Mixing and melting device and auxiliary control method using mixing and melting device

Abstract

Provided is a mixing and melting device capable of maintaining the quality of a manufactured fused material even when the operation state is affected by disturbances such as a sudden change in the environment or a malfunction of a machine. The mixing and melting device (1) includes: a motor (8) which generates a driving force; a rotating shaft (5) rotated by the motor (8); a plurality of blade members (10) formed on the outer periphery of the rotating shaft (5) on the other side in the axial direction; a material input unit (14) into which materials are input; and a mixing container (3) connected below the material input unit (14), through which the rotating shaft (5) passes, and which mixes and melts the materials input into the material input unit (14), the mixing and melting device (1) mixing and melting, in accordance with a predetermined work step, the materials by using the plurality of blade members (10). The mixing and melting device (1) further includes: a memory unit (41) that stores the work step; work units (8), (22), and (30) that operate in accordance with the work step stored in the memory unit (41); detection units (43) to (49) that detect the operation states of the work units (8), (22), and (30); and a control unit (40) that, when the work units (8), (22), and (30) are operated in accordance with the work step stored in the memory unit (41) according to the results detected by the detection units (43) to (49), predicts a predicted melting point arrival time (T1), which is the time it takes for the material to reach the melting point, and controls the work units (8), (22), and (30) so that the absolute value of the difference between the set melting point arrival time (T0) and the predicted melting point arrival time (T1) becomes smaller when the absolute value of the difference between the predetermined set melting point arrival time (T0) stored in the memory unit (41) and the predicted melting point arrival time (T1) is equal to a predetermined time threshold value or greater.

Description

Mixing and melting device and auxiliary control method using the mixing and melting device

[0001] The present invention relates to a mixing and melting device and an auxiliary control method for the mixing and melting device.

[0002] In recent years, the concentration of carbon dioxide in the atmosphere has increased, and climate change such as global warming has become a problem. In order to suppress environmental abnormalities such as global warming and reduce the use of fossil resources that are at risk of depletion, the use of biomass, a renewable resource that effectively utilizes biotechnology, has been promoted. Regarding the production of biomass materials, a technology has been disclosed for forming a wood-based composite resin material from a wood-based filler material, a lubricant, a thermoplastic resin raw material, and an oxygen modifier (see Patent Document 1).

[0003] Patent No. 4598194

[0004] The mixing and grinding device described in Patent Document 1 does not mention maintaining the quality of the fused material produced even if the operating condition is affected by external disturbances such as a sudden change in the environment or a malfunction of the machine.

[0005] In view of the above-mentioned problems, the present invention aims to provide a mixing and melting device and an auxiliary control method for the mixing and melting device that can maintain the quality of the fused material produced even if the operating state is affected by external disturbances such as a sudden change in the environment or a malfunction of the machine.

[0006] a mixing and melting device according to the present embodiment, comprising: a motor for generating a driving force; a rotary shaft rotated by the motor; a plurality of blade members formed on the outer periphery of the rotary shaft on the other side in the axial direction; a material charging section into which materials are charged; and a mixing container connected below the material charging section, through which the rotary shaft passes, for mixing and melting the materials charged into the material charging section; the mixing and melting device mixing and melting the materials by the plurality of blade members according to a predetermined work process, comprising: a memory section for storing the work process; a working section that operates according to the work process stored in the memory section; a detection section that detects the operating state of the working section; and a control section that predicts a predicted melting point arrival time, which is the time it takes for the material to reach its melting point, when the working section operates according to the work process stored in the memory section from the result detected by the detection section, and controls the working section so that the absolute value of the difference between the predetermined set melting point arrival time stored in the memory section and the predicted melting point arrival time becomes smaller when the absolute value of the difference between the set melting point arrival time and the predicted melting point arrival time is equal to or greater than a predetermined time threshold; Further provided are:

[0007] According to the mixing and melting apparatus and the auxiliary control method for the mixing and melting apparatus of the present invention, the quality of the produced fused material can be maintained even if the operating state is affected by external disturbances such as a sudden change in the environment or mechanical malfunction.

[0008] 1 shows an example of a mixing melting system to which the machine learning device of this embodiment is applied; FIG. 2 shows a partially enlarged view of a blade member and a supply screw in the mixing melting device of this embodiment; FIG. 3 shows a skeleton block diagram of the entire system of the mixing melting device of this embodiment; FIG. 4 shows an example of the system configuration of a mixing melting device 1 to which the machine learning device of this embodiment is applied; FIG. 5 shows an example of a block diagram of the machine learning device of this embodiment; FIG. 6 shows an example of an input unit of the machine learning device of this embodiment; FIG. 7 shows an example of a mixed melting information acquisition unit of the machine learning device of this embodiment; FIG. 8 shows an example of a neural network model used in the machine learning device of this embodiment; FIG. 9 shows an example of a flowchart of a machine learning method by the machine learning device of this embodiment; FIG. 10 shows an example of a block diagram of a mixed melting information inference device of this embodiment; FIG. 11 shows an example of a flowchart of a mixed melting information inference method by the mixed melting information inference device of this embodiment; FIG. 12 shows an example of a flowchart of an auxiliary control method by the mixing melting device 1 of this embodiment; FIG. 13 shows a first example of an auxiliary control method of this embodiment; FIG. 14 shows a second example of an auxiliary control method of this embodiment; FIG. 15 shows a third example of an auxiliary control method of this embodiment; and FIG. 16 shows a fourth example of an auxiliary control method of this embodiment.

[0009] The mixing and melting apparatus 1 of this embodiment will be specifically described with reference to the drawings.

[0010] Fig. 1 shows an example of a mixing and melting system 100 according to an embodiment of the present invention. Fig. 2 shows a partially enlarged view of a blade member 10 and a supply screw 12 in the mixing and melting device 1 according to this embodiment. Fig. 3 is a skeleton block diagram showing the entire system of the mixing and melting device 1 according to this embodiment.

[0011] The mixing melting system 100 according to this embodiment mainly comprises a mixing melting device 1, a machine learning device 60, and a mixing melting information deduction device 70. The control unit 40 of the mixing melting device 1, the machine learning device 60, and the mixing melting information deduction device 70 are connected to, for example, a wired or wireless network 90, and are configured to be able to send and receive various types of data. Note that the numbers of the control units 40, the machine learning devices 60, and the mixing melting information deduction devices 70 of the mixing melting device 1 and the connection configuration of the network 90 are not limited to the example in FIG. 1 and may be changed as appropriate.

[0012] The control unit 40, machine learning device 60, and mixed melt information inference device 70 of the mixing melting apparatus 1 may be configured with one or more arithmetic processing devices (CPU, MPU, GPU, DSP, etc.). The control unit 40, machine learning device 60, and mixed melt information inference device 70 may be implemented using, for example, a circuit board centered around a CPU (Central Processing Unit) or a dedicated circuit board. The control unit 40, machine learning device 60, and mixed melt information inference device 70 are supplied with power from a commercial power source via a power line (not shown). The control unit 40 may be installed separately from the mixing melting apparatus 1.

[0013] The memory unit 41 of the mixing and melting device 1 stores various data and programs, and may be composed of, for example, a volatile memory (DRAM, SRAM, etc.) that functions as a main memory and a non-volatile memory (ROM, flash memory, etc.). The memory unit 41 may be included in the control unit 40. Alternatively, it may be installed separately from the mixing and melting device 1.

[0014] The mixing and melting apparatus 1 of this embodiment includes a machine base 2, a cylindrical mixing container 3 arranged horizontally on the machine base 2, a motor 8 as a drive source installed on the machine base 2, a rotary shaft 5 driven to rotate by the motor 8, a plurality of blade members 10 disposed on the rotary shaft 5 inside the mixing container 3, a material charging section 14 for charging materials, and a supply screw 12 for supplying the materials charged to the material charging section 14 to the mixing container 3. The material charging section 14 has an upper hopper 14a and a material inlet section 14b located below the hopper 14a and sandwiching an opening / closing section 15 therebetween.

[0015] The motor 8 is driven by a power source (not shown). The motor 8 transmits driving force to the rotating shaft 5, which rotates the blade member 10 and the supply screw 12 via the rotating shaft 5. The motor 8 can control the rotation speed so that the peripheral speed of the tip of the blade member 10 is within a range of 5 m / s to 50 m / s. The output shaft of the motor 8 and the rotating shaft 5 are connected by a V-belt 7, which can slip to prevent a sudden change in load torque from being transmitted to the motor 8 when the load torque suddenly changes on the rotating shaft 5. The output shaft of the motor 8 and the rotating shaft 5 may be connected via a brake, a clutch, or the like. The rotating shaft 5 may also be formed coaxially with the output shaft of the motor 8.

[0016] The rotating shaft 5 is supported horizontally by bearings 4, 4, and is disposed coaxially through the center of the mixing vessel 3. One end of the rotating shaft 5 is in rotational communication with a motor 8 serving as a drive source via a pulley 6 and a V-belt 7. The rotating shaft 5 is hollow, with a small-diameter hole formed in the axial center for circulating fluid, and is provided with rotary joints 9 on both ends, so that fluid is supplied axially into the interior of the rotating shaft 5 through the rotary joints 9.

[0017] As shown in Figure 2, a total of six blade members 10a to 10f, each rectangular in cross section and having an overall rectangular shape, are provided on the outer periphery of the rotating shaft 5, projecting from the shaft at angular intervals of 180 degrees in the circumferential direction of the rotating shaft 5. As shown in Figure 2, the thickness of the blade members 10a to 10f is made thicker in approximately 40% of the outer periphery than in the inner periphery, allowing for effective crushing, mixing, and melting of the material. Note that if pre-crushed materials are used, crushing may not be necessary.

[0018] The blade members 10a and 10f at both ends in the axial direction are fixed to the outer periphery of the rotating shaft 5 at an angle of about 15 degrees from the tip to the base of the blade member 10 so that their leading edges come into sliding contact with the inner surfaces of the vertical walls 11, 11 at both ends of the mixing vessel 3 with almost no gap when rotating clockwise as viewed from the right side in Figure 1.

[0019] The four blade members 10b, 10c, 10d, and 10e in the middle section are fixed to the outer circumferential surface of the rotating shaft 5 in a staggered pattern, tilted from the tip to the base of each blade, and are arranged so that their leading edges face both ends of the mixing vessel 3 during rotation. That is, as shown in Figure 2, the four blade members 10b, 10d, 10c, and 10e are opposed to each other in the axial direction and are arranged on the rotating shaft 5 at an attachment angle (angle with respect to the circumferential direction) of 15 degrees so that the opposing spacing between them narrows in the rotation direction.

[0020] In addition, in the direction of the rotating shaft 5, the side of the mixing vessel 3 facing the motor 8 is the material supply section 3a, and the side opposite the motor 8 is the material mixing section 3b. A spiral supply screw 12 formed on the outer periphery of the rotating shaft 5 is housed within the material supply section 3a. The material supply section 3a surrounds the supply screw 12. A material input section 14 is provided above the material supply section 3a. After the material is input into the material input section 14, an open / close section 15 is provided that can be closed airtightly during crushing, mixing, and melting. In addition, a pair of balance wheels 16 are installed on both sides of the blade member 10 and the supply screw 12 of the rotating shaft 5 to ensure smooth rotation.

[0021] The material mixing section 3b surrounds the blade members 10 fixed to the rotary shaft 5. The material mixing section 3b has the material supply section 3a on the upstream side. The material fed into the material supply section 3a is moved to the material mixing section 3b by a supply screw 12 within the material supply section 3a. The material is mixed in the material mixing section 3b by the blade members 10. It is also possible to feed the material from above the material mixing section 3b without using the material supply section 3a and the supply screw 12.

[0022] As shown in Figure 3, continuous water passages 24 are formed in the peripheral wall of mixing vessel 3 and in the center of rotating shaft 5, and a fluid circulation unit 22 is formed that can adjust the temperature of the peripheral wall of mixing vessel 3 and rotating shaft 5 by circulating a fluid. The fluid circulation unit 22 adjusts the temperature of the fluid using a circulating fluid control unit 23, adjusts the temperatures of mixing vessel 3, rotating shaft 5, etc., and returns the fluid to the circulating fluid control unit 23. The circulating fluid control unit 23 controls the temperature, flow rate, etc. of the fluid according to instructions from the control unit 40.

[0023] Here, the fluid used may be oil, air, tap water, cold water, warm water, or hot water. Therefore, the mixing and melting apparatus 1 of this embodiment can appropriately adjust the pressure, temperature, humidity, and the like inside the mixing vessel 3. Furthermore, for example, when air or tap water is used, it can be operated at low cost, when cold water is used, it can cool the inside of the mixing vessel 3, and when warm water or hot water is used, it can warm the inside of the mixing vessel 3.

[0024] A discharge port lid 17 for removing the molten material is provided on the bottom wall of the mixing vessel 3. The discharge port lid 17 is rotatably supported by a shaft 18, which is connected to rotary cylinders 19, 19 so as to be able to open and close.

[0025] The collars 20, 20 on both sides shown in Figure 2 are for sending air into the mixing vessel 3, and the continuous grooves on each end are made up of right-handed and left-handed spiral grooves so that air is sent to the mixing vessel 3 by the rotation of the rotating shaft 5.

[0026] The control unit 40 is also connected to the motor 8 via a connection cable, and receives a continuous electrical signal representing the load torque of the main shaft from the motor 8. The control unit 40 may control the timing of opening and closing the discharge port lid 17 of the mixing vessel 3 based on changes in the load torque acting on the main shaft of the motor 8 in response to the grinding, mixing, and melting of the material in the mixing vessel 3, thereby removing the ground, mixed, and melted material.

[0027] A method for producing a fused material using the mixing and melting device 1 of this embodiment will be described. The user places the material inside the mixing container 3 of the mixing and melting device 1 through an opening (not shown), drives the motor 8, and rotates the rotating shaft 5, the blade member 10, and the feed screw 12 to finely crush the material. The size of the crushed particles is preferably about 2 to 7 mm. Note that the crushing step is not performed when materials that have been crushed in advance using another device are simply placed inside the device.

[0028] Subsequently, the motor 8 is driven to rotate the rotary shaft 5, the blade member 10, and the feed screw 12, thereby mixing the pulverized material. The rotation speed of the rotary shaft 5 is preferably set to 500 rpm or more.

[0029] Furthermore, in the production of the fusion material of this embodiment, it is not necessary to use additives for compatibilization. Not using additives improves environmental friendliness. Furthermore, in this embodiment, it is preferable to supply a fluid to the material using the fluid supply unit 30. By supplying a fluid, the internal pressure can be increased in a later process. The fluid may be supplied to the inside of the mixing container 3 in advance, or may be supplied to the inside of the mixing container 3 during operation.

[0030] Here, the fluid used may be air, tap water, cold water, warm water, or hot water. Therefore, the mixing and melting apparatus 1 of this embodiment can appropriately adjust the pressure, temperature, humidity, and the like inside the mixing vessel 3. Furthermore, for example, when air or tap water is used, it can be operated at low cost, when cold water is used, it can cool the inside of the mixing vessel 3, and when warm water or hot water is used, it can warm the inside of the mixing vessel 3.

[0031] Next, the motor 8 is driven to rotate the rotating shaft 5, the blade member 10, and the supply screw 12. When the blade member 10 is rotated at high speed, the temperature inside the mixing vessel 3 rises to 100°C or higher due to the movement of the materials themselves and the friction caused by the materials colliding and shearing. After that, steam is generated from the fluid, which applies pressure inside the mixing vessel 3, causing the materials to melt.

[0032] Next, the motor 8 is driven to rotate the rotating shaft 5, the blade member 10, and the supply screw 12, thereby promoting the reaction of the materials. As the reaction progresses, the materials are integrated. At the same time, the fluid is vaporized and released to the outside from an outlet (not shown) of the mixing vessel 3. Therefore, the inside of the mixing vessel 3 dries, and a fusion product is obtained from which moisture and the like have been removed.

[0033] The resulting fusion product is a new material in which multiple materials with different melting points are uniformly fused at the molecular level, and since it can be molded by injection, extrusion, blowing, pressing, etc., it can be used for a variety of purposes.

[0034] Furthermore, the method for producing a fusion material of this embodiment is performed in less than 1,800 seconds. Typically, the greater the heat input to a material, the more severe the deterioration. The method for producing a fusion material of this embodiment does not require external heat input, and the time required for heat to be applied to the material is short. Therefore, the fusion material produced by the method for producing a fusion material of this embodiment is less susceptible to deterioration and can maintain its strength. Furthermore, since the fusion material produced by the method for producing a fusion material of this embodiment is less susceptible to deterioration, the completed fusion product can be reused.

[0035] The mixing and melting apparatus 1 of this embodiment also has a fluid supply unit 30. The fluid supply unit 30 is connected to the material input unit 14 of the mixing and melting apparatus 1. The fluid supply unit 30 has a supply pipe 31, a first flow rate control unit 32 installed midway along the supply pipe 31 communicating with the material inlet unit 14b, a first fluid supply member 33 installed at the tip of the supply pipe 31 communicating with the material inlet unit 14b, a second flow rate control unit 34 installed midway along the supply pipe 31 communicating with the mixing vessel 3, and a second fluid supply member 35 installed at the tip of the supply pipe 31 communicating with the mixing vessel 3.

[0036] The supply pipe 31 is a pipe through which a fluid flows, such as air, tap water, cold water, warm water, hot water, or a circulating fluid adjusted by the circulating fluid control unit 23 shown in Fig. 3. The amount of fluid flowing through the supply pipe 31 is adjusted by at least one of a first flow rate control unit 32 and a second flow rate control unit 34. The first flow rate control unit 32 and the second flow rate control unit 34 are installed midway along the supply pipe 31, and the flow rate of the fluid passing through the supply pipe 31 may be controlled by adjusting the rotation angle of a valve, for example.

[0037] In this embodiment, the first fluid supply member 33 is installed in a hole formed in the material inlet portion 14b, and the second fluid supply member 35 is installed in a hole formed in the mixing vessel 3. In the mixing and melting apparatus 1 of this embodiment, at least one hole may be formed in the material inlet portion 14b and the mixing vessel 3, and at least one first fluid supply member 33 and one second fluid supply member 35 may be installed. The hole may also be formed in the hopper 14a.

[0038] The first fluid supply member 33 and the second fluid supply member 35 may be nozzles or showers that spray fluid directly or as a mist. Fluids such as air, tap water, cold water, warm water, hot water, or circulating fluids flowing through the supply pipe 31 are sprayed from the first fluid supply member 33 and the second fluid supply member 35. The sprayed fluid is supplied to the material inlet portion 14b or the mixing vessel 3 and mixed with the materials. The fluid may be supplied to the material inlet portion 14b in advance, or may be supplied to the material inlet portion 14b during operation. While it is sufficient for at least one of the multiple first fluid supply members 33 and second fluid supply members 35 to be in operation, it is preferable for all of the multiple first fluid supply members 33 and second fluid supply members 35 to be in operation to prevent uneven supply.

[0039] The mixing and melting apparatus 1 of this embodiment further includes a fluid circulation section 22 that adjusts the temperature of the mixing vessel 3 by circulating a circulating fluid, and the fluid circulation section 22 has a water passage 24 formed continuously in the peripheral wall of the mixing vessel 3, and communicates the water passage 24 with the supply pipe 31, and uses the circulating fluid as the fluid. Therefore, the mixing and melting apparatus 1 of this embodiment can appropriately adjust the temperature inside the mixing vessel 3.

[0040] In this way, by supplying fluids from the first fluid supply member 33 and the second fluid supply member 35, the amount of fluid in the mixing container 3 can be adjusted before, during, and after the materials are fused. For example, a fluid such as water can be added to the materials before melting, thereby increasing the internal pressure in a later process. The temperature inside the mixing container 3 after melting can also be adjusted. Furthermore, the appropriate amount of fluid added for each material to be fused can be automatically controlled and managed, enabling quantitative mass production. Furthermore, since a fluid such as water can be added after the materials are added, and the materials are added in a dry state, improper addition is less likely to occur.

[0041] Furthermore, by spraying mist from the first fluid supply member 33 and the second fluid supply member 35 before melting, the fluid can be uniformly dispersed on the material before melting. This allows the fluid to be uniformly applied to the entire fusion material, enabling uniform heat transfer during fusion or temperature adjustment. Furthermore, after fusion, a high-strength product with uniform physical properties can be obtained. During cooling, the fused material can be rapidly cooled, mitigating thermal degradation of the material.

[0042] FIG. 4 shows an example of the system configuration of a mixing and melting device 1 to which the machine learning device 60 according to this embodiment is applied.

[0043] This mixing and melting device 1 may be a device that actually uses data learned by this machine learning device 60 in the future, or may be a test device that simulates the same structure as the device that will actually be used.

[0044] The mixing and melting apparatus 1 has an operation panel 42 operated by an operator, a control unit 40 that drives and controls the first flow rate control unit 32, the second flow rate control unit 34, and the circulating fluid control unit 23 in response to commands from the operation panel 42, and a memory unit 41 that stores in advance the drive of the first flow rate control unit 32, the second flow rate control unit 34, and the circulating fluid control unit 23, as well as the flow rates and temperatures of the fluids flowing through them. The memory unit 41 may be included in the control unit 40 or may be separate.

[0045] Furthermore, the mixing and melting apparatus 1 has an inverter 43 that receives a signal from the control unit 40 and controls the motor 8 that rotates the rotating shaft 5, a torque detection unit 44 that detects the torque of the motor 8, an internal pressure detection unit 45 that detects the pressure inside the mixing container 3, an oxygen concentration detection unit 46 that detects the oxygen concentration inside the mixing container 3, an internal humidity detection unit 47 that detects the humidity inside the mixing container 3, an internal temperature detection unit 48 that detects the temperature inside the mixing container 3, and an external wall temperature detection unit 49 that detects the temperature of the external wall of the mixing container 3. It is sufficient to have at least one of each of the detection units 43 to 49.

[0046] The control unit 40 has a command unit 40a that sends command values ​​to each control unit, and a calculation unit 40b that calculates command values ​​from detected values ​​of each detection unit 43 to 49. The memory unit 41 stores a table or calculation formula for converting detected values ​​of each detection unit 43 to 49 into command values. The operation panel 42, the control unit 40, or the memory unit 41 may be installed separately from the mixing and melting apparatus 1.

[0047] Fig. 5 shows an example of a block diagram of a machine learning device 60 according to this embodiment. Fig. 6 shows an example of an input unit 50 of the machine learning device 60 according to this embodiment. Fig. 7 shows an example of a mixed melting information acquisition unit 54 of the machine learning device 60 according to this embodiment.

[0048] The input unit 50 has at least one of a filler material information acquisition unit 51, a resin material information acquisition unit 52, and an additive material information acquisition unit 53, and a mixed melting information acquisition unit 54 which indicates the operating state of the mixing and melting device 1. The input unit 50 may be configured to automatically input information using a sensor or the like, or may be configured to input information manually by a user.

[0049] The filler material information acquisition unit 51 acquires information about the filler material as input data. The information about the filler material includes, for example, material name information 51a of the filler material to be used and characteristic information about the filler material. The characteristic information about the filler material may include at least one of type information 51b, which inputs whether the filler material is plant-based or animal-based, property information 51c, such as the specific gravity, melting point, and hydrophilicity of the filler material, blending ratio information 51d of the filler material, and water content information 51e of the filler material.

[0050] The resin material information acquisition unit 52 acquires information about the resin material as input data. The information about the resin material includes, for example, material name information 52a of the resin material used and characteristic information about the resin material. The characteristic information about the resin material may include at least one of type information 52b, which inputs whether the resin material is crystalline or amorphous, property information 52c, such as the specific gravity, melting point, viscosity, hydrophilicity, and fluidity (melt mass-flow rate (MFR)) of the resin material, blending ratio information 52d of the resin material, and water content information 52e of the resin material.

[0051] The additive material information acquisition unit 53 acquires information about the additive material as input data. The information about the additive material includes, for example, material name information 53a of the additive material to be used and characteristic information about the additive material. The characteristic information about the additive material may include at least one of type information 53b, which inputs whether the additive material is a plasticizer, stabilizer, antioxidant, UV absorber, lubricant, release agent, antistatic agent, colorant, foaming agent, flame retardant, etc.; property information 53c, such as specific gravity, melting point, and hydrophilicity of the additive material; blending ratio information 53d of the additive material; and moisture content information 53e of the additive material.

[0052] The mixed melting information acquisition unit 54 acquires, as output data, information on the operating state of the mixed melting device 1. The mixed melting information may include at least one of motor rotation information 54a indicating the rotation time or number of rotations of the motor 8, added fluid information 54b indicating the timing or amount of fluid added from the fluid supply unit 30, and circulating fluid information 54c indicating the temperature of the fluid circulated by the fluid circulator 22. The mixed melting information acquisition unit 54 may acquire at least one of the motor rotation information 54a, added fluid information 54b, and circulating fluid information 54c in chronological order by detection by the control unit 40 having a timer function, the detection units 43 to 49, manual input, or the like.

[0053] When inputting data into the input unit 50 manually, a worker terminal 80 may be used. The worker terminal 80 is connected to the input unit 50 and the machine learning device 60 via a network 90. ​​The worker terminal 80 may also be connected to the mixing and melting device 1. The worker terminal 80 may be a general-purpose product, and may include a processor, memory, an input device, a display device, a storage device, a communication interface, an external device interface, an input / output device interface, and a media input / output unit. Note that the above components may be omitted as appropriate depending on the application of the worker terminal 80.

[0054] The machine learning device 60 includes a learning data acquisition unit 600, a learning data storage unit 601, a machine learning unit 602, and a trained model storage unit 603. The machine learning device 60 is configured, for example, by a computer or the like. In this case, the learning data acquisition unit 600 is configured by a communication interface or an input / output device or the like, the machine learning unit 602 is configured by a processor or the like, and the learning data storage unit 601 and the trained model storage unit 603 are configured by storage or the like.

[0055] The learning data acquisition unit 600 is an interface unit that is connected to various external devices via a network 90 or the like and acquires learning data including at least input data. The external devices are, for example, the input unit 50 connected to the mixing and melting device 1, the input unit 50 provided in a simulated testing device or the like, and the operator terminal 80 used by an operator.

[0056] The learning data storage unit 601 is a database that stores one or more sets of learning data acquired by the learning data acquisition unit 600. The specific configuration of the database that constitutes the learning data storage unit 601 may be designed as appropriate.

[0057] The machine learning unit 602 performs machine learning using the learning data stored in the learning data storage unit 601. That is, the machine learning unit 602 inputs a plurality of sets of learning data into the learning model 610, and causes the learning model 610 to learn the correlation between the input data included in the learning data and the setting data of the mixing melting apparatus 1, thereby generating a trained learning model 610. In this embodiment, a case where a neural network is adopted as a specific method of supervised learning by the machine learning unit 402 will be described.

[0058] The trained model storage unit 603 is a database that stores the trained learning model 610 generated by the machine learning unit 602. The trained learning model 610 stored in the trained model storage unit 603 is provided to the actual system via any communication network, recording medium, etc. Note that although the training data storage unit 601 and the trained model storage unit 603 are shown as separate storage units in FIG. 5 , they may be configured as a single storage unit.

[0059] The training data includes at least one of filler material information, resin material information, and additive material information as input data. When "supervised learning" is adopted as machine learning, the training data further includes mixed melt information as output data associated with the input data. In supervised learning, the output data is referred to as, for example, training data or correct answer labels.

[0060] Therefore, the learning data according to this embodiment is configured by associating input data including at least one of filler material information, resin material information, and additive material information with output data including mixed melt information.

[0061] Here, the relationship between the input data included in the learning data (at least one of filler material information, resin material information, and additive material information) and the mixed melt information will be described.

[0062] The mixing and melting information is determined almost entirely by information on at least one of the materials used, namely, filler material information, resin material information, and additive material information. That is, by inputting information on the materials used as input data for learning, it becomes possible to infer the mixing and melting information as an operating condition of the mixing and melting device 1.

[0063] When acquiring the above-mentioned learning data, the learning data acquisition unit 600 uses at least one of the filler material information, resin material information, and additive material information stored in the storage unit 41 as input data.

[0064] When an operator obtains mixing and melting information as an operating condition of the mixing and melting device 1 and inputs it into the operator terminal 80, the learning data acquisition unit 600 acquires the setting values ​​inputted into the operator terminal 80 as output data (teaching data) from the operator terminal 80. Then, the learning data acquisition unit 600 configures one learning data by associating the input data with the output data, and stores it in the learning data storage unit 601.

[0065] FIG. 8 shows an example of a neural network model used in the machine learning device 60 according to this embodiment.

[0066] The learning model 610 is configured as a neural network model shown in Fig. 8. The neural network model is configured from one neuron (x1 to x1) in the input layer, m neurons (y11 to y1m) in the first hidden layer, n neurons (y21 to y2n) in the second hidden layer, and o neurons (z1 to zo) in the output layer.

[0067] Each neuron in the input layer is associated with a respective piece of input data included in the training data. Each neuron in the output layer is associated with a respective piece of output data included in the training data. Note that the input data may be subjected to predetermined pre-processing before being input to the input layer, and the output data may be subjected to predetermined post-processing after being output from the output layer.

[0068] The first and second hidden layers are also called hidden layers, and the neural network may have multiple hidden layers in addition to the first and second hidden layers, or may have only the first hidden layer as a hidden layer. Furthermore, synapses connecting the neurons of each layer are established between the input layer and the first hidden layer, between the first hidden layer and the second hidden layer, and between the second hidden layer and the output layer, and each synapse is assigned a weight wi (i is a natural number).

[0069] A neural network model uses training data to input input data contained in the training data into the input layer, and compares the output data output from the output layer as the inference result with the output data (teacher data) contained in the training data, thereby learning the correlation between the input data and the output data.

[0070] Specifically, each neuron in the input layer receives input data included in the training data, and the value of each neuron in the output layer is calculated by performing a process for all neurons other than the input layer, in which the value of the neuron on the input side connected to the neuron in question is calculated as the sum of a series of multiplication values ​​of the value of the neuron on the input side connected to the neuron in question and the weight wi associated with the synapse connecting the neuron on the output side and the neuron on the input side.

[0071] Then, the values ​​(z1 to zo) output to each neuron in the output layer as the inference results are compared with the values ​​(t1 to to) of the teacher data corresponding to each of the output data included in the training data to determine the error, and a process (backpropagation) is performed to adjust the weight wi associated with each synapse so that the error is reduced.

[0072] When a predetermined learning termination condition is met, such as repeating the above series of steps a predetermined number of times or the above error becoming smaller than an allowable value, the machine learning is terminated and a trained neural network model (all weights wi associated with each synapse) is generated.

[0073] FIG. 9 shows an example of a flowchart of a machine learning method performed by the machine learning device 60 according to this embodiment.

[0074] First, in step S11, the learning data acquisition unit 600 prepares a desired number of pieces of learning data as a preliminary preparation for starting machine learning, and stores the prepared learning data in the learning data storage unit 601. The number of pieces of learning data to be prepared here may be set in consideration of the inference accuracy required for the learning model 610 to be finally obtained.

[0075] Several methods can be used to prepare training data. For example, when inferring mixing and melting information for a specific mixing and melting device 1 or testing device, at least one of filler material information, resin material information, and additive material information is obtained, and an operator uses the operator terminal 80 to input the results in association with these inputs, thereby preparing input data and output data that constitute the training data. Then, by repeating this process, it is possible to prepare multiple sets of training data.

[0076] Next, in step S12, the machine learning unit 602 prepares a pre-learning learning model 610 to start machine learning. The pre-learning learning model 610 prepared here is configured with the neural network model exemplified in FIG. 8, and the weights of each synapse are set to initial values. Each neuron in the input layer is associated with at least one of filler material information, resin material information, and additive material information, which are input data included in the learning data. Each neuron in the output layer is associated with mixed melt information.

[0077] Next, in step S13 , the machine learning unit 602 acquires, for example, one piece of learning data at random from the multiple sets of learning data stored in the learning data storage unit 601 .

[0078] Next, in step S14, the machine learning unit 602 inputs the input data included in one piece of training data to the input layer of the prepared pre-learning (or training) training model 610. As a result, output data is output as an inference result from the output layer of the training model 610, and this output data was generated by the pre-learning (or training) training model 610. Therefore, in the pre-learning (or training) state, the output data output as an inference result represents information different from the output data (teacher data) included in the training data.

[0079] Next, in step S15, the machine learning unit 602 performs machine learning by comparing the output data (teacher data) included in the one learning data acquired in step S12 with the output data output from the output layer as an inference result in step S13 and adjusting the weights of each synapse. In this way, the machine learning unit 602 causes the learning model 610 to learn the correlation between the input data and the output data.

[0080] Next, in step S16, the machine learning unit 602 determines whether or not it is necessary to continue machine learning based on, for example, the error between the output data and the teacher data, or the remaining amount of unlearned learning data stored in the learning data storage unit 601.

[0081] If the machine learning unit 602 determines in step S16 to continue machine learning (No in step S16), the process returns to step S13, and steps S13 to S15 are performed multiple times using unlearned training data on the currently-trained learning model 610. On the other hand, if the machine learning unit 602 determines in step S16 to end machine learning (Yes in step S16), the process proceeds to step S17.

[0082] Then, in step S17, the machine learning unit 602 stores the trained learning model 610 generated by adjusting the weights associated with each synapse in the trained model storage unit 603, thereby completing the series of machine learning methods shown in Fig. 9. In the machine learning method, step S11 corresponds to a training data storage step, steps S12 to S16 correspond to a machine learning step, and step S17 corresponds to a trained model storage step.

[0083] As described above, the machine learning device 60 and machine learning method according to this embodiment can provide a learning model 610 capable of inferring mixed melting information from at least one of the filler material information, resin material information, and additive material information stored in the memory unit 41 as input data included in the learning data.

[0084] FIG. 10 shows an example of a block diagram of a mixed melting system 100 according to this embodiment.

[0085] The mixed melting information inference device 70 includes an input data acquisition unit 700, an inference unit 701, a trained model storage unit 702, and an output processing unit 703. The mixed melting information inference device 70 is configured, for example, by a computer. In this case, the input data acquisition unit 700 is configured by a communication interface or an input / output device, the inference unit 701 and the output processing unit 703 are configured by a processor, and the trained model storage unit 702 is configured by storage. The mixed melting system 100 may be incorporated into the mixed melting device 1, or may be incorporated into a higher-level management device of the mixed melting device 1 (for example, an equipment controller, an equipment management system that manages multiple equipment, etc.).

[0086] The input data acquisition unit 700 is an interface unit that is connected to the memory unit 41 and the input unit 50 provided in the mixing and melting device 1, and acquires input information (at least one of filler material information, resin material information, or additive material information) stored in the memory unit 41.

[0087] The inference unit 701 inputs the input data acquired by the input data acquisition unit 700 into the learning model 610 and performs an inference process to infer mixed melting information. The inference process uses the machine learning device 60 and the trained learning model 610 that has undergone supervised learning using the machine learning method.

[0088] The inference unit 701 not only performs inference processing using the learning model 610, but also includes a preprocessing function of adjusting the input data acquired by the input data acquisition unit 700 into a desired format, etc., and inputting the adjusted data into the learning model 610 as preprocessing for the inference processing, and a postprocessing function of applying a predetermined logical formula or calculation formula to the values ​​of the output data output from the learning model 610 to finally infer mixed melt information as postprocessing for the inference processing. Note that the inference results of the inference unit 701 are preferably stored in the trained model storage unit 702 or another storage device (not shown), and past inference results can be used, for example, as learning data to be used for online learning or relearning in order to further improve the inference accuracy of the learning model 610.

[0089] The trained model storage unit 702 is a database that stores trained learning models 610 used in the inference process of the inference unit 701. The number of learning models 610 stored in the trained model storage unit 702 is not limited to one. For example, multiple learning models 610 with different numbers of input data or different machine learning methods may be stored and selectively available.

[0090] The output processing unit 703 performs processing to output the inference result of the inference unit 701, i.e., the mixed melting information. Specific output means can be various means. For example, the output processing unit 703 may notify the worker of the mixed melting information by display or sound, or may transmit the mixed melting information as history to, for example, the mixed melting device 1 or a management device (not shown) higher than the mixed melting device 1, where it may be stored in a memory unit of the management device for the mixed melting device 1, or used to control the drive of the motor 8.

[0091] FIG. 11 shows an example of a flowchart of a mixing and melting information inference method using the mixing and melting device 1 according to this embodiment.

[0092] First, in step S21, the input data acquisition unit 700 acquires input data (at least one of filler material information, resin material information, and additive material information) input from the operation panel 42 and stored in the storage unit 41.

[0093] Next, in step S22, the inference unit 701 performs preprocessing on the input data and inputs the preprocessed data to the input layer of the learning model 610, and obtains the output data output from the output layer of the learning model 610.

[0094] Next, in step S23, the inference unit 701 infers mixed melting information.

[0095] As described above, the mixed melt information inference device 70 and mixed melt information inference method according to this embodiment can infer mixed melt information without relying on the experience of the worker.

[0096] Fig. 12 shows a flowchart of an auxiliary control method by the mixing and melting apparatus 1 according to this embodiment. Fig. 13 shows a first example of an auxiliary control method according to this embodiment. Fig. 14 shows a second example of an auxiliary control method according to this embodiment. In this embodiment, the operation of the mixing and melting apparatus 1 is divided into the steps of material input + stirring, crushing + dehydration, dispersion + melting, cooling, and discharge, and auxiliary control is performed in the dispersion + melting step. Note that auxiliary control may be performed in any step, or may be performed in multiple steps.

[0097] Furthermore, the auxiliary control is not limited to the mixing and melting system 100 of this embodiment, but can be used in various types of mixing and melting devices 1 that automatically execute work processes in a predetermined manner. For example, it may be used in a mixing and melting device 1 that automatically executes work processes stored in advance in a program or the like by inputting input data and performs output processing, without using the machine learning device 60 and the mixing and melting information inference device 70 in Figures 1 and 4. The storage unit 41 that stores the work processes may be installed in the mixing and melting device 1, or may be a storage device of a separate device, a cloud, or the like.

[0098] The mixing and melting device 1 of this embodiment can automatically execute work processes previously stored in the memory unit 41 by inputting input data and performing output processing. However, if there is an influence from external disturbances such as a sudden change in the environment or a malfunction of the machine, the time it takes for the material to reach its melting point may change, as shown in Figure 13, and the quality of the produced fused material may decrease. Therefore, the mixing and melting device 1 of this embodiment performs an operating state change rate correction process, which is an auxiliary control to adjust the time it takes for the material to reach its melting point.

[0099] In the auxiliary control method using the mixing and melting apparatus 1 according to this embodiment, first, in step S31, the detectors 43 to 49 shown in Fig. 4 detect the operating state. The detectors 43 to 49 for detecting the operating state in the mixing and melting apparatus 1 according to this embodiment may be, for example, at least one of the inverter 43 that receives a signal from the control unit 40 and controls the motor 8 that rotates the rotating shaft 5, the torque detector 44 that detects the torque inside the mixing container 3, the internal pressure detector 45 that detects the pressure inside the mixing container 3, the oxygen concentration detector 46 that detects the oxygen concentration inside the mixing container 3, the internal humidity detector 47 that detects the humidity inside the mixing container 3, the internal temperature detector 48 that detects the temperature inside the mixing container 3, or the outer peripheral wall temperature detector 49 that detects the temperature of the outer peripheral wall of the mixing container 3.

[0100] Next, in step S32, the control unit 40 determines whether the rate of change of the operating state is equal to or greater than a predetermined change rate threshold value α. If the rate of change of the operating state is smaller than the change rate threshold value α, the process proceeds to step S36. If the rate of change of the operating state is equal to or greater than the change rate threshold value α, in step S33, the control unit 40 calculates a predicted melting point arrival time T1.

[0101] For example, if the temperature change rate of the internal temperature or the outer wall temperature of the mixing vessel 3 in this embodiment is taken as the operating state change rate, the operating state change rate correction process is taken as the temperature change rate correction process, and it is determined whether the temperature change rate is equal to or greater than a predetermined change rate threshold value α. If the temperature change rate is equal to or greater than the change rate threshold value α, there is a risk that an external disturbance will affect the mixing vessel 3, changing the time it takes for the material to reach its melting point. Therefore, the auxiliary control method of this embodiment calculates a predicted melting point arrival time T1.

[0102] Next, in step 34, the control unit 40 compares whether the absolute value of the difference between the preset melting point arrival time T0 and the predicted melting point arrival time T1 is smaller than a predetermined time threshold β. If the absolute value of the difference between the preset melting point arrival time T0 and the predicted melting point arrival time T1 is equal to or greater than the predetermined time threshold β, the control unit 40 executes an operating state change rate correction process to correct the operating state change rate in step 35. If the absolute value of the difference between the preset melting point arrival time T0 and the predicted melting point arrival time T1 is smaller than the predetermined time threshold β, the process proceeds to step 36.

[0103] In the first example of the auxiliary control method shown in Figure 13, when the difference between the set melting point arrival time T0 and the predicted melting point arrival time T1 is a positive value, a temperature change rate reduction process is executed as an operating state change rate correction process. The temperature change rate reduction process reduces the temperature change rate by reducing the rotational speed of the motor 8, adding coolant through the fluid supply unit 30, circulating coolant through the fluid circulation unit 22, etc. By reducing the temperature change rate, the predicted melting point arrival time T1 becomes longer. Here, at least one of the motor 8, the fluid supply unit 30, and the fluid circulation unit 22 constitutes a working unit.

[0104] In addition, the second example of the auxiliary control method shown in Figure 14 executes a temperature change rate increase process as an operating state change rate correction process when the difference between the set melting point arrival time T0 and the predicted melting point arrival time T1 is a negative value. The temperature change rate increase process increases the temperature change rate by increasing the rotational speed of the motor 8, adding hot or warm water through the fluid supply unit 30, circulating hot or warm water through the fluid circulator 22, etc. Increasing the temperature change rate shortens the predicted melting point arrival time T1. Here, at least one of the motor 8, the fluid supply unit 30, and the fluid circulator 22 constitutes a working unit.

[0105] Next, in step 36, the control unit 40 determines whether the set melting point arrival time T0 has elapsed. If the set melting point arrival time T0 has not elapsed, the process returns to step 31. If the set melting point arrival time T0 has elapsed, the auxiliary control is terminated. The passage of time can be determined by providing the control unit 40 with a timer function.

[0106] In this way, according to the mixing and melting device 1 of this embodiment, even if the operating state of the mixing container 3 is affected by external disturbances such as a sudden change in the environment or a malfunction of the machine, and the time until the material reaches its melting point is likely to change, the quality of the fused material produced can be maintained without deterioration by controlling the operating state of the working unit.

[0107] In addition, according to the first example of the auxiliary control method, the quality of the fused material produced can be maintained without degradation by executing an operating state change rate correction process according to the temperature change rate of the internal temperature or the external wall temperature detected by the internal temperature detection unit 48 or the external wall temperature detection unit 49 of the mixing and melting device 1.

[0108] 15 shows a third example of the auxiliary control method according to the present embodiment. In the third example of the auxiliary control method, the operating state change rate correction process is performed in accordance with the internal pressure in the mixing vessel 3 detected by the internal pressure detection unit 45 of the mixing and melting device 1.

[0109] In the third example of the auxiliary control method, in step S31, the internal pressure detection unit 45 detects the pressure inside the mixing container 3. Subsequently, in step S32, the control unit 40 determines whether the rate of change of the pressure inside the mixing container 3 is equal to or greater than a predetermined change rate threshold value α. If the operating state change rate is equal to or greater than the change rate threshold value α, in step S33, the control unit 40 calculates a predicted melting point arrival time T1.

[0110] Next, in step 34, the control unit 40 compares whether the absolute value of the difference between the preset melting point arrival time T0 and the predicted melting point arrival time T1 is smaller than a predetermined time threshold β. If the absolute value of the difference between the preset melting point arrival time T0 and the predicted melting point arrival time T1 is equal to or greater than the predetermined time threshold β, in step 35, the control unit 40 executes an operating state change rate correction process to correct the operating state change rate.

[0111] In the third example of the auxiliary control method shown in Figure 15, when the difference between the set melting point arrival time T0 and the predicted melting point arrival time T1 is a positive value, a temperature change rate reduction process is executed as the operating state change rate correction process. The temperature change rate reduction process reduces the temperature change rate by reducing the rotational speed of the motor 8, adding coolant through the fluid supply unit 30, circulating coolant through the fluid circulation unit 22, etc. By reducing the temperature change rate, the predicted melting point arrival time T1 becomes longer. The temperature change rate increase process may be executed in the same manner as in the example shown in Figure 14.

[0112] Thus, according to the third example of the auxiliary control method, the operating state change rate correction process is performed in accordance with the internal pressure in the mixing container 3 detected by the internal pressure detection unit 45 of the mixing and melting device 1, thereby making it possible to maintain the quality of the fused material produced without any deterioration.

[0113] 16 shows a fourth example of the auxiliary control method according to the present embodiment. In the fourth example of the auxiliary control method, the operating state change rate correction process is performed in accordance with the oxygen concentration in the mixing vessel 3 detected by the oxygen concentration detection unit 46 of the mixing and melting apparatus 1.

[0114] In the fourth example of the auxiliary control method, in step S31, the oxygen concentration detection unit 46 detects the oxygen concentration in the mixing container 3. Subsequently, in step S32, the control unit 40 determines whether the rate of change of the oxygen concentration in the mixing container 3 is equal to or greater than a predetermined change rate threshold value α. If the rate of change of the operating state is equal to or greater than the change rate threshold value α, in step S33, the control unit 40 calculates a predicted melting point arrival time T1.

[0115] Next, in step 34, the control unit 40 compares whether the absolute value of the difference between the preset melting point arrival time T0 and the predicted melting point arrival time T1 is smaller than a predetermined time threshold β. If the absolute value of the difference between the preset melting point arrival time T0 and the predicted melting point arrival time T1 is equal to or greater than the predetermined time threshold β, in step 35, the control unit 40 executes an operating state change rate correction process to correct the operating state change rate.

[0116] In the fourth example of the auxiliary control method shown in Figure 16, when the difference between the set melting point arrival time T0 and the predicted melting point arrival time T1 is a positive value, a temperature change rate reduction process is executed as the operating state change rate correction process. The temperature change rate reduction process reduces the temperature change rate by reducing the rotational speed of the motor 8, adding coolant through the fluid supply unit 30, circulating coolant through the fluid circulation unit 22, etc. By reducing the temperature change rate, the predicted melting point arrival time T1 becomes longer. Note that the temperature change rate increase process may be executed in the same manner as in the example shown in Figure 14.

[0117] Thus, according to the fourth example of the auxiliary control method, the operating state change rate correction process is performed in accordance with the oxygen concentration in the mixing container 3 detected by the oxygen concentration detection unit 46 of the mixing and melting device 1, thereby making it possible to maintain the quality of the fused material produced without any deterioration.

[0118] The present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit and scope of the present invention, all of which are included in the technical concept of the present invention.

[0119] In the above embodiment, a case has been described in which a neural network is employed as a specific method of machine learning by the machine learning unit 602, but any other machine learning method may be employed by the machine learning unit 602. Examples of other machine learning methods include tree-type methods such as decision trees and regression trees, ensemble learning such as bagging and boosting, neural network-type methods (including deep learning) such as recurrent neural networks and convolutional neural networks, clustering-type methods such as hierarchical clustering, non-hierarchical clustering, k-nearest neighbors, and k-means, multivariate analyses such as principal component analysis, factor analysis, and logistic regression, and support vector machines.

[0120] The present invention can also be provided in the form of a program (machine learning program) for causing a general-purpose computer to execute each step of the machine learning method according to the above embodiment. Also, the present invention can also be provided in the form of a program (mixed melt information inference program) for causing a general-purpose computer to execute each step of the mixed melt information inference method according to the above embodiment.

[0121] The present invention can be provided not only in the form of the mixed melt information inference device 70 (mixed melt information inference method or mixed melt information inference program) according to the above embodiment, but also in the form of an inference device (inference method or inference program) used to infer mixed melt information. In this case, the inference device (inference method or inference program) can include a memory and a processor, and the processor executes a series of processes. The series of processes includes an input data acquisition process (input data acquisition step) for acquiring input data including input data (at least one of filler material information, resin material information, and additive material information) stored in the storage unit 41, and an inference process (inference step) for inferring mixed melt information inference information upon acquisition of the input data in the input data acquisition process.

[0122] By providing it in the form of an inference device (inference method or inference program), it can be more easily applied to various devices than when implementing the mixed melt information inference device 70. It will be naturally understood by those skilled in the art that when the inference device (inference method or inference program) infers mixed melt information, it may apply the inference method implemented by the inference unit 701 of the mixed melt information inference device 70 using the machine learning device 60 according to the above embodiment and the trained learning model 610 generated by the machine learning method.

[0123] 1...mixing and melting device, 3...mixing container, 3a...material supply section, 3b...material mixing container, 4...bearing, 5...rotating shaft, 6...pulley, 7...V-belt, 8...motor, 9...rotary joint, 10...blade member, 11...vertical wall, 12...supply screw, 14...material input section, 15...opening and closing section, 16...balance wheel, 17...discharge outlet cover, 18...shaft, 19...rotary cylinder, 20...collar, 22...fluid circulation section, 23...circulating fluid control section, 24...water passage, 30...fluid supply section, 31...supply pipe, 32...first flow rate control section, 33...first fluid supply member, 34...second flow rate control section, 35...second fluid supply member, 40...Control unit, 41...Memory unit, 42...Operation panel, 43...Inverter, 44...Torque detection unit, 45...Internal pressure detection unit, 46...Oxygen concentration detection unit, 47...Humidity detection unit, 48...Internal temperature detection unit, 49...Outer peripheral wall temperature detection unit, 50...Input unit, 51...Filler material information acquisition unit, 52...Resin material information acquisition unit, 53...Additive material information acquisition unit, 54...Mixed melt information acquisition unit, 60...Machine learning device, 600...Learning data acquisition unit, 601...Learning data storage unit, 602...Machine learning unit, 603...Model storage unit, 610...Learning model, 70...Mixed melt information inference device, 700...Input data acquisition unit, 701...Inference unit, 702...Model storage unit, 703...Output processing unit

Claims

1. A mixing and melting device comprising: a motor that generates a driving force; a rotating shaft rotated by the motor; a plurality of blade members formed on the outer periphery of the rotating shaft on the other side in the axial direction; a material input section into which materials are input; and a mixing container connected below the material input section, through which the rotating shaft passes, for mixing and melting the materials input to the material input section; the mixing and melting device mixing and melting the materials by the plurality of blade members according to a predetermined work process, further comprising: a memory section that stores the work process; a working section that operates according to the work process stored in the memory section; a detection section that detects the operating state of the working section; and a control section that predicts a predicted melting point arrival time, which is the time it takes for the material to reach its melting point, when the working section operates according to the work process stored in the memory section from the result detected by the detection section, and controls the working section so that the absolute value of the difference between the predetermined set melting point arrival time stored in the memory section and the predicted melting point arrival time becomes smaller when the absolute value of the difference between the set melting point arrival time and the predicted melting point arrival time is equal to or greater than a predetermined time threshold value. Mixing and melting equipment.

2. The mixing and melting device according to claim 1, further comprising: a fluid supply unit which supplies a fluid to the material input unit or the mixing vessel; and a fluid circulation unit which adjusts the temperature of the material in the mixing vessel, the temperature in the peripheral wall of the mixing vessel, and the temperature of the rotating shaft by circulating the fluid, and wherein the working unit is at least one of the motor, the fluid supply unit, or the fluid circulation unit.

3. The mixing and melting device according to claim 2, wherein the detection unit is at least one of an inverter which detects the rotation speed of the rotating shaft, an internal pressure detection unit which detects the pressure inside the mixing vessel, an internal temperature detection unit which detects the temperature inside the mixing vessel, an oxygen concentration detection unit which detects the oxygen concentration inside the mixing vessel, an internal humidity detection unit which detects the humidity inside the mixing vessel, or an outer wall temperature detection unit which detects the temperature inside the outer wall of the mixing vessel.

4. The mixing and melting device according to claim 2 or 3, wherein when the difference between the set melting point arrival time and the predicted melting point arrival time is a positive value, the control unit executes a temperature change rate reduction process to reduce a temperature change rate by at least one of reducing the rotation speed of the motor, adding cooling water by the fluid supply unit, or circulating cooling water by the fluid circulation unit.

5. The mixing and melting device according to claim 2 or 3, wherein when the difference obtained by subtracting the predicted melting point arrival time from the set melting point arrival time is a negative value, the control unit executes a temperature change rate increase process to increase a temperature change rate by at least one of increasing the rotation speed of the motor, adding warm water or hot water by the fluid supply unit, or circulating warm water or hot water by the fluid circulation unit.

6. A method of auxiliary control for a mixing and melting device comprising: a motor that generates a driving force; a rotating shaft rotated by the motor; a plurality of blade members formed on the outer periphery of the rotating shaft on the other side in the axial direction; a material input section into which materials are input; a mixing container connected below the material input section and through which the rotating shaft passes, for mixing and melting the materials input to the material input section; a memory section that stores a work process; a working section that operates according to the work process stored in the memory section; and a detection section that detects the operating state of the working section, the method comprising the steps of: detecting the operating state with the detection section; determining whether a rate of change in the operating state is equal to or greater than a predetermined change rate threshold; and calculating a predicted melting point arrival time, which is the time at which the material will reach its melting point when the working section operates according to the work process. The auxiliary control method for a mixing melting device includes the steps of: comparing whether an absolute value of a difference obtained by subtracting the predicted melting point arrival time from a predetermined set melting point arrival time stored in the memory unit is smaller than a predetermined time threshold; executing an operating state change rate correction process to correct the change rate of the operating state when the absolute value of the difference obtained by subtracting the predicted melting point arrival time from the set melting point arrival time is equal to or greater than the predetermined time threshold; and determining whether the set melting point arrival time has elapsed.

7. The mixing and melting apparatus further comprises a fluid supply unit which supplies a fluid to the material input unit or the mixing container, and a fluid circulation unit which adjusts the temperature of the material in the mixing container, the temperature in the peripheral wall of the mixing container, and the temperature of the rotating shaft by circulating the fluid, and the operating state change rate correction process is a temperature change rate correction process which corrects at least one of the rotation speed of the motor, the addition of fluid by the fluid supply unit, and the circulation of fluid by the fluid circulation unit. An auxiliary control method for a mixing and melting apparatus as described in claim 6.

8. The auxiliary control method for a mixing and melting device as described in claim 7, wherein the operating state change rate correction process executes a temperature change rate reduction process for reducing the temperature change rate by at least one of reducing the rotation speed of the motor, adding cooling water by the fluid supply unit, or circulating cooling water by the fluid circulation unit when the difference obtained by subtracting the predicted melting point arrival time from the set melting point arrival time is a positive value.

9. The auxiliary control method for the mixing and melting device according to claim 7, wherein the operating state change rate correction process executes a temperature change rate increase process for increasing the temperature change rate by at least one of increasing the rotation speed of the motor, adding warm water or hot water by the fluid supply unit, or circulating warm water or hot water by the fluid circulation unit when the difference obtained by subtracting the predicted melting point arrival time from the set melting point arrival time is a negative value.

Images