Injection molding system

Abstract

To shorten a trial shot period.SOLUTION: An injection molding machine repeatedly performs injection molding of a resin using a mold according to a molding cycle based on predetermined molding conditions. A measurement system outputs a plurality of measurement values corresponding to a plurality of physical quantities every time injection molding is performed. An adjustment device 1 stores the plurality of measurement values corresponding to the plurality of physical quantities in time series, and adjusts the molding conditions based on a plurality of time series data corresponding to the plurality of physical quantities, which are generated thereby. The molding conditions include a planned cooling time value related to a cooling time included in the molding cycle. The adjustment device 1 includes a first calculation unit 12 and an adjustment unit 13, and the first calculation unit 12 calculates a theoretical cooling time value using the plurality of time series data and a calculation formula. When a feasible condition is satisfied that the planned cooling time value is longer than the theoretical cooling time value by a threshold value or more, the adjustment unit 13 adjusts the molding conditions so that a trial shot period before the mold reaches a thermal equilibrium state is shortened.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present disclosure relates to an injection molding system, and more particularly to an injection molding system that repeatedly performs injection molding of resin using a mold.

Background Art

[0002] In Patent Document 1, the temperature of a mold is measured, and until the measured temperature reaches the target temperature, the cooling medium is not guided to the mold so that the mold temperature is rapidly increased, thereby reducing the number of waste shots (molded products having defects such as partial defects or non-uniformity due to the low mold temperature) at the start of mass production. A mold temperature control device is described.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the above mold temperature control device, when the mold temperature is increased, the cooling time until the resin injected into the mold is cooled and becomes a molded product that can be taken out of the mold becomes longer, and as a result, the waste shot period (waste injection) during which waste shots occur may become longer.

[0005] An object of the present disclosure is to provide an injection molding system capable of shortening the waste injection period.

Means for Solving the Problems

[0006] An injection molding system according to one aspect of the present disclosure comprises a mold, an injection molding machine, a measurement system, and an adjustment device. The injection molding machine repeatedly performs injection molding of resin using the mold according to a molding cycle based on predetermined molding conditions. The measurement system measures a plurality of physical quantities related to the injection molding each time the injection molding machine performs the injection molding and outputs a plurality of measured values ​​corresponding to the plurality of physical quantities. The adjustment device adjusts the molding conditions based at least on a plurality of time-series data corresponding to the plurality of physical quantities, which are generated by storing the plurality of measured values ​​output by the measurement system in time series. The molding cycle includes a cooling time. The cooling time is the time from when the resin is injected into the mold by the injection molding machine until the injected resin cools and can be removed from the mold. The molding conditions include a predetermined value for the cooling time. The predetermined value for the cooling time is a predetermined value for the cooling time. The adjustment device comprises a calculation unit and an adjustment unit. The calculation unit calculates the theoretical value of the cooling time, which is the theoretical value of the cooling time, using the plurality of time-series data and a calculation formula or information equivalent to the calculation formula. The adjustment unit adjusts the molding conditions so that the test molding period is shortened when the possible conditions are met. The possible conditions are that the planned cooling time is longer than the theoretical cooling time by a threshold or more. The test molding period is the period before the mold reaches a state of thermal equilibrium. [Effects of the Invention]

[0007] The injection molding system disclosed herein has the effect of shortening the time required for test molding. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is a conceptual diagram of an injection molding system according to an embodiment of this disclosure. [Figure 2] Figure 2 is a block diagram of the adjustment device that constitutes the injection molding system described above. [Figure 3] Figure 3 is a flowchart illustrating the operation of the injection molding system described above. [Figure 4] Figure 4 is a flowchart illustrating the molding condition adjustment process (reduction of cooling time included in the molding cycle) using the adjustment device described above. [Figure 5] Figure 5 is a bar graph showing an example of adjustments made by the molding condition adjustment process described above (reduction of the holding pressure time, which is included in the molding cycle and involves holding pressure while cooling). [Figure 6] Figure 6 is a bar graph showing another example of the above adjustment (reduction of the holding time and cooling time (cooling without holding pressure) included in the molding cycle). [Figure 7] Figure 7 is a bar graph showing another example of the above adjustment (reduction of the holding time, cooling time, and metering time included in the molding cycle). [Figure 8] Figure 8 is a graph showing an example of the above adjustment (an adjustment that shortens the cooling time included in the molding cycle while bringing the predicted rise curve closer to the target rise curve). [Modes for carrying out the invention]

[0009] The various physical quantities, measuring instruments and their arrangements, measured values, time-series data, and calculation formulas mentioned in the following explanation are all illustrative examples and may be modified as appropriate.

[0010] (1) Overview As shown in Figure 1, the injection molding system 100 according to the embodiment of this disclosure comprises a mold 20, an injection molding machine 10, a measurement system 2, and an adjustment device 1.

[0011] (1-1) Mold The mold 20 is a mold for injection molding of resin. The resin is a thermoplastic resin such as polypropylene or polycarbonate, but it may also be a thermosetting resin such as melamine resin. The resin may contain fillers such as metal powder or glass fibers, and the material of the resin is not limited. The resin measured by the injection molding machine 10 is injected into the mold 20. The resin injected into the mold 20 is cooled until it can be removed from the mold 20 as a molded product.

[0012] (1-2) Injection molding machine The injection molding machine 10 performs injection molding of resin using the mold 20. The injection molding machine 10 repeatedly executes injection molding according to a molding cycle based on predetermined molding conditions. The repeated execution is, for example, continuous execution, but may also be intermittent execution. The injection molding machine 10 is, for example, a vented molding machine or a DSI (Die Slide Injection Molding) molding machine, etc., but is not limited thereto.

[0013] (1-2-1) Molding conditions The molding conditions are conditions related to injection molding, and are, for example, values such as time, temperature, or pressure preset for each process of injection molding. The predetermined molding conditions are, for example, the molding conditions used during the mass production period.

[0014] (1-2-1a) Mass production period The mass production period is the period after the discard period described later, and is the period after the mold 20 reaches a thermal equilibrium state. The mass production period is a period in which "mold thermal equilibrium temperature - mold temperature ≤ predetermined temperature" is satisfied. In other words, the mass production period is a period in which the necessary conditions described later are not satisfied.

[0015] Note that during the mass production period, since the mold temperature is relatively high (reaching the mold thermal equilibrium temperature or the difference from the mold thermal equilibrium temperature is below the predetermined temperature), the possibility of defects occurring in the molded product due to a low mold temperature is low.

[0016] (1-3) Measurement system The measurement system 2 measures a plurality of physical quantities related to injection molding. Each time the injection molding machine 10 executes injection molding, the measurement system 2 measures a plurality of physical quantities related to injection molding and outputs a plurality of measurement values corresponding to the plurality of physical quantities.

[0017] (1-3-1) A plurality of physical quantities and a plurality of measurement values Multiple physical quantities include, for example, mold temperature and resin temperature. Multiple measured values ​​include, for example, mold temperature measurement values ​​and resin temperature measurement values.

[0018] (1-4) Adjustment device The adjustment device 1 adjusts the molding conditions. Adjustments to the molding conditions include, for example, adjusting the time, temperature, and pressure. In particular, adjustments to the molding conditions involve shortening the cooling time (at least one of the holding pressure time with cooling and the non-holding pressure cooling time) in order to shorten the sacrificial molding period. As shown in Figure 5, adjustments to the molding conditions may also involve bringing the predetermined cooling time T2 (hereinafter referred to as "planned cooling time") included in the molding cycle closer to the theoretical cooling time T20 if it is longer than the theoretical cooling time T20.

[0019] The adjustment device 1 stores multiple measured values ​​corresponding to multiple physical quantities output by the measurement system 2 in a time series. Based at least on the multiple time-series data corresponding to multiple physical quantities generated by storing them in a time series, the adjustment device 1 adjusts the molding conditions.

[0020] Storing data chronologically could involve associating measured values, such as mold temperature, with a cycle number "i" indicating which molding cycle the measurement occurred in. Alternatively, the measured values ​​could be stored in association with the current time information, or they could be stored in chronological order from oldest to newest, without being associated with a cycle number or anything similar.

[0021] (1-4-1) Multiple time series data Time-series data refers to data obtained by arranging multiple measurement results corresponding to a single physical quantity in a time series. Multiple time-series data include mold temperature time-series data and resin temperature time-series data.

[0022] For example, if the current molding cycle is the nth molding cycle (cycle number n), the time series data consists of n measured values ​​corresponding to the n molding cycles from the first to the nth molding cycle. The time series data shows the change in physical quantities over time.

[0023] In the adjustment device 1, multiple time-series data corresponding to multiple physical quantities are input to the neural work's trained model 131a, and multiple calculation data are output from the trained model 131a. Then, the adjustment device 1 calculates adjustment values ​​based on the output calculation data and adjusts the molding conditions based on the calculated adjustment values.

[0024] (1-4-2) Cooling time The molding cycle includes a cooling time T2, as shown in Figure 5. The cooling time T2 is the time it takes for the resin injected into the mold 20 by the injection molding machine 10 to cool and become removable from the mold 20.

[0025] (1-4-3) Planned cooling time The molding conditions include a predetermined cooling time. The predetermined cooling time is a fixed value for the cooling time T2.

[0026] (1-4-4) Calculation unit and adjustment unit As shown in Figure 2, the adjustment device 1 comprises a calculation unit 12 and an adjustment unit 13.

[0027] The calculation unit 12 calculates the theoretical value of the cooling time T2, which is the theoretical value of the cooling time T2, using multiple time-series data and a calculation formula or information equivalent to a calculation formula. Information equivalent to a calculation formula may include, for example, a data table relating the values ​​of variables included in the calculation formula to the calculated values ​​obtained by the formula, or a pre-trained machine learning model that takes the values ​​of variables as input and outputs the calculated values.

[0028] The adjustment unit 13 adjusts the molding conditions so that the test molding period is shortened by at least one step, when the possible conditions are met.

[0029] (1-4-4a) Possible conditions The feasibility condition is the condition regarding whether or not it is possible to shorten the cooling time T2. The feasibility condition is that the planned cooling time is longer than the theoretical cooling time T20 by a predetermined amount of time (for example, the planned cooling time is longer than the theoretical cooling time by a first threshold or more).

[0030] (1-4-4b) Period of throwaway betting The sacrificial firing period is the period before the mold 20 reaches thermal equilibrium. The sacrificial firing period is the period during which the condition "mold thermal equilibrium temperature - mold temperature > predetermined temperature" (for example, "predicted mold thermal equilibrium temperature - measured mold temperature > second threshold") is satisfied. In other words, the sacrificial firing period is the period during which the necessity conditions described later are satisfied.

[0031] During the test injection period, the mold temperature is relatively low (for example, more than a predetermined temperature lower than the mold's thermal equilibrium temperature), which can result in incomplete resin injection molding and potentially lead to defects in the molded product.

[0032] (1-5) Advantages Thus, according to this embodiment, when it is possible to shorten the cooling time T2, the adjustment unit 13 can adjust the molding conditions to shorten at least the test molding period.

[0033] Furthermore, by adjusting the molding conditions, both the test run period and the mass production period may be shortened. However, during the test run period, the mold temperature has not yet reached the mold thermal equilibrium temperature and there is still considerable room for improvement, so it is often possible to shorten the cooling time T2. In contrast, during the mass production period, the mold temperature has already reached the mold thermal equilibrium temperature and there is little room for improvement, so it is less likely that the cooling time T2 can be shortened. Therefore, at least the test run period can be shortened.

[0034] (2) Details The calculation unit 12 described in the overview is referred to as the first calculation unit 12 in the following description.

[0035] (2-1) Details of multiple physical quantities As mentioned above, the multiple physical quantities include mold temperature and resin temperature. Mold temperature is the temperature of the mold 20. More specifically, mold temperature is the temperature of the surface of the mold 20 (the side that is in contact with the outside air). Resin temperature is the temperature of the resin injected into the mold 20. More specifically, resin temperature is the temperature of the portion of the resin injected into the mold 20 that is in contact with the inner surface of the mold 20 (the side that is in contact with the resin), and may be considered equal to the temperature of the inner surface of the mold 20.

[0036] (2-2) Details of multiple measurements As mentioned above, the multiple measurements include mold temperature and resin temperature. Mold temperature refers to the measurement corresponding to the mold temperature. Resin temperature refers to the measurement corresponding to the resin temperature.

[0037] (2-3) Details of multiple time series data As mentioned above, the multiple time series data include mold temperature time series data and resin temperature time series data. Mold temperature time series data is time series data corresponding to mold temperature. Mold temperature time series data is data arranged in time series from multiple mold temperature measurements corresponding to mold temperature. Resin temperature time series data is time series data corresponding to resin temperature. Resin temperature time series data is data arranged in time series from multiple resin temperature measurements corresponding to resin temperature.

[0038] (2-4) Judgment regarding feasibility conditions by the adjustment unit The adjustment unit 13 determines whether the feasibility conditions are met. As mentioned above, the feasibility conditions relate to whether the cooling time T2 can be shortened, and are that the planned cooling time is longer than the theoretical cooling time T20 by a predetermined time or more ("planned cooling time - theoretical cooling time ≥ predetermined time"). In this embodiment, as mentioned above, the feasibility condition is that the planned cooling time is longer than the theoretical cooling time by a first threshold or more ("planned cooling time - theoretical cooling time ≥ first threshold").

[0039] If it is determined that the feasibility conditions are met, that is, if it is possible to shorten the cooling time T2, the adjustment unit 13 adjusts the molding conditions within a range that satisfies, for example, "(planned cooling time after shortening) - theoretical cooling time ≥ 0". If it is determined that the feasibility conditions are not met, that is, if it is impossible to shorten the cooling time T2, no adjustment of the molding conditions is performed.

[0040] (2-5) Details of the adjustment section As shown in Figure 2, the adjustment unit 13 includes a data output unit 131, a second calculation unit 132, and a shortening unit 133.

[0041] (2-5-1) Data Output Section The data output unit 131 includes a pre-trained neural network model 131a.

[0042] (2-5-1a) Neural Networks The neural network in this embodiment is, for example, a recurrent neural network suitable for handling time-series data. However, the neural network may be a neural network other than a recurrent type, or it may be any machine learning algorithm (such as deep learning) that applies a neural network.

[0043] (2-5-1b) Pre-trained model The trained model 131a takes multiple time-series data as input and outputs multiple calculation data. Calculation data refers to data used to calculate adjustment values ​​(described later).

[0044] Multiple calculation data include a predicted mold thermal equilibrium temperature. The predicted mold thermal equilibrium temperature is the predicted temperature of the mold when it reaches a state of thermal equilibrium.

[0045] (2-5-1c) Thermal equilibrium state and mold thermal equilibrium temperature Thermal equilibrium is a state in which the temperature of the mold increases as the low-temperature mold 20 repeatedly comes into contact with the high-temperature resin, and the temperature difference between the mold and the resin decreases until a constant temperature is reached. The mold thermal equilibrium temperature is the constant temperature at which the mold 20 reaches thermal equilibrium.

[0046] In this embodiment, the thermal equilibrium state is defined as a state in which the temperature difference of the mold temperature between a predetermined number of consecutive molding cycles is less than or equal to a third threshold. This allows for the determination of whether or not the mold 20 has reached a thermal equilibrium state based on the mold temperature time series data.

[0047] Furthermore, in this embodiment, the mold thermal equilibrium temperature is the average value of the mold temperature over a predetermined number of consecutive molding cycles while in a thermal equilibrium state (i.e., while the mold temperature remains within a third threshold fluctuation during continuous injection molding). This allows the mold thermal equilibrium temperature to be calculated based on mold temperature time-series data.

[0048] (2-5-2) Second Calculation Unit The second calculation unit 132 calculates adjustment values ​​for the molding conditions based on multiple calculation data output by the data output unit 131, provided that the necessary conditions are met.

[0049] (2-5-2a) Necessity condition The necessity condition is the condition regarding whether or not it is necessary to shorten the cooling time T2, and by extension, the condition regarding whether the current or next molding cycle belongs to a sacrificial period in which shortening the cooling time T2 is necessary, or to a mass production period in which shortening the cooling time T2 is not necessary.

[0050] The necessary condition is that the difference between the mold thermal equilibrium temperature and the mold temperature is greater than or equal to a predetermined temperature ("Mold thermal equilibrium temperature - Mold temperature ≥ predetermined temperature"). In this embodiment, the necessary condition is that the measured mold temperature is lower than the predicted mold thermal equilibrium temperature by a second threshold or more.

[0051] (2-5-2b) Adjustment values An adjustment value is a value used to adjust the molding conditions. In this embodiment, for example, the adjustment value is a value used to increase the rate of rise in mold temperature by shortening the cooling time T2 included in the molding cycle.

[0052] The mold temperature increase rate is the increase in mold temperature (°C / cycle) between two consecutive molding cycles. For example, the mold temperature increase rate is the difference between the mold temperature measurement in the previous molding cycle and the mold temperature measurement in the current molding cycle (the increase based on the two measured values ​​from the previous and current cycles).

[0053] Alternatively, the rate of increase in mold temperature may be, for example, the difference between the measured mold temperature in the current molding cycle and the predicted mold temperature based on the predicted rate of increase for the next molding cycle (the predicted rate of increase based on the current measured value and the next predicted value).

[0054] (2-5-3) Judgment by the Adjustment Department regarding the necessity conditions The adjustment unit 13 further determines whether the necessity condition has been met if the possibility condition has been met.

[0055] If the feasibility conditions are met and the necessity conditions are met, that is, if it is possible to shorten the cooling time T2 and the current or next molding cycle falls within a trial period where shortening the cooling time T2 is required, the adjustment unit 13 adjusts the molding conditions. The adjustment of the molding conditions may, for example, be by shortening the cooling time within the range that satisfies "(shortened cooling time T2) - theoretical cooling time ≥ 0", while making the rise prediction curve CV1 follow the rise target curve CV2 (described later).

[0056] If the feasibility conditions are met and the necessity conditions are not met, that is, even if it is possible to shorten the cooling time T2, if the current or next molding cycle falls within a mass production period where shortening the cooling time T2 is unnecessary, no adjustments to the molding conditions will be made.

[0057] (2-5-4) Shortened section The shortening unit 133 shortens the test molding period based on the adjustment value calculated by the second calculation unit 132. As a result, if the current or next molding cycle falls within the test molding period, the mold temperature will rise at a higher rate (for example, more steeply) compared to when it falls within the mass production period.

[0058] Thus, the cooling time T2 can be shortened, and if the mold has not yet reached thermal equilibrium, the shortened section 133 can shorten the test molding period by adjusting the molding conditions to increase the rate of rise in mold temperature.

[0059] (2-5-4a) Reduction of cooling time based on cooling time adjustment value The adjustment value includes the cooling time adjustment value. The cooling time adjustment value is an adjustment value relative to the planned cooling time. The cooling time adjustment value is a value such that the planned cooling time after reduction (planned cooling time after reduction) based on the cooling time adjustment value does not fall below the theoretical cooling time T20 (theoretical cooling time). In other words, the cooling time adjustment value is an adjustment value that satisfies "(cooling time after reduction) - theoretical cooling time ≥ 0", and may also be a value that satisfies, for example, "(cooling time after reduction) - theoretical cooling time = 0".

[0060] The shortening unit 133 shortens the planned cooling time value based on the cooling time adjustment value.

[0061] Thus, the cooling time T2 can be shortened, and when the mold 20 has not yet reached thermal equilibrium, the shortening section 133 can shorten the test run period by shortening the cooling time T2 and increasing the rate of rise in the mold temperature.

[0062] (2-6) Calculation formula The formula used by the first calculation unit 12 to calculate the theoretical cooling time T20 (theoretical cooling time value) includes a first variable Tm, a second variable Tc, a first constant Tx, and at least one second constant. The first variable Tm is substituted with the measured mold temperature. The second variable Tc is substituted with the measured resin temperature. The first constant Tx is a constant corresponding to the removable temperature. The removable temperature is the temperature at which the resin injected into the mold 20 cools and becomes removable from the mold 20.

[0063] The removal temperature may be, for example, the heat distortion temperature or the solidification temperature. The resin, once cooled to the removal temperature, does not need to be completely solidified, but it is sufficient that it can be safely removed (without significant deformation or damage).

[0064] At least one second constant corresponds to the wall thickness t of the injection-molded product, and at least one of the following: thermal diffusivity α, thermal conductivity, specific heat, density, glass transition temperature, latent heat of crystallization, and primary crystallinity of the resin.

[0065] The calculation formula is, for example, the formula “T2=(t)” which includes the first variable Tm, the second variable Tc, the first constant Tx, the wall thickness t, and the thermal diffusivity α. 2 / π 2 α)ln[8(Tc-Tm) / {π 2 It can also be expressed as (Tx-Tm)}]”; in this formula, “ln” represents the natural logarithm.

[0066] By using such a calculation formula, the first calculation unit 12 can accurately calculate the theoretical value of the cooling time.

[0067] (2-7) Details of the injection molding machine As shown in Figure 1, the injection molding machine 10 includes a cylinder 10a and a screw 10b, a hopper 10c, and a plurality of heaters 10d. The mold 20 has a plurality of water tubes 20a.

[0068] The cylinder 10a and screw 10b are components for measuring the resin injected into the mold 20. Measuring means melting the resin to be used in the next molding cycle and measuring its quantity. The hopper 10c is a component that stores the resin taken into the cylinder 10a by the screw 10b. The heater 10d is a device (temperature controller) that adjusts the temperature of the resin taken into the cylinder 10a to a preset temperature.

[0069] The water pipe 20a is a pipe through which cooling water flows to cool the mold 20. In Figure 1, the water pipe 20a is embedded in the mold 20, but it may also be attached to the surface of the mold 20.

[0070] (2-8) Details of the measurement system The measurement system 2 includes at least one measuring instrument from among a temperature sensor and a flow meter. The measurement system 2 includes, for example, a first temperature sensor for measuring the temperature of the mold 20 (mold temperature), a second temperature sensor for measuring the temperature of the resin injected into the mold 20 (resin temperature), a third temperature sensor for measuring the ambient temperature (ambient temperature), and a flow sensor for measuring the flow rate of the cooling water flowing through the water pipe 20a (see Figure 1) (water pipe flow rate).

[0071] The first temperature sensor is attached, for example, to the surface of the mold 20. The second temperature sensor is embedded, for example, in the part of the inner surface of the mold 20 that is in contact with the resin. The third temperature sensor is attached, for example, to an appropriate position on the injection molding machine 10.

[0072] Measurement system 2 measures the mold temperature with a first temperature sensor and outputs the measured mold temperature. Measurement system 2 also measures the resin temperature with a second temperature sensor and outputs the measured resin temperature. Furthermore, measurement system 2 measures the ambient temperature with a third temperature sensor and outputs the ambient temperature. Finally, measurement system 2 measures the water pipe flow rate with a flow sensor and outputs the water pipe flow rate.

[0073] (2-9) Start of measurement at the beginning of each molding cycle At the start of each molding cycle, the measurement system 2 begins measuring multiple physical quantities, such as mold temperature, resin temperature, ambient temperature, and water pipe flow rate, using multiple measuring instruments including the first to third temperature sensors and flow rate sensors.

[0074] In this way, at the start of each molding cycle, the measurement system 2 initiates multiple measurements corresponding to multiple physical quantities, and the adjustment device 1 stores the multiple measurement values ​​corresponding to the multiple physical quantities, thereby generating multiple time-series data corresponding to the multiple physical quantities.

[0075] (2-10) Other examples of multiple physical quantities, and other examples of multiple measured values The multiple physical quantities may further include at least one of the following: cylinder temperature, ambient temperature, screw rotation speed, and water pipe flow rate. The cylinder temperature is the temperature of cylinder 10a. The ambient temperature is the temperature of the ambient air. The screw rotation speed is the number of rotations of screw 10b per unit time. The water pipe flow rate is the flow rate of cooling water flowing through water pipe 20a per unit time.

[0076] Multiple measurements may further include at least one of the following: cylinder temperature measurement, ambient temperature measurement, screw rotation speed measurement, and water pipe flow rate measurement. The cylinder temperature measurement is the measurement corresponding to the cylinder temperature. The ambient temperature measurement is the measurement corresponding to the ambient temperature. The screw rotation speed measurement is the measurement corresponding to the screw rotation speed. The water pipe flow rate measurement is the measurement corresponding to the water pipe flow rate.

[0077] Multiple time series data may further include at least one of the following: cylinder temperature time series data, ambient temperature time series data, screw rotation speed time series data, and water pipe flow rate time series data. Cylinder temperature time series data is time series data corresponding to cylinder temperature. Ambient temperature time series data is time series data corresponding to ambient temperature. Screw rotation speed time series data is time series data corresponding to screw rotation speed. Water pipe flow rate time series data is time series data corresponding to water pipe flow rate.

[0078] In this way, by including at least one of the following physical quantities to be measured by the measurement system 2—in addition to mold temperature and resin temperature—as well as cylinder temperature, ambient temperature, screw rotation speed, and water pipe flow rate, the accuracy of the adjustment of molding conditions by the adjustment unit 13 can be improved.

[0079] Furthermore, the measurement targets of the measurement system 2 may include the adjustment targets of the adjustment device 1, or physical quantities that can be adjusted. That is, the multiple physical quantities may further include at least one of the times of various processes included in the molding cycle (injection time T1, filling time T11, holding pressure time T12, cooling time T2, removal time T3, and metering time T4), and back pressure. Back pressure is the pressure applied to the resin in the metering process.

[0080] Multiple measurements may further include one or more of the following: injection time measurement, filling time measurement, holding pressure time measurement, cooling time measurement, removal time measurement, and back pressure measurement. Multiple time series data may further include one or more of the following: injection time time series data, filling time time series data, holding pressure time time series data, cooling time time series data, removal time series data, and back pressure time series data.

[0081] In this way, by including multiple physical quantities that are measured by the measurement system 2 as quantities that are adjusted by the adjustment device 1 (e.g., injection time T1) or quantities that can be adjusted (e.g., extraction time T3, back pressure), the adjustment accuracy can be improved.

[0082] (2-11) Constants input to the adjustment unit The adjustment unit 13 may adjust the molding conditions based on one or more constants (values ​​that do not change over time) in addition to multiple time-series data. One or more constants may be one or more values ​​from the following: the set temperature of the heater 10d, the weight of the mold 20, the weight of the molded product, the type of resin, or the diameter or radius (water tube diameter) of the water tube 20a.

[0083] Furthermore, if the set temperature of heater 10d can be changed during the molding cycle (the period from the start to the end of production), the set temperature may be treated as a variable, and multiple time series data may further include time series data of the set temperature corresponding to the set temperature.

[0084] (2-12) Details of the molding cycle The molding cycle includes injection time T1, cooling time T2, removal time T3, and metering time T4, as shown in Figures 5 to 7.

[0085] (2-12-1) Injection time The injection time T1 is the time corresponding to the injection process. The injection process is the process of injecting the resin metered in the previous molding cycle into the mold 20.

[0086] (2-12-2) Details of Cooling Time The cooling time T2 is the time corresponding to the cooling process. The cooling process is the process of cooling the resin injected into the mold 20 until it can be removed from the mold 20. The resin injected into the mold 20 is cooled until it reaches a temperature at which it can be removed. Specifically, the molten resin inside the mold 20 is cooled and solidified by the cooling water flowing through the water pipe 20a, becoming a molded product that can be removed from the mold 20. The cooling time T2 is the time it takes for the resin injected into the mold 20 to cool and reach a temperature at which it can be removed.

[0087] (2-12-3) Removal time The removal time T3 is the time corresponding to the removal process. The removal process is performed after the cooling process and is the process of removing the molded product by injection molding from the mold 20. More specifically, the removal process consists of mold opening (opening the mold 20), ejection (ejecting the molded product from inside the mold 20), removal (removing the ejected molded product), and mold closing (closing the mold 20). In other words, the removal process includes a series of steps from mold opening through ejection and removal to mold closing, but in this embodiment, it is not necessary to particularly distinguish between the series of steps.

[0088] (2-12-4) Weighing time The metering time T4 is the time corresponding to the metering process. The metering process is performed in parallel with the cooling process and is the process of metering the resin to be injected into the mold 20 in the next molding cycle. The metering process is usually started when the injection process is completed and completed before the cooling process is completed.

[0089] If the cooling process is completed and the removal process has started but the weighing process has not yet been completed, the removal process will not take place, and the system will remain in a waiting state until the weighing process is completed.

[0090] Furthermore, if it is anticipated that the metering process will not be completed by the end of the cooling process as a result of shortening the cooling time T2, the metering time T4 included in the molding cycle may be shortened, as shown in Figure 7.

[0091] (2-12-5) Details of molding conditions The molding conditions may further include, in addition to the planned cooling time, planned injection time, planned metering time, and planned removal time. The planned injection time is a predetermined value for the injection time T1. The planned removal time is a predetermined value for the removal time T3. The planned metering time is a predetermined value for the metering time T4.

[0092] (2-12-6) Details of adjustment values The adjustment values ​​may include, in addition to the cooling time adjustment value mentioned above, at least one more adjustment value from among the injection time adjustment value, the extraction time adjustment value, and the metering time adjustment value. The injection time adjustment value is an adjustment value relative to the planned injection time. The extraction time adjustment value is an adjustment value relative to the planned extraction time. The metering time adjustment value is an adjustment value relative to the planned metering time.

[0093] However, in this embodiment, no special adjustment is required for the removal time T3. In other words, the shortening unit 133 may further shorten at least one time scheduled value corresponding to at least one adjustment value among the planned cooling time and the planned injection time.

[0094] Thus, by further shortening at least one of the injection time T1 and metering time T4 in addition to the cooling time T2 (at least one of the time spent cooling while holding pressure and the time spent cooling without holding pressure), the shortening unit 133 can further shorten the test shot period.

[0095] (2-12-7) Details of the injection process: Filling process and holding pressure process The injection process includes a filling process and a holding pressure process. The filling process is the process of filling the mold 20 with resin. The holding pressure process is the process of cooling the resin filled in the mold 20 while maintaining the pressure.

[0096] The injection time T1 includes the filling time T11 and the holding pressure time T12, as shown in Figure 5. The filling time T11 is the time corresponding to the filling process. The holding pressure time T12 is the time corresponding to the holding pressure process.

[0097] The planned injection time includes the planned filling time and the planned holding time. The planned filling time is a predetermined value for the filling time T11. The planned holding time is a predetermined value for the holding time T12.

[0098] Thus, when the injection process is divided into a filling process and a holding pressure process, the theoretical value of the cooling time is the time from when the resin filled into the mold 20 in the filling process begins to cool while being held under pressure in the holding pressure process until it reaches the temperature at which it can be removed (= the first constant Tx). The holding pressure time T12 overlaps with a part (the beginning) of the cooling time T2. In other words, it can be said that the cooling time T2 includes the holding pressure time T12.

[0099] (2-12-7a) Shortening of holding time The molding conditions further include conditions related to the holding pressure time T12. The adjustment values ​​further include holding pressure time adjustment values. The holding pressure time adjustment values ​​are adjustment values ​​relative to the planned holding pressure time.

[0100] The shortening section 133 shortens the holding pressure time T12 included in the injection time T1 and cooling time T2, respectively, based on the holding pressure time adjustment value, as shown in Figure 5.

[0101] Thus, the injection process includes a filling process and a holding pressure process, in which the resin filled in the filling process is cooled while being held under pressure. By shortening the holding pressure time T12, which is part of the cooling time T2, the shortened section 133 can shorten the test injection period.

[0102] (2-12-7b) Reduction of holding time and cooling time Alternatively, as shown in Figure 6, the shortening section 133 may shorten the holding pressure time T12 included in the injection time T1 and the cooling time T2, and further shorten the cooling time T2 excluding the holding pressure time T12.

[0103] In this way, the shortened section 133 shortens the holding pressure time T12 with cooling (in other words, the cooling time T2 with holding pressure), and further shortens the cooling time T2 without holding pressure, thereby further shortening the test firing period.

[0104] (2-12-7c) Reduction of holding time, cooling time, and weighing time The multiple measurements include mold temperature and resin temperature, as well as screw rotation speed.

[0105] The multiple time-series data includes mold temperature time-series data and resin temperature time-series data, as well as screw rotation speed time-series data.

[0106] The multiple calculation data includes, in addition to the predicted mold thermal equilibrium temperature, a predicted screw rotation speed. The predicted screw rotation speed is the number of screw rotations required to complete the metering process before the end of the cooling process, assuming that the holding pressure time T12 included in both the injection time T1 and the cooling time T2 is shortened, and the cooling time T2 excluding the holding pressure time T12 is further shortened.

[0107] The molding conditions further include conditions related to the metering time T4. The adjustment values ​​further include metering time adjustment values.

[0108] The shortening section 133 shortens the holding pressure time T12 included in both the injection time T1 and the cooling time T2, and further shortens the cooling time T2 excluding the holding pressure time T12. If the metering process is not completed by the end of the cooling process, the metering time T4 included in the molding cycle may be shortened based on the predicted screw rotation speed, as shown in Figure 7, so that the metering process is completed by the end of the cooling process.

[0109] Thus, if metering is not completed within the cooling time T2, the shortening unit 133 can further shorten the metering time T4 based on the predicted screw rotation speed so that metering is completed within the cooling time T2, thereby shortening the time spent firing without a test shot.

[0110] (2-12-8) Upward forecast curve and upward target curve The multiple calculation data further include predicted values ​​for the rate of rise in mold temperature. The adjustment value, as shown in Figure 8, is a value that shortens the planned cooling time based on the cooling time adjustment value while aligning the predicted rise curve CV1, which is based on the predicted rate of rise, with the target rise curve CV2.

[0111] As mentioned above, the rate of increase in mold temperature is the increase in mold temperature per molding cycle (°C / cycle). The predicted rate of increase in mold temperature is, for example, the difference between the measured mold temperature in the current molding cycle and the predicted mold temperature in the next molding cycle.

[0112] The predicted mold temperature increases as shown by the rise prediction curve CV1, which is shown as a solid line in Figure 8. Specifically, in the first period from the initial temperature (50°C) to a first temperature near the mold thermal equilibrium temperature (59°C) (for example, 58°C: a temperature 1°C lower than the mold thermal equilibrium temperature of 59°C), the predicted mold temperature increases at a first rate of increase (for example, 4°C / cycle). Then, in the second period from the first temperature (58°C) to above the mold thermal equilibrium temperature (59°C), the predicted mold temperature increases at a second rate of increase (for example, 1°C / cycle), which is smaller than the first rate of increase (4°C / cycle). The rise prediction curve CV1 is a curve (piece line) that shows the time change of such a predicted mold temperature.

[0113] The target mold temperature increases as shown by the dashed line in Figure 8, for example, in the rise target curve CV2. That is, in the first period from the initial temperature (50°C) to the first temperature (approximately 58°C), the target mold temperature increases at a first rate of increase (e.g., 4°C / cycle), similar to the predicted mold temperature. Then, in the second period from the first temperature (approximately 58°C) to above the mold thermal equilibrium temperature (59°C), the rate of increase of the target mold temperature monotonically decreases from the first rate of increase to 0. The rise target curve CV2 is a curve (monotonically increasing curve) that shows the time change of the target mold temperature in this way.

[0114] In this way, by using an adjustment value that shortens the cooling time T2 while making the predicted rise curve CV1 follow the target rise curve CV2, the shortening unit 133 can shorten the cooling time T2 while controlling the rise in mold temperature.

[0115] The dashed line in Figure 8, representing the rise curve CV0, is an example (a broken line) of the rise in mold temperature when the molding conditions are manually adjusted. While it is possible to increase the mold temperature as shown by the rise curve CV0 and shorten the test molding period through manual adjustment, this requires considerable effort and time, and it is not easy to raise the mold temperature along the target rise curve CV2.

[0116] (2-12-9) Example of an upward prediction curve As mentioned above, the temperature rise prediction curve CV1 is a curve that shows the temperature change such that the predicted mold temperature rises at a first rate of increase in the first period and at a second rate of increase in the second period. The first period is the period from the initial temperature to reaching the first temperature. The first temperature is a temperature near the mold thermal equilibrium temperature. The first rate of increase is the rate of increase based on the theoretical value of the cooling time. The second period is the period from the first temperature to above the mold thermal equilibrium temperature. The second rate of increase is a smaller rate of increase than the first rate of increase.

[0117] The first period is a test run period after the molding conditions have been adjusted by the adjustment unit 13 (for example, after the cooling time T2 has been shortened by the shortening unit 133), and the second period may be the beginning of the mass production period. The beginning of the mass production period refers to the period in the mass production period until the mold temperature reaches the mold thermal equilibrium temperature.

[0118] In this way, by raising the mold temperature to a first temperature at a first rate of increase (for example, a rapid increase), and then raising it from the first temperature to the mold thermal equilibrium temperature at a second rate of increase (< first rate of increase) (for example, a gradual increase), it is possible to suppress the phenomenon that occurs when the mold temperature is rapidly raised to the mold thermal equilibrium temperature, where the mold temperature exceeds the mold thermal equilibrium temperature and then decreases to the mold thermal equilibrium temperature, while also shortening the cooling time T2.

[0119] (2-13) Generating a pre-trained model The injection molding system 100 further includes a model generation unit 134. The model generation unit 134 generates a trained model 131a by performing a training process on a neural network (described above) based on multiple time-series data.

[0120] The learning process is, for example, supervised learning based on a set of multiple time-series data and multiple measured data corresponding to multiple calculation data (training data), but unsupervised learning without training data is also acceptable.

[0121] Furthermore, the model generation unit 134 may generate a second trained model 132a, as described in the modified example, by performing neural network training based on multiple calculation data and adjustment values.

[0122] This allows the model generation unit 134 within the adjustment device 1 to generate a trained model 131a. However, the trained model 131a may be generated externally.

[0123] (3) Specific examples Next, a specific example of the injection molding system 100 will be described. Note that explanations of previously mentioned topics have been omitted or simplified.

[0124] In this specific example, the injection molding system 100 comprises an injection molding machine 10, a mold 20, and a controller 30, as shown in Figure 1. The controller 30 controls the injection molding machine 10 and the mold 20. The controller 30 comprises an adjustment device 1 and a measurement system 2. The measurement system 2 outputs multiple measurement values, including mold temperature measurement values ​​and resin temperature measurement values, to the adjustment device 1.

[0125] As shown in Figure 2, the adjustment device 1 comprises a storage unit 11, a first calculation unit 12, and an adjustment unit 13. The adjustment unit 13 comprises a data output unit 131, a second calculation unit 132, a shortening unit 133, and a model generation unit 134.

[0126] The storage unit 11 stores multiple measurement values ​​output by the measurement system 2 in a time series, thereby generating multiple time series data, including mold temperature time series data and resin temperature time series data, in the storage unit 11.

[0127] Multiple time-series data generated in the memory unit 11 are input to the model generation unit 134. The model generation unit 134 generates a trained model 131a by performing neural network training.

[0128] Furthermore, the generated time-series data are input to the first calculation unit 12. The first calculation unit 12 calculates a theoretical cooling time based on the multiple time-series data and outputs the calculated theoretical cooling time to the adjustment unit 13.

[0129] The adjustment unit 13 determines whether the possible condition "planned cooling time - theoretical cooling time ≥ first threshold" is met, based on the planned cooling time value that constitutes the molding conditions and the calculated theoretical cooling time value.

[0130] The data output unit 131 includes the trained model 131a generated by the model generation unit 134. If it is determined that the conditions for possibility are met, the data output unit 131 inputs the generated mold temperature time series data and resin temperature time series data, along with the calculated theoretical cooling time, into the trained model 131a. The trained model 131a outputs the predicted mold thermal equilibrium temperature and the predicted rate of rise.

[0131] The adjustment unit 13 determines whether the necessity condition "predicted mold thermal equilibrium temperature - measured mold temperature ≤ second threshold" is met, based on the mold thermal equilibrium temperature prediction value output by the trained model 131a and the mold temperature measurement value included in the mold temperature time series data.

[0132] If it is determined that the necessity conditions are met, the second calculation unit 132 calculates a cooling time adjustment value that shortens the cooling time T2 while making the predicted rise curve CV1 follow the target rise curve CV2, based on the predicted mold thermal equilibrium temperature value and the predicted rise rate value output from the trained model 131a.

[0133] The shortened section 133 adjusts the planned cooling time value that constitutes the molding condition based on the calculated cooling time adjustment value.

[0134] (4) Means for implementing the adjustment device The adjustment device 1 includes a processor and memory. The memory stores programs and various types of information, and the processor operates based on the various programs (described later) and various types of information stored in the memory. This enables the functions of the adjustment device 1 (storage unit 11, first calculation unit 12, and adjustment unit 13).

[0135] Various types of information include, for example, the value of the cooling time T2 (planned cooling time) that constitutes the molding conditions, multiple time-series data, and a trained model 131a. The first calculation unit 12 is implemented by a theoretical cooling time calculation program that calculates the theoretical cooling time T20. The adjustment unit 13 is implemented by a feasibility judgment program that makes a judgment regarding possible conditions, a necessity judgment program that makes a judgment regarding necessity conditions, a calculation data output program that outputs calculation data using the trained model 131a, an adjustment value calculation program that calculates adjustment values, and an adjustment program that adjusts the molding conditions.

[0136] (5) Example of operation The injection molding system 100 operates, for example, according to the flowchart in Figure 3. Note that the operation shown in Figure 3 begins when the injection molding system 100 is started.

[0137] When the injection molding system 100 is started, the adjustment device 1 performs a molding condition adjustment process to adjust the molding conditions (step S1). The molding condition adjustment process will be explained using the flowchart in Figure 4.

[0138] Once the molding conditions have been adjusted, the injection molding machine 10, under the control of the controller 30, fills the mold 20 with resin based on the filling conditions (such as the planned filling time) (step S2).

[0139] Once filling is complete, the injection molding machine 10 starts cooling under the control of the controller 30, based on the cooling conditions among the molding conditions (such as the planned cooling time) (step S3).

[0140] In response to the start of cooling, the injection molding machine 10 performs holding pressure under the control of the controller 30, based on the holding pressure conditions among the molding conditions (such as the planned holding pressure time) (step S4).

[0141] Once the holding pressure is complete, the injection molding machine 10 performs weighing based on the weighing conditions (such as the planned weighing time) among the molding conditions, under the control of the controller 30 (step S5).

[0142] After weighing is complete, the injection molding machine 10, under the control of the controller 30, terminates cooling when the time corresponding to the planned holding pressure time and planned cooling time has elapsed since the start of cooling in step S3 (step S6).

[0143] After cooling is complete, the injection molding machine 10 removes the molded product from the mold 20 under the control of the controller 30, based on the removal conditions among the molding conditions (such as the planned removal time) (step S7).

[0144] Once the molded product has been removed, the controller 30 determines whether the current molding cycle, which includes the seven operations corresponding to steps S1 to S7, is the last molding cycle among the multiple molding cycles scheduled (step S8). If it is determined that the current molding cycle is not the last molding cycle (No in step S8), the operation returns to step S1.

[0145] If step S8 determines that this molding cycle is the final molding cycle (Yes), the operation is terminated.

[0146] The molding condition adjustment process in step S1 is performed, for example, according to the flowchart in Figure 4.

[0147] The first calculation unit 12, which constitutes the adjustment device 1, acquires multiple time-series data from the storage unit 11 (step S101).

[0148] Next, the first calculation unit 12 calculates the theoretical cooling time T20 based on the multiple time-series data acquired in step S101 and obtains the theoretical value of the cooling time (step S102).

[0149] Next, the adjustment unit 13 determines whether the possibility condition that the planned cooling time is longer than the theoretical cooling time by at least a first threshold is met (step S103). If the planned cooling time that constitutes the molding conditions is longer than the theoretical cooling time obtained in step S102 by at least a first threshold, it is determined that the possibility condition is met. If the difference between the planned cooling time and the theoretical cooling time (planned cooling time - theoretical cooling time) is less than the first threshold, it is determined that the possibility condition is not met. If it is determined that the possibility condition is not met (No in step S103), the process returns to the higher-level flowchart (step S2 in Figure 3).

[0150] If it is determined in step S103 that the conditions for possibility are met (Yes), the data output unit 131 inputs multiple time series data into the trained model 131a (step S104).

[0151] Next, the data output unit 131 outputs multiple calculation data from the trained model 131a (step S105). The output calculation data includes the predicted mold thermal equilibrium temperature.

[0152] Next, the adjustment unit 13 determines whether the necessity condition that the mold temperature measurement value is lower than the mold thermal equilibrium temperature prediction value by a second threshold or more has been met (step S106). If the most recent mold temperature measurement value included in the mold temperature time series data is lower than the mold thermal equilibrium temperature prediction value output in step S105 by a second threshold or more, it is determined that the necessity condition has been met. If the difference between the most recent mold temperature measurement value and the mold thermal equilibrium temperature prediction value (mold thermal equilibrium temperature prediction value - most recent mold temperature measurement value) is less than the second threshold, it is determined that the necessity condition has not been met. If it is determined that the necessity condition has not been met (No in step S106), the process returns to the higher-level flowchart (step S2 in Figure 3).

[0153] If it is determined in step S106 that the necessity condition is met (Yes), the second calculation unit 132 calculates the adjustment value based on the multiple calculation data output from the data output unit 131 in step S105 (step S107). Here, for example, the cooling time adjustment value is calculated.

[0154] Next, the shortening unit 133 adjusts the molding conditions based on the adjustment value calculated in step S107 (step S108). Here, for example, the cooling time T2 is shortened based on the cooling time adjustment value. After that, the process returns to the higher-level flowchart (step S2 in Figure 3).

[0155] (6) Variant (6-1) Modified version of the second calculation section The trained model 131a described in the embodiment is the first trained model 131a in this modification.

[0156] The second calculation unit 132 includes a second pre-trained neural network model 132a. The second pre-trained model 132a receives multiple calculation data as input and outputs an adjustment value.

[0157] According to this modified version, the second calculation unit 132 includes a second trained model 132a that receives multiple calculation data and outputs an adjusted value, thereby enabling the output of an adjusted value using the trained model.

[0158] (6-2) Modification of trained models In this modified example, the pre-trained neural network model 131a described in the embodiment is a first pre-trained model obtained using machine learning other than a neural network. Similarly, the pre-trained neural network model 132a described in the modified example of the second calculation unit is a second pre-trained model obtained using machine learning other than a neural network. Machine learning other than neural networks may include, but are not limited to, decision trees, random forests, or support vector machines (SVMs).

[0159] (6-3) Modification of the memory section The memory unit 11 may be located outside the adjustment device 1. The memory unit 11 may be provided by, for example, the measurement system 2, or by elements within the controller 30 other than the adjustment device 1 and the measurement system 2. Furthermore, the memory unit 11 may be located outside the controller 30 (for example, on a cloud server), and its location is irrelevant as long as it is accessible to the processor of the adjustment device 1.

[0160] (7) Summary The injection molding system (100) according to the first embodiment comprises a mold (20), an injection molding machine (10), a measurement system (2), and an adjustment device (1). The injection molding machine (10) repeatedly performs injection molding of resin using the mold (20) according to a molding cycle based on predetermined molding conditions. The measurement system (2) measures multiple physical quantities related to injection molding each time the injection molding machine (10) performs injection molding and outputs multiple measured values ​​corresponding to the multiple physical quantities. The adjustment device (1) adjusts the molding conditions based at least on multiple time-series data corresponding to the multiple physical quantities, which are generated by storing the multiple measured values ​​output by the measurement system (2) in time series. The molding cycle includes a cooling time (T2). The cooling time (T2) is the time from when the resin is injected into the mold (20) by the injection molding machine (10) until the injected resin cools and can be removed from the mold (20). The molding conditions include a planned value for the cooling time. The planned cooling time is a predetermined value of the cooling time (T2). The adjustment device (1) comprises a calculation unit (12) and an adjustment unit (13). The calculation unit (12) uses multiple time-series data and a calculation formula or information equivalent to a calculation formula to calculate the theoretical cooling time, which is the theoretical value of the cooling time (T2). The adjustment unit (13) adjusts the molding conditions so that the test molding period is shortened when the possible conditions are met. The possible conditions are that the planned cooling time is longer than the theoretical cooling time by a threshold or more. The test molding period is the period before the mold (20) reaches a state of thermal equilibrium.

[0161] According to this embodiment, if the cooling time (T2) can be shortened, the test molding period can be shortened by adjusting the molding conditions.

[0162] Furthermore, both the trial run period and the mass production period may be shortened. However, during the trial run period, the mold temperature has not yet reached the mold thermal equilibrium temperature, and there is still considerable room for improvement, so it is often possible to shorten the cooling time (T2). In contrast, during the mass production period, the mold temperature has already reached the mold thermal equilibrium temperature, and there is little room for improvement, so it is less likely that the cooling time (T2) can be shortened. Therefore, at least the trial run period can be shortened.

[0163] In the injection molding system (100) according to the second embodiment, in the first embodiment, the calculation unit (12) is the first calculation unit (12). Multiple physical quantities include mold temperature and resin temperature. Mold temperature is the temperature of the mold (20). Resin temperature is the temperature of the resin injected into the mold (20). Multiple measured values ​​include mold temperature measured values ​​and resin temperature measured values. Mold temperature measured values ​​are measured values ​​corresponding to the mold temperature. Resin temperature measured values ​​are measured values ​​corresponding to the resin temperature. Multiple time series data include mold temperature time series data and resin temperature time series data. Mold temperature time series data are time series data corresponding to the mold temperature. Resin temperature time series data are time series data corresponding to the resin temperature. The threshold is the first threshold. The adjustment unit (13) further comprises a data output unit (131), a second calculation unit (132), and a shortening unit (133). The data output unit (131) includes a machine learning trained model (131a). The trained model (131a) takes multiple time-series data as input and outputs multiple calculation data. The multiple calculation data includes a predicted mold thermal equilibrium temperature. The predicted mold thermal equilibrium temperature is the predicted value of the mold thermal equilibrium temperature when the mold reaches thermal equilibrium. The second calculation unit (132) calculates adjustment values ​​for the molding conditions based on the multiple calculation data when the necessity conditions are met. The adjustment values ​​are values ​​that increase the rate of rise of the mold temperature. The necessity condition is that the measured mold temperature is lower than the predicted mold thermal equilibrium temperature by a second threshold or more. The shortening unit (133) shortens the test run period based on the adjustment values.

[0164] According to this embodiment, the cooling time (T2) can be shortened, and if the mold (20) is not in thermal equilibrium, the molding conditions can be adjusted to increase the rate of rise in mold temperature, thereby shortening the test molding period.

[0165] In the injection molding system (100) according to the third embodiment, in the second embodiment, the adjustment value includes a cooling time adjustment value. The cooling time adjustment value is an adjustment value relative to the planned cooling time. The shortening unit (133) shortens the planned cooling time based on the cooling time adjustment value.

[0166] According to this embodiment, the cooling time (T2) can be shortened, and if the mold (20) is not in thermal equilibrium, the cooling time (T2) can be shortened to increase the rate of rise in mold temperature, thereby shortening the trial firing period.

[0167] In the injection molding system (100) according to the fourth embodiment, in the third embodiment, the calculation formula includes a first variable (Tm), a second variable (Tc), a first constant (Tx), and at least one second constant. The first variable (Tm) is substituted with the measured mold temperature. The second variable (Tc) is substituted with the measured resin temperature. The first constant (Tx) is a constant corresponding to the removable temperature. The removable temperature is the temperature at which the resin injected into the mold (20) cools and becomes removable from the mold (20). At least one second constant corresponds to the wall thickness (t) of the molded product by injection molding, and at least one of the following: thermal diffusivity (α), thermal conductivity, specific heat, density, glass transition temperature, latent heat of crystallization, and primary crystallinity of the resin.

[0168] According to this embodiment, the theoretical value of the cooling time can be calculated with high accuracy.

[0169] In the injection molding system (100) according to the fifth embodiment, in the third or fourth embodiment, the injection molding machine (10) includes a cylinder (10a) and a screw (10b), and a water pipe (20a). The cylinder (10a) and screw (10b) are components for measuring the resin to be injected into the mold (20). The water pipe (20a) is a pipe through which cooling water flows to cool the mold (20). The plurality of physical quantities further include at least one physical quantity from among cylinder temperature, ambient temperature, screw rotation speed, and water pipe flow rate. Cylinder temperature is the temperature of the cylinder (10a). Ambient temperature is the temperature of the ambient air. Screw rotation speed is the number of rotations of the screw (10b) per unit time. Water pipe flow rate is the flow rate of cooling water flowing through the water pipe (20a) per unit time. The plurality of measured values ​​further include at least one measured value corresponding to at least one of the plurality of measured values ​​corresponding to the plurality of physical quantities. Furthermore, the multiple measurements corresponding to multiple physical quantities include cylinder temperature measurements, ambient temperature measurements, screw rotation speed measurements, and water pipe flow rate measurements. The cylinder temperature measurement is the measurement corresponding to the cylinder temperature. The ambient temperature measurement is the measurement corresponding to the ambient temperature. The screw rotation speed measurement is the measurement corresponding to the screw rotation speed. The water pipe flow rate measurement is the measurement corresponding to the water pipe flow rate. The multiple time series data further includes at least one time series data corresponding to at least one of the multiple time series data corresponding to multiple physical quantities. Furthermore, the multiple time series data corresponding to multiple physical quantities further include cylinder temperature time series data, ambient temperature time series data, screw rotation speed time series data, and water pipe flow rate time series data. The cylinder temperature time series data is the time series data corresponding to the cylinder temperature. The ambient temperature time series data is the time series data corresponding to the ambient temperature. The screw rotation speed time series data is the time series data corresponding to the screw rotation speed. The water pipe flow rate time series data is the time series data corresponding to the water pipe flow rate.

[0170] According to this embodiment, the accuracy of adjusting molding conditions can be improved by further including at least one of the following physical quantities to be measured by the measurement system (2): mold temperature, resin temperature, cylinder temperature, screw rotation speed, and water pipe flow rate.

[0171] In the injection molding system (100) according to the sixth embodiment, in the fifth embodiment, the molding cycle includes injection time (T1), cooling time (T2), and metering time (T4). Injection time (T1) is the time corresponding to the injection process. The injection process is the process of injecting the resin metered in the previous molding cycle into the mold (20). Cooling time (T2) is the time corresponding to the cooling process. The cooling process is the process of cooling the resin injected into the mold (20) in the injection process. Metering time (T4) is the time corresponding to the metering process. The metering process is performed in parallel with the cooling process and is the process of metering the resin to be injected into the mold (20) in the next molding cycle. The molding conditions further include a planned injection time value and a planned metering time value. The planned injection time value is a predetermined value of the injection time (T1). The planned metering time is a predetermined value of the metering time (T4). The adjustment value further includes at least one adjustment value from among the injection time adjustment value and the metering time adjustment value. The injection time adjustment value is an adjustment value relative to the planned injection time. The metering time adjustment value is an adjustment value relative to the planned metering time. The shortening unit (133) further shortens at least one planned time value corresponding to at least one adjustment value from among the planned injection time and the planned metering time.

[0172] According to this embodiment, in addition to the cooling time (T2: at least one of the time spent cooling while holding pressure and the time spent cooling without holding pressure), the test firing period can be further shortened by further reducing at least one of the injection time (T1) and metering time (T4).

[0173] In the injection molding system (100) according to the seventh embodiment, in the fifth or sixth embodiment, the molding cycle includes an injection time (T1), a cooling time (T2), a metering time (T4), and a removal time (T3). The injection time (T1) is the time corresponding to the injection process. The injection process is the process of injecting the resin metered in the previous molding cycle into the mold (20). The cooling time (T2) is the time corresponding to the cooling process. The cooling process is the process of cooling the resin injected into the mold (20) in the injection process. The metering time (T4) is the time corresponding to the metering process. The metering process is performed in parallel with the cooling process and is the process of metering the resin to be injected into the mold (20) in the next molding cycle. The removal time (T3) is performed after the cooling process and is the process of removing the molded product by injection molding from the mold (20). The molding conditions further include a planned injection time, a planned metering time, and a planned removal time. The planned injection time is a predetermined value for the injection time (T1). The planned metering time is a predetermined value for the metering time (T4). The planned removal time is a predetermined value for the removal time (T3). The injection process includes a filling process and a holding pressure process. The filling process is the process of filling the mold (20) with resin. The holding pressure process is the process of cooling the resin filled in the mold (20) while holding pressure. The injection time (T1) includes the filling time (T11) and the holding pressure time (T12). The filling time (T11) is the time corresponding to the filling process. The holding pressure time (T12) is the time corresponding to the holding pressure process. The planned injection time includes the planned filling time and the planned holding pressure time. The planned filling time is a predetermined value for the filling time (T11). The planned holding time is a predetermined value for the holding time (T12). The theoretical cooling time is the time from when the resin filled into the mold (20) in the filling process begins to cool while being held under pressure in the holding process until it reaches the temperature at which it can be removed. The cooling time (T2) includes the holding time (T12). The molding conditions further include conditions related to the holding time (T12). The adjustment value further includes a holding time adjustment value. The holding time adjustment value is an adjustment value relative to the planned holding time. The shortening section (133) shortens the holding time (T12) included in the cooling time (T2) based on the holding time adjustment value.

[0174] According to this embodiment, the injection process includes a filling process and a holding pressure process, in which the resin filled in the filling process is cooled while being held under pressure. In this way, by shortening the holding pressure time (T12), which is part of the cooling time (T2), the time required for test injection can be shortened.

[0175] In the injection molding system (100) according to the eighth embodiment, in the seventh embodiment, the shortened portion (133) shortens the holding pressure time (T12) included in the cooling time (T2), and further shortens the cooling time (T2) excluding the holding pressure time (T12).

[0176] According to this embodiment, the holding pressure time with cooling (T1: in other words, the cooling time with holding pressure T2) is shortened, and the non-holding pressure cooling time (T2) is also shortened, thereby shortening the period of sacrificial firing.

[0177] In the injection molding system (100) according to the ninth embodiment, in the eighth embodiment, the plurality of measured values ​​further include screw rotation speed measured values ​​corresponding to the screw rotation speed. The plurality of time series data further include screw rotation speed time series data corresponding to the screw rotation speed. The plurality of calculation data further include screw rotation speed predicted values. The screw rotation speed predicted values ​​are predicted screw rotation speeds required to complete the metering process by the end of the cooling process when the holding pressure time (T12) included in the cooling time (T2) is shortened, and the cooling time (T2) excluding the holding pressure time (T12) is further shortened. The molding conditions further include conditions related to the metering time (T4). The adjustment value further includes a metering time adjustment value. The metering time adjustment value is an adjustment value for the metering time (T4). The shortening section (133) shortens the holding pressure time (T12) included in the cooling time (T2), and further shortens the cooling time (T2) excluding the holding pressure time (T12). As a result, if the metering process is not completed by the end of the cooling process, the metering time (T4) is shortened based on the predicted screw rotation speed so that the metering process is completed by the end of the cooling process.

[0178] According to this embodiment, if metering is not completed within the cooling time (T2), the metering time (T4) can be shortened based on the predicted screw rotation speed so that metering is completed within the cooling time (T2), thereby further shortening the trial firing period.

[0179] In the injection molding system (100) according to the tenth embodiment, in the ninth embodiment, the multiple calculation data further include predicted values ​​for the rate of rise of the mold temperature. The adjustment value is a value that shortens the planned cooling time based on the cooling time adjustment value while aligning the predicted rise curve (CV1) based on the predicted rate of rise value with the target rise curve (CV2).

[0180] According to this embodiment, by using an adjustment value that shortens the cooling time (T2) while making the predicted rise curve (CV1) follow the target rise curve (CV2), it is possible to control the rise in mold temperature while shortening the cooling time (T2).

[0181] In the injection molding system (100) according to the 11th embodiment, the temperature rise prediction curve (CV1) is a curve showing a temperature change such that the mold temperature prediction value, based on the temperature rise rate prediction value, rises at a first rate of rise in the first period and at a second rate of rise in the second period. The first period is the period from the initial temperature to reaching the first temperature. The first temperature is a temperature near the mold thermal equilibrium temperature. The first rate of rise is a rate of rise based on the theoretical value of the cooling time. The second period is the period from the first temperature to above the mold thermal equilibrium temperature. The second rate of rise is a rate of rise smaller than the first rate of rise.

[0182] According to this embodiment, by raising the mold temperature to a first temperature at a first rate of increase (for example, a rapid increase), and then raising it from the first temperature to the mold thermal equilibrium temperature at a second rate of increase (< first rate of increase) (for example, a gradual increase), it is possible to suppress the phenomenon in which the mold temperature exceeds the mold thermal equilibrium temperature and then decreases to the mold thermal equilibrium temperature, which occurs when the mold temperature is rapidly raised to the mold thermal equilibrium temperature, while also shortening the cooling time (T2).

[0183] In the injection molding system (100) according to the twelfth embodiment, in any of the second to eleventh embodiments, the thermal equilibrium state is a state in which the temperature difference of the mold temperature between a predetermined number of consecutive molding cycles is less than or equal to a third threshold.

[0184] According to this embodiment, it is possible to determine whether or not the mold (20) has reached a state of thermal equilibrium based on the mold temperature time series data.

[0185] In the injection molding system (100) according to the 13th embodiment, in the 12th embodiment, the mold thermal equilibrium temperature is the average value of the mold temperature over a predetermined number of consecutive molding cycles in a thermal equilibrium state.

[0186] According to this embodiment, the mold thermal equilibrium temperature can be calculated based on mold temperature time series data.

[0187] In the injection molding system (100) according to the 14th embodiment, in any of the second to 13th embodiments, the measurement system (2) starts measuring a plurality of physical quantities each time a molding cycle begins.

[0188] According to this embodiment, at the start of each molding cycle, multiple measurements corresponding to multiple physical quantities are initiated, and multiple measurement values ​​corresponding to multiple physical quantities are stored, thereby generating multiple time-series data corresponding to multiple physical quantities.

[0189] In the injection molding system (100) according to the 15th embodiment, in any of the second to 14th embodiments, the trained model (131a) is the first trained model (131a). The second calculation unit (132) includes a second trained machine learning model (132a). The second trained model (132a) receives multiple calculation data as input and outputs an adjustment value.

[0190] In this embodiment, the second calculation unit (132) includes a second trained model (132a) that takes multiple calculation data as input and outputs an adjusted value, thereby enabling the output of an adjusted value using the trained model.

[0191] The injection molding system (100) according to the 16th embodiment further comprises a model generation unit (134) in any of the second to 15th embodiments. The model generation unit (134) generates a trained model (131a) by performing machine learning training based on multiple time series data.

[0192] According to this embodiment, a trained model (131a) can be generated. [Explanation of Symbols]

[0193] 1 Adjustment device 12. First Calculation Unit (Calculation Unit) 13 Adjustment section 131 Data Output Section 131a First pre-trained model (pre-trained model) 132 Second Calculation Unit 132a Second pre-trained model 133 Abbreviated section 134 Model Generation Unit 2. Measurement System 100 Injection Molding Systems 10 injection molding machine 10a Cylinder 10b Screw 20 molds 20a water pipe T1 Injection time T11 Filling time T12 Holding pressure time T2 Cooling Time T4 Weighing time T20 Theoretical Cooling Time

Claims

1. mold and An injection molding machine that repeatedly performs resin injection molding using the aforementioned mold according to a molding cycle based on predetermined molding conditions, Each time the injection molding machine performs the injection molding, a measurement system measures a plurality of physical quantities related to the injection molding and outputs a plurality of measured values ​​corresponding to the plurality of physical quantities. The device includes an adjustment device that adjusts the molding conditions based on at least a plurality of time-series data corresponding to the plurality of physical quantities, which are generated by storing the plurality of measured values ​​output by the measurement system in a time series, The molding cycle includes a cooling time from the time the resin is injected into the mold by the injection molding machine until the injected resin cools down and can be removed from the mold. The molding conditions include a predetermined cooling time value, which is a predetermined value for the cooling time. The adjustment device is, A calculation unit that calculates a theoretical value of the cooling time, which is the theoretical value of the cooling time, using the plurality of time-series data and a calculation formula or information equivalent to the calculation formula, The system includes an adjustment unit that adjusts the molding conditions so as to shorten the sacrificial molding period before the mold reaches thermal equilibrium, when the possible condition is met that the planned cooling time is longer than the theoretical cooling time by a threshold or more. Injection molding system.

2. The calculation unit is the first calculation unit, The aforementioned plurality of physical quantities include the mold temperature, which is the temperature of the mold, and the resin temperature, which is the temperature of the resin injected into the mold. The plurality of measured values ​​include a mold temperature measurement value corresponding to the mold temperature and a resin temperature measurement value corresponding to the resin temperature. The aforementioned plurality of time series data include mold temperature time series data corresponding to the mold temperature and resin temperature time series data corresponding to the resin temperature. The aforementioned threshold is the first threshold, The adjustment unit is, A data output unit includes a trained machine learning model that takes the aforementioned multiple time-series data as input and outputs multiple calculation data, including a predicted mold thermal equilibrium temperature value which is a predicted value of the mold thermal equilibrium temperature when the mold reaches a thermal equilibrium state. If the requirement that the measured mold temperature is lower than the predicted mold thermal equilibrium temperature by a second threshold is further met, a second calculation unit calculates an adjustment value for the molding conditions to increase the rate of rise of the mold temperature, based on the plurality of calculation data, The system further includes a shortening unit that shortens the trial run period based on the aforementioned adjustment value. The injection molding system according to claim 1.

3. The adjustment value includes a cooling time adjustment value which is the adjustment value relative to the planned cooling time value. The shortening unit shortens the planned cooling time value based on the adjusted cooling time value. The injection molding system according to claim 2.

4. The above calculation formula is, The first variable into which the mold temperature measurement value is substituted, The second variable into which the resin temperature measurement value is substituted, A first constant corresponding to the removable temperature, which is the temperature at which the resin injected into the mold cools and becomes removable from the mold, The thickness of the molded product by injection molding, and at least one second constant corresponding to at least one of the thermal diffusivity, thermal conductivity, specific heat, density, glass transition temperature, latent heat of crystallization, and primary crystallinity of the resin, The injection molding system according to claim 3.

5. The injection molding machine is, A cylinder and screw for measuring the resin to be injected into the mold, It has a water pipe through which cooling water flows for cooling the mold, The aforementioned multiple physical quantities are, The cylinder temperature is the temperature of the cylinder, The outside temperature is the temperature of the outside air. The screw rotation speed, which is the number of rotations of the screw per unit time, and The water pipe flow rate is the flow rate of the cooling water flowing through the water pipe in a unit time, and further includes at least one physical quantity, The plurality of measured values ​​further include at least one measured value corresponding to at least one of the plurality of measured values ​​corresponding to the plurality of physical quantities, The plurality of time series data further includes at least one time series data corresponding to at least one of the plurality of physical quantities, among the plurality of time series data corresponding to the plurality of physical quantities. The injection molding system according to claim 3.

6. The molding cycle described above is: An injection process in which the resin measured in the previous molding cycle is injected into the mold, and an injection time corresponding to this process, The cooling time, which corresponds to the cooling step for cooling the resin injected into the mold in the injection step, and This includes a metering time, which is performed in parallel with the cooling process and corresponds to a metering process for measuring the resin to be injected into the mold in the next molding cycle, The molding conditions are as follows: The injection time is a predetermined value, and The system further includes a predetermined value for the measurement time, which is a predetermined value for the measurement time. The aforementioned adjustment value, The injection time adjustment value relative to the planned injection time value, and The system further includes at least one adjustment value among the measurement time adjustment values ​​for the planned measurement time, The shortening section further shortens at least one time-scheduled value corresponding to the at least one adjustment value among the injection time-scheduled value and the metering time-scheduled value. The injection molding system according to claim 5.

7. The molding cycle described above is: An injection process in which the resin measured in the previous molding cycle is injected into the mold, and an injection time corresponding to this process, A cooling step for cooling the resin injected into the mold in the injection step, the cooling time corresponding to the cooling step, The metering time, which is performed in parallel with the cooling process and corresponds to the metering process for measuring the resin to be injected into the mold in the next molding cycle, and This includes a removal time corresponding to a removal step performed after the cooling step, in which the molded product produced by injection molding is removed from the mold, The molding conditions are as follows: The injection time is a predetermined value, which is the injection time. The predetermined measurement time is the scheduled measurement time, and The aforementioned extraction time is a predetermined value, which is the planned extraction time, further includes The injection process is as follows: A filling step of filling the mold with the resin, and The process includes a holding pressure step of cooling the resin filled into the mold while maintaining pressure, The injection time is, The filling time corresponding to the aforementioned filling process, and This includes the holding pressure time corresponding to the holding pressure process, The aforementioned planned injection time value is, The predetermined value of the filling time, and This includes a predetermined value for the holding pressure time, which is the holding pressure time, The theoretical value of the cooling time is the time from when the resin filled into the mold in the filling step begins to cool while being held under pressure in the holding pressure step until it reaches the temperature at which it can be removed from the mold. The cooling time includes the holding pressure time, The molding conditions further include conditions relating to the holding pressure time, The adjustment value further includes a holding pressure time adjustment value which is the adjustment value relative to the planned holding pressure time value, The shortened portion shortens the holding pressure time included in the cooling time based on the holding pressure time adjustment value. The injection molding system according to claim 5 or 6.

8. The shortened portion shortens the holding pressure time included in the cooling time, and further shortens the cooling time excluding the holding pressure time. The injection molding system according to claim 7.

9. The plurality of measured values ​​further include screw rotation speed measured values ​​corresponding to the screw rotation speed, The aforementioned plurality of time series data further include screw rotation speed time series data corresponding to the screw rotation speed, The aforementioned plurality of calculation data further include a predicted screw rotation speed value, which is a predicted value of the screw rotation speed, for completing the metering process by the end of the cooling process, when the holding pressure time included in the cooling time is shortened, and the cooling time excluding the holding pressure time is further shortened. The molding conditions further include the conditions relating to the metering time, The adjustment value further includes a measurement time adjustment value, which is an adjustment value for the measurement time. The shortened portion shortens the holding pressure time included in the cooling time, and further shortens the cooling time excluding the holding pressure time, so that if the metering process is not completed by the end of the cooling process, the metering time is shortened based on the predicted screw rotation speed so that the metering process is completed by the end of the cooling process. The injection molding system according to claim 8.

10. The aforementioned multiple calculation data further include the predicted rate of increase of the mold temperature, The adjustment value is a value that shortens the planned cooling time based on the cooling time adjustment value, while making the predicted rise curve based on the predicted rise rate value follow the target rise curve. The injection molding system according to claim 9.

11. The aforementioned rise prediction curve is based on the mold temperature prediction value derived from the rise rate prediction value. During the first period from the initial temperature to reaching a first temperature near the mold thermal equilibrium temperature, the temperature rises at a first rate of increase based on the theoretical value of the cooling time. The curve shows a temperature change in the second period from the first temperature to a temperature equal to or higher than the mold thermal equilibrium temperature, where the temperature rises at a second rate of increase that is smaller than the first rate of increase. The injection molding system according to claim 10.

12. The thermal equilibrium state is a state in which the temperature difference of the mold temperature between a predetermined number of consecutive molding cycles is less than or equal to a third threshold. The injection molding system according to claim 2.

13. The mold thermal equilibrium temperature is the average value of the mold temperature during a predetermined number of consecutive molding cycles in the thermal equilibrium state. The injection molding system according to claim 12.

14. The measurement system starts measuring the plurality of physical quantities each time the molding cycle begins. The injection molding system according to claim 2.

15. The aforementioned trained model is the first trained model, The second calculation unit includes the second trained machine learning model, which receives the plurality of calculation data and outputs the adjustment value. The injection molding system according to claim 2.

16. The system further includes a model generation unit that generates the trained model by performing the machine learning training process based on the aforementioned multiple time series data. The injection molding system according to claim 2.

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