System for determining qualification of injection molding machine

Abstract

In order to perform molding disqualification determination by combining a monitoring data group and waveform data in the same dimension, the present invention is provided with: a set timing data acquisition unit (112) that acquires, from monitoring data when molding a molded article by an injection molding machine (1), set timing data (91), which is monitoring data at a plurality of preset timings; a waveform data acquisition unit (113) that acquires waveform data (95), which is monitoring data that is continuous during a predetermined period, from among the monitoring data; a sample point data acquisition unit (114) that acquires sample point data (97), which is data at a plurality of sample points (96) in the plurality of pieces of waveform data (95); an integrated data generation unit (115) for generating integrated data (90) by integrating the plurality of sample point data (97) and a monitoring data group (92) comprising the plurality of set timing data (91); and a quality assessment unit (127) that assesses whether or not a defective product has occurred in the molded article on the basis of the integrated data (90).

Description

Technical Field

[0001] The present invention relates to a quality judgment system for an injection molding machine that injects molten resin material to mold a molded product. Background Art

[0002] As a function for monitoring the quality of molded products molded by the injection molding machine, there is a function for determining the quality of molded products by setting allowable values ​​for each monitoring data such as pressure and temperature obtained from the sensor for each shot, and comparing the obtained monitoring data with the allowable values. For example, if the barrel temperature deviates from the allowable value set relative to the reference, the molded product molded by this shot is determined to be a defective product.

[0003] However, in setting such an allowable value, a certain degree of experience is required of the operator. Therefore, among the conventional methods for determining the pass / failure of injection molding machines, there is a method that can reliably and simply adjust to a good state without being undertaken by a skilled operator. For example, in the method for determining the pass / failure of the plasticizing process of an injection molding machine described in Patent Document 1, a reference data value calculated from the data of parameters related to the dynamics of the molding material when the predetermined number of shots is performed and an MD value calculated from the data of parameters calculated from any shot is calculated, and the pass / failure of the operation in the plasticizing process is determined based on the size of the MD value.

[0004] Prior art literature

[0005] Patent Literature

[0006] Patent Document 1: Japanese Patent Application Publication No. 2008-246734 Summary of the invention

[0007] Problems to be solved by the invention

[0008] Here, as an example of a method for obtaining monitoring data during molding with an injection molding machine, the following method can be cited: a timing that can obtain data that best presents characteristics is pre-set in one molding cycle, and data of one point is obtained at the timing determined in each cycle. The quality of the molded product can be determined by comparing the monitoring data obtained in this way with the allowable value of the monitoring data to determine whether the molded product is acceptable or not.

[0009] In addition, as another method for determining the quality of molded products based on monitoring data, there is a method of determining using waveform data obtained during injection molding by an injection molding machine. For example, the injection pressure waveform when the mold is filled with resin material is recorded, and when the maximum value exceeds a preset threshold, it is assumed that the filler is oversaturated and the product is determined to be defective, and when the maximum value of the injection pressure waveform does not exceed the threshold, it is determined to be a qualified product.

[0010] When using monitoring data to make a pass / fail judgment, there are methods of making a judgment using a monitoring data group each consisting of one point and methods of making a judgment using waveform data, but in the method of making a pass / fail judgment using data of one point, by performing a multivariate analysis, it is possible to judge a failure based on a combination of multiple data. On the other hand, when making a pass / fail judgment based on waveform data, the pass / fail judgment is made using characteristic quantities such as the maximum value and the minimum value of the waveform data, independently of the judgment using the monitoring data group.

[0011] In the pass / fail judgment using monitoring data, since the judgment using the monitoring data group each consisting of 1 point and the judgment using the waveform data are performed independently, there is no problem as long as the unqualified phenomenon can be captured only based on the monitoring data group or the waveform data, but among the unqualified phenomena, there is a phenomenon that it is difficult to judge the pass / fail without establishing a correlation between the monitoring data group and the waveform data for analysis. However, since the data format is different between the monitoring data group and the waveform data, it is impossible to perform calculations under the same dimension, and it becomes very difficult to combine the two to judge the molding failure.

[0012] The present invention is made in view of the above, and an object of the present invention is to provide a pass / fail judgment system for an injection molding machine that can combine a monitoring data group and waveform data in the same dimension to perform molding failure judgment.

[0013] Means used to solve problems

[0014] In order to solve the above-mentioned problems and achieve the purpose, the pass / fail judgment system of the injection molding machine of the present invention comprises: a set timing data acquisition unit, which respectively acquires a plurality of monitoring data of a predetermined timing, namely, set timing data, from monitoring data of the injection molding machine when the injection molding machine molds a molded product; a waveform data acquisition unit, which acquires the monitoring data, namely, waveform data, which are continuous during a specified period of time from the monitoring data; a sample point data acquisition unit, which acquires data at a plurality of sample points at different times from the waveform data acquired by the waveform data acquisition unit, namely, sample point data; a combined data generation unit, which combines a monitoring data group composed of a plurality of set timing data acquired by the set timing data acquisition unit and a plurality of sample point data acquired by the sample point data acquisition unit to generate combined data; and a pass / fail judgment unit, which judges whether a defective product has occurred in the molded product molded by the injection molding machine based on the combined data generated by the combined data generation unit.

[0015] Effects of the Invention

[0016] The pass / fail judgment system for an injection molding machine according to the present invention has the effect of being able to combine the monitoring data group and the waveform data in the same dimension to perform a molding failure judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram showing a configuration example of a quality determination system for an injection molding machine according to an embodiment.

[0018] Figure 2 yes Figure 1 An illustration of the control device shown.

[0019] Figure 3 This is an explanatory diagram showing an example of setting timing data.

[0020] Figure 4 This is an explanatory diagram showing an example of waveform data.

[0021] Figure 5 It is an explanatory diagram showing an example of sample point data.

[0022] Figure 6 This is an explanatory diagram showing an example of combined data.

[0023] Figure 7 This is an explanatory diagram showing a state in which combined data of a plurality of shots is obtained.

[0024] Figure 8 This is an illustration of the monitoring data judgment screen.

[0025] Fig. 9 This is a flowchart showing the flow of control when performing determination control on whether monitoring data satisfies a prescribed value.

[0026] Fig.10 This is a flowchart showing the procedure for generating basic data.

[0027] Fig.11 This is a flowchart showing a procedure for setting parameters used in determining whether a molded product is acceptable.

[0028] Fig.12 This is a diagram of the pass / fail judgment setting main screen used when setting the pass / fail judgment parameters.

[0029] Fig.13 This is a schematic diagram of the pass / fail judgment setting sub-screen.

[0030] Fig.14 This is a diagram for explaining the grouping of monitoring data.

[0031] Fig.15 This is an illustration of the pass / fail judgment screen.

[0032] Fig.16 This is a flowchart showing the flow of control when performing control to determine whether or not a defective product has occurred in a molded product based on monitoring data.

[0033] Fig.17 It is an explanatory diagram showing a modified example of acquisition of sample point data in the pass / fail determination system according to the embodiment. DETAILED DESCRIPTION

[0034] The following is a detailed description of the implementation of the pass / fail judgment system for the injection molding machine disclosed in the present invention based on the accompanying drawings. In addition, the invention is not limited by the implementation. In addition, the components of the following implementation include components that can be replaced and easily thought of by a person skilled in the art, or substantially the same components.

[0035] [Implementation Method]

[0036] Figure 1 1 is a schematic diagram showing a configuration example of a pass / fail determination system 200 of an injection molding machine 1 according to an embodiment. In the following description, the vertical direction of the injection molding machine 1 in a normal use state is also described as the vertical direction in the injection molding machine 1, and the horizontal direction of the injection molding machine 1 in a normal use state is also described as the horizontal direction in the injection molding machine 1.

[0037] <Injection molding machine 1>

[0038] The injection molding machine 1 according to the present embodiment includes an injection device 10 and a mold clamping device 30, which are mounted on a frame 2 disposed at the lower end of the injection molding machine 1. The injection molding machine 1 melts the molding material by the injection device 10 to make it a plasticized material, and cools and solidifies the plasticized material injected from the injection device 10 by the mold clamping device 30, thereby being able to manufacture various desired molded products.

[0039] The injection device 10 includes a heating barrel 11, a screw 13, a metering unit 20, and an injection device driving unit 25. The heating barrel 11 can heat and melt the molding material inside to make it a plasticized material. In addition, the heating barrel 11 has a nozzle 12 for injecting the plasticized material at one end, and the other end is connected to a hopper 15 for feeding raw materials. The screw 13 is arranged in the heating barrel 11 and can move in the axial direction inside the heating barrel 11.

[0040] The metering unit 20 can introduce the resin as the molding material from the hopper 15 into the heating barrel 11 by rotating the screw 13 in the heating barrel 11 .

[0041] The injection device driving unit 25 can move the screw 13 in the horizontal direction in the heating barrel 11. In addition, the injection device driving unit 25 can push the molding material out from the nozzle 12 by moving the screw 13 toward the nozzle 12 side in a state where the molten molding material is accumulated in the heating barrel 11 at the end side where the nozzle 12 is located. Thus, the molding material in the heating barrel 11 can be injected from the nozzle 12.

[0042] The mold clamping device 30 includes a fixed platen 31, a movable platen 32, a mold clamping drive mechanism 40, and an ejection mechanism 45. The fixed platen 31 is arranged on the frame 2, and the movable platen 32 is arranged on the opposite side of the fixed platen 31 on the frame 2 to the side where the injection device 10 is located, so as to be movable relative to the fixed platen 31. A fixed mold 35 is installed on the surface of the fixed platen 31 on the side where the movable platen 32 is located, and a movable mold 36 is installed on the surface of the movable platen 32 on the side where the fixed platen 31 is located. The movable mold 36 installed on the movable platen 32 is opposite to the fixed mold 35 installed on the fixed platen 31, and when the movable platen 32 approaches the fixed platen 31, it approaches the fixed mold 35 and is combined with the fixed mold 35.

[0043] The mold clamping drive mechanism 40 can move 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 and the fixed mold 35 can be closed, or the movable mold 36 and the fixed mold 35 can be opened. In the present embodiment, the mold clamping drive mechanism 40 includes a so-called toggle mechanism 41, and the movable platen 32 can be moved relative to the fixed platen 31 by the toggle mechanism 41.

[0044] The ejector mechanism 45 includes an ejector member 46 for ejecting the molded product adhering to the inner surface of the movable mold 36 after molding, and can remove the molded product from the movable mold 36 after molding.

[0045] <Control device 100>

[0046] The injection molding machine 1 includes a control device 100 that performs various controls on the injection molding machine 1, an input unit 160 for an operator to perform input operations on the injection molding machine 1, and a display unit 170 that displays various information. The input unit 160 and the display unit 170 are both connected to the control device 100, and the input unit 160 transmits information of the input operation to the control device 100. In addition, 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 being configured with a so-called touch panel type display.

[0047] The control device 100 is connected to various actuators such as motors that are power sources for the operation of the injection molding machine 1, and various sensors that obtain information during the operation of the injection molding machine 1. Thus, the control device 100 can control the injection molding machine 1 by obtaining information during the operation of the injection molding machine 1 from the sensors and sending control signals to the actuators of the injection molding machine 1.

[0048] Figure 2 yes Figure 1 1 is an explanatory diagram of the control device 100 shown in FIG. The control device 100 includes a processing unit 110, a storage unit 140, and an input / output unit 150. The processing unit 110 includes a CPU (Central Processing Unit) that performs calculations, and a RAM (Random Access Memory) and a ROM (Read Only Memory) that function as memories for storing various information. All or part of the functions of the processing unit 110 are realized by downloading an application program stored in the ROM into the RAM and executing it by the CPU, and by reading and writing data in the RAM and the ROM.

[0049] The storage unit 140 is electrically connected to the processing unit 110 and is a storage device for storing information. When the control device 100 controls the injection molding machine 1, information obtained 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 the processing unit 110 retrieves information stored in the storage unit 140 and uses it to control the injection molding machine 1.

[0050] In addition, each function implemented by the processing unit 110 may be stored in advance as a program in the storage unit 140. In this case, the processing unit 110 calls out the program stored in the storage unit 140 by the processing unit 110, and the processing unit 110 executes the operation according to the program to execute each function. In addition, the storage unit 140 may be integrally equipped in the control device 100, or may be configured to be detachable from the control device 100.

[0051] The input / output unit 150 is a so-called interface for inputting and outputting signals with external devices of the control device 100. That is, various actuators, various sensors, the input unit 160, and the display unit 170 of the injection molding machine 1 connected to the control device 100 are connected to the input / output unit 150. The processing unit 110 included in the control device 100 transmits and receives signals with these external devices via the input / output unit 150.

[0052] The processing unit 110 functionally includes a monitoring data acquisition unit 111, a reference value acquisition unit 116, an allowable value acquisition unit 117, a monitoring data determination unit 118, a basic data generation unit 121, a monitoring data extraction unit 122, a high-impact monitoring data extraction unit 123, a determination parameter calculation unit 124, a molding parameter calculation unit 125, a recommended value calculation unit 126, a pass / fail determination unit 127 and an abnormal data extraction unit 128.

[0053] Among them, the monitoring data acquisition unit 111 can acquire monitoring data as detection results of various sensors of the injection molding machine 1 when the injection molding machine 1 is in operation. Examples of the monitoring data include the temperature when the molding material is melted in the heating barrel 11 of the injection device 10 of the injection molding machine 1, the time for introducing the molding material into the heating barrel 11 and measuring the molding material, and the rotation speed of the screw 13. The monitoring data acquisition unit 111 stores the acquired monitoring data together with the acquired time in the storage unit 140. That is, the monitoring data acquisition unit 111 associates the acquired monitoring data with the acquired date and time and stores them in the storage unit 140.

[0054] Furthermore, the monitoring data acquisition unit 111 includes a setting timing data acquisition unit 112, a waveform data acquisition unit 113, a sample point data acquisition unit 114, and a combined data generation unit 115. The setting timing data acquisition unit 112 and the waveform data acquisition unit 113 acquire monitoring data of different forms, respectively, and the sample point data acquisition unit 114 and the combined data generation unit 115 treat the monitoring data of different forms acquired by the setting timing data acquisition unit 112 and the waveform data acquisition unit 113 as data of the same type.

[0055] Figure 3 1 is an explanatory diagram showing an example of the set timing data 91. The set timing data acquisition unit 112 included in the monitoring data acquisition unit 111 can acquire the set timing data 91 as monitoring data of the timings set in advance respectively from among a plurality of monitoring data of the injection molding machine 1 when the injection molding machine 1 molds the molded product. The set timing data 91 acquired by the set timing data acquisition unit 112 is monitoring data of the timing set for each monitoring data as the timing showing the most characteristic in the monitoring data in one molding cycle when the injection molding machine 1 molds the molded product.

[0056] The setting timing data acquisition unit 112 acquires a plurality of monitoring data to be acquired as the setting timing data 91 for each molding cycle when the molded product is molded in the injection molding machine 1. Figure 3As shown, a monitoring data group 92 composed of a plurality of set timing data 91 is acquired for each molding cycle in the injection molding machine 1 , and the acquired date and time are associated with each set timing data 91 and stored in the storage unit 140 .

[0057] Figure 4 : is an explanatory diagram showing an example of waveform data 95. The waveform data acquisition unit 113 included in the monitoring data acquisition unit 111 can acquire the waveform data 95 which is continuous monitoring data in a predetermined period, among the monitoring data of the injection molding machine 1 when the injection molding machine 1 molds the molded product. The waveform data 95 acquired by the waveform data acquisition unit 113 is monitoring data whose values ​​change with the passage of time in one molding cycle when the injection molding machine 1 molds the molded product, and is acquired as continuous data corresponding to the time in the molding cycle. That is, unlike the set timing data acquisition unit 112, the waveform data acquisition unit 113 does not acquire monitoring data of a specific timing in one molding cycle, but acquires the entire monitoring data whose values ​​change in one molding cycle including the form of change.

[0058] For example, Figure 4 95 is waveform data about the filling pressure when the injection device 10 fills the mold with resin during molding in the injection molding machine 1. During molding in the injection molding machine 1, the filling pressure changes in one molding cycle as follows: Figure 4 Although the pressure changes as shown, the waveform data acquisition unit 113 also acquires the entire value of the pressure that changes with the passage of time, including the form of the change, as the waveform data 95.

[0059] The waveform data acquisition unit 113 acquires a plurality of monitoring data to be acquired as waveform data 95 for each molding cycle when the molded product is molded in the injection molding machine 1, and respectively acquires the plurality of waveform data 95 as waveform data 95. Thus, the waveform data acquisition unit 113 acquires a plurality of waveform data 95 for each molding cycle in the injection molding machine 1, and stores the waveform data 95 in the storage unit 140 in association with the acquired date and time.

[0060] The monitoring data acquired as the waveform data 95 may include, in addition to the filling pressure, the temperature of the mold, the flow rate of cooling water for cooling the mold, the pressure inside the mold when the resin is filled into the mold, i.e., the mold internal pressure, etc. These monitoring data are acquired as the waveform data 95 by the waveform data acquisition unit 113 for each molding cycle, and are associated with the acquired date and time and stored in the storage unit 140.

[0061] Figure 51 is an explanatory diagram showing an example of sample point data 97. The sample point data acquisition unit 114 provided in the monitoring data acquisition unit 111 acquires sample point data 97, which is data at a plurality of sample points 96 at different times in the waveform data 95 acquired by the waveform data acquisition unit 113. The sample point 96 mentioned here is a plurality of points set at different times for extracting the value of the waveform data 95 for the waveform data 95 acquired by the waveform data acquisition unit 113.

[0062] The sample points 96 are set, for example, by presetting the number of samples extracted from one waveform data 95 and setting the sample points at a time interval sufficient to meet the number of samples for one waveform data 95. Alternatively, the sample points 96 may be directly set at an arbitrary time interval for one waveform data 95.

[0063] The sample point data acquisition unit 114 acquires monitoring data at the position of the sample point 96 set for the waveform data 95 as the sample point data 97. Since a plurality of sample points 96 are set for one waveform data 95, the sample point data acquisition unit 114 acquires monitoring data at the position of the sample point 96 set for the waveform data 95 as the sample point data 97. Figure 5 As shown, a plurality of sample point data 97 are acquired from one waveform data 95 corresponding to a sample point 96 .

[0064] Figure 6 It is an explanatory diagram showing an example of the combined data 90. Figure 7 1 is an explanatory diagram showing a state where combined data 90 of a plurality of shots is acquired. The combined data generating unit 115 provided in the monitoring data acquiring unit 111 combines the monitoring data group 92 consisting of the plurality of set timing data 91 acquired by the set timing data acquiring unit 112 with the plurality of sample point data 97 acquired by the sample point data acquiring unit 114, and generates the combined data 90. Thus, the combined data generating unit 115 treats the plurality of set timing data 91 acquired by the set timing data acquiring unit 112 and the plurality of sample point data 97 acquired by the sample point data acquiring unit 114 from the waveform data 95 acquired by the waveform data acquiring unit 113 as separate and independent monitoring data of the same type.

[0065] Since the set timing data 91 and the sample point data 97 are acquired for each molding cycle in the injection molding machine 1 , the combined data generating unit 115 generates the combined data 90 for each molding cycle, that is, for each shot when the injection molding machine 1 performs injection molding.

[0066] In addition, Figure 6 and Figure 7In the example, the sample point data 97 combined with the monitoring data group 92 is the sample point data 97 of the waveform data 95 regarding the filling pressure, but the sample point data 97 combined with the monitoring data group 92 may be the sample point data 97 of the waveform data 95 other than the filling pressure. In addition, the sample point data 97 combined with the monitoring data group 92 may be the sample point data 97 of a plurality of waveform data 95.

[0067] The reference value acquisition unit 116 acquires the reference value of the monitoring data during the operation of the injection molding machine 1, which is input by the user of the injection molding machine 1 using the input unit 160. The monitoring data mentioned here includes both the setting timing data 91 acquired by the setting timing data acquisition unit 112 and the sample point data 97 acquired by the sample point data acquisition unit 114. The same applies to the following description. The reference value acquisition unit 116 stores the acquired reference value of the monitoring data in the storage unit 140.

[0068] The allowable value acquisition unit 117 acquires the allowable value relative to the reference value of the monitoring data during the operation of the injection molding machine 1, which is input by the user of the injection molding machine 1 using the input unit 160. The allowable value acquisition unit 117 stores the acquired allowable value relative to the reference value of the monitoring data in the storage unit 140.

[0069] The monitoring data determination unit 118 compares the monitoring data acquired by the monitoring data acquisition unit 111 with the reference value acquired by the reference value acquisition unit 116 and the allowable value acquired by the allowable value acquisition unit 117 , and determines whether the monitoring data is within the allowable value range.

[0070] The basic data generation unit 121 generates basic data that links the result of determination of whether the molded product molded by the injection molding machine 1 is a good product or a bad product with the monitoring data of the injection molding machine 1 when the molded product is molded, that is, the monitoring data acquired by the monitoring data acquisition unit 111. That is, the basic data generation unit 121 generates basic data that links the result of determination of whether the molded product molded by the injection molding machine 1 is a good product or a bad product with the combined data 90 generated by the combined data generation unit 115 of the monitoring data acquisition unit 111. The basic data generated by the basic data generation unit 121 is stored in the storage unit 140 by associating the date and time when the monitoring data is acquired or the date and time when the molded product is molded.

[0071] The monitoring data extraction unit 122 extracts the monitoring data of the molding of multiple qualified products, i.e., the monitoring data of qualified products, and the monitoring data of the molding of unqualified products, i.e., the monitoring data of unqualified products, from the basic data generated by the basic data generation unit 121. That is, since the basic data establishes a connection between the judgment result of the molded product molded by the injection molding machine 1 and the monitoring data of the molding of the molded product, i.e., the combined data 90, the monitoring data of qualified products and the monitoring data of unqualified products are distinguished and extracted separately from the judgment result of the molded product and the monitoring data of each molded product. That is, the monitoring data extraction unit 122 extracts the setting timing data 91 and the sample point data 97 contained in the combined data 90 established in connection with the judgment result of the molded product as the monitoring data of qualified products or the monitoring data of unqualified products.

[0072] In addition, the monitoring data extraction unit 122 extracts the monitoring data of the same type of qualified products and the monitoring data of the unqualified products according to each unqualified type of unqualified products. That is, the monitoring data extraction unit 122 associates the setting timing data 91 and the sample point data 97 extracted as the monitoring data of the unqualified products, and the setting timing data 91 and the sample point data 97 of the same type as these setting timing data 91 and the sample point data 97 in the monitoring data of the qualified products with the unqualified types of the unqualified products and extracts them. The monitoring data of the qualified products and the monitoring data of the unqualified products extracted by the monitoring data extraction unit 122 are respectively stored in the storage unit 140.

[0073] Examples of the types of defects mentioned here include short shots, burrs, flow marks, silver streaks, spray marks, burns, fogging, whitening, weld lines, warping, cracks, yellowing, shrinkage cavities, and blowholes.

[0074] Short shots are defects caused by incomplete filling of the resin. Burrs are defects caused by the molten material entering the gap of the mold and the remaining part on the periphery of the molded product. Flow marks are defects caused by the resin flowing in the mold forming streaks and appearing on the surface of the molded product. Silver streaks are defects caused by white lines that appear along the flow of the resin starting from the gate, the opening of the mold's flow path for the molded product.

[0075] Spray marks are defects that show traces of the flow of the resin after passing through the gate, and burn marks are defects that the end of the resin is burnt as if it has been burned. Fogging is a defect that the surface is fogged white, especially in the molding of transparent resins. Whitening is a defect that the solidified resin is locally stretched due to excessive force. Weld lines are defects that appear as linear marks at the part where the flow fronts that branched due to the shape of the mold cavity meet each other.

[0076] Warpage is a defect in which the molded product is warped. Cracking is a defect in which cracks occur on the surface of the molded product. Yellowing is a defect in which the resin surface changes color to yellow. Shrinkage is a defect in which the surface becomes concave due to molding shrinkage. Air holes are a defect in which a vacuum is generated inside due to molding shrinkage.

[0077] The high-impact monitoring data extracting unit 123 extracts a plurality of monitoring data in order from the one with the highest influence on the defective type of the defective product among the plurality of monitoring data extracted by the monitoring data extracting unit 122. That is, when the injection molding machine 1 performs molding, since the operation state of the injection molding machine 1 and the state of the resin during molding have an influence on the quality of the molded product, it is conceivable that there is monitoring data of the cause of the defectiveness due to deviation from the reference value during molding of the molded product among the molded products determined as defective products.

[0078] Therefore, the high-impact monitoring data extraction unit 123 extracts a plurality of monitoring data having a high degree of influence on the unqualified type of the molded product determined as a defective product from the plurality of monitoring data extracted by the monitoring data extraction unit 122, for each defective type, as high-impact monitoring data. That is, the high-impact monitoring data extraction unit 123 extracts a plurality of setting timing data 91 and sample point data 97 having a high degree of influence on the defective type of the molded product determined as a defective product, for each defective type, as high-impact monitoring data. The high-impact monitoring data extracted by the high-impact monitoring data extraction unit 123 is associated with the defective type and stored in the storage unit 140.

[0079] The judgment parameter calculation unit 124 calculates the judgment parameter as a parameter used in the qualification judgment of the molded product molded by the injection molding machine 1 based on the monitoring data extracted by the monitoring data extraction unit 122. The judgment parameter is a value calculated based on the Mahalanobis distance calculated by applying the well-known MT (Mahalanobis-Taguchi) method. Specifically, in this embodiment, the value of the square of the Mahalanobis distance (MD value) calculated by applying the MT method is used as the judgment parameter. In this embodiment, the value of the square of the MD value calculated by applying the MT method to the monitoring data is called the judgment parameter. The judgment parameter calculation unit 124 calculates the judgment parameter of the monitoring data of the qualified product and the monitoring data of the unqualified product extracted by the monitoring data extraction unit 122, as the parameter of the qualified product. In addition, the judgment parameter calculation unit 124 calculates the judgment parameter of the monitoring data of the qualified product and the monitoring data of the unqualified product extracted by the monitoring data extraction unit 122, as the parameter of the unqualified product.

[0080] Furthermore, the determination parameter calculation unit 124 calculates the conforming parameters of the conforming monitor data and the failing parameters of the failing monitor data, among the high-impact monitor data extracted by the high-impact monitor data extraction unit 123 .

[0081] The molding parameter calculation unit 125 calculates molding parameters, which are parameters for determining the quality of a molded product during molding by the injection molding machine 1 based on the same type of monitoring data as the high-impact monitoring data among the plurality of monitoring data of the setting timing data 91 and the sample point data 97 included in the combined data 90 generated by the combined data generation unit 115. The molding parameters are calculated as determination parameters of the monitoring data acquired by the monitoring data acquisition unit 111 during molding by the injection molding machine 1, corresponding to the monitoring data extracted by the monitoring data extraction unit 122. That is, the molding parameter calculation unit 125 calculates the determination parameters obtained by applying the known MT method in the same manner as the determination parameter calculation unit 124, as molding parameters, based on the same type of monitoring data as the high-impact monitoring data during molding by the injection molding machine 1 and the monitoring data extracted by the monitoring data extraction unit 122. The molding parameter calculation unit 125 that calculates the molding parameters in this way calculates the molding parameters for each defective type of defective products in the monitoring data extracted by the monitoring data extraction unit 122 .

[0082] The recommended value calculation unit 126 calculates a recommended judgment value based on the monitoring data extracted by the monitoring data extraction unit 122. The recommended judgment value is a threshold value recommended as a reference for judging whether a defective product has occurred in a molded product during molding by the injection molding machine 1. The recommended judgment value calculated by the recommended value calculation unit 126 is calculated as a threshold value of a judgment parameter of the monitoring data acquired by the monitoring data acquisition unit 111 during molding in the injection molding machine 1, corresponding to the monitoring data extracted by the monitoring data extraction unit 122.

[0083] The pass / fail judgment unit 127 judges whether a defective product has occurred in the molded product molded by the injection molding machine 1 based on the combined data 90 generated by the combined data generation unit 115. That is, the pass / fail judgment unit 127 compares the molding parameters calculated by the molding parameter calculation unit 125 based on the setting timing data 91 and the sample point data 97 included in the combined data 90 with the judgment threshold value as the threshold value for the molding parameters, and judges whether a defective product has occurred in the molded product molded by the injection molding machine 1. The judgment threshold value used by the pass / fail judgment unit 127 to judge whether a defective product has occurred in the molded product uses the judgment recommended value calculated by the recommended value calculation unit 126 as the judgment threshold value, or uses the value set by the user based on the judgment recommended value as the judgment threshold value. In addition, the pass / fail judgment unit 127 judges whether a defective product has occurred in the molded product molded by the injection molding machine 1 for each defective type of the defective product.

[0084] When the pass / fail judgment unit 127 judges that a defective product has occurred in the molded product during molding in the injection molding machine 1, the abnormal data extraction unit 128 extracts the monitoring data with the highest degree of abnormality from the plurality of monitoring data of the same type as the defective monitoring data extracted by the monitoring data extraction unit 122. In the present embodiment, the abnormal data extraction unit 128 extracts the monitoring data with the largest deviation from the conforming monitoring data in the high-impact monitoring data from the plurality of monitoring data of the same type as the plurality of high-impact monitoring data during molding in the injection molding machine 1 as the monitoring data with the highest degree of abnormality.

[0085] <Function of the Acceptability Judgment System 200 of the Injection Molding Machine 1>

[0086] The pass / fail judgment system 200 of the injection molding machine 1 of the present embodiment includes the above-mentioned structure, and its function is described below. The injection molding machine 1 takes one injection-molding action as one cycle, and repeatedly executes the cycle of the injection-molding action. Each cycle includes a plurality of processes for the injection of molding materials and the molding of products. Each cycle includes, for example, a mold closing process, a pressure increasing process, a filling (injection) process, a pressure holding process, a metering process, a mold opening process, and an ejection process.

[0087] When the injection molding machine 1 performs injection molding as described above, the control device 100 obtains monitoring data detected by each sensor of the injection molding machine 1, and repeatedly performs the injection molding operation while monitoring the state of the operation of the injection molding machine 1 based on the monitoring data. In this embodiment, as monitoring of the state of the operation of the injection molding machine 1 based on the monitoring data, two types of monitoring are performed: determining whether the monitoring data satisfies a specified value, and determining whether a defective product occurs in a molded product molded by the injection molding machine 1.

[0088] <Monitoring data judgment screen 50>

[0089] First, the monitoring data determination screen 50 displayed on the display unit 170 and used in the control for determining whether the monitoring data satisfies the prescribed value will be described. Figure 8 50 is an explanatory diagram of the monitoring data determination screen. Figure 8 While the monitoring data determination screen 50 shown is displayed on the display unit 170 , the control device 100 determines whether the monitoring data satisfies the prescribed value. Figure 8 The monitoring data determination screen 50 shown includes a monitoring data name display portion 51 , a reference value input portion 52 , an allowable value input portion 53 , and an alarm selection portion 54 .

[0090] The monitoring data name display unit 51 displays the type of monitoring data for which the control device 100 determines whether it is normal, that is, the name of the monitoring data. Specifically, the monitoring data name display unit 51 displays the names of the set timing data 91 and the sample point data 97 as monitoring data. When the control device 100 determines whether the monitoring data is normal, the monitoring data to be determined can be selected, and the monitoring data name display unit 51 of the monitoring data determination screen 50 can display the selected monitoring data.

[0091] 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 input the reference value for each monitoring data using the input unit 160. When the user inputs the reference value to the reference value input unit 52, the reference value acquisition unit 116 of 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.

[0092] The allowable value input unit 53 is a unit for inputting an allowable value for the reference value of the monitoring data input by the reference value input unit 52, that is, a positive and negative range centered on the reference value. The allowable value input unit 53 can input an allowable value for the reference value for each monitoring data using the input unit 160. When the user inputs an allowable value to the allowable value input unit 53, the allowable value acquisition unit 117 of the processing unit 110 of the control device 100 acquires the allowable value input by the user and stores it in the storage unit 140.

[0093] The alarm selection unit 54 is a part that selects whether to enable or disable an alarm to notify the user that the monitoring data exceeds the allowable value. The alarm selection unit 54 can select whether to enable or disable the alarm for each monitoring data using the input unit 160 .

[0094] <Determination control of whether monitoring data meets the specified value>

[0095] Next, a description will be given of a control flow when the injection molding machine 1 monitors the operating state of the injection molding machine 1 while determining whether or not monitoring data satisfies a predetermined value when molding a molded product.

[0096] Fig. 9 1 is a flowchart showing the control flow when performing the control for determining whether the monitoring data satisfies the prescribed value. In the control for determining whether the monitoring data satisfies the prescribed value, while the injection molding machine 1 is molding the molded product, the monitoring data is acquired from each sensor provided in the injection molding machine 1 (step ST11). The monitoring data is acquired by the monitoring data acquisition unit 111 of the processing unit 110 of the control device 100.

[0097] The monitoring data acquisition unit 111 acquires the setting timing data 91 from each sensor provided in the injection molding machine 1 through the setting timing data acquisition unit 112, and acquires the waveform data 95 through the waveform data acquisition unit 113. Furthermore, the monitoring data acquisition unit 111 acquires the sample point data 97 from the waveform data 95 acquired by the waveform data acquisition unit 113 through the sample point data acquisition unit 114. Thus, the monitoring data acquisition unit 111 acquires the setting timing data 91 and the sample point data 97 as monitoring data.

[0098] Once the monitoring data is acquired, it is determined whether the acquired monitoring data deviates from the allowable value (step ST12). The determination of whether the monitoring data deviates from the allowable value is performed by the monitoring data determination unit 118 of the processing unit 110 of the control device 100. The monitoring data determination unit 118 determines whether the monitoring data acquired by the monitoring data acquisition unit 111 deviates from the range of allowable values ​​centered on the reference value of the monitoring data, or whether the monitoring data falls within the range of allowable values. In this case, the reference value is a reference value acquired by the reference value acquisition unit 116 through input by the user to the reference value input unit 52 of the monitoring data determination screen 50, and the allowable value is an allowable value acquired by the allowable value acquisition unit 117 through input to the allowable value input unit 53 of the monitoring data determination screen 50.

[0099] When the monitoring data determination unit 118 determines that the monitoring data acquired by the monitoring data acquisition unit 111 does not deviate from the allowable value (step ST12 : “No” determination), molding of the molded product by the injection molding machine 1 is continued.

[0100] On the other hand, when the monitoring data determination unit 118 determines that the monitoring data acquired by the monitoring data acquisition unit 111 deviates from the allowable value (step ST12: "Yes" determination), an alarm is displayed (step ST13). For example, the control device 100 causes the display unit 170 to display an alarm. With regard to the alarm, when the monitoring data for which the alarm selection unit 54 of the monitoring data determination screen 50 has selected to enable the alarm deviates from the allowable value, the display unit 170 is caused to display an alarm indicating that the monitoring data deviates from the allowable value.

[0101] In addition, when the injection molding machine 1 is equipped with a removal robot (omitted in the figure) or a chute (omitted in the figure) for removing defective products, when it is determined that the monitoring data deviates from the allowable value, the molded products determined to be defective products are allocated by the removal robot or the chute to a storage place for defective products.

[0102] When the molded product is molded by the injection molding machine 1, these steps are repeated to perform molding while determining whether the monitoring data satisfies the prescribed value.

[0103] Furthermore, in the pass / fail determination system 200 of the injection molding machine 1 according to the present embodiment, in addition to determining whether the monitoring data satisfies the specified value, it is also possible to determine whether a defective product has occurred in the molded product based on the monitoring data when the molded product is molded by the injection molding machine 1. Next, a method for monitoring the state of the operation of the injection molding machine 1 by determining whether a defective product has occurred in the molded product molded by the injection molding machine 1 based on the monitoring data will be described.

[0104] <Generation of basic data>

[0105] When determining whether or not a defective product has occurred in a molded product based on monitoring data, first, basic data serving as a basis for the determination method is generated.

[0106] Fig.10 1 is a flowchart showing the order of generating basic data. When generating basic data, first, the injection molding machine 1 is used to perform molding using a mold for which the molded product is to be judged as acceptable (step ST21). In this case, the molding performed by the injection molding machine 1 is not the actual molding of the product, but the molding for generating basic data.

[0107] Once molding is performed, a finished product inspection of the molded product is performed for each shot performed by the injection molding machine 1, and the result of the pass / fail judgment is input to the control device 100 using the input unit 160 (step ST22). The finished product inspection of the molded product is performed manually by the user to determine whether the molded product is a pass or a fail product. That is, the finished product inspection of the molded product is performed by the user visually determining whether the molded product is a pass or a fail product.

[0108] When inputting to the control device 100, for example, a screen for inputting whether the molded product is a good product or a bad product is displayed on the display unit 170, and the input unit 160 is used to input the result of determining whether the molded product is a good product for each shot performed by the injection molding machine 1 on the input screen of the display unit 170. At this time, if the molded product is a bad product, the name of the bad product type, that is, the bad name, is also input together.

[0109] Once the pass / fail judgment result of the molded product is input to the control device 100, the control device 100 associates the input pass / fail judgment result with the monitoring data and stores it in the storage unit 140 (step ST23). In detail, the control device 100 associates the input pass / fail judgment result of the molded product with the monitoring data acquired by the monitoring data acquisition unit 111 of the processing unit 110 when the molded product is molded by the injection molding machine 1. The monitoring data associated by the basic data generation unit 121 is the combined data 90 generated by the combined data generation unit 115 of the monitoring data acquisition unit 111. That is, the basic data generation unit 121 associates the monitoring data group 92 composed of a plurality of set timing data 91 and a plurality of sample point data 97, that is, the combined data 90, with the input pass / fail judgment result of the molded product. At this time, the basic data generation unit 121 also associates the unqualified name of the unqualified product. The basic data generating unit 121 stores the data thus linked as basic data in the storage unit 140 .

[0110] When the basic data generated in the above steps has acquired a preset number or more of the monitoring data of shots of qualified products and the monitoring data of shots of defective products, the acquisition of the basic data is terminated.

[0111] Once the basic data is generated in these steps, the parameters used for determining the quality of the molded product when the molded product is molded as a product are set. The procedure for setting the parameters used for determining the quality of the molded product is described below.

[0112] <Setting parameters used in determining whether molded products are acceptable>

[0113] Fig.11 This is a flowchart showing a procedure for setting parameters used in determining whether a molded product is acceptable. Fig.12 60 is a schematic diagram of the pass / fail determination setting parent screen 60 used when setting the pass / fail determination parameters. After generating the basic data, when setting the parameters used in the pass / fail determination of the molded product, first, Fig.12The pass / fail determination setting parent screen 60 shown in the figure is displayed on the display unit 170, and the pass / fail determination setting parent screen 60 is used. The pass / fail determination setting parent screen 60 is operated by using the input unit 160 to operate the control device 100, and the program related to the pass / fail determination setting parent screen 60 is called and displayed on the display unit 170.

[0114] The pass / fail determination setting parent screen 60 includes a failure name input section 61 for inputting a failure name of a failure product, and a setting button 62 corresponding to the failure name input section 61. The pass / fail determination setting parent screen 60 includes a plurality of failure name input sections 61, and the setting button 62 is set for each failure name input section 61. In addition, in the pass / fail determination setting parent screen 60, each failure name input section 61 is assigned a different number and displayed.

[0115] When setting the parameters used in the pass / fail judgment of the molded product, the display unit 170 displays the pass / fail judgment setting parent screen 60, and the input unit 160 is used to input the unqualified name to be logged into the unqualified name input unit 61 of the pass / fail judgment setting parent screen 60, and the setting button 62 corresponding to the unqualified name input unit 61 in which the unqualified name is input is pressed (step ST31).

[0116] <Pass / Fail Judgment Setting Subscreen 70>

[0117] Fig.13 1 is a schematic diagram of the pass / fail determination setting sub-screen 70. When the setting button 62 is pressed in the pass / fail determination setting parent screen 60, the control device 100 displays the pass / fail determination setting sub-screen 70 corresponding to the pressed setting button 62 on the display unit 170. The pass / fail determination setting sub-screen 70 is, for example, Fig.13 As shown, it has an unqualified information display unit 71, an extraction period input unit 72, a data display unit 73 for pass / fail judgment, a sample injection number display unit 74, an unqualified parameter display unit 75, a judgment parameter histogram display unit 76 and a judgment recommended value display unit 77.

[0118] The failure information display section 71 is a section that displays information related to the failure name input to the failure name input section 61 corresponding to the setting button 62 pressed on the pass / fail determination setting parent screen 60. The failure information display section 71 displays the failure name input by the failure name input section 61, the number assigned to the failure name input section 61, the molding condition number indicating the type of mold used when generating the basic data, and the like.

[0119] The extraction period input unit 72 is a part for inputting the period of the monitoring data extracted by the monitoring data extracting unit 122 from the monitoring data included in the generated basic data. That is, the extraction period input unit 72 is a part for inputting the monitoring data of which period is extracted as the monitoring data extracted by the monitoring data extracting unit 122.

[0120] The pass / fail determination data display section 73 is a portion that displays monitoring data used for determining the failure type, which is the failure name for which the setting button 62 is pressed in the pass / fail determination setting parent screen 60 , among the monitoring data extracted by the monitoring data extraction section 122 .

[0121] The sample shot count display unit 74 is a portion for displaying the shot count of the injection molding machine 1 when generating the basic data. Specifically, the sample shot count display unit 74 is a portion for displaying the shot count of qualified products and the shot count of unqualified products in the monitoring data extracted by the monitoring data extraction unit 122, respectively.

[0122] The unqualified parameter display unit 75 is a part for displaying the unqualified parameters in the basic data, and the unqualified parameters are the judgment parameters of the monitoring data extracted by the monitoring data extraction unit 122 for the unqualified product name when the setting button 62 is pressed in the pass / fail judgment setting parent screen 60. The judgment parameter mentioned here is the value of the square of the Mahalanobis distance calculated by applying the well-known MT method. In the unqualified parameter display unit 75, the average unqualified parameters, the maximum unqualified parameters and the minimum unqualified parameters of the unqualified monitoring data in the monitoring data extracted from the basic data by the monitoring data extraction unit 122 are displayed.

[0123] The judgment parameter histogram display unit 76 is a part that displays the monitoring data for the qualified product and the monitoring data for the unqualified product extracted from the basic data by the monitoring data extraction unit 122 in the form of a histogram for each judgment parameter for the monitoring data extracted by the monitoring data extraction unit 122. That is, the judgment parameter histogram display unit 76 is a part that displays the parameters for the qualified product and the parameters for the unqualified product calculated by the judgment parameter calculation unit 124.

[0124] The recommended judgment value display section 77 is a section that displays the recommended value of the threshold value for judging whether the molded product is a defective product with the defective name of which the setting button 62 is pressed in the pass / fail judgment setting parent screen 60 based on the judgment parameters calculated based on the monitoring data when molding is performed by the injection molding machine 1.

[0125] In the pass / fail determination setting parent screen 60 (refer to Fig.12), once the user presses the setting button 62, the pass / fail determination setting sub-screen 70 (refer to Fig.13 ) is displayed on the display unit 170, and the user uses the pass / fail determination setting sub-screen 70 to input the data extraction period (step ST32). The data extraction period is input using the extraction period input unit 72 of the pass / fail determination setting sub-screen 70 using the input unit 160.

[0126] Once the data extraction period is input, the control device 100 extracts the monitoring data of the shot that matches the unqualified name for which the user pressed the setting button 62 in the pass / fail judgment setting parent screen 60 and the monitoring data of the shot of the qualified product from the basic data (step ST33). The extraction is performed by the monitoring data extraction unit 122 of the processing unit 110 of the control device 100. That is, the monitoring data extraction unit 122 extracts the monitoring data when the unqualified product is formed, that is, the monitoring data when the unqualified product is formed, from the basic data, which corresponds to the unqualified name for which the user pressed the setting button 62 in the pass / fail judgment setting parent screen 60. Furthermore, the monitoring data extraction unit 122 extracts the monitoring data when the qualified product is formed, which is the same type of monitoring data as the monitoring data when the unqualified product is formed, from the basic data.

[0127] Specifically, the monitoring data for failure is the setting timing data 91 and the sample point data 97 included in the combined data 90 of the shot for molding the failure product corresponding to the failure type of the failure name for which the setting button 62 was pressed. Furthermore, the monitoring data for good products is the setting timing data 91 and the sample point data 97 of the same type as the monitoring data for failure, and is the setting timing data 91 and the sample point data 97 included in the combined data 90 of the shot for molding the good products.

[0128] Once the monitoring data is extracted from the basic data by the monitoring data extraction unit 122, the influence is calculated for each monitoring data (step ST34). The influence in this case is the influence of the monitoring data extracted by the monitoring data extraction unit 122 corresponding to the failure type of the failure name for which the user pressed the setting button 62 in the pass / fail determination setting parent screen 60. That is, the influence in this case is an indicator indicating whether a failure has occurred by how much the monitoring data of the setting timing data 91 and the sample point data 97 extracted by the monitoring data extraction unit 122 contribute to the failure type selected by the user in the pass / fail determination setting parent screen 60.

[0129] The calculation of the influence degree is performed by the high influence degree monitoring data extraction unit 123 of the processing unit 110 of the control device 100. The high influence degree monitoring data extraction unit 123 calculates the influence degree by the following formula (1) based on the plurality of conforming monitoring data and non-conforming monitoring data extracted by the monitoring data extraction unit 122. The calculation of the influence degree is performed for each type of monitoring data, that is, for the same type of setting timing data 91 or sample point data 97.

[0130] Influence = (average value of monitoring data for qualified products - average value of monitoring data for unqualified products) / standard deviation of monitoring data for qualified products σ…(1)

[0131] Once the influence of the monitoring data is calculated, a plurality of monitoring data with a high influence is then extracted (step ST35). The extraction of monitoring data with a high influence is continuously performed by the high influence monitoring data extraction unit 123 that has calculated the influence. The high influence monitoring data extraction unit 123 extracts a plurality of monitoring data in sequence from the one with the highest influence on the type of defective product selected by the user in the pass / fail determination setting parent screen 60 among the plurality of monitoring data extracted by the monitoring data extraction unit 122. That is, the high influence monitoring data extraction unit 123 extracts a plurality of monitoring data, i.e., a plurality of setting timing data 91 and sample point data 97 in sequence from the one with a larger influence calculated by the above formula (1).

[0132] In this case, the number of monitoring data extracted by the high-impact monitoring data extraction unit 123 in order from the one with the highest impact is arbitrary. The number of monitoring data extracted by the high-impact monitoring data extraction unit 123 based on the calculated impact can be arbitrarily selected by the user, for example, between 2 and 10. Therefore, for example, when the number of monitoring data extracted by the high-impact monitoring data extraction unit 123 is set to 3, the high-impact monitoring data extraction unit 123 extracts the monitoring data of the three set timing data 91 and the sample point data 97 in order from the one with the highest impact on the non-conforming type.

[0133] Regarding the number of monitoring data extracted by the high-impact monitoring data extraction unit 123, for example, in the case of unqualified types of monitoring data whose causes are known to some extent, the number of monitoring data may be set to be small, and in the case of unqualified types of monitoring data whose causes are unknown, the number of monitoring data may be set to be large. The number of monitoring data extracted by the high-impact monitoring data extraction unit 123 may be set to any number by the user according to the unqualified types, etc.

[0134] <Grouping of monitoring data>

[0135] In addition, when extracting monitoring data with a high impact on the type of unqualified products, the high-impact monitoring data extraction unit 123 groups them so that the monitoring data with a high correlation coefficient in the setting timing data 91 and the sample point data 97 contained in the combined data 90 are in the same group, and only one monitoring data with the highest impact is extracted from the same group.

[0136] Fig.14 This is an illustration of the grouping of monitoring data. For monitoring data, the correlation coefficients of different types of monitoring data are calculated, and multiple monitoring data with high correlation coefficients, such as Fig.14 The monitoring data is grouped in advance and stored in the storage unit 140. Fig.14 In FIG. 1 , two groups are illustrated as an example, but 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.

[0137] When extracting monitoring data with a high degree of influence on the type of non-conformity, the high-impact monitoring data extracting unit 123 refers to the grouped data stored in the storage unit 140, extracts one monitoring data with the highest degree of influence from one group, and extracts a set number of monitoring data. The monitoring data extracted by the high-impact monitoring data extracting unit 123 in this way is displayed on the pass / fail determination data display unit 73 of the pass / fail determination setting sub-screen 70 together with the calculated degree of influence.

[0138] Next, the parameters for the conforming product as the determination parameters for the conforming product monitoring data and the parameters for the non-conforming product as the determination parameters for the non-conforming product monitoring data are calculated from the monitoring data extracted by the monitoring data extraction unit 122 (step ST36). The calculation of the parameters for the conforming product and the parameters for the non-conforming product is performed by the determination parameter calculation unit 124 of the processing unit 110 of the control device 100.

[0139] In this embodiment, the conforming product parameter and the failing product parameter are calculated based on the conforming product monitoring data and the failing product monitoring data among the high-impact monitoring data extracted by the high-impact monitoring data extracting unit 123 as monitoring data.

[0140] When the number of high-impact monitoring data extracted by the high-impact monitoring data extraction unit 123 is, for example, three, the judgment parameter calculation unit 124 uses the qualified product monitoring data and the unqualified product monitoring data of each of the three high-impact monitoring data to calculate the qualified product parameters and the unqualified parameters by the following formula (2).

[0141] [Formula 1]

[0142]

[0143] In formula (2), MD 2 The parameters used to determine whether the molded product is qualified or not based on the monitoring data, i.e., the judgment parameters, are satisfied for both qualified and unqualified products. In this embodiment, the MD calculated in formula (2) is 2 As parameters for qualified products and unqualified products, that is, parameters for judging monitoring data. In addition, in formula (2), a, b, and c are monitoring data, and the order of a, b, and c is the monitoring data with a higher degree of influence on the unqualified type, and S represents the sum of squares. In formula (2), since three high-impact monitoring data are used to calculate the parameters for qualified products and the parameters for unqualified products, the monitoring data are three, namely a, b, and c, but the number of monitoring data in formula (2) increases or decreases according to the number of high-impact monitoring data extracted by the high-impact monitoring data extraction unit 123.

[0144] Furthermore, the determination parameter calculation unit 124 calculates the failure parameters based on the failure monitoring data extracted by the monitoring data extraction unit 122, also in the monitoring data other than the high-impact monitoring data. These failure parameters calculated by the determination parameter calculation unit 124 are displayed on the failure parameter display unit 75 of the pass / fail determination setting sub-screen 70.

[0145] Next, based on the monitoring data for qualified products, a recommended value for judging whether the molded product is qualified or not is calculated (step ST37). The recommended value for judging is calculated by the recommended value calculation unit 126 of the processing unit 110 of the control device 100. In this embodiment, the recommended value calculation unit 126 calculates the recommended value for judging by the following formula (3) using the qualified product parameter calculated based on the monitoring data for qualified products and the unqualified parameter calculated based on the monitoring data for unqualified products.

[0146] [Formula 2]

[0147]

[0148] In formula (3), MD 2 Parameters for qualified products, MD′ 2 In the formula (3), σ is the standard deviation of the parameter when the product is good, and σ′ is the standard deviation of the parameter when the product is bad.

[0149] The recommended determination value calculated by the recommended value calculation unit 126 is displayed on the recommended determination value display unit 77 of the pass / fail determination setting sub-screen 70 .

[0150] In this embodiment, by setting the parameters for the pass / fail judgment of the molded product using these pass / fail judgment setting parent screen 60 and the pass / fail judgment setting child screen 70 and then performing molding of the molded product, it is possible to determine whether defective products have occurred in the molded product molded by the injection molding machine 1 based on the monitoring data.

[0151] <Pass / Fail Judgment Screen 80>

[0152] Next, the pass / fail determination screen 80 displayed on the display unit 170 and used for control for determining whether or not defective products have occurred in the molded products molded by the injection molding machine 1 based on the monitoring data will be described. Fig.15 80. Fig.15 While the pass / fail determination screen 80 shown is displayed on the display unit 170 , the control device 100 determines whether or not a defective product has occurred in the molded product molded by the injection molding machine 1 . Fig.15 The pass / fail determination screen 80 shown includes a failure name display portion 81 , a determination threshold input portion 82 , and an alarm selection portion 83 .

[0153] The defective name display unit 81 displays the name of the defective type of defective products when the control device 100 performs the pass / fail judgment of the molded product molded by the injection molding machine 1, that is, the defective name. When the control device 100 performs the judgment on whether the molded product is a defective product, the defective name to be judged can be selected, and the defective name display unit 81 of the pass / fail judgment screen 80 can display the selected defective name.

[0154] The judgment threshold input unit 82 is a part for inputting a threshold value for the judgment parameter, i.e., a judgment threshold value, when judging whether the molded product is a defective product based on the judgment parameter of the monitoring data obtained when the molded product is molded. The judgment threshold input unit 82 can input the judgment threshold value for each defective name using the input unit 160. The judgment threshold input unit 82 inputs the judgment recommended value calculated by the recommended value calculation unit 126 as the default value of the judgment threshold value, and the user appropriately changes the judgment recommended value according to the molded product, the defective type, etc., so that a value suitable for the molded product or the defective type, etc., can be set as the judgment threshold value.

[0155] The alarm selection unit 83 is a part for selecting whether to enable or disable the alarm to notify the user that the determination parameter of the monitoring data exceeds the determination threshold when the determination parameter of the monitoring data exceeds the determination threshold. The alarm selection unit 83 can use the input unit 160 to select whether to enable or disable the alarm for each failure name.

[0156] <Determination and control of whether defective products have occurred in molded products>

[0157] By setting parameters for performing pass / fail judgment of molded products using the pass / fail judgment setting parent screen 60 and the pass / fail judgment setting child screen 70, once the judgment threshold is set, control for judging whether a defective product has occurred in the molded product can be implemented. Next, control for judging whether a defective product has occurred in the molded product molded by the injection molding machine 1 based on monitoring data will be described.

[0158] Fig.16 This is a flowchart showing the control process when performing control based on monitoring data to determine whether a defective product has occurred in a molded product. In the control of determining whether a molded product is qualified or not, while the molded product is molded by the injection molding machine 1, monitoring data is obtained from each sensor provided in the injection molding machine 1 (step ST41). The monitoring data is obtained by the monitoring data acquisition unit 111 of the processing unit 110 of the control device 100. The monitoring data acquisition unit 111 acquires the monitoring data for each shot performed by the injection molding machine 1 and updates the data. That is, the monitoring data acquisition unit 111 acquires the setting timing data 91 and the sample point data 97 for each shot performed by the injection molding machine 1, and updates the data by generating the combined data 90.

[0159] Once the monitoring data is acquired, the molding parameters are calculated (step ST42). The molding parameters are calculated by the molding parameter calculation unit 125 included in the processing unit 110 of the control device 100. The molding parameter calculation unit 125 calculates the molding parameters by calculating the judgment parameters of the same type of monitoring data as the high-impact monitoring data extracted by the high-impact monitoring data extraction unit 123, among the monitoring data acquired by the monitoring data acquisition unit 111. That is, the molding parameter calculation unit 125 calculates the molding parameters by calculating the judgment parameters of the same type of monitoring data as the high-impact monitoring data, corresponding to the plurality of monitoring data of the plurality of setting timing data 91 and the sample point data 97 extracted from the basic data by the monitoring data extraction unit 122.

[0160] Since the high-impact monitoring data is calculated for each type of non-conformity, that is, for each non-conformity name, the molding parameters calculated based on the monitoring data of the same type as the high-impact monitoring data are also calculated for each non-conformity name. Similar to the case where the judgment parameters for monitoring data are obtained by the judgment parameter calculation unit 124, the molding parameters are calculated by applying the MT method to obtain the square value of the Mahalanobis distance of the monitoring data of the same type as the high-impact monitoring data corresponding to the multiple monitoring data extracted from the basic data. Regarding the molding parameters, the monitoring data is obtained by the monitoring data acquisition unit 111, and the molding parameters are calculated at the timing when the data is updated.

[0161] Once the molding parameters are calculated, it is then determined whether the molding parameters are greater than the determination threshold value (step ST43). This determination is performed by the pass / fail determination unit 127 included in the processing unit 110 of the control device 100. The pass / fail determination unit 127 compares the molding parameters calculated by the molding parameter calculation unit 125 with the determination threshold value, which is a threshold value for the molding parameters, and determines whether a defective product has occurred in the molded product molded by the injection molding machine 1.

[0162] The determination threshold used in this determination is a value set by the determination threshold input unit 82 of the pass / fail determination screen 80. As the determination threshold, a determination recommended value calculated by the recommended value calculation unit 126 or a value set by the user based on the determination recommended value is set. The pass / fail determination unit 127 compares the determination threshold set in this way with the molding parameters calculated by the molding parameter calculation unit 125 for each unacceptable name, and determines for each unacceptable name whether the molding parameters are greater than the determination threshold.

[0163] When the pass / fail determination unit 127 determines that the molding parameter calculated by the molding parameter calculation unit 125 is not greater than the determination threshold value (step ST43: "No" determination), molding of the molded product by the injection molding machine 1 is continued.

[0164] In contrast, when it is determined by the pass / fail determination unit 127 that the molding parameter calculated by the molding parameter calculation unit 125 is greater than the determination threshold (step ST43: "Yes" determination), the monitoring data with the highest abnormality is extracted (step ST44). The monitoring data with the highest abnormality is extracted by the abnormal data extraction unit 128 of the processing unit 110 of the control device 100. Since the determination of whether the molding parameter is greater than the determination threshold is performed for each unqualified name, the abnormal data extraction unit 128 extracts the monitoring data with the highest abnormality for the unqualified name whose molding parameter is determined to be greater than the determination threshold. That is, the abnormal data extraction unit 128 extracts the setting timing data 91 or sample point data 97 with the highest abnormality for the unqualified name whose molding parameter is determined to be greater than the determination threshold.

[0165] The abnormal data extraction unit 128 extracts the monitoring data with the largest deviation from the qualified product monitoring data in the high-impact monitoring data from the monitoring data obtained by the monitoring data acquisition unit 111, as the monitoring data with the highest abnormality. Specifically, the abnormal data extraction unit 128 calculates the deviation by the following formula (4), and extracts the monitoring data with the largest deviation, that is, the setting timing data 91 or the sample point data 97 with the largest deviation, as the monitoring data with the highest abnormality.

[0166] Deviation = |(average value of monitoring data for qualified products - monitoring data for corresponding injection) / (standard deviation σ of monitoring data for qualified products)|…(4)

[0167] 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-influence monitoring data, calculates the degree of deviation for each monitoring data, and extracts the monitoring data with the highest degree of abnormality.

[0168] Once the monitoring data with the highest abnormality is extracted, a message is displayed (step ST45). The display of the message is, for example, displayed by the control device 100 on the display unit 170. Regarding the message, when it is determined that the molding parameter of the unqualified name for which the alarm is set to be effective by the alarm selection unit 83 of the pass / fail judgment screen 80 is greater than the judgment threshold, the display unit 170 is caused to display a message reporting the monitoring data with the highest abnormality together with the unqualified name. That is, if a failure of the unqualified name occurs, the display unit 170 is caused to display a message that the value of the monitoring data with the highest abnormality is abnormal.

[0169] When a message is displayed on the display unit 170 , the injection molding machine 1 is adjusted so that the value of the displayed monitoring data becomes a normal value, thereby removing the failure type of the failure name displayed on the display unit 170 .

[0170] In addition, when the injection molding machine 1 is equipped with a removal robot (omitted in the figure) or a chute (omitted in the figure) for removing defective products, when it is determined that the parameters during molding are greater than the determination threshold, the molded products formed by the injection molding can also be allocated by the removal robot or the chute to a storage place for defective products.

[0171] <Effects of Implementation Methods>

[0172] In the above-described pass / fail determination system 200 for the injection molding machine 1 of the embodiment, when the molded product is molded by the injection molding machine 1, the set timing data acquisition unit 112 acquires the set timing data 91, which is monitoring data of each preset timing, and the sample point data acquisition unit 114 acquires the sample point data 97, which is data at a plurality of sample points 96 at different times, in the waveform data 95 acquired by the waveform data acquisition unit 113. Furthermore, the combined data generation unit 115 combines the monitoring data group 92 composed of the plurality of set timing data 91 acquired by the set timing data acquisition unit 112 and the plurality of sample point data 97 acquired by the sample point data acquisition unit 114 to generate the combined data 90, and the pass / fail determination unit 127 determines whether a defective product has occurred in the molded product molded by the injection molding machine 1 based on the combined data 90.

[0173] In this way, by setting a plurality of sample points 96 in the waveform data 95, obtaining the value of the waveform data 95 at the position of the sample point 96 as the sample point data 97 and combining it with the monitoring data group 92, the setting timing data 91 and the sample point data 97 can be treated as the same dimension to determine whether a defective product has occurred in the molded product. As a result, the monitoring data group 92 and the waveform data 95 can be combined as the same dimension to perform a molding defect determination.

[0174] Furthermore, the pass / fail judgment system 200 extracts a plurality of monitoring data for pass and a plurality of monitoring data for failure from the basic data that establishes a connection between the judgment result of whether the molded product is a pass or a failure and the combined data 90 before molding the molded product as a product, and extracts the high-impact monitoring data as monitoring data with a high degree of influence on the failure type of the failure product from the extracted plurality of monitoring data. Then, when the molded product as a product is molded by the injection molding machine 1, the molding parameters as parameters for performing the pass / fail judgment of the molded product are calculated based on the set timing data 91 included in the combined data 90 and the monitoring data of the same type as the high-impact monitoring data in the sample point data 97, and the calculated molding parameters are compared with the judgment threshold value as the threshold value for the molding parameters to judge whether a failure has occurred in the molded product molded by the injection molding machine 1.

[0175] Thus, for the molded product molded by the injection molding machine 1, even when the setting timing data 91 and the waveform data 95 included in the combined data 90 interact, the pass / fail judgment of the molded product is performed by using the molding parameters calculated based on the setting timing data 91 and the sample point data 97 included in the combined data 90, and the setting timing data 91 and the sample point data 97 can be treated as the same dimension to judge whether a defective product has occurred in the molded product. As a result, the monitoring data group 92 and the waveform data 95 can be combined as the same dimension to perform the molding defect judgment.

[0176] In addition, since the monitoring data are pre-grouped so that the monitoring data with higher correlation coefficients are set to be in the same group, the high-impact monitoring data extraction unit 123 extracts only one monitoring data with the highest influence from the same group, so it is possible to suppress the monitoring data with higher correlation coefficients from being extracted as high-impact monitoring data. Therefore, when the recommended value calculation unit 126 calculates the recommended value, it is possible to suppress the bias in the monitoring data that serves as the basis for the calculation of the recommended value due to the monitoring data with higher correlation coefficients being extracted as high-impact monitoring data. Therefore, when determining whether a molded product is qualified or not, it is possible to suppress the determination only by referring to the monitoring data with higher correlation coefficients, and the monitoring data that is the cause of defective products can be monitored from multiple angles. As a result, the accuracy of the qualified or not determination can be further improved, and the cause of the defect can be determined more reliably.

[0177] [Modifications]

[0178] In addition, in the above-mentioned embodiment, the monitoring data is detected by sensors provided at various parts of the injection molding machine 1, but the sensors that detect the monitoring data during the operation of the injection molding machine 1 may be added or reduced as needed. In this way, when the number of sensors arranged in the injection molding machine 1 is changed, it is preferred to set the grouping of the monitoring data contained in the combined data 90 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 the monitoring data are grouped based on the calculated correlation coefficient so that the monitoring data with higher correlation coefficients are in the same group. For example, for the monitoring data detected by each sensor, the monitoring data with a correlation coefficient of more than 0.5 are set to be correlated with each other and set to the same group. Alternatively, it may be decided in advance that the monitoring data with significantly higher correlation are set to the same group regardless of the correlation coefficient.

[0179] Thus, even when the number of sensors disposed in the injection molding machine 1 is changed, it is possible to suppress the occurrence of bias in the monitoring data that is the basis for calculating the recommended value because the monitoring data with high correlation coefficients are extracted as high-influence monitoring data. As a result, even when the number of sensors detecting monitoring data is changed, the accuracy of the pass / fail judgment can be improved, and the cause of the failure can be determined more reliably.

[0180] In addition, the monitoring data can also be grouped based on factors other than correlation coefficients. Fig.17FIG. 1 is an explanatory diagram showing a modified example of how the sample point data 97 is obtained in the pass / fail determination system 200 according to the embodiment. In the waveform data 95, it is obvious that the correlation between the sample point data 97 adjacent to each other in the time direction becomes high, so the sample point data 97 within a certain interval in the waveform data 95 may be grouped in advance. That is, the sample point data acquisition unit 114 may also be configured as follows, for example: Fig.17 As shown, a plurality of groups 98 composed of a plurality of sample points 96 that are continuous in the time-advancing direction are set for one waveform data 95 , and one sample point data 97 is acquired from one group 98 .

[0181] Thus, in the waveform data 95, the sample point data 97 close to each other in time within the molding cycle are extracted as highly influential monitoring data, and it is possible to suppress the use of a plurality of sample point data 97 close to each other in time within the molding cycle in the monitoring data used for calculating the molding parameters as parameters for determining the quality of the molded product. Therefore, the quality of the molded product can be determined based on a plurality of monitoring data with low correlation, so the accuracy of the quality determination can be improved, and the cause of the failure can be determined more reliably.

[0182] Furthermore, when extracting the sample point data 97 as the high-impact monitoring data, only one sample point data 97 may be extracted from one waveform data 95. That is, when the high-impact monitoring data extracted by the high-impact monitoring data extraction unit 123 includes the sample point data 97, the high-impact monitoring data extraction unit 123 may extract one sample point data 97 as the high-impact monitoring data for each waveform data 95.

[0183] Thus, it is possible to suppress the concentration of sample point data 97 extracted as high-influence monitoring data on sample point data 97 of specific waveform data 95, and suppress the occurrence of bias in the monitoring data used as the basis for calculating the judgment recommended value. Therefore, it is possible to perform the pass / fail judgment of the molded product based on a plurality of monitoring data with low correlation, so that the accuracy of the pass / fail judgment can be improved, and the cause of the failure can be determined more reliably.

[0184] In addition, in the above-mentioned embodiment, regarding basic data, in the process of generating basic data, the user visually inspects the finished product of the molded product, and the basic data is generated by the user inputting whether the molded product is a qualified product or a defective product to the control device 100, but the control device 100 can also automatically generate the basic data when molding is performed by the injection molding machine 1.

[0185] For example, a photographing unit such as a camera that photographs the molded product and converts it into image data may be provided in the injection molding machine 1. In the process of generating basic data, the photographing unit photographs the molded product, and the image data of the photographed molded product is analyzed to determine whether the molded product is a qualified product or a defective product. In this way, the molded product is determined to be a qualified product or a defective product based on the image data photographed by the photographing unit, and the basic data is generated by linking the determination result with the monitoring data, so that the basic data can be easily generated.

[0186] In addition, in the above-mentioned embodiment, the recommended value is calculated by formula (3) using the qualified product parameter calculated based on the qualified product monitoring data and the unqualified parameter calculated based on the unqualified monitoring data, but the recommended value can also be calculated by other methods. For example, the recommended value can also be calculated by the following formula (5) without using the unqualified parameter.

[0187] [Formula 3]

[0188]

[0189] The method of calculating the recommended determination value is preferably appropriately set according to the type of defective product, whether molded products deviating from conforming products are to be allowed, and the like.

[0190] Description of symbols

[0191] 1…injection molding machine; 2…frame; 10…injection device; 11…heating barrel; 12…nozzle; 13…screw; 15…hopper; 20…metering unit; 25…injection device drive unit; 30…mold clamping device; 31…fixed plate; 32…movable plate; 35…fixed mold; 36…movable mold; 40…mold clamping drive mechanism; 41…toggle mechanism; 45…ejection mechanism; 46…ejection component; 50…monitoring data judgment screen; 51…monitoring data name display unit; 52…reference value input unit; 5 3…allowable value input section; 54…alarm selection section; 60…pass / fail judgment setting parent screen; 61…failure name input section; 62…setting button; 70…pass / fail judgment setting child screen; 71…failure information display section; 72…extraction period input section; 73…pass / fail judgment data display section; 74…sample shot number display section; 75…failure parameter display section; 76…judgment parameter histogram display section; 77…judgment recommended value display section; 80…pass / fail judgment screen; 81… Unqualified name display unit; 82... judgment threshold input unit; 83... alarm selection unit; 90... combined data; 91... setting timing data; 92... monitoring data group; 95... waveform data; 96... sample point; 97... sample point data; 98... group; 100... control device; 110... processing unit; 111... monitoring data acquisition unit; 112... setting timing data acquisition unit; 113... waveform data acquisition unit; 114... sample point data acquisition unit; 115... combined data generation unit; 116... reference value acquisition unit Obtaining unit; 117…allowable value acquiring unit; 118…monitoring data judging unit; 121…basic data generating unit; 122…monitoring data extracting unit; 123…high-impact monitoring data extracting unit; 124…judgment parameter calculating unit; 125…molding parameter calculating unit; 126…recommended value calculating unit; 127…qualified or unqualified judging unit; 128…abnormal data extracting unit; 140…storage unit; 150…input / output unit; 160…input unit; 170…display unit; 200…qualified or unqualified judging system.

Claims

1. A qualification judgment system for an injection molding machine, characterized in that: have: a setting timing data acquisition unit for acquiring, from monitoring data of the injection molding machine when the molded product is molded by the injection molding machine, the monitoring data of a plurality of preset timings, namely, setting timing data; A waveform data acquisition unit acquires the waveform data of the monitoring data that is continuous within a predetermined period from among the monitoring data; a sample point data acquisition unit that acquires data at a plurality of sample points at different times from among the waveform data acquired by the waveform data acquisition unit, that is, sample point data; a combined data generating unit that combines a monitoring data group consisting of a plurality of the set timing data acquired by the set timing data acquiring unit and a plurality of the sample point data acquired by the sample point data acquiring unit to generate combined data; and The pass / fail determination unit determines whether a defective product has occurred in the molded product molded by the injection molding machine based on the combined data generated by the combined data generation unit.

2. The pass / fail judgment system for an injection molding machine according to claim 1, characterized in that: have: A monitoring data extraction unit extracts the monitoring data when the plurality of the above-mentioned qualified products are molded, i.e., the monitoring data when the qualified products are formed, and the monitoring data when the above-mentioned unqualified products are molded, i.e., the monitoring data when the unqualified products are formed, respectively, from basic data that establishes a connection between the judgment result of whether the above-mentioned molded product molded by the above-mentioned injection molding machine is a qualified product or a unqualified product and the above-mentioned combined data generated by the above-mentioned combined data generation unit; A high-impact monitoring data extraction unit extracts, from among the plurality of monitoring data extracted by the monitoring data extraction unit, the plurality of monitoring data in order of the highest impact on the type of non-conforming product as the high-impact monitoring data; a molding parameter calculation unit that calculates molding parameters for determining whether a molded product is acceptable during molding by the injection molding machine based on the monitoring data of the same type as the high-impact monitoring data among the plurality of monitoring data of the set timing data and the sample point data included in the combined data generated by the combined data generation unit; The pass / fail determination unit compares the molding parameters calculated by the molding parameter calculation unit with a determination threshold value which is a threshold value for the molding parameters, and determines whether the defective product has occurred in the molded product molded by the injection molding machine.

3. The pass / fail judgment system for an injection molding machine according to claim 2, characterized in that: The high-impact monitor data extraction unit extracts one sample point data from one waveform data as the high-impact monitor data when the high-impact monitor data extracted by the high-impact monitor data extraction unit includes the sample point data.

4. The pass / fail judgment system for an injection molding machine according to claim 1 or 2, characterized in that: The sample point data acquisition unit sets a plurality of groups consisting of a plurality of the sample points that are continuous in a time-advancing direction for one of the waveform data, and acquires one of the sample point data from one of the groups.

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