Device and method for detecting the mixing state of an extrusion molding machine.

Abstract

A kneading state detection device (50) comprises: an AE wave acquisition unit (71) (acquisition unit) that acquires, when a twin-screw extruder (30) (extruder) for kneading a raw material or kneading a raw material with an additive is in operation, an AE output (M(t)) of an AE sensor (20) installed in a housing (32) of the twin-screw extruder (30); and a kneading state determination unit (72) (determination unit) that determines the kneading state of the raw material on the basis of a comparison between a change in the intensity of the AE output (M(t)) of the AE sensor (20) acquired by the AE wave acquisition unit (71) and a threshold value (Th).

Description

Technical Field

[0001] The present invention relates to a kneading state detection device and a kneading state detection method for an extrusion molding machine.

Background Art

[0002] Conventionally, the kneading state of raw materials by an extrusion molding machine has been determined based on, for example, the elapsed time since the start of kneading, the measured value of a pressure sensor that measures the internal pressure of the extrusion molding machine, the measured value of a temperature sensor that measures the internal temperature of the extrusion molding machine, the torque fluctuation value of a motor that drives the extrusion molding machine, the current value flowing through the motor, etc. (see Patent Documents 1, 2, and 3).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0004] All of such conventional determination methods indirectly monitor the kneading state of raw materials and do not directly monitor the kneading state. In addition, depending on the raw materials to be kneaded, the amount of variation in the measured physical quantity becomes very small, so experience and intuition had to be relied on to determine the kneading state.

[0005] The present invention has been made in view of the above, and an object thereof is to provide a kneading state detection device and a kneading state detection method for an extrusion molding machine that can reliably detect the kneading state of raw materials in real time.

Means for Solving the Problems

[0006] To solve the above-mentioned problems and achieve the objective, the kneading state detection device for an extrusion molding machine according to the present invention is characterized by comprising: an acquisition unit that acquires the output of an AE sensor installed in the housing of an extrusion molding machine when the extrusion molding machine that kneads raw materials or kneads raw materials and additives is in operation; and a determination unit that determines the kneading state of raw materials, or raw materials and additives, based on a comparison between the intensity change of the AE sensor output over a predetermined time period acquired by the acquisition unit and a threshold value. [Effects of the Invention]

[0007] The kneading state detection device and kneading state detection method for an extrusion molding machine according to the present invention can reliably detect the kneading state of the raw material in real time. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is an explanatory diagram of acoustic emissions. [Figure 2] Figure 2 is a schematic diagram of the AE sensor. [Figure 3] Figure 3 is a schematic diagram showing an example of a kneading state detection device for a twin-screw extruder. [Figure 4] Figure 4 is a cross-sectional view of the output shaft of a twin-screw extruder. [Figure 5] Figure 5 is a hardware block diagram showing an example of the hardware configuration of the kneading state detection device for a twin-screw extrusion molding machine according to the embodiment. [Figure 6] Figure 6 shows an example of the results of frequency analysis of AE waves acquired by an AE wave analyzer. [Figure 7] Figure 7 shows an example of the time evolution of the integrated power spectrum of an AE wave acquired by an AE wave analyzer from 60 kHz to 80 kHz. [Figure 8] Figure 8 illustrates a method for determining whether the fracture state of the glass fibers has stabilized based on the AE wave waveform acquired by an AE wave analyzer. [Figure 9]Figure 9 is a functional block diagram showing an example of the functional configuration of the kneading state detection device for a twin-screw extrusion molding machine according to the embodiment. [Figure 10] Figure 10 is a flowchart showing an example of the processing flow performed by the mixing state detection device. [Figure 11] Figure 11 shows an example of the time change in the integrated power spectrum when the screw rotation speed is changed while a twin-screw extruder is continuously operated and the resin raw material and glass fibers are sufficiently mixed. [Figure 12] Figure 12 shows an example of the time change in the integrated power spectrum when the resin raw material input flow rate is changed while the twin-screw extruder is running continuously and the resin raw material and glass fibers are sufficiently mixed. [Figure 13] Figure 13 shows an example of the time change in the integrated power spectrum when the glass fiber input flow rate is changed while the twin-screw extruder is running continuously and the resin raw material and glass fiber are sufficiently mixed. [Modes for carrying out the invention]

[0009] [Explanation of Acoustic Emission (AE)] Before describing the embodiments, we will explain acoustic emissions (hereinafter referred to as AE), which are used to detect the mixing state of an extrusion molding machine in operation. AE is a phenomenon in which, for example, when mixing resin raw materials (pellets) in an extrusion molding machine, the accumulated strain energy is released as sound waves (elastic waves, AE waves) when the solid resin pellets are crushed or when reinforcing materials such as glass fibers, carbon fibers, or cellulose fibers added to strengthen the resin raw materials break. By detecting and analyzing the AE waves generated in conjunction with the crushing of resin pellets or the breakage of reinforcing materials, it is possible to predict the mixing state of the resin material fed into the extrusion molding machine and the mixing state of the reinforcing materials. The frequency band of AE waves is said to be around several tens of kHz to several MHz, and it has a frequency band that cannot be detected by general vibration sensors or acceleration sensors. Therefore, a dedicated AE sensor is used to detect AE waves. More details about the AE sensor will be described later. In this embodiment, we will explain that reinforcing materials are mixed into the resin raw materials, but the materials mixed in are not limited to reinforcing materials. For example, the following explanation also applies when additives such as talc, calcium carbonate, or magnesium carbonate are mixed in.

[0010] Figure 1 is an explanatory diagram of acoustic emissions. As shown in Figure 1, for example, when crushing of resin pellets or rupture of reinforcing material occurs at point P inside the twin-screw extruder 30, AE waves W are generated. The AE waves W spread radially from point P and enter the inside of the housing of the twin-screw extruder 30. Once inside the housing, the AE waves W propagate through the inside of the housing of the twin-screw extruder 30.

[0011] The AE wave W propagating inside the housing of the twin-screw extruder 30 is detected by the AE sensor 20 installed on the surface of the housing of the twin-screw extruder 30. Then, the AE sensor 20 outputs a detection signal D. Since the detection signal D is a signal representing vibration, as shown in FIG. 1, it is an AC signal having positive and negative values. However, as it is, it is difficult to handle when performing various operations on the detection signal D (AE wave W), so it is common to handle it as a rectified waveform obtained by half-wave rectifying the negative part of the detection signal D. Also, when analyzing the AE wave W, generally, the squared value of the rectified waveform is averaged over a predetermined time and then the square root is taken, that is, it is handled as the effective value (RMS (Root Mean Square) value).

[0012] The propagation speed of the AE wave W is different between longitudinal waves and transverse waves (longitudinal waves are faster than transverse waves), but considering the size (propagation distance) of the solid material Q, the difference is negligible. Therefore, in this embodiment, without distinguishing between longitudinal waves and transverse waves, the AE wave W detected within a predetermined time is taken as a measurement signal and is the subject of analysis.

[0013] FIG. 2 is a schematic structural diagram of the AE sensor. As shown in FIG. 2, the AE sensor 20 is installed at the tip of a waveguide rod 21 (waveguide) that is abutted against the surface of the housing (barrel) 32 of the twin-screw extruder 30 to be detected, while being enclosed in a shield case 20a. The waveguide rod 21 is formed of ceramic or stainless steel and transmits the AE wave W that has traveled inside the housing 32 to the AE sensor 20.

[0014] A heater 39 for melting resin pellets is mounted on the surface of the housing 32 of the twin-screw extruder 30, and since it becomes a high temperature of about 200°C, the AE sensor 20 cannot be directly installed on the housing 32. Therefore, the AE sensor 20 is installed via the waveguide rod 21. A magnet 22 is installed at the tip of the waveguide rod 21 on the side of the twin-screw extruder 30, and the waveguide rod 21 is fixed to the surface of the housing 32 of the twin-screw extruder 30 by the magnet 22, avoiding the position of the heater 39. Alternatively, the tip of the waveguide rod 21 on the side of the twin-screw extruder 30 may be fixed to the surface of the housing 32 by screwing.

[0015] The other end of the waveguide rod 21 is connected to the wave-receiving surface 20b of the AE sensor 20. To improve the adhesion between the AE sensor 20 and the waveguide rod 21, grease may be applied to the wave-receiving surface 20b of the AE sensor 20. A vapor-deposited film 20c of copper or the like is formed on the upper part of the wave-receiving surface 20b. A piezoelectric element 20d of lead zirconate titanate (PZT) or the like is installed on the upper part of the vapor-deposited film 20c. The piezoelectric element 20d receives the AE wave W transmitted through the inside of the waveguide rod 21 via the wave-receiving surface 20b and outputs an electrical signal corresponding to the AE wave W. The electrical signal output by the piezoelectric element 20d is output as a detection signal D via the vapor-deposited film 20e and connector 20f. Since the detection signal D is weak, a preamplifier (not shown in Figure 2) may be installed inside the AE sensor 20 to amplify the detection signal D before output in order to suppress the influence of noise.

[0016] Embodiments of the kneading state detection device for an extrusion molding machine according to this disclosure will be described in detail below with reference to the drawings. However, the present invention is not limited by these embodiments. Furthermore, the components in the embodiments described below include those that are substituted and readily conceivable by those skilled in the art, or that are substantially identical.

[0017] [First Embodiment] The first embodiment of this disclosure is an example of a kneading state detection device for a twin-screw extruder that determines the kneading state of glass fibers mixed into a resin raw material. Note that the twin-screw extruder is just one example, and this embodiment is applicable to all types of extruders, such as single-screw extruders and multi-screw extruders.

[0018] [Outline structure of a twin-screw extruder] First, the schematic structure of the kneading state detection device 50 of the twin-screw extruder 30 in this embodiment will be described using Figures 3 and 4. Figure 3 is a schematic structural diagram showing an example of a kneading state detection device for a twin-screw extruder. Figure 4 is a cross-sectional view of the output shaft of the twin-screw extruder.

[0019] The twin-screw extruder 30 is driven according to the output of the gearbox 40. More specifically, the gearbox 40 reduces the rotational force of the motor 24 to rotate the two output shafts 42 of the twin-screw extruder 30 in the same direction. Screws 44 and kneading discs 46, described later, are installed on the outer circumference of the output shafts 42. As the output shafts 42 rotate, the resin raw material (resin pellets) fed into the twin-screw extruder 30 is plasticized and melted, then kneaded and molded, and the strength of the resin raw material is improved by mixing and kneading in reinforcing materials such as glass fibers. Note that the twin-screw extruder 30 is an example of an extruder in this disclosure.

[0020] The two output shafts 42 are arranged parallel to each other along the Y-axis, separated by a constant inter-axis distance C along the X-axis, inside the cylindrical housing (barrel) 32 of the twin-screw extrusion molding machine 30.

[0021] Figure 4(a) is a cross-sectional view AA of the twin-screw extruder 30. As shown in Figure 4(a), the output shaft 42 is inserted into a spline hole 43 formed in the screw 44. The output shaft 42 then engages with the spline hole 43, causing the screw 44 to rotate inside the insertion hole 34.

[0022] Figure 4(b) is a cross-sectional view of the BB of the twin-screw extruder 30. As shown in Figure 4(b), the output shaft 42 is inserted into a spline hole 43 formed in the kneading disc 46. The output shaft 42 then engages with the spline hole 43, causing the kneading disc 46 to rotate inside the insertion hole 34.

[0023] The screw 44 rotates at a speed of, for example, 300 revolutions per minute, to transport the molten resin material and glass fibers mixed into the resin material, which have been fed into the twin-screw extruder 30, to the downstream side of the twin-screw extruder 30. The screws 44 on each output shaft 42 mesh with each other to transport the molten resin material downstream. The glass fibers mixed into the resin material are subjected to a large shear force and broken as they pass through the meshing parts of the screws 44 on each output shaft 42.

[0024] The kneading disc 46 has a structure in which multiple elliptical discs are arranged in a direction perpendicular to the output shaft 42, and the orientation of adjacent discs is offset along the output shaft 42. By offsetting the adjacent discs, the flow of the resin material is interrupted between the discs, thereby promoting the kneading of the conveyed resin material and the glass fibers mixed into the resin material. Specifically, the kneading disc 46 is heated by the heater 39 and provides shear energy to the resin material conveyed by the screw 44, thereby completely melting the resin material.

[0025] The housing 32 is provided with through-holes 34 into which each output shaft 42 is inserted. The through-holes 34 are holes provided along the longitudinal direction of the housing 32 and have a shape in which parts of a cylinder overlap. This allows the screws 44 and kneading discs 46 to be inserted into the through-holes 34 in a state where they are interlocked with each other.

[0026] Returning to Figure 3, a supply port 36a is provided at one longitudinal end of the housing 32 for introducing the pelletized resin raw material and the powdered filler material into the insertion hole 34. Then, reinforcing material such as glass fiber is introduced from a supply port 36b provided in the side feeder 37 downstream of the supply port 36a. Note that the direction of raw material supply through the supply ports 36a and 36b is not limited to the example shown in Figure 3.

[0027] An outlet 38 is provided at the other end of the housing 32 in the longitudinal direction for discharging the material that has been kneaded while passing through the insertion hole 34. In addition, a heater 39 is provided on the outer circumference of the housing 32 for heating the resin raw material introduced into the insertion hole 34 by heating the housing 32.

[0028] In the example shown in Figure 3, the output shaft 42 of the twin-screw extruder 30 is equipped with two screws 44 and one kneading disc 46. However, the number of screws 44 and kneading discs 46 is not limited to the example shown in Figure 3. For example, kneading discs 46 may be installed in multiple locations to knead the resin raw material and glass fibers.

[0029] The AE sensor 20 is installed on the surface of the housing 32 of the twin-screw extruder 30, downstream of the supply port 36b, via a waveguide rod 21. The output of the AE sensor 20 is input to the AE wave analyzer 10. The AE sensor 20 and the AE wave analyzer 10 constitute the kneading state detection device 50. In order to detect the AE wave W with higher sensitivity, it is desirable to install the AE sensor 20 near the kneading disc 46, which is the main source of the AE wave W during raw material kneading, as shown in Figure 3.

[0030] The configuration and function of the AE sensor 20 are as described above.

[0031] The AE wave analyzer 10 determines whether the fracture state of the glass fibers fed into the twin-screw extruder 30 has stabilized by analyzing the frequency components of the AE wave W output by the AE sensor 20. A stable fracture state of the glass fibers means that the resin raw material and glass fibers are sufficiently mixed, and molded products that meet quality standards are discharged from the discharge port 38. In other words, it means that there are no longer any temporal changes in the amount of glass fiber fracture or the degree of mixing with the resin raw material at each element point inside the twin-screw extruder 30. This state is also called a steady state. Hereafter, the state of stable mixing of the raw materials will also be referred to as a steady state. The method for analyzing the frequency components of the AE wave W will be described later.

[0032] [Hardware configuration of the mixing state detection device] Next, the hardware configuration of the kneading state detection device 50 of the twin-screw extruder 30 will be described using Figure 5. Figure 5 is a hardware block diagram showing an example of the hardware configuration of the kneading state detection device of the twin-screw extruder according to the embodiment.

[0033] The kneading state detection device 50 is used in connection with the twin-screw extrusion molding machine 30 and comprises an AE wave analyzer 10 and an AE sensor 20. The AE wave analyzer 10 comprises a control unit 13, a storage unit 14, and a peripheral device controller 16.

[0034] The control unit 13 comprises a CPU (Central Processing Unit) 13a, a ROM (Read Only Memory) 13b, and a RAM (Random Access Memory) 13c. The CPU 13a is connected to the ROM 13b and the RAM 13c via a bus line 15. The CPU 13a reads the control program P1 stored in the memory unit 14 and loads it into the RAM 13c. The CPU 13a controls the operation of the control unit 13 by operating according to the control program P1 loaded into the RAM 13c. In other words, the control unit 13 has the configuration of a typical computer that operates based on the control program P1.

[0035] The control unit 13 is further connected to the storage unit 14 and the peripheral device controller 16 via the bus line 15.

[0036] The memory unit 14 is a non-volatile memory such as flash memory, or an HDD (Hard Disk Drive), which retains stored information even when the power is turned off. The memory unit 14 stores a program including the control program P1 and the AE output M(t) output from the AE sensor 20 at time t. The control program P1 is a program that enables the control unit 13 to perform its functions. The AE output M(t) is a signal obtained by converting the effective value of the detection signal D output by the AE sensor 20 into a digital signal using the A / D converter 17.

[0037] The control program P1 may be provided pre-installed in the ROM 13b. Alternatively, the control program P1 may be provided as a file in an installable or executable format for the control unit 13, recorded on a computer-readable recording medium such as a CD-ROM, flexible disk (FD), CD-R, or DVD (Digital Versatile Disc). Furthermore, the control program P1 may be provided by storing it on a computer connected to a network such as the Internet and allowing users to download it via the network. Finally, the control program P1 may be provided or distributed via a network such as the Internet.

[0038] The peripheral device controller 16 connects to the A / D converter 17, the display device 18, and the operation device 19. Based on commands from the control unit 13, the peripheral device controller 16 controls the operation of each connected device.

[0039] The A / D converter 17 converts the AE wave W output by the AE sensor 20 into a digital signal and outputs an AE output M(t). As described above, the AE sensor 20 detects the AE wave W transmitted through the housing 32 of the twin-screw extrusion molding machine 30 via the waveguide rod 21. Although not shown in Figure 5, the AE wave W output by the AE sensor 20 is amplified by an amplifier before being input to the A / D converter 17.

[0040] The display device 18 is, for example, a liquid crystal display. The display device 18 displays various information related to the operating status of the kneading state detection device 50. The display device 18 also notifies the kneading state detection device 50 that the resin raw material and glass fiber fed into the twin-screw extruder 30 have been kneaded and reached a steady state.

[0041] The operating device 19 is, for example, a touch panel superimposed on the display device 18. The operating device 19 acquires operation information related to various operations performed by the operator on the kneading state detection device 50 of the twin-screw extruder 30.

[0042] The AE sensor 20 is installed on the surface of the housing 32 of the twin-screw extruder 30 in the configuration described in Figures 2 and 3. Furthermore, the frequency band of the detectable signal differs depending on the type of AE sensor 20. Therefore, when selecting an AE sensor 20 to use, it is desirable to select one that has high sensitivity to the frequency band of the AE wave W expected to be generated during mixing, taking into consideration the raw material to be measured and the operating conditions of the twin-screw extruder 30.

[0043] [Analysis of AE waves generated by the fracture of glass fibers] The inventors temporarily supplied a fixed amount of glass fibers, intended to increase the strength of the resin raw material, from a supply port 36b to molten resin pellets being transported inside the twin-screw extruder 30 shown in Figure 3, and observed the AE wave W emitted when the glass fibers ruptured using an AE sensor 20.

[0044] Figure 6 shows an example of the results of frequency analysis of AE waves acquired by an AE wave analyzer. Graph 60 in Figure 6 shows an example of the frequency distribution of the amplitude X(f) of the AE output M(t) acquired by the AE sensor 20 when the molten resin raw material, which had reached a steady state, was transported by the twin-screw extruder 30. The horizontal axis of graph 60 represents the frequency f. The vertical axis of graph 60 represents the amplitude of the frequency f component obtained by performing a discrete Fourier transform on the AE output M(t). The discrete Fourier transform was performed using the FFT algorithm. The AE output M(t) was sampled at a sampling frequency of 250 kHz.

[0045] Graph 61 shows an example of the frequency distribution of the amplitude X(f) of the AE output M(t) obtained when a fixed amount of glass fiber was temporarily supplied to a molten resin raw material that had reached a steady state, and then transported by a twin-screw extruder 30. The AE output M(t) was sampled at a sampling frequency of 250 kHz, similar to Graph 60.

[0046] Graph 60 shows peaks at several frequencies, which are inherent frequency components generated when the twin-screw extruder 30 is in operation. These frequency components are generated by, for example, motor vibration, valve opening and closing noise, inverter noise, etc. It was found that when the twin-screw extruder 30 is transporting only molten resin raw material, AE waves W caused by the resin raw material are not generated.

[0047] In contrast, when glass fiber breakage occurs inside the twin-screw extruder 30, a comparison of graphs 60 and 61 reveals that a large amplitude AE ​​output M(t) is generated, particularly in the frequency band from 60kHz to 80kHz.

[0048] Next, to visualize how the glass fiber fracture progresses over time, the inventors applied a 60kHz to 80kHz bandpass filter to the amplitude X(f) shown in Figure 6. Then, they calculated the power spectrum from 60kHz to 80kHz. Finally, they calculated the integral value S(t) of the calculated power spectrum over 0.2 seconds and observed its time evolution.

[0049] The power spectrum P(f) of a signal with frequency f is calculated using equation (1). P(f)=|X(f)| 2 =(X(f)*X(f)) / n 2 ...(1) Here, X(f) is the amplitude as described above, and n is the number of data points.

[0050] Figure 7 shows an example of the time evolution of the integrated power spectrum of an AE wave acquired by an AE wave analyzer from 60 kHz to 80 kHz.

[0051] Graphs 62a, 62b, and 62c in Figure 7 show the time evolution of the integrated power spectrum of the AE output M(t) acquired by the AE wave analyzer 10 from 60kHz to 80kHz when the screw 44 is rotated at different rotational speeds. Graph 62a shows the integrated value S(t) when the screw rotational speed is 50 rpm. Graph 62b shows the integrated value S(t) when the screw rotational speed is 100 rpm. Graph 62c shows the integrated value S(t) when the screw rotational speed is 150 rpm.

[0052] As can be seen from graphs 62a, 62b, and 62c, in all graphs, the integral value S(t) increases monotonically over time, and then decreases monotonically. In other words, it was found that glass fiber breakage occurs frequently immediately after the glass fiber is introduced. Furthermore, as time passes, the length of the glass fiber decreases, so the frequency of breakage decreases, and it was found that the size (length) of the glass fiber does not change over time, that is, the mixing state of the raw materials reaches a steady state.

[0053] Furthermore, it was found that as the rotational speed of screw 44 increased, the integral value S(t), i.e., the amplitude of the 60-80 kHz component of the AE wave W, also increased. This is thought to be because the frequency of glass fiber fracture increases with increasing rotational speed of screw 44.

[0054] Based on these results, the inventors concluded that when the integral value S(t) decreases monotonically and the change becomes gradual, it can be determined that the fracture state of the glass fibers has reached a steady state.

[0055] Figure 8 illustrates a method for determining whether the fracture state of the glass fibers has reached a steady state based on the waveform of the AE wave acquired by the AE wave analyzer.

[0056] Because the waveform of the integral value S(t) fluctuates greatly, the AE wave analyzer 10 first smooths the waveform by calculating a moving average A(t) of the integral value S(t). A moving average is a type of low-pass filter and is used to analyze the overall trend of a waveform by smoothing the given waveform. The time interval for calculating the moving average can be set arbitrarily, but if the time interval is too short, noise components will remain, and if the time interval is too long, the waveform will become too blunted, so it is desirable to set an appropriate time interval by conducting evaluation experiments. For example, the moving average A(t) can be obtained from the integral value S(t) shown in graph 63 of Figure 8.

[0057] Next, the AE wave analyzer 10 analyzes the time change of the moving average A(t). Specifically, as shown in graph 64 of Figure 8, it calculates the rate of change G(t) of the moving average A(t) by taking the time derivative of the moving average A(t). That is, the rate of change G(t) is calculated by equation (2).

[0058] G(t) = dA(t) / dt ... (2)

[0059] From the results of the evaluation experiment described above, it was found that when the glass fibers are broken and reach a steady state, the moving average A(t) decreases monotonically and gradually. Therefore, the AE wave analyzer 10 first searches for an interval in which the moving average A(t) is decreasing monotonically. The interval in which the moving average A(t) is decreasing monotonically is identified, for example, by searching for a place in which an interval satisfying G(t)≦0 is continuous over a predetermined time Δt. In the case of graph 64, interval K is identified as an interval in which the moving average A(t) is decreasing monotonically.

[0060] Next, the AE wave analyzer 10 searches for a time t in the interval K where the moving average A(t) is monotonically decreasing, where the absolute values ​​of the rate of change G(t) are all below the threshold Th over a predetermined time Δt. In the case of graph 64, time ta is found. That is, from time ta to time tb, separated by a predetermined time Δt, the absolute values ​​of the rate of change G(t) are all below the threshold Th. The AE wave analyzer 10 then determines that at time ta, the glass fiber fracture has reached a steady state, that is, it is ready to start molding the product using the twin-screw extruder 30.

[0061] On the other hand, if the aforementioned conditions are not met, the AE wave analyzer 10 determines that the glass fiber fracture has not reached a steady state, that is, that the operation of the twin-screw extruder 30 needs to continue.

[0062] [Functional Configuration of the Mixing State Detection Device] Next, the functional configuration of the kneading state detection device 50 of the embodiment will be explained using Figure 9. Figure 9 is a functional block diagram showing an example of the functional configuration of the kneading state detection device for a twin-screw extrusion molding machine according to the embodiment. The control unit 13 of the kneading state detection device 50 operates by loading the control program P1 into the RAM 13c, thereby realizing the AE wave acquisition unit 71, the kneading state determination unit 72, and the kneading state output unit 73 shown in Figure 9 as functional units.

[0063] The AE wave acquisition unit 71 acquires the output of the AE sensor 20 installed in the housing 32 of the twin-screw extruder 30 when the twin-screw extruder 30, which kneads raw materials or kneads raw materials with a reinforcing material to improve the strength of the raw materials, is in operation. More specifically, the AE wave acquisition unit 71 is equipped with an amplifier to amplify the detection signal D detected by the AE sensor 20, and the A / D converter 17 converts the effective value of the analog detection signal D into a digital signal, the AE output M(t). Note that the AE wave acquisition unit 71 is an example of an acquisition unit in this disclosure.

[0064] The mixing state determination unit 72 determines the mixing state of the resin raw material and glass fiber based on a comparison between the intensity change of the AE output M(t) of the AE sensor 20 over a predetermined time period, acquired by the AE wave acquisition unit 71, and a threshold value. Note that the mixing state determination unit 72 is an example of a determination unit in this disclosure. The mixing state determination unit 72 further comprises an FFT processing unit 72a, a BPF processing unit 72b, a power spectrum calculation unit 72c, an integral value calculation unit 72d, a moving average calculation unit 72e, a rate of change calculation unit 72f, and a threshold value processing unit 72g.

[0065] The FFT processing unit 72a performs an FFT on the AE output M(t).

[0066] The BPF processing unit 72b performs an FFT and applies a bandpass filter (BPF) of a predetermined frequency range to the result. The predetermined frequency range is, for example, 60 to 80 kHz.

[0067] The power spectrum calculation unit 72c calculates the power spectrum P(f) for a predetermined frequency range calculated by the BPF processing unit 72b.

[0068] The integral value calculation unit 72d calculates the integral value S(t) of the power spectrum P(f) in a predetermined frequency range over a predetermined time. The predetermined time is, for example, 0.2 seconds.

[0069] The moving average calculation unit 72e calculates the moving average A(t) of the integral value S(t).

[0070] The rate of change calculation unit 72f calculates the rate of change G(t) of the moving average A(t).

[0071] The threshold processing unit 72g searches for a time t in which the absolute values ​​of the rate of change G(t) of the moving average A(t) are all less than or equal to the threshold Th over a predetermined time Δt. Then, the threshold processing unit 72g determines that the mixing state of the raw materials has reached a steady state at time t.

[0072] The thresholding unit 72g may determine that the mixing state of the raw materials has reached a steady state if, without using a moving average A(t), the amplitude R(t) of the integral value S(t) (see Figure 8) remains between a first threshold and a second threshold that is greater than the first threshold for a predetermined time Δt. The first and second thresholds are set appropriately according to the conditions for mixing the raw materials.

[0073] The kneading state output unit 73 outputs the determination result regarding the kneading state of the raw materials determined by the kneading state determination unit 72. The determination result is displayed on the display device 18. The output method of the kneading state output unit 73 is not limited to this, and it may also be indicated that the glass fiber fracture state has reached a steady state by lighting up or flashing an indicator (not shown in Figure 5), or by outputting sound or voice from a speaker or buzzer (not shown in Figure 5).

[0074] [Process flow performed by the mixing state detection device] Next, the processing flow of the kneading state detection device 50 according to the embodiment will be explained using Figure 10. Figure 10 is a flowchart showing an example of the processing flow of the kneading state detection device.

[0075] The AE wave acquisition unit 71 acquires the AE output M(t) within a predetermined time range (step S11). The predetermined time is the time range in which the number of data necessary to perform the FFT in step S12 can be acquired.

[0076] The FFT processing unit 72a performs an FFT on the AE output M(t) (step S12).

[0077] The BPF processing unit 72b applies a bandpass filter to the result of the FFT, cutting out outputs outside a predetermined frequency range (step S13). In this embodiment, the predetermined frequency range is 60 to 80 kHz.

[0078] The power spectrum calculation unit 72c calculates the power spectrum based on the result of applying the bandpass filter (step S14).

[0079] Next, the integral value calculation unit 72d calculates the integral value S(t) of the power spectrum (step S15).

[0080] The moving average calculation unit 72e calculates the moving average A(t) of the integral value S(t) (step S16).

[0081] The rate of change calculation unit 72f calculates the rate of change G(t) of the moving average A(t) (step S17).

[0082] The threshold processing unit 72g determines whether the absolute value of the rate of change G(t) of the moving average A(t) is less than or equal to the threshold Th over a predetermined time Δt (step S18). If it is determined that the condition is satisfied (step S18: Yes), the process proceeds to step S19. On the other hand, if it is determined that the condition is not satisfied (step S18: No), the process returns to step S11.

[0083] In step S18, if it is determined that the absolute value of the rate of change G(t) of the moving average A(t) is less than or equal to the threshold Th over a predetermined time Δt, the threshold processing unit 72g determines that the mixing state of the raw materials has reached a steady state (step S19).

[0084] The kneading state output unit 73 notifies the display device 18 that the kneading state of the raw materials has reached a steady state (step S20). After that, the kneading state detection device 50 completes the process shown in Figure 10.

[0085] Furthermore, if the kneading state output unit 73 determines in step S18 that the absolute value of the rate of change G(t) of the moving average A(t) is not below a threshold Th for a predetermined time Δt, it may notify the display device 18 that the kneading state of the raw materials has not reached a steady state.

[0086] Furthermore, the threshold processing unit 72g may determine in step S18 that the mixing state of the raw materials has reached a steady state if the rate of change G(t) of the moving average A(t) is negative over a predetermined time Δt, that is, the rate of change G(t) is monotonically decreasing, and the absolute value of the rate of change G(t) is less than or equal to the threshold Th over a predetermined time Δt.

[0087] As described above, the kneading state detection device 50 of the first embodiment includes an AE wave acquisition unit 71 (acquisition unit) that acquires the AE output M(t) of an AE sensor 20 installed in the housing 32 of a twin-screw extruder 30 when the twin-screw extruder 30 that kneads raw materials or kneads raw materials and additives is in operation, and a kneading state determination unit 72 (determination unit) that determines the kneading state of raw materials or raw materials and additives based on a comparison of the intensity change of the AE output M(t) of the AE sensor 20 over a predetermined time period acquired by the AE wave acquisition unit 71 with a threshold Th.Therefore, it is possible to reliably detect in real time whether there has been no change over time in the amount of glass fiber breakage or the degree of kneading with the resin raw material at each element point inside the twin-screw extruder 30, that is, whether the kneading state of the raw materials has stabilized.In addition, it is possible to determine the kneading state of the raw materials even in areas that cannot be determined by pressure sensors, temperature sensors, torque sensors, etc.In addition, since the kneading state of the raw materials can be determined in real time, it is possible to reduce raw material waste.

[0088] Furthermore, the kneading state detection device 50 of the first embodiment includes an integral value calculation unit 72d that calculates the integral value S(t) of the power spectrum of the AE output M(t) of the AE sensor 20 in a predetermined frequency range, and a moving average calculation unit 72e that calculates the moving average A(t) of the time change of the integral value S(t). The kneading state determination unit 72 (determination unit) determines that the kneading state of the raw material, or the raw material and additive, is stable when the absolute value of the rate of change G(t) of the moving average A(t) is less than or equal to a predetermined threshold Th over a predetermined time Δt. Therefore, the kneading state of the raw material can be reliably detected in real time.

[0089] Furthermore, in the kneading state detection device 50 of the first embodiment, the kneading state determination unit 72 (determination unit) determines that the kneading state of the raw materials has stabilized when the moving average A(t) of the time change of the integral value S(t) decreases monotonically with time, and the absolute value of the rate of change G(t) of the moving average A(t) is less than or equal to a predetermined threshold Th over a predetermined time Δt. Therefore, the kneading state of the raw materials can be reliably determined in real time.

[0090] Furthermore, in the kneading state detection device 50 of the first embodiment, the kneading state determination unit 72 (determination unit) determines that the kneading state of the raw materials is stable when the time change of the integral value S(t) falls between a first threshold and a second threshold that is greater than the first threshold over a predetermined time Δt. Therefore, the kneading state of the raw materials can be reliably determined in real time.

[0091] Furthermore, in the kneading state detection device 50 of the first embodiment, the AE sensor 20 is installed downstream of the twin-screw extruder 30 from the input ports for the resin raw materials and glass fibers. Therefore, it is possible to reliably determine whether the kneading state of the raw materials has stabilized. In addition, because the AE sensor 20 is installed near the kneading area, it has a fast response speed and can determine the kneading state of the raw materials in real time.

[0092] Furthermore, in the kneading state detection device 50 of the first embodiment, the kneading state determination unit 72 (determination unit) determines that when glass fibers (reinforcement material) are introduced into the molten resin raw material being transported inside the twin-screw extruder 30, the size of the glass fibers does not change over time. Therefore, it is possible to reliably determine whether there is no longer any change in the amount of glass fiber breakage at each element point inside the twin-screw extruder 30, that is, whether the glass fiber breakage (kneading) state has stabilized.

[0093] Furthermore, in the kneading state detection device 50 of the first embodiment, the reinforcing material is glass fiber. Therefore, molded products with improved strength can be reliably manufactured.

[0094] [Modified version of the first embodiment] In the embodiment described above, an example was explained in which, when glass fibers are added to molten resin pellets, the kneading state detection device 50 determines whether the glass fibers have broken and reached a steady state. The kneading state detection device 50 can also further determine whether the unmelted resin pellets fed into the twin-screw extruder 30 have been crushed and melted and reached a steady state by observing the AE wave W and performing the same signal processing as described above.

[0095] Furthermore, it is possible to determine whether, in a state where unmelted resin pellets and glass fibers are mixed, the resin pellets have been crushed and melted, and whether a steady state has been reached in which the size of the glass fibers does not change over time.

[0096] As described above, in the kneading state detection device 50, a modified version of the first embodiment, the kneading state determination unit 72 (determination unit) determines, when unmelted resin pellets and glass fibers (reinforcement material) are fed into the twin-screw extruder 30, that the resin pellets have been crushed and melted, and that the size of the glass fibers has not changed over time. Therefore, the kneading state of the resin raw material and the reinforcement material can be determined reliably and easily.

[0097] [Second Embodiment] When the twin-screw extruder 30 is operated to mix the raw materials, the raw materials are continuously fed in for a long period of time. In addition, the operating conditions may be changed during the operation of the twin-screw extruder 30. Operating conditions include, for example, changing the rotation speed of the screw 44 or changing the flow rate of the resin raw material or glass fiber being fed in. The mixing state detection device 50 can determine in real time whether the mixing state of the raw materials has stabilized, even in such continuous operation situations. In the case of continuous operation, new raw materials and additives are constantly being fed in, so unlike the first embodiment, AE waveforms associated with the breakage of raw materials are constantly output. When the fed raw materials fill the inside of the twin-screw extruder 30, the time change of the output AE waveform becomes small. This state is the steady state in continuous operation. The mixing state detection device 50 can also determine when such a steady state has been reached in continuous operation.

[0098] [First example of operation of the second embodiment] Figure 11 shows an example of the time change in the integrated power spectrum when the screw rotation speed is changed while a twin-screw extruder is continuously operated and the resin raw material and glass fibers are sufficiently mixed.

[0099] In Figure 11, the screw 44 rotates at 50 rpm from time 0 to time td. Then, at time td, when the mixing state reaches a steady state, the rotation speed of the screw 44 is increased to 100 rpm, and the screw 44 rotates at 100 rpm from time td to time te. The resin raw material is continuously fed into the supply port 36a (see Figure 3) from time 0, and the glass fiber is continuously fed into the side feeder 37 (see Figure 3) from time tc. The flow rate of the resin raw material is 5 kg / h, and the flow rate of the glass fiber is 0.5 kg / h.

[0100] The kneading state determination unit 72 of the kneading state detection device 50 determines that the kneading state of the raw materials has stabilized (reached a steady state) when the absolute value of the rate of change G(t) of the moving average A(t) of the integral value S(t) is less than or equal to a predetermined threshold Th over a predetermined time Δt. The graph showing the rate of change G(t) in Figure 11 is difficult to understand due to the high compression of the time axis, but the inventors confirmed that in the latter half of the interval under the same operating conditions, the time change of the rate of change G(t) remains below a predetermined threshold over a predetermined time.

[0101] Therefore, even if the operating conditions are changed during continuous operation, the determination method described in the first embodiment (see Figure 10) can be applied as is in sections with the same operating conditions.

[0102] [Second example of operation of the second embodiment] Figure 12 shows an example of the time change in the integrated power spectrum when the screw rotation speed is changed while the twin-screw extruder is running continuously and the resin raw material and glass fibers are sufficiently mixed.

[0103] In Figure 12, from time 0 to time th, the resin raw material is continuously fed at a flow rate of 2 kg / h. Then, at time th, when the mixing state reaches a steady state, the flow rate of the resin raw material is increased, and from time th to time ti, the resin raw material is continuously fed at a flow rate of 9 kg / h. In addition, glass fibers are continuously fed at a flow rate of 0.5 kg / h from time tg. The rotation speed of screw 44 (100 rpm) is constant.

[0104] The kneading state determination unit 72 of the kneading state detection device 50a determines that the kneading state of the raw materials has stabilized (reached a steady state) when the absolute value of the rate of change G(t) of the moving average A(t) of the integral value S(t) is less than or equal to a predetermined threshold Th over a predetermined time Δt. The graph showing the rate of change G(t) in Figure 12 is difficult to understand due to the high compression of the time axis, but the inventors confirmed that in the latter half of the interval under the same operating conditions, the time change of the rate of change G(t) remains below a predetermined threshold over a predetermined time.

[0105] Therefore, even if the operating conditions are changed during continuous operation, the determination method described in the first embodiment (see Figure 10) can be applied as is in sections with the same operating conditions.

[0106] [Third example of operation in the second embodiment] Figure 13 shows an example of the time change in the integrated power spectrum when the glass fiber input flow rate is changed while the twin-screw extruder is running continuously and the resin raw material and glass fiber are sufficiently mixed.

[0107] In Figure 13, from time tk to time tl, glass fibers are continuously fed at a flow rate of 0.5 kg / h. Then, at time tl, when the mixing state reaches a steady state, the flow rate of glass fibers is increased, and from time tl to time tm, glass fibers are continuously fed at a flow rate of 1 kg / h. In addition, the resin raw material is continuously fed at a flow rate of 5 kg / h from time 0. The rotation speed of screw 44 (100 rpm) is constant.

[0108] The kneading state determination unit 72 of the kneading state detection device 50a determines that the kneading state of the raw materials has stabilized (reached a steady state) when the absolute value of the rate of change G(t) of the moving average A(t) of the integral value S(t) is less than or equal to a predetermined threshold Th over a predetermined time Δt. The graph showing the rate of change G(t) in Figure 13 is difficult to understand due to the high compression of the time axis, but the inventors confirmed that in the latter half of the interval under the same operating conditions, the time change of the rate of change G(t) remains below a predetermined threshold over a predetermined time.

[0109] Therefore, even if the operating conditions are changed during continuous operation, the determination method described in the first embodiment (see Figure 10) can be applied as is in sections with the same operating conditions.

[0110] As described above, in the second embodiment, the kneading state detection device 50, when raw materials are continuously supplied, includes an integral value calculation unit 72d that calculates the integral value S(t) of the power spectrum of the AE output M(t) of the AE sensor 20 in a predetermined frequency range, and a moving average calculation unit 72e that calculates the moving average A(t) of the integral value S(t). When the absolute value of the rate of change G(t) of the moving average A(t) is less than or equal to a predetermined threshold Th over a predetermined time Δt, it is determined that no temporal change is observed in the degree of kneading of the raw materials fed into the twin-screw extruder 30 at each element point inside the twin-screw extruder 30, that is, that a steady state has been reached. Therefore, even when the twin-screw extruder 30 is in continuous operation and raw materials are continuously supplied, the kneading state of the raw materials can be reliably determined.

[0111] Although embodiments of the present invention have been described above, these embodiments are illustrative and are not intended to limit the scope of the invention. This novel embodiment can be carried out in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims of the invention and its equivalents. [Explanation of Symbols]

[0112] 10…AE wave analyzer, 20…AE sensor, 30…Twin-screw extruder (extruder), 32…Housing (barrel), 36a,36b…Feed port, 42…Output shaft, 44…Screw, 46…Kneading disc, 50…Kneading state detection device, 71…AE wave acquisition unit (acquisition unit), 72…Kneading state determination unit (determination unit), 72a…FFT processing unit, 72b…BPF processing unit, 72c…Power spectrum calculation unit, 72d…Integral value calculation unit, 72e…Moving average calculation unit, 72f…Rate of change calculation unit, 72g…Threshold processing unit, 73…Kneading state output unit, A(t)…Moving average, f…Frequency, G(t)…Rate of change, K…Interval, M(t)…AE output, P(f)…Power spectrum, Th…Threshold, W…AE wave, X(f)…Amplitude, Δt…Determined time

Claims

1. An acquisition unit that acquires the output of an AE sensor installed on the housing of an extrusion molding machine that kneads raw materials or kneads raw materials and additives when the extrusion molding machine is in operation, An integral value calculation unit that calculates the integral value of the power spectrum in a predetermined frequency range of the output of the AE sensor, A moving average calculation unit calculates the moving average of the time change of the aforementioned integral value, A determination unit that determines that the mixing state of the raw material, or the raw material and additive, has stabilized when the absolute value of the rate of change of the moving average is below a predetermined threshold over a predetermined period of time, A kneading state detection device for an extrusion molding machine.

2. (delete)

3. The determination unit determines that the mixing state has stabilized when the moving average decreases monotonically over time, and the absolute value of the rate of change of the moving average is below a predetermined threshold over a predetermined period of time. A kneading state detection device for an extrusion molding machine according to claim 1.

4. The determination unit determines that the kneading state is stable when the time change of the integral value remains between a first threshold and a second threshold that is greater than the first threshold over a predetermined period of time. The kneading state detection device for an extrusion molding machine according to claim 3.

5. The aforementioned AE sensor is A port located downstream of the raw material inlet, or the inlet for the raw material and the additive, is installed on the side of the extrusion molding machine. A kneading state detection device for an extrusion molding machine according to claim 1.

6. The determination unit determines, when an additive is added to the molten resin raw material being transported inside the extrusion molding machine, that the size of the additive does not change over time. A kneading state detection device for an extrusion molding machine according to claim 1 or claim 3.

7. The determination unit determines, when the unmelted resin raw material and additive are fed into the extrusion molding machine, that the resin raw material is crushed and melted, and that the size of the additive does not change over time. A kneading state detection device for an extrusion molding machine according to claim 1 or claim 3.

8. The aforementioned additive is glass fiber. A kneading state detection device for an extrusion molding machine according to claim 1 or claim 3.

9. When an extrusion molding machine that kneads raw materials, or kneads raw materials and additives, is in operation, the output of an AE sensor installed on the housing of the extrusion molding machine is acquired, and the integral value of the power spectrum in a predetermined frequency range of the acquired AE sensor output is calculated. The moving average of the time evolution of the aforementioned integral value is calculated, If the absolute value of the rate of change of the moving average remains below a predetermined threshold over a predetermined period of time, it is determined that the raw material, or the mixing state of the raw material and additive, has stabilized. A method for detecting the mixing state of an extrusion molding machine.

10. (delete)

11. The mixing state is determined to be stable when the moving average decreases monotonically over time, and the absolute value of the rate of change of the moving average remains below a predetermined threshold over a predetermined period of time. A method for detecting the kneading state of an extrusion molding machine according to claim 9.

12. If the time change of the integral value remains between a first threshold and a second threshold that is greater than the first threshold over a predetermined period of time, it is determined that the mixing state has stabilized. A method for detecting the kneading state of an extrusion molding machine according to claim 9.

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