WO2023002946A1 - 射出成形機の良否判定システム - Google Patents
射出成形機の良否判定システム Download PDFInfo
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- WO2023002946A1 WO2023002946A1 PCT/JP2022/027867 JP2022027867W WO2023002946A1 WO 2023002946 A1 WO2023002946 A1 WO 2023002946A1 JP 2022027867 W JP2022027867 W JP 2022027867W WO 2023002946 A1 WO2023002946 A1 WO 2023002946A1
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- injection molding
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- 238000001746 injection moulding Methods 0.000 title claims abstract description 124
- 238000012544 monitoring process Methods 0.000 claims abstract description 446
- 230000002950 deficient Effects 0.000 claims abstract description 162
- 238000000465 moulding Methods 0.000 claims abstract description 106
- 230000007547 defect Effects 0.000 claims abstract description 104
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Images
Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
- B29C45/17—Component parts, details or accessories; Auxiliary operations
- B29C45/76—Measuring, controlling or regulating
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
- B29C45/17—Component parts, details or accessories; Auxiliary operations
- B29C45/76—Measuring, controlling or regulating
- B29C45/768—Detecting defective moulding conditions
Definitions
- the present invention relates to a quality judgment system for an injection molding machine that injects a molten resin material to mold a molded product.
- allowable values are set for each monitoring data such as pressure and temperature obtained from sensors for each shot, and the obtained monitoring data and allowable values are compared. By doing so, the quality of the molded product can be determined. For example, if the barrel temperature deviates from the tolerance set for the standard, the molded product from that shot is determined to be defective.
- the parameters in molding with an injection molding machine may cause interaction with the molded product. There is a possibility that it may become difficult to perform determination appropriately. In addition, if a defect occurs due to the interaction of parameters during molding with an injection molding machine, it becomes difficult to identify the cause of the defect. may be difficult to adjust. For this reason, there is room for improvement in terms of improving judgment accuracy and identifying the cause of defects in the judgment of good or bad when molding is performed by an injection molding machine.
- a quality determination system for an injection molding machine provides a determination result as to whether a molded product molded by an injection molding machine is a good product or a defective product.
- the monitoring data at the time of good product which is the monitoring data at the time of molding the good product, and the monitoring data at the time of molding the defective product
- a monitoring data extracting unit for extracting a plurality of pieces of monitoring data at the time of failure, which is the monitoring data;
- a high-impact monitoring data extracting unit that extracts a plurality of monitoring data in order from the above as high-impact monitoring data, and determines whether the molded product is good or bad during molding by the injection molding machine using the same type as the high-impact monitoring data.
- a molding parameter calculation unit that calculates a molding parameter that is a parameter when performing based on the monitoring data, the molding parameter calculated by the molding parameter calculation unit, and a determination threshold that is a threshold value for the molding parameter. and a good/bad judging unit for judging whether or not the defective product has occurred in the molded product molded by the injection molding machine.
- the quality determination system for an injection molding machine has the effect of improving the accuracy of quality determination and more reliably identifying the cause of a defect.
- FIG. 1 is a schematic diagram showing a configuration example of a quality determination system for an injection molding machine according to an embodiment.
- FIG. 2 is an explanatory diagram of the control device shown in FIG.
- FIG. 3 is an explanatory diagram of a monitoring data determination screen.
- FIG. 4 is a flowchart showing the flow of control when determining whether or not monitoring data satisfies a predetermined value.
- FIG. 5 is a flowchart showing the procedure for generating basic data.
- FIG. 6 is a flowchart showing a procedure for setting parameters used for determining whether a molded product is good or bad.
- FIG. 7 is a schematic diagram of a pass/fail judgment setting parent screen used when setting parameters for pass/fail judgment.
- FIG. 1 is a schematic diagram showing a configuration example of a quality determination system for an injection molding machine according to an embodiment.
- FIG. 2 is an explanatory diagram of the control device shown in FIG.
- FIG. 3 is an explanatory diagram of a monitoring
- FIG. 8 is a schematic diagram of the quality judgment setting child screen.
- FIG. 9 is an explanatory diagram of grouping of monitoring data.
- FIG. 10 is an explanatory diagram of the pass/fail judgment screen.
- FIG. 11 is a flow chart showing the flow of control when performing control to determine whether or not a defective molded product has occurred based on monitoring data.
- FIG. 1 is a schematic diagram showing a configuration example of a quality determination system 200 for an injection molding machine 1 according to an embodiment.
- the up-down direction of the injection molding machine 1 in normal use will be described as the up-down direction of the injection molding machine 1 as well, and the horizontal direction in the normal use of the injection molding machine 1 will be referred to as the injection molding machine. 1 is also described as the horizontal direction.
- the injection molding machine 1 has an injection device 10 and a mold clamping device 30.
- the injection device 10 and the mold clamping device 30 are connected to the frame 2 arranged at the lower end of the injection molding machine 1. placed on top.
- the injection molding machine 1 melts the molding material into a plasticized material by the injection device 10, and cools and solidifies the plasticized material injected from the injection device 10 by the mold clamping device 30, thereby performing various desired moldings. It is possible to manufacture goods.
- the injection device 10 includes a heating barrel 11 , a screw 13 , a weighing section 20 and an injection device driving section 25 .
- the heating barrel 11 is capable of heating and melting the molding material inside to turn it into a plasticized material.
- the heating barrel 11 has a nozzle 12 for injecting the plasticizing material on one end side, and the other end side is connected to a hopper 15 for charging raw materials.
- the screw 13 is arranged in the heating barrel 11 and is axially movable inside the heating barrel 11 .
- the weighing unit 20 can introduce resin, which is a molding material, from the hopper 15 into the heating barrel 11 .
- the injection device driving section 25 is capable of moving the screw 13 horizontally within the heating barrel 11 .
- the injection device drive unit 25 moves the screw 13 toward the nozzle 12 while the molten molding material is stored in the end portion of the heating barrel 11 where the nozzle 12 is located. , the molding material can be extruded from the nozzle 12 . Thereby, the molding material in the heating barrel 11 can be injected from the nozzle 12 .
- the mold clamping device 30 has a fixed platen 31 , a movable platen 32 , a mold clamping drive mechanism 40 and an extrusion mechanism 45 .
- the fixed platen 31 is arranged on the frame 2 and fixed to the frame 2.
- the movable platen 32 is arranged on the side of the fixed platen 31 on the frame 2 opposite to the side where the injection device 10 is located. are arranged movably with respect to the A fixed mold 35 is attached to the surface of the fixed platen 31 on which the movable platen 32 is positioned, and a movable mold 36 is attached to the surface of the movable platen 32 on which the fixed platen 31 is positioned.
- a movable mold 36 attached to the movable platen 32 faces a fixed mold 35 attached to the fixed platen 31, and when the movable platen 32 approaches the fixed platen 31, it approaches the fixed mold 35. It is combined with the fixed mold 35 .
- the mold clamping drive mechanism 40 is capable of moving the movable platen 32 relative to the fixed platen 31 , and by moving the movable platen 32 relative to the fixed platen 31 , the movable mold 36 is fixed. It is possible to close the mold 35 and to open the movable mold 36 and the fixed mold 35 .
- the mold clamping drive mechanism 40 includes a so-called toggle mechanism 41 , and the toggle mechanism 41 can move the movable platen 32 relative to the fixed platen 31 .
- the extrusion mechanism 45 is equipped with an extrusion member 46 that pushes out the molded product adhered to the inner surface of the moving mold 36, so that the molded product after molding can be removed from the moving mold 36.
- the injection molding machine 1 has a control device 100 that performs various controls of the injection molding machine 1, an input section 160 that allows an operator to perform input operations to the injection molding machine 1, and a display section 170 that displays various information.
- a control device 100 that performs various controls of the injection molding machine 1
- an input section 160 that allows an operator to perform input operations to the injection molding machine 1
- a display section 170 that displays various information.
- the input unit 160 and the display unit 170 are connected to the control device 100 , and the input unit 160 transmits information to the control device 100 that has been input.
- the display unit 170 displays information transmitted from the control device 100 .
- the input unit 160 and the display unit 170 may be configured separately, or may be integrally formed by configuring a so-called touch panel type display.
- the control device 100 is connected to various actuators such as motors that serve as power sources for the operation of the injection molding machine 1, various sensors that acquire information during operation of the injection molding machine 1, and the like. As a result, the control device 100 can control the injection molding machine 1 by transmitting control signals to the actuators of the injection molding machine 1 while acquiring information on the operation of the injection molding machine 1 from the sensor. is possible.
- FIG. 2 is an explanatory diagram of the control device 100 shown in FIG.
- the control device 100 has a processing section 110 , a storage section 140 and an input/output section 150 .
- the processing unit 110 has a CPU (Central Processing Unit) that performs arithmetic processing, and a RAM (Random Access Memory) and ROM (Read Only Memory) that function as memories for storing various information. All or part of each function of the processing unit 110 is realized by reading and writing data in the RAM and ROM by loading an application program held in the ROM into the RAM and executing it by the CPU.
- CPU Central Processing Unit
- RAM Random Access Memory
- ROM Read Only Memory
- the storage unit 140 is a storage device that is electrically connected to the processing unit 110 and stores information.
- the control device 100 controls the injection molding machine 1
- information acquired from the injection molding machine 1 by the processing unit 110 and information calculated by the processing unit 110 are stored in the storage unit 140 or stored in the storage unit 140.
- the information is called by the processing unit 110 and used for controlling the injection molding machine 1 .
- Each function realized by the processing unit 110 may be stored in the storage unit 140 in advance as a program.
- the processing unit 110 executes each function by calling the program stored in the storage unit 140 and executing the operation in accordance with the program.
- the storage unit 140 may be provided integrally with the control device 100 or may be detachably attached to the control device 100 .
- the input/output unit 150 is a so-called interface for inputting/outputting signals between the control device 100 and external devices. That is, various actuators and various sensors of the injection molding machine 1 connected to the control device 100 , the input section 160 and the display section 170 are connected to the input/output section 150 .
- the processing unit 110 of the control device 100 transmits and receives signals to and from these external devices via the input/output unit 150 .
- the processing unit 110 functionally includes a monitoring data acquisition unit 111, a reference value acquisition unit 112, an allowable value acquisition unit 113, a monitoring data determination unit 114, a basic data generation unit 121, a monitoring data extraction unit 122, It has a high-impact monitoring data extraction unit 123, a judgment parameter calculation unit 124, a molding parameter calculation unit 125, a recommended value calculation unit 126, a quality judgment unit 127, and an abnormal data extraction unit 128. .
- the monitoring data acquisition unit 111 is capable of acquiring monitoring data, which are detection results of various sensors of the injection molding machine 1, when the injection molding machine 1 is in operation.
- the monitoring data includes, for example, the temperature at which the molding material is melted in the heating barrel 11 of the injection device 10 of the injection molding machine 1, and the time spent introducing the molding material into the heating barrel 11 and measuring the molding material. , the rotation speed of the screw 13, and the like.
- the monitoring data acquisition unit 111 stores the acquired monitoring data in the storage unit 140 together with the acquisition time. That is, the monitoring data acquisition unit 111 stores the acquired monitoring data in the storage unit 140 in association with the acquired date and time.
- the reference value acquisition unit 112 acquires the reference value of the monitoring data during operation of the injection molding machine 1 that is input by the user using the injection molding machine 1 using the input unit 160 .
- the reference value acquiring unit 112 stores the acquired reference value of the monitoring data in the storage unit 140 .
- the allowable value acquisition unit 113 acquires the allowable value for the reference value of the monitoring data when the injection molding machine 1 is in operation, which is input by the user using the injection molding machine 1 using the input unit 160 .
- the allowable value obtaining unit 113 stores the obtained allowable value for the reference value of the monitoring data in the storage unit 140 .
- the monitoring data determination unit 114 compares the monitoring data acquired by the monitoring data acquisition unit 111, the reference value acquired by the reference value acquisition unit 112, and the allowable value by the allowable value acquisition unit 113, and determines that the monitoring data is within the allowable value. It is determined whether or not it is within the range.
- the basic data generation unit 121 generates the determination result as to whether the molded product molded by the injection molding machine 1 is good or defective, and the monitoring data of the injection molding machine 1 when the molded product is molded.
- Basic data associated with the monitoring data acquired by the data acquisition unit 111 is generated.
- the basic data generated by the basic data generating unit 121 is stored in the storage unit 140 in association with the date and time when the monitoring data is acquired or the date and time when the molded product is molded.
- the monitoring data extraction unit 122 extracts, from the basic data generated by the basic data generation unit 121, monitoring data for non-defective products, which is monitoring data for molding non-defective products, and monitoring data for defective products, which is monitoring data for molding defective products. Extract multiple of each. In other words, since the basic data is linked to the determination result of the molded product molded by the injection molding machine 1 and the monitoring data when the molded product was molded, the determination result of the molded product and the evaluation result of each molded product are linked. Monitoring data for non-defective products and monitoring data for defective products are separately extracted from monitoring data during molding.
- the monitoring data extracting unit 122 extracts the same type of monitoring data for non-defective products and monitoring data for defective products for each type of defective product. In other words, the monitoring data extraction unit 122 extracts the monitoring data for non-defective products and the monitoring data for defective products in association with the defect type of the defective product.
- the non-defective monitoring data and the defective monitoring data extracted by the monitoring data extraction unit 122 are respectively stored in the storage unit 140 .
- defect types here include shorts, burrs, flow marks, silver streaks, jetting, burns, clouds, whitening, weld lines, warping, cracks, yellowing, sink marks, voids, and the like.
- a short circuit is a defect in which the resin is not completely filled.
- a burr is a defect in which a surplus portion is generated on the periphery of a molded product due to molten material entering the gap of the mold.
- a flow mark is a defect that appears on the surface of a molded product in the form of striped patterns caused by the resin that has flowed through the mold.
- a silver streak is a defect in which white streaks are generated along the flow of the resin starting from the gate portion, which is the opening to the flow path of the resin for the molded product in the mold.
- Jetting is a defect in which traces of resin flow appear after passing through the gate, and burning is a defect in which the end of the resin is scorched.
- Cloudiness is a defect in which the surface becomes white and cloudy, especially in the molding of transparent resin.
- Whitening is a defect in which solidified resin is locally elongated due to excessive force applied thereto.
- a weld line is a linear trace defect that appears at a portion where flow fronts branched by the mold cavity shape meet.
- a warp is a defect in which the molded product warps.
- a crack is a defect in which cracks occur on the surface of a molded product.
- Yellowing is a defect in which the surface of the resin turns yellow.
- a sink mark is a defect in which the surface portion becomes concave due to mold shrinkage.
- a void is a defect in which a vacuum is generated inside due to mold shrinkage.
- the high-impact monitoring data extraction unit 123 extracts a plurality of pieces of monitoring data from among the pieces of monitoring data extracted by the monitoring data extraction unit 122 in descending order of the degree of influence on the defect type of the defective product.
- the operating state of the injection molding machine 1 and the state of the resin at the time of molding affect the quality of the molded product. It is conceivable that there is monitoring data that caused the defect due to deviation from the standard value during molding of the molded product.
- the high-impact monitoring data extraction unit 123 selects monitoring data that has a high degree of impact on the type of defect in the molded product determined to be defective from among the plurality of monitoring data extracted by the monitoring data extraction unit 122. , multiple data are extracted for each defect type as high-impact monitoring data.
- the high-impact monitoring data extracted by the high-impact monitoring data extraction unit 123 is stored in the storage unit 140 in association with the defect type.
- the determination parameter calculation unit 124 calculates, from the monitoring data extracted by the monitoring data extraction unit 122, determination parameters that are parameters used to determine the quality of the molded product molded by the injection molding machine 1.
- the determination parameter is a value calculated based on the Mahalanobis distance obtained by applying the known MT (Mahalanobis-Taguchi) method. Specifically, in the present embodiment, the square value of the Mahalanobis distance (MD value) obtained by applying the MT method is used as the determination parameter. In this embodiment, the value of the square of the MD value obtained by applying the MT method to the monitoring data in this manner is referred to as a determination parameter.
- the determination parameter calculation unit 124 calculates, as a non-defective parameter, a determination parameter for the non-defective monitoring data in the non-defective monitoring data and the defective monitoring data extracted by the monitoring data extraction unit 122 . In addition, the determination parameter calculation unit 124 calculates, as a failure parameter, a determination parameter for the defective monitoring data in the monitoring data of the non-defective monitoring data and the defective monitoring data extracted by the monitoring data extracting unit 122. .
- the determination parameter calculation unit 124 calculates the non-defective parameter of the non-defective monitoring data and the defective parameter of the defective monitoring data in the high-impact monitoring data extracted by the high-impact monitoring data extracting unit 123. calculate.
- the molding parameter calculation unit 125 determines the quality of the molded product during molding by the injection molding machine 1 based on the monitoring data of the same type as the high- influence monitoring data acquired by the monitoring data acquisition unit 111. Calculate the parameters at the time of molding.
- the parameter during molding is calculated as a determination parameter of the monitoring data acquired by the monitoring data acquisition unit 111 during molding by the injection molding machine 1 with respect to the monitoring data extracted by the monitoring data extraction unit 122 . That is, the molding parameter calculation unit 125 uses the monitoring data of the same type as the high- influence monitoring data during molding by the injection molding machine 1 and the monitoring data extracted by the monitoring data extraction unit 122 to determine the determination parameter calculation unit. Similar to 124, the parameters for judgment obtained by applying the known MT method are calculated as parameters at the time of molding.
- the molding-time parameter calculator 125 that calculates the molding-time parameters in this manner calculates the molding-time parameters for each defect type of defective products in the monitoring data extracted by the monitoring data extraction unit 122 .
- the recommended value calculation unit 126 outputs a recommended determination value, which is a threshold recommended as a criterion for determining whether a defective product has occurred in a molded product during molding by the injection molding machine 1, to the monitoring data extracting unit 122. It is calculated based on the monitoring data extracted when the product is good.
- the recommended judgment value calculated by the recommended value calculation unit 126 is a threshold value of the judgment parameter of the monitoring data acquired by the monitoring data acquisition unit 111 during molding by the injection molding machine 1 with respect to the monitoring data extracted by the monitoring data extraction unit 122. calculate.
- the pass/fail determination unit 127 compares the molding parameter calculated by the molding parameter calculation unit 125 with a determination threshold that is a threshold for the molding parameter, and determines whether a defective product has occurred in the molded product molded by the injection molding machine 1. A determination is made as to whether or not The determination threshold used in determining whether a defective product has occurred in the molded product in the quality determination unit 127 is determined by using the determination recommended value calculated by the recommended value calculation unit 126 as the determination threshold, or by using the determination recommended value as a standard. A value set by the user is used as the determination threshold. Also, the good/bad judgment unit 127 judges whether or not a defective product has occurred in the molded product molded by the injection molding machine 1 for each defect type of the defective product.
- the abnormal data extractor 128 extracts the same monitoring data as the defective monitoring data extracted by the monitoring data extractor 122 when the quality determiner 127 determines that a defective molded product has occurred during molding by the injection molding machine 1. Extract the monitoring data with the highest degree of anomaly from the plurality of types of monitoring data. In the present embodiment, the abnormal data extraction unit 128 selects the non-defective monitoring data in the high-influence monitoring data among the plurality of monitoring data of the same type as the plurality of high-influence monitoring data during molding of the injection molding machine 1. The monitoring data with the largest discrepancy is extracted as the monitoring data with the highest degree of anomaly.
- the quality determination system 200 for the injection molding machine 1 includes the configuration described above, and the operation thereof will be described below.
- the injection molding machine 1 repeats this cycle of injection/molding operations, with one injection/molding operation as one cycle.
- Each cycle includes multiple steps for injection of molding material and molding of the product.
- Each cycle includes, for example, a mold closing process, a pressurization process, a filling (injection) process, a holding pressure process, a metering process, a mold opening process, and an extrusion process.
- the monitoring data detected by each sensor provided in the injection molding machine 1 is acquired by the control device 100, and the injection molding machine 1 is operated based on the monitoring data.
- the injection and molding operations are repeated while monitoring the state of the operation.
- as monitoring of the operation state of the injection molding machine 1 based on the monitoring data it is determined whether or not the monitoring data satisfies a predetermined value, and a defective product is found in the molded products molded by the injection molding machine 1. Two kinds of determination are performed based on monitoring data as to whether or not an error has occurred.
- FIG. 3 is an explanatory diagram of the monitoring data determination screen 50. As shown in FIG. Whether or not the monitoring data satisfies the predetermined value is determined by the control device 100 while displaying the monitoring data determination screen 50 as shown in FIG. A monitoring data determination screen 50 shown in FIG.
- the monitoring data name display unit 51 displays the type of monitoring data for determining whether or not the control device 100 is normal, that is, the name of the monitoring data.
- the control device 100 determines whether the monitoring data is normal, it is possible to select the monitoring data to be determined, and the monitoring data name display section 51 of the monitoring data determination screen 50 is , it is possible to display selected monitoring data.
- the reference value input unit 52 is a part for inputting the reference value of the monitoring data used for determining whether the monitoring data is normal.
- the reference value input unit 52 can use the input unit 160 to input a reference value for each monitoring data.
- the reference value acquisition unit 112 included in the processing unit 110 of the control device 100 acquires the reference value input by the user and stores it in the storage unit 140 .
- the allowable value input unit 53 is a portion for inputting the allowable value for the reference value of the monitoring data input to the reference value input unit 52, that is, the range of plus and minus around the reference value.
- the allowable value input unit 53 can use the input unit 160 to input the allowable value with respect to the reference value for each monitoring data.
- the permissible value acquiring unit 113 included in the processing unit 110 of the control device 100 acquires the permissible value input by the user and stores it in the storage unit 140 .
- the alarm selection unit 54 selects whether to enable or disable an alarm that notifies the user that the monitoring data is outside the allowable range when the monitoring data exceeds the allowable value. There is The alarm selection unit 54 can use the input unit 160 to select whether to enable or disable the alarm for each monitoring data.
- FIG. 4 is a flowchart showing the flow of control when determining whether monitoring data satisfies a predetermined value.
- the monitoring data is acquired from each sensor provided in the injection molding machine 1 while molding the molded product in the injection molding machine 1 (step ST11). .
- the monitoring data is acquired by the monitoring data acquisition unit 111 included in the processing unit 110 of the control device 100 .
- the monitoring data determination unit 114 of the processing unit 110 of the control device 100 determines whether or not the monitoring data is out of the allowable value.
- the monitoring data determination unit 114 determines whether the monitoring data acquired by the monitoring data acquisition unit 111 is outside the range of allowable values centered on the reference value of the monitoring data, or whether the monitoring data is within the range of allowable values. It is determined whether or not
- the reference value in this case is the reference value acquired by the reference value acquisition unit 112 by the user inputting to the reference value input unit 52 of the monitoring data judgment screen 50, and the allowable value is the reference value of the monitoring data judgment screen 50. This is the allowable value acquired by the allowable value acquisition unit 113 by inputting to the allowable value input unit 53 .
- step ST12 No determination
- the molding of the molded product by the injection molding machine 1 is stopped. continue.
- step ST12 determines that the monitoring data acquired by the monitoring data acquisition unit 111 is outside the allowable value (step ST12: Yes determination)
- an alarm is displayed (step ST13 ).
- the display of the alarm is displayed on the display unit 170 by the control device 100, for example.
- the alarm indicates that the monitoring data is out of the allowable value. is displayed on the display unit 170 .
- the injection molding machine 1 is equipped with a take-out robot (not shown) or a shooter (not shown) for taking out defective products
- a take-out robot not shown
- a shooter not shown
- the molded products are sorted by a take-out robot or shooter to a storage area for defective products.
- molding is performed while repeating these steps to determine whether the monitoring data satisfies a predetermined value.
- the quality determination system 200 of the injection molding machine 1 when molding a molded product by the injection molding machine 1, in addition to determining whether the monitoring data satisfies a predetermined value, the molded product is determined. Based on the monitoring data, it is possible to determine whether or not defective products have occurred.
- a method for monitoring the operating state of the injection molding machine 1 by determining whether or not a defective product has occurred in the molded product molded by the injection molding machine 1 based on monitoring data will be described.
- FIG. 5 is a flow diagram showing the procedure for generating basic data.
- generating the basic data first, using the injection molding machine 1, molding is performed with a mold for which quality determination of the molded product is desired (step ST21). It should be noted that the molding by the injection molding machine 1 in this case is not the molding of the actual product, but the molding for generating the basic data.
- the molded product is inspected for each shot by the injection molding machine 1, and the result of quality determination is input to the control device 100 using the input unit 160 (step ST22).
- the user manually determines whether the molded product is good or defective. That is, in the inspection of the molded product, the user visually determines whether the molded product is good or defective.
- a screen for inputting whether the molded product is a good product or a defective product is displayed on the display unit 170, and the input screen of the display unit 170 indicates whether the molded product is a good product.
- the input unit 160 is used to input the determination result as to whether there is any for each shot by the injection molding machine 1 . At that time, if the molded product is defective, the name of the defect type of the defective product, that is, the defect name is also input.
- the control device 100 After inputting the quality determination result of the molded product to the control device 100, the control device 100 associates the input quality determination result with the monitoring data and stores it in the storage unit 140 (step ST23). More specifically, the control device 100 receives the quality determination result of the molded product and the monitoring data obtained by the monitoring data obtaining unit 111 of the processing unit 110 when the molded product is molded by the injection molding machine 1. are linked by the basic data generation unit 121 of the processing unit 110 . At that time, the basic data generation unit 121 also associates the defect names of the defective products with each other. The basic data generation unit 121 stores the data linked in this way in the storage unit 140 as basic data.
- the basic data generated in this way are the monitoring data of the shots of good products and the monitoring data of the shots of defective products, respectively.
- the acquisition of the basic data is terminated. .
- FIG. 6 is a flowchart showing a procedure for setting parameters used for determining whether a molded product is good or bad.
- FIG. 7 is a schematic diagram of a pass/fail judgment setting master screen 60 used when setting parameters for pass/fail judgment. After generating the basic data, when setting the parameters used for determining the quality of the molded product, first, the quality determination setting main screen 60 as shown in FIG. is used. The pass/fail judgment setting main screen 60 calls up a program related to the pass/fail judgment setting main screen 60 and displays it on the display unit 170 by operating the control device 100 using the input unit 160 .
- the quality determination setting main screen 60 has a defect name input section 61 for inputting the defect name of the defective product, and a setting button 62 corresponding to the defect name input section 61 .
- the quality judgment setting main screen 60 has a plurality of defect name input sections 61 , and a setting button 62 is set for each defect name input section 61 . Also, on the pass/fail determination setting main screen 60, each defect name input section 61 is displayed with a different number.
- step ST31 When setting parameters to be used for quality judgment of a molded product, display the quality judgment setting main screen 60 on the display unit 170, and use the input unit 160 to register in the defect name input unit 61 of the quality judgment setting main screen 60. Enter the defect name and press the setting button 62 corresponding to the defect name input section 61 in which the defect name is input (step ST31).
- FIG. 8 is a schematic diagram of the quality judgment setting child screen 70.
- the control device 100 displays the pass/fail judgment setting child screen 70 corresponding to the pushed setting button 62 on the display unit 170 .
- the pass/fail judgment setting child screen 70 includes a fault information display portion 71, an extraction period input portion 72, a pass/fail judgment data display portion 73, a sample shot number display portion 74, and a It has a parameter display section 75 , a judgment parameter histogram display section 76 , and a judgment recommended value display section 77 .
- the defect information display portion 71 is a portion that displays information about the defect name entered in the defect name input portion 61 corresponding to the setting button 62 pressed on the pass/fail determination setting main screen 60 .
- the defect information display section 71 displays the defect name input to the defect name input section 61, the number assigned to the defect name input section 61, the molding condition number representing the type of mold used when generating the basic data, and the like. is displayed.
- the extraction period input section 72 is a section for inputting the monitoring data period extracted by the monitoring data extraction section 122 from the monitoring data included in the generated basic data. That is, the extraction period input section 72 is a section for inputting which period of monitoring data is to be extracted as the monitoring data extracted by the monitoring data extraction section 122 .
- the pass/fail judgment data display unit 73 displays the monitoring data used for judging the type of failure, which is the name of the fault for which the setting button 62 is pressed on the pass/fail judgment setting master screen 60, among the monitoring data extracted by the monitoring data extraction unit 122. It's part of what you do.
- the sample shot number display section 74 is a section that displays the number of shots in the injection molding machine 1 when the basic data was generated. More specifically, the sample shot number display section 74 is a section that displays the number of good shots and the number of defective shots in the monitoring data extracted by the monitoring data extraction section 122 .
- the defective parameter display unit 75 displays the monitoring data extracted by the monitoring data extracting unit 122 in the basic data when the defective product with the defective name for which the setting button 62 was pressed on the quality judgment setting master screen 60 was molded. This is the part that displays the failure parameter, which is the parameter for judgment.
- the parameter for judgment here is the value of the square of the Mahalanobis distance obtained by applying the known MT method.
- the failure parameter display unit 75 displays the average failure parameter, the maximum failure parameter, and the minimum failure parameter of the monitoring data extracted from the basic data by the monitoring data extraction unit 122. do.
- the determination parameter histogram display unit 76 displays each determination parameter for the monitoring data extracted by the monitoring data extraction unit 122 for the monitoring data for non-defective products and the monitoring data for defective products extracted by the monitoring data extraction unit 122 from the basic data, This is the part that is displayed in the histogram.
- the determination parameter histogram display section 76 is a section that displays the non-defective parameter and the defective parameter calculated by the determination parameter calculation section 124 .
- the recommended determination value display section 77 indicates whether or not the molded product molded by the injection molding machine 1 is a defective product with the defect name for which the setting button 62 has been pressed on the quality determination setting main screen 60. This is a portion for displaying a recommended value of a threshold value for determination when making a determination based on a determination parameter calculated from monitoring data.
- step ST32 When the user presses a setting button 62 on the pass/fail judgment setting parent screen 60 (see FIG. 7) to display the pass/fail judgment setting child screen 70 (see FIG. 8) corresponding to the setting button 62 on the display unit 170, the user , inputs the data extraction period using the pass/fail judgment setting child screen 70 (step ST32).
- the data extraction period is input to the extraction period input section 72 of the pass/fail judgment setting child screen 70 using the input section 160 .
- the control device 100 After inputting the data extraction period, the control device 100 converts the monitoring data of the shots that match the name of the defect whose name the user has pressed the setting button 62 on the pass/fail determination setting main screen 60 and the monitoring data of the shots that are good to basic data. It extracts from (step ST33). This extraction is performed by the monitoring data extraction unit 122 of the processing unit 110 of the control device 100 . In other words, the monitoring data extracting unit 122 extracts from the basic data the monitoring data when the user presses the setting button 62 on the quality determination setting main screen 60 and the defective product corresponding to the defect type of the defect name, that is, when the defective product is molded. Extract monitoring data. Furthermore, the monitoring data extracting unit 122 extracts, from the basic data, good-quality monitoring data, which is the same type of monitoring data as the bad-quality monitoring data and is monitoring data during molding of a good quality product.
- the degree of influence is calculated for each monitoring data (step ST34).
- the degree of influence in this case is the degree of influence of the monitoring data extracted by the monitoring data extraction unit 122 on the defect type of the defect name for which the user pressed the setting button 62 on the pass/fail judgment setting master screen 60 .
- the degree of influence in this case indicates how much each monitoring data extracted by the monitoring data extraction unit 122 contributes to the type of failure selected by the user on the pass/fail judgment setting main screen 60 to cause the failure. It is an index that shows
- the degree of influence is calculated by the high-impact monitoring data extraction unit 123 of the processing unit 110 of the control device 100 .
- the high-impact monitoring data extracting unit 123 calculates the impact using the following formula (1) from a plurality of non-defective monitoring data and defective monitoring data extracted by the monitoring data extracting unit 122 .
- a plurality of pieces of monitoring data having a high degree of influence are then extracted (step ST35).
- the high-impact monitoring data extraction unit 123 which has calculated the impact, continues to extract monitoring data with a high impact.
- the high-impact monitoring data extracting unit 123 selects, from among the plurality of monitoring data extracted by the monitoring data extracting unit 122, the one having the highest impact on the defect type of the defective product selected by the user on the quality judgment setting main screen 60. , to extract multiple monitoring data. That is, the high-impact monitoring data extraction unit 123 extracts a plurality of pieces of monitoring data in descending order of the degree of influence calculated by the above formula (1).
- the number of pieces of monitoring data that the high-impact monitoring data extraction unit 123 extracts in descending order of impact is arbitrary.
- the number of pieces of monitoring data to be extracted by the high-impact monitoring data extraction unit 123 can be arbitrarily selected by the user, for example, between 2 and 10 based on the calculated impact. For this reason, for example, when the number of monitoring items to be extracted by the high-impact monitoring data extracting unit 123 is set to 3, the high-impact monitoring data extracting unit 123 selects, in descending order of impact on the defect type, , to extract three monitoring data.
- the number of monitoring data extracted by the high-impact monitoring data extracting unit 123 for example, in the case of a defect type for which the monitoring data causing the cause is known to some extent, the number of monitoring data may be small, and the monitoring data causing the cause may be small. For an unknown defect type, the number of monitoring data may be increased.
- the number of pieces of monitoring data to be extracted by the high-impact monitoring data extraction unit 123 can be set by the user to an arbitrary number according to the types of failures and the like.
- the high-influence monitoring data extracting unit 123 groups the monitoring data with a high correlation coefficient into the same group. Extract only one monitoring data with the highest impact.
- FIG. 9 is an explanatory diagram of grouping of monitoring data.
- Monitoring data is grouped by calculating correlation coefficients between different types of monitoring data, and grouping a plurality of monitoring data with high correlation coefficients into one group as shown in FIG. 9, for example. Grouping of monitoring data is performed in advance and stored in the storage unit 140 . Although two groups are shown in FIG. 9 as an example, the number of groups to be set and the number of monitoring data in one group are appropriately set according to the correlation coefficient between the monitoring data.
- the high-impact monitoring data extracting unit 123 refers to the grouping data stored in the storage unit 140 when extracting monitoring data with a high degree of influence on the defect type, and selects one group with the highest degree of influence. While extracting one high monitoring data, a set number of multiple monitoring data are extracted. The monitoring data thus extracted by the high-impact monitoring data extraction unit 123 are displayed in the pass/fail judgment data display portion 73 of the pass/fail judgment setting child screen 70 together with the calculated influence.
- the non-defective parameter which is the parameter for determining the monitoring data for the non-defective product
- the defective parameter which is the parameter for determining the monitoring data for the defective product
- parameters for non-defective products and parameters for defective products are calculated from monitoring data for non-defective products and monitoring data for defective products in the high-impact monitoring data extracted by the high-impact monitoring data extraction unit 123 .
- the determination parameter calculation unit 124 extracts the non-defective monitoring data and the defective monitoring data for each of the three high-impact monitoring data. Using the time monitoring data, the parameter for good quality and the parameter for bad quality are calculated by the following equation (2).
- MD2 indicates a judgment parameter that is used when judging the quality of a molded product based on monitoring data, and applies to both parameters for non-defective products and parameters for defective products.
- the MD2 calculated in Equation ( 2 ) in this way is treated as a good parameter or a defective parameter, that is, a monitoring data determination parameter.
- a, b, and c are monitoring data, and in the order of a, b, and c, the monitoring data have a high degree of influence on the defect type, and S indicates the sum of squares.
- the three high-impact monitoring data are used to calculate the parameters for non-defective products and parameters for defective products, so there are three monitoring data, a, b, and c. , increases or decreases according to the number of high-impact monitoring data extracted by the high-impact monitoring data extraction unit 123 .
- the determination parameter calculation unit 124 also calculates failure time parameters from the failure time monitoring data extracted by the monitoring data extraction unit 122 for monitoring data other than the high-impact monitoring data. These failure parameters calculated by the determination parameter calculation unit 124 are displayed in the failure parameter display unit 75 of the pass/fail determination setting child screen 70 .
- the recommended judgment value for judging the quality of the molded product is calculated based on the monitoring data for non-defective products (step ST37).
- the recommended determination value is calculated by the recommended value calculation unit 126 included in the processing unit 110 of the control device 100 .
- the recommended value calculation unit 126 uses the parameters for non-defective products calculated based on the monitoring data for non-defective products and the parameters for defective products calculated based on the monitoring data for defective products, using the following formula (3): Calculate the recommended judgment value.
- Equation ( 3 ) MD2 is the good parameter and MD'2 is the bad parameter. Also, in the equation (3), ⁇ is the standard deviation of the parameters when good, and ⁇ ′ is the standard deviation of the parameters when defective.
- the recommended judgment value calculated by the recommended value calculation unit 126 is displayed in the recommended judgment value display unit 77 of the pass/fail judgment setting child screen 70 .
- the quality determination setting main screen 60 and the quality determination setting child screen 70 are used to set the parameters for determining the quality of the molded product, and then the molded product is molded. Based on the monitoring data, it is possible to determine whether or not defective products have occurred.
- FIG. 10 is an explanatory diagram of the pass/fail judgment screen 80. As shown in FIG. Determination of whether or not a defective product has occurred in the molded product molded by the injection molding machine 1 is performed by the control device 100 while displaying a quality determination screen 80 as shown in FIG. The pass/fail judgment screen 80 shown in FIG.
- the defect name display section 81 displays the name of the defect type of the defective product when the control device 100 determines the quality of the molded product molded by the injection molding machine 1, that is, the defect name.
- the control device 100 determines whether or not the molded product is defective, it is possible to select the name of the defect to be determined. can display the selected defect name.
- the determination threshold value input unit 82 is a threshold value for determination parameters when determining whether or not a molded product is defective based on the determination parameters of monitoring data acquired during molding of the molded product. This is the part for inputting the judgment threshold.
- the determination threshold value input unit 82 can use the input unit 160 to input a determination threshold value for each defect name.
- the recommended determination value calculated by the recommended value calculation unit 126 is input to the determination threshold value input unit 82 as a default determination threshold value, and the user can appropriately change the recommended determination value according to the molded product to be molded, the type of defect, and the like. By doing so, it is possible to set a value suitable for the molded product, the type of defect, etc., as the determination threshold value.
- the alarm selection unit 83 selects whether to enable or disable an alarm that notifies the user that the monitoring data determination parameter exceeds the determination threshold when the monitoring data determination parameter exceeds the determination threshold. It is a part to choose.
- the alarm selection unit 83 can use the input unit 160 to select whether to enable or disable the alarm for each defect name.
- FIG. 11 is a flow chart showing the flow of control when determining whether or not a defective molded product has occurred based on monitoring data.
- monitoring data is acquired from each sensor provided in the injection molding machine 1 while molding the molded product in the injection molding machine 1 (step ST41).
- the monitoring data is acquired by the monitoring data acquisition unit 111 included in the processing unit 110 of the control device 100 .
- the monitoring data acquisition unit 111 acquires monitoring data for each shot by the injection molding machine 1 and updates the data.
- the molding parameters are calculated (step ST42).
- the calculation of the parameters during molding is performed by the parameter calculation unit 125 included in the processing unit 110 of the control device 100 .
- the molding-time parameter calculation unit 125 calculates a determination parameter for monitoring data of the same type as the high-impact monitoring data extracted by the high-impact monitoring data extraction unit 123 in the monitoring data acquired by the monitoring data acquisition unit 111.
- the molding parameters are calculated. That is, the molding-time parameter calculation unit 125 calculates the determination parameter of the same type of monitoring data as the high-impact monitoring data for a plurality of pieces of monitoring data extracted from the basic data by the monitoring data extraction unit 122. Calculate parameters.
- the molding time parameter is the same type as the high-impact monitoring data for a plurality of monitoring data extracted from the basic data by applying the MT method in the same manner as when determining the monitoring data determination parameter in the determination parameter calculation unit 124. is calculated by obtaining the square value of the Mahalanobis distance of the monitoring data.
- the parameters during molding the monitoring data is acquired by the monitoring data acquisition unit 111, and the parameters during molding are calculated at the timing when the data is updated.
- step ST43 After calculating the parameters during molding, it is next determined whether or not the parameters during molding are greater than the determination threshold (step ST43). This determination is performed by the pass/fail determination section 127 of the processing section 110 of the control device 100 .
- the pass/fail determination unit 127 compares the molding parameter calculated by the molding parameter calculation unit 125 with a determination threshold that is a threshold for the molding parameter, and determines whether a defective product has occurred in the molded product molded by the injection molding machine 1. A determination is made as to whether or not
- the determination threshold used for this determination is the value set in the determination threshold input section 82 of the pass/fail determination screen 80.
- the determination threshold is the recommended determination value calculated by the recommended value calculation section 126, or the recommended determination value. Based on the value set by the user is set.
- the pass/fail determination unit 127 compares the determination threshold value set in this manner with the molding parameter calculated by the molding parameter calculation unit 125 for each defect name, and the molding parameter is greater than the determination threshold value for each defect name. or not.
- step ST43 No determination
- step ST43 when it is determined by the quality determination unit 127 that the molding parameter calculated by the molding parameter calculation unit 125 is larger than the determination threshold value (step ST43: Yes determination), the monitoring with the highest degree of abnormality Data is extracted (step ST44).
- the monitoring data with the highest degree of anomaly is extracted by the anomaly data extracting section 128 of the processing section 110 of the control device 100 . Since the determination of whether or not the molding parameter is greater than the determination threshold is performed for each defect name, the abnormality data extraction unit 128 determines that the defect name whose molding parameter is greater than the determination threshold has the highest degree of abnormality. Extract monitoring data.
- the abnormal data extracting unit 128 extracts the non-defective monitoring data in the high-impact monitoring data from among the plurality of monitoring data of the same type as the plurality of high-impact monitoring data in the monitoring data acquired by the monitoring data acquiring unit 111.
- the monitoring data with the largest discrepancy is extracted as the monitoring data with the highest degree of anomaly.
- the abnormal data extraction unit 128 calculates the degree of deviation using the following formula (4), and extracts the monitoring data with the highest degree of deviation as the monitoring data with the highest degree of abnormality.
- the abnormal data extraction unit 128 calculates the above equation (4) for each of the plurality of monitoring data of the same type as the plurality of high-impact monitoring data, calculates the degree of deviation for each monitoring data, and detects an abnormality. Extract the monitoring data with the highest intensity.
- a message is displayed (step ST45).
- the message is displayed on the display unit 170 by the control device 100, for example.
- the message is sent when it is determined that the molding parameter of the defect name for which the alarm is enabled is greater than the determination threshold in the alarm selection section 83 of the quality determination screen 80, and the monitoring with the highest degree of abnormality.
- the display unit 170 is caused to display a message notifying the data together with the defect name. That is, the display unit 170 is caused to display a message that a defect with the defect name has occurred and that the value of the monitoring data with the highest degree of abnormality is abnormal.
- the defect type of the defect name displayed on the display unit 170 is corrected by adjusting the injection molding machine 1 so that the displayed monitoring data value becomes a normal value. can be resolved.
- the injection molding machine 1 is equipped with a take-out robot (not shown) or a shooter (not shown) that takes out defective products, when it is determined that the molding parameter is greater than the determination threshold, the shot will be used for molding.
- the molded products that have been removed may be distributed to storage locations for defective products by a take-out robot or a shooter.
- the quality of the molded product is determined based on the high-influence monitoring data extracted in advance.
- the high-impact monitoring data extracted in advance it is possible to identify the monitoring data that causes the defective product when a defective product occurs in the molded product. can. As a result, it is possible to improve the accuracy of quality determination and more reliably identify the cause of the defect.
- the monitoring data extraction unit 122 extracts the monitoring data for non-defective products and the monitoring data for defective products from the basic data for each defect type of defective products, and the molding parameter calculation unit 125 calculates the molding parameters for each defect type. do. Furthermore, the good/bad judgment unit 127 judges whether or not a defective product has occurred in the molded product for each defect type. As a result, when it is determined that a molded product is defective, the type of defective product, that is, the name of the defective product, can also be identified and determined. can be specified for each fault name. As a result, the accuracy of quality determination can be further improved, and the cause of the defect can be specified more reliably.
- the abnormal data extraction unit 128 extracts monitoring data with the highest degree of abnormality. It is possible to more reliably identify the monitoring data that is the cause of the occurrence. As a result, when a defective molded product occurs, the cause of the defect can be specified more reliably.
- the quality of the molded product can be determined for each defect type, and monitoring data with a high degree of abnormality can be specified for each defect type. can be easily eliminated when the molding defect is eliminated by adjusting . As a result, even if a defective product occurs in the molded product, the defective molding can be eliminated more reliably, and the occurrence of defective products can be easily suppressed.
- a recommended value calculation unit 126 is provided for calculating a recommended value for judgment when determining whether a defective product has occurred in the molded product based on the monitoring data for non-defective products. Since the recommended determination value calculated in the section 126 is used as the determination threshold value, it is possible to more appropriately determine whether or not the molded product molded by the injection molding machine 1 is defective. As a result, the accuracy of quality determination can be further improved, and the cause of the defect can be specified more reliably.
- the monitoring data are grouped in advance so that the monitoring data with high correlation coefficients belong to the same group, and the high-impact monitoring data extracting unit 123 extracts the monitoring data 1 with the highest impact from the same group. Since only one is extracted, monitoring data with high correlation coefficients can be suppressed from being extracted as high-impact monitoring data.
- the recommended value calculation unit 126 calculates the recommended determination value
- monitoring data with high correlation coefficients are extracted as high-impact monitoring data, resulting in the calculation of the recommended determination value. It is possible to suppress bias in the monitoring data that is the basis.
- the basic data is obtained by visually inspecting the molded product by the user in the procedure for generating the basic data, and determining whether the molded product is a non-defective product or a defective product to the control device 100.
- the control device 100 may automatically generate the basic data when the injection molding machine 1 performs molding.
- the injection molding machine 1 is provided with a photographing unit such as a camera that photographs a molded product and converts it into image data. may be analyzed to determine whether the molded product is a non-defective product or a defective product. In this way, based on the image data captured by the imaging unit, it is determined whether the molded product is good or defective. Data can be easily generated.
- a photographing unit such as a camera that photographs a molded product and converts it into image data.
- image data may be analyzed to determine whether the molded product is a non-defective product or a defective product.
- the imaging unit it is determined whether the molded product is good or defective. Data can be easily generated.
- the recommended judgment value is calculated by Equation (3) using the parameters for non-defective products calculated based on the monitoring data for non-defective products and the parameters for defective products calculated based on the monitoring data for defective products.
- the recommended determination value may be calculated by other methods.
- the recommended judgment value may be calculated by the following formula (5) without using the failure parameter.
- monitoring data is detected by sensors provided in each part of the injection molding machine 1, but sensors for detecting monitoring data during operation of the injection molding machine 1 may be added as necessary. You can reduce it.
- the number of sensors arranged in the injection molding machine 1 it is preferable to set monitoring data grouping each time the number of sensors is changed. That is, when the number of sensors arranged in the injection molding machine 1 is changed, the correlation coefficient of the monitoring data detected by each sensor is calculated for each monitoring data, and based on the calculated correlation coefficient, the correlation coefficient group so that monitoring data with high values are in the same group. For example, in the monitoring data detected by each sensor, the monitoring data having a correlation coefficient of 0.5 or more are considered to be correlated and set in the same group.
- monitoring data with high correlation coefficients are extracted as high-influence monitoring data. It is possible to suppress the occurrence of bias in monitoring data. As a result, even if the number of sensors that detect monitoring data is changed, the accuracy of quality determination can be improved, and the cause of failure can be identified more reliably.
- Control device 110 Processing unit 111 Monitoring data acquisition unit 112 Reference value acquisition unit 113 Allowable value acquisition unit 114 Monitoring data determination unit 121 Basic data generation unit 122 Monitoring data extraction unit 123 High impact monitoring data extraction unit , 124... Determination parameter calculation unit 125... Molding parameter calculation unit 126... Recommended value calculation unit 127... Good/bad determination unit 128... Abnormal data extraction unit 140... Storage unit 150... Input/output unit 160... Input unit 170 Display unit 200 Good/bad judgment system
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Abstract
Description
図1は、実施形態に係る射出成形機1の良否判定システム200の構成例を示す模式図である。なお、以下の説明では、射出成形機1の通常の使用状態における上下方向を、射出成形機1においても上下方向として説明し、射出成形機1の通常の使用状態における水平方向を、射出成形機1においても水平方向として説明する。
本実施形態に係る射出成形機1は、射出装置10と、型締装置30とを有しており、射出装置10と型締装置30とは、射出成形機1における下端に配置されるフレーム2上に載置されている。射出成形機1は、射出装置10で成形材料を溶融して可塑化材料にし、射出装置10から射出された可塑化材料を、型締装置30によって冷却・固化することにより、所望の各種の成形品を製造することが可能になっている。
射出成形機1は、射出成形機1の各種制御を行う制御装置100と、オペレータが射出成形機1への入力操作を行う入力部160と、各種情報を表示する表示部170とを有している。入力部160と表示部170とは、共に制御装置100に接続されており、入力部160は、入力操作された情報を制御装置100に伝達する。また、表示部170は、制御装置100から伝達された情報を表示する。入力部160と表示部170とは、別体で構成されていてもよく、または、いわゆるタッチパネル式のディスプレイによって構成されることにより、一体に形成されていてもよい。
本実施形態に係る射出成形機1の良否判定システム200は、以上のような構成を含み、以下、その作用について説明する。射出成形機1は、1回の射出・成形動作を1サイクルとして、この射出・成形動作のサイクルを繰り返し実行する。各サイクルは、成形材料の射出、及び製品の成形のために複数の工程を含む。各サイクルは、例えば、型閉工程、昇圧工程、充填(射出)工程、保圧工程、計量工程、型開工程、押出工程を含む。
まず、モニタリングデータが所定値を満たしているか否かの判定の制御に用いる、表示部170に表示するモニタリングデータ判定画面50について説明する。図3は、モニタリングデータ判定画面50の説明図である。モニタリングデータが所定値を満たしているか否かの判定は、図3に示すようなモニタリングデータ判定画面50を、表示部170に表示しながら制御装置100で行う。図3に示すモニタリングデータ判定画面50は、モニタリングデータ名称表示部51と、基準値入力部52と、許容値入力部53と、アラーム選択部54とを有している。
次に、射出成形機1による成形品の成形時に、モニタリングデータが所定値を満たしているか否を判定しながら射出成形機1の動作の状態を監視する際の制御の流れについて説明する。
成形品に不良品が発生したか否かをモニタリングデータに基づいて判定する際には、まず、当該判定方法のベースとなる基礎データの生成を行う。
図6は、成形品の良否判定に用いるパラメータを設定する手順を示すフロー図である。図7は、良否判定のパラメータを設定する際に用いる良否判定設定親画面60の模式図である。基礎データを生成した後、成形品の良否判定に用いるパラメータを設定する際には、まず、図7に示すような良否判定設定親画面60を表示部170に表示し、良否判定設定親画面60を用いて行う。良否判定設定親画面60は、入力部160を用いて制御装置100を操作することにより、良否判定設定親画面60に関するプログラムを呼び出して表示部170に表示する。
図8は、良否判定設定子画面70の模式図である。良否判定設定親画面60で設定ボタン62を押すと、制御装置100は、押した設定ボタン62に対応する良否判定設定子画面70を表示部170に表示する。良否判定設定子画面70は、例えば、図8に示すように、不良情報表示部71と、抽出期間入力部72と、良否判定用データ表示部73と、サンプルショット数表示部74と、不良時パラメータ表示部75と、判定用パラメータヒストグラム表示部76と、判定推奨値表示部77とを有している。
影響度=(良品時モニタリングデータの平均値-不良時モニタリングデータの平均値)/良品時モニタリングデータの標準偏差σ・・・(1)
また、高影響度モニタリングデータ抽出部123は、不良種類に対する影響度が高いモニタリングデータを抽出する際には、相関係数の高いモニタリングデータ同士が同じグループになるようにグルーピングし、同一グループからは最も影響度が高いモニタリングデータ1つのみを抽出する。
次に、射出成形機1で成形した成形品に不良品が発生したか否かをモニタリングデータに基づいて判定する制御に用いる、表示部170に表示する良否判定画面80について説明する。図10は、良否判定画面80の説明図である。射出成形機1で成形した成形品に不良品が発生したか否かの判定は、図10に示すような良否判定画面80を、表示部170に表示しながら制御装置100で行う。図10に示す良否判定画面80は、不良名称表示部81と、判定閾値入力部82と、アラーム選択部83とを有している。
良否判定設定親画面60と良否判定設定子画面70とを用いて、成形品の良否判定を行う際におけるパラメータを設定することにより、判定閾値が定まったら、成形品に不良品が発生したか否かの判定を行う制御が可能となる。次に、射出成形機1で成形した成形品に不良品が発生したか否かの判定をモニタリングデータに基づいて行う制御について説明する。
以上の実施形態に係る射出成形機1の良否判定システム200は、製品となる成形品を成形する前に、成形品が良品であるか不良品であるかの判定結果とモニタリングデータとが紐付けられた基礎データから、良品時モニタリングデータと不良時モニタリングデータとをそれぞれ複数抽出し、抽出したモニタリングデータのうち、不良品の不良種類に対する影響度が高いモニタリングデータである高影響度モニタリングデータを抽出する。その後、射出成形機1によって製品となる成形品を成形する際に、高影響度モニタリングデータと同じ種類のモニタリングデータの判定用パラメータである成形時パラメータを算出し、算出した成形時パラメータと、成形時パラメータに対する閾値である判定閾値とを比較することにより、射出成形機1で成形した成形品に不良品が発生したか否かの判定を行う。
なお、上述した実施形態では、基礎データは、基礎データの生成を行う手順において成形品の検品をユーザが目視で行い、成形品が良品であるか不良品であるかを制御装置100に対してユーザが入力することにより基礎データの生成を行っているが、射出成形機1で成形を行った際に、制御装置100が自動的に基礎データを生成するようにしてもよい。
Claims (4)
- 射出成形機により成形された成形品が良品であるか不良品であるかの判定結果と、前記成形品を成形した際における前記射出成形機のモニタリングデータとが紐付けられた基礎データから、前記良品の成形時における前記モニタリングデータである良品時モニタリングデータと、前記不良品の成形時における前記モニタリングデータである不良時モニタリングデータとをそれぞれ複数抽出するモニタリングデータ抽出部と、
前記モニタリングデータ抽出部で抽出した複数の前記モニタリングデータのうち、前記不良品の不良種類に対する影響度が最も高い方から順に複数の前記モニタリングデータを高影響度モニタリングデータとして抽出する高影響度モニタリングデータ抽出部と、
前記射出成形機による成形時における成形品の良否判定を、前記高影響度モニタリングデータと同じ種類の前記モニタリングデータに基づいて行う際のパラメータである成形時パラメータを算出する成形時パラメータ算出部と、
前記成形時パラメータ算出部で算出した前記成形時パラメータと、前記成形時パラメータに対する閾値である判定閾値とを比較し、前記射出成形機で成形した前記成形品に前記不良品が発生したか否かの判定を行う良否判定部と、
を備えることを特徴とする射出成形機の良否判定システム。 - 前記モニタリングデータ抽出部は、前記良品時モニタリングデータと前記不良時モニタリングデータとを前記不良品の不良種類ごとに抽出し、
前記成形時パラメータ算出部は、前記成形時パラメータを前記不良種類ごとに算出し、
前記良否判定部は、前記成形品に前記不良品が発生したか否かの判定を前記不良種類ごとに行う請求項1に記載の射出成形機の良否判定システム。 - 前記良否判定部で前記成形品に前記不良品が発生したと判定をした場合に、異常度が最も高い前記モニタリングデータを抽出する異常データ抽出部を備え、
前記異常データ抽出部は、前記射出成形機の成形時における複数の前記高影響度モニタリングデータと同じ種類の複数の前記モニタリングデータのうち、前記高影響度モニタリングデータにおける前記良品時モニタリングデータに対して最も乖離が大きい前記モニタリングデータを、異常度が最も高い前記モニタリングデータとして抽出する請求項1または2に記載の射出成形機の良否判定システム。 - 前記成形品に前記不良品が発生したか否かの判定を行う際における判定推奨値を、前記モニタリングデータ抽出部で抽出した前記良品時モニタリングデータに基づいて算出する推奨値算出部を備え、
前記良否判定部は、前記推奨値算出部で算出した前記判定推奨値を前記判定閾値として用いる請求項1~3のいずれか1項に記載の射出成形機の良否判定システム。
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