Anaesthetic mask sprue freezing time judgment method based on melt pulsation analysis

Abstract

The invention provides an anaesthetic mask sprue freezing time judgment method based on melt pulsation analysis, and relates to the technical field of monitoring and optimization of a precise injection molding process of medical instruments. The method comprises the following steps: mounting a sensor at the tail end of a mold sprue or a runner to collect melt pressure data; extracting a pressure pulsation signal after pressure maintaining is finished; calculating a pulsation attenuation characteristic value representing amplitude attenuation and a period stability factor representing period stability; establishing a two-parameter synchronous triggering judgment model; judging and recording sprue freezing time in real time; and finally, the time is used for optimizing a cooling process or carrying out online quality monitoring and sorting. According to the method, high-precision and high-robustness on-line automatic judgment of the pouring gate freezing moment is realized, and the core problems of insufficient precision, high hysteresis and poor reliability of a traditional method depending on empirical formulas, off-line simulation and macroscopic pressure trend judgment are solved; and a key technical support is provided for improving the product quality consistency, excavating the productivity and realizing intelligent production.

Description

Technical Field

[0001] This invention relates to the field of precision injection molding process monitoring and optimization technology for medical devices, and in particular to a method for determining the freezing time of the gate of anesthesia masks based on melt pulsation analysis. Background Technology

[0002] In the field of injection molding of plastic medical devices, especially for products like anesthesia masks that have stringent requirements for dimensional stability, sealing, and flawless appearance, precise control of every step of the molding process is crucial. The gate, as a key channel connecting the runner and the cavity, directly determines the termination time of the melt pressure holding and shrinkage compensation process within the cavity. Premature gate freezing can lead to insufficient compensation for cooling shrinkage inside the cavity, resulting in defects such as shrinkage marks, depressions, or even insufficient dimensions; while delayed freezing can cause melt backflow, excessive wear at the gate, or unnecessary extended cooling time, reducing production efficiency and increasing energy consumption. Therefore, accurately determining the gate freezing time is a core technical aspect for optimizing the cooling process, ensuring product quality, and improving production efficiency.

[0003] For a long time, the industry has relied primarily on empirical formulas based on classical heat conduction theory to determine gate freezing time. These methods estimate cooling time using parameters such as product wall thickness, material thermal diffusivity, and the temperature difference between the melt and the mold. However, the actual injection molding process is a dynamic process involving complex non-Newtonian fluid flow, unsteady heat transfer, and phase changes, influenced by various factors such as batch-to-batch material variations, uneven mold temperature distribution, and fluctuations in injection molding machine conditions. Empirical formulas, based on numerous simplifications and assumptions, often yield results that significantly deviate from reality, making them unsuitable for the production of high-precision medical devices. Engineers typically add substantial safety margins, unnecessarily extending cooling time and sacrificing production cycle time.

[0004] With the development of computer-aided engineering, mold flow analysis software has been used to predict gate freezing behavior. This method simulates the filling, holding, and cooling processes of the melt by building a three-dimensional model. Although its theoretical accuracy is high, the modeling process is complex and heavily relies on accurate material rheological and thermal property data, boundary condition settings, and mesh generation quality. Modifications to mold design or material changes require time-consuming and specialized simulation analysis, making it unsuitable for rapid production demands. More importantly, the simulation results are predictions of ideal operating conditions and cannot detect or respond in real time to process fluctuations caused by various random disturbances occurring on the production line, thus making it unsuitable for online monitoring and real-time control.

[0005] To obtain more direct measurement data, some techniques attempt to implant temperature sensors near the mold gate, indirectly inferring freezing by monitoring the melt temperature drop to a certain critical point. However, the implantation of miniature thermocouples interferes with melt flow and the thermal field; their response delay and even slight deviations in installation position can introduce non-negligible errors. Furthermore, this method places higher demands on mold processing, is costly, and inconvenient to maintain. Another destructive method, the "short-shot method," involves interrupting production during sequential cooling times and checking the weight or dimensions of the finished product to infer the freezing time. This method is completely unusable for normal continuous production and is extremely wasteful.

[0006] In recent years, some studies have also focused on using cavity pressure curves for process monitoring. A common method is to observe the decay curve of cavity pressure after the holding pressure ends. When the pressure drops to a certain threshold or tends to plateau, it is considered that the gate has frozen. However, this method lacks sensitivity because the overall pressure drop is a slow and continuous process, affected by both the cooling rate and the melt compressibility, making it difficult to accurately identify the "freezing point" that marks the cessation of flow. The appearance of the pressure plateau often lags behind the actual freezing moment and is easily affected by sensor zero drift and process noise, resulting in ambiguous judgment results and poor repeatability.

[0007] In summary, existing technologies either rely on theoretical estimations detached from actual working conditions, require complex and expensive offline analysis or destructive testing, or employ indirect monitoring methods with low sensitivity and weak anti-interference capabilities. The lack of a method for online, real-time, accurate, and non-invasive direct determination of gate freezing time has become a prominent bottleneck restricting the achievement of lean, intelligent, and high-quality control in the production of high-end plastic medical devices. Summary of the Invention

[0008] To address the technical problems of low accuracy in determining gate freezing time, reliance on offline experience or simulation, and inability to achieve sensitive online monitoring and automatic control in existing technologies, this invention provides a method for determining the gate freezing time of anesthesia masks based on melt pulsation analysis.

[0009] The technical solution provided by this invention is as follows:

[0010] This invention provides a method for determining the freezing time of the gate of anesthesia masks based on melt pulsation analysis, comprising:

[0011] S1. Install a melt pressure sensor near the gate or at the end of the runner of the injection molding mold to continuously collect melt pressure data in the cavity during and after the injection holding pressure stage.

[0012] S2. Preprocess the collected melt pressure data to filter out noise and extract the pressure pulsation signal during the natural decay process of melt pressure after the holding stage ends.

[0013] S3. Based on the pressure pulsation signal, calculate the first parameter, namely the pulsation attenuation characteristic value, used to characterize the attenuation rate and periodic variation law of the pressure pulsation amplitude, and the second parameter, namely the periodic stability factor, used to characterize the periodic stability of the pressure pulsation.

[0014] S4. Based on the real-time change curves of the pulsation decay characteristic value and the periodic stability factor, establish a gate freezing determination model; the triggering condition of the determination model is: the pulsation decay characteristic value drops to below a preset first proportional threshold of its peak value, and at the same time the periodic stability factor rises to above a preset second proportional threshold of its initial value.

[0015] S5. When the real-time calculated pulsation decay characteristic value and periodic stability factor meet the triggering conditions of the judgment model, it is determined that the gate has been completely frozen, and the time at this moment is recorded as the gate freezing time.

[0016] S6. Output the gate freezing time for use in optimizing the cooling time setting of the injection molding process or for online monitoring and sorting of product quality.

[0017] The beneficial effects of the technical solution provided by this invention include at least the following:

[0018] (1) In this invention, by extracting and analyzing the microscopic pressure pulsation signal contained in the natural decay of cavity pressure after the holding pressure is completed, and by innovatively constructing two parameters with clear physical meanings, namely the pulsation decay characteristic value and the periodic stability factor, the direct and sensitive capture of the phase transition process from flow to freezing of the melt state is realized. The pressure pulsation originates from the compressibility of the melt itself and the elastic coupling of the mold system. Its decay rate and periodic stability directly reflect the changes in the melt flow capacity and internal structure. Compared with the traditional method of only observing the macroscopic pressure decline trend, this invention improves the sensitivity and specificity of monitoring from the signal essence level. It can clearly distinguish the physical characteristics of the flow dynamic and the frozen state, fundamentally overcoming the defects of the traditional pressure monitoring method of response lag and ambiguous judgment, and laying a solid physical and information foundation for the accurate online determination of the gate freezing time.

[0019] (2) In this invention, a smart judgment model based on the synchronous triggering of two parameters—pulsation decay characteristic value and periodic stability factor—and combined with a delayed confirmation mechanism is established to achieve highly robust and reliable automatic identification of the gate freezing moment. This model does not rely on a single threshold but integrates two parameters characterizing changes in different physical dimensions and introduces a confirmation mechanism in the time series, effectively filtering out instantaneous false signals caused by sensor noise or accidental process fluctuations. This design makes the judgment process sensitive to minute state changes while possessing strong immunity to random interference. This replaces the original method of relying entirely on operator experience to observe pressure curves or simple threshold alarms, transforming a vague and subjective process judgment into an objective, repeatable, and automated measurement process. This significantly improves the automation level of process monitoring and the consistency of judgment results, providing a stable and reliable input signal for subsequent closed-loop control.

[0020] (3) In this invention, by directly comparing the real-time determined gate freezing time with the theoretically calculated value and applying it to the online optimization suggestions of cooling process parameters, and by incorporating this time as a key quality characteristic into the statistical process control system, a complete closed loop from process status perception to process parameter optimization to product quality assurance is realized. The system can not only accurately tell "when to freeze," but also intelligently judge whether the current cooling time is "too long" or "insufficient," and provide specific adjustment suggestions to drive the production process to continuously move towards the optimal. At the same time, by monitoring the stability of the key process parameter of gate freezing time, it can provide early warning of quality risks caused by abnormal material, mold, or equipment conditions, and realize product quality prediction and sorting based on process parameters. This completely changes the traditional situation of conservative cooling time setting and passive quality inspection, and maximizes the potential of equipment capacity while ensuring zero product defects, thereby improving production efficiency and overall quality control level. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a method for determining the freezing time of anesthesia mask gate based on melt pulsation analysis, provided in an embodiment of the present invention;

[0023] Figure 2 A flowchart illustrating the calculation of pulsation attenuation characteristic values ​​in a method for determining the freezing time of an anesthesia mask gate based on melt pulsation analysis, provided in an embodiment of the present invention.

[0024] Figure 3 This is a flowchart illustrating the calculation of the periodic stability factor in a method for determining the freezing time of anesthesia mask gate based on melt pulsation analysis, provided in an embodiment of the present invention. Detailed Implementation

[0025] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0026] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0027] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0028] In embodiments of the present invention, sometimes the subscript is as follows: It may be mistakenly written as a non-subscript form such as W1. When the distinction is not emphasized, the meaning they express is the same.

[0029] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0030] Reference manual attached Figure 1 The diagram shows a flowchart of a method for determining the freezing time of anesthesia mask gate based on melt pulsation analysis, provided by an embodiment of the present invention.

[0031] This invention provides a method for determining the freezing time of anesthesia mask gate based on melt pulsation analysis. The processing flow may include the following steps:

[0032] S1. Install a melt pressure sensor near the gate or at the end of the runner of the injection molding mold to continuously collect melt pressure data in the cavity during and after the injection holding stage.

[0033] Specifically, the melt pressure sensor employs a high-temperature piezoelectric or piezoresistive sensor, with its measuring probe directly contacting the molten plastic through a pre-drilled mounting hole in the mold. The sensor is activated before the injection stage begins, continuously recording the melt pressure changes inside the cavity at a sampling frequency of at least 1 kHz until the product cools and the mold is opened. The acquired pressure-time series data is transmitted in real-time to the injection molding machine control system or a separate industrial computer for further processing.

[0034] S2. Preprocess the collected melt pressure data to filter out noise and extract the pressure pulsation signal during the natural decay process of melt pressure after the holding stage.

[0035] Specifically, the preprocessing first employs a moving average method or a low-pass filter to eliminate high-frequency noise introduced by mechanical vibration. Subsequently, the system automatically identifies the precise moment when the pressure holding period switches to cooling. From this point onward, the subsequent pressure decay curve is used as the analysis object. To separate the microscopic pulsation components superimposed on the overall pressure decay trend, the trend term needs to be removed from the original pressure decay curve. This can be achieved through polynomial fitting or high-pass filtering, thereby obtaining a pressure pulsation signal reflecting the energy transfer and dissipation characteristics within the melt.

[0036] S3. Based on the pressure pulsation signal, calculate the first parameter, namely the pulsation attenuation characteristic value, used to characterize the attenuation rate and periodic variation law of the pressure pulsation amplitude, and the second parameter, namely the periodic stability factor, used to characterize the periodic stability of the pressure pulsation.

[0037] Specifically, the system performs time-domain analysis on the pressure pulsation signal extracted in step S2. The core of calculating the pulsation attenuation characteristic value lies in quantifying the relative attenuation rate of adjacent pressure pulsation amplitudes and considering the influence of pulsation period variation on the attenuation process. The core of calculating the period stability factor lies in analyzing the dispersion of the pressure pulsation period and the magnitude of its range relative to the average period, used to characterize the process of pulsation moving from disorder to stability. Both parameters are calculated in a rolling manner according to a preset time window or event window, generating a parameter sequence that varies with cooling time.

[0038] S4. Based on the real-time change curves of the pulsation decay characteristic value and the periodic stability factor, establish a gate freezing determination model. The triggering condition for the determination model is: the pulsation decay characteristic value drops below a preset first proportional threshold of its peak value, and at the same time, the periodic stability factor rises above a preset second proportional threshold of its initial value.

[0039] Specifically, multiple experiments were conducted under stable injection molding conditions to obtain parameter variation curves for the entire process from the end of pressure holding to gate confirmation and freezing. Analysis revealed that the pulsation decay characteristic value initially decreased rapidly as the melt solidified, then tended to level off, while the periodic stability factor gradually increased from a low value. The physical state of complete gate freezing corresponds to the moment when the melt stops flowing and the pressure pulsation characteristics undergo a fundamental change. This moment is represented on the parameter curves by the pulsation decay characteristic value crossing its downward inflection point and reaching a low-level stable range, while the periodic stability factor crosses its upward inflection point and reaches a high-level stable range. Therefore, the proportional relationship between the characteristic stability levels after the inflection points of the two parameters and their respective characteristic peaks or initial values ​​was quantified as a threshold, constructing a logical "AND" dual-condition synchronous triggering model.

[0040] S5. When the real-time calculated pulsation decay characteristic value and periodic stability factor meet the triggering conditions of the judgment model, it is determined that the gate has been completely frozen, and the time at this moment is recorded as the gate freezing time.

[0041] Specifically, during real-time production, the system compares the pulsation decay characteristic value and cycle stability factor obtained from rolling calculations with the thresholds set in step S4 in real time. Once the system detects that within a calculation cycle, the pulsation decay characteristic value is less than or equal to the product of a preset first proportional threshold and its historical peak value, and the cycle stability factor is greater than or equal to the product of a preset second proportional threshold and its initial value, a freeze trigger signal is immediately generated. The system records the time interval from the end of the holding pressure to the generation of this trigger signal; this time interval is the gate freeze time for this injection molding.

[0042] S6. Output the gate freezing time for use in optimizing the cooling time setting of the injection molding process or for online monitoring and sorting of product quality.

[0043] Specifically, the system outputs the determined gate freezing time as a key process parameter. This time can be directly displayed on the human-machine interface for operators to manually adjust the cooling time. Alternatively, it can be transmitted to the injection molding machine control system via a communication interface as input for process parameter self-optimization, achieving automatic closed-loop control of the cooling time. Simultaneously, this time is stored in a database for generating statistical process control charts. If the gate freezing time of a particular production run significantly deviates from the controlled range, the system can coordinate with the production execution system to automatically mark or sort the anesthesia masks produced in that batch, achieving quality prediction and control based on process parameters.

[0044] In one possible implementation, such as Figure 2 As shown, the method for calculating the pulsation attenuation characteristic value in step S3 includes:

[0045] S301. After the pressure holding period ends, at fixed time intervals... Extract a pressure pulsation signal segment containing N consecutive pressure pulsation waves;

[0046] S302. For each pressure pulsation wave, identify its peaks and troughs, and calculate the amplitude of the wave. and cycle ,in ;

[0047] S303. Calculate the pulsation attenuation characteristic value of the current signal segment using the following formula. :

[0048]

[0049] in, The amplitude of the i-th pressure pulsation wave is expressed in MPa. The period of the i-th pressure pulsation wave is expressed in seconds. and Each of the following is a list of all signals within the current signal segment. The maximum and minimum values ​​in; The attenuation coefficient is related to the properties of the melt material, and is a constant greater than 0; The data acquisition time interval is in seconds; N is the number of pressure pulsations in the current signal segment.

[0050] Regarding the method for calculating the pulsation attenuation characteristic value, in its specific implementation, the time interval... The selection of the attenuation coefficient needs to match the system's data processing capabilities and the typical period of the pulsating signal, typically ranging from tens to hundreds of milliseconds. The value of parameter N must ensure that each analysis window covers a sufficient number of pulsating events to reflect statistical regularities, generally not less than 3. In specific calculations, the attenuation coefficient... Obtaining this coefficient is crucial. This coefficient is not a weight, but a material constant with a clear physical meaning. Its calibration requires analyzing the pressure pulsation curve of a standard melt freely decaying in the mold at specific temperatures and shear rates, and fitting the decay trend of its envelope. For different medical polymer materials, such as polycarbonate, ABS, or thermoplastic elastomers, The values ​​differ significantly, therefore they need to be calibrated separately and stored in the materials and processes database.

[0051] Attenuation coefficient The specific calibration process is as follows: Under the premise of fixed mold and process conditions, a set of samples with differences only in cooling time were prepared. By analyzing the pressure pulsation signal during the complete cooling process after the pressure holding period, the pressure pulsation amplitude sequence within each time window was extracted. The amplitude envelope decay function is fitted using the nonlinear least squares method. In Parameters. Repeated fitting was performed within multiple time windows before the gate physically freezes, and these parameters were used. The statistical average value is used as the attenuation coefficient for this material-mold combination. The calibration value. This process ensures... This reflects the true energy dissipation characteristics of the melt under specific flow channel geometric constraints.

[0052] In one possible implementation, such as Figure 3 As shown, the method for calculating the periodic stability factor in step S3 includes:

[0053] S311. After the pressure holding period ends, extract a pressure pulsation signal segment containing M consecutive pressure pulsation waves at a fixed time interval $\Delta t$.

[0054] S312. For each pressure pulsation wave, accurately measure its period. ,in ;

[0055] S313. Calculate the periodic stability factor of the current signal segment using the following formula. :

[0056]

[0057] in, The period of the j-th pressure pulsation wave is expressed in seconds. For the current signal segment, there are M cycles. The arithmetic mean of , in seconds; For the current signal segment, there are M cycles. The standard deviation of , in seconds; and Each of the following is a list of all signals within the current signal segment. The maximum and minimum values ​​in; is an adjustment coefficient related to the viscoelasticity of the melt, and is a constant greater than 0.

[0058] Regarding the method for calculating the periodic stability factor, its specific implementation requires... This is to meet the basic requirements for sample size in statistics, in order to ensure the calculated coefficient of variation. Representative. Adjustment coefficient. The method for determining it is as follows:

[0059] Multiple pressure pulsation cycle sequences were collected before and after the known physical moment of gate freezing. Through iterative optimization... The value of the periodic stability factor makes the periodic stability factor... At this physical moment, the most significant abrupt changes occur, such as the gradient reaching its maximum value. This is determined by... Able to make It is most sensitive to changes in periodic disorder during the transition of melt from fluid to solid state.

[0060] In specific optimization, the objective function is defined as the periodic stability factor. Given the physical time of gate freezing Nearby Time Window The absolute value of the average gradient of the internal variation curve. By adjusting... The candidate values ​​are calculated for each candidate. corresponding The sequence and its average gradient within the target time window. Selecting the sequence that maximizes this average gradient. This serves as the optimal adjustment coefficient. This process is completed through numerical iteration, ensuring... The phase transition point has the strongest signal characteristics, thus maximizing the model's decision sensitivity.

[0061] In one possible implementation, extracting the melt pressure pulsation signal in step S2 specifically involves: using a bandpass filter to filter the pre-processed melt pressure data. The passband frequency range of the bandpass filter is preset according to the injection molding machine screw specifications, melt material characteristics, and gate size, in order to retain the characteristic frequency pulsations caused by melt compressibility and mold system elasticity.

[0062] Regarding the method of extracting signals using a bandpass filter, the specific implementation depends on the dynamic analysis of the injection molding system. The lower frequency limit is set to filter out low-frequency interference from the hydraulic system, clamping mechanism, and other components. The upper frequency limit is related to the inherent oscillation frequency of the melt-mold system before the gate freezes. This frequency can be obtained by performing a Fast Fourier Transform analysis on the pressure signal at the end of the holding pressure period or the beginning of cooling to determine its dominant frequency components. The filter is typically implemented using a finite impulse response (FIR) filter or an infinite impulse response (IOR) filter to ensure that the phase response characteristics do not affect subsequent time-domain analysis.

[0063] Specifically, the lower cutoff frequency of the passband. It should be set to at least twice the pulsating main frequency of the injection molding machine's hydraulic system, typically not lower than 20 Hz, to completely eliminate periodic interference from the equipment. Upper limit cutoff frequency of the passband. The fundamental frequency is determined as follows: During the process stage where the gate is confirmed to be unfrozen (e.g., at the end of the holding pressure stage), a pressure signal is collected, and its power spectral density is estimated. The frequency corresponding to the highest amplitude peak in the power spectrum is recorded as the fundamental frequency. Upper limit cutoff frequency It should be set to to This ensures that the dominant frequency and second harmonic of the system's inherent oscillation are covered, while filtering out higher-frequency irrelevant noise.

[0064] In one possible implementation, the first proportional threshold in step S4 is determined by conducting multiple experiments under a known gate freezing state and statistically analyzing the relationship between the stable value of the pulsation attenuation characteristic value and its peak value; the second proportional threshold is determined by statistically analyzing the relationship between the stable value of the periodic stability factor and its initial value under the same experimental conditions.

[0065] The method for determining the preset proportional threshold is implemented through an offline calibration process. In the calibration experiment, the physical moment when the gate is completely frozen is confirmed using one of the following standard methods: First, using microscopic sectioning technology, the injection molding process is interrupted and the product is removed at consecutive different cooling time points. The gate cross-section is then subjected to metallographic treatment, and the thickness of the solidified layer is measured under a microscope. The moment when the solidified layer thickness reaches 99% of the gate thickness is determined as the gate freezing moment. Second, a miniature thermocouple with a response time of less than 100 milliseconds is implanted at the center of the gate to monitor temperature changes in real time. The moment when the melt temperature drops to 5 degrees Celsius below the material's glass transition temperature or crystallization temperature is determined as the gate freezing moment. Both of these methods can provide a reliable benchmark for the physical moment of gate freezing. When statistically analyzing the relationship between the stable value and peak value of the pulsation decay characteristic value, the stable value is taken as the average value of the parameter over a period of time after the gate freezes, and the peak value is taken as the maximum value of the parameter after the holding pressure ends. The same applies to the processing of the periodic stability factor; its initial value is the calculation result of the first effective analysis window after the holding pressure ends.

[0066] In one possible implementation, the range of the preset first proportional threshold is 45%-65%, and the range of the preset second proportional threshold is 150%-300%.

[0067] The specific range of the proportional threshold is an empirical range derived from extensive experimental statistics on widely used medical plastics, such as polyvinyl chloride, polypropylene, polycarbonate, and silicone rubber, under typical anesthesia mask wall thicknesses and process windows. Selecting a threshold within this range allows the judgment model to have good adaptability to different materials and process conditions. For specific highly transparent or highly viscous materials, the threshold may approach the upper or lower limit of this range.

[0068] In one possible implementation, the determination model in step S4 also introduces a delayed confirmation mechanism: after the trigger condition is met for the first time, the parameter status is monitored for the next K consecutive calculation cycles. If the trigger condition is still met for more than a preset proportion of the calculation cycles, the determination result is finally confirmed; otherwise, the determination is invalid and monitoring continues.

[0069] Regarding the specific implementation of the delayed confirmation mechanism, the value of parameter K is related to the system's calculation cycle and data update rate. Within a calculation cycle, the parameter may momentarily meet the condition due to signal noise. Introducing monitoring for K consecutive cycles, and requiring the condition to be met for more than a preset proportion, such as two-thirds of the cycles, essentially introduces a low-pass filtering decision process, which can effectively suppress false judgments caused by occasional interference. If the condition is no longer met within the monitoring window, the system resets and continues to search for the next possible trigger point.

[0070] The selection of parameter K and the system calculation cycle and the main period of pressure pulsation Relevant, should meet This ensures the observation window covers at least two complete pulse cycles, avoiding misjudgments due to periodic signal fluctuations. The preset ratio is typically set to greater than 50%, but the specific value can be adjusted based on analysis of false trigger events in historical data. For example, by analyzing normal production data and statistically analyzing the proportion of events that momentarily meet the triggering conditions but subsequently recover quickly, setting the preset ratio to a value higher than this statistical proportion, such as two-thirds or seventy-five percent, can effectively improve the robustness of the judgment while ensuring response speed.

[0071] In one possible implementation, the cooling time setting for optimizing the injection molding process in step S6 specifically involves comparing the gate freezing time determined in the current injection cycle with the theoretical cooling time. If the gate freezing time is significantly shorter than the theoretical cooling time, a suggestion to shorten the cooling time is automatically provided. If the gate freezing time is significantly longer than the theoretical cooling time, an alarm for insufficient cooling is automatically issued.

[0072] Regarding the specific implementation of optimizing cooling time settings, theoretical cooling time is typically calculated based on the classical heat conduction equation, considering the time required for the center temperature at the thickest part of the product to cool below the material's heat distortion temperature. After comparison, the system provides "suggestions" or "alarms" indicating specific modifications to the process parameters. For example, it might suggest "cooling time can be shortened to X seconds," or issue an alarm stating "cooling time is insufficient; it is recommended to check the mold water temperature or extend the cooling time by Y seconds." This achieves a closed loop from state determination to process optimization.

[0073] Specifically, a one-dimensional flat plate cooling model can be used for estimation, and the formula is as follows:

[0074]

[0075] Where s is the thickest wall thickness of the product, in meters; a is the thermal diffusivity of the plastic material, in square meters per second; This refers to the melt injection temperature, expressed in degrees Celsius. This refers to the temperature of the mold cooling water, in degrees Celsius. This is the allowable center temperature of the product upon demolding, typically taken as the material's heat distortion temperature, expressed in degrees Celsius. This formula is a simplified calculation model known in the art, used as a benchmark for estimating theoretical cooling time.

[0076] In one possible implementation, the online monitoring of product quality in step S6 specifically involves: during continuous production, statistically analyzing and plotting a historical control chart of gate freezing time; when the gate freezing time of the current injection molding cycle exceeds the control limit set based on historical data, automatically marking the products produced in that cycle as potentially defective.

[0077] Regarding the specific implementation of online monitoring, the establishment of historical control charts is based on the principles of statistical process control. Control limits are typically set as the average of historical data plus or minus three standard deviations. When a new gate freezing time data point exceeds the control limits, not only is the product flagged, but the system can also automatically trace the key process parameters of that batch, such as melt temperature and holding pressure, to assist in the root cause analysis of quality problems.

[0078] In practice, under stable process conditions, at least 25 sets of qualified gate freezing time data must be continuously collected. The arithmetic mean of these data sets should then be calculated. and standard deviation The upper control limit (UCL) of the control chart is then set to... The lower control limit LCL is set to In subsequent continuous production, the freezing time data point of each newly generated gate... All of these are compared with these control limits in real time.

[0079] In one possible implementation, before step S1, step S0 is further included: according to the material and weight of the anesthesia mask to be produced and the gate design of the mold, the installation position and measurement range of the melt pressure sensor are calibrated, and the initial values ​​of the preset first proportional threshold and the preset second proportional threshold in the judgment model are preset.

[0080] Regarding the specific implementation of the initial step S0, when calibrating the sensor installation location, mold flow analysis software is often used to simulate the pressure field distribution during melt filling and holding, selecting a region near the gate that is sensitive to pressure changes. The measurement range needs to cover the peak injection pressure and the residual pressure at the end of cooling. The preset initial threshold can be set based on recommended process data provided by the material supplier or mature production experience of similar molds, providing a basic basis for the first trial molding, followed by precise calibration through step S4.

[0081] In practice, the initial value of the preset first proportional threshold can be set to 55%, and the initial value of the preset second proportional threshold can be set to 220%. These initial values ​​are general starting points based on the statistical median of various common plastic materials. During the first trial molding, the system uses these initial thresholds for preliminary judgment and records the corresponding peak value of the pulsation attenuation characteristic. and the initial value of the periodic stability factor Subsequently, using the data obtained from this trial molding, the process was immediately switched to offline calibration to accurately calculate and update the threshold ratios in order to quickly adapt to the current specific material and mold combination.

[0082] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0083] (1) In this invention, by extracting and analyzing the microscopic pressure pulsation signal contained in the natural decay of cavity pressure after the holding pressure is completed, and by innovatively constructing two parameters with clear physical meanings, namely the pulsation decay characteristic value and the periodic stability factor, the direct and sensitive capture of the phase transition process from flow to freezing of the melt state is realized. The pressure pulsation originates from the compressibility of the melt itself and the elastic coupling of the mold system. Its decay rate and periodic stability directly reflect the changes in the melt flow capacity and internal structure. Compared with the traditional method of only observing the macroscopic pressure decline trend, this invention improves the sensitivity and specificity of monitoring from the signal essence level. It can clearly distinguish the physical characteristics of the flow dynamic and the frozen state, fundamentally overcoming the defects of the traditional pressure monitoring method of response lag and ambiguous judgment, and laying a solid physical and information foundation for the accurate online determination of the gate freezing time.

[0084] (2) In this invention, a smart judgment model based on the synchronous triggering of two parameters—pulsation decay characteristic value and periodic stability factor—and combined with a delayed confirmation mechanism is established to achieve highly robust and reliable automatic identification of the gate freezing moment. This model does not rely on a single threshold but integrates two parameters characterizing changes in different physical dimensions and introduces a confirmation mechanism in the time series, effectively filtering out instantaneous false signals caused by sensor noise or accidental process fluctuations. This design makes the judgment process sensitive to minute state changes while possessing strong immunity to random interference. This replaces the original method of relying entirely on operator experience to observe pressure curves or simple threshold alarms, transforming a vague and subjective process judgment into an objective, repeatable, and automated measurement process. This significantly improves the automation level of process monitoring and the consistency of judgment results, providing a stable and reliable input signal for subsequent closed-loop control.

[0085] (3) In this invention, by directly comparing the real-time determined gate freezing time with the theoretically calculated value and applying it to the online optimization suggestions of cooling process parameters, and by incorporating this time as a key quality characteristic into the statistical process control system, a complete closed loop from process status perception to process parameter optimization to product quality assurance is realized. The system can not only accurately tell "when to freeze," but also intelligently judge whether the current cooling time is "too long" or "insufficient," and provide specific adjustment suggestions to drive the production process to continuously move towards the optimal. At the same time, by monitoring the stability of the key process parameter of gate freezing time, it can provide early warning of quality risks caused by abnormal material, mold, or equipment conditions, and realize product quality prediction and sorting based on process parameters. This completely changes the traditional situation of conservative cooling time setting and passive quality inspection, and maximizes the potential of equipment capacity while ensuring zero product defects, thereby improving production efficiency and overall quality control level.

[0086] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0087] The following points need to be explained:

[0088] (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0089] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the invention, i.e., these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element or there may be intermediate elements.

[0090] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0091] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for determining the freezing time of the gate of anesthesia masks based on melt pulsation analysis, characterized in that, The method comprises the following steps: S1, a melt pressure sensor is installed near the gate or at the end of the flow channel of an injection molding mold, for continuously collecting melt pressure data in the cavity during the injection holding stage and after the injection holding stage; S2, the collected melt pressure data is pre-processed to filter out noise, and a pressure pulsation signal in the natural decay process of the melt pressure after the end of the holding stage is extracted; S3, based on the pressure pulsation signal, a first parameter, i.e., a pulsation decay characteristic value, for representing the decay rate and periodic variation law of the pulsation amplitude, and a second parameter, i.e., a periodic stability factor, for representing the periodic stability of the pressure pulsation are calculated; S4, a gate freezing judgment model is established according to the real-time variation curves of the pulsation decay characteristic value and the periodic stability factor; the trigger condition of the judgment model is that the pulsation decay characteristic value drops below a preset first proportion threshold of the peak value, and at the same time, the periodic stability factor rises above a preset second proportion threshold of the initial value; S5, when the real-time calculated pulsation decay characteristic value and the periodic stability factor meet the trigger condition of the judgment model, it is judged that the gate has been completely frozen, and the time at this moment is recorded as the gate freezing time; S6, the gate freezing time is output for optimizing the cooling time setting of the injection molding process or for online monitoring and sorting of product quality.

2. A melt-pulse analysis based method of determining freeze time of an anesthesia mask gate as claimed in claim 1, wherein, The method for calculating the pulsation decay characteristic value in step S3 comprises: S301、After the pressure maintaining is ended, the fixed time interval is taken The pressure pulsation signal segment containing the continuous N pressure pulsation waves is intercepted; S302、For each pressure pulsation wave, identify its wave crest and wave trough, calculate the amplitude of the wave and period wherein ; S303, adopt the following formula to calculate the pulsation attenuation characteristic value of the current signal segment : wherein, is the amplitude of the i-th pressure fluctuation wave, in MPa; is the period of the i-th pressure fluctuation wave, in s; and are the maximum and minimum values, respectively, of all in the current signal segment; is the attenuation coefficient related to the melt material properties, and is a constant greater than 0; is the data acquisition time interval, in s; and N is the number of pressure fluctuation waves in the current signal segment.

3. A melt-pulse analysis based frozen gate time determination method for anesthetic mask according to claim 1, characterized in that, The method for calculating the periodic stability factor in step S3 comprises: S311, after the end of the holding, a pressure pulsation signal segment containing M continuous pressure pulsation waves is intercepted at a fixed time interval Δt; S312, for each pressure pulsation wave, accurately measure its period wherein ; S313、adopt the following formula to calculate the period stability factor of the current signal segment : wherein, is the period of the jth pressure pulsation wave, in s; is the arithmetic mean of the M periods within the current signal segment, in s; is the standard deviation of the M periods within the current signal segment, in s; and are the maximum and minimum values, respectively, of all within the current signal segment; is an adjustment factor related to the melt viscoelasticity, and is a constant greater than 0.

4. A melt-pulse analysis based frozen gate time determination method for anesthetic masks as claimed in claim 1, wherein, In step S2, the melt pressure pulsation signal is extracted by filtering the pre-processed melt pressure data with a band-pass filter, and the passband frequency range of the band-pass filter is pre-set according to the screw specification of the injection molding machine, the melt material properties and the gate size, so as to retain the characteristic frequency pulsation caused by the melt compressibility and the mold system elasticity.

5. A melt-pulse analysis based frozen gate time determination method for anesthetic masks as claimed in claim 1, wherein, In step S4, the preset first proportion threshold is determined by performing multiple experiments under the known gate freezing state, and the relationship between the stable value of the pulsation decay characteristic value and the peak value is counted; the preset second proportion threshold is determined by counting the relationship between the stable value of the periodic stability factor and the initial value under the same experimental conditions.

6. A melt-pulse analysis based frozen gate time determination method for anesthetic masks as defined in claim 5, characterized in that, The method comprises the following steps: The range of the preset first proportion threshold is 45%-65%, and the range of the preset second proportion threshold is 150%-300%.

7. A melt-pulse analysis based frozen gate time determination method for anesthetic masks as defined in claim 1, characterized in that, In step S4, the judgment model also introduces a delay confirmation mechanism: when the trigger condition is first met, the parameter state in the subsequent K continuous calculation periods is continuously monitored, if the trigger condition still meets in more than a preset proportion of the calculation periods, the final determination result is confirmed; otherwise, the determination is invalid and continues to be monitored.

8. A melt-pulse analysis based frozen gate time determination method for anesthetic masks as defined in claim 1, characterized in that, The cooling time setting for optimizing the injection molding process in step S6 is specifically: comparing the determined sprue freezing time in the current injection molding cycle with the theoretical cooling time, if the sprue freezing time is significantly shorter than the theoretical cooling time, a suggestion of shortening the cooling time is automatically prompted; if the sprue freezing time is significantly longer than the theoretical cooling time, an alarm of insufficient cooling is automatically sent.

9. A melt-pulse analysis based frozen gate time determination method for anesthetic masks as claimed in claim 1, wherein, The online monitoring for product quality in step S6 is specifically: in continuous production, the history control chart of the sprue freezing time is counted and drawn, when the sprue freezing time of the current injection molding cycle exceeds the control limit set based on the historical data, the product produced in this cycle is automatically marked as a potential unqualified product.

10. A melt-pulse analysis based frozen gate time determination method for anesthetic masks as claimed in claim 1, wherein, Before step S1, step S0 is further included: according to the material, weight of the anesthesia mask to be produced and the sprue design scheme of the mold, the installation position and measurement range of the melt pressure sensor are calibrated, and the initial values of the preset first proportion threshold and the preset second proportion threshold in the determination model are pre-set.

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