Lifetime prediction method, lifetime prediction device, and computer program

Abstract

To provide a life prediction method that can accurately predict a life of a servo amplifier that drives a servo motor installed in a component of a molding machine.SOLUTION: A life prediction method for predicting a life of a servo amplifier that drives a servo motor installed in a component of a molding machine, by accumulating a number of process that required the servo motor to be driven, detecting a physical quantity pertaining to a load generated in the servo amplifier in one process, and multiplying a unit wear of the servo amplifier according to the detected physical quantity by the number of processes that have been accumulated.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to a lifespan prediction method, a lifespan prediction device, and a computer program. [Background technology]

[0002] There is an information management device for injection molding that manages information on parts of an injection molding machine. The information management device for injection molding calculates the amount of wear as the product of the value of a physical quantity that represents the load on a part of an injection molding machine during one shot and the number of shots, and detects the degree of deterioration of the part by integrating the amount of wear since the part was first used (for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2017-087587 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the life prediction method based on the amount of wear calculated for each shot, there is a problem in that the accuracy of predicting the life of the servo amplifier mounted on the molding machine is low.

[0005] The junction temperature of the semiconductor elements that make up the servo amplifier changes depending on the current flowing through the semiconductor element. This repeated temperature change causes the chip part of the semiconductor element to expand and contract repeatedly, which damages the servo amplifier. The amount of wear on the semiconductor element varies depending on how the molding machine is used, and it is difficult to accurately predict the lifespan of a servo amplifier using a lifespan prediction based on the amount of wear calculated on a shot-by-shot basis.

[0006] An object of the present disclosure is to provide a life prediction method, a life prediction device, and a computer program that can accurately predict the life of a servo amplifier that drives a servo motor provided in a component of a molding machine. [Means for solving the problem]

[0007] A life prediction method according to one embodiment of the present disclosure is a life prediction method for predicting the life of a servo amplifier that drives a servo motor provided in a component of a molding machine, which predicts the life of the servo amplifier by accumulating the number of processes required to drive the servo motor, detecting a physical quantity related to the load generated on the servo amplifier in one process, and multiplying the unit wear amount of the servo amplifier corresponding to the detected physical quantity by the accumulated number of processes.

[0008] A life prediction device according to one embodiment of the present disclosure is a life prediction device that predicts the life of a servo amplifier that drives a servo motor provided in a component of a molding machine, and is equipped with a detection unit that detects a physical quantity related to the load generated on the servo amplifier in one process, and a calculation unit, wherein the calculation unit calculates the number of processes that required driving the servo motor, and predicts the life of the servo amplifier by multiplying the unit wear amount of the servo amplifier corresponding to the detected physical quantity by the calculated number of processes.

[0009] A computer program according to one embodiment of the present disclosure is a computer program for causing a computer to execute a process for predicting the lifespan of a servo amplifier that drives a servo motor provided in a component of a molding machine, and causes the computer to execute a process for predicting the lifespan of the servo amplifier by accumulating the number of processes required to drive the servo motor, detecting a physical quantity related to the load generated on the servo amplifier in one process, and multiplying the unit wear amount of the servo amplifier corresponding to the detected physical quantity by the accumulated number of processes. [Effects of the Invention]

[0010] According to the present disclosure, it is possible to accurately predict the life of a servo amplifier that drives a servo motor provided in a component of a molding machine. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a schematic diagram showing a configuration example of an injection molding machine according to a first embodiment. [Figure 2] 1 is a schematic diagram showing an example of the configuration of a control device, a servo amplifier, and the like provided in an injection molding machine according to a first embodiment. [Figure 3] FIG. 10 is an explanatory diagram showing thermal fatigue life data of a servo amplifier. [Figure 4] 4 is a flowchart showing a processing procedure of a processor according to the first embodiment. [Figure 5] FIG. 2 is an explanatory diagram showing the configuration of a process DB according to the first embodiment. [Figure 6] 10 is a flowchart showing a processing procedure of a processor according to the second embodiment. [Figure 7] FIG. 10 is an explanatory diagram showing the configuration of a process DB according to the second embodiment. [Figure 8] FIG. 10 is a schematic diagram showing an example of the configuration of a control device, a servo motor, and the like provided in an injection molding machine according to a third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Specific examples of an injection molding machine (lifespan prediction device), a lifespan prediction method, and a computer program according to embodiments of the present invention will be described below with reference to the drawings. At least some of the embodiments described below may be combined in any manner. Note that the present invention is not limited to these examples, but is defined by the claims, and is intended to include all modifications within the meaning and scope of the claims.

[0013] (Embodiment 1) Fig. 1 is a schematic diagram showing a configuration example of an injection molding machine 1 according to embodiment 1, and Fig. 2 is a schematic diagram showing a configuration example of a control device 4 and servo amplifiers 11a, 12a, ... provided in the injection molding machine 1 according to embodiment 1. The injection molding machine 1 according to embodiment 1 includes a mold clamping device 2 that clamps a mold 21, an injection device 3 that melts and injects a molding material, and a control device 4. The control device 4 functions as a life prediction device according to embodiment 1.

[0014] The mold clamping device 2 includes a fixed platen 22 fixed on a bed 20, a mold clamping housing 23 provided slidably on the bed 20, and a movable platen 24 that similarly slides on the bed 20. The fixed platen 22 and the mold clamping housing 23 are connected by a plurality of, for example, four tie bars 25, 25, .... The movable platen 24 is configured to be slidable between the fixed platen 22 and the mold clamping housing 23. A mold clamping mechanism 26 is provided between the mold clamping housing 23 and the movable platen 24.

[0015] The mold clamping mechanism 26 is composed of, for example, a toggle mechanism. The mold clamping device 2 is equipped with a mold opening and closing servo motor 13, and the mold clamping mechanism 26 operates by the torque output by the mold opening and closing servo motor 13. The mold opening and closing servo motor 13 is driven by power supplied from a servo amplifier 13a. The fixed platen 22 and the movable platen 24 are provided with a fixed mold 21a and a movable mold 21b, respectively, and when the mold clamping mechanism 26 is driven, the mold 21 is opened and closed.

[0016] The mold clamping unit 2 is equipped with an ejector pin for ejecting and removing a molded product from a mold 21, a ball screw mechanism, and an ejector servomotor 14. The ejector pin is operated by torque output by the ejector servomotor 14. Specifically, the ejector servomotor 14 advances and retreats the ejector pin via a ball screw mechanism or the like. The ejector servomotor 14 is driven by power supplied from a servo amplifier 14a.

[0017] The injection device 3 is provided on a base 30. The injection device 3 includes a heating cylinder 31 having a nozzle 31a at its tip, and a screw 32 disposed within the heating cylinder 31 so as to be rotatable in the circumferential and axial directions. A heater for melting the molding material is provided inside or on the outer periphery of the heating cylinder 31. The screw 32 is driven by a drive device 33 in the rotational and axial directions.

[0018] The drive device 33 includes an injection servomotor 11 and a ball screw mechanism for driving the screw 32 in the axial direction. The drive device 33 also includes a screw rotation servomotor 12 for driving and rotating the screw 32. The injection servomotor 11 and the screw rotation servomotor 12 are driven by power supplied from servo amplifiers 11a and 12a, respectively.

[0019] A hopper 34 into which molding material is poured is provided near the rear end of the heating cylinder 31. The injection molding machine 1 also includes a nozzle touch device 35 that moves the unit of the injection device 3 in the front-to-rear direction (left-to-right direction in FIG. 1). The nozzle touch device 35 operates by torque output from the unit advance / retract servo motor 15. The unit advance / retract servo motor 15 is driven by power supplied from a servo amplifier 15a. When the nozzle touch device 35 is driven, the injection device 3 moves forward and the nozzle 31a of the heating cylinder 31 touches the contact portion of the fixed platen 22.

[0020] The servo amplifiers 11a, 12a, 13a, 14a, and 15a described above include, for example, inverter circuits. The servo amplifiers 11a, 12a, 13a, 14a, and 15a are configured with power switching elements such as IGBTs (Insulated Gate Bipolar Transistors), IPMs (Intelligent Power Modules), or power MOSFETs (Metal Oxide Semiconductor Field Effect Transistors), and convert DC power generated by the rectifier circuit back into AC power, supplying the required power to the injection servomotor 11, the screw rotation servomotor 12, the mold opening / closing servomotor 13, the ejector servomotor 14, and the unit advance / retract servomotor 15. The operations of the servo amplifiers 11a, 12a, 13a, 14a, and 15a are controlled by the control device 4.

[0021] The control device 4 is a computer that controls the operations of the mold clamping unit 2 and the injection unit 3, and includes a processor (arithmetic unit) 41, a storage unit 42, a signal input / output unit 43, and an operation panel 40 as its hardware configuration. The control device 4 also executes a life prediction method for predicting the life of the servo amplifiers 11a, 12a, .... The control device 4 may be a server device connected to a network. The control device 4 may also be configured to perform distributed processing using multiple computers, or may be realized by multiple virtual machines provided in a single server, or may be realized by using a cloud server.

[0022] The processor 41 includes an arithmetic circuit such as a central processing unit (CPU), a multi-core CPU, a graphics processing unit (GPU), a general-purpose computing on graphics processing units (GPGPU), a tensor processing unit (TPU), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a neural processing unit (NPU), an internal storage device such as a read-only memory (ROM) or a random access memory (RAM), an I / O terminal, a timer, etc. The processor 41 implements the life prediction method according to the first embodiment by executing a computer program (program product) 42a stored in a storage unit 42 (described later). Note that each functional unit of the control device 4 may be realized by software, or some or all of them may be realized by hardware.

[0023] The storage unit 42 is a non-volatile memory such as a hard disk, an EEPROM (Electrically Erasable Programmable ROM), or a flash memory. The storage unit 42 stores a computer program 5 for causing a computer to execute a life prediction process. The storage unit 42 also stores thermal fatigue life data 6 for predicting the life of the servo amplifiers 11a, 12a, .... The storage unit 42 also stores a process DB 7 that stores information regarding the amount of wear of the servo amplifiers 11a, 12a, ... for each process in a molding process cycle. The thermal fatigue life data 6 and the process DB 7 will be described in detail below.

[0024] The computer program 5 according to the first embodiment may be recorded on a recording medium 50 in a computer-readable manner. The storage unit 42 stores the computer program 5 read from the recording medium 50 by a reading device. The recording medium 50 is a semiconductor memory such as a flash memory. The recording medium 50 may also be an optical disc such as a CD (Compact Disc)-ROM, a DVD (Digital Versatile Disc)-ROM, or a BD (Blu-ray (registered trademark) Disc). Furthermore, the recording medium 50 may also be a magnetic disc such as a flexible disk or a hard disk, a magneto-optical disc, or the like.

[0025] Furthermore, the computer program 5 according to the first embodiment may be downloaded from an external server connected to a communication network and stored in the storage unit .

[0026] The signal input / output unit 43 outputs control signals to the injection molding machine 1 to control the operation of the injection molding machine 1 in accordance with the control of the processor 41 based on the molding conditions. For example, the signal input / output unit 43 outputs drive control signals to servo amplifiers 11a, 12a, ... to control the operation of the injection servomotor 11, the screw rotation servomotor 12, the mold opening / closing servomotor 13, the ejector servomotor 14, and the unit advance / retract servomotor 15.

[0027] The injection molding machine 1 is equipped with current sensors 11b, 12b, 13b, 14b, and 15b that detect currents flowing through the servo amplifiers 11a, 12a, 13a, 14a, and 15a. The current sensors 11b, 12b, and so on are connected to the signal input / output unit 43, and a current value signal indicating the current value detected by each of the current sensors 11b, 12b, and so on is input.

[0028] The injection molding machine 1 also includes an ambient temperature sensor 16 that detects the ambient temperature. The ambient temperature sensor 16 is connected to the signal input / output unit 43, and a temperature signal indicating the temperature detected by the ambient temperature sensor 16 is input. The signal input / output unit 43 converts the input current value signal and temperature signal into current value data and ambient temperature data via analog-to-digital conversion, and provides them to the processor 41 .

[0029] The operation panel 40 is an interface for setting molding conditions for the injection molding machine 1 and for operating the injection molding machine 1. The operation panel 40 includes a display panel 40a and an operation unit 40b.

[0030] The display panel 40a is a display device such as a liquid crystal display panel or an organic EL display panel, and receives settings of molding conditions for the injection molding machine 1 under the control of the processor 41. The display panel 40a also displays the lifespans of the servo amplifiers 11a, 12a, ... predicted by the lifespan prediction method according to the first embodiment.

[0031] The operation unit 40b is an input device for inputting and adjusting molding conditions for the injection molding machine 1, and includes operation buttons, a touch panel, etc. The operation unit 40b provides the processor 41 with data indicating the received molding conditions.

[0032] The injection molding machine 1 is set with set values ​​that define molding conditions such as the injection start time, resin temperature in the mold, nozzle temperature, cylinder temperature (heater temperature), hopper temperature, mold clamping force, injection speed, injection acceleration, injection peak pressure (injection pressure), and injection stroke.

[0033] In addition, the injection molding machine 1 is set with set values ​​that determine molding conditions such as cylinder tip resin pressure, backflow prevention ring seating state, dwell pressure, dwell pressure switching speed, dwell pressure switching position, dwell pressure completion position, cushion position, metering back pressure, and metering torque.

[0034] Furthermore, set values ​​that determine molding conditions such as the metering completion position, screw retraction speed, cycle time, mold closing time, injection time, pressure dwell time, metering time, mold opening time, etc. are set in the injection molding machine 1. Then, once these set values ​​have been set, the injection molding machine 1 operates in accordance with these set values.

[0035] The molding process cycle is outlined below. Injection molding involves the well-known mold closing process, mold clamping process, injection unit advancement process, injection process, measurement process, mold opening process, and ejection process, which are carried out in this order. The control device 4 detects the current flowing through each servo amplifier and the ambient temperature for each process, and stores the detected current value data and ambient temperature data in the process DB 7. The control device 4 then predicts the lifespan of each servo amplifier 11a, 12a, ...

[0036] In the mold closing process and mold clamping process, the control device 4 drives the mold opening / closing servomotor 13. By driving the mold opening / closing servomotor 13, the mold 21 closes and the movable mold 21b comes into close contact with the fixed mold 21a.

[0037] In the injection unit advancement process, the control device 4 drives the unit advancement / retraction servomotor 15. By driving the unit advancement / retraction servomotor 15, the injection device 3, which is the injection unit, advances toward the mold 21, and the nozzle 31a of the injection device 3 comes into close contact with the contact portion of the mold 21.

[0038] In the injection process, the control device 4 drives the injection servo motor 11. Driving the injection servo motor 11 moves the screw 32 forward, and the molten molding material stored in the tip region of the heating cylinder 31 is injected from the nozzle 31a into and fills the mold 21. The screw 32 moves to a set holding pressure switching position, whereby a fixed amount of molding material is injected into the mold 21.

[0039] In the metering process, the control device 4 drives the injection servomotor 11 and the screw rotation servomotor 12. The molding material supplied from the hopper 34 to the heating cylinder 31 is heated by the heater, plasticized by the shearing action of the screw 32 rotated by the drive of the screw rotation servomotor 12, and fed into the tip region of the heating cylinder 31. Driven by the injection servomotor 11, the screw 32 gradually moves backward as the plasticized molding material accumulates in the tip region of the screw 32. The screw 32 moves to a set metering completion position, and a fixed amount of molding material is metered.

[0040] In the mold opening step, the control device 4 drives the mold opening / closing servomotor 13. By driving the mold opening / closing servomotor 13, the mold 21 opens.

[0041] In the ejection step, the control device 4 drives the ejector servomotor 14. By driving the ejector servomotor 14, the ejector pin operates, and the molded product is removed from the mold 21.

[0042] FIG. 3 is an explanatory diagram showing thermal fatigue life data 6 of servo amplifiers 11a, 12a, .... The horizontal axis indicates the temperature change dT of the junction of the semiconductor elements that make up the servo amplifiers 11a, 12a, ..., and the vertical axis indicates the number of repetitions to failure. The thermal fatigue life data 6 is data showing the relationship between the temperature change dT of the semiconductor elements and the number of repetitions to failure. The number of repetitions to failure is the number of times that a semiconductor element is estimated to be damaged when a certain temperature change dT occurs repeatedly. The reciprocal of the number of repetitions to failure corresponds to the load applied to the semiconductor element or the amount of wear of the semiconductor element.

[0043] Since the relationship between the temperature change dT of the semiconductor element and the number of repeated damage events also depends on the ambient temperature of the semiconductor element or the servo amplifiers 11a, 12a, ..., the memory unit 42 stores the relationship between the temperature change dT at a plurality of ambient temperatures and the number of repeated damage events. For example, the memory unit 42 stores the relationship between the temperature change dT at 25°C, 75°C, and 150°C and the number of repeated damage events.

[0044] 4 is a flowchart showing the processing procedure of the processor 41 according to embodiment 1. The processor 41 drives the injection servomotor 11, the screw rotation servomotor 12, the mold opening / closing servomotor 13, the ejector servomotor 14, or the unit advance / retract servomotor 15 according to the content of each step in the molding process cycle (step S111).

[0045] Then, the processor 41 detects the current flowing through the driven servo amplifiers 11a, 12a, ... for each step (step S112). The processor 41 also detects the ambient temperature (step S113).

[0046] Next, the processor 41 stores in the process DB 7, for each process, an amplifier ID for identifying the driven servo amplifier 11a, 12a, ..., current value data indicating the current value flowing through the servo amplifier 11a, 12a, ... indicated by the amplifier ID, and ambient temperature data indicating the ambient temperature at that time, in association with the data ID (step S114).

[0047] FIG. 5 is an explanatory diagram showing the configuration of the process DB 7 according to the first embodiment. The process DB 7 is a database in which data IDs, amplifier IDs, current value data, and ambient temperature data are associated with each other. The data IDs are serial numbers or the like for identifying each record. The amplifier IDs are IDs for identifying the servo amplifiers 11a, 12a, 13a, 14a, and 15a. The current value data are current value data indicating the current value flowing through the servo amplifiers 11a, 12a, ... indicated by the amplifier IDs. The ambient temperature data are data indicating the ambient temperature when the current value was detected. Records containing these data are registered for each process of the molding process cycle and for each servo amplifier 11a, 12a, ...

[0048] The processor 41 reads out records registered in the process DB 7 and calculates the amount of temperature change occurring in the servo amplifiers 11a, 12a, ... in each process (step S115). Specifically, the processor 41 reads out current value data for each of the servo amplifiers 11a, 12a, and calculates the amount of temperature change occurring in the servo amplifiers 11a, 12a, ... based on the read current value data. For example, the amount of temperature change occurring in the servo amplifiers 11a, 12a, ... is expressed by the following formula (1). The amount of temperature change occurring in the servo amplifiers 11a, 12a, ... is, for example, the amount of temperature change occurring at the junction of a semiconductor element.

[0049] ΔT=(ab×e -t / τ )×I…(1) however, ΔT: Amount of temperature change occurring at the junctions of the semiconductor elements that make up the servo amplifiers 11a, 12a, ... a, b: Coefficients determined by the characteristics of the servo amplifiers 11a, 12a, ... and the heat dissipation members τ: time constant determined by the characteristics of the servo amplifiers 11a, 12a, ... and the heat dissipation member t: time I: Current value flowing through servo amplifiers 11a, 12a, ...

[0050] Although an example of calculating the temperature change amount based on current value data has been described, the current value for each process may be calculated using other statistical quantities such as the average or maximum current value to determine the temperature change amount. Alternatively, a table that associates current data with the amount of temperature change may be stored in the storage unit 42, and the processor 41 may convert the current into the amount of temperature change by referring to the table.

[0051] Next, the processor 41 calculates the unit consumption amount of each of the servo amplifiers 11a, 12a, ... (step S116). Specifically, the processor 41 uses the temperature change amount as a key to read out the corresponding number of repetitions to failure from the thermal fatigue life data 6. The reciprocal of the read number of repetitions to failure is the unit consumption amount.

[0052] The processing of step S116 calculates the unit wear amount of the servo amplifier 11a of the injection servo motor 11 and the unit wear amount of the servo amplifier 12a of the screw rotation servo motor 12. The processing of step S116 also calculates the unit wear amount of the servo amplifier 13a of the mold opening / closing servo motor 13 and the unit wear amount of the servo amplifier 14a of the ejector servo motor 14. Furthermore, the processing of step S116 calculates the unit wear amount of the servo amplifier 15a of the unit advance / retract servo motor 15.

[0053] Then, the processor 41 reads out the records registered in the process DB 7 and calculates the total number of processes for each of the servo amplifiers 11a, 12a, ... (step S117). That is, the processor 41 adds up the number of operations of the servo amplifiers 11a, 12a, ... for each of the servo amplifiers 11a, 12a, ....

[0054] Next, the processor 41 calculates the wear amount of each of the servo amplifiers 11a, 12a, ... by multiplying the unit wear amount calculated in steps S116 and S117 by the total number of processes (step S118). Note that the processor 41 may also calculate the wear amount by dividing the total number of processes by the number of repeated breakages. The wear amount is expressed, for example, by the following formula (2).

[0055] Wear amount = total number of processes × unit wear rate = total number of processes / number of breakages…(2)

[0056] Next, the processor 41 calculates the lifespan of each of the servo amplifiers 11 a, 12 a, ... based on the calculated amount of wear (step S119), and then ends the process. For example, the remaining useful life, etc. can be obtained by subtracting the amount of wear calculated in step S118 from the amount of wear corresponding to the average lifespan of the servo amplifiers 11 a, 12 a, ... The processor 41 displays the calculated results of the life expectancy prediction of each of the servo amplifiers 11a, 12a, . . . on the display panel 40a.

[0057] As described above, the control device 4 according to the first embodiment is configured to calculate the unit wear amount of the servo amplifiers 11a, 12a, ... for each process, and therefore can accurately predict the lifespan of the servo amplifiers 11a, 12a, ....

[0058] Furthermore, the control device 4 can predict the life of the servo amplifiers 11a, 12a, . . . taking into consideration the ambient temperature.

[0059] The time t during which current flows through each of the servo amplifiers 11a, 12a, ... is stored as a known value determined by the molding process cycle in the storage unit 42. The time t during which current flows in each process may be measured, and the measured time t may be stored in the process DB 7 in association with the process ID.

[0060] In the first embodiment, an example has been described in which the amount of temperature change is calculated from the current flowing through the servo amplifiers 11a, 12a, ..., but a temperature sensor that detects the temperature of the servo amplifiers 11a, 12a, ... may be provided. Specifically, the processor 41 stores temperature data indicating the temperatures of the servo amplifiers 11a, 12a, ... at the start and end of each process in the process DB 7. The processor 41 can calculate the amount of temperature change occurring in the servo amplifiers 11a, 12a, ... based on the temperature data at the start and end of each process detected using the temperature sensor.

[0061] (Embodiment 2) The injection molding machine 1 according to the second embodiment differs from the first embodiment in the calculation processing method of the life prediction method and the contents of the process DB 7. The other configurations of the injection molding machine 1 are the same as those of the injection molding machine 1 according to the first embodiment, so the same parts are denoted by the same reference numerals and detailed description will be omitted.

[0062] 6 is a flowchart showing the processing procedure of the processor 41 according to the embodiment 2. As in the embodiment 1, the processor 41 according to the embodiment 2 drives various servo motors (step S211), detects the current flowing through each of the servo amplifiers 11 a, 12 a, ... in units of steps (step S212), and detects the ambient temperature (step S213).

[0063] Next, the processor 41 stores, for each process, the amplifier ID, the type of process, the current value data, and the ambient temperature data in association with the data ID in the process DB 7 (step S214).

[0064] 7 is an explanatory diagram showing the configuration of the process DB 7 according to the second embodiment. The process DB 7 according to the second embodiment is a database that associates process types with data IDs, amplifier IDs, current value data, and ambient temperature data. The process types include, for example, a mold closing process, a mold clamping process, an injection unit forward movement process, a primary injection process, a secondary injection process, a measuring process, a screw backward movement process, a mold opening process, an ejection process, and an ejection-back process.

[0065] Then, the processor 41 reads out the records registered in the process DB 7, and calculates the amount of temperature change occurring in the servo amplifiers 11a, 12a, . . . in each process for each type of process (step S215).

[0066] Next, the processor 41 calculates the unit consumption amount of each of the servo amplifiers 11a, 12a, . . . for each type of process (step S216).

[0067] Then, the processor 41 reads out the records registered in the process DB 7 and calculates the total number of processes of each of the servo amplifiers 11 a, 12 a, ... for each process type (step S217). That is, the processor 41 adds up the number of operations of the servo amplifiers 11 a, 12 a, ... for each servo amplifier 11 a, 12 a, ... and for each process type.

[0068] Next, the processor 41 multiplies the unit consumption amount calculated in steps S216 and S217 by the total number of processes for each type of process, and calculates the sum of the multiplied values ​​to calculate the consumption amount of each of the servo amplifiers 11a, 12a, ... (step S218). The consumption amount is expressed, for example, by the following formula (3).

[0069] Consumption amount = Σ (total number of processes in the nth process × unit consumption rate in the nth process) =Σ(total number of processes in the nth process / number of repeated breakages in the nth process)…(3)

[0070] Note that Σ means calculating the sum of the consumption amounts calculated for each type of process. For example, if there are N types of processes, the consumption amount is expressed as follows: total number of processes in the first process × unit consumption rate of the first process + total number of processes in the second process × unit consumption rate of the second process + ... total number of processes in the Nth process × unit consumption rate of the Nth process.

[0071] The processor 41 can also calculate the consumption amount using the following formula (4): Σ means that the sum of the consumption amounts calculated for each unit process is calculated. Wear amount = Σ Unit wear rate = Σ (1 / number of repeated breakages)…(4)

[0072] Next, the processor 41 calculates the life of each of the servo amplifiers 11a, 12a, . . . in the same manner as in the first embodiment (step S219), and ends the process.

[0073] According to the injection molding machine 1 of this embodiment 2, the unit wear amount of the servo amplifiers 11a, 12a, ... is calculated on a process-by-process basis for each process type, so that the lifespan of the servo amplifiers 11a, 12a, ... can be predicted more accurately.

[0074] (Embodiment 3) The injection molding machine 1 according to the third embodiment differs from the first and second embodiments in the calculation processing method of the life prediction method and the physical quantities used for the life prediction. The other configurations of the injection molding machine 1 are the same as those of the injection molding machines 1 according to the first and second embodiments, so the same reference numerals are used for the same parts and detailed descriptions are omitted.

[0075] 8 is a schematic diagram showing an example of the configuration of the control device 4, servo motors, etc. provided in the injection molding machine 1 according to embodiment 3. Of the servo amplifiers 11a, 12a, etc., only the servo amplifier 11a that drives the injection servo motor 11 is shown, but the other servo amplifiers 12a, etc. have the same configuration.

[0076] The servo amplifier 11a includes a semiconductor element 11d enclosed in a package 11c. The package 11c of the servo amplifier 11a is disposed on a heat sink 11e for dissipating heat from the package 11c to the outside. The package 11c and the heat sink 11e are thermally connected, and the heat from the package 11c is conducted to the heat sink 11e.

[0077] The injection molding machine 1 according to the third embodiment includes a first temperature sensor 11f that detects the temperature of the package 11c and a second temperature sensor 11g that detects the temperature of the heat sink 11e. The processor 41 acquires temperature data from the first temperature sensor 11f and the second temperature sensor 11g via the signal input / output unit 43.

[0078] The processing contents of the processor 41 are the same as those in the first or second embodiment. However, in step S112 or step S212, the processor 41 detects the temperatures of the servo amplifiers 11a, 12a, ... at the start and end of each step.

[0079] In step S114 or step S214, the processor 41 stores the temperature data instead of the current value data.

[0080] In step S115 or step S215, the processor 41 reads out records registered in the process DB 7 and calculates the amount of temperature change at the junction of the semiconductor element 11d based on the temperature data of the servo amplifiers 11a, 12a, ... at the start and end of each process. For example, the amount of temperature change at the junction of the semiconductor element 11d is expressed by the following equation (5).

[0081] ΔT = ΔT1 + ΔT2…(5) however, ΔT1: Amount of temperature change in package 11c occurring in one process ΔT2: Amount of temperature change of the heat sink 11e occurring in one process

[0082] The above formula (5) is an example, and the method for calculating the temperature change amount of the junction of the semiconductor element 11d is not particularly limited as long as it is a function of the temperature change amounts ΔT1 and ΔT2. The other processes are the same as those in the first and second embodiments, and therefore detailed description thereof will be omitted.

[0083] According to the injection molding machine 1 of embodiment 3, the lifespan of the servo amplifiers 11a, 12a, ... can be predicted more accurately by estimating the temperature of the junction of the semiconductor element 11d taking into account the temperatures of the packages 11c and heat sinks 11e of the servo amplifiers 11a, 12a, .... [Explanation of symbols]

[0084] 1 injection molding machine 2 Mold clamping device 3 Injection device 4. Control device (life prediction device) 5. Computer Programs 6 Thermal fatigue life data 7 Process DB 11 Injection servo motor 12. Screw rotation servo motor 13 Servo motor for mold opening and closing 14 Ejector servo motor 15 Servo motor for unit movement 11a, 12a, 13a, 14a, 15a Servo amplifier 11b, 12b, 13b, 14b, 15b Current sensors 11c package 11d Semiconductor elements 11e heat sink 41 processors 50 Recording Media

Claims

1. A life prediction method for predicting the life of a servo amplifier that drives a servo motor provided in a component of a molding machine, comprising: The number of steps that require the servo motor to be driven is calculated. Detecting a physical quantity related to a load generated on the servo amplifier in one process; The life of the servo amplifier is predicted by multiplying the unit consumption amount of the servo amplifier corresponding to the detected physical quantity by the integrated number of processes. Life expectancy prediction method.

2. the physical quantity is a current flowing through the servo amplifier or a temperature, Calculating the temperature change amount of the servo amplifier in the step based on the detected current or temperature; by referring to thermal fatigue life data in which the temperature change amount of the servo amplifier is associated with information relating to the unit consumption amount of the servo amplifier, the unit consumption amount corresponding to the temperature change amount is identified; The specified unit consumption amount is multiplied by the accumulated number of processes to predict the life of the servo amplifier. The life prediction method according to claim 1 .

3. The thermal fatigue life data is a plurality of relationships between the temperature change amount of the servo amplifier and information relating to the unit consumption amount of the servo amplifier are stored for each ambient temperature; Detects the ambient temperature, The thermal fatigue life data is referenced based on the detected ambient temperature and the calculated temperature change amount, thereby specifying the unit consumption amount corresponding to the temperature change amount. The life prediction method according to claim 2 .

4. the servo amplifier includes a semiconductor element mounted on a heat sink, Detecting the temperature of the package of the semiconductor element and the temperature of the heat sink; calculating a temperature change amount of the junction of the semiconductor element based on the temperature change amount of the package and the temperature change amount of the heat sink in the process; The life of the servo amplifier is predicted by multiplying the unit consumption amount corresponding to the temperature change amount of the junction by the integrated number of processes. The life prediction method according to claim 2 or 3.

5. the molding process cycle includes at least a first process and a second process that require driving the servo motor, The number of steps in the first step and the number of steps in the second step are respectively added up, Detecting a physical quantity related to a load generated on the servo amplifier in the first step; Detecting a physical quantity related to a load generated on the servo amplifier in the second step; The life of the servo amplifier is predicted by adding a multiplied value obtained by multiplying the unit wear amount of the servo amplifier corresponding to the detected physical quantity related to the first process by the integrated number of processes in the first process, and a multiplied value obtained by multiplying the unit wear amount of the servo amplifier corresponding to the detected physical quantity related to the second process by the integrated number of processes in the second process. The life prediction method according to any one of claims 1 to 4.

6. the molding machine includes a plurality of servo motors; Detects the number of processes and physical quantities for each of multiple servo motors to predict the lifespan of each servo motor The life prediction method according to any one of claims 1 to 5.

7. A life prediction device that predicts the life of a servo amplifier that drives a servo motor provided in a component of a molding machine, a detection unit that detects a physical quantity related to a load generated on the servo amplifier in one process; Calculation unit and Equipped with The calculation unit The number of steps that require the servo motor to be driven is calculated. The life of the servo amplifier is predicted by multiplying the unit consumption amount of the servo amplifier corresponding to the detected physical quantity by the integrated number of processes. Lifespan prediction device.

8. A computer program for causing a computer to execute a process for predicting the life of a servo amplifier that drives a servo motor provided in a component of a molding machine, The number of steps that require the servo motor to be driven is calculated. Detecting a physical quantity related to a load generated on the servo amplifier in one process; The life of the servo amplifier is predicted by multiplying the unit consumption amount of the servo amplifier corresponding to the detected physical quantity by the integrated number of processes. A computer program for causing the computer to execute a process.

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