A method and apparatus for tracing defects in articles during a plastics forming process

Abstract

This application provides a method and apparatus for tracing the source of defects in plastic products during the plastic molding process, relating to the field of plastic molding technology. The method includes: applying a target disturbance signal to target process parameters during the operation of the plastic molding equipment; simultaneously acquiring multi-source response parameters of the plastic molding equipment; determining the target correlation degree between each target process parameter and the defect status of the plastic product based on the multi-source response parameters; constructing a target mapping relationship between a process parameter matrix and the defect status of the plastic product based on the target correlation degree; determining the target process parameter matrix corresponding to each defect status of the plastic product; and determining the source tracing result of the plastic product defect based on the types of process parameters present in the target process parameter matrix. This application solves the problem that, during the plastic molding production process, due to the complexity and mutual coupling of process parameters, it is difficult to quickly and accurately determine the cause of defects when quality defects occur in plastic products.

Description

Technical Field

[0001] This application relates to the field of plastic molding technology, and in particular to a method and apparatus for tracing the source of defects in plastic products during the plastic molding process. Background Technology

[0002] In the field of plastic molding and processing, the quality of plastic products is of paramount importance, directly affecting their performance, lifespan, and market competitiveness. However, in the actual plastic molding process, various defects often occur in the products, such as shrinkage marks, bubbles, cracks, and warping. These defects not only reduce the appearance quality of the products but may also affect their mechanical properties and functionality, leading to an increased product defect rate, increased production costs, and significant economic losses for enterprises.

[0003] Plastic molding is a complex process involving the synergistic effects of multiple process parameters, including temperature, pressure, speed, and time. These parameters are not independent but rather exhibit complex coupling relationships. For example, in injection molding, melt temperature affects the fluidity of the plastic, while injection pressure and speed are closely related to the filling of the melt within the mold cavity. This coupling between multiple parameters makes the plastic molding process highly nonlinear and uncertain. A slight change in any one process parameter can trigger a chain reaction in other parameters through coupling effects, ultimately leading to defects in the finished product.

[0004] Currently, traditional methods for analyzing defects in plastic products mainly rely on experience-based judgment and simple statistical analysis. Experience-based judgment methods typically rely on the long-term accumulated experience of operators, subjectively assessing phenomena observed during the molding process and the appearance defects of the finished product to infer possible causes. However, this method is limited by individual experience and knowledge, lacks scientific rigor and systematic approach, and often struggles to accurately trace the source of some complex defects. Summary of the Invention

[0005] This application provides a method and apparatus for tracing the source of defects in plastic products during the plastic molding process. It solves the problem that, due to the complexity and mutual coupling of process parameters in the plastic molding production process, it is difficult to quickly and accurately determine the cause of defects when quality defects occur in plastic products.

[0006] The first aspect of this application provides a method for tracing the source of defects in plastic products during the molding process. Applied to plastic molding equipment, the method includes: generating a target disturbance signal based on the coupling strength of various target process parameters during the operation of the plastic molding equipment; applying the target disturbance signal to the target process parameters during the operation of the plastic molding equipment; simultaneously acquiring multi-source response parameters of the plastic molding equipment, the multi-source response parameters including at least one of real-time values ​​of process parameters, quality characteristics of plastic products, and equipment status data; determining the target correlation degree between each target process parameter and the defect status of the plastic product based on the multi-source response parameters; and constructing a target mapping relationship between a process parameter matrix and the defect status of the plastic product based on the target correlation degree, wherein each defect status of the plastic product corresponds to multiple process parameter matrices; filtering the mapping relationship between the process parameter matrix and the defect status of the plastic product based on the constraints of the plastic molding equipment under operating conditions, and determining the target process parameter matrix corresponding to each defect status of the plastic product; and determining the source tracing result of the plastic product defects based on the types of process parameters present in the target process parameter matrix.

[0007] Optionally, based on the coupling strength of each target process parameter during the operation of the plastic molding equipment, a target disturbance signal is generated, including: constructing a parameter coupling matrix based on the coupling strength between each target process parameter, where the elements in the parameter coupling matrix represent the coupling strength between two target process parameters; configuring a corresponding initial disturbance period and initial disturbance intensity for each target process parameter; adjusting the initial disturbance period and initial disturbance intensity corresponding to each target process parameter based on the parameter coupling matrix to obtain the target disturbance period and target disturbance intensity corresponding to each target process parameter; and using the target disturbance period and target disturbance intensity as time-domain and frequency-domain signals, respectively, to obtain the target disturbance signal.

[0008] Optionally, the initial disturbance period and initial disturbance intensity are configured for each target process parameter, including: for any target process parameter, obtaining the target process step in which the target process parameter has an effect within the operating cycle of the plastic molding equipment; determining the initial disturbance period corresponding to the target process parameter based on the target process step; and determining the initial disturbance intensity based on the duration of the initial disturbance period corresponding to any target process parameter; wherein, the initial disturbance intensity increases as the duration of the initial disturbance period increases; the operating cycle of the plastic molding equipment represents the time period required to produce a target plastic product.

[0009] Optionally, based on the parameter coupling matrix, the initial disturbance period and initial disturbance intensity corresponding to each target process parameter are adjusted to obtain the target disturbance period and target disturbance intensity corresponding to each target process parameter. This includes: for any element in the parameter coupling matrix corresponding to a first target process parameter and a second target process parameter, respectively obtaining the first initial disturbance period corresponding to the first target process parameter and the second initial disturbance period corresponding to the second target process parameter; obtaining the temporal overlap relationship between the first initial disturbance period and the second initial disturbance period; adjusting the initial disturbance intensity of the first target process parameter based on the temporal overlap relationship and the coupling strength between the first target process parameter and the second target process parameter to obtain the target disturbance intensity corresponding to the first target process parameter; and / or, adjusting the initial disturbance period of the first target process parameter based on the temporal overlap relationship to obtain the target disturbance period corresponding to the first target process parameter.

[0010] Optionally, based on multi-source response parameters, the target correlation degree between each target process parameter and the defect status of the plastic product is determined, including: constructing the temporal relationship between real-time values ​​of process parameters, quality characteristics of plastic products, and equipment status data; obtaining the target time period in which the change amplitude of equipment status data is less than the preset change amplitude; calculating the correlation degree between the change of real-time values ​​of process parameters and the change of quality characteristics of plastic products within the target time period, and obtaining the target correlation degree between each target process parameter and the defect status of the plastic product; wherein, the defect status of the plastic product is determined based on the quality characteristics of the plastic product.

[0011] Optionally, based on the degree of target correlation, a target mapping relationship between the process parameter matrix and the defect status of plastic products is constructed, including: determining at least two target process parameters corresponding to each type of plastic product defect status based on the degree of target correlation; obtaining the parameter variation range of at least two target process parameters during the operation of the plastic molding equipment; and generating multiple process parameter matrices corresponding to each type of plastic product defect status based on the parameter variation range, wherein the values ​​of each process parameter in the process parameter matrix are within the parameter variation range.

[0012] Optionally, based on the constraints of the plastic molding equipment in operation, the mapping relationship between the process parameter matrix and the defects of the plastic products is screened to determine the target process parameter matrix corresponding to each defect of the plastic products. This includes: obtaining the constraints of the plastic molding equipment in operation, which include a first constraint and a second constraint; wherein the first constraint is determined based on a pre-configured range of process parameter variations, and the second constraint is determined based on the hardware configuration of the plastic molding equipment.

[0013] A second aspect of this application provides a device for tracing defects in plastic products during the molding process, the method comprising: The signal generation module is used to generate target disturbance signals based on the coupling strength of various target process parameters during the operation of the plastic molding equipment. The parameter acquisition module is used to apply the target disturbance signal to the target process parameters during the operation of the plastic molding equipment; at the same time, it acquires the multi-source response parameters of the plastic molding equipment, which include at least one of the real-time values ​​of process parameters, quality characteristics of plastic products, and equipment status data. The parameter processing module is used to determine the degree of correlation between each target process parameter and the defect status of plastic products based on multi-source response parameters; and to construct the target mapping relationship between the process parameter matrix and the defect status of plastic products based on the degree of correlation, wherein each defect status of plastic products corresponds to multiple process parameter matrices; The matrix filtering module is used to filter the mapping relationship between the process parameter matrix and the defects of plastic products based on the constraints of the plastic molding equipment under operating conditions, and to determine the target process parameter matrix corresponding to each defect of plastic products. The matrix analysis module is used to determine the source of defects in plastic products based on the types of process parameters present in the target process parameter matrix.

[0014] A third aspect of this application provides an electronic device including a processor, a communication bus, a user interface, a network interface, and a memory. The memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, the communication bus is used to realize the connection and communication between the components within the electronic device, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described above.

[0015] In a fourth aspect, this application provides a non-transitory computer-readable storage medium storing instructions that, when executed, perform the method described in any of the foregoing descriptions.

[0016] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By adopting the above method, a target disturbance signal is generated based on the coupling characteristics of each target process parameter during the operation of the plastic molding equipment, and applied to the process parameters. At the same time, multi-source response parameters are obtained to determine the correlation between each process parameter and the defects of the plastic product. This allows for the accurate determination of the target correlation between each target process parameter and the defect of the plastic product, thereby constructing an accurate mapping relationship and ultimately achieving accurate source tracing of defects in the plastic product.

[0017] 2. By determining the correlation between process parameters and product defects, and constructing and screening accurate mapping relationships, key process parameters that cause defects in plastic products can be quickly located. This allows manufacturers to adjust their production processes accordingly, avoiding blind investigation and trial and error, effectively reducing production stagnation and waste caused by product defects, and significantly improving production efficiency.

[0018] 3. This application takes into account the coupling characteristics of process parameters and the constraints under the equipment operating conditions, generates disturbance signals and obtains multi-source response parameters, which can adapt to the high nonlinearity and uncertainty in the plastic molding process, so that the cause of defects can still be accurately determined in complex and ever-changing production environments. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a method for tracing the source of defects in plastic products during the molding process, as provided in an embodiment of this application. Figure 2 This is a schematic diagram of a module for tracing the source of defects in plastic products during the molding process, provided in an embodiment of this application. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0020] Explanation of reference numerals in the attached figures: 21. Acquisition module; 22. Processing module; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0022] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0023] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0024] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0025] Please refer to Figure 1 The flowchart illustrates a multimedia video streaming method provided in an embodiment of this application. The flowchart mainly includes the following steps: steps S101 to S105.

[0026] Step S101: Based on the coupling strength of each target process parameter during the operation of the plastic molding equipment, generate a target disturbance signal; In the embodiments of this application, plastic molding equipment is equipment used to process plastic raw materials into products of desired shapes through specific processes, including but not limited to injection molding machines, extruders, blow molding machines, etc.

[0027] In the embodiments of this application, the target process parameters are process parameters that have a significant impact on the quality of the product during the plastic molding process and that need to be closely monitored and controlled, such as temperature, pressure, speed, and time.

[0028] In the embodiments of this application, the target disturbance signal is generated based on the coupling strength of the target process parameters and is used to apply to the target process parameters during the operation of the plastic molding equipment to observe the equipment response and changes in product quality.

[0029] Specifically, the coupling strength between process parameters reflects the degree of mutual influence between parameters. In this application, the coupling strength between each process parameter is determined by conducting parameter correlation analysis in advance. The coupling strength between the i-th process parameter and the j-th process parameter can be determined based on the following formula:

[0030] in, The first step in the process of characterizing plastic molding equipment One step, It is in the In each step, the ratio of the i-th process parameter to the j-th process parameter after numerical normalization is... It is the first Numerical weights at each step It is the distance weight between the i-th process parameter and the j-th process parameter, reflecting the difference in the distribution of the i-th process parameter and the j-th process parameter in all process steps; For example, if the i-th process parameter and the j-th process parameter are involved in all process steps of a plastic molding equipment, then the distance weight between the i-th process parameter and the j-th process parameter is... The value is 0. Conversely, the greater the difference in the distribution of process parameters across all process steps, the greater the distance weight. The larger.

[0031] Furthermore, after determining the coupling strength between various process parameters, a parameter coupling matrix is ​​constructed to quantify the coupling strength between parameters. Initial disturbance periods and intensities are configured for each parameter, and adjustments are made based on the coupling matrix to ultimately obtain the target disturbance signal. Based on the inherent relationship between process parameters, the generated disturbance signal becomes more targeted and reasonable, contributing to a more accurate analysis of the impact of process parameters on the quality of plastic products.

[0032] Step S102: During the operation of the plastic molding equipment, the target disturbance signal is applied to the target process parameters; at the same time, the multi-source response parameters of the plastic molding equipment are acquired, including at least one of the real-time values ​​of process parameters, quality characteristics of plastic products, and equipment status data. In the embodiments of this application, the multi-source response parameters are various parameters related to equipment operation and product quality acquired during the operation of the plastic molding equipment, including real-time values ​​of process parameters, such as actual temperature and pressure, plastic product quality characteristics, such as dimensional accuracy and surface defects, and equipment status data, such as mold temperature and motor speed.

[0033] Specifically, after determining the target disturbance signal to be applied to the plastic molding equipment, the target disturbance signal corresponding to each target process parameter is obtained. During the operation of the plastic molding equipment, if one of the process steps involves a certain target process parameter, the target disturbance signal corresponding to the target process parameter is applied to the target process parameter. In this way, by actively introducing controllable disturbances, the sensitivity of the parameters to the output results and the interaction between parameters are quantified.

[0034] Step S103: Based on the multi-source response parameters, determine the degree of target correlation between each target process parameter and the defect status of the plastic product; and based on the degree of target correlation, construct the target mapping relationship between the process parameter matrix and the defect status of the plastic product, wherein each defect status of the plastic product corresponds to multiple process parameter matrices. In the embodiments of this application, the process parameter matrix is ​​a matrix composed of target process parameters related to the defect status of a certain plastic product, used to represent the relationship between different combinations of process parameters and defects.

[0035] In the embodiments of this application, the target mapping relationship is the correspondence between the process parameter matrix and the defect status of the plastic product, and each defect status of the plastic product corresponds to multiple process parameter matrices.

[0036] Specifically, real-time values ​​of process parameters, quality characteristics of plastic products, and equipment status data all change over time. First, the multi-source response parameters are arranged chronologically to establish their temporal relationships. This allows for subsequent analysis of the interrelationships between these parameters on the same time scale. For example, recording the temperature at a specific moment (i.e., the real-time value of the process parameter), whether defects appear on the surface of the plastic product, and whether a component of the equipment is operating normally. This allows for a clear visual observation of how different types of parameters influence and correlate with each other over time. Since significant changes in equipment status data can interfere with the analysis of the relationship between process parameters and the quality characteristics of plastic products, a preset variation range needs to be set. For example, a temperature change exceeding 10°C or a pressure change exceeding 5 MPa can be considered a significant change. This preset range can be determined based on actual equipment operation and experience. Then, time periods where the variation range of equipment status data is less than this preset range are selected as the target time period. Within this target time period, the equipment is considered to be in a relatively stable operating state, thus eliminating the interference of significant fluctuations in equipment status on the correlation analysis between process parameters and the quality characteristics of plastic products. For example, during the injection molding process, a period of time when the screw speed, temperature, and other conditions of the equipment are relatively stable can be used as a target time period to study the relationship between process parameters and defects in plastic products.

[0037] For example, within a defined target time period, the changes in real-time values ​​of process parameters and the changes in quality characteristics of plastic products are analyzed. For real-time values ​​of process parameters, the amount of change and rate of change, etc., are calculated within the target time period. For example, if the temperature increases from an initial 180℃ to 200℃, the change is 20℃, and the rate of change is calculated based on the time span. For quality characteristics of plastic products, the corresponding changes are calculated based on their quantitative indicators, such as shrinkage depth, number of bubbles, and dimensional deviations. For example, the shrinkage depth increases from 0.1mm to 0.3mm.

[0038] Furthermore, correlation analysis was used to calculate the degree of correlation between changes in real-time process parameters and changes in the quality characteristics of plastic products. Correlation analysis yields a correlation coefficient between -1 and 1; the closer the absolute value of the correlation coefficient is to 1, the stronger the correlation between the two. A positive correlation coefficient indicates that the changes in real-time process parameters and the changes in the quality characteristics of plastic products follow the same trend; a negative correlation coefficient indicates the opposite trend.

[0039] Using the above method, the degree of correlation between each target process parameter and the defects in plastic products can be obtained, that is, the degree of correlation between each target process parameter and defects such as shrinkage marks and bubbles in plastic products can be determined.

[0040] In the embodiments of this application, after obtaining the target correlation degree, a target mapping relationship between the process parameter matrix and the defects of the plastic product is constructed. For example, based on the target correlation degree, it is determined that a certain defect of the plastic product has the highest correlation degree with process parameters A and B. Multiple process parameter matrices including process parameters A and B are obtained, with different values ​​for process parameters A and B in each matrix, thereby constructing a target mapping relationship between the process parameter matrix and the defects of the plastic product. As another example, in the target correlation degree, if the correlation degree between process parameters A, B, and C and a certain defect of the plastic product is ranked as the top three, the process parameter matrix corresponding to that defect of the plastic product must include at least one of process parameters A, B, or C.

[0041] For example, if product warpage defects are caused by injection stage temperature (T1) or holding stage pressure (P2), then the matrix of multiple process parameters includes: Process parameter matrix 1, {T1=230℃, P2=80MPa}; Process parameter matrix 2, {T1=220℃, P2=90MPa}; Process parameter matrix 3, {T1=240℃, P2=70MPa}.

[0042] Step S104: Based on the constraints of the plastic molding equipment in operation, the mapping relationship between the process parameter matrix and the defects of the plastic products is screened to determine the target process parameter matrix corresponding to each defect of the plastic products. In the embodiments of this application, constraints are conditions that the plastic molding equipment needs to meet during operation, including constraints determined based on pre-configured ranges of process parameter variations and constraints determined based on equipment hardware configuration. After constraint filtering, a target process parameter matrix corresponding to each plastic product defect is obtained.

[0043] Step S105: Based on the types of process parameters present in the target process parameter matrix, determine the source tracing results of defects in plastic products.

[0044] Specifically, the causes of defects in plastic products are determined based on the types of process parameters present in the target process parameter matrix. For example, after screening the process parameter matrix, the target process parameter matrix for each type of plastic product defect includes the types and values ​​of the target process parameters. Based on these types and values ​​of the target process parameters, the causes of the defects in plastic products can be traced and analyzed.

[0045] In the embodiments of this application, a type of defect in a plastic product corresponds to a target process parameter matrix. Based on the data in the target process parameter matrix, the causes of defects and ways to improve them can be analyzed in a targeted manner. This application does not specifically limit the specific application of the data involved in the target process parameter matrix.

[0046] For example, the target process parameter matrix corresponding to the shrinkage mark defect is as follows: Temperature: 180℃ to 200℃; Pressure: 4MPa-5MPa; Holding time: 15s-20s.

[0047] Analysis of the types and values ​​of these process parameters yields the following tracing results: 1. The viscosity of plastics is quite sensitive to temperature. When the temperature is in the range of 180℃ to 200℃, the temperature is relatively low, which may cause the viscosity of the plastic melt to be higher and the fluidity to be worse.

[0048] The pressure range of 2.4MPa to 5MPa is relatively low. During the injection molding process, the lower pressure cannot provide enough power to allow the melt to fill the mold quickly and fully. This is especially true for some plastic products with complex shapes or uneven wall thicknesses. This can lead to insufficient filling in some areas, resulting in shrinkage marks after cooling.

[0049] 3. The holding time is 15-20 seconds, which is relatively short. The purpose of the holding stage is to continuously replenish the melt in the mold cavity. The short holding time means that the melt in the mold cavity cannot be replenished in time during the cooling and shrinkage of the plastic product, which leads to the formation of shrinkage marks on the surface.

[0050] It should be noted that the traceability results may differ depending on the specific application of the data involved in the target process parameter matrix. In practical applications, equipment managers can apply the data involved in the target process parameter matrix to different equipment improvement directions according to specific needs.

[0051] By employing the above method, a perturbation signal is generated based on the coupling characteristics of the target process parameters and applied to the process parameters. Simultaneously, multi-source response parameters are acquired, enabling a comprehensive capture of the dynamic changes and interactions of various parameters during plastic molding. Based on this, the correlation between process parameters and product defects is determined, a mapping relationship is constructed and filtered, ultimately achieving accurate defect tracing.

[0052] In one possible implementation, in step S101, a target disturbance signal is generated based on the coupling strength of each target process parameter during the operation of the plastic molding equipment. This includes: constructing a parameter coupling matrix based on the coupling strength between each target process parameter, where each element in the parameter coupling matrix represents the coupling strength between two target process parameters; configuring a corresponding initial disturbance period and initial disturbance intensity for each target process parameter; adjusting the initial disturbance period and initial disturbance intensity corresponding to each target process parameter based on the parameter coupling matrix to obtain the target disturbance period and target disturbance intensity corresponding to each target process parameter; and using the target disturbance period and target disturbance intensity as time-domain and frequency-domain signals, respectively, to obtain the target disturbance signal.

[0053] Specifically, a parameter coupling matrix is ​​constructed to quantify the coupling strength between various target process parameters, providing a basis for subsequent adjustments to the disturbance duration and intensity. First, the initial disturbance duration and intensity are configured, then adjusted according to the parameter coupling matrix. Finally, the adjusted duration and intensity are converted into target disturbance signals, making the generated signals more accurately reflect the coupling relationship between process parameters and improving the accuracy of subsequent source tracing.

[0054] By employing the above method, a parameter coupling matrix is ​​constructed to quantify the coupling strength between various target process parameters. Initial disturbance periods and intensities are configured for each target process parameter, and adjustments are made based on the parameter coupling matrix. This allows the generated disturbance signals to more accurately simulate the interaction of process parameters in actual production and to more effectively observe the impact of process parameter changes on the quality of plastic products. In one possible implementation, configuring corresponding initial disturbance period and initial disturbance intensity for each target process parameter includes: for any target process parameter, obtaining the target process step in which the target process parameter has an effect within the operating cycle of the plastic molding equipment; determining the initial disturbance period corresponding to the target process parameter based on the target process step; and determining the initial disturbance intensity for any target process parameter based on the duration of the initial disturbance period corresponding to the target process parameter. Among them, the initial disturbance intensity increases with the increase of the initial disturbance period; the operating cycle of the plastic molding equipment characterizes the time required to produce a target plastic product.

[0055] Specifically, for each target process parameter, the specific process stage within the operating cycle of the plastic molding equipment where that parameter takes effect needs to be identified. This process stage is then determined as the initial disturbance period corresponding to that parameter. For example, in the plastic molding process, the temperature parameter is effective during the raw material melting and injection filling stages; therefore, the time range of this stage is the initial disturbance period for the temperature parameter. Next, the initial disturbance intensity is determined based on the length of the determined initial disturbance period. As the length of the initial disturbance period increases, the initial disturbance intensity also increases accordingly. For example, assuming a proportional relationship, such as the disturbance intensity increasing by a fixed coefficient for every certain increase in duration, the initial disturbance intensity for each target process parameter can be determined in this way.

[0056] By adopting the above method, the disturbance of process parameters is closely related to the actual process, which enables a more targeted analysis of the impact of process parameter changes on the quality of plastic products.

[0057] In one possible implementation, based on the parameter coupling matrix, the initial disturbance period and initial disturbance intensity corresponding to each target process parameter are adjusted to obtain the target disturbance period and target disturbance intensity corresponding to each target process parameter. This includes: for any element in the parameter coupling matrix corresponding to a first target process parameter and a second target process parameter, respectively obtaining the first initial disturbance period corresponding to the first target process parameter and the second initial disturbance period corresponding to the second target process parameter; obtaining the time overlap relationship between the first initial disturbance period and the second initial disturbance period; adjusting the initial disturbance intensity of the first target process parameter based on the time overlap relationship and the coupling strength between the first target process parameter and the second target process parameter to obtain the target disturbance intensity corresponding to the first target process parameter; and / or, adjusting the initial disturbance period of the first target process parameter based on the time overlap relationship to obtain the target disturbance period corresponding to the first target process parameter.

[0058] Specifically, for any two target process parameters in the parameter coupling matrix, designated as the first and second target process parameters, the initial disturbance periods for each are first obtained. For example, for the process parameters of temperature and pressure, the initial disturbance periods for temperature and pressure are obtained respectively. Then, the temporal overlap relationship between the two initial disturbance periods is analyzed to determine their temporal overlap, such as complete overlap, partial overlap, or no overlap. Based on the temporal overlap relationship and the coupling strength between the two target process parameters as reflected in the parameter coupling matrix, the initial disturbance strength of the first target process parameter is adjusted. For example, if the coupling strength between the two parameters is high and the overlap time is long, the initial disturbance strength of the first target process parameter may be increased significantly; conversely, if the coupling strength is low or the overlap time is short, the adjustment will be smaller. Simultaneously, based on the temporal overlap relationship, the initial disturbance period of the first target process parameter is adjusted, for example, by fine-tuning the start or end time of the initial disturbance period according to the proportion of the overlapping portion in the initial disturbance period.

[0059] By adopting the above method, the influence of the coupling characteristics between process parameters on the disturbance setting is fully considered, so that the final target disturbance period and target disturbance intensity are more in line with the actual interaction relationship between process parameters, which helps to study the influence of process parameters on defects in plastic products more accurately.

[0060] The following is a detailed implementation example of determining the target disturbance signal S.

[0061] For example, the coupling strength between temperature and pressure is 0.8, the coupling strength between temperature and velocity is 0.6, and the coupling strength between pressure and velocity is 0.7. Then the parameter coupling matrix C can be expressed as:

[0062]

[0063] The elements on the diagonal of matrix C are 1, which means that the coupling strength between itself is 1. The other elements represent the coupling strength between different process parameters.

[0064] If the operating cycle of plastic molding equipment It lasts for 60 seconds.

[0065] Temperature primarily plays a role in the melting and injection filling stages of the plastic raw material. Assuming this stage begins at the 10th second and ends at the 30th second, the initial temperature disturbance period is obtained. The duration is [10, 30] seconds. This is based on the initial disturbance period length Δ. =30-10=20 seconds, assuming the initial disturbance strength It is directly proportional to the length of time, with a proportionality constant of 1. =0.05, the proportionality coefficient can be adjusted according to the actual situation. = ×Δ =0.05×20=1.

[0066] Among them, injection pressure is more important in the injection filling and holding pressure stages. Assuming that the initial pressure disturbance period starts from the 20th second and ends at the 40th second... The duration is [20, 40] seconds. The initial disturbance period length Δ =40-20=20 seconds. Following the same proportional relationship, the initial pressure disturbance intensity... = ×Δ =0.05×20=1.

[0067] Among these factors, the critical speed has an impact throughout the entire injection molding process. Assuming the initial speed disturbance period begins at second 10 and ends at second 50, this is the key data point. The duration is [10, 50] seconds. The initial disturbance period length Δ =50-10=40 seconds, therefore the initial disturbance intensity of velocity = ×Δ =0.05×40=2.

[0068] Based on the parameter coupling matrix, adjust the initial disturbance period and initial disturbance intensity corresponding to each target process parameter: Take temperature and pressure as examples: First, obtain the initial temperature disturbance period. =[10,30] seconds and the second initial disturbance period of pressure =[20,40] seconds, their time overlap relationship can be represented by calculating the overlap time length.

[0069] The overlap time period is [20, 30] seconds, and the overlap time length is Δ. =30-20=10 seconds.

[0070] Based on the time overlap relationship and the coupling strength between temperature and pressure =0.8 to adjust the initial temperature disturbance strength. The adjustment formula is:

[0071] = +α× ×

[0072] Where α is the adjustment coefficient, set to 0.5 (which can be adjusted according to the actual situation).

[0073] The target disturbance intensity of the adjusted temperature =1+0.5×0.8×10 / 20=1+0.2=1.2.

[0074] Simultaneously, the initial temperature disturbance period can also be adjusted based on the time overlap relationship. Assuming the adjustment rule is to fine-tune the start time based on the proportion of the overlap time in the two initial disturbance periods, let the fine-tuning coefficient be β=0.2.

[0075] The overlap time accounts for the proportion of the initial temperature disturbance period. 10 / 20=0.5, therefore the starting time of the target disturbance period for the adjusted temperature is: =10+β× =10 + 0.2 × 10 = 12; End time is: =30-β× =30-0.2×10=28; That is, the target disturbance period =[12,28] seconds.

[0076] Pressure and speed, temperature and speed are adjusted in a similar manner to obtain the target disturbance period and target disturbance intensity corresponding to each target process parameter.

[0077] Finally, the target perturbation period of the adjusted temperature will be... Target disturbance period of pressure Target disturbance period of speed Combined as a time-domain signal .

[0078] The target perturbation intensity of temperature Target disturbance intensity of pressure Target disturbance intensity of velocity Combined as a frequency domain signal .

[0079] Finally, the target disturbance signal S is obtained as ( ).

[0080] In one possible implementation, in step S103, the degree of correlation between each target process parameter and the defect status of the plastic product is determined based on the multi-source response parameters, including steps S201 to S203.

[0081] Step S201: Construct the time sequence relationship between real-time values ​​of process parameters, quality characteristics of plastic products, and equipment status data.

[0082] Specifically, during the operation of the plastic molding equipment, multi-source response parameters are collected at regular time intervals. Real-time values ​​of process parameters and quality characteristics of the plastic products are recorded at each collection point, such as the number of surface defects and dimensional deviations obtained through online inspection equipment, as well as equipment status data. This results in a dataset arranged chronologically, showing the changes of each parameter over time and establishing the temporal relationships between the multi-source response parameters.

[0083] For example, in the first second, the temperature is 200°C, the pressure is 5MPa, the injection speed is 30mm / s, a minor defect is detected on the surface of the plastic product, the operating temperature of a key component of the equipment is 40°C, and the vibration amplitude is within the normal range; in the second second, the parameters change accordingly, and so on, recording the data of the entire molding process.

[0084] Step S202: Obtain the target time period in which the change in device status data is less than the preset change range.

[0085] Specifically, based on the equipment's characteristics and experience, preset variation ranges for equipment status data are set. For example, the preset variation range for the operating temperature of a key component is ±5℃, and the preset variation range for vibration amplitude is ±10%. From the recorded equipment status data, time periods are selected where the operating temperature of the key component varies between 35℃ and 45℃, and the vibration amplitude variation is within ±10% of the normal range. For example, after selection, the period from the 10th second to the 30th second is chosen as the target time period. During this period, the equipment status is relatively stable and can be used to analyze the correlation between real-time values ​​of process parameters and the quality characteristics of plastic products.

[0086] Step S203: Calculate the correlation between the changes in real-time values ​​of process parameters and the changes in the quality characteristics of plastic products within the target time period, and obtain the target correlation between each target process parameter and the defect status of plastic products; wherein, the defect status of plastic products is determined based on the quality characteristics of plastic products.

[0087] Specifically, within the target time period from the 10th to the 30th second, the changes in each process parameter are calculated. For example, if the temperature rises from 200℃ in the 10th second to 210℃ in the 30th second, the temperature change is 10℃, and the average temperature change rate per second is 10℃÷(30-10)=0.5℃ / s; if the pressure decreases from 5MPa in the 10th second to 4.8MPa in the 30th second, the pressure change is 4.8-=-0.2MPa, and the average pressure change rate per second is -0.2MPa÷(30-10)=-0.01MPa / s; if the injection speed increases from 30mm / s in the 10th second to 32mm / s in the 30th second, the injection speed change is 32-30=2mm / s, and the average injection speed change rate per second is 2mm / s÷(30-10)=0.1mm / s.

[0088] Similarly, within the target time period from the 10th to the 30th second, we analyze the changes in the quality characteristics of the plastic product. Assume the number of surface defects on the plastic product increases from 2 at the 10th second to 5 at the 30th second, a change of 3 defects; and the length of the plastic product increases from 100mm at the 10th second to 100.5mm at the 30th second, a change of 0.5mm (100.5 - 100 = 0.5mm).

[0089] In the embodiments of this application, the Pearson correlation coefficient can be used to calculate the degree of correlation between real-time changes in process parameters and changes in the quality characteristics of plastic products. Taking temperature and the number of surface defects in plastic products as an example, the temperature change rate sequence and the defect number change sequence are substituted into the Pearson correlation coefficient formula:

[0090]

[0091] Among them, among them, For each value in the temperature change rate sequence, This represents the average rate of temperature change. For each value in the defect quantity variation sequence, is the average value of the change in the number of defects, and n is the number of data points.

[0092] Similarly, the correlation coefficients between pressure, injection speed and quality characteristics of plastic products are calculated to determine the degree of correlation between each target process parameter and the defects of plastic products.

[0093] By employing the above method, a time-series relationship of multi-source response parameters is constructed. A target time period with relatively stable equipment status is selected to calculate the correlation between real-time process parameter values ​​and changes in the quality characteristics of plastic products. Constructing this time-series relationship allows for a comprehensive observation of the dynamic changes of each parameter and their interrelationships over time. Selecting a target time period where the amplitude of equipment status changes is less than a preset value effectively eliminates the interference of large fluctuations in equipment status on the correlation analysis between process parameters and product quality characteristics, making the analysis results more reflective of the impact of process parameters themselves on product quality.

[0094] In one possible implementation, step S103, based on multi-source response parameters, determines the target correlation degree between each target process parameter and the defect status of the plastic product, and further includes: Through collaborative change indicators To measure process parameters The degree of co-change between the quality characteristic Q of the plastic product and time t.

[0095] Based on device status data To investigate the influence of process parameters on the quality characteristics of plastic products, a weighting function w( w(S(t)) is a function of the device status data, and 0 ≤ w(S(t)) ≤ 1. When the device is in good condition, w( When the equipment is in poor condition, w(S(t)) approaches 1; when the equipment is in poor condition, w(S(t)) approaches 0. It is determined by the following formula:

[0096] =

[0097] Where k is a constant, It is a reference value for device status data, which is adjusted by k and To adapt to the impact of different device states on the degree of correlation.

[0098] Cooperative change indicators The calculation formula is as follows:

[0099] in, and These are the process parameters and quality characteristics of plastic products The normalized value in the time interval Rate of change within.

[0100] For each process parameter during the entire observation period T Co-change indicators By integrating, the degree of synergistic change between the process parameter and the quality characteristics of the plastic product is obtained. The formula is as follows:

[0101] Finally, the degree of comprehensive and coordinated change will be considered. Transformed into target relevance , That is, the target process parameters The degree of correlation with the target of plastic product defects The higher the value, the greater the correlation between the process parameter and the defects in the plastic product.

[0102] In one possible implementation, in step S103, based on the degree of target correlation, a target mapping relationship between the process parameter matrix and the defect status of the plastic product is constructed, including: based on the degree of target correlation, determining at least two target process parameters corresponding to each defect status of the plastic product; during the operation of the plastic molding equipment, obtaining the parameter variation range of at least two target process parameters; based on the parameter variation range, generating multiple process parameter matrices corresponding to each defect status of the plastic product, wherein the values ​​of each process parameter in the process parameter matrix are within the parameter variation range.

[0103] Specifically, firstly, based on the correlation between each target process parameter and the defect status of plastic products, at least two target process parameters with a high correlation to each type of defect status are selected. Then, the process parameters that have a more significant impact on the defect status of plastic products are determined, so as to more accurately analyze the relationship between these parameters and defects in subsequent analyses.

[0104] Furthermore, during the operation of the plastic molding equipment, the range of parameter variation is determined for the target process parameters corresponding to each type of plastic product defect. This range reflects the potential fluctuation range of these target process parameters under actual production conditions; combinations of parameters within this range may lead to corresponding plastic product defects. Finally, based on the determined parameter ranges, multiple process parameter matrices are generated for each type of plastic product defect. During generation, a series of values ​​are selected from the range of variation of each target process parameter, and these values ​​are then combined to form different process parameter matrices. Each process parameter matrix represents a combination of process parameters that may lead to the defect in that plastic product; that is, the values ​​of each process parameter in the matrix are all within their respective ranges of variation.

[0105] For example, for shrinkage defects, temperature and pressure are highly correlated with shrinkage defects; for bubble defects, temperature and injection speed are highly correlated with bubble defects.

[0106] Based on historical production data, it was determined that plastic products are prone to shrinkage defects when the temperature varies between 180℃ and 220℃ and the pressure varies between 4MPa and 6MPa. Therefore, the temperature range for shrinkage defects is 180℃ to 220℃, and the pressure range is [4MPa, 6MPa].

[0107] Similarly, when the temperature is between 190℃ and 210℃ and the injection speed is between 20mm / s and 40mm / s, plastic products are prone to bubble defects. Therefore, the temperature parameter range corresponding to bubble defects is 190℃ to 210℃, and the injection speed parameter range is [20mm / s, 40mm / s].

[0108] Discrete values ​​are selected from the temperature range of 180℃ to 220℃, such as 180℃, 200℃, and 220℃; discrete values ​​are also selected from the pressure range of [4MPa, 6MPa], such as 4MPa, 5MPa, and 6MPa. These discrete values ​​are then combined to generate a process parameter matrix. For example, the process parameter matrix could be:

[0109]

[0110] By adopting the above method, a correspondence between the process parameter matrix and the defects of plastic products is established, providing a key mapping basis for subsequent tracing of defects in plastic products. This helps to determine the possible causes of defects in specific plastic products by analyzing the combination of process parameters.

[0111] In one possible implementation, in step S105, based on the constraints of the plastic molding equipment in operation, the mapping relationship between the process parameter matrix and the defects of the plastic products is screened to determine the target process parameter matrix corresponding to each defect of the plastic products. This includes: obtaining the constraints of the plastic molding equipment in operation, the constraints including a first constraint and a second constraint; wherein the first constraint is determined based on a pre-configured range of process parameter variations, and the second constraint is determined based on the hardware configuration of the plastic molding equipment.

[0112] In the embodiments of this application, the first constraint is determined based on a pre-configured range of process parameter variations. During plastic molding, each process parameter has a reasonable value range, determined by factors such as the properties of the plastic material, molding process requirements, and product quality standards. For example, excessively high or low temperatures may prevent the plastic from molding properly or cause serious defects, while pressure exceeding a certain range may damage the mold or affect the density of the product. The pre-set range of process parameter variations constitutes the first constraint, providing fundamental limitations on the parameter values ​​in the process parameter matrix.

[0113] In the embodiments of this application, the second constraint is determined based on the hardware configuration of the plastic molding equipment. The hardware performance of the equipment imposes practical limitations on the process parameters. For example, the heating system of the equipment may not be able to push the temperature above a certain upper limit, the power of the injection molding device limits the maximum values ​​of pressure and speed, and the capacity of the cooling system determines the adjustable range of cooling time and temperature, etc. The constraints determined by the equipment hardware configuration form the second constraint, further constraining the range of possible values ​​for the parameters in the process parameter matrix.

[0114] Please refer to Figure 2 This document illustrates a schematic diagram of a device for tracing defects in plastic products during the molding process, as provided in an embodiment of this application. The device includes a signal generation module 21, a parameter acquisition module 22, a parameter processing module 23, a matrix filtering module 24, and a matrix analysis module 25. The signal generation module 21 is used to generate target disturbance signals based on the coupling strength of various target process parameters during the operation of the plastic molding equipment. The parameter acquisition module 22 is used to apply the target disturbance signal to the target process parameters during the operation of the plastic molding equipment; at the same time, it acquires the multi-source response parameters of the plastic molding equipment, which include at least one of the real-time values ​​of process parameters, quality characteristics of plastic products, and equipment status data. The parameter processing module 23 is used to determine the degree of correlation between each target process parameter and the defect status of the plastic product based on the multi-source response parameters; and to construct the target mapping relationship between the process parameter matrix and the defect status of the plastic product based on the degree of correlation, wherein each defect status of the plastic product corresponds to multiple process parameter matrices. The matrix filtering module 24 is used to filter the mapping relationship between the process parameter matrix and the defects of plastic products based on the constraints of the plastic molding equipment under the operating state, and to determine the target process parameter matrix corresponding to each defect of plastic products. The matrix analysis module 25 is used to determine the source of defects in plastic products based on the types of process parameters present in the target process parameter matrix.

[0115] In one possible implementation, the signal generation module 21 is further configured to: construct a parameter coupling matrix based on the coupling strength between each target process parameter, wherein the elements in the parameter coupling matrix represent the coupling strength between two target process parameters; configure a corresponding initial disturbance period and initial disturbance intensity for each target process parameter; adjust the initial disturbance period and initial disturbance intensity corresponding to each target process parameter based on the parameter coupling matrix to obtain the target disturbance period and target disturbance intensity corresponding to each target process parameter; and use the target disturbance period and target disturbance intensity as time-domain signals and frequency-domain signals, respectively, to obtain the target disturbance signal.

[0116] In one possible implementation, the signal generation module 21 is further configured to: acquire, for any given target process parameter, the target process step in which the target process parameter acts within the operating cycle of the plastic molding equipment; determine the initial disturbance period corresponding to the target process parameter based on the target process step; and determine the initial disturbance intensity for any given target process parameter based on the duration of the initial disturbance period corresponding to the target process parameter; wherein, the initial disturbance intensity increases as the duration of the initial disturbance period increases; and the operating cycle of the plastic molding equipment characterizes the time period required to produce a target plastic product.

[0117] In one possible implementation, the signal generation module 21 is further configured to: obtain a first initial disturbance period corresponding to the first target process parameter and a second initial disturbance period corresponding to the second target process parameter for any element in the parameter coupling matrix; obtain the time overlap relationship between the first initial disturbance period and the second initial disturbance period; adjust the initial disturbance intensity of the first target process parameter based on the time overlap relationship and the coupling strength between the first target process parameter and the second target process parameter to obtain the target disturbance intensity corresponding to the first target process parameter; and / or adjust the initial disturbance period of the first target process parameter based on the time overlap relationship to obtain the target disturbance period corresponding to the first target process parameter.

[0118] In one possible implementation, the parameter processing module 23 is further configured to construct the temporal relationship between real-time values ​​of process parameters, quality characteristics of plastic products, and equipment status data; obtain a target time period in which the change range of equipment status data is less than a preset change range; calculate the degree of correlation between the change of real-time values ​​of process parameters and the change of quality characteristics of plastic products within the target time period, and obtain the target degree of correlation between each target process parameter and the defect status of plastic products; wherein, the defect status of plastic products is determined based on the quality characteristics of plastic products.

[0119] In one possible implementation, the parameter processing module 23 is further configured to determine at least two target process parameters corresponding to each type of plastic product defect based on the degree of target correlation; acquire the parameter variation range of at least two target process parameters during the operation of the plastic molding equipment; and generate multiple process parameter matrices corresponding to each type of plastic product defect based on the parameter variation range, wherein the values ​​of each process parameter in the process parameter matrix are within the parameter variation range.

[0120] In one possible implementation, the matrix filtering module 24 is further configured to obtain the constraints of the plastic molding equipment in its operating state, the constraints including a first constraint and a second constraint; wherein the first constraint is determined based on a pre-configured range of process parameter variations, and the second constraint is determined based on the hardware configuration of the plastic molding equipment.

[0121] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0122] This application also provides an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, and a memory 305.

[0123] The communication bus 302 is used to enable communication between these components.

[0124] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0125] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0126] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0127] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a traceability program for product defects during the plastic molding process.

[0128] exist Figure 3In the illustrated electronic device, the user interface 303 is primarily used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call the traceability program for product defects stored in the memory 305 during the plastic molding process. When executed by one or more processors 301, the electronic device performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0129] This application also provides a non-transitory computer-readable storage medium storing instructions. When executed by one or more processors, these instructions cause an electronic device to perform one or more of the methods described in the above embodiments.

[0130] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0131] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0132] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0134] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0135] The above description is merely an exemplary embodiment disclosed in this application and should not be construed as limiting the scope of this application. Any equivalent changes and modifications made in accordance with the teachings of this application shall still fall within the scope of this application.

[0136] This application is intended to cover any variations, uses, or adaptations disclosed herein that follow the general principles disclosed herein and include common knowledge or customary technical means in the art that are not described in this application.

Claims

1. A method for tracing the source of defects in plastic products during the molding process, applied to plastic molding equipment, characterized in that, The method includes: Based on the coupling strength of various target process parameters during the operation of the plastic molding equipment, a target disturbance signal is generated; During the operation of the plastic molding equipment, the target disturbance signal is applied to the target process parameters; at the same time, multi-source response parameters of the plastic molding equipment are acquired, the multi-source response parameters including at least one of real-time values ​​of process parameters, quality characteristics of plastic products, and equipment status data; Based on the multi-source response parameters, the target correlation degree between each target process parameter and the defect status of the plastic product is determined; and based on the target correlation degree, a target mapping relationship between the process parameter matrix and the defect status of the plastic product is constructed, wherein each defect status of the plastic product corresponds to multiple process parameter matrices. Based on the constraints of the plastic molding equipment under operating conditions, the mapping relationship between the process parameter matrix and the defects of plastic products is screened to determine the target process parameter matrix corresponding to each defect of plastic products. Based on the types of process parameters present in the target process parameter matrix, the source tracing results of defects in plastic products are determined.

2. The method according to claim 1, characterized in that, The generation of target disturbance signals based on the coupling strength of various target process parameters during the operation of the plastic molding equipment includes: Based on the coupling strength between various target process parameters, a parameter coupling matrix is ​​constructed, where the elements in the parameter coupling matrix represent the coupling strength between two target process parameters. Configure the corresponding initial disturbance period and initial disturbance intensity for each target process parameter; Based on the parameter coupling matrix, the initial disturbance period and initial disturbance intensity corresponding to each target process parameter are adjusted to obtain the target disturbance period and target disturbance intensity corresponding to each target process parameter; The target disturbance period and the target disturbance intensity are respectively used as time-domain signals and frequency-domain signals to obtain the target disturbance signal.

3. The method according to claim 2, characterized in that, The configuration of the corresponding initial disturbance period and initial disturbance intensity for each target process parameter includes: For any given target process parameter, obtain the target process step in which the target process parameter has an effect during the operating cycle of the plastic molding equipment; Based on the target process step, determine the initial disturbance period corresponding to the target process parameters; For any target process parameter, the initial disturbance intensity is determined based on the duration of the initial disturbance period corresponding to the target process parameter. The initial disturbance intensity increases as the duration of the initial disturbance period increases; the operating cycle of the plastic molding equipment represents the time required to produce a target plastic product.

4. The method according to claim 2, characterized in that, The step of adjusting the initial disturbance period and initial disturbance intensity corresponding to each target process parameter based on the parameter coupling matrix to obtain the target disturbance period and target disturbance intensity corresponding to each target process parameter includes: For any element in the parameter coupling matrix corresponding to the first target process parameter and the second target process parameter, the first initial disturbance period corresponding to the first target process parameter and the second initial disturbance period corresponding to the second target process parameter are obtained respectively. Obtain the temporal overlap relationship between the first initial disturbance period and the second initial disturbance period; Based on the time overlap relationship and the coupling strength between the first target process parameter and the second target process parameter, the initial disturbance strength of the first target process parameter is adjusted to obtain the target disturbance strength corresponding to the first target process parameter. And / or, based on the time overlap relationship, adjust the initial disturbance period of the first target process parameter to obtain the target disturbance period corresponding to the first target process parameter.

5. The method according to claim 1, characterized in that, The determination of the target correlation degree between each target process parameter and the defect status of the plastic product based on the multi-source response parameters includes: Construct a temporal relationship between the real-time values ​​of the process parameters, the quality characteristics of the plastic products, and the equipment status data; The target time period in which the change in the device status data is less than a preset change range is obtained; Calculate the correlation between the changes in the real-time values ​​of the process parameters and the changes in the quality characteristics of the plastic products within the target time period, and obtain the target correlation between each target process parameter and the defects of the plastic products; The defects in the plastic products are determined based on the quality characteristics of the plastic products.

6. The method according to claim 1, characterized in that, The step of constructing a target mapping relationship between the process parameter matrix and the defect status of plastic products based on the target correlation degree includes: Based on the degree of correlation of the targets, at least two target process parameters are determined for each type of defect in plastic products. During the operation of the plastic molding equipment, the parameter variation range of the at least two target process parameters is obtained; Based on the range of parameter variation, multiple process parameter matrices are generated corresponding to each type of defect in plastic products, and the values ​​of each process parameter in the process parameter matrix are within the range of parameter variation.

7. The method according to claim 1, characterized in that, The process parameter matrix and the mapping relationship between the process parameter matrix and the defects of the plastic products are filtered based on the constraints of the plastic molding equipment under operating conditions, and the target process parameter matrix corresponding to each defect of the plastic products is determined, including: Obtain the constraint conditions of the plastic molding equipment in its operating state, the constraint conditions including a first constraint condition and a second constraint condition; The first constraint is determined based on a pre-configured range of process parameter variations, and the second constraint is determined based on the hardware configuration of the plastic molding equipment.

8. A device for tracing defects in plastic products during the molding process, characterized in that, The method includes: The signal generation module is used to generate target disturbance signals based on the coupling strength of various target process parameters during the operation of the plastic molding equipment. The parameter acquisition module is used to apply the target disturbance signal to the target process parameters during the operation of the plastic molding equipment; at the same time, it acquires the multi-source response parameters of the plastic molding equipment, the multi-source response parameters including at least one of the real-time values ​​of process parameters, quality characteristics of plastic products, and equipment status data; The parameter processing module is used to determine the target correlation degree between each target process parameter and the defect status of the plastic product based on the multi-source response parameters; and to construct the target mapping relationship between the process parameter matrix and the defect status of the plastic product based on the target correlation degree, wherein each defect status of the plastic product corresponds to multiple process parameter matrices; The matrix filtering module is used to filter the mapping relationship between the process parameter matrix and the defects of plastic products based on the constraints of the plastic molding equipment under the operating state, and to determine the target process parameter matrix corresponding to each defect of plastic products. The matrix analysis module is used to determine the source tracing results of defects in plastic products based on the types of process parameters present in the target process parameter matrix.

9. An electronic device, characterized in that, The device includes a processor, a communication bus, a user interface, a network interface, and a memory. The memory is used to store instructions. The user interface and the network interface are both used to communicate with other devices. The communication bus is used to enable communication between the components within the electronic device. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 7.

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