Multi-dimensional full-process plastic particle quality comprehensive detection method

Abstract

The invention discloses a multi-dimensional full-process plastic particle quality comprehensive detection method, and relates to the technical field of full-process quality detection.The multi-dimensional full-process plastic particle quality comprehensive detection method comprises stage analysis and process analysis. First-stage quality data, second-stage quality data, third-stage quality data and fourth-stage quality data are analyzed, a stage supervision scheme is set, then a process analysis step is adopted, and the quality data of the four stages are divided into material quality data, reactor quality data and process quality data according to types. Setting a quality detection main body according to change data of the material quality data, the reactor quality data and the process quality data in four stages, and then generating a process supervision scheme. Through multi-dimensional and full-process quality comprehensive detection, the accuracy and comprehensiveness of plastic particle quality detection can be effectively improved; quality risks in production can be found and processed in time, and product quality stability is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of end-to-end quality testing technology, specifically to a multi-dimensional, end-to-end comprehensive testing method for the quality of plastic particles. Background Technology

[0002] In modern industrial systems, plastic granules are a crucial basic raw material with a wide range of applications, from everyday food packaging and electronic product casings to the manufacturing of automotive parts and medical devices. The quality of plastic granules directly affects the performance, safety, and lifespan of downstream products. Therefore, a comprehensive, multi-dimensional, end-to-end plastic granule quality testing method is needed.

[0003] Traditional quality inspection methods are often limited to a single dimension or part of the production stage, making it difficult to comprehensively and accurately assess the quality of plastic granules. They only test the final product performance of plastic granules, while ignoring the material quality in the early stages of production, the reactor status and process parameter changes during the production process. This makes it impossible to detect problems in the early stages of production in a timely manner, resulting in a large number of defective products.

[0004] Traditional methods often focus on a single production stage or the final product, lacking a systematic coverage of the entire process. For example, some technologies only test the purity of raw materials or the mechanical properties of finished products, ignoring the impact of parameter fluctuations in intermediate stages such as melting and granulation on the final quality, making it difficult to trace quality problems back to their source.

[0005] In terms of dynamic parameter adjustment, existing technologies mostly use fixed thresholds in their testing standards, which cannot be dynamically corrected based on historical production data and stage-specific quality indices. When factors such as raw material batches and equipment aging change, fixed thresholds are prone to misjudgment or missed detection, reducing testing accuracy. Regarding risk warning mechanisms, existing technologies lack a tiered response system. Most only issue simple alarms when indicators exceed limits, without setting warning thresholds based on quality-sensitive parameters or multi-level processing schemes, making it difficult to achieve early intervention in risks. Summary of the Invention

[0006] To address the aforementioned technical shortcomings, the present invention aims to provide a multi-dimensional, end-to-end comprehensive method for detecting the quality of plastic particles.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a multi-dimensional, full-process comprehensive detection method for plastic particle quality, including the following steps: Step 1, stage analysis: collect quality data of stage 1, stage 2, stage 3 and stage 4, analyze the quality data of stage 1, stage 2, stage 3 and stage 4, set quality sensitive parameters according to the analysis results, and then generate a stage supervision plan.

[0008] Step 2, Process Analysis: Obtain material quality data, reactor quality data, and process quality data from the first-stage, second-stage, third-stage, and fourth-stage quality data. Analyze the material quality data, reactor quality data, and process quality data, set up the quality inspection subject based on the analysis results, and then generate a process supervision plan.

[0009] Preferably, the generation process of the monitoring scheme in the generation stage is as follows: obtain the detection frequency correction rate corresponding to each quality sensitive parameter from the database, thereby obtaining the first-stage detection frequency correction rate corresponding to the first-stage quality sensitive parameter; obtain the first-stage detection frequency from the database; and multiply the first-stage detection frequency correction rate by the first-stage detection frequency to obtain the first-stage detection correction frequency.

[0010] The average quality index for a preset period is obtained from the database. The quality-sensitive parameter for the first period is divided by the threshold of the quality-sensitive parameter in the database to obtain the quality risk correction rate for the first period. The quality risk correction rate for the first period is multiplied by the average quality index for the first period to obtain the quality risk warning threshold for the first period.

[0011] Phase 1 monitoring plan: During the production phase, quality data is collected based on the Phase 1 testing and correction frequency. When the Phase 1 quality index exceeds the Phase 1 quality risk warning threshold, a Phase 1 warning is issued. If the number of Phase 1 warnings exceeds the preset number within a preset time period, a Phase 1 shutdown is initiated, and staff are notified to carry out repairs.

[0012] Based on the process of generating the first-stage regulatory plan, the second-stage, third-stage, and fourth-stage regulatory plans are generated.

[0013] Preferably, the generation process monitoring scheme is generated as follows: First-stage quality inspection subject early warning: Collect various types of inspection data of the first-stage quality inspection subject within a preset time period. When a certain type of inspection data of the first-stage quality inspection subject collected in a certain period does not belong to the corresponding interval, a first-stage subject early warning is issued.

[0014] Based on the first-stage main body early warning plan, early warnings were issued for the second-stage, third-stage, and fourth-stage main bodies, and the number of early warnings for the first-stage, second-stage, third-stage, and fourth-stage main bodies within the preset time period was statistically obtained.

[0015] If the number of warnings in the first, second, third, and fourth stages all exceed the preset number of warnings, the plastic particles within the preset time will be recycled. At the same time, the stage with the highest number of warnings will be marked as the risk stage, and staff will be prompted to carry out maintenance in the risk stage.

[0016] The beneficial effects of this invention are as follows: 1. This invention first adopts a stage analysis step, analyzing the quality data of the first stage, second stage, third stage, and fourth stage to set up a stage supervision plan. Secondly, it adopts a process analysis step, classifying the quality data of the four stages into material quality data, reactor quality data, and process quality data. Based on the changes in material quality data, reactor quality data, and process quality data in the four stages, a quality inspection subject is set up, thereby generating a process supervision plan. This invention, through multi-dimensional and full-process comprehensive quality inspection, can effectively improve the accuracy and comprehensiveness of plastic particle quality inspection, promptly identify and handle quality risks in production, and ensure product quality stability.

[0017] 2. In terms of comprehensiveness of detection, this invention breaks through the limitations of being limited to a single stage or finished product by collecting and analyzing data throughout the entire process from stage one to stage four. Every link from raw material processing to the final product is included in the supervision, which can not only detect potential quality problems at each stage in a timely manner, but also accurately trace the source of the problem, greatly improving the efficiency of quality problem investigation.

[0018] 3. In terms of the correlation of indicators, this invention establishes a quantitative correlation model between various quality parameters by means of correlation calculation and weight factor setting. By analyzing the correlation between additive dispersion and screen passing rate, it can identify the quality risks caused by the synergistic effect of parameters, making the quality assessment more scientific and accurate.

[0019] 4. Regarding dynamic parameter adjustment, based on historical data in the database and stage quality index, dynamic correction of quality-sensitive parameters, detection frequency, and early warning thresholds is achieved. When factors such as raw material batches and equipment status change, the system can automatically adapt and adjust, avoiding misjudgments or missed judgments caused by fixed thresholds, and significantly improving detection accuracy.

[0020] 5. This invention establishes a tiered response system for risk warning mechanisms, setting warning thresholds based on quality-sensitive parameters and implementing multi-level solutions including warnings and shutdown procedures. This allows for timely intervention at the nascent stage of quality problems, effectively reducing defect rates and production losses. Regarding the identification of responsible parties, by calculating quality deviation rates across different dimensions such as materials, reactors, and processes, the invention clarifies the quality inspection entities and their influence weights at each stage. When problems arise, it can precisely pinpoint improvement directions, significantly improving quality control efficiency and ensuring the stability and consistency of product quality. Attached Figure Description

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

[0022] Figure 1 This is a schematic diagram of the implementation steps of the method of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] according to Figure 1 As shown, this invention provides a multi-dimensional, end-to-end comprehensive quality testing method for plastic granules, comprising the following steps: Step 1, Stage Analysis: Collecting quality data for Stage 1, Stage 2, Stage 3, and Stage 4; analyzing the quality data for Stage 1, Stage 2, Stage 3, and Stage 4; setting quality-sensitive parameters based on the analysis results; and then generating a stage monitoring plan.

[0025] In one specific embodiment, the collection of first-stage quality data, second-stage quality data, third-stage quality data, and fourth-stage quality data is specifically carried out as follows: the first-stage quality data includes the first-stage additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, screen passing rate, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate.

[0026] It should be noted that the first stage is the raw material pretreatment stage, the second stage is the melt extrusion stage, the third stage is the granulation and molding stage, and the fourth stage is the cooling and drying stage.

[0027] The concentration was measured using an ultraviolet spectrophotometer and an inductively coupled plasma atomic emission spectrometer. The additive concentration deviation was calculated, and the maximum additive concentration deviation was recorded as the additive dispersion. Color difference uniformity was collected using a colorimeter. The residual moisture content was collected by an online near-infrared moisture sensor installed at the dryer outlet. An automatic weighing device was installed at the screen waste outlet to accumulate the total weight of impurities removed in each batch, thus obtaining the total amount of impurities removed. An electronic scale was installed at the vibrating screen outlet to weigh the oversize and undersize materials separately. The screen pass rate was obtained by dividing the undersize weight by the total weight.

[0028] Temperature and vibration sensors are installed on the mixer blades, motor, mixing chamber, and bearings to collect data on temperature and vibration amplitude. If the temperature of the mixer blades, motor, mixing chamber, or bearing exceeds the corresponding temperature threshold, it is recorded as a temperature anomaly. If the vibration amplitude exceeds the corresponding vibration threshold, it is recorded as a vibration anomaly. The number of temperature anomalies is divided by the number of temperature data collections to obtain the first-stage temperature anomaly rate. The number of vibration anomalies is divided by the number of vibration data collections to obtain the first-stage vibration anomaly rate. An infrared photoelectric sensor is installed at the discharge flange. When material particles are detected falling, the sensor automatically counts the particles. The number of material particles is divided by the total amount of material to obtain the sealing anomaly rate.

[0029] The second-stage quality data includes melt index, melt viscosity, average temperature difference between sections, average melt pressure, average phase size, volatile concentration, carbonyl absorption peak, crosslinking content, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate.

[0030] The melt flow index was obtained by testing under set temperature and load using a melt flow indexer and calculating the average value. The melt viscosity was obtained by real-time monitoring of the viscosity value using an online rotational viscometer. Armored thermocouples were installed in each section of the extruder and at the die to record the temperature in real time and obtain the temperature difference between each section. The temperature difference between adjacent sections was calculated and the average temperature difference between sections was obtained. A melt pressure sensor was installed at the die inlet to continuously collect pressure data and calculate the amplitude of each fluctuation. The average melt pressure was obtained by averaging the average value. Samples were taken from each batch, and the phase structure of the blend was observed using a scanning electron microscope. The phase size was measured using image analysis software to obtain the average size of the phase. A gas chromatograph was installed at the extruder exhaust port to detect the concentration of volatiles online. The carbonyl absorption peak of the melt was analyzed using a Fourier transform infrared spectroscopy.

[0031] Temperature sensors are used to collect the inlet and outlet hot air temperatures of the dryer. When the collected temperature exceeds the corresponding temperature threshold, it is recorded as a temperature anomaly. The number of temperature anomalies is divided by the number of temperature collections to obtain the two-stage temperature anomaly rate. Vibration sensors on the fan bearing housing and the side of the dryer body collect vibration amplitude. When the collected vibration amplitude exceeds the corresponding vibration amplitude threshold, it is recorded as a vibration anomaly. The number of vibration anomalies is divided by the number of vibration collections to obtain the two-stage vibration anomaly rate. Infrared photoelectric sensors are installed at the flange gaps of the inlet and outlet to monitor material particle leakage. A differential pressure sensor is installed on the top of the dryer cavity to monitor the difference between the internal pressure and the external atmospheric pressure. When the infrared sensor detects material particles or cavity pressure fluctuations exceeding the pressure fluctuation threshold, it is recorded as a sealing anomaly. The number of sealing anomalies per unit time is divided by the total monitoring time to obtain the two-stage sealing anomaly rate.

[0032] The three-stage quality data includes surface roughness, cut smoothness, roundness pass rate, particle weight deviation rate, static electricity deviation rate, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate.

[0033] The surface roughness of the particles was measured using an atomic force microscope. The particle cut was scanned using a laser profilometer to measure the burr height. The percentage of particles with burr heights less than a preset height was recorded as the cut smoothness. The particle projection profile was extracted using a machine vision system, and the roundness of the profile was calculated. A profile with roundness greater than a preset roundness was recorded as having acceptable roundness, and the roundness acceptance rate was calculated. A preset number of particles were randomly weighed using a high-precision electronic balance, and the deviation rate between the weight of each particle and the average weight was calculated. The maximum deviation rate was recorded as the particle weight deviation rate. The surface charge density was measured non-contactly in the storage hopper of each measured particle using an electrostatic field meter. The maximum surface charge density was divided by the average surface charge density to obtain the electrostatic value deviation rate.

[0034] Temperature sensors are installed on the pelletizer barrel, cutter holder, and water ring cooling water. When the collected temperature exceeds the corresponding temperature threshold, it is recorded as a temperature anomaly. The number of temperature anomalies is divided by the number of temperature collections to obtain the three-stage temperature anomaly rate. Vibration amplitude is collected by vibration sensors on the pelletizer main shaft, motor bearings, and vibrating screen body. When the collected vibration amplitude exceeds the corresponding vibration amplitude threshold, it is recorded as a vibration anomaly. The number of vibration anomalies is divided by the number of vibration collections to obtain the three-stage vibration anomaly rate. A capacitive proximity sensor is installed at the flange joint of the pelletizing chamber sealing surface to monitor changes in the sealing gap. A humidity sensor is installed below the cooling water interface pipe connection to monitor condensate leakage. An absolute pressure sensor is installed on the side of the pelletizing chamber cavity to monitor the internal air pressure. When the sealing gap exceeds a preset threshold, or the humidity sensor reading exceeds the threshold, or the pelletizing chamber pressure exceeds the threshold, it is recorded as a sealing anomaly. The number of sealing anomaly events per unit time is divided by the total monitoring time to obtain the three-stage sealing anomaly rate.

[0035] The four-stage quality data includes the following parameters: elongation at break, impact strength, sieve pass rate, final moisture content, fine powder content, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate.

[0036] The elongation at break was collected using a universal testing machine, and the impact strength was collected using a cantilever beam impact testing machine. By installing automatic counting devices at the discharge ports of different sieve layers of the grading sieve, the proportion of particles in each sieve layer was calculated to determine the proportion of qualified particles and obtain the sieve qualification rate. An online capacitive moisture meter was installed at the discharge port of the dryer to monitor the final moisture content in real time. The fine powder after sieving was collected, weighed using an electronic balance, and its percentage of the total sample weight was calculated to obtain the fine powder content.

[0037] Temperature sensors are installed in the straight sections of the inlet and outlet ducts of the vibrating fluidized bed and at the dryer outlet. When the collected temperature exceeds the corresponding temperature threshold, it is recorded as a temperature anomaly. The number of temperature anomalies is divided by the number of temperature collections to obtain the four-stage temperature anomaly rate. Vibration amplitude is collected by vibration sensors on the sides of the fluidized bed body, fan bearings, and grading screen. When the collected vibration amplitude exceeds the corresponding vibration amplitude threshold, it is recorded as a vibration anomaly. The number of vibration anomalies is divided by the number of vibration collections to obtain the four-stage vibration anomaly rate. Laser dust sensors are installed at the gap between the sealing caps and flanges at the inlet and outlet to monitor the concentration of leaked dust. A diaphragm pressure sensor is installed below the flange connection of the duct to collect the change in negative pressure. A differential pressure transmitter is installed at the top of the fluidized bed cavity to monitor the change in internal negative pressure. When the concentration of leaked dust exceeds the preset dust concentration threshold, or the change in diaphragm pressure exceeds the preset negative pressure change threshold, or the change in internal negative pressure exceeds the preset internal negative pressure change threshold, it is recorded as a sealing anomaly. The number of sealing anomaly events per unit time is divided by the total monitoring time to obtain the four-stage sealing anomaly rate.

[0038] In one specific embodiment, the analysis of the first-stage quality data, second-stage quality data, third-stage quality data, and fourth-stage quality data is carried out as follows: The additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, first-stage temperature anomaly rate, first-stage vibration anomaly rate, and first-stage sealing anomaly rate of each production in the database are correlated with the screen passing rate of the corresponding production. Weighting factors are set, and the additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate of the first stage are normalized and then weighted to obtain the first-stage quality index.

[0039] Based on the analysis of the first-stage quality data, the second-stage, third-stage, and fourth-stage quality data were analyzed to obtain the second-stage quality index, the third-stage quality index, and the fourth-stage quality index.

[0040] In a specific embodiment, the weighted calculation is performed as follows: After the correlation calculation, the correlation of additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate in the first stage are obtained. These are added together to obtain the total correlation of the first stage. The correlations of additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate in the first stage are divided by the total correlation of the first stage to obtain the weight factors of additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate in the first stage.

[0041] The additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate of the first stage are divided by their respective preset thresholds to obtain the normalized values ​​for additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate.

[0042] The first-stage quality index is obtained by multiplying the normalized values ​​of additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate of the first stage by their respective weighting factors and then summing them.

[0043] In one specific embodiment, the analysis of the second-stage, third-stage, and fourth-stage quality data is carried out as follows: The melt index, melt viscosity, average temperature difference of each segment, average melt pressure, volatile matter concentration, carbonyl absorption peak, crosslinking content, second-stage temperature anomaly rate, second-stage vibration anomaly rate, and second-stage sealing anomaly rate of each production in the database are correlated with the corresponding average size of the phase region. The correlations of melt index, melt viscosity, average temperature difference of each segment, average melt pressure, volatile matter concentration, carbonyl absorption peak, crosslinking content, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate of the second stage are obtained. Then, weighting factors are set, and the second-stage quality index is calculated by weighting.

[0044] The correlation between the surface roughness, cut smoothness, particle weight deviation rate, electrostatic value deviation rate, three-stage temperature anomaly rate, three-stage vibration anomaly rate, and three-stage sealing anomaly rate of each production in the database and the corresponding roundness pass rate is calculated to obtain the correlation of surface roughness, cut smoothness, particle weight deviation rate, electrostatic value deviation rate, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate in the second stage. Then, weighting factors are set, and the three-stage quality index is calculated by weighting.

[0045] The correlation between the breaking elongation, impact strength, final moisture content, fine powder content, four-stage temperature anomaly rate, four-stage vibration anomaly rate, and four-stage sealing anomaly rate of each production in the database and the corresponding screening pass rate is calculated to obtain the correlation of breaking elongation, impact strength, final moisture content, fine powder content, four-stage temperature anomaly rate, four-stage vibration anomaly rate, and four-stage sealing anomaly rate. Then, weighting factors are set, and the four-stage quality index is calculated by weighting.

[0046] In one specific embodiment, the process of setting the quality sensitive parameters is as follows: Analyze the historical quality data in the database to obtain the first-stage defective product correlation, second-stage defective product correlation, third-stage defective product correlation, fourth-stage defective product correlation, first-stage defective product weight factor, second-stage defective product weight factor, third-stage defective product weight factor, and fourth-stage defective product weight factor.

[0047] The product quality index is obtained by multiplying the first-stage quality index, second-stage quality index, third-stage quality index, and fourth-stage quality index by their respective weighting factors and then summing them.

[0048] The basic quality sensitivity parameters and the correlation parameter correction factors corresponding to each product quality index are obtained from the database. In this way, the basic quality sensitivity parameters and the correction factors of the first-stage defective product correlation parameters, the second-stage defective product correlation parameters, the third-stage defective product correlation parameters, and the fourth-stage defective product correlation parameters are obtained.

[0049] The basic quality sensitivity parameters are multiplied by the first-stage defective product related parameter correction factor, the second-stage defective product related parameter correction factor, the third-stage defective product related parameter correction factor, and the fourth-stage defective product related parameter correction factor, respectively, to obtain the first-stage quality sensitivity parameters, the second-stage quality sensitivity parameters, the third-stage quality sensitivity parameters, and the fourth-stage quality sensitivity parameters.

[0050] In one specific embodiment, the analysis of historical quality data in the database is carried out as follows: historical quality data is obtained from the database, which includes the first-stage quality index, second-stage quality index, third-stage quality index, fourth-stage quality index, and defect rate of each production run. The correlation of the first-stage quality index, second-stage quality index, third-stage quality index, fourth-stage quality index, and defect rate of each production run is calculated to obtain the first-stage defect correlation, second-stage defect correlation, third-stage defect correlation, and fourth-stage defect correlation.

[0051] The relevance of defective products in the first, second, third, and fourth stages is added together to obtain the total process relevance. The relevance of defective products in the first, second, third, and fourth stages is then divided by the total process relevance to obtain the weighting factors for defective products in the first, second, third, and fourth stages, respectively.

[0052] In one specific embodiment, the generation process of the generation phase supervision scheme is as follows: obtain the detection frequency correction rate corresponding to each quality sensitive parameter from the database, thereby obtaining the first-stage detection frequency correction rate corresponding to the first-stage quality sensitive parameter; obtain the first-stage detection frequency from the database; and multiply the first-stage detection frequency correction rate by the first-stage detection frequency to obtain the first-stage detection correction frequency.

[0053] The average quality index for a preset period is obtained from the database. The quality-sensitive parameter for the first period is divided by the threshold of the quality-sensitive parameter in the database to obtain the quality risk correction rate for the first period. The quality risk correction rate for the first period is multiplied by the average quality index for the first period to obtain the quality risk warning threshold for the first period.

[0054] Phase 1 monitoring plan: During the production phase, quality data is collected based on the Phase 1 testing and correction frequency. When the Phase 1 quality index exceeds the Phase 1 quality risk warning threshold, a Phase 1 warning is issued. If the number of Phase 1 warnings exceeds the preset number within a preset time period, a Phase 1 shutdown is initiated, and staff are notified to carry out repairs.

[0055] Based on the process of generating the first-stage regulatory plan, the second-stage, third-stage, and fourth-stage regulatory plans are generated.

[0056] Step 2, Process Analysis: Obtain material quality data, reactor quality data, and process quality data from the first-stage, second-stage, third-stage, and fourth-stage quality data. Analyze the material quality data, reactor quality data, and process quality data, set up the quality inspection subject based on the analysis results, and then generate a process supervision plan.

[0057] In one specific embodiment, the acquisition process of material quality data, reactor quality data, and process quality data is as follows: the first-stage additive dispersion, the first-stage color difference uniformity, the second-stage melt index, the second-stage melt viscosity, the second-stage average temperature difference of the melting zone, the second-stage average melt pressure, the third-stage surface roughness, the third-stage cut smoothness, the fourth-stage elongation at break, and the fourth-stage impact strength are recorded as material quality data.

[0058] The residual moisture content of stage one, the total amount of impurities removed in stage one, the concentration of volatile matter in stage two, the carbonyl absorption peak in stage two, the content of crosslinked matter in stage two, the particle weight deviation rate in stage three, the electrostatic value deviation rate in stage three, the final moisture content in stage four, and the fine powder content in stage four are recorded as process quality data.

[0059] The abnormal rates of temperature, vibration, and sealing in the first stage, the abnormal rates of temperature, vibration, and sealing in the second stage, the abnormal rates of temperature, vibration, and sealing in the third stage, and the abnormal rates of temperature, vibration, and sealing in the third stage are recorded as process quality data.

[0060] In one specific embodiment, the analysis of material quality data, reactor quality data, and process quality data is carried out as follows: the material quality data is analyzed to obtain the first-stage material quality index, the second-stage material quality index, the third-stage material quality index, and the fourth-stage material quality index.

[0061] The first-stage material quality deviation rate is obtained by subtracting the initial material quality index from the first-stage material quality index and dividing the result by the initial material quality index. The second-stage material quality deviation rate is obtained by subtracting the first-stage material quality index from the second-stage material quality index and dividing the result by the first-stage material quality index. The third-stage material quality deviation rate is obtained by subtracting the second-stage material quality index from the third-stage material quality index and dividing the result by the second-stage material quality index. The fourth-stage material quality deviation rate is obtained by subtracting the third-stage material quality index from the fourth-stage material quality index and dividing the result by the third-stage material quality index.

[0062] Based on the analysis methods for material quality data, the reactor quality data and process quality data are analyzed to obtain the quality deviation rates of the first-stage reactor, the second-stage reactor, the third-stage reactor, the fourth-stage reactor, the first-stage process quality deviation rate, the second-stage process quality deviation rate, the third-stage process quality deviation rate, and the fourth-stage process quality deviation rate.

[0063] In one specific embodiment, the analysis of material quality data is carried out as follows: the product of the first-stage additive dispersion and the corresponding correlation is added to the product of the first-stage color difference uniformity and the corresponding correlation to obtain the first-stage material quality reference index; the first-stage material quality reference index is divided by the sum of the first-stage additive dispersion correlation and the color difference uniformity correlation to obtain the first-stage material quality index.

[0064] Similarly, based on the two-stage melt index, the correlation of the two-stage melt index, the two-stage melt viscosity, the correlation of the two-stage melt viscosity, the correlation of the two-stage average temperature difference in the molten zone, the correlation of the two-stage average temperature difference in the molten zone, the correlation of the two-stage average melt pressure, and the correlation of the two-stage average melt pressure, the two-stage material quality index is obtained. Based on the three-stage surface roughness, the correlation of the three-stage surface roughness, the correlation of the three-stage cut smoothness, and the correlation of the three-stage cut smoothness, the three-stage material quality index is obtained. Based on the four-stage elongation at break, the correlation of the four-stage elongation at break, the correlation of the four-stage impact strength, and the correlation of the four-stage impact strength, the four-stage material quality index is obtained.

[0065] In one specific embodiment, reactor quality data and process quality data are analyzed. The specific analysis process is as follows: Based on the first-stage temperature anomaly rate, first-stage vibration anomaly rate, first-stage sealing anomaly rate, and the correlation of the first-stage temperature anomaly rate, first-stage vibration anomaly rate, and first-stage sealing anomaly rate, a first-stage reactor quality index is obtained. Based on the second-stage temperature anomaly rate, second-stage vibration anomaly rate, second-stage sealing anomaly rate, and the correlation of the second-stage temperature anomaly rate, second-stage vibration anomaly rate, and second-stage sealing anomaly rate, a second-stage reactor quality index is obtained. Based on the third-stage temperature anomaly rate, third-stage vibration anomaly rate, third-stage sealing anomaly rate, and the correlation of the third-stage temperature anomaly rate, third-stage vibration anomaly rate, and third-stage sealing anomaly rate, a third-stage reactor quality index is obtained.

[0066] The quality deviation rate of the first-stage reactor is obtained by subtracting the initial reactor quality index from the quality index of the first-stage reactor and dividing the result by the initial reactor quality index. The quality deviation rate of the second-stage reactor is obtained by subtracting the difference between the quality indices of the first-stage reactor from the quality index of the second-stage reactor and dividing the result by the quality index of the first-stage reactor. The quality deviation rate of the third-stage reactor is obtained by subtracting the difference between the quality indices of the second-stage reactor from the quality index of the third-stage reactor and dividing the result by the quality index of the second-stage reactor. The quality deviation rate of the fourth-stage reactor is obtained by subtracting the difference between the quality indices of the third-stage reactor from the quality index of the fourth-stage reactor and dividing the result by the quality index of the third-stage reactor.

[0067] The first-stage process quality index is obtained based on the residual moisture content, the correlation of residual moisture content, the total amount of impurities removed in the first stage, and the correlation of the total amount of impurities removed in the first stage. The second-stage process quality index is obtained based on the volatile matter concentration, the correlation of volatile matter concentration, carbonyl absorption peak, carbonyl absorption peak, crosslinking content, and crosslinking content. The third-stage process quality index is obtained based on the particle weight deviation rate, the correlation of particle weight deviation rate, the electrostatic value deviation rate, and the correlation of particle weight deviation rate. The fourth-stage process quality index is obtained based on the final moisture content, the correlation of final moisture content, and the correlation of fine powder content.

[0068] The process quality deviation rate is obtained by subtracting the initial process quality index from the first-stage process quality index and dividing the result by the initial process quality index. The process quality deviation rate is obtained by subtracting the difference between the first-stage process quality index and the second-stage process quality index from the second-stage process quality index. The process quality deviation rate is obtained by subtracting the difference between the second-stage process quality index and the third-stage process quality index from the third-stage process quality index. The process quality deviation rate is obtained by subtracting the difference between the third-stage process quality index and the fourth-stage process quality index from the fourth-stage process quality index.

[0069] In one specific embodiment, the process of setting up the quality inspection subject is as follows: the type corresponding to the maximum deviation rate among the material quality deviation rate, reactor quality deviation rate and process quality deviation rate in the same stage is recorded as the quality inspection subject, thereby obtaining the first-stage quality inspection subject, the second-stage quality inspection subject, the third-stage quality inspection subject and the fourth-stage quality inspection subject.

[0070] The correction factors for sensitive parameters corresponding to each deviation rate are obtained from the database to obtain the correction factors for the primary sensitive parameters of the first-stage quality inspection. The primary sensitive parameters of the first-stage quality inspection are multiplied by the correction factors to obtain the primary sensitive parameters of the first-stage quality inspection. The interval deviation rates corresponding to each primary sensitive parameter of the first-stage quality inspection are obtained from the database to obtain the interval deviation rates of the primary quality inspection. The mean values ​​of various types of test data of the primary quality inspection are obtained from the database to obtain the mean values ​​of various types of test data of the primary quality inspection. The interval deviation values ​​of various types of test data of the primary quality inspection are multiplied by the interval deviation rates of the primary quality inspection.

[0071] Subtracting the corresponding interval deviation value from the mean of various test data of the first-stage quality inspection subject yields the minimum value of various test data of the first-stage quality inspection subject, which is recorded as the lower limit of the interval of various test data of the first-stage quality inspection subject. Adding the corresponding interval deviation value to the mean of various test data of the first-stage quality inspection subject yields the maximum value of various test data of the first-stage quality inspection subject, which is recorded as the upper limit of the interval of various test data of the first-stage quality inspection subject. This is how the interval of various test data of the first-stage quality inspection subject is obtained.

[0072] Based on the method for obtaining the various test data ranges of the first-stage quality inspection entity, the various test data ranges of the second-stage quality inspection entity, the various test data ranges of the third-stage quality inspection entity, and the various test data ranges of the fourth-stage quality inspection entity are obtained.

[0073] In one specific embodiment, the generation process monitoring scheme is generated as follows: First-stage quality inspection subject early warning: Collect various types of inspection data of the first-stage quality inspection subject within a preset time period. When a certain type of inspection data of the first-stage quality inspection subject collected in a certain period does not belong to the corresponding interval, a first-stage subject early warning is issued.

[0074] Based on the first-stage main body early warning plan, early warnings were issued for the second-stage, third-stage, and fourth-stage main bodies, and the number of early warnings for the first-stage, second-stage, third-stage, and fourth-stage main bodies within the preset time period was statistically obtained.

[0075] If the number of warnings in the first, second, third, and fourth stages all exceed the preset number of warnings, the plastic particles within the preset time will be recycled. At the same time, the stage with the highest number of warnings will be marked as the risk stage, and staff will be prompted to carry out maintenance in the risk stage.

[0076] The database stores data on additive dispersion, color uniformity, residual moisture content, total amount of impurities removed, first-stage temperature anomaly rate, first-stage vibration anomaly rate, first-stage sealing anomaly rate, sieve throughput, melt flow index, melt viscosity, average temperature difference between sections, average melt pressure, volatile matter concentration, carbonyl absorption peak, crosslink content, second-stage temperature anomaly rate, second-stage vibration anomaly rate, second-stage sealing anomaly rate, average phase region size, surface roughness, cut smoothness, particle weight deviation rate, electrostatic value deviation rate, third-stage temperature anomaly rate, and other parameters for each production cycle. Three-stage vibration anomaly rate, three-stage sealing anomaly rate of each production run, roundness pass rate of each production run, elongation at break of each production run, impact strength of each production run, final moisture content of each production run, fine powder content of each production run, four-stage temperature anomaly rate of each production run, four-stage vibration anomaly rate of each production run, four-stage sealing anomaly rate of each production run, screening pass rate of each production run, historical quality data, basic quality sensitive parameters corresponding to each product quality index, correction factors for relevant parameters corresponding to each correlation, detection frequency correction rate corresponding to each quality sensitive parameter, one-stage detection frequency, average one-stage quality index within the preset time period, quality sensitive parameter threshold, initial material quality index, initial reactor quality index, initial process quality index, correction factors for sensitive parameters corresponding to each deviation rate, interval deviation rate corresponding to each quality detection subject sensitive parameter, and average values ​​of various detection data of the one-stage quality detection subject.

[0077] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A multi-dimensional, end-to-end comprehensive method for detecting the quality of plastic granules, characterized in that, Includes the following steps: Step 1, Phase Analysis: Collect quality data for Phase 1, Phase 2, Phase 3, and Phase 4; analyze the quality data for Phase 1, Phase 2, Phase 3, and Phase 4; set quality-sensitive parameters based on the analysis results; and then generate a phase supervision plan. Step 2, Process Analysis: Obtain material quality data, reactor quality data, and process quality data from the first-stage, second-stage, third-stage, and fourth-stage quality data. Analyze the material quality data, reactor quality data, and process quality data, set up the quality inspection subject based on the analysis results, and then generate a process supervision plan.

2. The multi-dimensional, end-to-end comprehensive quality testing method for plastic particles according to claim 1, characterized in that, The quality data for the first stage, second stage, third stage, and fourth stage are as follows: The first-stage quality data includes the first-stage additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, screen passing rate, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate. The second-stage quality data includes melt index, melt viscosity, average temperature difference between sections, average melt pressure, average phase region size, volatile concentration, carbonyl absorption peak, crosslink content, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate. The three-stage quality data includes surface roughness, cut smoothness, roundness pass rate, particle weight deviation rate, static electricity deviation rate, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate for each stage. The four-stage quality data includes the following parameters: elongation at break, impact strength, sieve pass rate, final moisture content, fine powder content, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate.

3. The multi-dimensional, end-to-end comprehensive quality testing method for plastic particles according to claim 2, characterized in that, The analysis of the first-stage, second-stage, third-stage, and fourth-stage quality data is described in the following process: The additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, first-stage temperature anomaly rate, first-stage vibration anomaly rate, and first-stage sealing anomaly rate of each production in the database are correlated with the screen passing rate of the corresponding production, and then weighted to obtain the first-stage quality index. Based on the analysis process of the first-stage quality index, the second-stage quality index, the third-stage quality index, and the fourth-stage quality index are obtained.

4. The multi-dimensional, end-to-end comprehensive quality testing method for plastic particles according to claim 3, characterized in that, The weighted calculation process is as follows: After correlation calculation, the correlations of additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate are obtained. These are added together to obtain the total first-stage correlation. The correlations of additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate in the first stage are divided by the total first-stage correlation to obtain the first-stage weighting factors of additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate. The additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate of the first stage are divided by their respective preset thresholds to obtain the normalized values ​​for additive dispersion, color difference uniformity, residual moisture content, total amount of removed impurities, temperature anomaly rate, vibration anomaly rate, and sealing anomaly rate. These values ​​are then weighted and calculated to obtain the first-stage quality index.

5. The multi-dimensional, end-to-end comprehensive quality testing method for plastic particles according to claim 3, characterized in that, The specific process for setting the quality-sensitive parameters is as follows: By analyzing the historical quality data of the database, we obtained the correlation of defective products in the first stage, the correlation of defective products in the second stage, the correlation of defective products in the third stage, the correlation of defective products in the fourth stage, the weight factor of defective products in the first stage, the weight factor of defective products in the second stage, the weight factor of defective products in the third stage, and the weight factor of defective products in the fourth stage. The product quality index is obtained by multiplying the first-stage quality index, the second-stage quality index, the third-stage quality index, and the fourth-stage quality index by their respective weighting factors and then summing them. The database is used to obtain the basic quality sensitive parameters and the correlation parameter correction factors corresponding to each product quality index. In this way, the basic quality sensitive parameters and the correction factors of the first-stage defective product related parameters, the second-stage defective product related parameters, the third-stage defective product related parameters, and the fourth-stage defective product related parameters are obtained. The basic quality sensitivity parameters are multiplied by the first-stage defective product related parameter correction factor, the second-stage defective product related parameter correction factor, the third-stage defective product related parameter correction factor, and the fourth-stage defective product related parameter correction factor, respectively, to obtain the first-stage quality sensitivity parameters, the second-stage quality sensitivity parameters, the third-stage quality sensitivity parameters, and the fourth-stage quality sensitivity parameters.

6. The multi-dimensional, end-to-end comprehensive quality testing method for plastic particles according to claim 5, characterized in that, The analysis of historical database quality data is performed as follows: Historical quality data is obtained from the database, including the first-stage quality index, second-stage quality index, third-stage quality index, fourth-stage quality index, and defect rate for each production run. The correlation between the first-stage quality index, second-stage quality index, third-stage quality index, fourth-stage quality index, and defect rate for each production run is calculated to obtain the first-stage defect correlation, second-stage defect correlation, third-stage defect correlation, and fourth-stage defect correlation. Then, the first-stage defect weight factor, second-stage defect weight factor, third-stage defect weight factor, and fourth-stage defect weight factor are obtained.

7. The multi-dimensional, end-to-end comprehensive quality testing method for plastic particles according to claim 5, characterized in that, The specific generation process of the regulatory scheme during the generation phase is as follows: The detection frequency correction rate corresponding to each quality sensitive parameter is obtained from the database. The first-stage detection frequency correction rate corresponding to the first-stage quality sensitive parameter is obtained from the database. The first-stage detection frequency is obtained by multiplying the first-stage detection frequency correction rate by the first-stage detection frequency to obtain the first-stage detection correction frequency. The average quality index for a given period is obtained from the database. The quality sensitive parameter for the first period is divided by the threshold of the quality sensitive parameter in the database to obtain the quality risk correction rate for the first period. The quality risk correction rate for the first period is multiplied by the average quality index for the first period to obtain the quality risk warning threshold for the first period. Phase 1 monitoring plan: During the production phase, quality data is collected based on the Phase 1 testing and correction frequency. When the Phase 1 quality index exceeds the Phase 1 quality risk warning threshold, a Phase 1 warning is issued. If the number of Phase 1 warnings exceeds the preset number within a preset time period, a Phase 1 shutdown is initiated, and staff are notified to carry out repairs. Based on the process of generating the first-stage regulatory plan, the second-stage, third-stage, and fourth-stage regulatory plans are generated.

8. The multi-dimensional, end-to-end comprehensive quality testing method for plastic particles according to claim 6, characterized in that, The analysis of material quality data, reactor quality data, and process quality data is as follows: The material quality data was analyzed to obtain the first-stage material quality index, the second-stage material quality index, the third-stage material quality index, and the fourth-stage material quality index. The difference between the first-stage material quality index and the initial material quality index in the database is divided by the initial material quality index to obtain the first-stage material quality deviation rate. The second-stage material quality deviation rate, the third-stage material quality deviation rate, and the fourth-stage material quality deviation rate are obtained in sequence. Based on the analysis methods for material quality data, the reactor quality data and process quality data are analyzed to obtain the quality deviation rates of the first-stage reactor, the second-stage reactor, the third-stage reactor, the fourth-stage reactor, the first-stage process quality deviation rate, the second-stage process quality deviation rate, the third-stage process quality deviation rate, and the fourth-stage process quality deviation rate.

9. The multi-dimensional, end-to-end comprehensive quality testing method for plastic particles according to claim 8, characterized in that, The specific setup process for the quality inspection entity is as follows: The type corresponding to the maximum deviation rate among the material quality deviation rate, reactor quality deviation rate and process quality deviation rate in the same stage is recorded as the quality inspection subject. In this way, the first-stage quality inspection subject, the second-stage quality inspection subject, the third-stage quality inspection subject and the fourth-stage quality inspection subject are obtained, and the various inspection data ranges of the first-stage quality inspection subject are set. Based on the method for obtaining the various test data ranges of the first-stage quality inspection entity, the various test data ranges of the second-stage quality inspection entity, the various test data ranges of the third-stage quality inspection entity, and the various test data ranges of the fourth-stage quality inspection entity are obtained.

10. The multi-dimensional, end-to-end comprehensive quality testing method for plastic particles according to claim 9, characterized in that, The specific generation process of the aforementioned process monitoring scheme is as follows: Early warning for the first-stage quality inspection subject: Collect various types of inspection data of the first-stage quality inspection subject within a preset time period. If a certain type of inspection data of the first-stage quality inspection subject collected in a certain period does not belong to the corresponding interval, an early warning for the first-stage subject will be issued. Based on the first-stage main body early warning plan, early warnings were issued for the second-stage, third-stage, and fourth-stage main bodies, and the number of early warnings for the first-stage, second-stage, third-stage, and fourth-stage main bodies within the preset time period was statistically obtained. If the number of warnings in the first, second, third, and fourth stages all exceed the preset number of warnings, the plastic particles within the preset time will be recycled. At the same time, the stage with the highest number of warnings will be marked as the risk stage, and staff will be prompted to carry out maintenance in the risk stage.

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