A fault early warning method and system for an injection molding device for producing a mobile phone acoustic module

Abstract

The present application belongs to the technical field of injection molding equipment, and particularly relates to a method and system for early warning of faults of injection molding equipment for producing a mobile phone acoustic module. The method comprises: the system acquires real-time pulling force and real-time injection pressure in an injection cycle in real time, aligns and denoises the two to be recorded as a pulling force sequence and an injection pressure sequence; a dynamic structure response index is calculated; a partial load factor is constructed; a pressure impact rate is obtained; a four-corner dynamic balance index is calculated; fault diagnosis is performed in combination with the four-corner dynamic balance index; a fault type is identified and hierarchical early warning is performed; and a compensation amount of injection speed is calculated. The present application effectively solves the technical problem that the injection molding machine monitoring system cannot finely and frequently capture micro abnormalities of the equipment in the key stage of production of the mobile phone acoustic module, leading to difficulties in early warning of faults and identification of the type.

Description

Technical Field

[0001] This invention relates to the field of injection molding equipment technology. More specifically, this invention relates to a fault early warning method and system for injection molding equipment used in the production of mobile phone acoustic modules. Background Technology

[0002] Existing injection molding machine monitoring systems typically monitor equipment operation status by sampling throughout the entire cycle or recording only the maximum value. However, in the precision production process of mobile phone acoustic modules, the V / P switching stage where the melt fills the mold cavity and instantly enters the holding pressure stage is considered the most dangerous moment for the equipment. During this stage, the pressure inside the mold cavity will instantly reach its peak. If there are minor defects in the equipment's clamping mechanism, it is very easy for uneven force or insufficient strength to cause the mold gap to increase at this moment, resulting in quality problems such as flash and unstable dimensions in the product, or even causing mold damage and machine shutdown.

[0003] Existing injection molding machine monitoring systems have clear technical paths for monitoring equipment operating status and are practical in conventional production scenarios. The full-cycle sampling mode can completely cover the entire process of equipment operation. By continuously collecting core parameters such as clamping force, injection pressure, and temperature, it can achieve full data traceability of the production process, which is convenient for subsequent source analysis of batch product quality problems. It plays an important role, especially in troubleshooting periodic failures or process parameter drift.

[0004] However, existing technologies fail to capture and analyze data in a refined and high-frequency manner for specific and critical time windows. For example, when a mold experiences a slight tilt or twist during V / P switching, and the clamping force is not evenly distributed or insufficiently responsive, traditional monitoring systems may not be able to detect such sudden microscopic anomalies in a timely manner. This makes early warning and type identification of equipment failures difficult, often only being discovered after the failure has already occurred and has a significant impact on product quality, leading to reduced production efficiency, increased scrap rates, and higher maintenance costs. Summary of the Invention

[0005] To address the technical problem that the aforementioned injection molding machine monitoring system is unable to accurately and frequently capture microscopic anomalies in the critical stages of mobile phone acoustic module production, leading to difficulties in early fault warning and type identification, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a fault early warning method for injection molding equipment used in the production of mobile phone acoustic modules, comprising:

[0007] The system acquires real-time tensile force and injection pressure during the injection molding cycle, aligns and denoises them, and records them as tensile force sequence and injection pressure sequence. Based on the difference between the standard deviation and average tensile force at any given moment in the tensile force sequence, a dynamic structural response index is calculated. The torque imbalance state of the mold is inferred from the tensile force distribution of the golem pillars, and an off-center loading factor is constructed in conjunction with the dynamic structural response index. The injection pressure sequence is subjected to first-order difference to obtain the pressure impact rate. Based on the interaction between the aggravating effect of the pressure impact rate on the off-center loading factor and the amplifying effect of the pressure impact rate on the mold by the off-center loading factor, a four-corner dynamic balance index is calculated. A dynamic safety boundary is constructed based on historical data, and fault diagnosis is performed in conjunction with the four-corner dynamic balance index. Based on the fluctuation characteristics of the off-center loading vector formed by the four-corner dynamic balance index and the off-center loading factor, fault types are identified and graded early warnings are executed. Based on the degree and rate of deviation between the four-corner dynamic balance index and the dynamic safety boundary, combined with the injection speed characteristics under historical healthy operating conditions, the compensation amount for the injection speed is calculated.

[0008] This invention addresses the problems of inaccurate fault warnings, difficulty in identifying fault types, and inability to intervene in advance during the critical stage of injection molding equipment production of mobile phone acoustic modules. By accurately collecting and processing equipment operating data, it constructs multi-dimensional equipment status assessment indicators, enabling accurate calculation of equipment conditions such as stress imbalance and pressure impact. Based on historical data, a dynamic fault judgment standard is built, which can meet the equipment status requirements under different operating conditions, accurately identify faults and distinguish fault types, and dynamically adjust process parameters according to the equipment's risk status. The overall solution effectively solves the problems of inaccurate data, one-sided assessments, fixed thresholds, and the inability to detect faults only after the fact in traditional monitoring methods. It improves the accuracy and timeliness of fault warnings, reducing product quality problems and equipment damage caused by missed or misjudged faults.

[0009] Preferably, after aligning and denoising the two sequences, they are denoised as a tension sequence and an injection pressure sequence, including:

[0010] By using high-sensitivity strain sensors installed on the surfaces of four goring pillars, real-time tensile force values ​​at four locations (upper left, upper right, lower left, and lower right) are collected at a fixed sampling frequency during the injection molding cycle to obtain a real-time tensile force value sequence. The real-time injection pressure is read from the injection molding machine controller to obtain a real-time injection pressure sequence. The real-time tensile force value sequence and the real-time injection pressure sequence are aligned on the time axis, and the aligned data is denoised to obtain the tensile force sequence and the injection pressure sequence.

[0011] Preferably, the calculation of the dynamic structural response index includes:

[0012] Calculate the standard deviation and average value of the tension values ​​at the top left, top right, bottom left, and bottom right at any same time in the tension sequence, and denot them as the tension standard deviation and tension average value, respectively. Calculate the ratio of the tension standard deviation to the tension average value, and add 1 to the ratio of the tension standard deviation to the tension average value to obtain the dynamic structural response index that characterizes the dynamic structural response state of the mold.

[0013] This invention calculates a dynamic structural response index based on the statistical characteristics of tensile data from multiple locations. It can assess the degree of imbalance of forces on the mold clamping structure of the equipment without relying on fixed evaluation criteria. It can reflect the current structural response state of the equipment in real time, providing a dynamic reference for assessing the tilt state of the mold and making subsequent judgments on the stress imbalance of the mold more consistent with the actual operation of the equipment.

[0014] Preferably, the off-center loading factor satisfies the following expression:

[0015] ;

[0016] In the formula, Indicates the horizontal off-load factor. Indicates the vertical off-center load factor; This represents the tension values ​​at any four points (top left, top right, bottom left, and bottom right) at any given time in the tension sequence. Indicates the dynamic structural response index; It represents a very small positive number, and guarantees that the denominator is not 0.

[0017] This invention constructs an off-center load factor by combining the real-time structural response status of the equipment. By inferring the stress imbalance of the mold through the tensile force distribution, it can adaptively identify the tilting state of the mold in different directions. This avoids the limitations of traditional fixed parameter evaluation methods, accurately reflects the dynamic stress changes of the mold, reduces the misjudgment of mold tilt or the failure to detect faults, and provides a core indicator that fits reality for subsequent risk assessment.

[0018] Preferably, obtaining the pressure impact rate includes:

[0019] Perform a first-order difference operation on the injection pressure sequence to obtain the pressure change at adjacent time points, calculate the sampling time interval of the pressure change at adjacent time points, and divide the pressure change by the corresponding sampling time interval to obtain the pressure impact rate.

[0020] Preferably, the four-corner dynamic balance index satisfies the following expression:

[0021] ;

[0022] In the formula, Indicates the dynamic balance index of the four corners; Indicates the horizontal off-load factor. Indicates the vertical off-center load factor; Indicates the pressure impact rate; Represents a very small positive number, ensuring that the denominator is not zero; This represents the absolute value function.

[0023] This invention integrates mold tilt, torsion state, and pressure impact intensity to calculate the dynamic balance index at the four corners, which can comprehensively assess the complex risks faced by the mold in the critical stages of injection molding. It breaks through the limitations of single-dimensional assessment, can accurately assess the degree of risk, avoid risk distortion caused by one-sided assessment, and make equipment failure warnings more consistent with the actual risk characteristics of mold damage.

[0024] Preferably, fault diagnosis is performed in conjunction with the four-corner dynamic balance index, including:

[0025] The peak values ​​of multiple four-corner dynamic balance indices within a historical healthy operating cycle are obtained to form a historical peak sequence; the mean and standard deviation of the historical peak sequence are calculated; the standard deviation of the historical peak sequence is multiplied by the coefficient of variation of the historical peak sequence, and then added to the mean of the historical peak sequence to obtain the dynamic safety boundary; the four-corner dynamic balance indices are compared with the dynamic safety boundary, and when the four-corner dynamic balance indices exceed the dynamic safety boundary, the equipment is determined to have failed.

[0026] Preferably, identifying fault types and executing tiered early warnings includes:

[0027] The system monitors the off-center load vector formed by the four-corner dynamic balance index and the off-center load factor. When the four-corner dynamic balance index shows a peak exceeding the dynamic safety boundary, and the off-center load vector continuously points to a specific quadrant, the equipment fault type is determined to be single-corner looseness, and a first-level warning is triggered. When the direction of the off-center load vector is randomly distributed, the equipment fault type is determined to be insufficient overall rigidity, and a second-level warning is triggered.

[0028] Preferably, the compensation amount for the injection speed satisfies the following expression:

[0029] ;

[0030] In the formula, The amount of compensation for injection speed; Indicates the dynamic balance index of the four corners; Represents a dynamic safety boundary; The standard deviation of the historical peak sequence; This represents the average injection rate over a historical healthy operating cycle. The coefficient of variation of injection velocity during historical healthy operating cycles.

[0031] This invention calculates the compensation amount of injection speed based on the degree of equipment risk and the rate of deterioration, enabling dynamic adjustment of injection molding process parameters. This avoids the drawbacks of fixed parameters or linear adjustments, smoothly meets changes in equipment risk, effectively mitigates the impact risk faced by the mold, and proactively intervenes in the equipment's operating status without requiring shutdown, reducing the probability of failure.

[0032] Secondly, the present invention provides a fault early warning system for injection molding equipment for producing mobile phone acoustic modules, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned fault early warning method for injection molding equipment for producing mobile phone acoustic modules is implemented.

[0033] By adopting the above technical solution, a fault early warning method for injection molding equipment used to produce mobile phone acoustic modules is generated into a computer program and stored in a memory for loading and execution by a processor. This allows for the creation of terminal devices based on the memory and processor, making them convenient to use.

[0034] The beneficial effects of this invention are as follows: This invention forms a complete solution for equipment operation monitoring and fault early warning in the injection molding production of mobile phone acoustic modules. From data acquisition, status assessment, fault diagnosis to process intervention, it constructs a dynamic control system covering the entire process. Compared with traditional monitoring methods, this invention eliminates the reliance on fixed parameters and single-dimensional assessments, using actual equipment operating data as the core driver to achieve precise perception of equipment status, comprehensive risk assessment, accurate fault identification, and adaptive process adjustment. This solution not only improves the operational stability and fault early warning capabilities of a single injection molding machine but also meets the equipment operating characteristics under different production scenarios. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating a fault early warning method for injection molding equipment used in the production of mobile phone acoustic modules according to the present invention. Detailed Implementation

[0036] This invention discloses a fault early warning method for injection molding equipment used in the production of mobile phone acoustic modules, referring to... Figure 1 This includes steps S1-S4:

[0037] S1: The system acquires the real-time tension and injection pressure during the injection cycle, aligns and denoises the two, and records them as the tension sequence and injection pressure sequence.

[0038] It should be noted that the V / P switching stage is a critical stage in injection molding, where V represents injection speed and P represents holding pressure. In order to ensure accurate capture of the microscopic response of the equipment during the V / P switching stage and to eliminate potential errors in data transmission and acquisition, this invention integrates and refines the data capture and preprocessing process.

[0039] Specifically, the system acquires real-time tension and injection pressure during the injection molding cycle, aligns and denoises them, and records them as tension sequence and injection pressure sequence, including:

[0040] Using high-sensitivity strain sensors installed on the surfaces of four columns, real-time tensile force values ​​at four locations (upper left, upper right, lower left, and lower right) are collected during the injection molding cycle at a sampling frequency of no less than 1000Hz to obtain a real-time tensile force value sequence. The real-time injection pressure is read from the injection molding machine controller to obtain a real-time injection pressure sequence. The real-time tensile force value sequence and the real-time injection pressure sequence are aligned on the time axis, and the aligned data is denoised to obtain the tensile force sequence and the injection pressure sequence.

[0041] It should be noted that the tie rod is a core component in the injection molding machine's mold clamping system, and is also often called a tie rod. It is also a key component connecting the fixed mold plate and the moving mold plate of the injection molding machine; the strain resolution of the high-sensitivity strain sensor is not less than 1με.

[0042] Thus, the tensile force sequence and the injection pressure sequence were obtained.

[0043] S2: Calculate the dynamic structural response index based on the difference between the standard deviation of the tension and the average tension at any time in the tension sequence; infer the moment imbalance state of the mold by the tension distribution of the Golling column, and construct the off-center loading factor in combination with the dynamic structural response index.

[0044] It should be noted that, in order to achieve adaptive recognition of the mold tilt state, this invention introduces a dynamic structural response index driven by real-time data. This index can calculate the degree of imbalance in the force distribution of the tie rods, providing dynamic weights for the subsequent calculation of the off-center loading factor. In other words, the dynamic structural response index transforms the stress state of the injection molding machine's clamping structure during the critical V / P switching stage into a core indicator for calculating the degree of structural imbalance. It includes the mapping relationship between the uniformity of the tie rod tension distribution and the overall stress strength. Instead of using a fixed imbalance judgment coefficient, it combines real-time multi-point tension data and statistical analysis methods to calculate a dimensionless evaluation value that reflects the current structural response to pressure impact.

[0045] Specifically, the dynamic structural response index is calculated based on the difference between the standard deviation and the average value of the tensile force at any given moment in the tensile force sequence, including:

[0046] Calculate the standard deviation and average value of the tension values ​​at the top left, top right, bottom left, and bottom right at any same time in the tension sequence, and denot them as the tension standard deviation and tension average value, respectively. Calculate the ratio of the tension standard deviation to the tension average value, and add 1 to the ratio of the tension standard deviation to the tension average value to obtain the dynamic structural response index that characterizes the dynamic structural response state of the mold.

[0047] It should be noted that the horizontal and vertical tilt of the mold is the direct cause of flash. This invention can reflect the tilting trend of the mold by the difference in the product of the diagonal tension of the columns. The dynamic structural response index is introduced as a weighting factor so that these off-center loading factors can adaptively reflect the dynamic structural response of the mold, rather than just the static mechanical balance.

[0048] Preferably, the moment imbalance state of the mold is inferred from the tensile force distribution of the tie rod, and an off-center loading factor is constructed by combining the dynamic structural response index, including:

[0049] It should be noted that traditional fixed-parameter or single-dimensional driven off-center load assessment methods cannot meet the dynamic force requirements of the V / P switching stage of injection molding for mobile phone acoustic modules, which can easily lead to misjudgment of mold tilt or missed fault detection. The current off-center load factor expression calculates the torque imbalance state by the difference of the product of the diagonal or same-side tie column tensions. It uses the dynamic structural response index to realize the nonlinear amplification and adaptive weighting of the force difference. It eliminates the difference in working conditions by normalizing the square of the average tension and introduces a very small positive number to avoid calculation anomalies. It not only accurately matches the physical nature of the torque balance of the mold as a rigid body and the engineering logic of the tie column tension acquisition, but also realizes the assessment, identification and dynamic adaptation of the horizontal or vertical off-center load of the mold.

[0050] The off-center loading factor satisfies the following expression:

[0051] ;

[0052] In the formula, Indicates the horizontal off-load factor. Indicates the vertical off-center load factor; This represents the tension values ​​at any four points (top left, top right, bottom left, and bottom right) at any given time in the tension sequence. Indicates the dynamic structural response index; It represents a very small positive number, and guarantees that the denominator is not 0.

[0053] In the formula, This represents the sum of the tensile forces of the four columns; for the horizontal eccentric load factor... When the diagonal pillar and The product of the tensile forces is greater than and When the product of the tension, A positive value indicates a horizontal eccentric load on the mold; conversely, a negative value indicates a negative value. The larger the absolute value, the greater the degree of horizontal tilt. For the vertical eccentric load factor... ,when and The product of the tensile forces is greater than and When the product of the tension, A positive value indicates that the mold has a vertical eccentric load; conversely, a negative value indicates a lower value. The larger the absolute value, the greater the degree of vertical tilt. As a dynamic weighting factor, it enables the off-center loading factor expression to nonlinearly enhance the amplification effect of force difference, thereby achieving adaptive identification of mold tilt state without introducing any external parameters.

[0054] For example, in scenario one, the forces on the four tie rods of the mold are evenly distributed, i.e. ,at this time, , Calculations yielded This indicates that the mold is in a balanced state; however, in case two, the mold experiences a horizontal eccentric load, i.e. ,at this time, , Calculations yielded This indicates that the mold has a relatively obvious horizontal tilt; in case three, the mold has a complex tilt, that is... ,at this time, , Calculations yielded This indicates that the mold is tilted in both the horizontal and vertical directions.

[0055] S3: Perform first-order difference on the injection pressure sequence to obtain the pressure impact rate; calculate the dynamic balance index of the four corners based on the interaction between the aggravating effect of the pressure impact rate on the off-center loading factor and the amplifying effect of the off-center loading factor on the harmful effect of the pressure impact rate on the mold.

[0056] It should be noted that the instantaneous pressure impact during the V / P switching stage of injection molding of mobile phone acoustic modules is the core driving force for mold damage. During this stage, the mold cavity pressure jumps sharply from the dynamic filling state to the holding pressure peak. The instantaneous high pressure can easily open the clamping mechanism with minor defects. The rate of change of pressure over time is the core indicator for calculating the impact intensity, namely the pressure impact rate. It is calculated by the first difference of the pressure data after high-frequency filtering. The larger the value, the more severe the impact. Its evaluation value far exceeds that of the static peak value. It can provide a key basis for constructing a balance index by integrating the off-center loading factor and identifying high-risk faults. It is the core support for realizing dynamic fault early warning.

[0057] Specifically, the injection pressure sequence is subjected to first-order difference to obtain the pressure impact rate, including:

[0058] Perform a first-order difference operation on the injection pressure sequence to obtain the pressure change at adjacent time points, calculate the sampling time interval of the pressure change at adjacent time points, and divide the pressure change by the corresponding sampling time interval to obtain the pressure impact rate.

[0059] It should be noted that mold failure is not only a static tilt, but also a complex deformation under dynamic impact. This invention integrates tilt potential energy, torsional effect and pressure impact kinetic energy, which can more comprehensively and dynamically assess the risk of mold damage.

[0060] It should be noted that the four-corner dynamic balance index is used to accurately represent the degree of combined risk of tilting, twisting, and high-pressure impact on the mold during the V / P switching stage in the injection molding production of mobile phone acoustic modules, at the critical moment of filling to holding pressure. The value of the four-corner dynamic balance index directly corresponds to the level of mold damage risk. The larger the value, the more serious the tilting and twisting of the mold under instantaneous high pressure, and the higher the probability of structural damage or product quality problems.

[0061] Specifically, based on the interaction between the aggravating effect of the pressure impact rate on the eccentric loading factor and the amplifying effect of the eccentric loading factor on the harmful effect of the pressure impact rate on the mold, the dynamic balance index at the four corners is calculated, including:

[0062] It should be noted that traditional single-dimensional or simple superposition methods for mold risk assessment cannot meet the dynamic characteristics of tilting, torsion, and impact during the V / P switching phase of mobile phone acoustic module injection molding, which can easily lead to distorted risk assessment. The current four-corner dynamic balance index expression calculates tilting through a sum of squares, amplifies composite deformation through a torsional coupling term, strengthens the impact weight through a pressure impact sum of squares, and introduces... To avoid computational anomalies, composite risk nonlinearity is achieved through the product relationship of multiple effects coupled together.

[0063] The four-corner dynamic balance index satisfies the following expression:

[0064] ;

[0065] In the formula, Indicates the dynamic balance index of the four corners; Indicates the horizontal off-load factor. Indicates the vertical off-center load factor; Indicates the pressure impact rate; It represents a very small positive number, and guarantees that the denominator is not 0.

[0066] In the formula, The sum of the squares of the horizontal and vertical eccentric load factors is used to calculate the overall tilt of the mold in the horizontal and vertical directions. The larger the sum of the squares, the more severe the tilt of the mold plane. middle This indicates that when the mold is simultaneously subjected to horizontal and vertical eccentric loads, this value increases; when the mold is subjected to eccentric loads in only one direction, this value approaches 0, thus distinguishing between simple tilting and torsional deformation. Its function is to adaptively amplify the weight of the torsion effect; that is, the more severe the mold torsion, the larger the value of this item, which will ultimately enhance the system's sensitivity to the combined deformation of tilting and torsion. This indicates the V / P switching stage of injection molding for mobile phone acoustic modules. It integrates the four-corner dynamic balance comprehensive index, which combines the mold plane tilting effect, dynamic torsional coupling effect, and pressure impact intensity, to calculate the complex deformation risk of the mold under instantaneous high pressure impact.

[0067] For example, in scenario one, there is no tilting and no pressure impact. Calculations yielded This indicates that the mold is in a stable state; Scenario two exists: there is horizontal tilt but the pressure impact is low. Calculations yielded Scenario 3 exists, involving complex tilting and high-pressure impact, resulting in saddle-shaped torsion. Calculations yielded The results show that the dynamic balance index value at the four corners increases significantly with complex tilting and high-pressure impact.

[0068] S4: Construct a dynamic safety boundary based on historical data and perform fault diagnosis by combining the four-corner dynamic balance index; identify fault types and execute graded early warnings based on the fluctuation characteristics of the off-center load vector formed by the four-corner dynamic balance index and the off-center load factor; calculate the compensation amount of the injection speed based on the degree and rate of deviation between the four-corner dynamic balance index and the dynamic safety boundary, combined with the injection speed characteristics under historical healthy working conditions.

[0069] It should be noted that the risk of damage to the mold during the V / P switching stage of the mobile phone acoustic module injection molding process is obviously dynamic and cumulative. Fixed threshold fault diagnosis methods cannot meet the differences in health status under different production batches and operating conditions. Therefore, constructing a self-driven dynamic safety boundary based on historical health operation data can avoid the subjectivity of manually preset coefficients and accurately match the actual operating characteristics of the equipment.

[0070] Specifically, a dynamic safety boundary is constructed based on historical data, and fault diagnosis is performed by combining the four-corner dynamic balance index, including:

[0071] The peak values ​​of multiple four-corner dynamic balance indices within a historical healthy operating cycle are obtained to form a historical peak sequence; the mean and standard deviation of the historical peak sequence are calculated; the standard deviation of the historical peak sequence is multiplied by the coefficient of variation of the historical peak sequence, and then added to the mean of the historical peak sequence to obtain the dynamic safety boundary; the four-corner dynamic balance indices are compared with the dynamic safety boundary, and when the four-corner dynamic balance indices exceed the dynamic safety boundary, the equipment is determined to have failed.

[0072] It should be noted that mold failures during the V / P switching phase of mobile phone acoustic module injection molding exhibit significant differences between localized failures and global degradation. Determining the occurrence of a failure solely through the balance index cannot pinpoint the root cause and is insufficient for targeted maintenance. Fixed failure determination criteria also cannot meet the characteristic differences of different failures such as single-corner loosening and insufficient rigidity. Therefore, combining the peak characteristics of the balance index with the off-center load vector distribution to achieve failure classification can overcome the limitations of single failure determination and match the specific characteristics of different failure types.

[0073] Preferably, based on the fluctuation characteristics of the off-center load vector formed by the four-corner dynamic balance index and the off-center load factor, the fault type is identified and a graded early warning is executed, including:

[0074] The system monitors the off-center load vector formed by the four-corner dynamic balance index and the off-center load factor. When the four-corner dynamic balance index shows a peak exceeding the dynamic safety boundary, and the off-center load vector continuously points to a specific quadrant, the equipment fault type is determined to be single-corner looseness, and a first-level warning is triggered. When the direction of the off-center load vector is randomly distributed, the equipment fault type is determined to be insufficient overall rigidity, and a second-level warning is triggered.

[0075] It should be noted that when a warning is triggered but immediate shutdown is not required, such as in the case of a yellow warning, the system has the capability to proactively intervene by dynamically adjusting the injection molding process parameters to achieve self-healing compensation of the equipment. This invention constructs a nonlinear control model entirely driven by real-time data, realizing adaptive gain and adaptive threshold anti-adaptive control.

[0076] Preferably, based on the degree and rate of deviation between the four-corner dynamic balance index and the dynamic safety boundary, and combined with the injection speed characteristics under historical healthy operating conditions, the compensation amount for the injection speed is calculated, including:

[0077] It should be noted that the injection speed compensation during the injection filling and holding pressure stage of mobile phone acoustic modules must simultaneously meet four core requirements: risk assessment, rate response, compensation smoothness, and compliance with operating conditions. Traditional solutions have shortcomings, namely, poor adaptability of fixed thresholds, easy occurrence of process fluctuations due to linear adjustments, and inability to cope with sudden risks due to single-dimensional design. The compensation expression for injection speed is standardized by calculating the risk amplitude, incorporating the deterioration rate to ensure response speed, avoiding sudden speed changes through nonlinear adjustment, adapting to operating conditions based on historical health data, and precisely suppressing the risk of mold damage through accurate compensation direction.

[0078] The compensation amount for injection speed satisfies the following expression:

[0079] ;

[0080] In the formula, The amount of compensation for injection speed; Indicates the dynamic balance index of the four corners; Represents a dynamic safety boundary; The standard deviation of the historical peak sequence; This represents the average injection rate over a historical healthy operating cycle. The coefficient of variation of injection velocity during historical healthy operating cycles.

[0081] In the formula, This indicates the standardized deviation of the four-corner dynamic balance index from the dynamic safety boundary. Exceed The more values ​​there are, the higher this value indicates, reflecting a greater degree to which the current composite risk of the mold exceeds the safe range; The rate of change index, representing the degree of standardization deviation, is used to adjust the system's sensitivity to the rate of risk deterioration. A higher value indicates a faster rate of risk deterioration, requiring a more responsive compensation mechanism. First-order numerical differentiation is used for calculation, and the sampling step size is consistent with the sampling time interval of real-time tension and real-time injection pressure; the denominator It takes the form of a Sigmoid function, which makes the compensation amount of injection speed change smoothly and non-linearly with the degree of risk, avoiding overcompensation or response lag. Based on historical health data, it is essentially the upper limit of reasonable fluctuation in injection speed under healthy operating conditions, providing a benchmark amplitude for compensation that meets the actual operating characteristics of the equipment; the negative sign indicates that the compensation direction is opposite to the trend of risk deterioration, that is, when hour, A negative value reduces the pressure impact intensity during the V / P switching stage by decreasing the injection speed, thereby mitigating the risk of mold tilting and twisting.

[0082] For example, in scenario one, the mold is in a stable state. , , , mm / s, Calculations yielded The speed of mm / s indicates that the injection speed needs to be slightly reduced to maintain stability; in scenario two, the mold risk is slightly exceeded and slowly deteriorates. , , , , mm / s, Calculations yielded The deceleration compensation needs to be appropriately increased; in scenario three, the mold risk is severely exceeding the standard and deteriorating rapidly. , , , , mm / s, Calculations yielded The speed needs to be close to the maximum reasonable fluctuation limit (mm / s) to decelerate rapidly and suppress risk deterioration. The above calculation results... Round to one decimal place; Round to two decimal places.

[0083] This completes the fault early warning system for injection molding equipment used in the production of mobile phone acoustic modules.

[0084] This invention also discloses a fault warning system for injection molding equipment used in the production of mobile phone acoustic modules, comprising a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a fault warning method for injection molding equipment used in the production of mobile phone acoustic modules according to the present invention.

[0085] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0086] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A fault early warning method for injection molding equipment producing mobile phone acoustic modules, characterized in that, include: The system acquires real-time tension and injection pressure during the injection molding cycle, aligns and denoises them, and records them as tension sequence and injection pressure sequence, including: High-sensitivity strain sensors installed on the surfaces of four goring pillars are used to collect real-time tensile force values ​​at four locations (upper left, upper right, lower left, and lower right) during the injection molding cycle at a fixed sampling frequency, thus obtaining a real-time tensile force value sequence. The real-time injection pressure is read from the injection molding machine controller to obtain a real-time injection pressure sequence. The real-time tensile force value sequence and the real-time injection pressure sequence are aligned on the time axis, and the aligned data is denoised to obtain the tensile force sequence and the injection pressure sequence. The dynamic structural response index is calculated based on the difference between the standard deviation and the average tensile force at any given moment in the tensile force sequence, including: Calculate the standard deviation and mean of the tension values ​​at the top left, top right, bottom left, and bottom right four points at any same time in the tension sequence, and denot them as the tension standard deviation and tension mean, respectively; calculate the ratio of the tension standard deviation to the tension mean, and add 1 to the ratio of the tension standard deviation to the tension mean to obtain the dynamic structural response index characterizing the dynamic structural response state of the mold; By inferring the moment imbalance state of the mold through the tensile force distribution of the tie rod, and combining it with the dynamic structural response index, an off-center loading factor is constructed. The pressure impact rate is obtained by performing a first-order difference on the injection pressure sequence. Based on the interaction between the aggravating effect of the pressure impact rate on the off-center loading factor and the harmful effect of the off-center loading factor on the pressure impact rate on the mold, the dynamic balance index of the four corners is calculated. A dynamic safety boundary is constructed based on historical data, and fault diagnosis is performed by combining the four-corner dynamic balance index; the fault type is identified and graded early warning is executed based on the fluctuation characteristics of the off-center load vector formed by the four-corner dynamic balance index and the off-center load factor; the compensation amount of the injection speed is calculated based on the degree and rate of deviation between the four-corner dynamic balance index and the dynamic safety boundary, combined with the injection speed characteristics under historical healthy working conditions.

2. The method for fault early warning of injection molding equipment for producing mobile phone acoustic modules according to claim 1, characterized in that, The off-load factor satisfies the following expression: ； In the formula, Indicates the horizontal off-load factor. Indicates the vertical off-center load factor; This represents the tension values ​​at any four points (top left, top right, bottom left, and bottom right) at any given time in the tension sequence. Indicates the dynamic structural response index; It represents a very small positive number, and guarantees that the denominator is not 0.

3. The method for fault early warning of injection molding equipment for producing mobile phone acoustic modules according to claim 1, characterized in that, The method of obtaining the pressure impact rate includes: Perform a first-order difference operation on the injection pressure sequence to obtain the pressure change at adjacent time points, calculate the sampling time interval of the pressure change at adjacent time points, and divide the pressure change by the corresponding sampling time interval to obtain the pressure impact rate.

4. A fault early warning method for injection molding equipment producing mobile phone acoustic modules according to claim 1, characterized in that, The four-corner dynamic balance index satisfies the following expression: ； In the formula, Indicates the dynamic balance index of the four corners; Indicates the horizontal offloading factor. Indicates the vertical off-center load factor; Indicates the pressure impact rate; Represents a very small positive number, ensuring that the denominator is not zero; This represents the absolute value function.

5. A fault early warning method for injection molding equipment producing mobile phone acoustic modules according to claim 1, characterized in that, The aforementioned fault diagnosis, combined with the four-corner dynamic balance index, includes: The peak values ​​of multiple four-corner dynamic balance indices within a historical healthy operating cycle are obtained to form a historical peak sequence; the mean and standard deviation of the historical peak sequence are calculated; the standard deviation of the historical peak sequence is multiplied by the coefficient of variation of the historical peak sequence, and then added to the mean of the historical peak sequence to obtain the dynamic safety boundary; the four-corner dynamic balance indices are compared with the dynamic safety boundary, and when the four-corner dynamic balance indices exceed the dynamic safety boundary, the equipment is determined to have failed.

6. A fault early warning method for injection molding equipment producing mobile phone acoustic modules according to claim 1, characterized in that, The process of identifying fault types and executing tiered early warnings includes: The system monitors the off-center load vector formed by the four-corner dynamic balance index and the off-center load factor. When the four-corner dynamic balance index shows a peak exceeding the dynamic safety boundary, and the off-center load vector continuously points to a specific quadrant, the equipment fault type is determined to be single-corner looseness, and a first-level warning is triggered. When the direction of the off-center load vector is randomly distributed, the equipment fault type is determined to be insufficient overall rigidity, and a second-level warning is triggered.

7. A fault early warning method for injection molding equipment producing mobile phone acoustic modules according to claim 1, characterized in that, The compensation amount for the injection speed satisfies the following expression: ； In the formula, The amount of compensation for injection speed; Indicates the dynamic balance index of the four corners; Represents a dynamic safety boundary; The standard deviation of the historical peak sequence; This represents the average injection rate over a historical healthy operating cycle. The coefficient of variation of injection velocity during historical healthy operating cycles.

8. A fault early warning system for injection molding equipment producing mobile phone acoustic modules, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement a fault early warning method for injection molding equipment producing mobile phone acoustic modules according to any one of claims 1-7.

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