Injection molding apparatus

Abstract

The present application relates to injection molding apparatus. The injection molding apparatus (1) is provided with: an injection device (20) provided with a cylinder (22), a screw (23), and a nozzle (24) provided at a front end of the cylinder (22) and discharging molten resin in conjunction with advancement of the screw (23); a mold (30) provided with a molded product cavity (C) and a resin flow path (P) between the molded product cavity (C) and a portion in contact with the nozzle (24); a flow path pressure measuring device (33) that acquires flow path pressure data of the resin flow path (P); and a determination section (61) that determines whether a gate (P3) in the resin flow path (P) is clogged based on a characteristic quantity of the flow path pressure data.

Description

Technical Field

[0001] This invention relates to injection molding apparatus. Background Technology

[0002] Patent document 1 describes a technology in which sensors are installed in the injection device and mold of an injection molding apparatus, and the quality of the molded product is inferred by using the detection data of the sensors and machine learning.

[0003] Patent Document 1: Japanese Patent Application Publication No. 2020-49929

[0004] However, molds used for injection molding include: a molded part cavity that forms the molded part, and a resin flow path (sprue, gate, or sprue) between the molded part cavity and the part that abuts against the nozzle of the injection unit. For example, sometimes the gate in the resin flow path may become temporarily blocked due to cold slag, foreign matter, etc. If the gate is blocked, the molten resin will not flow from the resin flow path to the molded part cavity, thus causing a decrease in the dimensional accuracy of the molded part and instability in dimensional accuracy.

[0005] During multiple consecutive molding cycles for forming a molded part, temporary gate blockage sometimes occurs, which is then cleared. Additionally, during the molding of a molded part, temporary gate blockage sometimes occurs when molten resin is supplied to the molded part's cavity, which is then cleared during the molding of that part. Thus, gate blockage can occur both continuously and temporarily, and is often difficult to detect. Summary of the Invention

[0006] The present invention was made in view of the above background, and aims to provide an injection molding apparatus capable of determining whether the gate is blocked.

[0007] One embodiment of the present invention is an injection molding apparatus comprising:

[0008] An injection device comprising a cylinder, a screw, and a nozzle disposed at the front end of the cylinder and discharging molten resin as the screw advances;

[0009] A mold having a molded article cavity and a resin flow path between the molded article cavity and the portion that abuts against the nozzle.

[0010] A flow path pressure measuring device that acquires flow path pressure data of the aforementioned resin flow path; and

[0011] The determination unit determines whether the gate in the resin flow path is blocked based on the characteristic values ​​of the aforementioned flow path pressure data.

[0012] During the molding process, molten resin is supplied from the injection unit to the resin flow path of the mold, and then from the resin flow path to the cavity of the molded part. If the gate in the resin flow path of the mold becomes blocked during molding, the molten resin will either not flow from the resin flow path to the cavity of the molded part or will be difficult to flow.

[0013] Considering the fluctuations in resin flow path pressure data due to gate blockage, the aforementioned injection molding apparatus determines whether the gate is blocked based on characteristic values ​​of the resin flow path pressure data in the mold. The determination unit of the injection molding apparatus can use the influence of gate blockage on the characteristic values ​​of the resin flow path pressure data to determine whether the gate is blocked. By determining whether the gate is blocked, it is possible to detect a decrease in the dimensional accuracy of the molded product, thereby achieving dimensional accuracy stabilization. Attached Figure Description

[0014] Figure 1 It is a diagram showing the mechanical structure of an injection molding device.

[0015] Figure 2 yes Figure 1 An enlarged sectional view of the mold.

[0016] Figure 3 This diagram only shows the spatial portion of the mold (the cavity of the molded part and the resin flow path), and it is a view taken from the axial direction of the valve core (from...). Figure 2 (See the right side of the image).

[0017] Figure 4 This is a flowchart illustrating the injection molding process.

[0018] Figure 5 This is a chart showing the time variation of screw pressure data during the injection and holding pressure processes. Solid lines indicate a gate without blockage, while dashed lines indicate a gate with blockage.

[0019] Figure 6 This is a graph showing the time-varying flow path pressure data during the injection and holding pressure processes. Solid lines indicate unblocked gates, while dashed lines indicate blocked gates.

[0020] Figure 7 This is a functional block diagram illustrating the injection molding apparatus of the first embodiment.

[0021] Figure 8 This is a functional block diagram illustrating the processing of the computer device for determining gate blockage in the injection molding apparatus of the first embodiment.

[0022] Figure 9 This is a diagram illustrating the threshold value stored in the storage unit in a first modified embodiment of the first embodiment.

[0023] Figure 10 This is a functional block diagram illustrating a second modified embodiment of the injection molding apparatus according to the first embodiment.

[0024] Figure 11 This is a graph showing the time variation of flow path pressure data for the injection and holding processes in the third modified embodiment of the first embodiment. Solid lines indicate cases where the gate is not blocked, and dashed lines indicate cases where the gate is blocked.

[0025] Figure 12 This is a functional block diagram illustrating the injection molding apparatus of the second embodiment.

[0026] Figure 13 This is a functional block diagram illustrating the processing of the computer device for determining gate blockage in the injection molding apparatus of the second embodiment.

[0027] Figure 14 This is a graph showing the time variation of screw pressure data during the injection and holding pressure processes. Thick lines indicate high resin viscosity, while thin lines indicate low resin viscosity.

[0028] Figure 15 It means to Figure 14 The graphs are magnified between time points t1~t2a and t1~t2b. The thick line represents the case where the resin viscosity is high, and the thin line represents the case where the resin viscosity is low.

[0029] Figure 16 This is a graph showing the time variation of flow path pressure data during the injection and holding pressure processes. Thick lines indicate high resin viscosity, while thin lines indicate low resin viscosity.

[0030] Figure 17 It means to Figure 16 The graphs are magnified between time points t1~t2a and t1~t2b. The thick line represents the case where the resin viscosity is high, and the thin line represents the case where the resin viscosity is low.

[0031] Figure 18 This is a functional block diagram illustrating the processing of the computer device for determining gate blockage in the first modified embodiment of the second embodiment.

[0032] Figure 19 This is a diagram illustrating the threshold value stored in the storage unit in a second variation of the second embodiment.

[0033] Figure 20 This is a functional block diagram of the injection molding apparatus corresponding to the first and second specific examples of the resin viscosity estimation method in the second embodiment.

[0034] Figure 21This is a functional block diagram of the injection molding apparatus corresponding to the third and fourth specific examples of the resin viscosity estimation method in the second embodiment.

[0035] Figure 22 This is a functional block diagram of the injection molding apparatus corresponding to the fifth specific example of the resin viscosity estimation method in the second embodiment.

[0036] Figure 23 This is a graph showing the time variation of nozzle pressure data during the cleaning process, indicating the case of low resin viscosity.

[0037] Figure 24 This is a graph showing the time variation of nozzle pressure data during the cleaning process, indicating the situation when the resin viscosity is high.

[0038] Figure 25 This is a functional block diagram of the injection molding apparatus corresponding to the sixth specific example of the resin viscosity estimation method in the second embodiment.

[0039] Figure 26 It is a chart showing the time change of the screw position during the metering process. Detailed Implementation

[0040] (1. Structure of injection molding device 1)

[0041] Reference Figure 1 Let me explain the injection molding apparatus 1. The injection molding apparatus 1 is a device that uses a mold 30 to mold resin articles. The injection molding apparatus 1 mainly includes a bed 10, an injection unit 20, a mold 30, a mold closing unit 40, a control unit 50, and a computer unit 60 for gate blockage detection. The bed 10 is a component set on the setting surface.

[0042] The injection unit 20 is mounted on the bed 10. The injection unit 20 is a device that melts resin, which is used as a molding material, applies pressure to the molten resin, and supplies it to the molded cavity C of the mold 30. The injection unit 20 includes a hopper 21, a cylinder 22, a screw 23, a nozzle 24, a heater 25, a drive unit 26, a screw pressure gauge 27, and a nozzle pressure gauge 28.

[0043] Hopper 21 is the inlet for feeding the molding material, i.e., resin granules (granular molding material). Cylinder 22 stores the molten resin after the granules fed into hopper 21 have been heated and melted. Furthermore, cylinder 22 is configured to move axially relative to bed 10. Screw 23 is disposed inside cylinder 22 and is configured to rotate and move axially. Nozzle 24 is a discharge port located at the front end of cylinder 22, discharging the molten resin inside cylinder 22 as screw 23 advances.

[0044] The heater 25 is disposed, for example, on the outer circumferential surface of the cylinder 22, or embedded inside the cylinder 22, to heat the resin inside the cylinder 22. That is, the heater 25 melts the particles and maintains the molten resin in a molten state. The drive device 26 performs axial movement (forward and backward) of the cylinder 22, rotation of the screw 23, and axial movement (forward and backward), etc.

[0045] The screw pressure gauge 27 is provided, for example, near the base end of the screw 23, to obtain pressure data (hereinafter referred to as "screw pressure data") exerted on the screw 23 by the molten resin inside the cylinder 22. Furthermore, the screw pressure gauge 27 corresponds to the screw pressure gauge, second screw pressure gauge, and third screw pressure gauge of the present invention. Similarly, the screw pressure data corresponds to the screw pressure data, second screw pressure data, and third screw pressure data of the present invention.

[0046] A nozzle pressure gauge 28 is installed at the nozzle 24 to acquire pressure data (hereinafter referred to as "nozzle pressure data") from the molten resin as it passes through the nozzle 24. In addition to the screw pressure gauge 27 and the nozzle pressure gauge 28, the injection unit 20 also includes sensors for acquiring the position of the cylinder 22, the position of the screw 23, the moving speed of the screw 23, the temperature of the heater 25, and the status of the drive unit 26.

[0047] Furthermore, the nozzle pressure measuring device 28 is equivalent to the nozzle pressure measuring device, the second nozzle pressure measuring device, the third nozzle pressure measuring device, and the fourth nozzle pressure measuring device of the present invention. Additionally, the nozzle pressure data is equivalent to the nozzle pressure data, the second nozzle pressure data, the third nozzle pressure data, and the fourth nozzle pressure data of the present invention.

[0048] The mold 30 includes a first mold 31 as a fixed side and a second mold 32 as a movable side. The mold 30 forms a molded article cavity C between the first mold 31 and the second mold 32 by closing the first mold 31 and the second mold 32. The first mold 31 and the second mold 32 have a resin flow path P between the molded article cavity C and a portion that abuts against the nozzle 24 of the injection device 20. The resin flow path P is a flow path (valve core, runner, gate) that guides the molten material supplied from the nozzle 24 of the injection device 20 to the molded article cavity C.

[0049] Furthermore, the mold 30 is equipped with a flow path pressure measuring device 33 for obtaining pressure data (hereinafter referred to as "flow path pressure data") of the resin flow path P. The flow path pressure data is the pressure data of the inner wall surface of the resin flow path P from the molten resin passing through the resin flow path P.

[0050] The mold clamping device 40 is positioned opposite the injection device 20 on the bed 10. The mold clamping device 40 performs the opening and closing action of the installed mold 30, and is configured such that the mold 30 will not open due to the pressure of the molten material injected into the cavity C of the molded article when the mold 30 is tightened.

[0051] The mold clamping device 40 includes a fixed plate 41, a movable plate 42, a connecting rod 43, a drive device 44, and a measuring device 45 for the mold clamping device. A first mold 31 is fixed to the fixed plate 41. A second mold 32 is fixed to the movable plate 42. The movable plate 42 can approach and separate relative to the fixed plate 41. The connecting rod 43 supports the movement of the movable plate 42. The drive device 44, for example, is a cylinder device, which moves the movable plate 42. The measuring device 45 for the mold clamping device obtains information such as the clamping force, mold temperature, and the status of the drive device 44.

[0052] The control device 50 controls the drive device 26 of the injection unit 20 and the drive device 44 of the mold clamping device 40. The computer device 60 determines the gate blockage by checking the gate P3 (e.g., in the resin flow path P of the mold 30) for the gate of the mold 30. Figure 2 (As shown) Determination of whether the gate is blocked. The gate blockage determination computer device 60 consists of an arithmetic unit and a storage unit, etc., and performs processing by executing a computer program. The gate blockage determination computer device 60 uses, for example, control data from the control device 50, pressure data obtained from the screw pressure gauge device 27, the nozzle pressure gauge device 28, the flow path pressure gauge device 33, etc., to determine whether the gate is blocked.

[0053] (2. Detailed structure of mold 30)

[0054] Reference Figure 2 as well as Figure 3 The detailed structure of mold 30 will now be described. Mold 30 has a molded article cavity C for molding the article. The molded article cavity C is formed by a first mold 31 and a second mold 32. In this embodiment, the molded article cavity C is formed, for example, in an annular shape, but it can be any shape such as a C-shape or a U-shape. The molded article cavity C may be formed in only one location or in multiple locations. Figure 2 as well as Figure 3 For ease of explanation, a cavity C of a molded product is shown in the figure.

[0055] Additionally, mold 30 has a nozzle 24 ( Figure 1 The resin flow path P connects the contact portion (shown) to the cavity C of the molded article. The resin flow path P includes a valve core P1 (also called a sprue), a runner P2, and a gate P3. The valve core P1 is a passage for introducing molten material from the nozzle 24. The valve core P1 is formed in a straight line, for example, from the contact portion with the nozzle 24.

[0056] The horizontal runner P2 is a flow path formed at an angle from the valve core P1. That is, the molten resin introduced into the valve core P1 flows into the horizontal runner P2. For example, as... Figure 3 As shown, in this embodiment, multiple runners P2 are formed radially from the valve core P1 toward a molded part cavity C. Furthermore, even when multiple molded part cavities C are formed, the multiple runners P2 are formed from the valve core P1 toward each branch of the multiple molded part cavities C.

[0057] Gate P3 is located at the front end of runner P2 and serves as a flow path guiding molten resin from runner P2 to the molded cavity C. The cross-sectional area of ​​the flow path of gate P3 is smaller than that of runner P2. In this embodiment, multiple gates P3 are formed, each connecting a plurality of runners P2 to a molded cavity C. Therefore, even if one of the multiple gates P3 is blocked, molten resin will still flow from the other gates P3 to the molded cavity C.

[0058] like Figure 2 As shown, a flow path pressure measuring device 33 is provided in the mold 30. The flow path pressure measuring device 33 is located at any one of the following locations in the resin flow path P: the middle of the valve core P1, the end of the valve core P1, the middle of the runner P2, or the front end of the runner P2. That is, the flow path pressure measuring device 33 obtains pressure data at a location in the resin flow path P that is different from the gate P3. Furthermore, the flow path pressure measuring device 33 can be located in one position or multiple positions within the mold 30.

[0059] (3. Injection Molding Method)

[0060] Reference Figure 4 The injection molding method of the molded article by the injection molding apparatus 1 will be described. The injection molding method is executed by the control device 50 of the injection molding apparatus 1.

[0061] First, before several consecutive molding cycles, the control device 50 performs a cleaning process S1, in which molten resin is discharged from the nozzle 24 from the mold 30. The cleaning process S1 is performed, for example, to discharge resin that has deteriorated due to heat or to replace different resin materials. In the cleaning process S1, the screw 23 is advanced to discharge molten resin from the nozzle 24 into the cylinder 22.

[0062] Next, the control device 50 executes metering step S2, which involves rotating the screw 23 while simultaneously retracting it to a predetermined position, thereby accumulating a predetermined amount of molten resin on the front side of the cylinder 22. In metering step S2, rotating the screw 23 at the forward position moves the molten resin towards the front end of the cylinder 22, and the reaction force of the forward movement of the molten resin causes the screw 23 to retract to the predetermined position. Thus, a predetermined amount of molten resin accumulates inside the cylinder 22 between the front end of the screw 23 and the nozzle 24.

[0063] Next, the control device 50 executes a nozzle contact step S3, in which the nozzle 24 comes into contact with the mold 30 by advancing the cylinder 22. At this time, the mold 30 is closed. However, mold closing can also be performed after the nozzle contact step S3.

[0064] Next, the control device 50 executes the following molding cycles S4 to S11. The control device 50 executes injection step S4, in which the screw 23 is advanced by speed control of the screw 23, and molten resin is injected from the nozzle 24 into the mold 30. In injection step S4, molten resin flows from the nozzle 24 into the resin flow path P, and from the resin flow path P into the molded part cavity C. In injection step S4, molten resin is supplied to a large portion (e.g., 90-95%) of the molded part cavity C.

[0065] Following the injection step S4, the control device 50 executes a holding pressure step S5, which applies a holding pressure to the molten resin in the molded article cavity C by controlling the pressure applied to the screw 23. In the holding pressure step S5, the screw 23 is further advanced by controlling the application of a predetermined pressure, supplying molten resin from the nozzle 24 to the molded article cavity C via the resin flow path P. In the holding pressure step S5, the molded article cavity C is completely filled with molten resin.

[0066] Next, the control device 50 executes a cooling step S6 to stop the pressure applied to the screw 23 and stop heating of the mold 30, thereby cooling the molten resin inside the mold 30. In the cooling step S6, the molten resin inside the mold 30 solidifies. Following the cooling step S6, the control device 50 executes a demolding / molding product removal step S7, controlling the mold closing device 40 to separate the second mold 32 from the first mold 31 and remove the molded product. Next, the control device 50 executes a mold closing step S8, controlling the mold closing device 40 to align the second mold 32 with the first mold 31 and perform mold closing.

[0067] In addition, after the pressure holding process S5 ends, the control device 50 executes the nozzle separation process S9, which involves retracting the cylinder 22 to separate the nozzle 24 from the mold 30. Following the nozzle separation process S9, the control device 50 executes the metering process S10, which involves rotating the screw 23 located in the forward position to move the molten resin toward the front end of the cylinder 22, and retracting the screw 23 to a predetermined position by the reaction force of the molten resin moving forward, thereby accumulating a predetermined amount of molten resin on the front side of the cylinder 22.

[0068] After the mold closing process S8 and the metering process S10 are completed, the control device 50 executes the nozzle contact process S11, which involves advancing the cylinder 22 to bring the nozzle 24 into contact with the mold 30. However, the mold closing process S8 can also be executed after the nozzle contact process S11. Moreover, after the nozzle contact process S11, the process is repeated starting from the injection process S4.

[0069] (4. Behavior of screw pressure data and flow path pressure data)

[0070] Reference Figure 5 as well as Figure 6 This section explains the behavior (time variation) of screw pressure data and flow path pressure data in injection process S4 and holding process S5. First, refer to... Figure 5 solid lines and Figure 6 The solid line is used to illustrate that the gate P3 is not blocked.

[0071] like Figure 5 As shown by the solid line, at time t1, injection process S4 begins, causing screw 23 to advance and screw pressure data to increase. Then, in this embodiment, after setting the advance speed of screw 23 to an initial low speed, it is switched to a high speed.

[0072] like Figure 5 solid lines and Figure 6 As shown by the solid line, if the forward speed of screw 23 is switched to high speed, molten resin flows into the resin flow path P of mold 30, causing a sharp increase in both screw pressure and flow path pressure. Furthermore, as the resin flows from the gate P3 of resin flow path P into the molded cavity C, the screw pressure and flow path pressure decrease slightly, then gradually increase. Therefore, in injection molding process S4, the screw pressure and flow path pressure reach their maximum values ​​at the pressure peak t2.

[0073] Let t4 be the time when the injection process S4 ends. In injection process S4, let t3 be the time between the pressure peak time t2 and the time between the injection process end time t4. Time t3 can be set as the midpoint between the pressure peak time t2 and the injection process end time t4, but it can also be set as a time that deviates from the midpoint.

[0074] By switching from injection step S4 to holding step S5, the screw pressure data and flow path pressure data decrease to near the desired holding pressure based on the pressure control in holding step S5. Typically, after just decreasing to a pressure slightly lower than the desired holding pressure, the screw pressure data and flow path pressure data rise towards the desired holding pressure.

[0075] Furthermore, the holding pressure process S5 continues, and when the molten resin is completely filled into the cavity C of the molded part, i.e., at the end of filling t5, the screw pressure data and the flow path pressure data rise. The screw pressure data is pressure controlled, so it then reaches the desired holding pressure. On the other hand, the flow path pressure data gradually increases after the end of filling t5.

[0076] Next, refer to Figure 5 The dashed lines and Figure 6 The dashed line illustrates the presence of blockage at gate P3. In injection molding step S4, both the screw pressure and flow path pressure data are higher than when gate P3 is not blocked. Here, the rate of increase in flow path pressure is greater than the rate of increase in screw pressure. The rate of increase is the proportion of the pressure increase under the condition of a blocked gate P3, compared to the condition of a blocked gate P3. Therefore, the impact of whether gate P3 is blocked depends on both the screw pressure and flow path pressure data. However, it can be seen that the flow path pressure data is more significantly affected than the screw pressure data.

[0077] (5. Overview of each implementation method)

[0078] Regarding the determination of blockage of gate P3, a summary of each implementation method is provided.

[0079] (A) First implementation method: Based on flow path pressure data (later time period of injection process S4) and screw pressure data (later time period of injection process S4), the presence or absence of gate blockage is determined by machine learning.

[0080] (A1) First variation of the first embodiment: The presence or absence of gate blockage is determined based on flow path pressure data (later time period of injection process S4) and screw pressure data (later time period of injection process S4) and a preset threshold.

[0081] (A2) Second variation of the first embodiment: using pressure data of the early time period between the peak time and the end time of the injection process S4.

[0082] (A3) A third variation of the first embodiment: using only flow path pressure data.

[0083] (B) Second implementation method: Based on flow path pressure data and inferred resin viscosity, the presence or absence of gate blockage is determined by machine learning.

[0084] (B1) First variation of the second embodiment: The presence or absence of gate blockage is determined by inferring resin viscosity, based on flow path pressure data and machine learning.

[0085] (B2) A second variation of the second embodiment: the presence or absence of gate blockage is determined based on flow path pressure data and a pre-set threshold, using the inferred resin viscosity as the unit.

[0086] (C1) A first specific example of a resin viscosity estimation method: using flow path pressure data, using the time up to the pressure peak in injection step S4.

[0087] (C2) A second specific example of the resin viscosity estimation method: using flow path pressure data, using the time from the end of the pressure holding process S5 to the end of filling the cavity of the molded part.

[0088] (C3) A third specific example of the resin viscosity estimation method: using screw pressure data, using the time until the pressure peak of injection step S4.

[0089] (C4) Fourth specific example of the resin viscosity estimation method: using screw pressure data, the time from the end of the holding pressure process S5 to the end of filling the cavity of the molded part.

[0090] (C5) Fifth specific example of the resin viscosity estimation method: using nozzle pressure data and screw movement speed data from cleaning process S1.

[0091] (C6) Sixth specific example of resin viscosity estimation method: using nozzle pressure data and screw speed data from injection process S4.

[0092] (C7) Seventh specific example of the resin viscosity estimation method: the time required for metering processes S2 and S10.

[0093] (C8) Eighth specific example of the resin viscosity estimation method: combining multiple examples from the first to seventh specific examples above.

[0094] (6. Injection molding apparatus 1 of the first embodiment)

[0095] Reference Figure 7 as well as Figure 8 To illustrate the injection molding apparatus 1 of the first embodiment. For example... Figure 7 As shown, the injection molding apparatus 1 includes an injection unit 20, a mold 30, a mold clamping unit 40, a control unit 50, and a computer unit 60. Figure 7The illustration only shows a portion of the functional parts of the injection molding apparatus 1. The computer device 60 will be described below.

[0096] The computer device 60 includes a determination unit 61 and a storage unit 62. The determination unit 61 is composed of an arithmetic unit constituting the computer device 60 and functions by executing a computer program. The storage unit 62 is composed of a storage device constituting the computer device 60.

[0097] The determination unit 61 determines whether there is a blockage of the gate P3 in the resin flow path P. As described above, the gate P3 is located between the runner P2 and the molded part cavity C, and its flow path cross-sectional area is smaller than that of the runner P2. Therefore, there is a concern that the gate P3 may be blocked due to cold slag, foreign matter, etc. The determination unit 61 determines whether such a blockage of the gate P3 exists.

[0098] In particular, the determination unit 61 determines whether a portion of the multiple gates P3 are blocked. Furthermore, during multiple consecutive executions of the molding cycle described above, there is a phenomenon where a temporary blockage of the gate P3 occurs, followed by the removal of the blockage. In this case, the determination unit 61 determines whether a temporary blockage of the gate P3 exists.

[0099] Furthermore, the determination unit 61 determines whether the gate P3 is blocked based on the flow path pressure data obtained from at least the flow path pressure measuring device 33. However, in this embodiment, in addition to the flow path pressure data, the determination unit 61 also uses the screw pressure data obtained from the screw pressure measuring device 27 to determine whether the gate P3 is blocked.

[0100] Furthermore, the determination unit 61 determines whether the gate P3 is blocked based on the characteristic values ​​of the flow path pressure data and the screw pressure data, rather than the flow path pressure data and screw pressure data themselves. Therefore, the determination unit 61 extracts characteristic values ​​from the acquired flow path pressure data and also extracts characteristic values ​​from the acquired screw pressure data.

[0101] In this embodiment, the determination unit 61 uses characteristic values ​​of the flow path pressure data and the screw pressure data of the injection process S4 (times t1 to t4) to determine whether the gate P3 is blocked. In particular, the determination unit 61 uses characteristic values ​​of the flow path pressure data and the screw pressure data at times t2 to t4 after the pressure reaches the pressure peak in the injection process S4.

[0102] More specifically, in this embodiment, the determination unit 61 uses the characteristic value of the flow path pressure data during the later time period (t3 to t4) of the injection process S4 near the holding pressure process S5, and the characteristic value of the screw pressure data during the later time period (t3 to t4) of the injection process S4 near the holding pressure process S5.

[0103] The characteristic quantities can be set as, for example, the time integral value of the flow path pressure data for the later time period (t3~t4) and the time integral value of the screw pressure data for the later time period (t3~t4). In addition, the characteristic quantities can also be the maximum value, minimum value, median, mean, first quartile, third quartile, variance, standard deviation, kurtosis, skewness, etc., of the flow path pressure data for the later time period (t3~t4). Furthermore, the decision unit 61 can also use multiple characteristic quantities.

[0104] Furthermore, in this embodiment, the determination unit 61 applies machine learning to determine whether the gate P3 is blocked. Therefore, the determination unit 61 uses a pre-generated, fully learned model to determine whether the gate P3 is blocked. In particular, when applying machine learning, the determination unit 61 can easily apply multiple feature quantities.

[0105] The storage unit 62 stores information used by the determination unit 61 to determine whether the gate P3 is blocked. In this embodiment, the storage unit 62 stores a learned model generated by machine learning using a training dataset. The determination unit 61 uses the learned model stored in the storage unit 62.

[0106] Reference Figure 8 To illustrate the function of the computer device 60 in the application of machine learning, a training dataset 63 is first prepared as a learning phase. The training dataset 63 contains feature values ​​of the flow path pressure data, feature values ​​of the screw pressure data, and label data indicating whether the gate P3 is blocked.

[0107] As a learning phase, the machine learning processing unit 64 of the computer device 60 performs machine learning using the training dataset 63 to generate a learned model. The learned model is stored in the storage unit 62. In this embodiment, the learned model sets the feature values ​​of the flow path pressure data and the screw pressure data as explanatory variables, and sets whether the gate P3 is blocked as the objective variable.

[0108] Next, as an inference phase, the determination unit 61 acquires the flow path pressure data and screw pressure data as detection data 65. Furthermore, the determination unit 61 calculates the characteristic values ​​of the flow path pressure data and the screw pressure data. Then, using the learned model stored in the storage unit 62, the determination unit 61 inputs the characteristic values ​​of the flow path pressure data and the screw pressure data, thereby determining (outputting) whether the gate P3 is blocked.

[0109] (7. Effects of the first implementation method)

[0110] During the molding of the molded article, molten resin is supplied from the injection device 20 to the resin flow path P of the mold 30, and then from the resin flow path P to the cavity C of the molded article. If the gate P3 in the resin flow path P of the mold 30 becomes blocked during molding, the molten resin will either not flow from the resin flow path P to the cavity C of the molded article, or it will become difficult to flow.

[0111] Furthermore, considering the fluctuations in the flow path pressure data of the resin flow path P due to the blockage of the gate P3, the determination unit 61 of the injection molding apparatus 1 determines whether the gate P3 is blocked based on the characteristic quantity of the flow path pressure data of the resin flow path P of the mold 30. The determination unit 61 of the injection molding apparatus 1 can determine whether the gate P3 is blocked by utilizing the influence of the characteristic quantity of the flow path pressure data of the resin flow path P on the blockage of the gate P3. By determining whether the gate P3 is blocked, a decrease in the dimensional accuracy of the molded product can be detected, and dimensional accuracy can be stabilized.

[0112] In particular, such as Figure 6 As shown, the difference in flow path pressure data indicates whether there is a blockage in gate P3. Therefore, by using the characteristic values ​​of the flow path pressure data, it is possible to determine with high accuracy whether gate P3 is blocked.

[0113] Furthermore, in determining whether gate P3 is blocked, the determination unit 61 uses not only the characteristic values ​​of the flow path pressure data but also the characteristic values ​​of the screw pressure data. For example... Figure 5 as well as Figure 6 As shown, in the injection process S4, whether or not the gate P3 is blocked will affect not only the flow path pressure data but also the screw pressure data. Therefore, the determination unit 61 uses the characteristic values ​​of the screw pressure data in addition to the characteristic values ​​of the flow path pressure data, thereby enabling it to determine whether the gate P3 is blocked with higher accuracy.

[0114] Furthermore, the determination unit 61 uses the flow path pressure data and the screw pressure data from the injection process S4. In particular, during the injection process S4, the pressure changes depending on whether the gate P3 is blocked. Therefore, by using the pressure data from the injection process S4, the determination unit 61 can accurately determine whether the gate P3 is blocked.

[0115] Furthermore, the determination unit 61 uses flow path pressure data and screw pressure data from the later time period (t3-t4) of the injection process S4, which is close to the holding pressure process S5. During the later time period (t3-t4) of the injection process S4, the pressure data is relatively stable. Therefore, the determination unit 61 can determine with high accuracy whether the gate P3 is blocked. In addition, the determination unit 61 uses machine learning to determine whether the gate P3 is blocked. In particular, machine learning-based determination is useful when using multiple feature quantities.

[0116] (8. A first modified embodiment of the first embodiment)

[0117] Reference Figure 9 Let me describe a first modified embodiment of the injection molding apparatus 1 according to the first embodiment. In the first embodiment described above, the storage unit 62 of the computer device 60 stores a learned model generated by machine learning, and the determination unit 61 uses the learned model to determine whether the gate P3 is blocked.

[0118] In this modified embodiment, the storage unit 62 stores a threshold Th used to determine whether the gate P3 is blocked. For example... Figure 9 As shown, the threshold Th is set in the relationship between screw pressure data and flow path pressure data. Figure 5 as well as Figure 6 As shown, both the flow path pressure and screw pressure data increased due to the blockage of gate P3. Moreover, when gate P3 was blocked, the increase in flow path pressure data was greater than the increase in screw pressure data.

[0119] Therefore, as Figure 9 As shown, in a two-dimensional coordinate system representing the relationship between screw pressure data and flow path pressure data, the determination unit 61 determines that the area above the threshold Th (represented by a solid line) is blocked by gate P3. On the other hand, the determination unit 61 determines that the area below the threshold Th is not blocked by gate P3.

[0120] The threshold Th can be set by obtaining multiple flow path pressure data and screw pressure data for various scenarios, including whether gate P3 is blocked. Furthermore, in... Figure 9 In this context, the threshold Th is represented by a straight line, but it can also be set using a curve.

[0121] (9. Second variation of the first embodiment)

[0122] Reference Figure 10 The second modified embodiment of the injection molding apparatus 1 of the first embodiment will be described below. In the first embodiment described above, the determination unit 61 uses screw pressure data in addition to flow path pressure data to determine whether the gate P3 is blocked.

[0123] In this modified embodiment, such as Figure 10 As shown, the determination unit 61 uses flow path pressure data to determine whether gate P3 is blocked. The determination unit 61 does not use screw pressure data. Figure 6 As shown, the flow path pressure data varies considerably depending on whether the gate P3 is blocked. Therefore, the determination unit 61 can adequately determine whether the gate P3 is blocked.

[0124] (10. A third variation of the first embodiment)

[0125] Reference Figure 11 The third modified embodiment of the injection molding apparatus 1 of the first embodiment will be described below. In the first embodiment described above, the determination unit 61 uses the flow path pressure data and screw pressure data during the later time period (t3~t4) of the injection process S4 to determine whether the gate P3 is blocked.

[0126] In injection molding process S4, during the molding of a molded part, when molten resin is supplied to the cavity C of the molded part, there is a situation where a temporary blockage occurs at gate P3, which is then cleared. In such a case, the flow path pressure data in injection molding process S4 is as follows: Figure 11 The changes are as shown by the dashed line.

[0127] Therefore, in order to determine the temporary blockage of gate P3 under such circumstances, in this modified embodiment, the determination unit 61 uses characteristic quantities of the flow path pressure data and screw pressure data during the early time period (t2~t3) between the pressure peak time t2 and the end time t4 of the injection process S4. Specifically, the determination unit 61 uses the time integral value of the relevant time period (t2~t3) as the characteristic quantity. In this way, the determination unit 61 can detect the blockage caused by… Figure 11 The pressure change shown by the dashed line can be used to determine whether gate P3 is blocked.

[0128] (11. Injection molding apparatus 1 of the second embodiment)

[0129] Reference Figure 12 To illustrate the injection molding apparatus 1 of the second embodiment. For example... Figure 12 As shown, the computer device 60 of the injection molding apparatus 1 includes a determination unit 61, a storage unit 62, and a viscosity estimation unit 66. The determination unit 61 and the viscosity estimation unit 66 are composed of an arithmetic unit constituting the computer device 60, and function through the execution of a computer program.

[0130] The viscosity estimation unit 66 estimates the viscosity of the molten resin flowing in the resin flow path P of the mold 30. The viscosity estimation unit 66 estimates the viscosity of the molten resin based on flow path pressure data, screw pressure data, nozzle pressure data, control data from the control device 50, etc. Several specific examples of the viscosity estimation unit 66 are described below.

[0131] The determination unit 61 further adds the viscosity (hereinafter referred to as "inferred resin viscosity") inferred by the viscosity inference unit 66 to the flow path pressure data to determine whether the gate P3 is blocked. In addition to the flow path pressure data and the inferred resin viscosity, the determination unit 61 can also use screw pressure data, nozzle pressure data, and control data from the control device 50, in addition to the flow path pressure data and the inferred resin viscosity, just like in the first embodiment.

[0132] Furthermore, the determination unit 61 applies machine learning to determine whether the gate P3 is blocked. Therefore, the determination unit 61 uses a pre-generated, fully learned model to determine whether the gate P3 is blocked. The storage unit 62 stores the fully learned model used by the determination unit 61 to determine whether the gate P3 is blocked.

[0133] Reference Figure 13 To illustrate the function of the computer device 60 in the application of machine learning, a training dataset 63 is first prepared as a learning phase. The training dataset 63 includes feature values ​​of the flow path pressure data, the inferred resin viscosity inferred by the viscosity inference unit 66, and label data indicating whether the gate P3 is blocked. In addition, the training dataset 63 may also include feature values ​​of the screw pressure data, the nozzle pressure data, etc.

[0134] As a learning phase, the machine learning processing unit 64 of the computer device 60 performs machine learning using the training dataset 63 to generate a learned model. The learned model is stored in the storage unit 62. In this embodiment, the learned model uses the characteristic values ​​of the flow path pressure data and the inferred resin viscosity as explanatory variables, and whether the gate P3 is blocked as the objective variable.

[0135] Next, as an inference stage, the determination unit 61 acquires at least the flow path pressure data as detection data 65, and the inferred resin viscosity inferred by the viscosity inference unit 66. Furthermore, the determination unit 61 calculates the characteristic values ​​of the flow path pressure data. Then, by using the learned model stored in the storage unit 62, the determination unit 61 inputs the characteristic values ​​of the flow path pressure data and the inferred resin viscosity to determine whether the gate P3 is blocked.

[0136] (12. Behavior of screw pressure data and flow path pressure data)

[0137] Reference Figures 14-17 This explains the behavior (time variation) of screw pressure data and flow path pressure data during injection molding process S4 and holding pressure process S5. Figures 14-17 In the diagram, the thick line indicates a high resin viscosity in resin flow path P, and the thin line indicates a low resin viscosity in resin flow path P.

[0138] like Figure 14 as well as Figure 15 As shown, for screw pressure data, the peak pressure values ​​during injection process S4 are different between t2a, t2b and t4 at the end of the injection process. That is, within the relevant time periods (t2a~t4, t2b~t4), if the resin viscosity is low, the screw pressure data represents a low value; if the resin viscosity is high, the screw pressure data represents a high value.

[0139] In addition, such as Figure 15 As shown, in the screw pressure data, the time (t1~t2a, t1~t2b) from the start of injection step S4 to the pressure peak of injection step S4 varies depending on the resin viscosity. That is, the correlation time (t1~t2b) when the resin viscosity is low is shorter than the correlation time (t1~t2a) when the resin viscosity is high.

[0140] In addition, such as Figure 14 As shown, in the screw pressure data, the times (t4~t5a, t4~t5b) from the start of the holding pressure process S5 to the end of filling the molded cavity C in the holding pressure process S5 vary depending on the resin viscosity. That is, the relevant time (t4~t5b) when the resin viscosity is low is shorter than the relevant time (t4~t5a) when the resin viscosity is high. At the end of filling, t5a and t5b represent the pressure rise in the screw pressure data during the holding pressure process S5, although it is only a slight rise. In other words, at the end of filling, t5a and t5b represent the pressure change in the screw pressure data during the holding pressure process S5 exceeding a specified value.

[0141] Here, the injection process S4 ends after a predetermined time has elapsed since its start, so the end time t4 of the injection process S4 is constant and independent of the resin viscosity. Therefore, the same applies even if the above times (t4~t5a, t4~t5b) are replaced with the times (t1~t5a, t1~t5b) from the start time t1 of the injection process S4 to the end time t5a and t5b of the filling of the molded cavity C in the holding pressure process S5.

[0142] like Figure 16 as well as Figure 17 As shown, the flow path pressure data exhibits the same behavior as the screw pressure data. The peak pressure values ​​of the flow path pressure data during injection process S4 differ between t2a, t2b, and t4 at the end of the injection process. That is, within the relevant time periods (t2a~t4, t2b~t4), if the resin viscosity is low, the screw pressure data represents a low value, and if the resin viscosity is high, the screw pressure data represents a high value.

[0143] In addition, such as Figure 17 As shown, in the flow path pressure data, the time (t1~t2a, t1~t2b) from the start of injection step S4 to the pressure peak of injection step S4 varies depending on the resin viscosity. That is, the correlation time (t1~t2b) when the resin viscosity is low is shorter than the correlation time (t1~t2a) when the resin viscosity is high.

[0144] In addition, such as Figure 16As shown, in the flow path pressure data, the times (t4~t5a, t4~t5b) from the start of the holding pressure process S5 to the end of filling the molded cavity C in the holding pressure process S5 vary depending on the resin viscosity. That is, the relevant time (t4~t5b) when the resin viscosity is low is shorter than the relevant time (t4~t5a) when the resin viscosity is high. The times t5a and t5b at the end of filling are the pressure rise times in the flow path pressure data of the holding pressure process S5. That is, the times t5a and t5b at the end of filling are when the pressure change in the flow path pressure data of the holding pressure process S5 exceeds a specified value. Compared with the screw pressure data, this pressure change in the flow path pressure data varies more significantly.

[0145] This applies even when the aforementioned times (t4~t5a, t4~t5b) are replaced with times (t1~t5a, t1~t5b) from the start of injection process S4 to the end of filling of the molded cavity C in pressure holding process S5.

[0146] (13. Effects of the second embodiment)

[0147] If the resin viscosity is different, the flow path pressure data and screw pressure data will show different values. That is, it is often not easy to determine whether the changes in the flow path pressure data and screw pressure data are caused by the blockage of the gate P3 or by the resin viscosity.

[0148] In the injection molding apparatus 1 of the second embodiment, the determination unit 61 further adds an inferred resin viscosity to the flow path pressure data and screw pressure data to determine whether the gate P3 is blocked. Therefore, the determination unit 61 can determine whether the gate P3 is blocked with high accuracy.

[0149] (14. First modified embodiment of the second embodiment)

[0150] Reference Figure 18 To illustrate, we will describe a first modified embodiment of the injection molding apparatus 1 according to the second embodiment. Figure 18 The diagram illustrates the function of the computer device 60 when applying machine learning. As a learning phase, a training dataset 63 is first prepared. The training dataset 63 includes feature values ​​of flow path pressure data classified using resin viscosity inferred by the viscosity inference unit 66, and label data indicating whether the gate P3 is blocked. Furthermore, the training dataset 63 may also include feature values ​​of screw pressure data, nozzle pressure data, etc.

[0151] As a learning phase, the machine learning processing unit 64 of the computer device 60 performs machine learning using the training dataset 63 on a per-inference resin viscosity basis, generating multiple learned models for each inference resin viscosity. These multiple learned models for each inference resin viscosity are stored in the storage unit 62. In this modified embodiment, each learned model uses characteristic quantities of the flow path pressure data as explanatory variables and whether the gate P3 is blocked as the objective variable.

[0152] Next, as an inference stage, the determination unit 61 acquires at least the flow path pressure data as detection data 65, and the inferred resin viscosity inferred by the viscosity inference unit 66. Furthermore, the determination unit 61 calculates the characteristic values ​​of the flow path pressure data. Next, the determination unit 61 selects one learned model corresponding to the inferred resin viscosity from a plurality of learned models stored in the storage unit 62. Then, the determination unit 61 uses the selected learned model and inputs the characteristic values ​​of the flow path pressure data to determine whether the gate P3 is blocked. That is, the determination unit 61 classifies based on the inferred resin viscosity and determines whether the gate P3 is blocked using the classified inferred resin viscosity as the unit. Even in this case, the same effect as in the second embodiment described above is achieved.

[0153] (15. A second variation of the second embodiment)

[0154] Reference Figure 19 The second modified embodiment of the injection molding apparatus 1 of the second embodiment will be described below. In the first modified embodiment of the second embodiment described above, the storage unit 62 of the computer device 60 stores multiple learned models generated by machine learning in terms of inferred resin viscosity, and the determination unit 61 selects one of the multiple learned models and uses the selected learned model to determine whether the gate P3 is blocked.

[0155] In this modified embodiment, the storage unit 62 stores a threshold Th used to determine whether the gate P3 is blocked. For example... Figure 19 As shown, the threshold Th is set in units of inferred resin viscosity. The threshold Th, in units of inferred resin viscosity, is set based on the relationship between screw pressure data and flow path pressure data. Furthermore, as... Figure 19 As shown, the determination unit 61, using the inferred resin viscosity as the unit, determines the area above the threshold Th (represented by the solid line) as having a blockage of gate P3 in a two-dimensional coordinate system that represents the relationship between screw pressure data and flow path pressure data. On the other hand, the determination unit 61 determines the area below the threshold Th as not having a blockage of gate P3.

[0156] The threshold Th can be set by obtaining multiple flow path pressure data and screw pressure data for each condition, including whether the gate P3 is blocked. Furthermore, in... Figure 19In this context, the threshold Th is represented by a straight line, but it can also be set using a curve.

[0157] (16. The first specific example of resin viscosity deduction)

[0158] In the second embodiment and its modified embodiment, the viscosity estimation unit 66 estimates the viscosity of the molten resin in the resin flow path P. (Refer to...) Figures 16-17 as well as Figure 20 Here is a first specific example of a method for estimating resin viscosity by the viscosity estimation unit 66.

[0159] like Figure 16 as well as Figure 17 As shown, the time intervals (t1~t2a, t1~t2b) from the start of injection step S4 to the peak pressure times (t2a, t2b) of the flow path pressure data in injection step S4 vary depending on the resin viscosity of the resin flow path P. Therefore, the viscosity estimation unit 66 uses these time intervals (t1~t2a, t1~t2b) as a component representing resin viscosity to estimate the resin viscosity.

[0160] (17. A second specific example of resin viscosity deduction)

[0161] Reference Figures 16-17 ,as well as Figure 20 Here is a second specific example illustrating the method for determining the resin viscosity using the viscosity estimation unit 66. For example... Figure 16 as well as Figure 17 As shown, in the flow path pressure data, the time (t1~t5a, t1~t5b, or t4~t5a, t4~t5b) from the start of injection process S4 or the start of holding process S5 to the end of filling of the molded cavity C in holding process S5 varies depending on the resin viscosity of the resin flow path P.

[0162] Therefore, as Figure 20 As shown, the viscosity estimation unit 66 uses the time (t1~t5a, t1~t5b, or t4~t5a, t4~t5b) from the start time t1 of the injection process S4 or the start time t4 of the holding pressure process S5 to the end time t5a and t5b of the filling of the molded cavity C in the holding pressure process S5 as components representing resin viscosity to infer the resin viscosity.

[0163] (18. A third specific example of resin viscosity deduction)

[0164] Reference Figures 14-15 ,as well as Figure 21 This is a third specific example illustrating the method for determining the resin viscosity using the viscosity estimation unit 66. For example... Figure 14 as well as Figure 15 As shown, the time intervals (t1~t2a, t1~t2b) from the start of injection process S4 to the peak pressure times (t2a, t2b) of the screw pressure data in injection process S4 vary depending on the resin viscosity in the resin flow path P. Therefore, the viscosity estimation unit 66 uses these time intervals (t1~t2a, t1~t2b) as a component representing resin viscosity to estimate the resin viscosity.

[0165] (19. The fourth specific example of resin viscosity deduction)

[0166] Reference Figures 14-15 ,as well as Figure 21 This is a fourth specific example illustrating the method for determining the resin viscosity using the viscosity estimation unit 66. For example... Figure 14 as well as Figure 15 As shown, in the screw pressure data, the time (t1~t5a, t1~t5b, or t4~t5a, t4~t5b) from the start of injection process S4 or the start of holding process S5 to the end of filling of the molded cavity C in holding process S5 varies depending on the resin viscosity of the resin flow path P.

[0167] Therefore, as Figure 21 As shown, the viscosity estimation unit 66 uses the time (t1~t5a, t1~t5b, or t4~t5a, t4~t5b) from the screw pressure data, which is the time at the start of injection process S4 or the start of holding process S5, to the end of filling of the molded cavity C in holding process S5, as a component representing the resin viscosity, to infer the resin viscosity.

[0168] (20. The fifth specific example of resin viscosity deduction)

[0169] Reference Figures 22-24 This is the fifth specific example illustrating the method for determining the resin viscosity using the viscosity estimation unit 66. For example... Figure 22 As shown, the viscosity estimation unit 66 uses the nozzle pressure data obtained by the nozzle pressure gauge 28 and the control data of the control device 50 to estimate the resin viscosity.

[0170] The behavior (time variation) of nozzle pressure data in cleaning process S1 is as follows: Figure 23 as well as Figure 24 As shown. Here, in Figure 23 as well as Figure 24 In this case, the moving speed of screw 23 is set to be the same. When the moving speed of screw 23 is the same, Figure 23 The low resin viscosity shown is similar to Figure 24Compared to the case with high resin viscosity, the average value of the nozzle pressure data for cleaning step S1 is lower. Therefore, the nozzle pressure data for cleaning step S1 depends on the resin viscosity.

[0171] If the screw 23 moves quickly, the nozzle pressure data increases. Therefore, the viscosity estimation unit 66 uses the relationship between the nozzle pressure data and the screw 23 moving speed data in the cleaning process S1 as a component representing the resin viscosity to estimate the resin viscosity.

[0172] (21. The sixth specific example of resin viscosity deduction)

[0173] In the fifth specific example, the viscosity estimation unit 66 uses the nozzle pressure data and screw 23 movement speed data from the cleaning process S1 to estimate the resin viscosity. Here, the nozzle pressure data also changes in the injection process S4. Therefore, in the sixth specific example, the viscosity estimation unit 66 uses the relationship between the nozzle pressure data and screw 23 movement speed data from the injection process S4 as a component representing the resin viscosity to estimate the resin viscosity.

[0174] (22. The seventh specific example of resin viscosity deduction)

[0175] Reference Figure 25 as well as Figure 26 This is the seventh specific example illustrating the method for determining the resin viscosity using the viscosity estimation unit 66. For example... Figure 25 As shown, the viscosity estimation unit 66 uses control data from the control device 50 to estimate the resin viscosity.

[0176] The time required for metering processes S2 and S10 is affected by the resin viscosity. For example... Figure 26 As shown, the time from the start of metering processes S2 and S10 to the end of metering processes S2 and S10, t6a and t6b, is longer when the resin viscosity is low than when the resin viscosity is high. This is because the pressure exerted on the screw 23 by the molten resin is lower when the resin viscosity is low. Therefore, the viscosity estimation unit 66 uses the time required for metering processes S2 and S10 as a component representing the resin viscosity to estimate the resin viscosity.

[0177] (23. Eighth specific example of resin viscosity deduction)

[0178] An eighth specific example of the method for estimating resin viscosity by the viscosity estimation unit 66 will be described. In the first to seventh specific examples described above, the viscosity estimation unit 66 deduces the resin viscosity by treating each element as a component representing the resin viscosity.

[0179] Furthermore, the viscosity estimation unit 66 estimates the resin viscosity by including the elements described in the first to seventh examples as components of the resin viscosity. That is, the viscosity estimation unit 66 estimates the resin viscosity using multiple elements, with each element being a component of the resin viscosity. The viscosity estimation unit 66 can use multiple elements and apply machine learning. Alternatively, the viscosity estimation unit 66 can also estimate the viscosity by using a database that has accumulated multiple elements.

Claims

1. An injection molding apparatus, comprising: An injection device comprising a cylinder, a screw, and a nozzle disposed at the front end of the cylinder and discharging molten resin as the screw advances; A mold having a molded article cavity and a resin flow path between the molded article cavity and the portion that abuts against the nozzle. A flow path pressure measuring device that acquires the flow path pressure data of the aforementioned resin flow path; The viscosity estimation unit estimates the viscosity of the molten resin flowing in the resin flow path of the mold; and The determination unit determines whether the gate in the resin flow path is blocked based on the characteristic values ​​of the flow path pressure data and the viscosity inferred by the viscosity inference unit.

2. The injection molding apparatus according to claim 1, further comprising: A screw pressure gauge measuring device that acquires screw pressure data from the molten resin inside the cylinder; and Control device, which controls the aforementioned injection device. The aforementioned control device executes a molding cycle that includes the following processes. In the injection process, molten resin is injected from the nozzle into the mold by controlling the speed of the screw; and Following the injection process, a holding pressure is applied to the molten resin within the cavity of the molded article by controlling the pressure of the screw. The determination unit determines whether the gate is blocked based on the characteristic values ​​of the flow path pressure data of the injection process and the characteristic values ​​of the screw pressure data of the injection process.

3. The injection molding apparatus according to claim 1 or 2, wherein, The resin flow path described above has multiple gates connected to a cavity of the molded article described above. The aforementioned determination unit determines whether a portion of the aforementioned multiple gates is blocked.

4. The injection molding apparatus according to claim 1 or 2, wherein, When the above-mentioned determination unit performs a molding cycle for molding the molded article multiple times in succession, and the phenomenon of temporary blockage of the gate is eliminated after the gate blockage is temporarily generated, it determines whether the gate is temporarily blocked.

5. The injection molding apparatus according to claim 1 or 2, wherein, In the process of molding a molded article, when molten resin is supplied to the cavity of the molded article, if the gate is temporarily blocked and then the blockage is cleared, the determination unit determines whether the gate is temporarily blocked.

6. The injection molding apparatus according to claim 1 or 2, wherein, The above flow path pressure data refers to the pressure data at a location in the resin flow path that is different from the above gate.

7. The injection molding apparatus according to claim 2, wherein, The determination unit determines whether the gate is blocked based on the characteristic values ​​of the flow path pressure data in the later period of the injection process near the later period of the pressure holding process and the characteristic values ​​of the screw pressure data in the later period of the injection process near the later period of the pressure holding process.

8. The injection molding apparatus according to any one of claims 1, 2, and 7, further comprising: Control device, which controls the aforementioned injection device. The aforementioned control device executes a molding cycle that includes the following processes. In the injection process, molten resin is injected from the nozzle into the mold by controlling the speed of the screw; and Following the injection process, a holding pressure is applied to the molten resin within the cavity of the molded article by controlling the pressure of the screw. The viscosity estimation unit uses the time from the start of the injection process to the peak pressure of the flow path pressure data in the injection process as one of the components representing the viscosity to estimate the viscosity.

9. The injection molding apparatus according to claim 8, wherein, The viscosity estimation unit uses the time from the start of the injection process or the start of the holding pressure process to the end of the filling of the molded article cavity in the holding pressure process as one of the components representing the viscosity to estimate the viscosity.

10. The injection molding apparatus according to claim 9, wherein, The filling process ends when the pressure of the flow path pressure data in the pressure holding process increases.

11. The injection molding apparatus according to claim 10, wherein, The filling process ends when the pressure change of the flow path pressure data in the pressure holding process becomes greater than a specified value.

12. The injection molding apparatus according to claim 8, further comprising: The second screw pressure gauge device acquires the second screw pressure data received by the screw from the molten resin inside the cylinder. The viscosity estimation unit uses the second screw pressure data as one of the components representing the viscosity to estimate the viscosity.

13. The injection molding apparatus according to claim 12, wherein, The viscosity estimation unit uses the time from the start of the injection process to the peak pressure of the second screw pressure data in the injection process as one of the components representing the viscosity to estimate the viscosity.

14. The injection molding apparatus according to claim 8, further comprising: A nozzle pressure gauge measuring device that acquires nozzle pressure data from the molten resin flowing through the nozzle. The aforementioned control device performs a cleaning process, prior to multiple consecutive molding cycles, in which molten resin is discharged from the nozzle into the cylinder while the nozzle is separated from the mold. The viscosity estimation unit uses the relationship between the nozzle pressure data and the screw movement speed data of the cleaning process as one of the components representing the viscosity to estimate the viscosity.

15. The injection molding apparatus according to claim 8, further comprising: The second nozzle pressure gauge device acquires data on the second nozzle pressure received by the nozzle from the molten resin when the molten resin flows in the nozzle. The viscosity estimation unit uses the relationship between the second nozzle pressure data and the screw movement speed data of the injection process as one of the components representing the viscosity to estimate the viscosity.

16. The injection molding apparatus according to claim 8, wherein, The molding cycle described above also includes a metering process prior to the injection molding process, in which molten resin is moved toward the front end of the cylinder by rotating the screw located at the front position, and the screw is retracted to a predetermined position by the reaction force of the molten resin moving forward, thereby accumulating a predetermined amount of molten resin on the front side of the cylinder. The viscosity estimation unit uses the time required for the metering process as one of the components representing the viscosity to estimate the viscosity.

17. The injection molding apparatus according to any one of claims 1, 2, and 7, further comprising: Control device, which controls the aforementioned injection device. The aforementioned control device executes a molding cycle that includes the following processes. In the injection process, molten resin is injected from the nozzle into the mold by controlling the speed of the screw; and Following the injection process, a holding pressure is applied to the molten resin within the cavity of the molded article by controlling the pressure of the screw. The viscosity estimation unit uses the time from the start of the injection process or the start of the holding pressure process to the end of the filling of the molded article cavity in the holding pressure process as one of the components representing the viscosity to estimate the viscosity.

18. The injection molding apparatus according to claim 17, wherein, The filling process ends when the pressure of the flow path pressure data in the pressure holding process increases.

19. The injection molding apparatus according to claim 18, wherein, The filling process ends when the pressure change of the flow path pressure data in the pressure holding process becomes greater than a specified value.

20. The injection molding apparatus according to any one of claims 1, 2, and 7, further comprising: The third screw pressure gauge device acquires the third screw pressure data received by the screw from the molten resin inside the cylinder. The viscosity inference unit uses the third screw pressure data as one of the components representing the viscosity to infer the viscosity.

21. The injection molding apparatus according to claim 20, further comprising: Control device, which controls the aforementioned injection device. The aforementioned control device executes a molding cycle that includes the following processes. In the injection process, molten resin is injected from the nozzle into the mold by controlling the speed of the screw; and Following the injection process, a holding pressure is applied to the molten resin within the cavity of the molded article by controlling the pressure of the screw. The viscosity estimation unit uses the time from the start of the injection process to the peak pressure of the third screw pressure data in the injection process as one of the components representing the viscosity to estimate the viscosity.

22. The injection molding apparatus according to any one of claims 1, 2, and 7, further comprising: The third nozzle pressure measuring device acquires data on the third nozzle pressure received by the nozzle from the molten resin when the molten resin flows in the nozzle; and Control device, which controls the aforementioned injection device. The aforementioned control device executes a molding cycle that includes the following processes. In the injection process, molten resin is injected from the nozzle into the mold by controlling the speed of the screw; and Following the injection process, a holding pressure is applied to the molten resin within the cavity of the molded article by controlling the pressure of the screw. The aforementioned control device also performs a cleaning process, prior to multiple consecutive molding cycles, in which molten resin is discharged from the nozzle from the cylinder while the nozzle is separated from the mold. The viscosity estimation unit uses the relationship between the third nozzle pressure data and the screw movement speed data in the cleaning process as one of the components representing the viscosity to estimate the viscosity.

23. The injection molding apparatus according to any one of claims 1, 2, and 7, further comprising: The fourth nozzle pressure measuring device acquires the fourth nozzle pressure data received by the nozzle from the molten resin when the molten resin flows in the nozzle; and Control device, which controls the aforementioned injection device. The aforementioned control device executes a molding cycle that includes the following processes. In the injection process, molten resin is injected from the nozzle into the mold by controlling the speed of the screw; and Following the injection process, a holding pressure is applied to the molten resin within the cavity of the molded article by controlling the pressure of the screw. The viscosity estimation unit uses the relationship between the fourth nozzle pressure data and the screw movement speed data of the injection process as one of the components representing the viscosity to estimate the viscosity.

24. The injection molding apparatus according to any one of claims 1, 2, and 7, further comprising: Control device, which controls the aforementioned injection device. The aforementioned control device executes a molding cycle that includes the following processes. In the metering process, the molten resin is moved toward the front end of the cylinder by rotating the screw located at the front position, and the screw is retracted to a predetermined position by the reaction force of the molten resin moving forward, thereby accumulating a predetermined amount of molten resin on the front side of the cylinder. The injection process, following the metering process described above, involves injecting molten resin from the nozzle into the mold via the speed control of the screw; and Following the injection process, a holding pressure is applied to the molten resin within the cavity of the molded article by controlling the pressure of the screw. The viscosity estimation unit uses the time required for the metering process as one of the components representing the viscosity to estimate the viscosity.

25. The injection molding apparatus according to claim 2 or 7, further comprising: The storage unit stores a learned model generated through machine learning using a training dataset containing feature quantities of the aforementioned flow path pressure data, feature quantities of the aforementioned screw pressure data, and label data indicating whether the gate is blocked. The aforementioned determination unit uses the learned model and inputs the characteristic values ​​of the flow path pressure data and the characteristic values ​​of the screw pressure data to infer whether the gate is blocked.

26. The injection molding apparatus according to any one of claims 1, 2, and 7, further comprising: The storage unit stores a learned model generated through machine learning using a training dataset containing feature quantities of the aforementioned flow path pressure data, the inferred viscosity, and label data indicating whether the gate is blocked. The aforementioned determination unit uses the learned model and inputs the characteristic values ​​of the flow path pressure data and the inferred viscosity to determine whether the gate is blocked.

27. The injection molding apparatus according to any one of claims 1, 2, and 7, wherein, The determination unit classifies the viscosity inferred by the viscosity inference unit and uses the classified viscosity as the unit to determine whether the gate is blocked.

28. The injection molding apparatus according to claim 27, further comprising: The storage unit stores multiple learned models for each viscosity, generated through machine learning using a training dataset containing feature quantities of the aforementioned flow path pressure data and label data indicating whether the gate is blocked, with the inferred viscosity as the unit. The above-mentioned judgment department From the multiple learned models mentioned above, select the one that corresponds to the viscosity inferred by the viscosity inference unit. By using the selected learned model and inputting the characteristic values ​​of the flow path pressure data, it is determined whether the gate is blocked.

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