Injection molding part production parameter optimization system and method

Abstract

The invention relates to the technical field of injection molding production management, and discloses an injection molding part production parameter optimization system and method, and the system comprises an injection molding production module and an intelligent evaluation module. According to the injection molding part production parameter optimization system and method, injection molding raw material data of all batches, process parameters of all time points and quality inspection scores of all injection molding parts are obtained through an injection molding production module and are classified to form a data set, and an intelligent evaluation module analyzes the water content of different types of raw materials and the variable quantity of the preparation proportion between the batches; the intelligent evaluation module generates a corresponding preparation data set and a difference data set, deeply analyzes the defect problem of raw materials, and then generates a corresponding monitoring data set, the real-time defect monitoring accuracy is high, and the intelligent evaluation module analyzes the change trend of injection molding part quality inspection scores and the change amount of cooling duration according to a curing data set, generates a corresponding fluctuation index, and carries out the real-time monitoring of the defects. And then whether abnormal parameters exist in the production process of the injection molding part is judged, corresponding optimization signals are generated, and intelligent optimization is more timely and effective.

Description

Technical Field

[0001] The invention relates to the technical field of injection molding production management, and in particular to an injection molding production parameter optimization system and method. Background Art

[0002] Injection molded parts are plastic parts produced by the injection molding process. This production process is to heat and melt the plastic raw materials, inject them into the mold under high pressure, and cool and solidify to obtain a plastic product of a preset shape. Common materials for injection molding include polyethylene PE, polypropylene PP, polystyrene PS, acrylonitrile-butadiene-styrene copolymer ABS, etc. Injection molded parts have the characteristics of high flexibility, high production efficiency, low cost and strong durability. Injection molded parts can meet a variety of design requirements and can produce parts with complex shapes. The injection molding process has a high degree of automation and low production cost. It is suitable for mass production and is widely used in the fields of automobiles and aviation. It helps to reduce the overall weight of mechanical parts and improve fuel efficiency. The process parameters of injection molding parts include barrel temperature, mold temperature and injection pressure. The barrel temperature directly affects the plasticization and flow of the plastic, the mold temperature directly affects the cooling speed and quality of the product, and the injection pressure is the key factor to ensure that the plastic fills the mold cavity, which directly affects the size and weight of the product. In order to improve the quality and production efficiency of injection molded products, the injection molding production parameter optimization system needs to overcome the shortcomings of traditional processes through more intelligent and automated technical means.

[0003] At present, the traditional injection molding production parameter optimization method is highly dependent on manual experience, and it is difficult to discover the causes of product defects in a timely manner, resulting in waste of resources. In addition, different plastic materials have different flow properties. Traditional optimization methods lack intelligent decision-making capabilities, making it difficult to ensure the filling of products, and the quality of plastic parts is uneven. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a system and method for optimizing the production parameters of injection molded parts, which have the advantages of high accuracy in real-time defect monitoring and more timely and effective intelligent optimization. It solves the problem that the traditional method for optimizing the production parameters of injection molded parts is difficult to detect the causes of defects in a timely manner and lacks intelligent decision-making capabilities.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solutions: an injection molding production parameter optimization system, comprising an injection molding production module and an intelligent evaluation module; The injection molding production module is composed of a raw material preparation unit, a high-pressure injection molding unit and a cooling and curing unit. The raw material preparation unit is connected to a database through a network to collect a raw material data set, and the raw material data set includes all batches of injection molding raw material data. The high-pressure injection molding unit is connected to an injection molding machine through a network to collect an injection molding data set, and the injection molding data set includes process parameters at all time points. The cooling and curing unit is connected to an injection molding machine through a network to collect a curing data set, and the curing data set includes quality inspection scores of all injection molded parts. The injection molding production module transmits the raw material data set, the injection molding data set and the curing data set to the intelligent evaluation module through the network; The intelligent evaluation module consists of a raw material analysis unit, an injection molding analysis unit and an optimization management unit. The raw material analysis unit analyzes the moisture content of different types of raw materials and the change in the preparation ratio between each batch according to the raw material data set, and generates a corresponding preparation data set. and difference data set and transmitted to the optimization management unit through the network. The injection molding analysis unit is set with a fixed range of barrel temperature thresholds. , duration threshold , injection temperature threshold , Mold temperature threshold , Pressure threshold and holding pressure threshold , combined with the injection molding data set, real-time analysis of the parameter changes in the injection molding process, and generation of the corresponding monitoring data set , and transmitted to the optimization management unit through the network. The optimization management unit analyzes the change trend of the injection molded part quality inspection score and the change amount of the cooling time according to the solidification data set, and generates the corresponding fluctuation index The optimization management unit is set with a fixed range of quality thresholds , Storage Threshold and fluctuation threshold , combined with the preparation data set , difference data set , Monitoring Data Group and volatility index , determine whether there are abnormal parameters in the injection molding production process and generate corresponding optimization signals.

[0006] Preferably, the expression of the raw material data set is , to They are injection molding raw material data from the first batch to the C batch respectively. The injection molding raw materials include plastic particles, color powder and additives. S represents the storage time of a single batch of injection molding raw materials.

[0007] Preferably, the expression of the injection molding data set is: , to From the first time point to the The process parameters at each time point include barrel temperature, heating time, nozzle temperature, mold temperature, injection pressure and holding time. Indicates the specific time for obtaining a single set of process parameters.

[0008] Preferably, the expression of the solidified data set is , to The first to the The quality inspection score of each injection molded part is shown in Figure 2, and t represents the cooling time of a single injection molded part.

[0009] Preferably, the preparation data set The calculation process is as follows: Extract the raw material data set Batch of injection molding materials, and the first Batches of plastic pellets are marked , will The toner batch is marked as , will Batches of additives are marked as ; Detected by rapid moisture analyzer 、 and The water content in After the batches of plastic granules, color powder and additives are mixed evenly, the moisture content of the raw material mixture is tested by a rapid moisture meter; In the formula, represents the preparation data set, KL represents the water content of the plastic particles in the optimal state, Indicates The moisture content of the plastic pellet batch, It indicates the difference between the actual water content of plastic particles and the optimal water content. SF indicates the water content of toner in the optimal state. Indicates The moisture content of the toner batch, It indicates the difference between the actual water content of the toner and the optimum water content. TJ indicates the water content of the additive under the optimum state. Indicates The water content of the batch additive, It 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 under the optimal state. Indicates The moisture content of the batch raw material mixture, Indicates the difference between the actual moisture content of the raw material mixture and the optimal moisture content.

[0010] Preferably, the difference data set The calculation process is as follows: Detection by weighing mixer 、 and The quality of After the batches of plastic granules, color powder and additives are mixed evenly, the quality of the raw material mixture is tested by a weighing mixer and marked as ; In the formula, represents the difference data set, Indicates The mass ratio of the plastic particles, color powder and additives in the first batch is Batch raw material preparation ratio , Indicates Batch raw material preparation ratio, Indicates that the Comparison of batch raw material preparation ratio Batch raw material preparation ratio, Indicates The total mass of the batch raw material mixture, Represents the difference in the quality of the raw material mixture between two adjacent batches, Indicates Storage time of batch injection molding materials, Indicates Storage time of batch injection molding materials, Indicates the difference in storage duration between two adjacent batches.

[0011] Preferably, the monitoring data set The calculation process is as follows: Extract the process parameters at the Kth time point in the injection molding data set, and mark the barrel temperature at the Kth time point as , mark the heating time at the Kth time point as , mark the nozzle temperature at the Kth time point as , mark the mold temperature at the Kth time point as , the injection pressure at the Kth time point is marked as , mark the holding time at the Kth time point as ; The flow rate of the raw material mixture after melting in the barrel and the flow rate of the raw material mixture in the nozzle are detected by the flow meter and marked as and ,in, It indicates the flow rate of the raw material mixture after it is melted in the barrel. Indicates the flow rate of the raw material mixture in the nozzle; In the formula, represents the monitoring data set, and are the lowest and highest values ​​in the barrel temperature threshold, respectively. It means comparing the barrel temperature at the Kth time point with the barrel temperature threshold. and are the lowest and highest values ​​in the duration threshold respectively, It means comparing the heating time at the Kth time point with the time threshold. and are the lowest and highest values ​​of the injection temperature threshold, respectively. It means comparing the nozzle temperature at the Kth time point with the injection temperature threshold. and are the lowest and highest values ​​of the mold temperature thresholds, Indicates comparing the mold temperature at the Kth time point with the mold temperature threshold. and are the lowest and highest values ​​in the pressure threshold, respectively. Indicates comparing the injection pressure at the Kth time point to the pressure threshold. and are the lowest and highest values ​​of the pressure holding threshold, It means comparing the holding time at the Kth time point with the holding threshold. It indicates the ratio of the flow rate of the raw material mixture after it is melted in the barrel to the heating time. It indicates the ratio of the flow rate of the raw material mixture in the nozzle to the injection pressure. It indicates the ratio of the flow rate of the raw material mixture in the nozzle to the holding time.

[0012] Preferably, the volatility index The calculation process is as follows: In the formula, represents the volatility index, Indicates the average quality inspection score obtained by dividing the total quality inspection score by the number of injection molded parts , represents the quality inspection score of the e-th injection molded part in the solidification data set, It means that according to the variance formula, the variance value obtained is the changing trend of the quality inspection score of the injection molded parts. Indicates the cooling time of the e-th injection molded part, Indicates Cooling time of each injection molded part, Indicates the difference in cooling time between two adjacent injection molded parts.

[0013] Preferably, the preparation data set When any value is negative, it means that the moisture content of the raw material exceeds the standard, and a raw material optimization signal is generated. The raw material preparation ratio between two adjacent batches is inconsistent, and the quality difference of the raw material mixture exceeds the quality threshold Or the storage duration difference exceeds the storage threshold When the raw material preparation is abnormal, a raw material optimization signal is generated. When the process parameters exceed the threshold or the ratio is negative, it indicates that the production parameters are abnormal and a parameter optimization signal is generated. The variance exceeds the volatility threshold When , it indicates that the quality of the injection molded part is poor and a part optimization signal is generated.

[0014] A method for optimizing production parameters of injection molded parts comprises the following steps: Step 1: Connect the database and injection molding machine through the network to obtain the injection molding raw material data of all batches, the process parameters at all time points and the quality inspection scores of all injection molded parts, and classify them into raw material data set, injection molding data set and curing data set; Step 2: Use a rapid moisture meter to test the moisture content of different raw materials in the raw material data set, then mix them evenly according to batches, analyze the moisture content of different types of raw materials and the changes in the preparation ratio between each batch, and generate the corresponding preparation data set and difference data set ; Step 3: Use the flow meter to detect the flow rate of the raw material mixture after melting in the barrel and the flow rate of the raw material mixture in the nozzle, and then combine the injection molding data set to analyze the parameter changes during the injection molding process in real time to generate the corresponding monitoring data set. ; Step 4: Analyze the change trend of the quality inspection score of the injection molded parts and the change in cooling time based on the solidification data set, and generate the corresponding fluctuation index ; Step 5: Set a fixed range of quality thresholds , Storage Threshold and fluctuation threshold , combined with the preparation data set , difference data set , Monitoring Data Group and volatility index , determine whether there are abnormal parameters in the injection molding production process and generate corresponding optimization signals.

[0015] Compared with the prior art, the present invention provides a system and method for optimizing production parameters of injection molded parts, which has the following beneficial effects:

[0016] 1. The present invention 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 molded parts through the injection molding production module network connection database and injection molding machine, and classifies them into raw material data sets, injection molding data sets and curing data sets. The intelligent evaluation module analyzes the water content of different types of raw materials and the change in the preparation ratio between each batch according to the raw material data sets, and generates the corresponding preparation data sets. and difference data set , in-depth analysis of the defects of a single batch of raw materials and the defects of raw materials between two adjacent batches of the same product helps to ensure the stability of product quality. The intelligent evaluation module is set with a fixed range of barrel temperature thresholds , duration threshold , injection temperature threshold , Mold temperature threshold , Pressure threshold and holding pressure threshold , combined with the injection molding data set, real-time analysis of the parameter changes in the injection molding process, and generation of the corresponding monitoring data set , real-time defect monitoring with high accuracy.

[0017] 2. The present invention uses an intelligent evaluation module to analyze the change trend of the injection molded parts quality inspection score and the change in cooling time according to the solidification data set, and generates a corresponding fluctuation index , and then set a fixed range of quality thresholds , Storage Threshold and fluctuation threshold , combined with the preparation data set , difference data set , Monitoring Data Group and volatility index , prepare data set When any value is negative, it means that the moisture content of the raw material exceeds the standard, and a raw material optimization signal is generated. The raw material preparation ratio between two adjacent batches is inconsistent, and the quality difference of the raw material mixture exceeds the quality threshold Or the storage duration difference exceeds the storage threshold When the raw material preparation is abnormal, a raw material optimization signal is generated, and the monitoring data group When the process parameters exceed the threshold or the ratio is negative, it means that the production parameters are abnormal, and a parameter optimization signal is generated. The variance exceeds the volatility threshold When , it indicates that the quality of the injection molded parts is poor and stable, and a part optimization signal is generated, and intelligent optimization is more timely and effective. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the system flow of the present invention;

[0019] Figure 2 It is a step diagram of the method of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Since the traditional injection molding production parameter optimization method is highly dependent on manual experience, it is difficult to find the cause of product defects in time, resulting in waste of resources. In addition, different plastic materials have different flow properties. The traditional optimization method lacks intelligent decision-making ability and is difficult to ensure the filling of the product. The quality of plastic parts is uneven. Therefore, a system and method for optimizing the production parameters of injection molding parts are provided. Please refer to Figure 1-Figure 2 , an injection molding production parameter optimization system, including 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 and curing unit. The raw material preparation unit collects raw material data sets through a network connection database. The raw material data sets include all batches of injection molding raw material data. The expression of the raw material data set is: , to They are the injection molding raw material data from the first batch to the C batch. The injection molding raw materials include plastic particles, color powder and additives. S represents the storage time of a single batch of injection molding raw materials. In the raw material preparation process, moisture needs to be removed by drying equipment to prevent bubbles or silver streaks from appearing during the injection molding process. Comprehensive collection of raw material data helps to discover raw material problems in a timely manner and avoid waste of resources. The high-pressure injection molding unit is connected to the injection molding machine through the network to collect the injection molding data set. The injection molding data set includes the process parameters at all time points. The expression of the injection molding data set is: , to From the first time point to the The process parameters at each time point include barrel temperature, heating time, nozzle temperature, mold temperature, injection pressure and holding time. Indicates the specific time of obtaining a single set of process parameters. During the injection molding process, different plastic materials and product requirements correspond to different injection pressures, times, temperatures, and holding times. Comprehensive collection of production parameters can ensure product quality. The cooling and curing unit is connected to the injection molding machine through the network to collect the curing data set. The curing data set includes the quality inspection scores of all injection molded parts. The expression of the curing data set is: , to The first to the The quality inspection score of each injection molded part, t represents the cooling time of a single injection molded part. The quality inspection score is specifically for the appearance of the injection molded part to ensure that the product has no defects such as bubbles and cracks; The injection molding production module transmits the raw material data set, injection molding data set and curing data set to the intelligent evaluation module through the network; The intelligent evaluation module consists of a raw material analysis unit, an injection molding analysis unit, and an optimization management unit. The raw material analysis unit analyzes the moisture content of different types of raw materials and the change in the preparation ratio between each batch based on the raw material data set, and generates the corresponding preparation data set. and difference data set , and transmitted to the optimization management unit through the network. The calculation process is as follows: Extract the raw material data set Batch of injection molding materials, and the first Batches of plastic pellets are marked , will The toner batch is marked as , will Batches of additives are marked as ; Detected by rapid moisture analyzer 、 and The water content in After the batches of plastic granules, color powder and additives are mixed evenly, the moisture content of the raw material mixture is tested by a rapid moisture meter; In the formula, represents the preparation data set, KL represents the water content of the plastic particles in the optimal state, Indicates The moisture content of the plastic pellet batch, It indicates the difference between the actual water content of plastic particles and the optimal water content. SF indicates the water content of toner in the optimal state. Indicates The moisture content of the toner batch, It indicates the difference between the actual water content of the toner and the optimum water content. TJ indicates the water content of the additive under the optimum state. Indicates The water content of the batch additive, It 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 under the optimal state. Indicates The moisture content of the batch raw material mixture, Indicates the difference between the actual moisture content of the raw material mixture and the optimal moisture content, and deeply analyzes the defects of a single batch of raw materials, which helps to ensure the stability of the quality of a single batch of products; Detection by weighing mixer 、 and The quality of After the batches of plastic granules, color powder and additives are mixed evenly, the quality of the raw material mixture is tested by a weighing mixer and marked as ; In the formula, represents the difference data set, Indicates The mass ratio of the plastic particles, color powder and additives in the first batch is Batch raw material preparation ratio , Indicates Batch raw material preparation ratio, Indicates that the Comparison of batch raw material preparation ratio Batch raw material preparation ratio, Indicates The total mass of the batch raw material mixture, Represents the difference in the quality of the raw material mixture between two adjacent batches, Indicates Storage time of batch injection molding materials, Indicates Storage time of batch injection molding materials, Indicates the difference in storage time between two adjacent batches, and deeply analyzes the raw material defects between two adjacent batches of the same product, which helps to ensure the stability of the quality of multiple batches of products; The injection molding analysis unit is set with a fixed range of barrel temperature thresholds , duration threshold , injection temperature threshold , Mold temperature threshold , Pressure threshold and holding pressure threshold , combined with the injection molding data set, real-time analysis of the parameter changes in the injection molding process, and generation of the corresponding monitoring data set , and transmitted to the optimization management unit through the network. The calculation process is as follows: Extract the process parameters at the Kth time point in the injection molding data set, and mark the barrel temperature at the Kth time point as , mark the heating time at the Kth time point as , mark the nozzle temperature at the Kth time point as , mark the mold temperature at the Kth time point as , the injection pressure at the Kth time point is marked as , mark the holding time at the Kth time point as ; The flow rate of the raw material mixture after melting in the barrel and the flow rate of the raw material mixture in the nozzle are detected by the flow meter and marked as and ,in, It indicates the flow rate of the raw material mixture after it is melted in the barrel. Indicates the flow rate of the raw material mixture in the nozzle; In the formula, represents the monitoring data set, and are the lowest and highest values ​​in the barrel temperature threshold, respectively. It means comparing the barrel temperature at the Kth time point with the barrel temperature threshold. and are the lowest and highest values ​​in the duration threshold respectively, It means comparing the heating time at the Kth time point with the time threshold. and are the lowest and highest values ​​of the injection temperature threshold, respectively. It means comparing the nozzle temperature at the Kth time point with the injection temperature threshold. and are the lowest and highest values ​​of the mold temperature thresholds, Indicates comparing the mold temperature at the Kth time point with the mold temperature threshold. and are the lowest and highest values ​​in the pressure threshold, respectively. Indicates comparing the injection pressure at the Kth time point to the pressure threshold. and are the lowest and highest values ​​of the pressure holding threshold, It means comparing the holding time at the Kth time point with the holding threshold. It indicates the ratio of the flow rate of the raw material mixture after it is melted in the barrel to the heating time. It indicates the ratio of the flow rate of the raw material mixture in the nozzle to the injection pressure. It indicates the ratio of the flow rate of the raw material mixture in the nozzle to the holding time, and can monitor defects in real time with high accuracy. The optimization management unit analyzes the change trend of the injection molded parts quality inspection score and the change in cooling time based on the solidification data set, and generates the corresponding fluctuation index The calculation process is as follows: In the formula, represents the volatility index, Indicates the average quality inspection score obtained by dividing the total quality inspection score by the number of injection molded parts , represents the quality inspection score of the e-th injection molded part in the solidification data set, It means that according to the variance formula, the variance value obtained is the changing trend of the quality inspection score of the injection molded parts. Indicates the cooling time of the e-th injection molded part, Indicates Cooling time of each injection molded part, Indicates the difference in cooling time between two adjacent injection molded parts; The optimization management unit is set with a fixed range of quality thresholds , Storage Threshold and fluctuation threshold , prepare data set When any value is negative, it means that the moisture content of the raw material exceeds the standard, and a raw material optimization signal is generated. The raw material preparation ratio between two adjacent batches is inconsistent, and the quality difference of the raw material mixture exceeds the quality threshold Or the storage duration difference exceeds the storage threshold When the raw material preparation is abnormal, a raw material optimization signal is generated, and the monitoring data group When the process parameters exceed the threshold or the ratio is negative, it means that the production parameters are abnormal, and a parameter optimization signal is generated. The variance exceeds the volatility threshold When , it indicates that the quality of the injection molded parts is poor and stable, and a part optimization signal is generated, and intelligent optimization is more timely and effective.

[0022] A method for optimizing production parameters of injection molded parts comprises the following steps: Step 1: Connect the database and injection molding machine through the network to obtain the injection molding raw material data of all batches, the process parameters at all time points and the quality inspection scores of all injection molded parts, and classify them into raw material data set, injection molding data set and curing data set; Step 2: Use a rapid moisture meter to test the moisture content of different raw materials in the raw material data set, then mix them evenly according to batches, analyze the moisture content of different types of raw materials and the changes in the preparation ratio between each batch, and generate the corresponding preparation data set and difference data set ; Step 3: Use the flow meter to detect the flow rate of the raw material mixture after melting in the barrel and the flow rate of the raw material mixture in the nozzle, and then combine the injection molding data set to analyze the parameter changes during the injection molding process in real time to generate the corresponding monitoring data set. , real-time monitoring of defects with high accuracy; Step 4: Analyze the change trend of the quality inspection score of the injection molded parts and the change in cooling time based on the solidification data set, and generate the corresponding fluctuation index ; Step 5: Set a fixed range of quality thresholds , Storage Threshold and fluctuation threshold , combined with the preparation data set , difference data set , Monitoring Data Group and volatility index , determine whether there are abnormal parameters in the injection molding production process, and generate corresponding optimization signals, making intelligent optimization more timely and effective.

[0023] Example 1: In this experiment, a 1200mm×2400mm high-density polyethylene was selected as the experimental object. After testing, the water content of this batch of high-density polyethylene was 0.01%, the water content of the polypropylene masterbatch was 0.5%, the water content of the flame retardant was 0.2%, and the water content of this batch of raw materials after uniform mixing was 0.25%. The water content of the high-density polyethylene of this batch of injection moldings in the best state should be 0.01%, the water content of the polypropylene masterbatch in the best state should be 0.3%, the water content of the flame retardant in the best state should be 0.1%, and the water content of the raw material mixture in the best state should be 0.3%. Prepare a data set The calculation formula is as follows: In the formula, It represents the preparation data group, 0.01-0.01 represents the difference between the actual moisture content of the plastic particles and the optimal moisture content, -0.2 represents the difference between the actual moisture content of the color powder and the optimal moisture content, -0.1 represents the difference between the actual moisture content of the additive and the optimal moisture content, and 0.05 represents the difference between the actual moisture content of the raw material mixture and the optimal moisture content. It is judged that the moisture content of this batch of polypropylene masterbatch and flame retardant exceeds the standard, and a raw material optimization signal is generated.

[0024] Example 2: In this experiment, the raw materials used in production on October 1 and October 2 were selected as experimental objects. According to statistics, the raw material preparation ratio on October 1 was 3:2:1, the total mass of the raw material mixture was 6000 kg, and the raw material storage time was 3 months. The raw material preparation ratio on October 2 was 3:2:1, the total mass of the raw material mixture was 12000 kg, and the raw material storage time was 12 months. The quality threshold Set to 1000kg, storage threshold Set to 6 months, difference data group The calculation formula is as follows: In the formula, represents the difference data set, It means comparing the raw material preparation ratio on October 2 with that on October 1, 6000kg indicates the difference in mass of the raw material mixture between two adjacent batches, and 9 months indicates the difference in storage time of the raw materials produced on October 2 and October 1. It is determined that the storage time of the raw materials produced on October 2 was too long, the quality was difficult to guarantee, there was an abnormality in the raw material preparation, and a raw material optimization signal was generated.

[0025] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An injection molding production parameter optimization system, characterized in that: Includes injection molding production module and intelligent evaluation module; The injection molding production module is composed of a raw material preparation unit, a high-pressure injection molding unit and a cooling and curing unit. The raw material preparation unit is connected to a database through a network to collect a raw material data set, and the raw material data set includes all batches of injection molding raw material data. The high-pressure injection molding unit is connected to an injection molding machine through a network to collect an injection molding data set, and the injection molding data set includes process parameters at all time points. The cooling and curing unit is connected to an injection molding machine through a network to collect a curing data set, and the curing data set includes quality inspection scores of all injection molded parts. The injection molding production module transmits the raw material data set, the injection molding data set and the curing data set to the intelligent evaluation module through the network; The intelligent evaluation module consists of a raw material analysis unit, an injection molding analysis unit and an optimization management unit. The raw material analysis unit analyzes the moisture content of different types of raw materials and the change in the preparation ratio between each batch according to the raw material data set, and generates a corresponding preparation data set. and difference data set and transmitted to the optimization management unit through the network. The injection molding analysis unit is set with a fixed range of barrel temperature thresholds. , duration threshold , injection temperature threshold , Mold temperature threshold , Pressure threshold and holding pressure threshold , combined with the injection molding data set, real-time analysis of the parameter changes in the injection molding process, and generation of the corresponding monitoring data set , and transmitted to the optimization management unit through the network. The optimization management unit analyzes the change trend of the injection molded part quality inspection score and the change amount of the cooling time according to the solidification data set, and generates the corresponding fluctuation index The optimization management unit is set with a fixed range of quality thresholds , Storage Threshold and fluctuation threshold , combined with the preparation data set , difference data set , Monitoring Data Group and volatility index , determine whether there are abnormal parameters in the injection molding production process and generate corresponding optimization signals.

2. The injection molding production parameter optimization system according to claim 1, characterized in that: The expression of the raw material data set is , to They are injection molding raw material data from the first batch to the C batch respectively. The injection molding raw materials include plastic particles, color powder and additives. S represents the storage time of a single batch of injection molding raw materials.

3. The injection molding production parameter optimization system according to claim 2, characterized in that: The expression of the injection molding data set is: , to From the first time point to the The process parameters at each time point include barrel temperature, heating time, nozzle temperature, mold temperature, injection pressure and holding time. Indicates the specific time for obtaining a single set of process parameters.

4. The injection molding production parameter optimization system according to claim 3, characterized in that: The expression of the solidified data set is: , to The first to the The quality inspection score of each injection molded part is shown in Figure 2, and t represents the cooling time of a single injection molded part.

5. The injection molding production parameter optimization system according to claim 4, characterized in that: The preparation data set The calculation process is as follows: Extract the raw material data set Batch of injection molding materials, and the first Batches of plastic pellets are marked , will Batches of toner are marked as , will Batches of additives are marked as ; Detected by rapid moisture analyzer 、 and The water content in After the batches of plastic granules, color powder and additives are mixed evenly, the moisture content of the raw material mixture is tested by a rapid moisture meter; In the formula, represents the preparation data set, KL represents the water content of the plastic particles in the optimal state, Indicates The moisture content of the plastic pellet batch, It indicates the difference between the actual water content of plastic particles and the optimal water content. SF indicates the water content of toner in the optimal state. Indicates The moisture content of the toner batch, It indicates the difference between the actual water content of the toner and the optimum water content. TJ indicates the water content of the additive under the optimum state. Indicates The water content of the batch additive, It 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 under the optimal state. Indicates The moisture content of the batch raw material mixture, Indicates the difference between the actual moisture content of the raw material mixture and the optimal moisture content.

6. The injection molding production parameter optimization system according to claim 5, characterized in that: The difference data set The calculation process is as follows: Detection by weighing mixer 、 and The quality of After the batches of plastic granules, color powder and additives are mixed evenly, the quality of the raw material mixture is tested by a weighing mixer and marked as ; In the formula, represents the difference data set, Indicates The mass ratio of the plastic particles, color powder and additives in the first batch is Batch raw material preparation ratio , Indicates Batch raw material preparation ratio, Indicates that the Comparison of batch raw material preparation ratio Batch raw material preparation ratio, Indicates The total mass of the batch raw material mixture, Represents the difference in the quality of the raw material mixture between two adjacent batches, Indicates Storage time of batch injection molding materials, Indicates Storage time of batch injection molding materials, Indicates the difference in storage duration between two adjacent batches.

7. The injection molding production parameter optimization system according to claim 6, characterized in that: The monitoring data set The calculation process is as follows: Extract the process parameters at the kth time point in the injection molding data set, and mark the barrel temperature at the kth time point as , mark the heating time at the kth time point as , mark the nozzle temperature at the kth time point as , the mold temperature at the kth time point is marked as , the injection pressure at the kth time point is marked as , mark the holding time at the kth time point as ; The flow rate of the raw material mixture after melting in the barrel and the flow rate of the raw material mixture in the nozzle are detected by the flow meter and marked as and ,in, It indicates the flow rate of the raw material mixture after it is melted in the barrel. Indicates the flow rate of the raw material mixture in the nozzle; In the formula, represents the monitoring data set, and are the lowest and highest values ​​in the barrel temperature threshold, respectively. It means comparing the barrel temperature at the kth time point with the barrel temperature threshold. and are the lowest and highest values ​​in the duration threshold respectively, It means comparing the heating time at the Kth time point with the time threshold. and are the lowest and highest values ​​of the injection temperature threshold, respectively. It means comparing the nozzle temperature at the Kth time point with the injection temperature threshold. and are the lowest and highest values ​​of the mold temperature thresholds, Indicates comparing the mold temperature at the Kth time point with the mold temperature threshold. and are the lowest and highest values ​​in the pressure threshold, respectively. Indicates comparing the injection pressure at the Kth time point to the pressure threshold. and are the lowest and highest values ​​of the pressure holding threshold, It means comparing the holding time at the Kth time point with the holding threshold. It indicates the ratio of the flow rate of the raw material mixture after it is melted in the barrel to the heating time. The ratio of the flow rate of the raw material mixture in the nozzle to the injection pressure, It indicates the ratio of the flow rate of the raw material mixture in the nozzle to the holding time.

8. The injection molding production parameter optimization system according to claim 7, characterized in that: The volatility index The calculation process is as follows: In the formula, represents the volatility index, Indicates the average quality inspection score obtained by dividing the total quality inspection score by the number of injection molded parts , represents the quality inspection score of the e-th injection molded part in the solidification data set, It means that according to the variance formula, the variance value obtained is the changing trend of the quality inspection score of the injection molded parts. Indicates the cooling time of the e-th injection molded part, Indicates The cooling time of each injection molded part, Indicates the difference in cooling time between two adjacent injection molded parts.

9. The injection molding production parameter optimization system according to claim 8, characterized in that: The preparation data set When any value in is negative, it means that the moisture content of the raw material exceeds the standard, and a raw material optimization signal is generated. The raw material preparation ratio between two adjacent batches is inconsistent, and the quality difference of the raw material mixture exceeds the quality threshold Or the storage duration difference exceeds the storage threshold When , it indicates that there is an abnormality in the raw material preparation, and a raw material optimization signal is generated. The monitoring data group When the process parameters exceed the threshold or the ratio is negative, it indicates that the production parameters are abnormal and a parameter optimization signal is generated. The variance exceeds the volatility threshold When , it indicates that the quality of the injection molded part is poor and a part optimization signal is generated.

10. A method for optimizing production parameters of injection molded parts, applied to a system for optimizing production parameters of injection molded parts according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Connect the database and injection molding machine through the network to obtain the injection molding raw material data of all batches, the process parameters at all time points and the quality inspection scores of all injection molded parts, and classify them into raw material data set, injection molding data set and curing data set; Step 2: Use a rapid moisture meter to test the moisture content of different raw materials in the raw material data set, then mix them evenly according to batches, analyze the moisture content of different types of raw materials and the changes in the preparation ratio between each batch, and generate the corresponding preparation data set and difference data set ; Step 3: Use the flow meter to detect the flow rate of the raw material mixture after melting in the barrel and the flow rate of the raw material mixture in the nozzle, and then combine the injection molding data set to analyze the parameter changes during the injection molding process in real time to generate the corresponding monitoring data set. ; Step 4: Analyze the change trend of the quality inspection score of the injection molded parts and the change in cooling time based on the solidification data set, and generate the corresponding fluctuation index ; Step 5: Set a fixed range of quality thresholds , Storage Threshold and fluctuation threshold , combined with the preparation data set , difference data set , Monitoring Data Group and volatility index , determine whether there are abnormal parameters in the injection molding production process and generate corresponding optimization signals.

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