Control method for an injection molding machine, manufacturing method for an injection molded article, and injection molding system

Abstract

A control method for an injection molding machine, which is a control method for a manufacturing apparatus executed by a control device, the manufacturing apparatus including an injection molding machine and a mold attached to the injection molding machine, the control method including: a collecting step for collecting a plurality of process data measured during a first injection molding from a plurality of sensors provided on the manufacturing apparatus; an acquiring step for acquiring a plurality of parameters related to a plurality of material properties of a resin material used in the injection molding based on the process data; and a control step for setting molding conditions for a second injection molding after the first injection molding based on the plurality of parameters.

Description

Technical Field

[0001] This disclosure relates to control methods and injection molding systems for injection molding machines. Background Technology

[0002] In conventional injection molding machines, resin material is melted by heating with a heater, and the molten resin is metered by the rotation and retraction of a motor-driven screw. The metered molten resin is then filled into the mold and pressurized by the forward motion of the motor-driven screw. Afterward, the molten resin cools and solidifies sufficiently in the mold, and is removed as a plastic molded product.

[0003] The output of the heater used to melt the resin material is controlled to reach a set temperature based on detection by thermocouples installed in the barrel section. Furthermore, resin metering and melting are performed simultaneously by retracting the screw until it reaches its set position. At this time, the molten resin is controlled to reach a predetermined pressure state by detection using a pressure sensor (e.g., a load cell) installed on the screw. The filling operation of the molten resin is also controlled based on the screw position information, and operations are performed to ensure that the screw's movement speed and amount follow set values. After filling, a pressurization operation called pressure holding is performed. Its purpose is to generate a compensating flow to compensate for volume shrinkage caused by the shrinkage of the molten resin. This pressurization operation is typically controlled by detection using the aforementioned load cell installed on the screw, and the screw advances according to the set pressure value.

[0004] The aforementioned settings for heater temperature, screw speed, travel distance, pressure, etc., are called molding conditions. Molding conditions need to be determined by the equipment user based on the type of resin material used, the volume of the molded product, and the mold specifications. The user primarily determines the molding conditions to obtain the desired quality of the molded product. Even when the injection molding machine is in mass production operation, the user can change the molding conditions. However, deriving molding conditions to ensure quality requires considerable experience and knowledge, therefore, changes are generally avoided during mass production.

[0005] When external disturbances cause instability in the quality of molded products, it is necessary to change the molding conditions during mass production. In particular, it is preferable to keep the dimensions of the molded products stable.

[0006] It has been consistently pointed out that changes in material properties can significantly affect the quality of molded products, especially their dimensions. Therefore, techniques have been proposed to stabilize quality by detecting changes in material properties during mass production and adjusting molding conditions accordingly.

[0007] For example, Patent Document 1 (PTL 1) discloses a technique in which a plunger and sensor for measuring viscosity are newly installed in the plasticizing unit of an injection molding machine to calculate the viscosity during molding, and the speed, temperature and back pressure conditions are adjusted based on the calculation results.

[0008] [List of Citations]

[0009] [Patent Literature]

[0010] [PTL 1] Japanese Patent Application Publication No. 2005-238519 Summary of the Invention

[0011] [Technical Issues]

[0012] The inventors' research has shown that controlling molding conditions based on only one specific material property cannot prevent variations in the molded product. Since multiple material properties affect the quality of the molded product, sufficient quality improvement cannot be achieved by controlling molding conditions during molding by detecting a single material property when adequate quality cannot be obtained due to variations in multiple material properties.

[0013] This disclosure was made in consideration of the problems in the conventional techniques described above, and the purpose of this disclosure is to provide an injection molding technology that can achieve stable product quality even when the material properties change.

[0014] [Solution to the problem]

[0015] A first aspect of this disclosure is a control method for an injection molding machine, which is a control method for a manufacturing apparatus executed by a control device, the manufacturing apparatus including an injection molding machine and a mold attached to the injection molding machine, the control method comprising: a collection step for collecting a first pressure sensor value obtained from a first pressure sensor disposed in a plasticizing section of the injection molding machine, a second pressure sensor value obtained from a second pressure sensor disposed in a resin flow path inside the mold, and a temperature sensor value obtained from a temperature sensor disposed near the second pressure sensor and detecting the temperature of the resin material; and a control step for setting molding conditions for injection molding based on a relationship between a first pressure sensor value P1 and a second pressure sensor value P2 when the temperature sensor value is at least the glass transition temperature Tg of the resin material during an injection step and a holding step in injection molding.

[0016] A second aspect of this disclosure is a control method for an injection molding machine, the control method being executed by a control device, the control method comprising: a collection step for collecting a first pressure sensor value obtained from a first pressure sensor disposed in a plasticizing device of the injection molding machine, a second pressure sensor value obtained from a second pressure sensor disposed in a resin flow path inside the mold, and a temperature sensor value obtained from a temperature sensor disposed near the second pressure sensor and detecting the temperature of the resin material; and a control step for changing the molding conditions of the injection molding machine based on a difference P1-P2 between a first pressure sensor value P1 and a second pressure sensor value P2 when the temperature sensor value is at least the glass transition temperature Tg of the resin material during an injection step and a holding step in molding.

[0017] A third aspect of this disclosure is a control method for an injection molding machine, which is a control method for a manufacturing apparatus executed by a control device, the manufacturing apparatus including an injection molding machine and a mold attached to the injection molding machine. The control method includes: a collection step for collecting pressure sensor values ​​obtained from a pressure sensor disposed in a resin flow path inside the mold, a temperature sensor value obtained from a temperature sensor disposed near the pressure sensor and detecting the temperature of the resin material, and a screw pressure value of the injection molding machine; and a control step for using a pressure sensor value P3 when the temperature sensor value drops to no more than Tg+10°C during a holding pressure step in injection molding and the screw pressure value P of the injection molding machine. S The relationship between these factors determines the molding conditions used for injection molding, where Tg is the glass transition temperature of the resin material.

[0018] A fourth aspect of this disclosure is a control method for an injection molding machine, which is a control method for a manufacturing apparatus executed by a control device, the manufacturing apparatus including an injection molding machine and a mold attached to the injection molding machine, the control method comprising: a collection step for collecting pressure sensor values ​​obtained from a pressure sensor disposed in a resin flow path inside the mold and a screw position of the injection molding machine; and a control step for setting molding conditions for injection molding based on a relationship between a change in screw position ΔLs during an injection step in injection molding and a change in pressure sensor value ΔP4.

[0019] The fifth aspect of this disclosure is a control method for an injection molding machine, which is a control method for a manufacturing apparatus executed by a control device, the manufacturing apparatus including an injection molding machine and a mold attached to the injection molding machine, the control method comprising: a collection step for collecting pressure sensor values ​​obtained from a pressure sensor disposed in a resin flow path inside the mold, and temperature sensor values ​​obtained from a temperature sensor disposed near the pressure sensor and detecting the temperature of the resin material; and a control step for setting molding conditions for injection molding based on a relationship between a pressure sensor value P5 when the gate of the mold is sealed during a holding pressure step and a cooling step in injection molding, a pressure sensor value P6 when the molded product reaches a temperature not exceeding the load deflection temperature, and an injection volume Vi.

[0020] The sixth aspect of this disclosure is a control method for an injection molding machine, which is a control method for a manufacturing apparatus executed by a control device, the manufacturing apparatus including an injection molding machine and a mold attached to the injection molding machine, the control method comprising: a collection step for collecting pressure sensor values ​​obtained from a pressure sensor disposed in a resin flow path inside the mold, and a temperature sensor value obtained from a temperature sensor disposed near the pressure sensor and detecting the temperature of the resin material; and a control step for basing the control on a pressure sensor value P5 when the gate of the mold is sealed during a holding pressure step and a cooling step in injection molding, a pressure sensor value P6 when the molded product reaches a temperature not exceeding the load deformation temperature, and the weight W of the molded product. A The relationship between these factors determines the molding conditions used for injection molding.

[0021] [Beneficial effects of the invention]

[0022] According to this disclosure, stable molded product quality can be achieved even when various material properties change. Attached Figure Description

[0023] Figure 1 This is a configuration diagram of the injection molding system with control devices in Example 1.

[0024] Figure 2 This is the control block diagram in Example 1.

[0025] Figure 3 This is the flowchart in Example 1.

[0026] Figure 4 This is a flowchart of the injection molding process in Comparative Example 1.

[0027] Figure 5 This is a flowchart of the injection molding process in Comparative Example 2.

[0028] Figure 6 This is a diagram showing the configuration of Test Example 1.

[0029] Figure 7 The measurement results of the weight of the molded product in Test Example 1 are shown.

[0030] Figure 8 The weight of the molded product in Test Example 1 is shown in detail.

[0031] Figure 9 This is an example of measuring the elastic modulus in Test Example 1.

[0032] Figure 10 This is an example of measuring the viscosity of a resin material in Test Example 1.

[0033] Figure 11 This is an example of measuring shrinkage characteristics in Test Example 1.

[0034] Figure 12 (a) and Figure 12 (b) is a diagram showing the configuration of test cases 2 to 5.

[0035] Figure 13 This is a graph representing the time dependence of viscosity.

[0036] Figure 14 This is a graph showing the correlation between viscosity (MVR) and pressure difference ΔP in Example 2.

[0037] Figure 15 This is a graph showing the correlation between the elastic modulus and the injection volume at Tg in Example 3.

[0038] Figure 16 This represents the elastic modulus and elastic modulus parameter P at Tg in Example 3. S A graph showing the correlation between / P3.

[0039] Figure 17 This is a graph showing the change of screw position over time in test example 3.

[0040] Figure 18 This is a graph showing the correlation between the elastic modulus during melting in Example 4 and the elastic modulus parameter ∆P4 / ∆Ls.

[0041] Figure 19 This is a graph showing the relationship between resin temperature and pressure after 5.0 seconds in Test Example 4.

[0042] Figure 20This is a graph showing the correlation between specific volume tolerance and shrinkage parameter (P5-P6) / Vi in Example 5.

[0043] Figure 21 The molding conditions during the pre-test operation in Test Example 5 are shown.

[0044] Figure 22 (a) to Figure 22 (e) shows the estimated results of the material properties obtained in Test Example 5. Detailed Implementation

[0045] First, we will briefly explain the molding conditions in injection molding and the material properties that affect the quality of the molded product.

[0046] Molding conditions include heater temperature, screw speed, movement distance, and pressure. These conditions need to be determined based on the type of resin material used, the volume of the molded product, and the mold specifications. Even under the same molding conditions, changes in the resin material's properties will prevent the achievement of consistently high-quality molded products.

[0047] Even for the same type of resin material from the same manufacturer, material properties can vary and often fluctuate due to batch-to-batch differences. Furthermore, the use of recycled resin materials (recycled plastic materials) – that is, resin materials derived from recycled resin materials (waste plastics) – has been increasing in recent years. Recycled resin materials are produced by breaking down waste plastics into fragments and granules. However, waste plastics contain plastics derived from a wide variety of products, and recycled resin materials often exhibit greater variations in material properties compared to virgin materials. Injection-molded products made using recycled resin materials (such as recycled granules) are called recycled plastics. Producing and using recycled plastics through material recycling is effective in reducing CO2 emissions.

[0048] One of the material properties that affects the dimensions of molded products is resin viscosity. Because viscosity greatly influences resin flowability, differences in viscosity will lead to differences in resin filler content, even under the same molding conditions. At high viscosities, flowability decreases and resin filler content is reduced, while at low viscosities, flowability improves and resin filler content is increased. Therefore, the dimensions of the molded product change.

[0049] Furthermore, the elastic modulus (elastic constant) of the resin is also cited as one of the material properties that affects the dimensions of the molded product. Since the elastic modulus is a physical quantity that represents the relationship between external force and strain (deformation), it significantly influences the holding pressure step controlled by the pressure sensor's readings. For example, materials with a high elastic modulus exhibit larger response stresses associated with screw movement, resulting in smaller screw movement relative to the set pressure value. Consequently, the mass of the compensating flow entering the mold decreases, and the dimensions of the molded product tend to be smaller.

[0050] In addition, shrinkage characteristics are also one of the material properties that affect the dimensions of molded products. The shrinkage characteristics of a material are usually represented by its PVT (Potential Temperature Transformation) properties, which indicate the pressure [MPa], temperature [°C], and specific volume [cm³] during cooling. 3 The relationship between [ / g] is crucial. Therefore, materials with a large specific volume change during the process from the high temperature of the molten resin to the low temperature of the resin cooling and solidifying will have a large shrinkage. In other words, even if the same volume of molten resin is filled into the mold, a smaller molded product size will be obtained with a material that has a larger specific volume change.

[0051] In situations where multiple material properties vary, control based on a single material property may not yield stable molded product quality. This disclosure achieves more stable molded product quality than conventional methods by detecting multiple material properties during molding and controlling molding conditions based on changes in these properties. Through in-depth research, the inventors have discovered that, for stable molded product dimensions, it is preferable to consider at least two, preferably all three, of viscosity, elastic modulus, and shrinkage characteristics when performing control. Furthermore, it has been found that it is also preferable to consider the material's thermal properties (e.g., melt density, specific heat, and thermal conductivity) when controlling molding conditions. This disclosure also proposes accurately estimating the viscosity, elastic modulus, and shrinkage characteristics of the material used for the above-mentioned control. Resin materials suitable for injection molding according to this disclosure are typically thermoplastic resins, but can also be thermosetting resins, crystalline resins, or amorphous resins, and can be general-purpose plastics or engineering plastics. Examples of resin materials include polyolefins such as polyethylene (PE) and polypropylene (PP), polystyrene (PS), polyesters such as polyethylene terephthalate (PET) and polybutylene terephthalate (PBT), polyvinyl chloride (PVC), polyoxymethylene (POM), acrylonitrile butadiene styrene (ABS), polycarbonate (PC), polyphthalamide (PPA), and acrylic resin (PMMA). Resin materials suitable for material recycling include polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyvinyl chloride (PVC). Furthermore, resin materials suitable for injection molding according to this disclosure can be composite materials in which fillers or elastomers are dispersed in a resin matrix, or they can be mixtures of multiple resin materials.

[0052] (Configuration)

[0053] The following describes in detail the control device and control method for an injection molding machine according to this disclosure, as well as the method for estimating material properties. The control device for an injection molding machine according to this disclosure acquires multiple parameters related to various material properties of the resin material used in injection molding based on multiple process data measured during injection molding, and uses these parameters to set molding conditions.

[0054] Figure 1 This is a configuration diagram of an injection molding system illustrating an example embodiment of the present disclosure. The injection molding system includes an injection molding machine 10, a control device 51, and a mold 1.

[0055] The injection molding machine 10 consists of a plasticizing unit 11 and a mold clamping unit 12, and is controlled by a control device 51. The injection molding machine 10, together with the mold 1 attached to it, can be considered as equipment for manufacturing injection-molded products. The plasticizing unit 11 can be considered as the plasticizing section of the manufacturing equipment. The control device 51 can be considered as a device for controlling the manufacturing equipment.

[0056] The mold closing device 12 is equipped with a mold opening and closing servo motor 23 and an ejector motor 24. The mold opening and closing servo motor 23 is used not only to close the mold 1 installed in the mold closing device 12 with a predetermined force, but also for the mold opening and closing operation. The ejector motor 24 is used when demolding the injection molded product from the mold.

[0057] The plasticizing apparatus 11 includes a barrel 13, a screw 14, a barrel heater 31, a metering servo motor 21, an injection servo motor 22, and a load sensor 15. Inside the barrel 13, molten resin is metered and enters the front of the barrel as the screw 14 retracts. The retraction of the screw 14 is achieved by the molten material due to the action of the barrel heater 31 and the rotation of the metering servo motor 21. Then, the metered molten resin is filled into the mold by the forward movement of the screw 14. The forward movement of the screw 14 is achieved by the rotation of the injection servo motor 22. The load sensor 15 is located at the rear end of the screw 14 and is used to control the pressure state during metering and filling.

[0058] Each servo motor can immediately detect analog data such as rotational speed, torque, velocity, and position, and output this data from the data output unit 16 as molding machine data 101. Similarly, analog data about pressure acquired by the load sensor 15 is also included in the molding machine data 101. The output molding machine data 101 is input to the control device 51.

[0059] The molding machine data 101 may include numerical values ​​other than analog data. Examples of such numerical data include minimum buffer position, holding pressure completion position, peak filling pressure, VP switching position, etc., which are calculated by the computer in the injection molding machine as actual values ​​for a cycle.

[0060] The mold 1 is equipped with one or more mold sensors 61. The sensors 61 can be composed of different types of sensors, such as pressure sensors that detect the pressure state inside the mold and resin temperature sensors that detect the temperature state of the molded product. Additionally, gap sensors that detect the mold opening amount, thermocouples that detect the mold temperature, and speed sensors that detect the flow rate of the molten resin can be used. The detected data is output as mold data 201 and input to the control device 51.

[0061] The barrel sensor 71 can be installed at the barrel 13. Multiple sensors can be used, such as a resin temperature sensor, a pressure sensor, and a speed sensor. The detected data is output as barrel data 301 and input to the control device 51.

[0062] In this embodiment, the measurement results from the measuring machine can also be processed as data. For example, the molded product measuring machine 81 can be installed near the injection molding machine to enable online measurement of the molded product. Multiple measuring machines can be used, such as a weight measuring machine, a length measuring machine, and a density measuring machine. The measurement results are output as measurement data 401 and input to the control device 51.

[0063] The control device 51 can be implemented in any way, as long as it can communicate with the injection molding machine 10 and the molded product measuring machine 81. For example, the control device 51 can be implemented by a different device from the injection molding machine 10, or it can be located inside the injection molding machine 10. The control device 51 can also be a computer installed in the same facility as the injection molding machine 10, or a remote computer installed in a different facility and connected via a network. The remote computer includes a cloud computer or a distributed computer.

[0064] The data input to the control device 51 requires one or more of the following: molding machine data 101, mold data 201, barrel data 301, and measurement data 401. The control device 51 performs calculations based on the input data and outputs a correction condition 901 for the molding conditions. The correction condition 901 is input as a command signal from the data input unit 17 located in the injection molding machine and rewrites the molding conditions for the next injection.

[0065] Figure 2This is a control block diagram illustrating an example embodiment of the present disclosure. Injection molding machine 10 molds a product in each cycle and outputs molding machine data 101 and barrel data 301. A measuring machine 81 measures the molded product online in real time and outputs measurement data 401. Mold data 201 from mold 1 is output from a sensor mounted inside the mold in each cycle. One or more of the molding machine data 101, mold data 201, barrel data 301, and measurement data 401 are input to control device 51.

[0066] The data input to the control device 51 is aggregated in the data detection unit 52. The data detection unit 52 performs processing to convert continuous numerical information, such as analog data, into single numerical data. For example, the data detection unit 52 converts the input data into an integral value, rate of change, maximum value, or minimum value within a freely selected range. The data detection unit 52 also calculates other values ​​based on multiple numerical information. For example, the data detection unit 52 can calculate the injection volume by performing a calculation considering the cross-sectional area of ​​the barrel's inner diameter, based on the screw position information included in the molding machine data 101. The converted numerical set is input from the data detection unit 52 as a feature quantity 501 to the material property calculation unit 53.

[0067] Feature 501 is sent to material property calculation unit 53 and used to estimate (acquire) the material properties 511 of the resin material used. The estimation process uses test-based calculation formulas, machine learning-based prediction models, etc. Material property 511 is, for example, at least one of resin viscosity, elastic modulus, PVT characteristics, shrinkage rate, density, specific heat, and thermal conductivity. Material property calculation unit 53 estimates two or more of these material properties.

[0068] The material properties 511 estimated by the material property calculation unit 53 can be estimated as values ​​in any format, as long as they correspond to or are one-to-one with the actual material properties. The estimated values ​​obtained by the material property calculation unit 53 may deviate from the values ​​extracted according to ISO; furthermore, the unit system of the estimated values ​​may differ from the unit system of the original material properties. In this disclosure, the estimated values ​​obtained by the material property calculation unit 53 are processed as parameters related to the material properties. The estimated values ​​obtained by the material property calculation unit 53 can be considered as parameters related to the material properties. Parameters related to or associated with the material properties can, for example, be correlated one-to-one with the material properties through predetermined calculations.

[0069] In comparison unit 54, the estimated material properties 511 are compared with reference material properties. Reference material properties are values ​​that ensure the quality of the molded product and correspond to so-called high-quality molding target values. These reference material properties are derived by performing a trial run beforehand and using the same method as the calculation performed by the material property calculation unit 53 described above. Material property comparisons are performed for each type of property, and a difference 521 from the reference is calculated for each type of property. Since two or more material properties are tested, the difference 521 from the reference also consists of the same number of values. The reference material properties are represented by parameters in the same format as the estimated values, thus enabling comparison with the estimated values.

[0070] The difference 521 from the baseline is sent to the control quantity calculation unit 55, which extracts the molding correction condition 901 to suppress the influence on molding quality. The calculation of the correction condition uses test-based calculation formulas, machine learning-based prediction models, etc. As mentioned above, since multiple material properties need to be compared, the correction condition 901 is often composed of multiple molding machine control parameters.

[0071] The correction condition 901 output from the control device 51 is input to the injection molding machine 10. The molding conditions of the injection molding machine 10 are set according to the correction condition 901 and reflected in the subsequent injection molding.

[0072] For the corrected molding, various data are collected in the same manner as described above and sent to the data detection unit 52. Thereafter, the same process is repeated recursively.

[0073] Figure 3 This is a flowchart that further explains the control method of the injection molding machine 10 executed by the control device 51 described above.

[0074] First, manufacturing equipment including an injection molding machine 10 and a mold 1 attached to the injection molding machine 10 is prepared, and injection molding is performed by the injection molding machine 10 in the injection molding step ST1. The injection molding operation of concern here is referred to as the first injection molding operation. Figure 3 In the cyclic process shown in the flowchart, the injection molding operation that follows the first injection molding operation (which may be after multiple cycles, not just immediately after) is called the second injection molding operation.

[0075] In step ST2, data related to the injection molding operation is collected. Figure 1 and Figure 2 (One or more of data 101, 201, 301, and 401 in the data). Specifically, the control device 51 collects multiple process data measured during the first injection molding from multiple sensors located at the injection molding machine 10 or the mold 1.

[0076] In step ST3, characteristic quantity 501 is derived based on the data content. In steps ST4 and ST5, two or more material properties are estimated based on characteristic quantity 501. Figure 2 Material properties (511). Figure 3 In this process, material properties a and b are estimated. Steps ST3 to ST5 correspond to the acquisition step, which is used to acquire multiple material properties or multiple parameters related to multiple material properties of the resin material used in injection molding based on the collected process data.

[0077] In steps ST6 and ST7, comparisons are made between material property a and reference material property A, and between material property b and reference material property B, and the differences from the reference are calculated. Figure 2 The difference between the calculated value and the reference value is 521. In the following steps ST8 and ST9, it is determined whether the calculated difference between the value and the reference values ​​A and B is within the allowable range. If the difference is within the allowable range, the process proceeds to step ST13. If the difference is outside the allowable range, the process proceeds to steps ST10 and ST11, and the molding machine correction conditions α and β are derived. Figure 1 and Figure 2 (Correction condition 901 in the text). In step ST12, correction conditions α and β are reflected in the molding conditions, and the process proceeds to step ST13. Steps ST6 to ST12 correspond to control steps, which are used to set molding conditions for a second injection molding following the first injection molding based on a plurality of parameters acquired in the acquisition step.

[0078] In step ST13, it is determined whether the production quantity is necessary and sufficient. If sufficient, the process ends; otherwise, the process returns to step ST1 and molding is performed again.

[0079] Figure 3 The acquisition of process data, estimation of material properties, and correction of molding conditions performed in each cycle are illustrated, but this disclosure is not limited thereto. For example, process data can be acquired in multiple cycles, and material properties can be estimated based on the acquired process data. As an example, material properties can be estimated based on statistical information from process data in multiple cycles. In this disclosure, one or more cycles for collecting process data for estimating material properties are referred to as injection molding operations. Molding conditions corrected based on process data collected in a first injection molding operation are applied to a second injection molding operation following the first injection molding operation. The second injection molding operation does not need to immediately follow the first injection molding operation; it can be an injection molding operation performed at any time later than the first injection molding operation.

[0080] <Example 1>

[0081] A comparison is made between the quality of a molded product formed using the control device according to this disclosure and the quality of a molded product formed using a conventional injection molding method. Example 1 illustrates the use of, for example... Figures 1 to 3 The control device and control method shown are used to perform injection molding. As described above, in this disclosure, multiple sensors disposed in the injection molding machine and the mold are used to estimate various material properties of the resin material, and molding conditions are controlled based on their trends. In Example 1, viscosity and elastic modulus are estimated to perform molding condition control. In other words, in Example 1, Figure 3 The material property shown, a, is viscosity, and the material property b is elastic modulus.

[0082] To compare with Example 1, Comparative Example 1 and Comparative Example 2, which use conventional techniques to perform injection molding, will be described.

[0083] Figure 4 This is a flowchart illustrating the control method of Comparative Example 1. Comparative Example 1 is an example of performing injection molding without estimating material properties or controlling molding conditions. In Comparative Example 1, only the injection molding performed in step S61 and the determination of whether the desired product quantity has been reached in step ST69 are performed.

[0084] Figure 5 This is a flowchart illustrating the control method of Comparative Example 2. In Comparative Example 2, material property estimation is performed only for one material property. The process from the execution of injection molding in step ST51 to the derivation of the characteristic quantity in step ST53 is basically the same as in Example 1, but the material property estimation in step ST54 is performed only for one material property. Therefore, there are no branches in the process from steps ST55 to ST57. Thus, Comparative Example 2 is characterized by performing estimation and controlling molding conditions only for a specific material property. This Comparative Example 2 is an example of controlling molding conditions by estimating only viscosity.

[0085] Figure 6(a) is a diagram illustrating Test Configuration 1 used to compare the effects of Example 1 with Comparative Examples 1 and 2. In Test Configuration 1, two molding materials with different viscosities and elastic moduli were introduced into a hopper 18 capable of holding approximately 100 injections of material. In this test, HIPS (High Impact Resistance Polystyrene) material was used, and Material A (denoted by reference numeral 91) and Material B (denoted by reference numeral 92) with significantly different viscosities and elastic moduli were prepared. Viscosities were compared using MVR (Melting Volume Ratio: ISO 1133, 200°C / 5kg) measurements, and elastic moduli were compared using values ​​measured near the glass transition temperature (110°C) using a rotational rheometer. Material A had a lower MVR, higher viscosity, and higher elastic modulus than Material B. Incidentally, both materials had the same specific gravity. Figure 6 (b) shows the viscosity and elastic modulus of materials A and B.

[0086] In test configuration 1, material A for 50 injections is first loaded into the hopper, followed by material B for 50 injections. When injection molding is performed in this state, only material A is plasticized and shaped in the initial stage, but as material A is consumed, the interior of the hopper 18 is replaced by material B. At this point, due to the slow mixing of the two materials, the boundary 93 between the two materials in the hopper becomes blurred.

[0087] Under these conditions, molding is repeated until the material in the hopper is exhausted, and the weight of the molded product is measured for each injection. Figure 7 As shown in the figure. In Comparative Examples 1 and 2, which represent conventional technology, a weight change was observed from approximately the 40th injection to the 60th injection. However, compared to Comparative Example 1, Comparative Example 2 tended to have a smaller weight change. On the other hand, in Example 1, no weight change was observed until the 100th injection at the end of molding.

[0088] The weight changes in Comparative Examples 1 and 2 are considered to be due to changes in molding behavior accompanying material displacement. Until the 40th injection, injection molding was primarily performed using material A; however, between the 40th and 60th injections, the two materials slowly mixed, thus the material properties of these two materials were considered to have an effect. After the 60th injection, the hopper interior was completely replaced by material B, therefore the material properties of material B were considered to have an effect.

[0089] Detailed analysis results of the weight of the molded product are in Figure 8 (a) and Figure 8 As shown in (b). Figure 8 (a) shows the weight filled during the injection step, and Figure 8(b) shows the weight filled during the holding pressure step. The cross-sectional area A [cm] of the barrel inner diameter is used. 2 Screw stroke S during pressure holding period p [mm], and the specific gravity ρ of the resin material [g / cm³] 3 The filling amount W for the pressure holding step is derived from formula (1). p [g].

[0090] Filling amount during pressure holding step: W p =ρAS p / 10...(1)

[0091] On the other hand, the weight W of the molded product is used. A [g], The injection volume W is calculated using formula (2). i [g].

[0092] Injection step filler volume: W i =W A -W P ...(2)

[0093] exist Figure 8 (a) and Figure 8 In the results shown in (b), Comparative Example 1 illustrates the changes in filling volume during the pressure holding step and the injection step. Comparative Example 2 only shows the change in filling volume during the pressure holding step. On the other hand, in Example 1, there were no changes in either the filling volume during the pressure holding step or the filling volume during the injection step.

[0094] In a typical injection molding machine, the injection step is controlled by the movement speed and position of the screw. Therefore, even with different materials, the screw movement during the injection step is always the same, provided the molding conditions are identical. However, even with the same screw operation, the weight of resin flowing into the mold will vary depending on the material viscosity. This is because different viscosities lead to different pressure distributions upstream and downstream of the resin flow path, resulting in differences in the overall density distribution. Higher viscosity materials have lower downstream pressure and lower density. Therefore, higher viscosity materials have a smaller filler weight compared to lower viscosity materials.

[0095] Furthermore, in a typical injection molding machine, during the holding pressure step, screw operation is controlled based on the pressure value detected in the plasticizing unit. The pressure is typically determined by factors such as... Figure 1 The load sensor 15, installed at the rear end of the screw, is shown for detection. Therefore, screw operation during the holding pressure step is susceptible to the influence of the elastic modulus. For example, materials with a high elastic modulus experience greater response stress as the screw moves, resulting in a smaller amount of screw movement relative to the set pressure value. Consequently, the weight of the material filled into the mold is also reduced.

[0096] As described above, the viscosity of the resin material has a significant impact on the filling step, and the elastic modulus has a significant impact on the holding pressure step. In Comparative Example 1 of the conventional technology, because the material properties were not estimated and the molding conditions were not controlled, changes in the viscosity and elastic modulus of the material in the hopper caused fluctuations in the filling amount during the injection and holding pressure steps. Furthermore, in Comparative Example 2 of the conventional technology, molding condition control was only performed for viscosity; therefore, although the filling amount during the injection step remained stable, fluctuations in the filling amount during the holding pressure step could not be suppressed. In contrast, in Example 1, estimation and molding condition control were performed for both viscosity and elastic modulus, thus achieving stability in both the filling amount during the injection and holding pressure steps.

[0097] In Example 1, molding condition control is performed based on two material properties: viscosity and elastic modulus. This will be achieved using... Figure 9 and Figure 10 This example illustrates the modification of molding conditions.

[0098] Figure 9 An example of measuring the elastic modulus of a resin material using a rotational rheometer is shown. Figure 9 The values ​​of the elastic modulus (Pa) are shown when the temperature (°C) is changed, while keeping the rotational frequency at 1 Hz and the strain at a constant 0.5%. This measurement shows that the elastic modulus is temperature-dependent. Therefore, to match the elastic modulus of two different materials, the temperature conditions need to be adjusted. For example, in… Figure 9 In this case, to match the elastic modulus of material B at 110°C, the temperature of material A needs to be set to 118°C. Similarly, in an injection molding machine, the elastic modulus can be stabilized by adjusting the temperature of the barrel heater (resin temperature).

[0099] Figure 10 An example of measuring the viscosity of a resin material using a capillary rheometer is shown. Figure 10 The viscosity [Pa·s] values ​​are shown as the melting temperature [°C] and shear rate [1 / s] change. These measurements demonstrate that viscosity is both temperature-dependent and shear rate-dependent. Therefore, to match the viscosity of two different materials, it is necessary to adjust either the temperature or shear rate conditions. Figure 10 The trend suggests that when a decrease in viscosity is desired, either a higher temperature or a higher shear rate is required. In other words, it can be assumed that viscosity can be stabilized in an injection molding machine by adjusting the resin temperature or the injection speed.

[0100] As mentioned above, in an injection molding machine, the elastic modulus can be adjusted by the resin temperature, and the viscosity can be adjusted by the resin temperature and the injection speed. Therefore, the adjustment of the elastic modulus and viscosity can be represented by the following mathematical formulas (3) and (4) with the setting parameters of the injection molding machine as variables.

[0101] Elastic modulus: Δg = At... (3)

[0102] Viscosity: Δμ = Bt + Cd... (4)

[0103] Here, t is a variable corresponding to the resin temperature [°C], d is a variable corresponding to the injection speed [mm / sec], and A, B, and C are constants. The constants A to C for each material can be derived in advance through material analysis, etc. Δg and Δμ represent the deviation between the target and actual values ​​of the material properties. The target values ​​are set based on the actual values ​​of the good product. Then, by solving these formulas, the variables t and d are extracted. The extracted t and d become correction values ​​and are reflected in the molding conditions of the next injection.

[0104] Therefore, in Example 1, the molding conditions for the second injection molding are set using pre-obtained relationships (3) and (4) representing the relationship between the adjustment amount of the molding conditions and the change of the parameters for each of the multiple material property-related parameters. In Example 1, a method is used to control two target variables (elastic modulus and viscosity) through two explanatory variables (resin temperature and injection speed). However, a solution can be derived even if there are three or more target variables, as long as the number of explanatory variables is not greater than the number of target variables. Preferably, the explanatory variables include at least two of the material properties among viscosity, elastic modulus, and shrinkage characteristics.

[0105] For example, there could be three target variables: elastic modulus, viscosity, and shrinkage characteristics. Figure 11 This is a graph showing the shrinkage characteristics of the material. Shrinkage characteristics are typically measured using a PVT measuring instrument and are expressed as specific volume [cm³] under pressure [MPa] and temperature [°C] conditions. 3 The relationship between / g] is evident from the figure. It is clear that the shrinkage characteristics of the material can be adjusted by resin temperature and pressure. Therefore, the adjustment of shrinkage characteristics can be expressed as formula (5).

[0106] Contraction characteristics: Δρ=Ft+Gp...(5)

[0107] Here, t is a variable corresponding to the resin temperature [°C], p is a variable corresponding to the pressure [MPa], and F and G are constants. The constants F and G can be pre-calculated for each material through material analysis, etc. Δρ represents the deviation between the target and actual values ​​of the material property (shrinkage characteristics).

[0108] If we consider three target variables (i.e., elastic modulus, viscosity, and shrinkage characteristics), the explanatory variables included in equations (3) to (5) are t, d, and p. Therefore, the desired molding conditions can be adjusted by solving a system of simultaneous equations for several variables.

[0109] However, the problem lies in the decrease in computational accuracy as the number of material properties used as target variables increases. Extensive research indicates that at least two material properties need to be adjusted; however, it is equally evident that for injection molding, including any two of the three material properties (i.e., viscosity, elastic modulus, and shrinkage characteristics) is sufficient. In other words, sufficiently stable molded product quality can be obtained by performing control using at least viscosity and elastic modulus, elastic modulus and shrinkage characteristics, or shrinkage characteristics and viscosity as target variables. As mentioned above, viscosity affects the amount of resin filled during the injection step, and elastic modulus affects the amount of resin filled during the holding pressure step. Furthermore, shrinkage characteristics affect the amount of shrinkage of the molded product during the cooling step.

[0110] Therefore, by controlling the three material properties (i.e., viscosity, elastic modulus, and shrinkage characteristics) to be the same, changes in material properties that can significantly affect the entire injection molding process can be suppressed, thereby stabilizing quality.

[0111] In this example, because the changed molding conditions can be fed back to the injection molding machine within a shortest possible cycle, the generation of defective products can be minimized even if the material properties of the resin change during mass production. Furthermore, in this example, because multiple material properties can be estimated simultaneously by calculating the collected process data, variations in the dimensions of the molded product caused by multiple factors can be effectively suppressed. Additionally, the material properties to be estimated include at least viscosity, elastic modulus, and shrinkage characteristics. In this example, computational efficiency can be optimized because the calculation process is limited to the resin filling amount during the injection step, the mass of the compensation flow during the holding pressure step, and the shrinkage of the molded product during the cooling step.

[0112] <Example 2>

[0113] The following describes a method for estimating the viscosity of a resin material based on process data obtained during a molding cycle. In this example, the viscosity is estimated based on the relationship between an upstream pressure P1 obtained from a first pressure sensor located in the plasticizing section of the injection molding machine 10 and a downstream pressure P2 obtained from a second pressure sensor located in the resin flow path inside the mold. More specifically, in this example, the viscosity is estimated based on the relationship between P1 and P2 when the temperature sensor value obtained from a temperature sensor located near the second pressure sensor and detecting the temperature of the resin material is at least the glass transition temperature Tg.

[0114] Viscosity is determined by formulas (6) and (7) according to the Hagen-Poiseuille law.

[0115] Viscosity: μ[Pa·s] = (πa) 4 ΔP) / (SQL)...(6)

[0116] ΔP = P1 - P2…(7)

[0117] Here, Q is the flow rate of the molten resin [m]. 3 / s], L is the flow path length [m], a is the flow path radius [m], P1 is the upstream pressure [Pa], and P2 is the downstream pressure [Pa].

[0118] In injection molding, the flow path length L and flow path radius a are fixed values ​​determined by the specifications of the mold and injection molding machine. The flow rate Q is also fixed due to molding conditions (injection speed, etc.). In other words, the viscosity fluctuation of the resin used can be determined by measuring the difference (ΔP) between the upstream pressure P1 and the downstream pressure P2. That is, ΔP is a value proportional to the viscosity in the injection molding step under consideration and is a viscosity-related parameter. Therefore, in this example, by controlling the molding conditions based on ΔP, viscosity-based control of the molding conditions can be achieved. However, this requires measuring ΔP while the molten resin is flowing. Therefore, in injection molding, measurements need to be taken while the resin is in the molten state and during the injection and holding pressure steps.

[0119] The first pressure sensor for measuring upstream pressure P1 and the second pressure sensor for measuring downstream pressure P2 can be installed at any location along the resin flow path, as long as the first pressure sensor is upstream of the second pressure sensor. For example, the first pressure sensor can be installed in the barrel or nozzle of the plasticizing unit, or a component extending the flow path can be inserted between the mold and the nozzle, and the pressure can be measured inside this component. Alternatively, the detection value of a load sensor already located at the rear end of the screw of the plasticizing unit can be used as the first pressure sensor. Furthermore, the second pressure sensor can be installed at any location in the mold, such as a runner bushing, hot runner manifold, hot runner end, runner, or cavity.

[0120] Figure 12 (a) shows the sensor arrangement in test configuration 2. In test configuration 2, a first pressure sensor 72 is installed in the nozzle section of the plasticizing device 11, and a second pressure sensor 62 is installed near the gate in the mold to measure the viscosity-related parameter ΔP. At this time, a resin temperature sensor 63 is installed near the second pressure sensor and also measures the resin temperature. Figure 12 (b) shows the measurement waveform of a cycle measured in this manner.

[0121] When ΔP(x) at any given time x is expressed by the following formula (8),

[0122] ΔP(x)=P1(x)-P2(x)...(8)

[0123] The viscosity-related parameter ΔP is represented by formula (9).

[0124] [Mathematical Expression 1]

[0125]

[0126] Here, T S It measures the start time, T. E It is the measurement end time. In other words, ΔP is the average pressure difference from the start time to the end time of the measurement.

[0127] Since the viscosity-related parameter ΔP needs to be measured in the molten state of the resin and during the injection and holding steps, it is calculated over any time period from the start of injection until the resin temperature reaches the glass transition temperature Tg. Figure 12 The average pressure difference within the period from 0.0s to 4.5s in (b). The measurement time period can be arbitrarily set within the above time period, for example, set to the period during the injection step ( Figure 12 (b) 0.0s to 1.5s), during the pressure holding step (1.5s to 4.5s), or the entire range (0.0s to 4.5s).

[0128] However, when determining the measurement time period, it is preferable to consider the time dependence of viscosity and the shear rate dependence. Figure 13 This is a schematic diagram illustrating the time dependence of viscosity. When a shear rate is applied to the molten resin, the viscosity requires a certain amount of time to reach equilibrium. It has also been shown that the time to reach equilibrium varies depending on the shear rate conditions. As mentioned above, in injection molding involving high-speed shear, it is necessary to determine the measurement time period after identifying the timing for reaching equilibrium.

[0129] For example, in Figure 12 During the injection step (0.0s to 1.5s) in (b), P1 and P2 continuously increase over time and do not reach equilibrium. On the other hand, during the pressure holding step (1.5s to 4.5s), the pressure values ​​stabilize and reach near equilibrium. However, in the latter half of the pressure holding step, the pressure value of P2 begins to decrease. Therefore, it is preferable to measure ΔP during the first half of the pressure holding step.

[0130] Extensive research indicates that a pressure fluctuation within 5 MPa / sec can be considered a state of relative equilibrium, resulting in smaller errors in viscosity measurements. Therefore, during the injection and holding phases, viscosity is estimated based on the rates of change of both the first pressure sensor value P1 and the second pressure sensor value P2 over time, which are within 5 MPa / sec during both phases. More specifically, it is preferable to begin measurements after the holding phase has started and when the pressure fluctuation is within 5 MPa / sec, and to end measurements when the pressure fluctuation is at least 5 MPa / sec or at the end of the holding phase, subsequently determining the average pressure.

[0131] Figure 14 The results show the correlation between the pressure drop (viscosity parameter) ΔP and MVR (melt volume rate: ISO 1133, 200°C / 5kg) when injection molding is performed using various types of HIPS materials in test configuration 2. A strong correlation between ΔP and the viscosity (MVR) of the resin material can be confirmed.

[0132] The method in this example can be applied to the estimation of the viscosity of the resin material in Example 1, and the molding conditions can be changed based on the obtained estimated viscosity value.

[0133] In the example above, viscosity is estimated based on the difference P1-P2 between upstream pressure P1 and downstream pressure P2, but this difference ΔP can be either P2-P1 or |P1-P2|. Furthermore, when estimating viscosity based on a relationship corresponding to the difference between P1 and P2, relationships other than P1-P2 can be used. For example, the difference between P1 and P2 can be represented by αP1-βP2 (where α and β are predetermined coefficients greater than 0) or P1 / P2, and viscosity can be estimated based on such values. Additionally, viscosity can be estimated using values ​​that include the P1-P2 or P1 / P2 component through formula transformations.

[0134] <Example 3>

[0135] The following describes a method for estimating the elastic modulus of a resin material based on process data obtained during a molding cycle. In this example, the elastic modulus is estimated based on the relationship between pressure sensor values ​​obtained from pressure sensors located in the resin flow path inside the mold 1 and the screw pressure value of the injection molding machine 10. More specifically, the elastic modulus is estimated based on the pressure P3 in the resin path within the mold when the temperature of the resin material drops to no more than Tg+10°C during the holding pressure step in injection molding and the screw pressure value P of the plasticizing device of the injection molding machine 10. S The relationship between the elastic modulus and the elastic modulus is used to estimate the elastic modulus.

[0136] In this disclosure, the term "elastic modulus" is used as a general term for elastic constant, elastic coefficient, Young's modulus (longitudinal elastic modulus, flexural elastic modulus), rigidity modulus (transverse elastic modulus, torsional elastic modulus, shear elastic modulus), Poisson's ratio, and bulk elastic modulus. The elastic modulus of resin materials used in injection molding is evaluated by performing dynamic viscoelastic measurements using a rotational rheometer or similar instrument, and the storage modulus is evaluated as an elastic term of the material. The elastic modulus is measured as the value of the stress response to strain (displacement), and the unit is [Pa] or [Bar].

[0137] As mentioned above, the elastic modulus of a material has a significant impact on the holding pressure step. In particular, it has been demonstrated that the elastic modulus near the glass transition temperature (Tg) of the material has a strong influence. Figure 15 This graph shows the relationship between the elastic modulus of HIPS material at Tg (=110℃) and the injection capacity of the injection molding machine. The elastic modulus at Tg can be extracted using a rotational rheometer. Figure 9 The results are derived from the measurement results shown in Example 1. Figure 15 It can be seen that there is a strong correlation between the elastic modulus at Tg and the injection volume.

[0138] Calculate using formula (10) Figure 15 The injection volume Vi.

[0139] Injection volume: Vi = (Lm - Lc)π(D / 2) 2 ...(10)

[0140] Lm is the metering position [mm], Lc is the holding pressure completion position [mm], and D is the screw inner diameter [mm]. Since the metering position Lm and the screw inner diameter D are fixed values ​​independent of the material state, the change in injection volume Vi is caused by the change in the holding pressure completion position Lc.

[0141] Since changes in injection volume imply changes in the quality of the molded product, this indicates that the elastic modulus at Tg affects the dimensions of the molded product.

[0142] Test Example 3 will be described below, which provides an example of estimating the elastic modulus at Tg. In Test Example 3, the elastic modulus at Tg is used... Figure 12 (a) shows a pressure sensor 62 installed in the resin flow path inside the mold 1 and a temperature sensor 63 installed near the pressure sensor to detect the temperature of the resin material.

[0143] Extensive research indicates a correlation between the elastic modulus of resin materials at Tg and the decreasing trend of pressure inside the mold. Specifically, there is a negative correlation between the internal mold pressure P3 when the temperature of the molded product inside the mold decreases to Tg+10°C and the elastic modulus at Tg, and a stronger negative correlation exists when the temperature of the molded product decreases to Tg.

[0144] The elastic modulus at Tg in this example is estimated based on formula (11). That is, the screw pressure P of the injection molding machine. S The ratio P to the pressure sensor value P3 S / P3 is a parameter related to the elastic modulus. Therefore, in this example, it is based on P S / P3 controls the molding conditions, realizing the control of molding conditions based on the elastic modulus at Tg.

[0145] Elastic modulus (at Tg) ∝ P S (x) / P3(x)...(11)

[0146] x: The time point at which the temperature of the molded product reaches Tg.

[0147] As described above, since there is a negative correlation between the internal mold pressure P3 when the temperature of the molded product inside the mold drops to Tg+10°C and the elastic modulus at Tg, the time point when the temperature of the molded product drops to a temperature not exceeding Tg+10°C can be used as x. Furthermore, any temperature can be used, as long as it does not exceed Tg+10°C; for example, the time point when the temperature cools to Tg can be used as x.

[0148] exist Figure 12 In the example shown in (b), the temperature of the molded product reaches Tg at x = 4.5 s, so the pressure value P3 (4.5) at this time is approximately 28 MPa. Furthermore, since the screw pressure at this time is P... S (4.5) = 40MPa, therefore the elasticity-related parameter P S / P3 is 1.54MPa.

[0149] Figure 16 The elasticity-related parameter P is shown when injection molding is performed using various types of HIPS materials under the conditions of Test Example 3. S The measurement results show the correlation between / P3 and the elastic modulus at Tg for each material. The ratio P can be confirmed. S There is a strong correlation between the elastic modulus at / P3 and Tg.

[0150] The method in this example can be applied to the estimation of the elastic modulus of the resin material in Example 1, and the molding conditions can be changed based on the obtained estimate.

[0151] In the example above, based on the screw force P S The elastic modulus is estimated by the ratio of P3 to the pressure value P3, but based on P3 and P S When estimating the elastic modulus based on the relationship or ratio between P and other factors, it is also possible to use P in addition to P. S Relationships other than / P3 can be used. For example, αP can be used. S / P3、(P S +β) / P3, P S The elastic modulus can be estimated using / (P³ + γ) or its reciprocal. Here, α, β, and γ are predetermined coefficients. In addition, the elastic modulus can be estimated using formula transformations that include P. S / P3 or P3 / P S Viscosity is estimated using the values ​​of the components.

[0152] <Example 4>

[0153] The following describes another method for estimating the elastic modulus of a resin material based on process data obtained during a molding cycle. Example 3 describes a method for estimating the elastic modulus at the glass transition temperature (Tg), while Example 4 describes a method for estimating the elastic modulus during the melting phase. In this example, the elastic modulus is estimated based on the relationship between pressure sensor values ​​obtained from pressure sensors installed in the resin flow path inside the mold 1 and the screw position in the injection molding machine 10. More specifically, the elastic modulus is estimated based on the relationship between the change in screw position ΔLs during the injection step within the injection molding period and the pressure change ΔP4 in the resin path inside the mold.

[0154] The elastic modulus of a resin material when it is fully molten follows Hooke's Law. Hooke's Law uses the spring constant to represent the displacement relative to the load, and the formula is as follows (12).

[0155] F=Kx...(12)

[0156] Here, x is the displacement of the spring, F is the reaction force (load) of the spring, and K is the spring constant.

[0157] In injection molding, x can be considered to correspond to the amount of screw movement during injection, F to the stress generated (pressure inside the mold), and K to the elastic modulus during melting. Figure 17 It means Figure 12 The diagram shows the screw position in Test Example 3. The screw movement ΔLs during the injection step can be obtained using formula (13).

[0158] ΔLs=L m -L vp ...(13)

[0159] Here, Lm is the metering position [mm], and Lvp is the VP switching position [mm]. The fluctuation of the internal pressure of the mold during the same time period (during the injection step) is represented by ΔP4.

[0160] In this example, the elastic modulus during melting is estimated based on formula (14). That is, the ratio ΔP4 / ΔLs, where ΔP4 is the elastic modulus (during melting), is used as a parameter related to the elastic modulus (during melting). Therefore, in this example, molding conditions are controlled based on the elastic modulus during melting by controlling the molding conditions based on ΔP4 / ΔLs.

[0161] Elastic modulus (during melting) ∝ΔP⁴ / ΔLs...(14)

[0162] Figure 18 The results show the measurement results of the correlation between the elastic modulus-related parameter ΔP4 / ΔLs and the elastic modulus during melting of each material when injection molding various types of HIPS materials was performed under the conditions of Test Example 3. A strong correlation between ΔP4 / ΔLs and the melt elastic modulus can be confirmed.

[0163] The method in this example can be applied to the estimation of the elastic modulus of the resin material in Example 1, and the molding conditions can be changed based on the obtained estimate.

[0164] In the examples above, the elastic modulus was estimated based on the change in internal mold pressure ΔP4 relative to screw movement ΔLs. However, when estimating the elastic modulus based on the relationship or ratio between ΔLs and ΔP4, relationships other than ΔP4 / ΔLs can be used. For example, the elastic modulus can be estimated using αΔP4 / ΔLs, (ΔP4+β) / ΔLs, ΔP4 / (ΔLs+γ), or their reciprocals. Here, α, β, and γ are predetermined coefficients. Furthermore, viscosity can be estimated using values ​​including ΔP4 / ΔLs or ΔLs / ΔP4 components through formula transformations.

[0165] <Example 5>

[0166] The following describes a method for estimating the shrinkage characteristics of a resin material based on process data obtained during a molding cycle. In this example, shrinkage characteristics are estimated based on the relationship between pressure sensor values ​​obtained from pressure sensors located in the resin flow path inside mold 1 and the injection volume. More specifically, shrinkage characteristics are estimated based on the relationship between the internal mold pressure P5 when the mold gate is sealed during the holding and cooling steps in the injection molding period, the internal mold pressure P6 when the molded product reaches a temperature equal to or below the load deformation temperature, and the injection volume Vi.

[0167] Resin shrinkage in injection molding is mainly related to two factors: "linear thermal expansion" and "state change". Linear thermal expansion is the characteristic that the volume changes with the resin temperature, while state change is the volume change that occurs when the resin changes between the molten state and the solidified state.

[0168] This shrinkage property of materials is usually caused by Figure 11 The graph shown is called the PVT curve, which illustrates the pressure [MPa], temperature [°C], and specific volume [cm³] during cooling. 3 The relationship between [ / g] is primarily determined using a specialized measuring device known as a PVT measuring device. Typically, during injection molding, the resin material transitions from a high-temperature, high-pressure state to a low-temperature, low-pressure state; therefore, the shrinkage behavior in the mold can be assessed by reading the specific volume difference at this point.

[0169] Specific volume is the volume per unit weight. However, in injection molding, there is a time period during which the weight changes due to resin flowing into the mold. Therefore, in order to estimate the change in specific volume based on process data, it is necessary to analyze the data after the resin has stopped flowing into the mold.

[0170] Test Example 4 will be described below, which provides an example of estimating the shrinkage characteristics. In Test Example 4, a setting was used... Figure 12 (a) shows a pressure sensor 62 in the resin flow path inside mold 1 and a temperature sensor 63 located near the pressure sensor to detect the temperature of the resin material. Since the mold runner is a cold runner, the gate curing time (gate sealing time) is predetermined. In test example 4, the gate sealing time is 5.0 seconds after the start of injection. This means that after 5.0 seconds, no resin flows into the mold, and the resin weight does not change. Therefore, the process data measured during this period is not affected by fluctuations in resin weight.

[0171] Figure 19 The relationship between resin temperature and pressure after 5.0 seconds in Test Example 4 is summarized. The vertical axis represents pressure [MPa], and the horizontal axis represents resin temperature [°C]. The plotted data is generally linear. This is similar to... Figure 11 The linear slope of the PVT characteristics is shown. In other words, pressure fluctuations at constant weight can be considered as representing changes in the volume (specific volume) of the molded product.

[0172] In this example, the shrinkage characteristics (specific tolerance) are estimated based on formula (15). That is, the value (P5-P6) / Vi, obtained by dividing the pressure difference between the pressure sensor value P5 at the gate seal and the pressure sensor value P6 when the molded product is at or below the weighted deformation temperature by the injection volume Vi, is used as a parameter related to the shrinkage characteristics (specific tolerance). The injection volume Vi is obtained by formula (10). The reason for dividing by the injection volume Vi is to account for the fluctuation of the filler weight used for each molding due to changes in viscosity and elastic modulus. In this way, in this example, molding conditions based on shrinkage characteristics (specific tolerance) are controlled by controlling the molding conditions based on (P5-P6) / Vi.

[0173] Shrinkage characteristics (specific tolerance) ∝ (P5-P6) / Vi... (15)

[0174] Figure 20 The results show the correlation between the shrinkage characteristic parameter (P5-P6) / Vi and the specific volume difference of each material when injection molding was performed using various types of HIPS materials under the conditions of Test Example 4. The specific volume difference was calculated based on the difference between the specific volume at 110°C (glass transition temperature) and 20 MPa pressure obtained by PVT measurement and the specific volume at 50°C and 20 MPa pressure. The results confirm a strong correlation between (P5-P6) / Vi and the specific volume difference, i.e., the shrinkage characteristic.

[0175] As a variation of this example, shrinkage characteristics (specific tolerance) can be estimated based on formula (16). That is, when P5 is the pressure value at gate sealing and P6 is the pressure sensor value when the molded product is equal to or below the weighted deformation temperature, the shrinkage characteristics (specific tolerance) can be estimated by dividing the pressure difference by the weight of the molded product W. A The obtained value is (P5-P6) / W A It can be used as a parameter related to shrinkage characteristics (specific tolerance). Similar results can be obtained by estimating shrinkage characteristics (specific tolerance) in this way.

[0176] Shrinkage property ∝ (P5-P6) / W A ...(16)

[0177] The method in this example can be applied to the estimation of the shrinkage characteristics of the resin material in Example 1, and the molding conditions can be changed based on the obtained estimates.

[0178] In the examples above, based on (P5-P6) / Vi or (P5-P6) / W A To perform the estimation of shrinkage characteristics. However, based on P5, P6, and Vi or W AWhen estimating shrinkage characteristics using a relationship or ratio, other relationships can be used. For example, values ​​obtained by replacing P5-P6 with αP5-P6 or P5-βP6 can be used, or values ​​obtained by adding at least one of the denominator or numerator to a constant can be used. In addition, formula transformations including P5-P6, (P5-P6) / Vi, or (P5-P6) / W can be employed. A Viscosity is estimated using the values ​​of the components.

[0179] <Example 6>

[0180] This example aims to accurately determine the relationship between material properties and molding conditions through analysis based on pre-measurements, thereby enabling more accurate estimation of material properties. The example of pre-measurement is measurements taken during trial runs prior to mass production.

[0181] Example 1 describes an example of deriving molding conditions (explanatory variables) for obtaining target material properties (target variables) using formulas (3) and (4) and formula (5). However, in order to obtain highly accurate explanatory variables, the constants included in formulas (3) to (5) need to conform to reality. Therefore, Example 6 describes a process for deriving constants that conform to reality.

[0182] In this example, an analysis step is performed before the first injection molding to determine the relationship between material properties and molding conditions, and the determined relationship is used to set the molding conditions. For example, the analysis step can be performed during a pre-production trial run. The pre-production trial run is performed after mold making is completed and the mass production environment (e.g., molding machine and temperature controller) is determined. The test environment is the same as the mass production environment. Furthermore, methods such as... Figure 12 (a) shows a mold and injection molding machine equipped with sensors. However, the analysis steps can be performed using testing equipment separate from the equipment used for mass production.

[0183] The analysis step involves performing injection molding under a variety of different conditions. The molding conditions follow the explanatory variables in formulas (3) to (5). That is, since the explanatory variables consist of t (resin temperature), d (injection speed), and p (pressure), molding is performed by setting these explanatory variables to multiple different values. Typically, molding is performed by changing the explanatory variables to two or three levels. In the analysis step, material properties are measured during molding under different conditions, and the measurement results are used to determine the relationship between material properties and molding conditions.

[0184] Figure 21 The setup for Test Example 5, in which molding conditions were varied during the initial trial run, is shown. HIPS material was used as the resin material, and... Figure 12Molding was performed under the conditions shown in (a). The resin temperature conditions varied from 200°C to 230°C, and the injection speed varied from 20 mm / s to 40 mm / s. In Test Example 5, the pressure was adjusted by changing the VP position, which varied from 8 mm to 12 mm.

[0185] Figure 22 (a) to Figure 22 (e) shows the estimated results of the material properties obtained in Test Example 5. Estimates of the elastic modulus, viscosity, and shrinkage characteristics were obtained using the methods described in Examples 2 to 5. From these results, it can be seen that the various material properties exhibit a roughly linear variation with respect to molding conditions. That is, the constants included in formulas (3) to (5) can be extracted according to the linear approximation.

[0186] As mentioned above, by conducting trial runs in the same environment as mass production, a relationship between material properties and molding conditions that matches actual conditions can be established. By using this numerical formula during mass production, higher molding quality can be achieved. Although the relationship between material properties and molding conditions is presented here as a relational expression, this relationship can also be stored and used in the form of a LUT (Lookup Table).

[0187] Preliminary analysis can be performed at any time before the relationship between material properties and molding conditions determined through analysis is used for control. In other words, "preliminary" can refer to any time before the first injection molding operation. For example, the above analysis can be performed not only during the preliminary trial run but also during mass production. This is because there may be differences in material properties between the material used in the trial run and the material used during mass production. In this disclosure, the relationship between material properties and molding conditions is continuously updated based on the molding results of the injection molding operation. Continuous updating can be performed for each injection molding operation, for multiple injection molding operations, or at any other arbitrary time. In this disclosure, since the estimation of material properties and the modification (correction) of molding conditions are continuously performed even during mass production, the number of plotted points in the graph shown in Figure 24 can be continuously increased. Therefore, the linear formula can be updated to a high-precision form through a large number of measurement points.

[0188] <Example 7>

[0189] The accuracy of the control process of the injection molding machine described in Examples 1 through 6 may be reduced due to various factors. For example, this may occur if the number of sensors used to collect process data is insufficient due to mold specifications, or if the sensors cannot be installed in the desired positions. In such cases, errors will appear in the calculations for material property estimation and for condition correction. Furthermore, although a linear trend between molding conditions and material properties is observed in Example 6, nonlinear behavior may occur for various reasons. In such cases, the accuracy of the calculations for material property estimation and molding condition correction will also be reduced.

[0190] This example uses machine learning to solve these problems. Machine learning can perform processing and analysis on a scale exceeding the level of human thought, so if a large amount of information can be collected, it can predict situations with high accuracy.

[0191] In this example, a prediction model based on machine learning is used to achieve this. Figure 2 The material property calculation unit 53 and control quantity calculation unit 55 are shown. Therefore, this example also includes a learning step, wherein injection molding is performed under multiple different conditions, the material properties are measured at this time, and a model of the relationship between the material properties and the molding conditions is learned. The predictive model learning process used in the material property calculation unit 53 is performed using learning data consisting of paired process data and material properties obtained during injection molding. Learning data is obtained by performing injection molding in advance (before the first injection molding) under multiple different molding conditions and obtaining the process data and material properties at this time. The paired process data and material properties obtained in this way are processed as learning data. Furthermore, the learning data consisting of paired material property estimates and molding conditions is used to perform learning processing for the predictive model used in the control quantity calculation unit 55. Learning data is obtained by performing injection molding in advance (before the first injection molding) under multiple different molding conditions and obtaining the estimated material properties and molding conditions at this time. The paired estimated material properties and molding conditions obtained in this way are used as learning data.

[0192] By using these learning data and applying machine learning algorithms, prediction models for the material property calculation unit 53 and the control quantity calculation unit 55 can be trained. For the machine learning models, supervised regression analysis can be used, and examples include linear regression, SVM regression, random forest, neural networks, and deep neural networks.

[0193] The predictive model of material property calculation unit 53 takes process data as input and outputs the material properties of the resin material during the process. Control quantity calculation unit 55 takes estimated material properties as input and outputs molding conditions (or adjustment quantities) to obtain the desired material properties. The predictive model is learned by inputting material properties and outputting molding conditions used to obtain those material properties.

[0194] Furthermore, it is preferable to update the machine learning prediction model during mass production. Since machine learning tends to become more accurate with more learning data, the control system can be improved by continuously learning and rewriting the prediction model via online learning during mass production. This example may also include an update step for acquiring learning data in each loop during mass production, continuously learning online using the acquired learning data, and updating the prediction model. Learning does not need to be performed in every loop; there is no particular limitation on the retraining interval, provided the model has already been learned (generated) once and can be updated using data obtained from injection molding.

[0195] <Example 8>

[0196] The following describes a method for determining quality using the techniques disclosed herein.

[0197] exist Figure 3 In steps ST8 and ST9 of the control flow shown, it is determined whether the material properties are within acceptable values. If the material properties are determined to be outside acceptable values, the system issues a warning to determine the quality of the molding. Steps ST8 and ST9 can be considered as determination steps for judging whether the injection-molded product is a good or defective product based on whether multiple parameters related to various material properties fall within preset appropriate ranges.

[0198] To determine whether a product is good or defective, a threshold needs to be predetermined. Methods to derive the threshold include deriving it experimentally, or by using analytical software to analyze the relationship between material properties and molding quality. There are also methods that set thresholds based on past production results.

[0199] Ideally, the product should be classified as good or bad for each estimated material property. For example, even if the elastic modulus is deemed acceptable, the molding quality can still be considered unacceptable if the viscosity is deemed unacceptable. Essentially, even if only one material property is unacceptable, it is best to consider the molding quality unacceptable.

[0200] Furthermore, it is preferable that the material properties used to determine whether a product is good or defective include at least the elastic modulus, viscosity, and shrinkage characteristics. In this case, accurate material property estimation and good / defective product determination can be performed by using input values ​​(estimated values) of the elastic modulus, viscosity, and shrinkage characteristics, along with thresholds and parameters shown in Examples 2 to 5.

[0201] <Example 9>

[0202] The following describes a material property monitoring system using the techniques disclosed herein.

[0203] pass Figures 1 to 3 The injection molding system shown can control the injection molding machine 10 fully automatically. However, by allowing the operator to review the various types of information acquired during the control process, this information can be used for phenomenon analysis and to improve work efficiency.

[0204] This example also includes a display step for showing multiple parameters related to various material properties on a monitor. For example, if estimated material properties are displayed in real time on monitors set up for both the injection molding machine 10 and the control unit 51, the physical properties of the delivered material can be managed. Furthermore, material properties can be utilized in element studies.

[0205] The monitor can be placed at a remote location different from the installation location of the injection molding machine 10, thereby enabling remote inspection of the aforementioned information. With the ability to remotely manage mass production results, information from multiple locations can be organized simultaneously.

[0206] Furthermore, it is preferable to automatically store the information on a server or similar device for a certain period of time, after which the information can be freely retrieved. Therefore, this example preferably also includes a storage step for storing multiple parameters related to various material properties in a storage medium.

[0207] Ideally, operators should be able to manually rewrite molding conditions based on measurement results, rather than implementing fully automated control.

[0208] <Example 10>

[0209] The control cycle (i.e., the change of molding conditions) can be set arbitrarily, for example, for each resin batch or each production start-up.

[0210] exist Figures 1 to 3In the configuration shown, the injection molding machine is controlled for each cycle of a single operation. However, since the material properties change significantly when the manufacturing batch of resin changes, sufficient effect can be achieved even by controlling it within that cycle. In practice, molding conditions can be controlled using a cycle longer than one operation but shorter than the period of batch change. Preferably, a function that allows the operator to set the control cycle is provided.

[0211] This invention is not limited to the embodiments described above, and various modifications and variations can be made thereto without departing from the spirit and scope of the invention. Therefore, the following claims are appended to disclose the scope of the invention.

[0212] This application claims priority to Japanese Patent Application No. 2023-191420, filed on November 9, 2023, and incorporates the entire contents of its disclosure by reference.

[0213] [List of reference numerals]

[0214] 1. Mold

[0215] 10 Injection Molding Machine

[0216] 11 Plasticizing unit

[0217] 12 Mold Closing Device

[0218] 51 Control device

[0219] 52 Data Detection Units

[0220] 53 Material Property Calculation Unit

[0221] 54 Comparison Units

[0222] 55 Control Quantity Calculation Unit

[0223] 61 Mold Sensor

[0224] 62 Second pressure sensor

[0225] 63 Resin Temperature Sensor

[0226] 71 Barrel Sensor

[0227] 72 First pressure sensor

[0228] 81 Measuring Machine

Claims

1. A control method for an injection molding machine, wherein the control method for manufacturing equipment is executed by a control device. The manufacturing equipment includes an injection molding machine and a mold attached to the injection molding machine. The control method includes: A collection step, wherein the collection step is used to collect multiple process data measured during the first injection molding from multiple sensors disposed on the manufacturing equipment; The acquisition step is used to acquire multiple parameters related to various material properties of the resin material used in injection molding based on the process data; as well as A control step, wherein the control step is used to set molding conditions for a second injection molding after the first injection molding based on the plurality of parameters.

2. The control method for an injection molding machine according to claim 1, wherein... In the control step, the molding conditions for the second injection molding are set using a set of simultaneous equations, which are predetermined for each of the plurality of parameters and include the relationship between the adjustment amount of the molding conditions and the changes in the parameters.

3. The control method according to claim 1, wherein... The various material properties include at least two of the following: viscosity, elastic modulus, and shrinkage characteristics.

4. The control method according to claim 1, wherein The plurality of sensors include: Sensors installed on the injection molding machine; as well as Sensors are installed on the mold.

5. The control method according to claim 1, wherein... The plurality of sensors include: Pressure sensor; as well as Temperature sensor.

6. The control method according to claim 1, wherein The plurality of parameters relating to various material properties include at least two of the following parameters: The first parameter is based on the difference P1-P2 between a first pressure sensor value P1 in the resin flow path within the plasticizing section of the injection molding machine and a second pressure sensor value P2 in the resin flow path inside the mold, when the temperature of the resin material in the mold is at least the glass transition temperature Tg during the injection and holding steps. The second parameter is based on the screw pressure value P of the injection molding machine when the temperature of the resin material in the mold has dropped to no more than Tg+10°C during the holding pressure step. S The pressure sensor value P3 of the resin material in the mold, or based on the change in screw position ΔLs in the injection molding machine during the injection step and the change in the pressure sensor value ΔP4; as well as The third parameter is based on the pressure sensor value P5 in the resin flow path inside the mold when the mold gate is sealed, the pressure sensor value P6 in the resin flow path inside the mold when the molded product reaches a temperature not exceeding the load deformation temperature, and the injection volume Vi or the weight W of the molded product during the holding and cooling steps. A .

7. The control method according to claim 6, wherein At least one of the following conditions must be met: The first parameter is based on the relationship between P1 and P2; The second parameter is based on P3 and P S The relationship between them; The second parameter is based on the relationship between ΔLs and ΔP4; The third parameter is based on the relationship between P5, P6, and Vi; and The third parameter is based on P5, P6, and W. A The relationship between them.

8. The control method according to claim 6, wherein At least one of the following conditions must be met: The first parameter is based on the relationship corresponding to the difference between P1 and P2; The second parameter is based on the parameter corresponding to P. S The relationship with the ratio of P3; The second parameter is based on the relationship corresponding to the ratio of ΔP4 to ΔLs; The third parameter is based on the relationship between the difference between P5 and P6 and Vi; and The third parameter is based on the difference between P5 and P6 and W. A The relationship between the ratios.

9. The control method according to claim 1, wherein The plurality of parameters relating to various material properties include at least two of the following parameters: Viscosity-related parameters, which correspond to the difference P1-P2, are the differences between the first pressure sensor value P1 in the resin flow path within the plasticizing section of the injection molding machine and the second pressure sensor value P2 in the resin flow path inside the mold when the temperature of the resin material in the mold is at least the glass transition temperature Tg during the injection and holding steps. Elastic modulus related parameters, the elastic modulus related parameters being those corresponding to the ratio P S The value of / P3 is either the value corresponding to the ratio ΔP4 / ΔLs, where the ratio P S / P3 is the screw pressure value P of the injection molding machine when the temperature of the resin material in the mold has dropped to no more than Tg+10°C during the holding pressure step. S The ratio ΔP4 / ΔLs is the ratio of the pressure sensor value P3 of the resin material in the mold to the change in the pressure sensor value ΔP4 in the mold during the injection step and the change in the screw position ΔLs of the injection molding machine. as well as Shrinkage characteristic related parameters, which correspond to the values ​​(P5-P6) / Vi or (P5-P6) / W. A The value, namely (P5-P6) / Vi or (P5-P6) / W A The pressure sensor value P5 in the resin flow path inside the mold when the mold gate is sealed during the holding and cooling steps, and the pressure sensor value P6 in the resin flow path inside the mold when the molded product reaches a temperature not exceeding the load deformation temperature, are divided by the injection volume Vi or the weight W of the molded product. A And thus obtained.

10. The control method according to claim 1, further comprising: The analysis step involves performing injection molding under multiple different conditions prior to the first injection molding, measuring the material properties at these conditions, and determining the relationship between the material properties and the molding conditions. In the control step, molding conditions based on the plurality of parameters are set by using the relationship obtained in the analysis step.

11. The control method of claim 10, further comprising the step of continuously updating the relationship based on the molding result of the injection molding.

12. The control method according to claim 1, further comprising: The learning step involves performing injection molding under multiple different conditions prior to the first injection molding, measuring the material properties at these conditions, and learning a model of the relationship between the material properties and the molding conditions. In the control step, molding conditions based on the plurality of parameters are set by using the model.

13. The control method according to claim 12, further comprising: The step of updating the model based on the result of the second injection molding.

14. The control method according to claim 1, further comprising: The determination step is used to determine whether the injection-molded product is a good product or a defective product based on whether the multiple parameters related to the multiple material properties fall within a preset appropriate range.

15. The control method according to claim 1, further comprising: The display step is used to display the plurality of parameters related to the plurality of material properties on a monitor.

16. The control method according to claim 1, further comprising: A storage step, wherein the storage step is used to store the plurality of parameters related to the plurality of material properties in a storage medium.

17. The control method according to claim 1, wherein The collection step, the acquisition step, and the control step are performed each time the manufacturing batch of the resin material is changed.

18. The control method according to claim 1, wherein The collection step, the acquisition step, and the control step are performed each time the manufacturing equipment is started.

19. A control method for manufacturing equipment executed by a control device, The manufacturing equipment includes an injection molding machine and a mold attached to the injection molding machine. The control method includes: The collection step is used to collect a first pressure sensor value obtained from a first pressure sensor located in the plasticizing section of the injection molding machine, a second pressure sensor value obtained from a second pressure sensor located in the resin flow path inside the mold, and a temperature sensor value obtained from a temperature sensor located near the second pressure sensor and detecting the temperature of the resin material. as well as A control step is used to set molding conditions for injection molding based on the relationship between a first pressure sensor value P1 and a second pressure sensor value P2 during the injection and holding pressure steps in injection molding, when the temperature sensor value is at least the glass transition temperature Tg of the resin material.

20. The control method according to claim 19, wherein The relationship described corresponds to the difference between P1 and P2.

21. The control method according to claim 19, wherein The relationship described corresponds to the difference between P1 and P2.

22. The control method according to claim 19, wherein In the control step, molding conditions for injection molding are set based on the first pressure sensor value and the second pressure sensor value when both the first pressure sensor value and the second pressure sensor value change at a rate of less than 5 MPa / sec over time during the injection step and the holding pressure step.

23. A control method for manufacturing equipment executed by a control device, The manufacturing equipment includes an injection molding machine and a mold attached to the injection molding machine. The control method includes: The collection step is used to collect pressure sensor values ​​obtained from pressure sensors located in the resin flow path inside the mold, temperature sensor values ​​obtained from temperature sensors located near the pressure sensors and detecting the temperature of the resin material, and screw pressure values ​​of the injection molding machine. as well as The control step is used to base the pressure sensor value P3 when the temperature sensor value drops to no more than Tg+10°C during the holding pressure step in injection molding and the screw pressure value P of the injection molding machine. S The relationship between these factors determines the molding conditions used for injection molding, where Tg is the glass transition temperature of the resin material.

24. The control method according to claim 23, wherein The relationship corresponds to P. S The relationship with the ratio of P3.

25. A control method for manufacturing equipment executed by a control device. The manufacturing equipment includes an injection molding machine and a mold attached to the injection molding machine. The control method includes: The collection step is used to collect pressure sensor values ​​obtained from pressure sensors located in the resin flow path inside the mold and the screw position of the injection molding machine. as well as A control step is used to set molding conditions for injection molding based on the relationship between the change in screw position ΔLs and the change in pressure sensor value ΔP4 during the injection step in injection molding.

26. The control method according to claim 25, wherein The relationship described corresponds to the ratio of ΔP4 to ΔLs.

27. A control method for manufacturing equipment executed by a control device. The manufacturing equipment includes an injection molding machine and a mold attached to the injection molding machine. The control method includes: The collection step is used to collect pressure sensor values ​​obtained from pressure sensors located in the resin flow path inside the mold, and temperature sensor values ​​obtained from temperature sensors located near the pressure sensors and detecting the temperature of the resin material. as well as A control step is used to set molding conditions for injection molding based on the relationship between pressure sensor value P5 when the gate of the mold is sealed during the holding and cooling steps in injection molding, pressure sensor value P6 when the molded product reaches a temperature not exceeding the load deformation temperature, and injection volume Vi.

28. The control method according to claim 27, wherein The relationship described corresponds to the ratio of the difference between P5 and P6 to Vi.

29. The control method according to claim 27, wherein The relationship described corresponds to the ratio of the difference between P5 and P6 to Vi.

30. A control method for manufacturing equipment executed by a control device. The manufacturing equipment includes an injection molding machine and a mold attached to the injection molding machine. The control method includes: The collection step is used to collect pressure sensor values ​​obtained from pressure sensors located in the resin flow path inside the mold, and temperature sensor values ​​obtained from temperature sensors located near the pressure sensors and detecting the temperature of the resin material. as well as The control steps are based on the pressure sensor value P5 when the mold gate is sealed during the holding and cooling steps in injection molding, the pressure sensor value P6 when the molded product reaches a temperature not exceeding the load deformation temperature, and the weight W of the molded product. A The relationship between these factors determines the molding conditions used for injection molding.

31. The control method according to claim 30, wherein the relationship corresponds to the difference between P5 and P6 and W. A The relationship between the ratios.

32. The control method according to claim 30, wherein The relationship corresponds to the difference between P5 and P6 and W. A The relationship between the ratios.

33. A method for manufacturing an injection-molded product, the method comprising: The steps of preparing manufacturing equipment, said manufacturing equipment including an injection molding machine and a mold attached to said injection molding machine; The step of injecting resin material into the mold using the injection molding machine; as well as The step of removing the injection-molded product from the mold, wherein The manufacturing equipment is controlled by the control method according to any one of claims 1 to 31.

34. The method for manufacturing injection-molded products according to claim 33, wherein, The resin material is a recycled resin material.

35. A system comprising: Manufacturing equipment, the manufacturing equipment including an injection molding machine and a mold attached to the injection molding machine; as well as Control device, in which The control device controls the manufacturing equipment using the control method according to any one of claims 1 to 32.

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