor the roughness and residual impurity characteristics of the substrate surface.

[0091] Compare the actual surface characteristics with the surface trend characteristics corresponding to the test treatment period in the substrate treatment trend characteristics diagram, and then output the comprehensive surface treatment parameters. If the ideal surface roughness corresponding to the 3rd - 5th hours in the trend diagram is Ra1.6, and the actual detection is also Ra1.6, and the surface cleanliness meets the trend diagram, it means that this period of treatment is qualified. Integrate these parameters that can reflect the surface treatment situation to form the comprehensive surface treatment parameters, and the subsequent powder coating process is adjusted based on this. For example, if the comprehensive surface treatment parameters show that the surface roughness meets the standard and the oil stain residue is less, then appropriately adjust parameters such as the spraying pressure during powder coating to make the powder coating effect better.

[0092] Compare the actual surface characteristics with the surface trend characteristics of the test treatment period in the substrate treatment trend characteristics diagram, and then output the comprehensive surface treatment parameters. The specific steps are as follows:

[0093] If the actual surface characteristics are consistent with the surface trend characteristics of the test treatment period in the substrate treatment trend characteristics diagram, then statistically obtain the comprehensive surface treatment parameters of the target optical fiber product according to the substrate treatment trend characteristics diagram, and then output the comprehensive surface treatment parameters.

[0094] Obtain the actual surface characteristics presented by the target optical fiber product after the specific test treatment period ends. At the same time, construct a substrate treatment trend characteristics diagram based on the product substrate characteristics and the pre-treatment duration. This diagram reflects the trend characteristics that the surface should have corresponding to the test treatment period under ideal conditions.

[0095] If the actual surface characteristics are consistent with the surface trend characteristics of the test treatment period in the substrate treatment trend characteristics diagram, this means that the surface pre-treatment in this period has achieved the expected process effect. At this time, according to the substrate treatment trend characteristics diagram, comprehensive surface treatment parameters covering surface roughness, cleanliness, and defect repair degree can be statistically obtained, and these parameters are output to provide a key basis for formulating subsequent powder coating process parameters. For example, subsequently, match the appropriate paint type according to this comprehensive parameter and determine the reasonable spraying pressure and spraying speed to ensure the powder coating quality and production efficiency.

[0096] Predicting the initial powder coating process parameters for the target optical fiber product based on comprehensive surface treatment parameters includes the following steps:

[0097] The material proportion values ​​are obtained by statistically analyzing the proportion of the substrate material in the target optical fiber product.

[0098] The target roughness value is obtained by statistically analyzing the surface roughness of the target powder coating area based on the substrate processing trend feature map;

[0099] Multiplying the target roughness value and the material ratio value yields the basic influence coefficient of the target optical fiber product substrate on paint adhesion;

[0100] The initial powder coating process parameters of the target optical fiber product are obtained by weighted calculation of the comprehensive surface treatment parameters and the basic influence coefficient.

[0101] First, the proportion of the substrate material of the target optical fiber product is statistically analyzed. By determining the proportion of different materials in the substrate, the material proportion value is obtained. Then, the surface roughness information related to the target powder coating area is extracted using the substrate processing trend feature map to obtain the target roughness value. This value reflects the morphological characteristics of the substrate surface in the powder coating area and plays a key role in the paint adhesion effect.

[0102] Multiplying the target roughness value by the material ratio value is a calculation that considers the synergistic effect of substrate material characteristics and surface micromorphology on paint adhesion. This quantifies the basic influence coefficient of the target optical fiber product substrate on paint adhesion, and this coefficient can reflect the basic degree of effect of the substrate's own properties on paint adhesion.

[0103] By weighting the comprehensive surface treatment parameters with the basic influence coefficient, and assigning appropriate weights to different parameters based on actual production needs and process experience, the initial powder coating process parameters adapted to the production requirements of the target optical fiber product are obtained. For example, this determines suitable spraying pressure, paint ratio, and spraying time, providing process guidance for subsequent powder coating processes and ensuring powder coating quality and production efficiency. For instance, in the production of a certain type of optical fiber connector, the metal content of the substrate is high, with a statistically determined material proportion of 0.7. From the substrate treatment trend characteristic map, the target roughness value of the powder coating area is Ra3.2. Multiplying this by the basic influence coefficient yields a basic influence coefficient of 2.24. This is then combined with information such as good surface cleanliness from the comprehensive surface treatment parameters for weighted calculation to obtain the initial powder coating process parameters.

[0104] The statistical analysis of historical production data includes comparing historical process adjustment characteristics with reference process characteristics of historical powder coating quality qualified areas. This process involves the following steps:

[0105] Obtain historical production data of the target optical fiber product under different production batches;

[0106] Extract historical process adjustment features from historical production data under the same substrate characteristics;

[0107] Extract historical powder coating areas that met the powder coating quality requirements under the historical process adjustment characteristics from historical production data;

[0108] Historical process adjustment characteristics and historical powder coating quality qualified areas are combined to form reference process comparison characteristics.

[0109] Historical production data of the target optical fiber product is obtained from different production batches. The production of optical fiber products can vary due to fluctuations in raw materials and changes in the environment. Data collection from multiple batches can cover diverse production scenarios. For example, some batches may have slightly different substrate purity, while others may have differences in the temperature and humidity of the production environment. These data collectively form the basis for subsequent analysis of stable process logic.

[0110] Extract historical process adjustment features from historical production data for products with similar substrate characteristics. Since substrate characteristics influence the powder coating process, similar substrate characteristics imply similar fundamental requirements for the powder coating process. For example, fiber optic products using the same alloy substrate and similar surface roughness can have their powder coating processes reused, such as adjusting spray pressure and coating viscosity. Extracting these adjustment features helps to identify process adjustment rules adapted to specific substrates.

[0111] Historical powder coating quality was extracted from historical production data, identifying areas that met the required standards under specific process adjustment conditions. A qualified powder coating indicates that the process parameters, substrate, and environment of that area were well-suited. By screening these qualified areas, the actual application scope where powder coating effects met standards under specific process adjustments was clarified. For example, some areas were deemed qualified due to accurate spraying distance and good paint adhesion; these areas serve as key evidence for verifying the effectiveness of process adjustments.

[0112] Historical process adjustment features and historical powder coating quality-compliant areas are combined to create reference process comparison features. Process adjustments and effective result areas are then correlated to form referable process adjustment parameters. If similar substrates and environments are encountered during subsequent production, these features can be compared to quickly determine the direction of process adjustments.

[0113] Based on the process adaptation prediction region and the paint adhesion performance parameters within that region, the powder coating process correction parameters for the current environment are obtained, specifically including the following steps:

[0114] The pre-treated adhesion parameters are obtained by detecting the paint adhesion performance parameters within the predicted area of ​​the detection process adaptation.

[0115] The target powder coating area is obtained by statistically analyzing the area of ​​the target powder coating region.

[0116] The treatment parameters per unit area are obtained by calculating the ratio between the comprehensive surface treatment parameters and the target powder coating area.

[0117] The comprehensive process adaptation parameters are obtained by multiplying the unit area processing parameters and the pretreatment adhesion parameters.

[0118] The pre-processed adhesion parameters and the target roughness value are multiplied to obtain the correction influence coefficient of paint adhesion under the current environment;

[0119] The powder coating process correction parameters for the current environment are obtained by calculating the difference between the comprehensive process adaptation parameters and the correction influence coefficient.

[0120] The pretreatment adhesion parameters are obtained by detecting the paint adhesion performance parameters within the predicted area of ​​the testing process. Before powder coating, the initial adhesion ability of the paint on the substrate surface is tested, for example, by tensile testing to obtain data such as the force required for the paint to peel off the substrate surface. These data reflect the basic state of paint adhesion on the substrate after pretreatment.

[0121] The target powder coating area is obtained by statistically analyzing the area of ​​the target powder coating region. The powder coating area is directly related to the process parameters of paint usage and spraying efficiency. Different sizes and shapes of powder coating areas have different requirements for spraying equipment parameters, such as spray gun movement speed and spray flow rate.

[0122] The treatment parameters per unit area are obtained by calculating the ratio between the comprehensive surface treatment parameters and the target powder coating area. The comprehensive surface treatment parameters include substrate surface roughness, cleanliness, and defect repair level.

[0123] The comprehensive process adaptation parameters are obtained by multiplying the unit area treatment parameters and the pretreatment adhesion parameters. For example, if a high level of cleanliness is required per unit area, the corresponding unit area treatment parameter value is high; if the paint adhesion is strong after pretreatment, the pretreatment adhesion parameter value is high. The comprehensive process adaptation parameters are used to guide the spraying equipment to increase the spraying pressure, thereby allowing the paint to adhere more densely.

[0124] The pretreatment adhesion parameters and the target roughness value are multiplied to obtain the correction influence coefficient of paint adhesion under the current environment. The target roughness value reflects the influence of the substrate surface microstructure on paint adhesion. Combined with the actual adhesion force after pretreatment, the dynamic influence of the environment on paint adhesion is quantified.

[0125] The modified parameters for the powder coating process under the current environment are obtained by calculating the difference between the comprehensive process adaptation parameters and the correction influence coefficient. The final parameters that meet both the theoretical process requirements and the actual production environment are then selected. For example, if the comprehensive process adaptation parameters require high spraying pressure, but the correction influence coefficient shows that the current environment is prone to paint sagging, the spraying pressure can be appropriately reduced by calculating the difference.

[0126] The initial powder coating process parameters are formulated based on the paint utilization rate and powder coating uniformity of other process steps to obtain the first process strategy, which specifically includes the following steps:

[0127] The auxiliary utilization rate value is obtained by statistically analyzing the paint utilization rate in other process steps;

[0128] Obtain the uniformity of powder coating at each stage of the process in historical periods;

[0129] The auxiliary test utilization rate and powder coating uniformity are integrated into a process optimization factor;

[0130] The factor weight ratio is obtained by calculating the ratio of each factor in the statistical process optimization factors to the comprehensive optimization factors.

[0131] Obtain the maximum process adjustment range value for each process step, and multiply the maximum process adjustment range value by the factor weight ratio to obtain the actual adjustment range for each process step;

[0132] The required adjustment deviation is calculated by taking the difference between the initial powder coating process parameters and the standard process parameters and the first process warning threshold.

[0133] Based on the adjustment and control conditions of the first process and the actual adjustment range, the required adjustment deviation is allocated and processed before the first process strategy is output.

[0134] The auxiliary utilization rate value is obtained by statistically analyzing the paint utilization rate in other process stages. The paint utilization rate reflects the degree of effective utilization of paint in the production process. For example, during the spraying process, some paint may not adhere to the workpiece due to the atomization effect of the spray gun and the spraying distance. Statistical analysis of the utilization rate of this stage can reveal the current status of paint waste or effective utilization.

[0135] Obtain the powder coating uniformity at each process stage during historical periods. Powder coating uniformity is crucial for product quality consistency; for example, poor powder coating uniformity on the casing of fiber optic equipment will result in areas where the coating is too thick or too thin. Collect historical data to analyze the uniformity patterns under different process parameters.

[0136] The auxiliary measurement utilization rate and powder coating uniformity are integrated into a process optimization factor. This means that cost efficiency indicators (paint utilization rate) and quality indicators (powder coating uniformity) are linked to form a composite factor that guides process adjustments. For example, the factor resulting from the integration of high paint utilization rate and low powder coating uniformity indicates the need to optimize spraying parameters to maintain or improve utilization rate while ensuring uniformity, so that process adjustments can simultaneously consider both quality and cost.

[0137] The weight ratio of each factor in the overall optimization factors is obtained by calculating the proportion of each factor in the statistical process optimization factors. The priorities of quality and cost differ under different production scenarios. Weighting calculations clarify the importance of paint utilization and powder coating uniformity in the current process. For example, in the production of high-end optical fiber precision components, powder coating uniformity has a significant impact on product performance, so its weight ratio is high, and process adjustments prioritize ensuring uniformity.

[0138] Obtain the maximum adjustment range for each process step, and multiply it by the factor weight ratio to obtain the actual adjustment range for each process step. The maximum adjustment range is the adjustable interval of the process parameter, such as the spray gun air pressure, which can be adjusted from 0.2 to 0.5 MPa. Combining this with the weight ratio, the specific adjustment amount for each step is calculated. For example, if the powder coating uniformity weight ratio is high, such as 0.6, the maximum adjustment range for the spray gun movement speed in the corresponding process step is ±10 mm / s. Multiplying this by the factor yields an actual adjustment range of ±6 mm / s, ensuring precise and controllable process adjustments.

[0139] The required adjustment deviation is calculated by subtracting the deviation between the initial powder coating process parameters and the standard process parameters from the first process warning threshold. The initial process parameters are the baseline set before production, while the standard parameters represent ideal values ​​for quality and efficiency.

[0140] Based on the adjustment control conditions and actual adjustment range of the first process, the required adjustment deviations are allocated and processed to output the first process strategy. Control conditions include equipment performance such as the maximum adjustment pressure of the spray gun, and production rhythm such as ensuring adjustments do not cause production line shutdowns. Combined with the actual adjustment range, the required adjustment deviations are rationally allocated to each process stage. For example, if the required adjustment deviation is to improve powder coating uniformity by 20% and increase paint utilization by 15%, based on the control conditions and the adjustment range of each stage, the allocation would be: spray gun air pressure adjustment +0.1MPa and spraying distance reduction by 5cm. Adjusting the spray gun air pressure improves uniformity, while shortening the spraying distance improves utilization. Finally, a complete first process strategy is output to guide production execution.

[0141] Then, hierarchical timing control is performed on each adjustment node in all process adjustment nodes, and the initial powder coating process parameters are formulated according to the powder coating process correction parameters to obtain the second process strategy, which specifically includes the following steps:

[0142] Obtain the initial adjustment capacity of environmentally sensitive adjustment nodes;

[0143] The actual adjustment capacity is obtained by subtracting the initial adjustment capacity from the powder coating process correction parameters.

[0144] Hierarchical timing adjustment control is implemented for each adjustment node in all process adjustment nodes;

[0145] If the required adjustment deviation is greater than the actual adjustment capacity, a second process strategy is output after coordinating and processing a stable environment selected from other process steps.

[0146] First, obtain the initial adjustment capacity of the environmentally sensitive adjustment nodes. In the powder coating production of optical fiber products, some processes are easily affected by environmental factors. These processes are the environmentally sensitive adjustment nodes, such as the air cleanliness control in the spray booth. Environmental changes can easily affect the powder coating quality. The initial adjustment capacity refers to the range of process parameters that can be adjusted at these nodes under ideal conditions, such as the fan speed adjustment range and temperature and humidity control accuracy of the air purification system.

[0147] The actual adjustment capacity is obtained by subtracting the initial adjustment capacity from the powder coating process correction parameters. The powder coating process correction parameters reflect the impact of the current environment on powder coating, such as the effect of actual temperature and humidity deviating from standard values. Combined with the initial adjustment capacity, the actual adjustable parameter range for easily disturbed nodes after environmental interference is calculated. For example, if the initial adjustment capacity allows for an air purification wind speed of 2-5 m / s, and the powder coating process correction parameters indicate that a 1 m / s reduction in wind speed is needed to ensure powder coating uniformity due to high humidity, the actual adjustment capacity becomes 1-4 m / s after subtracting, thus allowing the process adjustment to adapt to environmental changes.

[0148] All process adjustment nodes are subject to hierarchical and sequential adjustment control. The powder coating process involves multiple stages, such as pretreatment, spraying, and curing, each with different priorities regarding environmental and quality impacts. Hierarchical and sequential control helps avoid process adjustment conflicts.

[0149] If the required adjustment deviation exceeds the actual adjustment capacity, it indicates that the adjustment capability of environmentally sensitive nodes cannot meet the process requirements. In this case, a second process strategy is output after coordinating and processing an environmentally stable node from other process stages. For example, if the adjustment capacity is insufficient in the spraying stage due to high humidity, coordination can be achieved in the curing stage, such as increasing the curing temperature to accelerate paint drying. This compensates for the insufficient adjustment in the spraying stage, ensures the quality of powder coating, and ultimately forms a second process strategy adapted to complex environments.

[0150] A production line process strategy intelligent generation system based on optical fiber products, characterized in that it includes:

[0151] Acquisition Module: Acquires the substrate characteristics of the target optical fiber product, calculates the surface treatment requirements parameters of the target optical fiber product based on the substrate characteristics to obtain comprehensive surface treatment parameters, and calculates the substrate material ratio and surface roughness of the target powder-coated area of ​​the target optical fiber product.

[0152] The statistics module processes the comprehensive surface treatment parameters, substrate material ratio, and surface roughness of the target powder coating area to obtain the initial powder coating process parameters of the target optical fiber product. It also statistically analyzes the historical process adjustment characteristics and the reference process comparison characteristics of historical powder coating quality qualified areas in historical production data.

[0153] Matching module: Statistically analyzes the environmental variation characteristics between the current production environment and the standard production environment of the target optical fiber product, matches the environmental variation characteristics with the reference process comparison characteristics to obtain the process adaptation prediction area under the current environment, and obtains the powder coating process correction parameters under the current environment based on the process adaptation prediction area and the paint adhesion performance parameters belonging to the process adaptation prediction area.

[0154] Generation module: Processes and analyzes the powder coating process correction parameters to generate the first process strategy and the second process strategy.

[0155] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligently generating production line process strategies based on optical fiber products, characterized in that: The method includes the following steps: Obtain the characteristics of the substrate material of the target optical fiber product, and obtain comprehensive surface treatment parameters by statistically analyzing the surface treatment requirements of the target optical fiber product based on the substrate material characteristics. Calculate the substrate material ratio and surface roughness of the target powder coating area of ​​the target optical fiber product. The initial powder coating process parameters of the target optical fiber product are obtained by processing the comprehensive surface treatment parameters, the proportion of substrate material and the surface roughness of the target powder coating area. The historical process adjustment characteristics and the reference process comparison characteristics of the historical powder coating quality qualified areas are statistically analyzed in historical production data. The environmental variation characteristics between the current production environment and the standard production environment of the target optical fiber product are statistically analyzed. The environmental variation characteristics are matched with the reference process comparison characteristics to obtain the process adaptation prediction area under the current environment. Based on the process adaptation prediction area and the paint adhesion performance parameters within the process adaptation prediction area, the powder coating process correction parameters under the current environment are obtained. The powder coating process correction parameters are processed and analyzed to generate a first process strategy and a second process strategy, specifically including the following steps: If the deviation between the initial powder coating process parameters and the standard process parameters is greater than or equal to the preset first process warning threshold, then the remaining adjustment nodes excluding the environmentally disturbed adjustment nodes will be activated. The initial powder coating process parameters will be formulated based on the paint utilization rate and powder coating uniformity of other process steps to obtain the first process strategy. If the deviation between the initial powder coating process parameters and the standard process parameters is greater than or equal to the preset second process warning threshold, and the deviation between the initial powder coating process parameters and the standard process parameters is less than the first process warning threshold, then the adjustment nodes in all process adjustment nodes are adjusted and controlled in a hierarchical and sequential manner, and the initial powder coating process parameters are formulated according to the powder coating process correction parameters to obtain the second process strategy.

2. The intelligent generation method for production line process strategies based on optical fiber products according to claim 1, characterized in that, Based on the characteristics of the product substrate, the surface treatment requirements of the target optical fiber product are statistically analyzed to obtain comprehensive surface treatment parameters, which specifically include the following steps: The required pretreatment time for substrate surface pretreatment is calculated based on the characteristics of the product substrate. Develop a substrate processing trend feature map for the target optical fiber product based on the characteristics of the product substrate and the required preprocessing time. Based on the maximum surface defect value in the product substrate characteristics, select a portion of the pretreatment stage to be tested from the required pretreatment time; The actual surface characteristics of the target optical fiber product after the test processing period are detected based on the test processing period. The comprehensive surface treatment parameters are output by comparing the actual surface features with the surface trend features of the corresponding treatment period in the substrate treatment trend feature map.

3. The intelligent generation method for production line process strategies based on optical fiber products according to claim 2, characterized in that, The comprehensive surface treatment parameters are output by comparing the actual surface features with the surface trend features of the corresponding treatment period in the substrate treatment trend feature map. The specific steps include: If the actual surface characteristics are consistent with the surface trend characteristics of the time period to be tested in the substrate processing trend characteristic map, then the comprehensive surface processing parameters of the target optical fiber product are calculated based on the substrate processing trend characteristic map and the comprehensive surface processing parameters are output.

4. The intelligent generation method for production line process strategies based on optical fiber products according to claim 3, characterized in that, Predicting the initial powder coating process parameters for the target optical fiber product based on comprehensive surface treatment parameters includes the following steps: The material proportion values ​​are obtained by statistically analyzing the proportion of the substrate material in the target optical fiber product. The target roughness value is obtained by statistically analyzing the surface roughness of the target powder coating area based on the substrate processing trend feature map; Multiplying the target roughness value and the material ratio value yields the basic influence coefficient of the target optical fiber product substrate on paint adhesion; The initial powder coating process parameters of the target optical fiber product are obtained by weighted calculation of the comprehensive surface treatment parameters and the basic influence coefficient.

5. The intelligent generation method for production line process strategies based on optical fiber products according to claim 4, characterized in that, The statistical analysis of historical production data includes comparing historical process adjustment characteristics with reference process characteristics of historical powder coating quality qualified areas. This process involves the following steps: Obtain historical production data of the target optical fiber product under different production batches; Extract historical process adjustment features from historical production data under the same substrate characteristics; Extract historical powder coating areas that met the powder coating quality requirements under the historical process adjustment characteristics from historical production data; The historical process adjustment features and historical powder coating quality qualified areas are combined to form reference process comparison features.

6. The intelligent generation method for production line process strategies based on optical fiber products according to claim 5, characterized in that, Based on the process adaptation prediction region and the paint adhesion performance parameters within that region, the powder coating process correction parameters for the current environment are obtained, specifically including the following steps: The pre-treated adhesion parameters are obtained by detecting the paint adhesion performance parameters within the predicted area of ​​the detection process adaptation. The target powder coating area is obtained by statistically analyzing the area of ​​the target powder coating region. The treatment parameters per unit area are obtained by calculating the ratio between the comprehensive surface treatment parameters and the target powder coating area. The comprehensive process adaptation parameters are obtained by multiplying the unit area processing parameters and the pretreatment adhesion parameters. The pre-processed adhesion parameters and the target roughness value are multiplied to obtain the correction influence coefficient of paint adhesion under the current environment; The powder coating process correction parameters for the current environment are obtained by calculating the difference between the comprehensive process adaptation parameters and the correction influence coefficient.

7. The intelligent generation method for production line process strategies based on optical fiber products according to claim 6, characterized in that, The initial powder coating process parameters are formulated based on the paint utilization rate and powder coating uniformity of other process steps to obtain the first process strategy, which specifically includes the following steps: The auxiliary utilization rate value is obtained by statistically analyzing the paint utilization rate in other process steps; Obtain the uniformity of powder coating at each stage of the process in historical periods; The auxiliary test utilization rate value and powder coating uniformity are integrated into a process optimization factor. The factor weight ratio is obtained by calculating the ratio of each factor in the statistical process optimization factors to the comprehensive optimization factors. Obtain the maximum process adjustment range value for each process step, and multiply the maximum process adjustment range value by the factor weight ratio to obtain the actual adjustment range for each process step; The required adjustment deviation is calculated by taking the difference between the initial powder coating process parameters and the standard process parameters and the first process warning threshold. Based on the adjustment and control conditions of the first process and the actual adjustment range, the required adjustment deviation is allocated and processed before the first process strategy is output.

8. The intelligent generation method for production line process strategies based on optical fiber products according to claim 7, characterized in that, Then, hierarchical timing control is performed on each adjustment node in all process adjustment nodes, and the initial powder coating process parameters are formulated according to the powder coating process correction parameters to obtain the second process strategy, which specifically includes the following steps: Obtain the initial adjustment capacity of environmentally sensitive adjustment nodes; The actual adjustment capacity is obtained by subtracting the initial adjustment capacity from the powder coating process correction parameters. Hierarchical timing adjustment control is implemented for each adjustment node in all process adjustment nodes; If the required adjustment deviation is greater than the actual adjustment capacity, a second process strategy is output after coordinating and processing a stable environment selected from other process steps.

9. A production line process strategy intelligent generation system based on optical fiber products, applied to the production line process strategy intelligent generation method based on optical fiber products as described in any one of claims 1 to 8, characterized in that, include: Acquisition Module: Acquires the substrate characteristics of the target optical fiber product, calculates the surface treatment requirements parameters of the target optical fiber product based on the substrate characteristics to obtain comprehensive surface treatment parameters, and calculates the substrate material ratio and surface roughness of the target powder-coated area of ​​the target optical fiber product. The statistics module processes the comprehensive surface treatment parameters, substrate material ratio, and surface roughness of the target powder coating area to obtain the initial powder coating process parameters of the target optical fiber product. It also statistically analyzes the historical process adjustment characteristics and the reference process comparison characteristics of historical powder coating quality qualified areas in historical production data. Matching module: Statistically analyzes the environmental variation characteristics between the current production environment and the standard production environment of the target optical fiber product, matches the environmental variation characteristics with the reference process comparison characteristics to obtain the process adaptation prediction area under the current environment, and obtains the powder coating process correction parameters under the current environment based on the process adaptation prediction area and the paint adhesion performance parameters belonging to the process adaptation prediction area. Generation module: Processes and analyzes the powder coating process correction parameters to generate the first and second process strategies, specifically including the following steps: If the deviation between the initial powder coating process parameters and the standard process parameters is greater than or equal to the preset first process warning threshold, then the remaining adjustment nodes excluding the environmentally disturbed adjustment nodes will be activated. The initial powder coating process parameters will be formulated based on the paint utilization rate and powder coating uniformity of other process steps to obtain the first process strategy. If the deviation between the initial powder coating process parameters and the standard process parameters is greater than or equal to the preset second process warning threshold, and the deviation between the initial powder coating process parameters and the standard process parameters is less than the first process warning threshold, then the adjustment nodes in all process adjustment nodes are adjusted and controlled in a hierarchical and sequential manner, and the initial powder coating process parameters are formulated according to the powder coating process correction parameters to obtain the second process strategy.

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