CN119217669A - A system and method for optimizing production parameters of injection molded parts - Google Patents
A system and method for optimizing production parameters of injection molded parts Download PDFInfo
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 59
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000002347 injection Methods 0.000 title claims description 50
- 239000007924 injection Substances 0.000 title claims description 50
- 239000002994 raw material Substances 0.000 claims abstract description 174
- 238000001746 injection moulding Methods 0.000 claims abstract description 164
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 68
- 238000005457 optimization Methods 0.000 claims abstract description 58
- 238000002360 preparation method Methods 0.000 claims abstract description 58
- 238000001816 cooling Methods 0.000 claims abstract description 28
- 238000012544 monitoring process Methods 0.000 claims abstract description 26
- 238000007689 inspection Methods 0.000 claims abstract description 24
- 230000008569 process Effects 0.000 claims abstract description 20
- 238000007711 solidification Methods 0.000 claims abstract description 17
- 230000008023 solidification Effects 0.000 claims abstract description 17
- 230000008859 change Effects 0.000 claims abstract description 15
- 238000011156 evaluation Methods 0.000 claims abstract description 15
- 230000002159 abnormal effect Effects 0.000 claims abstract description 13
- 239000000203 mixture Substances 0.000 claims description 58
- 238000003860 storage Methods 0.000 claims description 31
- 239000004033 plastic Substances 0.000 claims description 28
- 229920003023 plastic Polymers 0.000 claims description 28
- 239000000654 additive Substances 0.000 claims description 25
- 239000002245 particle Substances 0.000 claims description 17
- 238000007726 management method Methods 0.000 claims description 14
- 238000004458 analytical method Methods 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 13
- 230000000996 additive effect Effects 0.000 claims description 12
- 238000010438 heat treatment Methods 0.000 claims description 12
- 239000012778 molding material Substances 0.000 claims description 9
- 229920000426 Microplastic Polymers 0.000 claims description 8
- 238000002844 melting Methods 0.000 claims description 6
- 230000008018 melting Effects 0.000 claims description 6
- 238000005303 weighing Methods 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 3
- 230000005856 abnormality Effects 0.000 claims description 2
- 239000000843 powder Substances 0.000 claims 4
- 238000010223 real-time analysis Methods 0.000 claims 1
- 230000007547 defect Effects 0.000 abstract description 16
- 238000002156 mixing Methods 0.000 description 7
- 239000004743 Polypropylene Substances 0.000 description 5
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- 229920001155 polypropylene Polymers 0.000 description 4
- RNFJDJUURJAICM-UHFFFAOYSA-N 2,2,4,4,6,6-hexaphenoxy-1,3,5-triaza-2$l^{5},4$l^{5},6$l^{5}-triphosphacyclohexa-1,3,5-triene Chemical compound N=1P(OC=2C=CC=CC=2)(OC=2C=CC=CC=2)=NP(OC=2C=CC=CC=2)(OC=2C=CC=CC=2)=NP=1(OC=1C=CC=CC=1)OC1=CC=CC=C1 RNFJDJUURJAICM-UHFFFAOYSA-N 0.000 description 3
- 239000003063 flame retardant Substances 0.000 description 3
- 238000005215 recombination Methods 0.000 description 3
- 239000002699 waste material Substances 0.000 description 3
- 239000004594 Masterbatch (MB) Substances 0.000 description 2
- 239000004698 Polyethylene Substances 0.000 description 2
- 239000004793 Polystyrene Substances 0.000 description 2
- XECAHXYUAAWDEL-UHFFFAOYSA-N acrylonitrile butadiene styrene Chemical compound C=CC=C.C=CC#N.C=CC1=CC=CC=C1 XECAHXYUAAWDEL-UHFFFAOYSA-N 0.000 description 2
- 229920000122 acrylonitrile butadiene styrene Polymers 0.000 description 2
- 239000004676 acrylonitrile butadiene styrene Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000003908 quality control method Methods 0.000 description 2
- BQCADISMDOOEFD-UHFFFAOYSA-N Silver Chemical compound [Ag] BQCADISMDOOEFD-UHFFFAOYSA-N 0.000 description 1
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
- B29C45/17—Component parts, details or accessories; Auxiliary operations
- B29C45/76—Measuring, controlling or regulating
- B29C45/768—Detecting defective moulding conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2945/00—Indexing scheme relating to injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould
- B29C2945/76—Measuring, controlling or regulating
- B29C2945/76929—Controlling method
- B29C2945/76939—Using stored or historical data sets
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- Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Mechanical Engineering (AREA)
- Injection Moulding Of Plastics Or The Like (AREA)
Abstract
本发明涉及注塑生产管理技术领域，且公开了一种注塑件生产参数优化系统及方法，包括注塑生产模块和智能评估模块。该注塑件生产参数优化系统及方法通过注塑生产模块获取所有批次的注塑原料数据、所有时间点的工艺参数和所有注塑件的质检评分，并分类组成数据集，智能评估模块分析不同类型原料的含水量和每个批次之间制备比例的变化量，生成对应的制备数据组和差异数据组，深入分析原料缺陷问题，再生成对应的监测数据组，实时监测缺陷精准度高，智能评估模块根据固化数据集，分析注塑件质检评分的变化趋势和冷却时长的变化量，生成对应的波动指数，再判断注塑件生产过程中是否存在异常参数，并生成对应的优化信号，智能优化更及时有效。
The present invention relates to the technical field of injection molding production management, and discloses a system and method for optimizing the production parameters of injection molding parts, including an injection molding production module and an intelligent evaluation module. The system and method for optimizing the production parameters of injection molding parts obtains the injection molding raw material data of all batches, the process parameters of all time points and the quality inspection scores of all injection molding parts through the injection molding production module, and classifies and forms a data set. The intelligent evaluation module analyzes the water content of different types of raw materials and the change in the preparation ratio between each batch, generates a corresponding preparation data group and a difference data group, deeply analyzes the raw material defect problem, and then generates a corresponding monitoring data group. The real-time monitoring of defects is high in accuracy. The intelligent evaluation module analyzes the change trend of the injection molding part quality inspection score and the change in the cooling time according to the solidification data set, generates a corresponding fluctuation index, and then determines whether there are abnormal parameters in the injection molding part production process, and generates a corresponding optimization signal. The intelligent optimization is more timely and effective.
Description
Technical Field
The invention relates to the technical field of injection molding production management, in particular to an injection molding production parameter optimization system and method.
Background
The injection molded part is a plastic part produced by an injection molding process. The production process is that after the plastic raw material is heated and melted, the plastic raw material is injected into a mould under high pressure, and the plastic product with a preset shape is obtained after cooling and solidification. Common materials for injection molding include polyethylene PE, polypropylene PP, polystyrene PS, acrylonitrile-butadiene-styrene copolymer ABS, and the like. The injection molding piece has the characteristics of high flexibility, high production efficiency, low cost and strong durability. The injection molding part can meet various design requirements, parts with complex shapes can be produced, the injection molding process is high in automation degree and low in production cost, the injection molding part is suitable for mass production, is widely applied to the fields of automobiles, aviation and the like, and is beneficial to reducing the overall weight of mechanical parts and improving the fuel efficiency. The injection molding process parameters include barrel temperature, mold temperature, and injection pressure. The temperature of the charging barrel directly influences the plasticization and flow of the plastic, the temperature of the mold directly influences the cooling speed and quality of the product, and the injection pressure is a key factor for ensuring that the plastic fills the mold cavity, and directly influences the size and weight of the product. In order to improve the quality and production efficiency of injection molding products, the injection molding production parameter optimization system needs to overcome the defects in the traditional process by more intelligent and automatic technical means.
At present, the traditional injection molding production parameter optimization method is highly dependent on manual experience, so that the cause of product defects is difficult to discover in time, the resource waste is caused, in addition, different plastic materials have different flow properties, the traditional optimization method lacks intelligent decision making capability, the filling property of products is difficult to ensure, and the quality of the plastic parts is uneven.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an injection molding production parameter optimization system and method, which have the advantages of high accuracy of real-time monitoring defects, more timely and effective intelligent optimization and the like, and solve the problems that the traditional injection molding production parameter optimization method is difficult to discover the defect reasons in time and lacks intelligent decision-making capability.
In order to achieve the aim, the invention provides the technical scheme that the injection molding production parameter optimization system comprises an injection molding production module and an intelligent evaluation module;
The injection molding production module comprises a raw material preparation unit, a high-pressure injection molding unit and a cooling solidification unit, wherein the raw material preparation unit is connected with a database through a network to collect a raw material data set, the raw material data set comprises injection molding raw material data of all batches, the high-pressure injection molding unit is connected with an injection molding machine through the network to collect an injection molding data set, the injection molding data set comprises technological parameters of all time points, the cooling solidification unit is connected with the injection molding machine through the network to collect a solidification data set, the solidification data set comprises quality inspection scores of all injection molding pieces, and the injection molding production module transmits the raw material data set, the injection molding data set and the solidification data set to the intelligent evaluation module through a network;
the intelligent evaluation module consists of a raw material analysis unit, an injection molding analysis unit and an optimization management unit, wherein the raw material analysis unit analyzes the water content of different types of raw materials and the variation of the preparation proportion between each batch according to a raw material data set to generate a corresponding preparation data set And a difference data setAnd the barrel temperature threshold value of a fixed range is set in the injection molding analysis unitThreshold of durationThreshold of temperature injectionThreshold of die temperatureThreshold of pressureAnd a dwell thresholdAnd then combining the injection molding data set, analyzing the parameter change in the injection molding process in real time to generate a corresponding monitoring data setThe quality control score variation trend and the cooling time variation amount of the injection molding part are analyzed according to the solidification data set by the optimizing management unit to generate a corresponding fluctuation indexThe optimization management unit is provided with a quality threshold value in a fixed rangeStorage thresholdAnd a fluctuation thresholdPreparing data set by recombinationData set of differencesData set for monitoringIndex of fluctuationJudging whether abnormal parameters exist in the production process of the injection molding, and generating corresponding optimized signals.
Preferably, the expression of the raw material data set is,To the point ofAnd the injection molding raw material data of the first batch to the C batch are respectively, the injection molding raw materials comprise plastic particles, toner and additives, and S represents the storage time of the injection molding raw materials of the single batch.
Preferably, the expression of the injection molding data set is,To the point ofRespectively from the first time point to the first time pointTechnological parameters of each time point, wherein the technological parameters comprise barrel temperature, heating time, nozzle temperature, mold temperature, injection molding pressure and pressure maintaining time,Indicating the specific time at which a single set of process parameters was obtained.
Preferably, the expression of the solidified data set is,To the point ofRespectively the first to the secondQuality control scores for individual injection molded parts, t representing the cooling time period for the individual injection molded parts.
Preferably, the preparation data setThe calculation flow is as follows:
extracting the raw material data set Batch of injection molding material and bringing aboutThe batches of plastic particles are marked asWill be at the firstThe batch of toner is marked asWill be at the firstThe batch of additive is marked as;
Detection by a rapid moisture meter、AndWater content in (a) and then to (b)After uniformly mixing the plastic particles, the toner and the additives in batches, detecting the water content of the raw material mixture by a rapid moisture detector; In the formula (i), Represents the preparation data set, KL represents the water content in the optimum state of the plastic granules,Represent the firstThe water content of the plastic granules in the batch,Represents the difference between the actual water content and the optimal water content of the plastic particles, SF represents the water content of the toner in the optimal state,Represent the firstThe water content of the batch toner,Indicating the difference between the actual water content of the toner and the optimum water content, TJ indicating the water content in the optimum state of the additive,Represent the firstThe water content of the batch of additives,Represents the difference between the actual water content of the additive and the optimal water content, H represents the water content of the raw material mixture in the optimal state,Represent the firstThe water content of the batch raw material mixture,Representing the difference between the actual moisture content of the raw material mixture and the optimum moisture content.
Preferably, the difference data setThe calculation flow is as follows:
respectively detect through weighing type blendor 、AndIs to be added again with the firstAfter uniformly mixing the plastic particles, the toner and the additives in batches, detecting the quality of the raw material mixture by a weighing mixer, and marking as;
In the formula (i),The set of difference data is represented as,Represent the firstThe mass ratio of the batch of plastic particles, the toner and the additive is the firstBatch raw material preparation ratio,Represent the firstThe preparation proportion of the batch raw materials,Representing the will beBatch raw material preparation ratio comparisonThe preparation proportion of the batch raw materials,Represent the firstThe total mass of the batch of the raw material mixture,Representing the difference in mass of the feed mixture between two adjacent batches,Represent the firstThe storage time of the batch of injection molding materials,Represent the firstThe storage time of the batch of injection molding materials,Representing the difference in storage time between two adjacent batches.
Preferably, the monitoring data setThe calculation flow is as follows:
extracting the process parameters at the Kth time point in the injection molding data, and marking the barrel temperature at the Kth time point as The heating duration at the kth time point is marked asMarking the nozzle temperature at the Kth time point asThe mold temperature at the kth time point is marked asThe injection pressure at the Kth time point is marked asMarking the dwell time of the Kth time point as;
The flow rate of the raw material mixture after being melted in the charging barrel and the flow rate of the raw material mixture in the nozzle are respectively detected by a flowmeter and marked asAndWherein, the method comprises the steps of, wherein,Indicating the flow rate of the raw material mixture after melting in the barrel,Indicating the flow rate of the raw mixture in the nozzle;
In the formula (i), Representing a set of monitored data,AndRespectively the lowest value and the highest value in the cylinder temperature threshold value,Indicating that the cartridge temperature at the kth time point is compared to the cartridge temperature threshold,AndRespectively the lowest and highest of the duration thresholds,The heating duration at the kth time point is compared to a duration threshold,AndRespectively the lowest value and the highest value in the injection temperature threshold values,The injection nozzle temperature at the kth time point is compared with the injection temperature threshold value,AndRespectively the lowest value and the highest value in the mode temperature threshold value,The mold temperature at the kth time point is compared to the mold temperature threshold,AndRespectively the lowest and highest of the pressure thresholds,The injection pressure at the kth time point is shown against the pressure threshold,AndThe lowest value and the highest value in the pressure maintaining threshold value are respectively,Indicating that the dwell time at the kth time point is compared with the dwell threshold,Indicating the ratio of the flow rate of the raw material mixture after melting in the cylinder to the heating period,Indicating the ratio of the flow rate of the raw mixture in the nozzle to the injection pressure,The ratio of the flow rate of the raw material mixture in the nozzle to the dwell time is indicated.
Preferably, the fluctuation indexThe calculation flow is as follows:
In the formula (i), The index of the fluctuation is represented as,Representing the average quality score obtained by dividing the total quality score by the number of injection molded parts,Representing the quality score of the e-th injection molded part in the curing dataset,The variance value obtained according to the variance formula is the variation trend of the quality inspection score of the injection molding,Indicating the cooling time period of the e-th injection molding,Represent the firstThe cooling time period of each injection molding piece,Representing the difference in cooling time between two adjacent injection molded parts.
Preferably, the preparation data setWhen any one of the numerical values is negative, the water content of the raw materials exceeds the standard, a raw material optimizing signal is generated, and the difference data setThe raw material preparation proportion between two adjacent batches is inconsistent, and the quality difference value of the raw material mixture exceeds the quality threshold valueOr the difference value of the storage time length exceeds the storage threshold valueWhen the raw material preparation is abnormal, generating a raw material optimizing signal, and monitoring the data setWhen the process parameter exceeds the threshold value or the ratio is negative, the abnormality of the production parameter is indicated, a parameter optimization signal is generated, and the fluctuation index is obtainedThe median variance value exceeds the fluctuation thresholdAnd when the quality stability of the injection molding part is poor, generating a part optimization signal.
An injection molding production parameter optimization method comprises the following steps:
Step one, acquiring injection molding raw material data of all batches, technological parameters of all time points and quality inspection scores of all injection molding parts through a network connection database and an injection molding machine, and classifying to form a raw material data set, an injection molding data set and a curing data set;
detecting the water content of different raw materials in the raw material data set by a rapid moisture detector, uniformly mixing the raw materials according to batches, analyzing the water content of different types of raw materials and the variation of the preparation proportion between each batch, and generating a corresponding preparation data set And a difference data set;
Detecting the flow rate of the raw material mixture after being melted in the charging barrel and the flow rate of the raw material mixture in the nozzle respectively through a flowmeter, and analyzing the parameter change in the injection molding process in real time by combining an injection molding data set to generate a corresponding monitoring data set;
Analyzing the variation trend of the quality inspection score and the variation of the cooling time length of the injection molding part according to the solidification data set to generate a corresponding fluctuation index;
Step five, setting a quality threshold value of a fixed rangeStorage thresholdAnd a fluctuation thresholdPreparing data set by recombinationData set of differencesData set for monitoringIndex of fluctuationJudging whether abnormal parameters exist in the production process of the injection molding, and generating corresponding optimized signals.
Compared with the prior art, the invention provides an injection molding production parameter optimization system and method, which have the following beneficial effects:
1. The invention acquires the injection molding raw material data of all batches, the technological parameters of all time points and the quality inspection scores of all injection molding parts through the network connection database of the injection molding production module and the injection molding machine, classifies the injection molding raw material data into a raw material data set, an injection molding data set and a curing data set, and an intelligent evaluation module analyzes the water content of different types of raw materials and the variation of the preparation proportion among each batch according to the raw material data set to generate a corresponding preparation data set And a difference data setDeep analysis of defect problems of single-batch raw materials and raw material defect problems between two adjacent batches of similar products is helpful for ensuring stability of product quality, and the intelligent evaluation module is provided with a barrel temperature threshold value in a fixed rangeThreshold of durationThreshold of temperature injectionThreshold of die temperatureThreshold of pressureAnd a dwell thresholdAnd then combining the injection molding data set, analyzing the parameter change in the injection molding process in real time to generate a corresponding monitoring data setThe accuracy of real-time defect monitoring is high.
2. According to the invention, the intelligent evaluation module analyzes the variation trend of the quality inspection score and the variation of the cooling time length of the injection molding part according to the solidification data set to generate a corresponding fluctuation indexSetting a quality threshold value of a fixed rangeStorage thresholdAnd a fluctuation thresholdPreparation of data sets in combinationData set of differencesData set for monitoringIndex of fluctuationPreparing a data setWhen any one of the numerical values is negative, the water content of the raw materials exceeds the standard, a raw material optimizing signal is generated, and a difference data set is generatedThe raw material preparation proportion between two adjacent batches is inconsistent, and the quality difference value of the raw material mixture exceeds the quality threshold valueOr the difference value of the storage time length exceeds the storage threshold valueWhen the raw material preparation is abnormal, generating raw material optimization signals, and monitoring a data setWhen the process parameter exceeds the threshold value or the ratio is negative, the abnormal production parameter is indicated, a parameter optimization signal is generated, and the fluctuation index is generatedThe median variance value exceeds the fluctuation thresholdAnd when the quality stability of the injection molding part is poor, part optimization signals are generated, and intelligent optimization is more timely and effective.
Drawings
FIG. 1 is a schematic flow chart of the system of the present invention;
FIG. 2 is a process step diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Because the traditional injection molding production parameter optimization method highly depends on manual experience, the cause of product defects is difficult to discover in time, so that resource waste is caused, in addition, different plastic materials have different flow properties, the traditional optimization method lacks intelligent decision capability, the filling property of products is difficult to ensure, and the quality of the plastic parts is uneven, therefore, the injection molding production parameter optimization system and the injection molding production parameter optimization method are provided, and referring to fig. 1-2, the injection molding production parameter optimization system comprises an injection molding production module and an intelligent evaluation module;
The injection molding production module consists of a raw material preparation unit, a high-pressure injection molding unit and a cooling solidification unit, wherein the raw material preparation unit is connected with a database through a network to collect a raw material data set, the raw material data set comprises injection molding raw material data of all batches, and the expression of the raw material data set is as follows ,To the point ofThe injection molding raw material data of the first batch to the C batch are respectively, the injection molding raw materials comprise plastic particles, toner and additives, S represents the storage time of the injection molding raw materials of a single batch, the water is removed through drying equipment in the raw material preparation link, the occurrence of bubbles or silver marks in the injection molding part forming process can be prevented, the raw material data are comprehensively collected, the raw material problems can be found in time, and the resource waste is avoided;
The high-pressure injection molding unit is connected with the injection molding machine through a network to collect an injection molding data set, wherein the injection molding data set comprises technological parameters at all time points, and the expression of the injection molding data set is as follows ,To the point ofRespectively from the first time point to the first time pointTechnological parameters of each time point, wherein the technological parameters comprise barrel temperature, heating time, nozzle temperature, mold temperature, injection molding pressure and pressure maintaining time,The specific time for acquiring a single set of process parameters is represented, and in the injection molding process, different plastic materials and product requirements correspond to different injection molding pressures, time, temperature, dwell time and the like, and the production parameters are comprehensively acquired to ensure the product quality;
the cooling and solidifying unit is connected with the injection molding machine through a network to collect a solidifying data set, the solidifying data set comprises quality inspection scores of all injection molding pieces, and the expression of the solidifying data set is as follows ,To the point ofRespectively the first to the secondQuality inspection scores of the individual injection molding pieces, t represents cooling time of the individual injection molding pieces, and the quality inspection scores are specially tested for the appearance of the injection molding pieces, so that the products are ensured to have no defects such as bubbles, cracks and the like;
The injection molding production module transmits the raw material data set, the injection molding data set and the solidification data set to the intelligent evaluation module through a network;
The intelligent evaluation module consists of a raw material analysis unit, an injection molding analysis unit and an optimization management unit, wherein the raw material analysis unit analyzes the water content of different types of raw materials and the variation of the preparation proportion between each batch according to a raw material data set to generate a corresponding preparation data set And a difference data setAnd the data is transmitted to an optimization management unit through a network, and the calculation flow is as follows:
extracting the raw material data set Batch of injection molding material and bringing aboutThe batches of plastic particles are marked asWill be at the firstThe batch of toner is marked asWill be at the firstThe batch of additive is marked as;
Detection by a rapid moisture meter、AndWater content in (a) and then to (b)After uniformly mixing the plastic particles, the toner and the additives in batches, detecting the water content of the raw material mixture by a rapid moisture detector; In the formula (i), Represents the preparation data set, KL represents the water content in the optimum state of the plastic granules,Represent the firstThe water content of the plastic granules in the batch,Represents the difference between the actual water content and the optimal water content of the plastic particles, SF represents the water content of the toner in the optimal state,Represent the firstThe water content of the batch toner,Indicating the difference between the actual water content of the toner and the optimum water content, TJ indicating the water content in the optimum state of the additive,Represent the firstThe water content of the batch of additives,Represents the difference between the actual water content of the additive and the optimal water content, H represents the water content of the raw material mixture in the optimal state,Represent the firstThe water content of the batch raw material mixture,Representing the difference value between the actual water content and the optimal water content of the raw material mixture, deeply analyzing the defect problem of the single-batch raw material, and helping to ensure the stability of the quality of the single-batch product;
respectively detect through weighing type blendor 、AndIs to be added again with the firstAfter uniformly mixing the plastic particles, the toner and the additives in batches, detecting the quality of the raw material mixture by a weighing mixer, and marking as;
In the formula (i),The set of difference data is represented as,Represent the firstThe mass ratio of the batch of plastic particles, the toner and the additive is the firstBatch raw material preparation ratio,Represent the firstThe preparation proportion of the batch raw materials,Representing the will beBatch raw material preparation ratio comparisonThe preparation proportion of the batch raw materials,Represent the firstThe total mass of the batch of the raw material mixture,Representing the difference in mass of the feed mixture between two adjacent batches,Represent the firstThe storage time of the batch of injection molding materials,Represent the firstThe storage time of the batch of injection molding materials,Representing the difference value of the storage time between two adjacent batches, deeply analyzing the raw material defect problem between the two adjacent batches of the similar product, and helping to ensure the stability of the quality of the multi-batch product;
the injection molding analysis unit is provided with a barrel temperature threshold value with a fixed range Threshold of durationThreshold of temperature injectionThreshold of die temperatureThreshold of pressureAnd a dwell thresholdAnd then combining the injection molding data set, analyzing the parameter change in the injection molding process in real time to generate a corresponding monitoring data setAnd the data is transmitted to an optimization management unit through a network, and the calculation flow is as follows:
extracting the process parameters at the Kth time point in the injection molding data, and marking the barrel temperature at the Kth time point as The heating duration at the kth time point is marked asMarking the nozzle temperature at the Kth time point asThe mold temperature at the kth time point is marked asThe injection pressure at the Kth time point is marked asMarking the dwell time of the Kth time point as;
The flow rate of the raw material mixture after being melted in the charging barrel and the flow rate of the raw material mixture in the nozzle are respectively detected by a flowmeter and marked asAndWherein, the method comprises the steps of, wherein,Indicating the flow rate of the raw material mixture after melting in the barrel,Indicating the flow rate of the raw mixture in the nozzle;
In the formula (i), Representing a set of monitored data,AndRespectively the lowest value and the highest value in the cylinder temperature threshold value,Indicating that the cartridge temperature at the kth time point is compared to the cartridge temperature threshold,AndRespectively the lowest and highest of the duration thresholds,The heating duration at the kth time point is compared to a duration threshold,AndRespectively the lowest value and the highest value in the injection temperature threshold values,The injection nozzle temperature at the kth time point is compared with the injection temperature threshold value,AndRespectively the lowest value and the highest value in the mode temperature threshold value,The mold temperature at the kth time point is compared to the mold temperature threshold,AndRespectively the lowest and highest of the pressure thresholds,The injection pressure at the kth time point is shown against the pressure threshold,AndThe lowest value and the highest value in the pressure maintaining threshold value are respectively,Indicating that the dwell time at the kth time point is compared with the dwell threshold,Indicating the ratio of the flow rate of the raw material mixture after melting in the cylinder to the heating period,Indicating the ratio of the flow rate of the raw mixture in the nozzle to the injection pressure,The ratio of the flow rate of the raw material mixture in the nozzle to the pressure maintaining time is represented, and the defect accuracy is high in real-time monitoring;
The optimizing management unit analyzes the change trend of the quality inspection score and the change quantity of the cooling time length of the injection molding part according to the solidification data set to generate a corresponding fluctuation index The calculation flow is as follows:
In the formula (i), The index of the fluctuation is represented as,Representing the average quality score obtained by dividing the total quality score by the number of injection molded parts,Representing the quality score of the e-th injection molded part in the curing dataset,The variance value obtained according to the variance formula is the variation trend of the quality inspection score of the injection molding,Indicating the cooling time period of the e-th injection molding,Represent the firstThe cooling time period of each injection molding piece,A difference representing a cooling time period between two adjacent injection molded parts;
The optimization management unit is provided with a quality threshold value in a fixed range Storage thresholdAnd a fluctuation thresholdPreparing a data setWhen any one of the numerical values is negative, the water content of the raw materials exceeds the standard, a raw material optimizing signal is generated, and a difference data set is generatedThe raw material preparation proportion between two adjacent batches is inconsistent, and the quality difference value of the raw material mixture exceeds the quality threshold valueOr the difference value of the storage time length exceeds the storage threshold valueWhen the raw material preparation is abnormal, generating raw material optimization signals, and monitoring a data setWhen the process parameter exceeds the threshold value or the ratio is negative, the abnormal production parameter is indicated, a parameter optimization signal is generated, and the fluctuation index is generatedThe median variance value exceeds the fluctuation thresholdAnd when the quality stability of the injection molding part is poor, part optimization signals are generated, and intelligent optimization is more timely and effective.
An injection molding production parameter optimization method comprises the following steps:
Step one, acquiring injection molding raw material data of all batches, technological parameters of all time points and quality inspection scores of all injection molding parts through a network connection database and an injection molding machine, and classifying to form a raw material data set, an injection molding data set and a curing data set;
detecting the water content of different raw materials in the raw material data set by a rapid moisture detector, uniformly mixing the raw materials according to batches, analyzing the water content of different types of raw materials and the variation of the preparation proportion between each batch, and generating a corresponding preparation data set And a difference data set;
Detecting the flow rate of the raw material mixture after being melted in the charging barrel and the flow rate of the raw material mixture in the nozzle respectively through a flowmeter, and analyzing the parameter change in the injection molding process in real time by combining an injection molding data set to generate a corresponding monitoring data setThe accuracy of real-time defect monitoring is high;
Analyzing the variation trend of the quality inspection score and the variation of the cooling time length of the injection molding part according to the solidification data set to generate a corresponding fluctuation index ;
Step five, setting a quality threshold value of a fixed rangeStorage thresholdAnd a fluctuation thresholdPreparing data set by recombinationData set of differencesData set for monitoringIndex of fluctuationJudging whether abnormal parameters exist in the production process of the injection molding part, generating corresponding optimized signals, and performing intelligent optimization more timely and effective.
Example 1 in this experiment, a 1200 mm.times.2400 mm high density polyethylene was selected as the subject, and tested to have a moisture content of 0.01% for the batch, a moisture content of 0.5% for the polypropylene masterbatch, a moisture content of 0.2% for the flame retardant, a moisture content of 0.25% for the batch after mixing uniformly, a moisture content of 0.01% for the injection molded part of the batch in the optimum state, a moisture content of 0.3% for the polypropylene masterbatch in the optimum state, a moisture content of 0.1% for the flame retardant in the optimum state, a moisture content of 0.3% for the raw material mixture in the optimum state, and a data set was preparedThe calculation formula is as follows:
In the formula (i), Representing a preparation data set, wherein 0.01-0.01 represents the difference value between the actual water content of the plastic particles and the optimal water content, 0.2 represents the difference value between the actual water content of the toner and the optimal water content, 0.1 represents the difference value between the actual water content of the additive and the optimal water content, 0.05 represents the difference value between the actual water content of the raw material mixture and the optimal water content, and the water content of the polypropylene color master batch and the water content of the flame retardant in the batch are over-standard after judging, so that a raw material optimization signal is generated.
Example 2 in this experiment, raw materials used for production on 10 months 1 and 10 months 2 were selected as experimental objects, and the raw materials were counted to have a raw material preparation ratio of 3:2:1 on 10 months 1, a total mass of the raw material mixture of 6000kg, a raw material storage period of 3 months, a total mass of the raw material mixture of 3:2:1 on 10 months 2, a raw material storage period of 12 months, and a mass threshold valueSet to 1000kg, store thresholdSet to 6 months, differential data setThe calculation formula is as follows:
In the formula (i), The set of difference data is represented as,The method is characterized in that the preparation ratio of the raw materials at the time of 10 months and 2 days is compared with the preparation ratio of the raw materials at the time of 10 months and 1 day, 6000kg is the difference of the mass of the raw material mixture between two adjacent batches, 9 months are the difference of the storage time of the raw materials at the time of 10 months and 2 days and 1 day, and the storage time of the raw materials at the time of 10 months and 2 days is too long, the mass is difficult to ensure, the raw material preparation is abnormal, and a raw material optimization signal is generated.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
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