A waterproof connector manufacturing process management method and system

Abstract

The application relates to the technical field of intelligent manufacturing, and particularly discloses a waterproof connector manufacturing process management method and system, which comprises the following steps: acquiring sealing performance data of each waterproof connector after assembly, grading the sealing performance data, and obtaining the quality grade of each waterproof connector; continuously monitoring the quantity proportion of the waterproof connectors corresponding to each quality grade to form a grade proportion distribution; when a raw material batch replacement event is identified or a preset negative change trend occurs in the grade proportion distribution, the key manufacturing process parameters affecting the sealing performance data are tentatively adjusted; the adjustment effect of the tentative adjustment is evaluated according to the grade proportion distribution of the waterproof connectors produced after the tentative adjustment, and the key manufacturing process parameters are iteratively corrected according to the adjustment effect. The technical scheme of the application suppresses batch quality problems caused by factors such as raw material fluctuations, and improves the stability of the production process and the consistency of the final product.

Description

Technical Field

[0001] This application relates to the field of intelligent manufacturing technology, and in particular to a method and system for managing the manufacturing process of waterproof connectors. Background Technology

[0002] In the industrial mass production of waterproof connectors, maintaining stable sealing performance has always been a challenge in manufacturing quality management. Existing manufacturing management methods typically employ fixed process parameters, only performing sealing performance testing after all assembly steps are completed, discarding defective products. However, the sealing performance of waterproof connectors is affected by multiple factors, including batch variations in raw materials, fluctuations in environmental temperature and humidity, and equipment aging. When raw material batches change or processes drift, fixed parameters cannot adapt to these dynamic changes, often leading to a batch-wide decline in sealing performance and significant material waste. More critically, existing technologies lack real-time, tiered monitoring and trend warning mechanisms for sealing performance data, failing to identify risks in the early stages of quality deterioration. By the time a large number of defective products are discovered, severe material waste and production efficiency losses have already occurred, and tracing the root cause of the problem is difficult. Furthermore, existing methods rely on trial-and-error adjustments based on manual experience for key manufacturing parameters such as sealant injection volume and curing temperature profiles. These adjustments are delayed and lack precision, making it difficult to quickly restore stable production when raw material fluctuations or process drift occur, severely restricting the continuity and consistency of production. Summary of the Invention

[0003] This application proposes a method and system for managing the manufacturing process of waterproof connectors, aiming to solve the technical problem that existing technologies are unable to adjust key manufacturing parameters in a timely manner when raw material batches change or process drifts, resulting in unstable sealing performance and reduced yield.

[0004] In a first aspect, this application discloses a method for managing the manufacturing process of a waterproof connector, comprising the following steps: Obtain the sealing performance data of each assembled waterproof connector, classify the sealing performance data, and obtain the quality level of each waterproof connector; Continuously monitor the quantity ratio of waterproof connectors corresponding to each quality level to form a grade ratio distribution; When a raw material batch change event is identified, or when a predetermined negative trend in the grade ratio distribution occurs, the key manufacturing process parameters affecting the sealing performance data are adjusted on a trial basis. The key manufacturing process parameters include at least one of the following: sealant injection volume, curing temperature profile, and injection pressure holding time. The trial adjustment is to make incremental or decremental adjustments to the selected key manufacturing process parameters by a predetermined parameter adjustment range. Based on the grade distribution of the waterproof connectors produced after the trial adjustments, the effectiveness of the adjustments is evaluated, and key manufacturing process parameters are iteratively revised based on the adjustment results.

[0005] This technical solution can solve the problem that fixed process parameters cannot adapt to dynamic changes in the production environment, and improve the self-adaptability of the production process and the stability of sealing performance.

[0006] Optionally, the steps of obtaining sealing performance data for each assembled waterproof connector, classifying the sealing performance data, and obtaining the quality grade of each waterproof connector include: Online airtightness monitoring is performed on each assembled waterproof connector to obtain the pressure leakage value of the waterproof connector, which serves as the sealing performance data of the waterproof connector; The quality level of the waterproof connector is determined by comparing the pressure leakage value with multiple preset grading thresholds.

[0007] This technical solution enables the quantitative grading of sealing performance, providing basic data support for subsequent quality trend analysis while ensuring testing efficiency.

[0008] Optionally, the steps of obtaining sealing performance data for each assembled waterproof connector, classifying the sealing performance data, and obtaining the quality grade of each waterproof connector include: Online airtightness monitoring was performed on each assembled waterproof connector to obtain the pressure leakage value of the waterproof connector; During online airtightness monitoring, signals of pressure changes inside the waterproof connector over time are collected to form a pressure-time curve. Dynamic characteristic parameters reflecting the viscoelastic behavior of the sealing material are extracted from the pressure-time curve. The dynamic characteristic parameters include at least one of the following: the instantaneous rate of pressure drop, the rate of change of the instantaneous rate, and the nonlinear deviation of the pressure-time curve from the preset ideal elastic decay curve. The creep risk index is calculated based on dynamic characteristic parameters; Pressure leakage value and creep risk index are used together as sealing performance data for waterproof connectors; Determine the leakage value range in which the pressure leakage value falls, and the risk index range in which the creep risk index falls; Based on the combination of leakage value range and risk index range, the quality level of the waterproof connector is obtained by querying the pre-stored level determination table; the level determination table defines the quality level corresponding to different combinations of leakage value range and risk index range.

[0009] This technical solution enables a technological leap from static sealing performance evaluation to dynamic sealing reliability prediction.

[0010] Optionally, the steps for calculating the creep risk index based on dynamic characteristic parameters include: When there is only one dynamic characteristic parameter, the value of the dynamic characteristic parameter is used as the creep risk index; When there are two or more dynamic characteristic parameters, preset weight coefficients are assigned to the dynamic characteristic parameters, and the dynamic characteristic parameters with assigned weight coefficients are weighted and summed to calculate the creep risk index; where the weight coefficients are pre-calibrated based on historical experimental data and sealing failure analysis results.

[0011] This technical solution can adapt to evaluation needs under different material properties and process conditions.

[0012] Optionally, the quality grades may include at least an excellent sealing grade, a critical pass grade, and a non-pass grade; The steps for continuously monitoring the quantity ratio of waterproof connectors corresponding to each quality level and forming a level ratio distribution include: Using a preset sliding time window or a preset product quantity sliding quantity window as a unit, the proportion of waterproof connectors with excellent sealing rating and critical qualified rating is statistically analyzed in real time to form a rating distribution.

[0013] This technical solution can provide a sensitive quality indicator for the early identification of process drift.

[0014] Optionally, the preset negative trends include: The proportion of waterproof connectors with a critical pass rating continues to rise within multiple consecutive sliding time windows or sliding number windows, and exceeds the preset rise threshold. And / or, the proportion of waterproof connectors with superior sealing ratings continuously decreases within multiple consecutive sliding time windows or sliding number windows, and exceeds a preset decrease threshold.

[0015] This technical solution can avoid false alarms while ensuring timely response to real quality degradation trends.

[0016] Optionally, prior to the step of making tentative adjustments to key manufacturing process parameters that affect sealing performance data, the following steps may also be included: Based on pressure sensors and infrared thermal imagers deployed in the injection mold, the pressure change curve and temperature field distribution information of the molten plastic during the injection molding process of the waterproof connector shell are collected as sensing information of the injection molding process behavior. By spraying a predetermined volume of droplets into the sealed area of ​​the waterproof connector housing and capturing a sequence of transient spread images of the droplets using a high-speed vision device, an interfacial activity index is calculated as an assessment of the surface activity of the waterproof connector housing. Among these, perceived information and / or evaluation information serve as part of the basis for making exploratory adjustments to key manufacturing process parameters that affect sealing performance data.

[0017] This technical solution enables the acquisition of underlying data on the injection molding quality and surface activation state of the outer shell before the sealing assembly process.

[0018] Optionally, the steps of making trial adjustments to key manufacturing process parameters that affect sealing performance data include: An abnormal state is determined when the similarity between the perceived information and the preset injection molding process reference behavior pattern is lower than the preset similarity threshold, and / or the evaluation information is lower than the preset activity target threshold; wherein, the injection molding process reference behavior pattern is established based on the standard pressure change curve and temperature field distribution information recorded during the injection molding process of a batch of plastic particles known to be able to produce waterproof connectors that meet the preset quality level. In response to an abnormal state, while making tentative adjustments to key manufacturing process parameters affecting sealing performance data, coordinated tentative adjustments are made to injection molding process parameters and / or shell surface activation treatment parameters based on the criteria for determining the abnormal state. The injection molding process parameters include at least one of the following: melt temperature, mold temperature, holding time, and cooling rate. The shell surface activation treatment parameters include at least one of the following: power and time parameters for plasma treatment or ultraviolet / ozone treatment of the sealing area of ​​the waterproof connector shell.

[0019] This technical solution enables the establishment of a collaborative control mechanism for the entire process from injection molding and surface treatment to sealing assembly, breaking through the limitations of adjusting a single process and achieving more comprehensive suppression of quality fluctuations.

[0020] Optionally, the steps of evaluating the effectiveness of the trial adjustments based on the grade distribution of the waterproof connectors produced after the trial adjustments, and iteratively correcting the key manufacturing process parameters based on the adjustment effectiveness, include: Based on the change in the grade distribution of waterproof connectors produced after and before the adjustment, a quantitative feedback value is calculated as the effect of the exploratory adjustment; among them, the feedback value is positively correlated with the change in the proportion of products with excellent sealing grade and negatively correlated with the change in the proportion of products with critical pass grade. Based on the feedback value, a subsequent correction strategy for the key manufacturing process parameters is determined. Iterative adjustments are made to the key manufacturing process parameters that affect the sealing performance data until the change in the proportion distribution of the adjustment effect level meets the preset optimization target. The subsequent correction strategy includes: if the feedback value is positive and exceeds the preset effective gain threshold, the key manufacturing process parameters are adjusted along the current adjustment direction; if the feedback value is negative or does not exceed the effective gain threshold, the key manufacturing process parameters are adjusted in the opposite direction or the parameters that have not been adjusted in the key manufacturing process parameters are adjusted.

[0021] This technical solution enables the establishment of a closed-loop control mechanism based on quality distribution feedback, avoiding the blindness of manual trial and error and ensuring that process parameters converge quickly to the optimal range.

[0022] Secondly, this application also proposes a waterproof connector manufacturing process management system, comprising: The data acquisition module is used to acquire the sealing performance data of each assembled waterproof connector, classify the sealing performance data, and obtain the quality level of each waterproof connector. The quality monitoring module is used to continuously monitor the quantity ratio of waterproof connectors corresponding to each quality level, forming a level ratio distribution. The parameter adjustment module is used to make exploratory adjustments to key manufacturing process parameters that affect sealing performance data when a raw material batch change event is identified, or when a preset negative change trend occurs in the grade ratio distribution. The key manufacturing process parameters include at least one of the following: sealant injection volume, curing temperature profile, and injection pressure holding time. The exploratory adjustment is to make incremental or decremental adjustments to the selected key manufacturing process parameters by a preset parameter adjustment range. The effect evaluation and correction module is used to evaluate the effect of the trial adjustment based on the grade ratio distribution of the waterproof connectors produced after the trial adjustment, and to iteratively correct the key manufacturing process parameters based on the adjustment effect.

[0023] This technical solution provides an automated execution platform that combines hardware and software for the aforementioned management methods.

[0024] The technical solution according to the embodiments of this application has at least the following beneficial effects: The original production adjustment process, which relied on manual experience and had a slow response, has been transformed into an adaptive control process based on real-time data and automatic iterative optimization. This process can proactively intervene in the early stages of quality problems and quickly find the optimal parameter combination that matches the new batch of raw materials or the new process state through small, rapid, exploratory adjustments. This suppresses batch quality problems caused by factors such as raw material fluctuations and improves the stability of the production process and the consistency of the final product.

[0025] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0026] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0027] Figure 1 This is a flowchart illustrating a method for managing the manufacturing process of a waterproof connector, as provided in an embodiment of this application.

[0028] Figure 2 This is an overall architecture diagram of a waterproof connector manufacturing process management method provided in an embodiment of this application.

[0029] Figure 3 This is a flowchart illustrating the iterative correction of key manufacturing process parameters in an embodiment of this application.

[0030] Figure 4 This is a schematic diagram of the architecture of a waterproof connector manufacturing process management system provided in an embodiment of this application. Detailed Implementation

[0031] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0032] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0033] In modern industrial manufacturing, particularly in automotive electronics, outdoor communication equipment, and consumer electronics, waterproof connectors play a crucial role. Their core function is to protect delicate internal electronic components from moisture and liquid corrosion, ensuring long-term stable operation of equipment in harsh environments. However, the mass production of waterproof connectors faces a persistent challenge: even with compliant raw materials, such as plastic particles for injection-molded housings or liquid silicone for sealing, subtle differences in microscopic properties exist between batches. These differences, coupled with fluctuations in temperature and humidity in the production environment and equipment wear, all affect the manufacturing process, causing fluctuations in sealing performance and even systemic quality drift in products produced using identical process parameters.

[0034] Traditional quality control methods typically involve setting up a pass / fail inspection checkpoint at the end of production. This approach is relatively passive; by the time a large number of defective products are discovered, significant material waste and capacity loss have often already occurred, and it is difficult to quickly pinpoint the root cause of the problem. The reason for this lies in the lack of a closed-loop feedback mechanism that can dynamically sense changes in product quality distribution and adjust key process parameters in real time accordingly. To address this, this application proposes a manufacturing process management method that can proactively adapt to changes in raw material batches and process drift.

[0035] like Figure 1 and Figure 2 As shown, this application discloses a method for managing the manufacturing process of a waterproof connector, including the following steps: S110: Obtain the sealing performance data of each assembled waterproof connector, classify the sealing performance data, and obtain the quality level of each waterproof connector; S120 continuously monitors the quantity ratio of waterproof connectors corresponding to each quality level to form a level ratio distribution; S130, when a raw material batch change event is identified, or a preset negative change trend occurs in the grade ratio distribution, the key manufacturing process parameters affecting the sealing performance data are adjusted tentatively; wherein, the key manufacturing process parameters include at least one of the following: sealant injection volume, curing temperature profile, and injection pressure holding time; the tentative adjustment is: to adjust the selected key manufacturing process parameters incrementally or subtractively with a preset parameter adjustment range; S140: Based on the grade ratio distribution of the waterproof connectors produced after the trial adjustment, evaluate the adjustment effect of the trial adjustment, and iteratively correct the key manufacturing process parameters based on the adjustment effect.

[0036] It should be noted that the quality grades mentioned in this application are not a simple binary division of "qualified" and "unqualified," but rather a more refined performance stratification. For example, the sealing performance of a product can be divided into "excellent sealing grade," "critically qualified grade," and "unqualified grade." "Excellent sealing grade" indicates that the product's sealing performance is far superior to the design standard, with a high reliability margin; "critically qualified grade" indicates that although the product's performance meets the factory standard, it is close to being unqualified, with a low reliability margin; "unqualified grade" is clearly a failed product. Through this refined grading, the management system can detect the deterioration trend of product quality earlier. The "grade proportion distribution" is a statistical description of the proportion of products of each quality grade within a production cycle (e.g., the most recently produced 1,000 products or products produced in the most recent hour), reflecting the overall quality status of the current production process. Specifically, a data acquisition module is deployed at the end-of-line inspection station of the production line. Whenever a waterproof connector completes inspection and is assigned a quality grade, this grade information is transmitted in real time to the database of the central control system via industrial Ethernet or fieldbus. The system maintains a circular buffer to store the quality grade records of the most recent N products (e.g., N = 1000). Whenever new product grade data is written, the system automatically updates the statistical counters, calculates the number of each quality grade (Excellent, Borderline Acceptable, Unacceptable) in the current N samples, and further calculates its percentage of the total sample, thus generating a real-time grade distribution chart. This distribution chart can be displayed on the monitoring interface as a bar chart or pie chart, allowing operators to intuitively understand the drift in the current production status.

[0037] The method described in this application constructs a complete closed-loop control system of "monitoring-decision-execution-evaluation". In a specific application scenario, the central control system on the production line continuously collects quality grade information for each connector from the end-of-line inspection station. The system calculates and updates the grade distribution map in units of a sliding window (e.g., every 500 products). Assuming that under normal production conditions, the proportion of products with a "superior sealing grade" remains stable at around 90%, and the proportion of "critically acceptable grade" is around 5%. When a new batch of sealant raw materials is introduced into the production line, the system identifies this "raw material batch change event" and enters a state of high attention. Even if the new batch of raw materials reports as acceptable, the system anticipates potential process mismatch risks. Indeed, in the following batches, the system detected that the proportion of "critically acceptable grade" continuously increased from 5% to 15%, while the proportion of "superior sealing grade" decreased accordingly. This trend triggered a preset alarm threshold.

[0038] At this point, the system automatically initiates a "tentative adjustment" procedure. Based on a pre-set rule base, the system may determine that a slight change in sealant viscosity is the most likely cause of the problem and select "sealant injection volume" as the primary key manufacturing process parameter for adjustment. The system will issue a new instruction to the dispensing machine with a small, pre-set adjustment increment, such as increasing the injection volume by 2%. After the adjustment, the system will continue to closely monitor the grade distribution of subsequent products. If the proportion of "critically acceptable grades" begins to decrease and the proportion of "excellent sealant grades" rebounds, it proves that the adjustment direction is correct, and the system may continue to fine-tune in this direction until the grade distribution returns to the ideal state. Conversely, if the situation does not improve or even worsens, the system will cancel the adjustment and attempt a reverse adjustment or select another key manufacturing process parameter (e.g., adjusting a characteristic temperature or the duration of a certain stage in the curing temperature profile) for a new round of trial and error.

[0039] The method described in this application transforms the original production adjustment process, which relied on manual experience and had a delayed response, into an adaptive control process based on real-time data and automatic iterative optimization. This process can proactively intervene in the early stages of quality problems and quickly find the optimal parameter combination that matches the new batch of raw materials or the new process state through small, rapid, exploratory adjustments. This suppresses batch quality problems caused by factors such as raw material fluctuations and improves the stability of the production process and the consistency of the final product.

[0040] Optionally, in a basic implementation, the steps of obtaining sealing performance data for each assembled waterproof connector, classifying the sealing performance data, and obtaining the quality grade of each waterproof connector include: Online airtightness monitoring is performed on each assembled waterproof connector to obtain the pressure leakage value of the waterproof connector, which serves as the sealing performance data of the waterproof connector; The quality level of the waterproof connector is determined by comparing the pressure leakage value with multiple preset grading thresholds.

[0041] At the end of the production line, an automated airtightness testing station is installed. This station is equipped with a dedicated sealing fixture, which consists of two parts: the lower part secures the connector housing, and the upper part, driven by a cylinder, presses the connector port to form a sealed test chamber. The fixture integrates a high-precision pressure sensor and a solenoid valve assembly. Once the waterproof connector is positioned, the control system first drives the solenoid valve to connect to the dry air source, filling the test chamber with dry air at a gauge pressure of P (e.g., 50 kPa) for a duration of t1 (e.g., 2 seconds). Subsequently, the solenoid valve switches to the closed state, entering a pressure stabilization phase for t2 (e.g., 1 second) to eliminate pressure fluctuations during the filling process. Then, the testing phase begins, lasting t (e.g., 5 seconds). During this period, the pressure sensor continuously monitors pressure changes within the chamber at a high sampling frequency (e.g., 1000 Hz). After the test, the system calculates the pressure drop ΔP = P1 - P2 based on the collected initial and final pressure values ​​P1 and P2 of the test phase, and further calculates the pressure leakage rate per unit time L = ΔP / t. The unit of L can be Pa / s, and the leakage rate is used as the pressure leakage value of the waterproof connector.

[0042] The system then compares this pressure leakage value with preset classification thresholds. The process for establishing these classification thresholds is as follows: Before mass production on the production line, leakage rate data of products known to have good quality (without leakage after long-term verification) are collected from historical production data, and the mean and standard deviation of their statistical distribution are calculated; simultaneously, leakage rate data of products known to have sealing defects are collected. Based on statistical process control principles, and combined with product design requirements and the distribution of known defect samples, a first classification threshold L1 and a second classification threshold L2 are set, where L2 > L1 > 0. The factory can define the following rules based on product design requirements and historical data: If the pressure leakage value is less than or equal to L1, it is judged as "excellent sealing grade"; If the pressure leakage value is greater than L1 and less than or equal to L2, it is judged as "critical qualification level"; If the pressure leakage value is greater than L2, it is judged as "unqualified".

[0043] In this way, each product can be assigned a clear quality grade within seconds, providing fast and reliable data input for subsequent grade distribution statistics. The advantages of this solution lie in its mature technology, fast testing speed, ease of automation, and ability to meet the needs of 100% full inspection on large-scale production lines.

[0044] Optionally, in a more refined implementation, the steps of obtaining sealing performance data for each assembled waterproof connector, classifying the sealing performance data, and obtaining the quality grade of each waterproof connector include: Online airtightness monitoring was performed on each assembled waterproof connector to obtain the pressure leakage value of the waterproof connector; During online airtightness monitoring, signals of pressure changes inside the waterproof connector over time are collected to form a pressure-time curve. Dynamic characteristic parameters reflecting the dynamic response behavior of the sealing system are extracted from the pressure-time curve. The dynamic characteristic parameters include at least one of the following: the instantaneous rate of pressure drop, the rate of change of the instantaneous rate, and the deviation of the pressure-time curve from the preset reference decay curve. Based on the dynamic characteristic parameters, the risk index is calculated. The pressure leakage value and the risk index are used together as the sealing performance data of the waterproof connector; the leakage value range in which the pressure leakage value falls and the risk index range in which the risk index falls are determined. Based on the combination of leakage value range and risk index range, the quality level of the waterproof connector is obtained by querying the pre-stored level determination table; the level determination table defines the quality level corresponding to different combinations of leakage value range and risk index range.

[0045] The core idea of ​​this specific implementation plan is that it no longer treats airtightness testing as a black-box test that only focuses on the final value, but further analyzes the dynamic behavior during the testing process. During airtightness testing, a high-frequency pressure sensor records the real-time changes in the internal pressure of the connector throughout the entire process from pressure stabilization to the end of the test, thereby generating a pressure-time curve. Specifically, during the testing phase, the sensor collects M pressure data points at a fixed sampling interval Δt (e.g., 1 ms), denoted as P(1), P(2), ..., P(M), forming a discrete pressure-time series.

[0046] For an idealized stable leakage model, the pressure decay curve typically appears as a monotonically changing smooth curve. However, actual sealing systems are affected by a variety of factors, including sealing materials, interface bonding conditions, fixture stiffness, cavity volume, and test noise. Therefore, the actual curve may deviate from the ideal model. This degree of deviation can serve as one of the auxiliary pieces of information for evaluating the stability and consistency of the sealing system.

[0047] The system will extract a series of dynamic feature parameters from this curve. The specific extraction method is as follows: For the instantaneous rate of pressure drop: the system calculates the rate of pressure change between adjacent sampling points. For example, the instantaneous rate V(i) at the i-th sampling point can be approximated by the following formula: V(i)=(P(i)-P(i-1)) / Δt.

[0048] The system can take the absolute value of the maximum instantaneous rate during the entire test process, or the average value of the absolute instantaneous rate within a specific time period as the characterization value. If the instantaneous rate changes drastically and exhibits irregular fluctuations, it may indicate that there is an unstable leakage channel at the sealing interface, fluctuations in the sealing state of the test fixture, or strong disturbances in the test signal. Therefore, it is usually necessary to combine the results of filtering, noise reduction, and repeatability verification for a comprehensive judgment.

[0049] For the rate of change of instantaneous velocity: this is a second-order characteristic reflecting the trend of pressure change rate over time. The system calculates the difference between adjacent instantaneous velocities. For example, it can calculate the difference between V(i) and V(i-1), or further divide by Δt to form a discrete rate of change characteristic. The system can count the number of inflection points in the direction of instantaneous velocity change throughout the entire test process, or calculate the magnitude of change within a specific time interval, as an indicator of curve stability. This characteristic can be used to identify abnormal fluctuations or dynamic instability phenomena, but its correspondence with long-term creep failure usually needs to be further calibrated using historical experimental data, and cannot be used to directly determine that the material will definitely undergo rapid creep based solely on a single short-term test.

[0050] Regarding deviations from a preset reference decay curve: The system first presets a reference decay curve based on the test chamber conditions, target product type, and historical stable sample data. This reference decay curve can be in exponential form, a linear approximation, or an empirical curve obtained statistically from standard samples. Then, the system calculates the degree of deviation between the actual collected pressure-time curve and this reference curve. Specifically, it can calculate the integral of the absolute value of the area enclosed by the two curves, or calculate the sum of squares of the deviations between the actual pressure value and the reference pressure value at each sampling point, or find the maximum deviation value. This deviation value quantifies the degree of deviation of the actual response from the reference response. The larger the deviation, the more significant the difference between the dynamic behavior of the current sample during the test and the stable sample, and thus it can be used as one of the inputs for risk assessment.

[0051] Subsequently, the system calculates a comprehensive index, namely the "risk index," based on these dynamic characteristic parameters. The higher this index, the greater the deviation of the sample from the characteristics of a stable sample in dynamic test response, and the higher the likelihood of potential sealing stability risks.

[0052] Ultimately, the product quality grade is no longer determined by a single pressure leakage value, but by a combination of both. The system queries a pre-stored two-dimensional grade determination table. The preset establishment process of this grade determination table is as follows: First, prepare multiple sets of samples, covering various sealing conditions from excellent to defective. Perform the aforementioned airtightness test on each set of samples, recording their pressure leakage value and various dynamic characteristic parameters. Subsequently, conduct accelerated aging tests on these samples (such as constant temperature and humidity storage and temperature cycling tests under high temperature and high humidity environments, or long-term stress relaxation tests under constant compression conditions), continuously monitoring until the samples experience sealing failure, and recording the failure time of each sample. Based on the length of the failure time, the samples are divided into a high-reliability group (no failure for a long time) and a low-reliability group (failure in a relatively short time). Then, analyze the distribution patterns of pressure leakage value and risk index in the high-reliability group and the low-reliability group to determine reasonable interval boundaries. For example, the leakage value interval can be set to [L]. min [L1], [L1, L2], [L2, L max The risk index range is [R]. low R mid ]、[R mid R high Based on experimental data statistics, the following mapping relationship is established: when the pressure leakage value of a product is within [L...] min The L1 interval and the risk index are in the [R] range. low R mid When the pressure leakage value is in the range [L1, L2], it indicates that the current sealing condition is good and the dynamic response is close to the stable sample, and it can be judged as "excellent sealing level"; when the pressure leakage value of a product is in the range [L1, L2] but the risk index is in the range [R ... low R mid When the leakage value is in the range [L], although the leakage value is relatively high, the dynamic response is relatively stable, and it can be judged as "critically qualified level"; while if the pressure leakage value of a product is low (within the range [L], it can be judged as "critically qualified level"); min The risk index is relatively high (within the [R] range), but it is within the L1 range. mid R high The range indicates a low current leakage level but abnormal dynamic behavior, suggesting potential reliability issues. In this case, the rating table may classify it as "critically acceptable" or even "unacceptable." By consulting this pre-stored rating table, the system can determine the final quality level of each product.

[0053] By introducing the risk index dimension, the assessment has been expanded from evaluating "current sealing performance" to assisting in the assessment of "long-term reliability risk." It can identify potentially risky products that pass traditional static testing but exhibit abnormal dynamic response, thereby improving the ability to control the long-term quality of products. This has significant practical value for manufacturing high-reliability connectors.

[0054] In a further embodiment of this application, the step of calculating the risk index based on dynamic characteristic parameters includes: When there is only one dynamic feature parameter, the value of the dynamic feature parameter is normalized or standardized and then used as the risk index. When there are two or more dynamic characteristic parameters, preset weight coefficients are assigned to the dynamic characteristic parameters, and the dynamic characteristic parameters with assigned weight coefficients are weighted and summed to calculate the risk index; where the weight coefficients are pre-calibrated based on historical experimental data and sealing failure analysis results.

[0055] This specific implementation plan provides a flexible and scientific calculation method for the quantification of the risk index. In some simplified application scenarios or failure mode analyses, a certain dynamic characteristic parameter (such as the deviation from a preset reference decay curve) may have a strong correlation with the final seal failure risk. In this case, the value of this parameter can be directly normalized and used as the risk index for rapid calculation.

[0056] In more common and complex scenarios, multiple dynamic characteristic parameters often reflect the dynamic response characteristics during the testing process from different perspectives. In this case, a weighted summation approach can construct a more comprehensive and robust risk assessment model. The determination of the weighting coefficients is crucial to this approach; they are not subjectively set but pre-calibrated based on extensive experimental data and failure analysis. The specific calibration process is as follows: The R&D team first prepares a batch of representative samples, covering different material batches, process conditions, and potential defect types. These samples undergo the aforementioned dynamic airtightness test, recording various dynamic characteristic parameters for each sample (instantaneous rate, rate of change, deviation from the preset reference decay curve, etc.). Subsequently, these samples are placed in an accelerated aging environment (e.g., constant temperature and humidity storage test at 85°C and 85% relative humidity, and / or temperature cycling test, or long-term stress relaxation test under constant mechanical compression), periodically testing their sealing performance and recording the time required for each sample to experience sealing failure (pressure leakage value exceeding the failure threshold). Based on these experimental data, correlation analysis was conducted: the correlation coefficient between each dynamic characteristic parameter and the failure time was calculated, or a predictive model between the characteristic parameters and failure risk was established using machine learning methods (such as multiple linear regression, random forest, etc.). The analysis results showed that some parameters contributed significantly to predicting failure risk, while others contributed less. Based on these contributions, corresponding weight coefficients were assigned to each dynamic characteristic parameter. For example, if the analysis found that parameter A contributed significantly to predicting sealing failure risk, followed by parameter B, and then parameter C, the weight coefficients for these three parameters could be preset as α, β, and γ, respectively, where α + β + γ = 1, and α > β > γ > 0. In subsequent actual production, after the system collects the dynamic characteristic parameters of a product, it multiplies them by the corresponding weight coefficients and sums them to obtain the product's risk index. For example, the risk index can be expressed as: R = αx1 + βx2 + γx3, where x1, x2, and x3 represent different dynamic characteristic parameters after normalization. By assigning weights in this data-driven manner, the calculation of the risk index is no longer based on guesswork. The assessment results are more correlated with and credible to the actual long-term performance of the product, making the entire quality assessment system more scientific and accurate.

[0057] In the aforementioned closed-loop control framework, the precise classification of quality levels and real-time monitoring of their proportional distribution are prerequisites for triggering subsequent parameter adjustment decisions. Simply counting the number of non-conforming products would only allow the system to react when the problem has become extremely serious, violating the principles of early warning and preventative adjustments. Therefore, defining the monitoring window and identifying meaningful negative trends from statistical data are crucial for ensuring the sensitivity and effectiveness of the entire system.

[0058] In a preferred embodiment of this application, the quality grades include at least an excellent sealing grade, a critical pass grade, and a fail grade; The steps for continuously monitoring the quantity ratio of waterproof connectors corresponding to each quality level and forming a level ratio distribution include: Using a preset sliding time window or a preset product quantity sliding quantity window as a unit, the proportion of waterproof connectors with excellent sealing rating and critical qualified rating is statistically analyzed in real time to form a rating distribution.

[0059] In one specific embodiment of this application, the above-mentioned sliding window statistics are implemented in the following way: The data receiving module of the quality monitoring system continuously receives grade judgment data from the production line inspection station and maintains two circular queue data structures in memory, which are used to store historical data in time window mode and quantity window mode, respectively. For the time-based sliding window mode, the window duration is set to T (e.g., 30 minutes), and the system maintains a queue covering all product grade records within the most recent T duration. Whenever the system clock advances by a statistical step Δt (e.g., 1 minute), the grade data of all connectors produced within the new statistical step is added to the tail of the queue, and historical data with timestamps earlier than the current time minus T are removed from the head of the queue. The system then recalculates the ratio of the number of excellent sealing grades to the number of critical pass grades based on all the data in the current queue. For the quantity-based sliding window mode, the window size is set to N (e.g., 500 products), and the system maintains a fixed-length array of length N as a circular buffer. Whenever a new product is launched and its rating is determined, its rating identifier is written to the current pointer position in the buffer. The pointer moves forward one position, overwriting the oldest data. The system immediately calculates the ratio of each rating based on the N data points in the current buffer. The specific processing method for ratio calculation is as follows: count the number N of the Excellent Sealing Rating within the current window. e Number of critical pass grades N c And the number of non-compliant grades N u Calculate the total number N of products within the window. t =N e +N c +N u The proportion of excellent sealing rating is N. e / N t ×100%, the proportion of critical pass grades is N c / N t ×100%.

[0060] This specific implementation plan clarifies the core objects and statistical methods of quality monitoring. The system focuses on two sensitive indicators that reflect the stability of the production process: "Excellent Sealing Grade" and "Critical Acceptance Grade," rather than solely on "Non-conforming Grade." This is because when the production process begins to show slight drift, the first change is often not a sudden increase in the non-conforming rate, but rather a decline in product performance from the "Excellent" grade, causing it to fall into the "Critical Acceptance" range. Therefore, monitoring changes in the proportion of these two grades allows for earlier detection of signals of quality deterioration.

[0061] The statistical method employs a sliding window mechanism, a common smoothing and trend extraction technique in time series analysis. The system can be configured in one of two modes: One method is a time-based sliding window, for example, setting the window duration to 30 minutes. The system continuously counts the number of "Excellent" and "Critical Pass" grades among all products produced within the last 30 minutes and calculates their proportion relative to the total number of products within the window. Every minute, the window slides forward by 1 minute, incorporating the data from the latest minute and discarding the earliest data outside the 30-minute window range, and then recalculates the proportion.

[0062] Another type is a quantity-based sliding window, for example, setting the window size to 500 products. The system will calculate the proportion of each grade among the 500 most recently produced products. Each time a new product is produced, the window slides forward one position, incorporating the data of this new product and removing the oldest data that is outside the window range, and then recalculates.

[0063] The advantage of using a sliding window is that it effectively smooths out random fluctuations in the test results of individual products, reflecting the average quality level and trend over a period of time, making the monitoring results more stable and representative. At the same time, continuously updating the data within the window ensures the real-time nature of the monitoring.

[0064] In this way, the system no longer views the test results of each product in isolation, but examines the health of the entire production process from a dynamic and statistical perspective. When the proportion of "near-qualified" products begins to rise quietly, or the proportion of "excellent" products begins to decline slowly, even if the non-conforming rate has not yet changed significantly, the system can already keenly detect potential risks, gaining valuable time for subsequent early warnings and adjustments.

[0065] It should be noted that the preset negative change trends preferably include: The proportion of waterproof connectors with critical qualification level continues to rise within multiple consecutive sliding time windows or sliding number windows, and the cumulative increase exceeds the preset increase threshold. And / or, the proportion of waterproof connectors with superior sealing ratings continuously decreases over multiple consecutive sliding time windows or sliding number windows, and the cumulative decrease exceeds a preset decrease threshold.

[0066] In one specific embodiment of this application, the identification of the aforementioned negative trend is achieved through the following information processing means: the system presets a consecutive window number M (e.g., M=5) and an increase threshold P (e.g., P=3%). The system maintains an array of length M to store the critical pass rate ratio data of the most recent M sliding windows. Whenever the statistics of a new sliding window are completed and a new ratio value is obtained, the system adds this value to the end of the array and removes the oldest data at the beginning. Subsequently, the system traverses the array, compares the ratio values ​​of adjacent windows, and determines whether the ratio value of each subsequent window is not less than the ratio value of the previous window, and whether there is at least one set of adjacent windows whose ratio values ​​strictly increase, i.e., whether the continuous increase condition is met. If this condition is met, the system further calculates the difference between the ratio value of the Mth window (the latest window) and the ratio value of the 1st window (the earliest window) to obtain the cumulative change amplitude. If the cumulative change amplitude exceeds the preset increase threshold P, it is determined that the ratio of the critical pass rate has shown a negative trend that requires intervention. For monitoring the excellent sealing rating, a similar processing logic is used, but the judgment direction is reversed: the system maintains a historical array of the excellent rating percentage, determines whether it meets the criteria of continuous non-increase and at least one decrease, and calculates the total decrease. If the total decrease exceeds a preset decrease threshold Q (e.g., Q=3%), then the excellent rating is determined to have a negative trend. The preset values ​​of the above thresholds P and Q are determined based on historical production data through statistical process control methods. For example, the control limits can be determined based on the fluctuation distribution of this percentage statistic under normal production conditions, and the corresponding trend judgment threshold can be selected accordingly.

[0067] This specific implementation plan provides clear and operational criteria for identifying "negative trends." It introduces considerations of two dimensions: "continuity" and "amplitude," thereby effectively distinguishing between systemic process drift and random short-term fluctuations.

[0068] Specifically, the system sets two key decision parameters: the number of consecutive windows N and the scaling threshold P. For example, N=5 and P=3%.

[0069] For monitoring the "critical pass level," the system checks whether the proportion of this quantity has been consistently increasing over the most recent five consecutive sliding windows. This means the proportion in subsequent windows is not lower than the proportion in the previous window, and there has been at least one actual increase. If this "consistent increase" condition is met, the system further calculates the total increase in the proportion over these five windows. If the total increase exceeds a preset threshold (e.g., from 5% to over 8%, the increase exceeds 3%), the system determines that a negative trend has occurred.

[0070] Similarly, for monitoring the "excellent sealing rating", the system will also check whether its proportion has been continuously decreasing over five consecutive windows, and whether the total decrease exceeds the preset decrease threshold.

[0071] These two conditions can trigger an alarm independently or in combination; when both occur simultaneously, the alarm has higher priority.

[0072] By introducing the condition of "continuous multiple windows," proportional jumps that occur only within one or two windows and may be caused by accidental factors (such as brief equipment vibrations or environmental interference) can be effectively filtered out. Only when such a trend of change persists is it considered a signal worthy of attention, which greatly reduces the possibility of false alarms. The setting of a "threshold" ensures that the system will only initiate adjustment procedures when the magnitude of the change reaches a certain level, sufficient to indicate a substantial deviation in the process state, avoiding unnecessary intervention in negligible normal fluctuations. This early warning mechanism based on statistical process control makes the system's decision-making more robust and reliable, ensuring the accuracy and necessity of production adjustments.

[0073] In some embodiments of this application, such as Figure 3 As shown, the steps for evaluating the effectiveness of the trial adjustments based on the grade distribution of the waterproof connectors produced after the trial adjustments, and iteratively correcting the key manufacturing process parameters based on the adjustment effectiveness, include: S141. Based on the change in the grade distribution of waterproof connectors produced after adjustment and before adjustment, a quantitative feedback value is calculated as the adjustment effect of the trial adjustment; among which, the feedback value is positively correlated with the change in the proportion of products with excellent sealing grade and negatively correlated with the change in the proportion of products with critical qualified grade.

[0074] In a specific embodiment of this application, the calculation of the above feedback value adopts the following specific processing method: The proportion of excellent sealing grades within the statistical period before adjustment is set as E1, and the proportion of critical qualified grades is set as C1; the proportion of excellent sealing grades within the corresponding statistical period after adjustment is set as E2, and the proportion of critical qualified grades is set as C2. First, the change in the excellent grade ΔE = E2 - E1 is calculated, and then the change in the critical qualified grade ΔC = C2 - C1 is calculated. The calculation logic of the feedback value F is as follows: the change in the excellent grade ΔE is taken as a positive contribution, and the change in the critical qualified grade ΔC is taken as a negative contribution, i.e., the feedback value F = ΔE - ΔC. For example, if the excellent proportion E1 is 85% and the critical proportion C1 is 10% before adjustment; after adjustment, the excellent proportion E2 rises to 88%, and the critical proportion C2 falls to 7%, then ΔE increases by 3 percentage points, and ΔC decreases by 3 percentage points, i.e., ΔC = -3%. At this time, the feedback value F = 3% - (-3%) = 6%, indicating that the adjustment has a positive effect. Conversely, if the excellent proportion decreases while the critical proportion increases after adjustment, the feedback value is negative.

[0075] S142, Based on the feedback value, determine the subsequent correction strategy for the key manufacturing process parameters, and iteratively perform exploratory adjustments to the key manufacturing process parameters that affect the sealing performance data until the change in the proportion distribution of the adjustment effect indicator level meets the preset optimization target; wherein, the subsequent correction strategy includes: if the feedback value is positive and exceeds the preset effective gain threshold, then continue to adjust the key manufacturing process parameters along the current adjustment direction; if the feedback value is negative or does not exceed the effective gain threshold, then adjust the key manufacturing process parameters in the opposite direction or adjust the parameters among the key manufacturing process parameters that have not been adjusted.

[0076] In a specific embodiment of this application, the iterative correction is implemented as follows: First, a preset effective gain threshold T (e.g., T=2%) and adjustment step size S are established. The system maintains a list of key manufacturing process parameters, including parameters such as sealant injection volume, curing temperature, and holding time, and their current settings. When a trial adjustment is required, the system selects the current parameter from the list and adjusts the step size S in a preset direction (increasing or decreasing). After adjustment, the system waits for at least one sliding window period, preferably a full evaluation period covering process lag and detection feedback, collects adjusted production data, and calculates the feedback value F. If the feedback value F is positive and F>T, it indicates that the current adjustment direction is correct and the effect is significant, and the system continues to further adjust the same parameter in the same direction with a step size of S. If the feedback value F is negative, it indicates that the adjustment direction is incorrect, and the system performs a reverse adjustment, first canceling the current adjustment (reversing the adjustment S to restore the original value), and then adjusting the step size S in the opposite direction. If the feedback value F is positive but F≤T, it indicates that the adjustment effect is not significant or has reached the marginal benefit point. The system keeps the current parameters unchanged and instead performs a trial adjustment on the next unadjusted key manufacturing process parameter in the parameter list. To ensure feasibility, the adjustment step size S is set according to the physical dimensions, equipment resolution, process allowable range, and historical sensitivity analysis results of the corresponding key manufacturing process parameter. When the key manufacturing process parameter is any one of temperature, time, pressure, or injection volume, its step size is denoted as S. T S t S P or S V This iterative process continues until the proportion of excellent sealing grades recovers to the target range (e.g., greater than 90%) and the proportion of critical acceptable grades drops to the target range (e.g., less than 5%), or the feedback values ​​of multiple consecutive adjustments are close to 0 and the parameters fluctuate within an allowable small range, indicating that the system has reached a stable state.

[0077] This specific implementation plan provides a clear quantitative assessment and decision-making logic for closed-loop control of parameter adjustments. First, it defines a quantitative "feedback value" to evaluate the adjustment effect. This feedback value is a comprehensive indicator that simultaneously considers the proportional changes in "good" products (excellent level) and "risky" products (critically acceptable level).

[0078] With this design, if the adjustment increases the proportion of excellent grades while decreasing the proportion of barely acceptable grades, the feedback value will be a large positive number, indicating a significant effect of the adjustment. Conversely, if the adjustment worsens the situation, the feedback value will be negative.

[0079] After receiving quantified feedback values, the system determines the next action based on preset decision rules, which constitutes an iterative optimization control logic based on feedback search.

[0080] The specific logic of the subsequent correction strategy is as follows: If the feedback value is positive and exceeds a preset "effective gain threshold" (e.g., 0.02, meaning the overall improvement exceeds 2%), the system will determine that the current adjustment direction is correct and effective. It will then continue to adjust the same key manufacturing process parameter in the same direction by the same amount. If the current parameter is defined using a relative step size, for example, if the sealant injection volume was increased by A% last time, it will be increased by A% this time; if the current parameter is defined using an absolute step size, it will continue to adjust according to the corresponding absolute increment.

[0081] If the feedback value is negative, it indicates that the adjustment direction is incorrect, leading to a deterioration in quality. In this case, the system will immediately make a reverse adjustment. For example, if the injection volume was increased by a preset step size last time, the system will not only undo this increase but also adjust by the same step size in the opposite direction, that is, change by one step size in the opposite direction relative to the original parameters, and then perform a new round of evaluation.

[0082] If the feedback value is positive but does not exceed the effective gain threshold, it may mean that the adjustment direction is correct but the effect is not obvious, or that the current parameter is already close to the optimal value, and further adjustment has little marginal benefit. In this case, the system may choose to make a trial adjustment to another key manufacturing process parameter that has not yet been adjusted. For example, if it has been adjusting the sealant injection volume, it may now turn to adjusting the curing temperature profile.

[0083] This iterative process continues until the grade distribution returns to the preset ideal state (e.g., the proportion of excellent grades returns to above 90%, and the proportion of critically qualified grades drops below 5%), or the feedback value fluctuates slightly around 0 after multiple adjustments, indicating that the system has reached a stable optimal or suboptimal state. Through this intelligent iterative correction strategy based on quantitative feedback, the system can, like an experienced engineer, automatically and quickly adjust the production process parameters to a better state through continuous "trial and error" and "learning," thereby achieving adaptive optimization control of the production process.

[0084] In a preferred embodiment of this application, prior to the step of making tentative adjustments to key manufacturing process parameters affecting sealing performance data, the method further includes: Based on pressure sensors and infrared thermal imagers deployed in the injection mold, the pressure change curve and temperature field distribution information of the molten plastic during the injection molding process of the waterproof connector shell are collected as sensing information of the injection molding process behavior. By spraying a predetermined volume of droplets into the sealed area of ​​the waterproof connector housing and capturing a sequence of transient spread images of the droplets using a high-speed vision device, an interfacial activity index is calculated as an assessment of the surface activity of the waterproof connector housing. Among these, perceived information and / or evaluation information serve as part of the basis for making exploratory adjustments to key manufacturing process parameters that affect sealing performance data.

[0085] This specific implementation plan introduces a feedforward control mechanism to monitor the upstream injection molding quality and surface treatment status before the sealing and assembly process, thereby providing more comprehensive pre-process information for adjusting subsequent key manufacturing process parameters. Specifically, in the injection molding stage, a high-precision cavity pressure sensor is deployed inside the mold cavity or near the gate. This sensor captures the pressure changes of the molten plastic in real time during the filling, holding, and cooling stages with a high-frequency sampling rate (e.g., collecting hundreds to thousands of data points per second), forming a complete pressure change curve. Simultaneously, an infrared thermal imager is placed on the mold opening and closing side or at a specific observation window position to perform non-contact temperature measurement on the injection molded part at the moment of demolding or at specific process moments, obtaining information on the temperature field distribution on the outer shell surface, or indirectly reflecting the temperature uniformity inside the melt. These pressure and temperature data together constitute the perceived information of the injection molding process behavior, reflecting the melting state, flowability, and molding stability of the current batch of plastic particles.

[0086] In the surface treatment stage, a precision micro-droplet jetting device is installed above the sealing area of ​​the outer shell (such as the sealing ring mounting groove or end face). This device uses piezoelectric drive or a micro-injection pump to jet a preset volume of test droplets (e.g., volume V, ranging from 1 μL to 10 μL) onto the surface with extremely high precision. Almost simultaneously with the droplet contacting the surface, a high-speed vision device (a high-frame-rate industrial camera equipped with a macro lens, capable of thousands of frames per second) begins continuous imaging, capturing the transient spreading process of the droplet from contact with the surface to reaching equilibrium, forming an image sequence containing multiple frames. The system analyzes the rate of change of the droplet's contact angle, spreading area, or spreading diameter over time in each frame image using image processing algorithms (such as edge detection and contour extraction), thereby calculating the interfacial activity index. This index quantifies the wetting performance of the outer shell surface and indirectly reflects the surface energy, cleanliness, or effectiveness of the activation treatment.

[0087] The aforementioned sensing and evaluation information is transmitted to the central control system in real time, serving as feedforward for tentative adjustments to key manufacturing process parameters such as subsequent sealant injection volume and curing temperature. For example, if the sensing information indicates that the pressure decay is too rapid during the injection molding and holding stage, it may indicate that the shell has shrinkage marks or insufficient density. The system can predict that there may be potential problems with the fit between the sealing ring and the shell, and thus appropriately increase the sealant injection volume in advance to compensate for potential leakage paths. Or, if the evaluation information shows that the interfacial activity index is low, indicating that poor surface wettability may affect the adhesion of the sealant, the system can adjust the curing temperature profile accordingly to promote the interfacial bonding between the colloid and the substrate.

[0088] By introducing injection molding process behavior perception and surface activity assessment, this solution moves the quality control node from the final assembly stage to the shell molding and surface treatment stage, enabling earlier risk warning and more accurate pre-adjustment, reducing the number of trial and error attempts in subsequent closed-loop adjustments, and improving the efficiency and response speed of overall process optimization.

[0089] It should be noted that the steps for making trial adjustments to key manufacturing process parameters that affect sealing performance data include: An abnormal state is determined when the similarity between the perceived information and the preset injection molding process reference behavior pattern is lower than the preset similarity threshold, and / or the evaluation information is lower than the preset activity target threshold; wherein, the injection molding process reference behavior pattern is established based on the standard pressure change curve and temperature field distribution information recorded during the injection molding process of a batch of plastic particles known to be able to produce waterproof connectors that meet the preset quality level. In response to an abnormal state, while making tentative adjustments to key manufacturing process parameters affecting sealing performance data, coordinated tentative adjustments are made to injection molding process parameters and / or shell surface activation treatment parameters based on the criteria for determining the abnormal state. The injection molding process parameters include at least one of the following: melt temperature, mold temperature, holding time, and cooling rate. The shell surface activation treatment parameters include at least one of the following: power and time parameters for plasma treatment or ultraviolet / ozone treatment of the sealing area of ​​the waterproof connector shell.

[0090] This specific implementation plan further establishes a collaborative control mechanism for the entire process, from upstream injection molding and midstream surface treatment to downstream sealing and assembly. First, the system needs to pre-establish a reference behavior model for the injection molding process. During the production line commissioning phase, trial production is conducted using batches of standard plastic particles with stable physical properties and a good historical performance. Under optimized standard process parameters (including melt temperature, mold temperature, holding pressure curve, etc.), pressure change curves and temperature field distribution data for multiple mold cycles or consecutive production cycles are collected using the aforementioned pressure sensors and infrared thermal imagers. Through statistical analysis of these standard data (such as calculating mean curves, envelopes, or principal component characteristics), a benchmark template or feature library is established as a reference behavior model for the injection molding process.

[0091] In actual production, the system compares the real-time collected sensing information with the reference behavior pattern to calculate the similarity. For example, a dynamic time warping algorithm can be used to calculate the similarity score between the real-time pressure curve and the standard pressure curve, or to calculate the correlation coefficient of the temperature field distribution. When the similarity is lower than a preset similarity threshold (e.g., the correlation coefficient is lower than 0.85 or the DTW distance is greater than a set threshold), it is determined that there is abnormal drift in the injection molding process. At the same time, the interface activity index calculated in real time is compared with a preset activity target threshold (e.g., the lower limit of the benchmark interface activity index measured after standard surface treatment). If it is lower than the threshold, it is determined that the surface activation is insufficient.

[0092] Once an anomaly is detected, the system not only initiates the aforementioned tentative adjustments to downstream key manufacturing process parameters such as sealant injection volume and curing temperature profile to compensate for the impact of upstream fluctuations on sealing performance, but also simultaneously makes coordinated tentative adjustments to upstream process parameters based on the specific source of the anomaly. If the anomaly originates from the injection molding process (low similarity), the system will send instructions to the injection molding machine control system to fine-tune the injection molding process parameters, such as appropriately increasing the melt temperature to improve fluidity, extending the holding pressure time to increase density, or adjusting the mold temperature to optimize cooling uniformity. If the anomaly originates from surface treatment (low activity index), the system will adjust the parameters of the surface activation treatment equipment, such as increasing the radio frequency power of plasma treatment, extending the treatment time, or adjusting the intensity and duration of ultraviolet / ozone irradiation to enhance surface energy.

[0093] This coordinated adjustment mechanism breaks through the limitations of traditional isolated control of each process, achieving full-chain optimization from molding and processing to assembly. By simultaneously adjusting upstream processes to eliminate the root causes of defects and adjusting downstream processes to compensate for residual effects, the system can correct quality fluctuations more quickly and thoroughly, avoiding over-adjustment or performance bottlenecks that may result from simple downstream compensation, thereby improving the overall robustness of the manufacturing process and the consistency of product quality.

[0094] like Figure 4As shown, this application also discloses a waterproof connector manufacturing process management system, including: The data acquisition module 210 is used to acquire the sealing performance data of each assembled waterproof connector, classify the sealing performance data, and obtain the quality level of each waterproof connector. The quality monitoring module 220 is used to continuously monitor the quantity ratio of waterproof connectors corresponding to each quality level, forming a level ratio distribution. The parameter adjustment module 230 is used to make exploratory adjustments to key manufacturing process parameters that affect sealing performance data when a raw material batch change event is identified, or when a preset negative change trend occurs in the grade ratio distribution. The key manufacturing process parameters include at least one of the following: sealant injection volume, curing temperature profile, and injection pressure holding time. The exploratory adjustment is to make incremental or decremental adjustments to the selected key manufacturing process parameters by a preset parameter adjustment range. The effect evaluation and correction module 240 is used to evaluate the effect of the trial adjustment based on the grade ratio distribution of the waterproof connectors produced after the trial adjustment, and to iteratively correct the key manufacturing process parameters based on the adjustment effect.

[0095] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0096] The system disclosed in this application provides an automated execution platform combining hardware and software for the management method of this application. Through modular design, it integrates functions such as data acquisition, quality monitoring, parameter adjustment, and effect evaluation, thereby improving the systematization and automation level of manufacturing process management.

[0097] The preferred embodiments of this application have been described in detail above, but this application is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application.

Claims

1. A method for managing the manufacturing process of a waterproof connector, characterized in that, Includes the following steps: Obtain the sealing performance data of each assembled waterproof connector, classify the sealing performance data, and obtain the quality grade of each waterproof connector; Continuously monitor the quantity ratio of waterproof connectors corresponding to each quality level to form a grade ratio distribution; When a raw material batch change event is identified, or when the grade ratio distribution shows a preset negative change trend, the key manufacturing process parameters affecting the sealing performance data are tentatively adjusted; wherein, the key manufacturing process parameters include at least one of the following: sealant injection volume, curing temperature profile, and injection pressure holding time; the tentative adjustment is: to make incremental or decremental adjustments to the selected key manufacturing process parameters by a preset parameter adjustment range; Based on the grade distribution of the waterproof connectors produced after the trial adjustments, the effectiveness of the trial adjustments is evaluated, and the key manufacturing process parameters are iteratively corrected based on the adjustment effectiveness.

2. The method for managing the manufacturing process of a waterproof connector according to claim 1, characterized in that, The steps of obtaining the sealing performance data of each assembled waterproof connector, classifying the sealing performance data, and obtaining the quality grade of each waterproof connector include: Online airtightness monitoring is performed on each assembled waterproof connector to obtain the pressure leakage value of the waterproof connector, which serves as the sealing performance data of the waterproof connector; The quality level of the waterproof connector is obtained by comparing the pressure leakage value with multiple preset grading thresholds.

3. The method for managing the manufacturing process of a waterproof connector according to claim 1, characterized in that, The steps of obtaining the sealing performance data of each assembled waterproof connector, classifying the sealing performance data, and obtaining the quality grade of each waterproof connector include: Online airtightness monitoring was performed on each assembled waterproof connector to obtain the pressure leakage value of the waterproof connector; During the online airtightness monitoring process, the signal of pressure change inside the waterproof connector over time is collected to form a pressure-time curve; From the pressure-time curve, dynamic characteristic parameters reflecting the viscoelastic behavior of the sealing material are extracted; wherein, the dynamic characteristic parameters include at least one of the following: the instantaneous rate of pressure drop, the rate of change of the instantaneous rate, and the nonlinear deviation of the pressure-time curve relative to a preset ideal elastic decay curve; Based on the aforementioned dynamic characteristic parameters, the creep risk index is calculated. The pressure leakage value and the creep risk index are used together as the sealing performance data of the waterproof connector; Determine the leakage value range in which the pressure leakage value falls, and the risk index range in which the creep risk index falls; Based on the combination of the leakage value range and the risk index range, a pre-stored grade determination table is consulted to obtain the quality grade of the waterproof connector; wherein, the grade determination table defines the quality grades corresponding to different combinations of leakage value ranges and risk index ranges.

4. The method for managing the manufacturing process of a waterproof connector according to claim 3, characterized in that, The step of calculating the creep risk index based on the dynamic characteristic parameters includes: When there is only one dynamic characteristic parameter, the value of the dynamic characteristic parameter is used as the creep risk index; When there are two or more dynamic characteristic parameters, preset weighting coefficients are assigned to the dynamic characteristic parameters, and the dynamic characteristic parameters with assigned weighting coefficients are weighted and summed to calculate the creep risk index; wherein, the weighting coefficients are pre-calibrated based on historical experimental data and sealing failure analysis results.

5. The method for managing the manufacturing process of a waterproof connector according to claim 1, characterized in that, The quality grades include at least an excellent sealing grade, a critical pass grade, and a non-pass grade; The step of continuously monitoring the quantity ratio of waterproof connectors corresponding to each quality level to form a level ratio distribution includes: Using a preset sliding time window or a preset product quantity sliding quantity window as a unit, the proportion of waterproof connectors with excellent sealing rating and critical qualified rating is statistically analyzed in real time to form a rating ratio distribution.

6. The method for managing the manufacturing process of a waterproof connector according to claim 5, characterized in that, The preset negative change trends include: The proportion of waterproof connectors with the critical qualification level continues to rise within multiple consecutive sliding time windows or sliding number windows, and exceeds a preset rise threshold. And / or, the proportion of waterproof connectors with the superior sealing rating continuously decreases within multiple consecutive sliding time windows or sliding number windows, and exceeds a preset decrease threshold.

7. The method for managing the manufacturing process of a waterproof connector according to claim 1, characterized in that, Prior to the step of making tentative adjustments to the key manufacturing process parameters that affect the sealing performance data, the method also includes: Based on pressure sensors and infrared thermal imagers deployed inside the injection mold, the pressure change curve and temperature field distribution information of the molten plastic are collected during the injection molding process of the housing of the waterproof connector, serving as sensing information of the injection molding process behavior. An interfacial activity index is calculated as an assessment of the surface activity of the waterproof connector housing by spraying a predetermined volume of droplets into the sealed area of ​​the waterproof connector housing and capturing a sequence of transient spread images of the droplets using a high-speed vision device. The perceived information and / or evaluation information serve as part of the basis for making exploratory adjustments to key manufacturing process parameters that affect the sealing performance data.

8. The method for managing the manufacturing process of a waterproof connector according to claim 7, characterized in that, The steps for making exploratory adjustments to key manufacturing process parameters that affect the sealing performance data include: When the similarity between the perceived information and the preset injection molding process reference behavior pattern is lower than the preset similarity threshold, and / or the evaluation information is lower than the preset activity target threshold, an abnormal state is determined to exist; wherein, the injection molding process reference behavior pattern is established based on the standard pressure change curve and temperature field distribution information recorded during the injection molding process of a batch of plastic particles known to be able to produce waterproof connectors that meet the preset quality level. In response to the abnormal state, while making tentative adjustments to the key manufacturing process parameters affecting the sealing performance data, coordinated tentative adjustments are made to the injection molding process parameters and / or the shell surface activation treatment parameters based on the criteria for determining the abnormal state; wherein, the injection molding process parameters include at least one of: melt temperature, mold temperature, holding time, and cooling rate; the shell surface activation treatment parameters include at least one of: power and time parameters for plasma treatment or ultraviolet / ozone treatment of the sealing area of ​​the waterproof connector shell.

9. The method for managing the manufacturing process of a waterproof connector according to claim 5, characterized in that, The steps of evaluating the effectiveness of the trial adjustments based on the grade distribution of the waterproof connectors produced after the trial adjustments, and iteratively correcting the key manufacturing process parameters based on the adjustment effectiveness, include: Based on the change in the grade distribution of waterproof connectors produced after and before the adjustment, a quantitative feedback value is calculated as the adjustment effect of the exploratory adjustment; wherein, the feedback value is positively correlated with the change in the proportion of products with the excellent sealing grade and negatively correlated with the change in the proportion of products with the critical pass grade. Based on the feedback value, a subsequent correction strategy for the key manufacturing process parameters is determined. Iterative adjustments are made to the key manufacturing process parameters affecting the sealing performance data until the adjustment effect indicates that the change in the grade ratio distribution meets a preset optimization target. The subsequent correction strategy includes: if the feedback value is positive and exceeds a preset effective gain threshold, the key manufacturing process parameters are adjusted along the current adjustment direction; if the feedback value is negative or does not exceed the effective gain threshold, the key manufacturing process parameters are adjusted in the opposite direction, or the unadjusted parameters among the key manufacturing process parameters are adjusted.

10. A waterproof connector manufacturing process management system, characterized in that, include: The data acquisition module is used to acquire the sealing performance data of each assembled waterproof connector, classify the sealing performance data, and obtain the quality grade of each waterproof connector. The quality monitoring module is used to continuously monitor the quantity ratio of waterproof connectors corresponding to each quality level, forming a level ratio distribution. The parameter adjustment module is used to make exploratory adjustments to key manufacturing process parameters affecting the sealing performance data when a raw material batch change event is identified, or when the grade ratio distribution shows a preset negative change trend; wherein, the key manufacturing process parameters include at least one of: sealant injection volume, curing temperature profile, and injection molding holding time; the exploratory adjustment is to make incremental or decremental adjustments to the selected key manufacturing process parameters by a preset parameter adjustment range; The effect evaluation and correction module is used to evaluate the adjustment effect of the trial adjustment based on the grade ratio distribution of the waterproof connectors produced after the trial adjustment, and to iteratively correct the key manufacturing process parameters based on the adjustment effect.

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