JP6876302B2 - Information processing equipment, information processing methods, and programs - Google Patents

Description

The present invention relates to an information processing apparatus or the like that acquires and outputs one or more parameters to be set in a molding machine.

Conventionally, a molding machine has been used for molding a resin or the like (see, for example, Patent Document 1). Further, in order to produce a non-defective product by such a molding machine, it is necessary to appropriately set parameters such as temperature, pressure, and speed related to the molding machine.

Special Table 2016-51901

However, in order to set an appropriate parameter, it is necessary to repeat molding many times while changing the parameter until a non-defective product can be molded, which causes a problem that complicated work is required. Even if the appropriate parameters found by the mold maker are used by the mass production maker (the factory where the mold is shipped) that mass-produces the molded product, the environment and machinery of the mold maker and the environment and machinery of the mass production maker, etc. There is also a problem that it is not always possible for a mass-produced manufacturer to manufacture a good product due to the difference. That is, there is also a problem that the appropriate parameters found in the molding machine in one environment cannot be used as they are in another molding machine in another environment.

The present invention has been made to solve the above problems, and an object of the present invention is to provide an information processing apparatus or the like capable of easily acquiring parameters for which a molding result becomes normal.

In order to achieve the above object, the information processing apparatus according to the present invention is obtained by using a mold with a sensor in a molding machine in which one or more parameters to be set in the molding machine and one or more parameters are set. A learning device storage unit that stores a learning device that has learned two or more learning information having the above sensor values and one or more molding-related information related to molding using a mold with a sensor by a machine learning algorithm. , A test reception unit that accepts one or more normal parameters, which are one or more parameters for normal molding results in test molding using a mold without a sensor, and one or more molding-related information related to test molding. By applying one or more normal parameters and one or more molding-related information received by the test reception unit to the learner, one or more normal sensor values, which are one or more sensor values that make the molding result normal, are acquired. A sensor value acquisition unit, a mounting reception unit that receives one or more molding-related information related to mounting molding using a mold without a sensor, one or more molding-related information received by the mounting reception unit, and a sensor value. By applying one or more normal sensor values acquired by the acquisition unit to the learner, the parameter acquisition unit that acquires one or more parameters that make the molding result in mounting molding normal, and the parameter acquisition unit acquired it. It is provided with an output unit that outputs one or more parameters.
With such a configuration, in test molding using a mold without a sensor, it is possible to obtain a normal sensor value at which the molding result becomes normal without using a sensor. Further, it is considered that the molding result becomes normal if the mounting molding is performed so that the sensor value becomes the normal sensor value. Therefore, by applying the normal sensor value and the molding-related information related to the molding using the mold without the sensor to the learner and acquiring the parameter that the molding result becomes normal, it is appropriate in the mounting molding. You can get the parameters. Further, by performing mounting molding using the parameters obtained in this way, a non-defective product can be manufactured.

Further, in the information processing apparatus according to the present invention, the molding-related information may include at least one or more of environmental information regarding the environment at the time of molding, material information regarding the material to be molded, and machine information of the molding machine.
With such a configuration, for example, even if the environment, materials, and the like are different between test molding and mounting molding, appropriate parameters can be obtained according to the difference.

Further, in the information processing apparatus according to the present invention, the molding-related information includes environmental information regarding the environment at the time of molding and material information regarding the material to be molded, and the learner storage unit learns each of two or more material information. The device is stored, and the parameter acquisition unit selects a learning device corresponding to the material information included in one or more molding-related information received by the mounting reception unit, and the molding result is normal using the selected learning device. One or more parameters may be acquired.
With such a configuration, it becomes possible to acquire appropriate parameters according to the material.

Further, in the information processing apparatus according to the present invention, one or more sensor values may include one or more of the flow velocity of the material, the displacement of the mold, the pressure of the material, and the temperature of the material.

Further, in the information processing apparatus according to the present invention, the learning information may include result information indicating that the molding result is normal or abnormal.
With such a configuration, a learning device is configured in which parameters, sensor values, and the like are input, and a molding result indicating that the condition is normal or abnormal is output.

Further, in the information processing apparatus according to the present invention, a condition storage unit that stores the conditions of one or more parameters and a set that generates two or more sets including one or more parameters by using the conditions of one or more parameters. The parameter acquisition unit further includes a generation unit, and the parameter acquisition unit applies two or more sets generated by the set generation unit to the learner, respectively, and obtains a score for which the molding result is normal for each set, and a score acquisition means. A parameter acquisition means for acquiring one or more parameters constituting a set in which the score acquired by the score acquisition means satisfies a predetermined condition may be provided.
With such a configuration, it is possible to acquire appropriate parameters by using a learner that inputs parameters, sensor values, and the like and outputs a molding result indicating that it is normal or abnormal.

Further, in the information processing apparatus according to the present invention, the output unit may set one or more parameters acquired by the parameter acquisition unit in the molding machine.
With such a configuration, a non-defective product can be manufactured by performing mounting molding using a molding machine whose parameters are set by the output unit.

Further, the information processing method according to the present invention includes one or more parameters to be set in the molding machine and one or more sensor values acquired by using a sensor-equipped mold in the molding machine in which one or more parameters are set. , A learning device storage unit that stores a learning device that has learned two or more learning information having one or more molding-related information related to molding using a mold with a sensor by a machine learning algorithm, and a test reception unit. , An information processing method that is processed using a sensor value acquisition unit, a mounting reception unit, a parameter acquisition unit, and an output unit, and the test reception unit is a molding in test molding using a mold without a sensor. A test acceptance step for receiving one or more normal parameters, which are one or more parameters for which the result is normal, and one or more molding-related information related to test molding, and a sensor value acquisition unit are accepted in the test acceptance step. By applying one or more normal parameters and one or more molding-related information to the learner, a sensor value acquisition step of acquiring one or more normal sensor values, which is one or more sensor values that make the molding result normal, and a sensor value acquisition step. The mounting reception unit receives one or more molding-related information related to mounting molding using a mold without a sensor, and the parameter acquisition unit receives one or more molding-related information received in the mounting reception step. By applying the one or more normal sensor values acquired in the sensor value acquisition step to the learner, the parameter acquisition step of acquiring one or more parameters that make the molding result in mounting molding normal, and the output unit It includes an output step that outputs one or more parameters acquired in the parameter acquisition step.

According to the information processing apparatus or the like according to the present invention, it is possible to acquire appropriate parameters in mounting molding. In addition, non-defective products can be manufactured by performing mounting molding using the acquired parameters.

A block diagram showing a configuration of an information processing device according to an embodiment of the present invention. A block diagram showing the configuration of the parameter acquisition unit in the same embodiment. A flowchart showing the operation of the information processing apparatus according to the same embodiment. A flowchart showing an example of parameter acquisition processing in the same embodiment. A flowchart showing an example of parameter acquisition processing in the same embodiment. Schematic diagram for explaining a specific example in the embodiment. The figure which shows an example of the mounting position of the sensor in the mold of the same embodiment. Schematic diagram showing an example of the appearance of a computer system in the same embodiment The figure which shows an example of the configuration of the computer system in the same embodiment.

Hereinafter, the information processing apparatus according to the present invention will be described with reference to embodiments. In the following embodiments, the components and steps having the same reference numerals are the same or correspond to each other, and the description thereof may be omitted again. The information processing apparatus according to the present embodiment uses a learner corresponding to molding using a mold with a sensor and a parameter that makes the molding result normal in test molding using a mold without a sensor in test molding. The sensor value at which the molding result becomes normal is estimated, and the sensor value, the molding-related information related to the mounting molding, and the learning device are used to acquire the parameters at which the molding result at the mounting molding becomes normal. Estimating a sensor value is usually the acquisition of a sensor value.

FIG. 1 is a block diagram showing a configuration of an information processing device 1 according to the present embodiment. The information processing device 1 according to the present embodiment includes a learning device storage unit 11, a test reception unit 12, a sensor value acquisition unit 13, a mounting reception unit 14, a condition storage unit 15, a set generation unit 16, and parameters. The acquisition unit 17 and the output unit 18 are provided. The information processing device 1 may be a stand-alone device or a server device in a server / client system. In the latter case, the output unit or the reception unit may receive or output information via a communication line such as the Internet, an intranet, or a public telephone line network. In the present embodiment, the case where the information processing device 1 is a server device will be mainly described.

In the learner storage unit 11, the learner is stored. The learner has one or more parameters to be set in the molding machine, one or more sensor values acquired by using a mold with a sensor in the molding machine in which the one or more parameters are set, and the sensor. Two or more learning information having one or more molding-related information related to molding using a mold is learned by a machine learning algorithm. The learning information may include result information indicating that the molding result is normal or abnormal. The learner may be considered as information as a result of machine learning of two or more learning information. The fact that the learning device is stored in the learning device storage unit 11 means that, for example, the learning device itself (for example, a function that outputs a value with respect to an input, a model of a learning result, etc.) is stored. It may be possible that information such as parameters necessary for configuring the learning device is stored. Even in the latter case, since the learning device can be configured by using the information such as the parameters, it can be considered that the learning device is substantially stored in the learning device storage unit 11. In the present embodiment, the case where the learning device itself is stored in the learning device storage unit 11 will be mainly described.

The molding machine may be, for example, an injection molding molding machine. In the present embodiment, a case where the molding machine is an injection molding molding machine will be mainly described. The injection molding machine may have, for example, an injection device, a mold, a mold clamping device, and a controller for controlling them.

A normal molding result means that the molded product molded by the molding machine is a good product, and an abnormal molding result means that the molded product is a defective product. For example, if the molded product has burrs, short shots, sink marks, weld lines, voids, flow marks, uneven thickness (uneven wall thickness), warpage, etc., it may be determined to be a defective product. The normality and abnormality of the molding result will differ depending on the required quality. Therefore, it is preferable that normality and abnormality are judged according to the required quality.

One or more parameters are set in the molding machine, and may be set in, for example, an injection device, a mold clamping device, a controller, or the like. The parameters are not particularly limited, but are, for example, mold temperature, cylinder temperature, injection pressure, injection speed, injection switching, holding pressure, holding time, screw rotation speed, back pressure, switching position, weighing, and sack. It may be one or more selected from the back amount, the cooling time, and the mold clamping force. Further, the parameters may be set for each of a plurality of parts of the molding machine, such as the nozzle, the front part, the middle front part, the middle part, the rear part cylinder temperature, and the like.

The sensor may be, for example, any of a flow velocity sensor, a displacement sensor, a pressure sensor, and a temperature sensor. Therefore, one or more sensor values may include one or more of the flow rate, displacement, pressure, and temperature of the material. Further, the sensors may be arranged in the order in which the materials flow, for example, in the mold. For example, the first sensor is placed at the position of the sprue bush of the mold, the second sensor is placed at the position of the runner of the mold, the third sensor is placed at the position of the gate of the mold, and the mold is placed. The fourth sensor is placed near the gate of the molded part of the mold, and the fifth sensor is placed at the position intermediate between the gate of the molded part of the mold and the final filling part (the part farthest from the gate). , The sixth sensor may be arranged in the final filling portion of the molded product portion of the mold. The sensor placed on the mold may acquire information about the mold (temperature of the mold itself, etc.), and information about the material flowing through the mold and the material formed by the mold (material). (Temperature, etc.) may be acquired.

The sensor value may be, for example, information according to a time series, or information at a predetermined time point. When the sensor value is information according to the time series, for example, a sensor value of 1 or more, a sensor value of 0 seconds based on a certain time point, a sensor value of 5 seconds, a sensor value of 10 seconds, etc. May include sensor values for each constant or indefinite time interval. Further, since the sensor value according to the time (for example, 0 seconds, 5 seconds, etc.) with respect to a certain time point is considered to be different depending on the injection speed or the like, the time may be normalized. The normalized time is, for example, the time from the start time (for example, "0.1") in which a predetermined unit (for example, "1") is set from the start time to the end time of one cycle of molding. May be shown. Further, when the sensor value is information at a predetermined time point, for example, the sensor value at the runner position is determined as the sensor value 5 seconds after the resin reaches the position. May be good.

FIG. 7 is a diagram showing an example of a mold on which a sensor is mounted. FIG. 7 (a) is a plan view showing a first mold member 31 having two gabities 31b, and FIG. 7 (b) shows a second mold member 32 having two cores 32b. It is a plan view which shows. The mold is formed by the first and second mold members 31, 32. The first mold member 31 is provided with four guide bushes 31a, and the second mold member 32 is provided with four guide pins 32a. Then, by inserting each guide pin 32a into the corresponding guide bush 31a, the first and second mold members 31 and 32 are positioned.

The first and second mold members 31 and 32 are provided with temperature sensors at positions indicated by reference numerals T11 to T13, and pressure sensors are provided at positions indicated by reference numerals P11 to P17. The displacement sensor may be provided at the position indicated by D11 to D14, and the flow velocity sensor may be provided at the position indicated by the reference numerals C11 and C12. It should be noted that the position and type of the sensor in the mold shown in FIG. 7 is an example, and it goes without saying that the arrangement and type of the sensor in the mold with a sensor may be different from those in FIG.

The molding-related information may include at least one or more of environmental information regarding the environment at the time of molding, material information regarding the material to be molded, and machine information of the molding machine.

The environmental information may include, for example, at least one of temperature, humidity, and atmospheric pressure. The temperature, humidity, and atmospheric pressure may be, for example, an external temperature (outside air temperature) of the molding machine, an external humidity, or an external atmospheric pressure. That is, the environmental information may be, for example, environmental information at a place where the molding machine is installed (for example, an installation room or the like). The environmental information is preferably information at the time when the actual molding is performed.

The material information may indicate the type of material to be molded (molded article). The material information regarding injection molding may be, for example, polyethylene, polyvinyl chloride, polystyrene, polypropylene, polyamide, polyethylene terephthalate, phenol resin, polyurethane resin and the like. Further, the material information may indicate the properties of the material, such as a thermoplastic resin or a thermosetting resin. Further, the material information may indicate the attributes of the material, such as the density and viscosity of the material.

The machine information is usually static information of the molding machine (information that does not change with time), and may be, for example, specifications of the molding machine. The specifications of the molding machine may include at least one of, for example, cylinder diameter, injection device type (direct pressure type, toggle type, etc.), power type (electric, hydraulic, etc.), molding machine part number, and the like. Good.

Machine learning is machine learning that uses learning information as teacher data (training data), and even if it is SVM (Support Vector Machine) or SVR (Support Vector Regression) machine learning, for example. Often, it may be machine learning of a neural network, or it may be other machine learning.

Here, a learning device obtained by performing machine learning using learning information including result information indicating normality or abnormality, and a learning device obtained by performing machine learning using learning information not including result information. Each of the learning devices will be described.

(1) Learning device which is a learning result using learning information including result information In this case, one learning information (teacher data) is set with one or more parameters and one or more parameters thereof. Inputs including one or more sensor values acquired by using a mold with a sensor in a molding machine and molding-related information related to molding in the molding machine, and whether the molding result is normal or abnormal. It has the result information (output) shown. In this case, the result information indicating that it is normal is a positive example, and the result information indicating that it is abnormal is a negative example. As described above, since this learner is a binary classifier that outputs result information indicating normality or abnormality, machine learning may be performed using, for example, an SVM or a neural network. In addition, one or more parameters and one or more sensor values acquired by using a mold with a sensor in a molding machine in which one or more parameters are set in the learner which is the result of the machine learning, and their molding. When the molding-related information related to molding on the machine is input, the normal or abnormal classification result is output. In addition, the score (confidence) of the classification result may be output together with the classification result. In this embodiment, the case of (1) will be mainly described.

(2) Learning device that is a learning result using learning information that does not include result information In this case, one learning information is one or more parameters related to molding in which the molding result is normal, and one or more thereof. It has an input that includes molding-related information related to molding in a molding machine with parameters set, and one or more sensor values (outputs) obtained using a mold with a sensor in the molding machine. There is. In this case, only the learning information about molding that makes the molding result normal is used for machine learning. Since this learner is not a binary classifier with an output, machine learning may be performed using, for example, an SVR, a neural network, or an SVM configured to perform multi-value classification. Further, when one or more parameters and molding-related information related to molding by a molding machine in which the one or more parameters are set are input to the learner which is the result of the machine learning, one or more parameters are input. The sensor value of is output. Further, the score (confidence) of the output sensor value may be output together with the sensor value.

In each of the above cases (1) and (2), the molding machine used to acquire the learning information which is the teacher data may be one molding machine or a plurality of molding machines. It may be. In the former case, for example, only the molding machine of the mold maker may be used, or only the molding machine of the mass production maker of the molded product may be used, and in the latter case, for example, the mold maker's molding machine may be used. A molding machine and a molding machine of a mass-produced manufacturer may be used. Further, the molding machine of the mass production maker may be one molding machine, or may be a plurality of molding machines of one or more mass production makers. From the viewpoint of making the learning device more accurate, it is preferable to use the learning information acquired by more molding machines (preferably more types of molding machines) in machine learning.

Further, when the range of each input value in machine learning is determined, for example, when each value is determined in the range of -1 to 1 or the range of 0 to 1, the learning information is used. Machine learning may be performed after preprocessing so that each value included is within the determined range, and the same preprocessing is performed for each value included in the information input to the learner. You may input to the learner after performing.

When a neural network is used for machine learning, the number of layers of the neural network, the number of nodes in each layer, the type of each layer (for example, fully connected layer, other layers, etc.), etc. are appropriately selected. You may. The number of nodes in the input layer and the output layer is usually determined by the inputs and outputs included in the learning information.

Further, two or more learners may be stored in the learner storage unit 11. For example, the learner may be stored for each material indicated by the material information, or the learner may be stored for each part number of the molding machine indicated by the molding-related information. The details of the case where two or more learners are stored in the learner storage unit 11 will be described later.

The process in which the learner is stored in the learner storage unit 11 does not matter. For example, the learning device may be stored in the learning device storage unit 11 via the recording medium, and the learning device transmitted via the communication line or the like may be stored in the learning device storage unit 11. You may. The memory in the learner storage unit 11 may be a temporary memory in a RAM or the like, or a long-term memory. The learner storage unit 11 can be realized by a predetermined recording medium (for example, a semiconductor memory, a magnetic disk, or the like).

The test reception unit 12 provides one or more normal parameters, which are one or more parameters for normal molding results in test molding using a mold without a sensor, and one or more molding-related information related to the test molding. Accept. The mold without a sensor may be, for example, one having the same shape as the mold with a sensor used for obtaining learning information, that is, a mold having the same shape as the mold with a sensor. It may have a shape different from that of the sensor-equipped mold, that is, it may mold a molded product different from the sensor-equipped mold. Test molding is molding performed to obtain normal parameters. For example, by setting a mold without a sensor in a molding machine and repeating molding while changing one or more parameters to various values, one or more parameters when a normal molding result is obtained are acquired. .. Then, the test reception unit 12 may accept a set of one or more parameters used in the test molding for which the normal molding result is obtained and the molding-related information of the test molding. Although not particularly limited, this test molding may be performed by, for example, a mold maker. When test molding is performed by a mold maker, for example, normal parameters may be acquired by test molding before delivery of the manufactured mold. The set of the normal parameter and the molding-related information received by the test reception unit 12 may be one or two or more. From the viewpoint of acquiring more appropriate parameters by the parameter acquisition unit 17 described later, it is preferable that more sets are accepted.

The test reception unit 12 may receive, for example, information input from an input device (for example, a keyboard, mouse, touch panel, etc.), or may receive information transmitted via a wired or wireless communication line. , Information read from a predetermined recording medium (for example, an optical disk, a magnetic disk, a semiconductor memory, etc.) may be accepted. In the present embodiment, the case where the test reception unit 12 receives the normal parameters and the like will be mainly described. The test reception unit 12 may or may not include a device for receiving (for example, a modem, a network card, etc.). Further, the test reception unit 12 may be realized by hardware, or may be realized by software such as a driver that drives a predetermined device.

The sensor value acquisition unit 13 is one or more sensor values at which the molding result becomes normal by applying one or more normal parameters and one or more molding-related information received by the test reception unit 12 to the learner. Acquire a normal sensor value of 1 or more. The one or more normal sensor values obtained in this way can be considered to be one or more sensor values at which the molding result becomes normal in the mold without a sensor used in the test molding.

For example, when the learner of (2) above is stored in the learner storage unit 11, the sensor value acquisition unit 13 has one or more normal parameters received by the test reception unit 12 and one or more molding-related items. By inputting the information into the learner, it is possible to acquire one or more normal sensor values which are the outputs of the learner.

On the other hand, for example, when the learner of (1) above is stored in the learner storage unit 11, the sensor value acquisition unit 13 has one or more normal parameters received by the test reception unit 12 and one or more normal parameters. Along with the molding-related information, one or more sensor values will be input to the learner. Then, when the output of the learner is "normal", the input sensor value of 1 or more becomes the normal sensor value of 1 or more. Here, one or more sensor values input by the sensor value acquisition unit 13 to the learner are referred to as normal sensor value candidates. Usually, it is preferable that there are two or more normal sensor value candidates. The two or more normal sensor value candidates may be stored, for example, in a recording medium (not shown in advance) in advance, or may be generated by the sensor value acquisition unit 13. In the latter case, the sensor value acquisition unit 13 generates a plurality of normal sensor value candidates by using, for example, a range of possible values or candidate values for each one or more sensor values included in the normal sensor value candidates. You may. Specifically, the normal sensor value candidates include the first and second sensor values, the first sensor value takes any of 1, 2, and 3, and the second sensor value is. , 11, 12, and 13, the sensor value acquisition unit 13 has (first sensor value, second sensor value) = (1,11), (1,12), Nine normal sensors such as (1,13), (2,11), (2,12), (2,13), (3,11), (3,12), (3,13) Value candidates may be generated. After that, the sensor value acquisition unit 13 inputs one or more normal parameters received by the test reception unit 12, one or more molding-related information, and one normal sensor value candidate into the learner, thereby causing the learner. Obtain the molding result which is the output from. Further, the sensor value acquisition unit 13 repeats the same process for each normal sensor value candidate. Then, the sensor value acquisition unit 13 sets one or more sensor values included in the normal sensor value candidates included in the input corresponding to the output “normal”, which is so high that the certainty degree satisfies a predetermined condition, to one or more normal. It may be acquired as a sensor value. A high degree of certainty that satisfies a predetermined condition may mean that the degree of certainty exceeds a predetermined threshold value, or a plurality of outputs corresponding to a plurality of normal sensor value candidates, respectively. In, it may be the highest degree of certainty. In the former case, a plurality of sets containing one or more normal sensor values may be acquired, but in that case, a plurality of sets may be used in the subsequent processing, and one selected set may be acquired. May be used in the subsequent processing. The selection may be, for example, a random selection or a selection according to a predetermined rule.

When two or more sets of one or more normal parameters and one or more molding-related information are received by the test reception unit 12, the sensor value acquisition unit 13 has one or more normals corresponding to each set. The sensor value may be acquired. In that case, a plurality of sets including one or more normal sensor values will be acquired.

The mounting reception unit 14 receives one or more molding-related information related to mounting molding using a mold without a sensor. A mold without a sensor is usually the same mold as the mold without a sensor used in the test molding, but another mold having the same shape as the mold without a sensor used in the test molding. It may be a mold. Mounting molding is molding performed to manufacture a molded product. Although not particularly limited, this mounting molding may be performed by a mass-produced manufacturer of molded products. When mounting molding is performed by a mass-produced manufacturer, for example, one or more molding-related information regarding mounting molding may be acquired before manufacturing a molded product. Further, when one or more molding-related information related to mounting molding includes environmental information, the environmental information may be an actually measured value measured immediately before mounting molding, and is a prediction at the time of mounting molding. It may be a value.

The mounting reception unit 14 may receive, for example, information input from an input device (for example, a keyboard, mouse, touch panel, etc.), or may receive information transmitted via a wired or wireless communication line. , Information read from a predetermined recording medium (for example, an optical disk, a magnetic disk, a semiconductor memory, etc.) may be accepted. In the present embodiment, the case where the mounting reception unit 14 receives the molding-related information will be mainly described. The mounting reception unit 14 may or may not include a device for receiving (for example, a modem, a network card, etc.). Further, the mounting reception unit 14 may be realized by hardware, or may be realized by software such as a driver that drives a predetermined device.

The condition storage unit 15 stores the conditions of one or more parameters. This condition may be a condition indicating a range of possible values or candidate values for each of one or more parameters. The process in which information is stored in the condition storage unit 15 does not matter. For example, the information may be stored in the conditional storage unit 15 via the recording medium, or the information transmitted via the communication line or the like may be stored in the conditional storage unit 15. Alternatively, the information input via the input device may be stored in the condition storage unit 15. The storage in the conditional storage unit 15 may be temporary storage in RAM or the like, or long-term storage. The condition storage unit 15 can be realized by a predetermined recording medium (for example, a semiconductor memory, a magnetic disk, or the like).

The set generation unit 16 generates two or more sets including one or more parameters by using the conditions of one or more parameters stored in the condition storage unit 15. Specifically, two parameters, that is, the first parameter and the second parameter are set targets, the first parameter takes any value of 1, 2, or 3, and the second parameter is. , 11, 12, and 13, the set generator 16 has (first parameter, second parameter) = (1,11), (1,12), (1,12). Generate 9 sets such as 13), (2,11), (2,12), (2,13), (3,11), (3,12), (3,13). May be good. The generated set may be stored on a recording medium (not shown).

The parameter acquisition unit 17 applies the one or more molding-related information received by the mounting reception unit 14 and the one or more normal sensor values acquired by the sensor value acquisition unit 13 to the learner, thereby performing the mounting molding. Acquire one or more parameters that make the molding result normal. Usually, it is considered that the sensor value in the mold and the quality of the molded product are closely related. Therefore, in mounting molding, even if one or more parameters that make the molding result normal are not acquired by trial and error, one or more sensor values in mounting molding are mounted so as to be one or more normal sensor values in test molding. By setting one or more parameters used in molding, it is considered that the molding result becomes normal. Therefore, the parameter acquisition unit 17 acquires such one or more parameters.

In the present embodiment, as shown in FIG. 2, the parameter acquisition unit 17 includes the score acquisition means 21 and the parameter acquisition means 22.

The score acquisition means 21 applies two or more sets generated by the set generation unit 16 to the learner, respectively, and acquires a score for which the molding result is normal for each set. For example, when the learning device of the above (1) is stored in the learning device storage unit 11, the score acquisition means 21 has one or more molding-related information received by the mounting reception unit 14 and a sensor value acquisition unit. One or more normal sensor values acquired by 13 and one or more parameters included in one set are input to the learner, and when the output of the learner is "normal", the learner gives the learner. Get the output score (confidence). Then, the process is performed for each set generated by the set generation unit 16. Further, when the sensor value acquisition unit 13 has acquired N sets including 1 or more normal sensor values, by using each set as an input, N times as many outputs can be acquired. (However, N is an integer of 2 or more). In this way, the score acquisition means 21 acquires the correspondence between the set and the score for which the molding result is normal.

Further, for example, when the learning device of the above (2) is stored in the learning device storage unit 11, the score acquiring means 21 may have one or more moldings received by the mounting receiving unit 14. The related information and one or more parameters included in one set are input to the learner, and one or more sensor values which are the outputs of the learner are one or more normals acquired by the sensor value acquisition unit 13. When it matches the sensor value, the score output from the learner is acquired. Then, the process is performed for each set generated by the set generation unit 16. It should be noted that the fact that the output sensor value of 1 or more and the normal sensor value of 1 or more match may mean, for example, that they completely match, or that the difference between the two is a predetermined threshold value. It may be smaller than. In the latter case, it will be judged that they match when they are more similar than the predetermined criteria. Further, when the sensor value acquisition unit 13 has acquired N sets including 1 or more normal sensor values, when the output sensor value of 1 or more matches any of the N sets, The score output from the learner will be acquired (however, N is an integer of 2 or more). In this way, the score acquisition means 21 acquires the correspondence between the set and the score for which the molding result is normal. In the case of the learner of (2) above, one or more normal sensor values will be used for comparison with the output of the learner. Therefore, the fact that one or more normal sensor values are applied to the learner includes the case where it is used in comparison with the output of the learner in this way.

The parameter acquisition means 22 acquires one or more parameters that constitute a set in which the score acquired by the score acquisition means 21 is so high that it satisfies a predetermined condition. That is, the parameter acquisition means 22 identifies a score higher than the score acquired by the score acquisition means 21 so as to satisfy a predetermined condition, and sets one or more parameters constituting a set corresponding to the specified score. Will get. The score is high enough to satisfy a predetermined condition, that the score may exceed a predetermined threshold value, or the score may be the highest. In the former case, when a plurality of scores exceed a predetermined threshold value, the parameter acquisition unit 17 selects one of the scores exceeding the predetermined threshold value. , One or more parameters constituting the set corresponding to the selected score may be acquired. The selection may be, for example, a random selection or a selection according to a predetermined rule. Further, a plurality of sets including one or more parameters corresponding to a plurality of scores exceeding a predetermined threshold value may be acquired by the parameter acquisition means 22.

The output unit 18 outputs one or more parameters acquired by the parameter acquisition unit 17. Depending on the output, one or more output parameters may be set in the molding machine that performs mounting molding. Further, the output unit 18 may set one or more parameters acquired by the parameter acquisition unit 17 in the molding machine. The molding machine for which the parameter is set is a molding machine that performs mounting molding. Further, when a plurality of sets including one or more parameters are acquired by the parameter acquisition unit 17, the output unit 18 may output the plurality of sets.

Here, this output may be, for example, transmitted to a predetermined device via a communication line, stored in a recording medium, delivered to another component, or set as a parameter to a molding machine. You may. The output unit 18 may or may not include a device that outputs (for example, a communication device). Further, the output unit 18 may be realized by hardware, or may be realized by software such as a driver that drives those devices.

The learning device storage unit 11 and the condition storage unit 15 may be realized by the same recording medium, or may be realized by different recording media. In the former case, the area that stores the learner may be the learner storage unit 11, and the area that stores the condition may be the condition storage unit 15.

Next, the operation of the information processing device 1 will be described with reference to the flowchart of FIG.
(Step S101) The test reception unit 12 determines whether or not one or more normal parameters related to test molding and one or more molding-related information have been received. Then, if one or more normal parameters related to test molding and one or more molding-related information are received, the process proceeds to step S102, and if not, the process proceeds to step S103. The one or more normal parameters and one or more molding-related information received by the test reception unit 12 are one or more normal parameters and one or more normal parameters that make the molding result normal in the test molding using the mold without the sensor. This is molding-related information.

(Step S102) The sensor value acquisition unit 13 applies one or more normal parameters received by the test reception unit 12 and one or more molding-related information to the learner stored in the learner storage unit 11. Acquires one or more normal sensor values that make the molding result normal. The normal sensor value of 1 or more may be stored in a recording medium (not shown). Then, the process returns to step S101.

(Step S103) The mounting receiving unit 14 determines whether or not one or more molding-related information regarding mounting molding has been received. Then, when one or more molding-related information regarding mounting molding is received, the process proceeds to step S104, and if not, the process returns to step S101. The one or more molding-related information received by the mounting reception unit 14 is one or more molding-related information related to mounting molding using a mold without a sensor.

(Step S104) The parameter acquisition unit 17 has one or more molding-related information received by the mounting reception unit 14 and 1 acquired by the sensor value acquisition unit 13 in the learning device stored in the learning device storage unit 11. By applying the above normal sensor values, one or more parameters that make the molding result in mounting molding normal are acquired. The details of this process will be described later.

(Step S105) The output unit 18 outputs one or more parameters acquired by the parameter acquisition unit 17. Then, a molded product is manufactured by mounting molding in a molding machine in which one or more parameters are set.

The order of processing in the flowchart of FIG. 3 is an example, and the order of each step may be changed as long as the same result can be obtained. Further, in the flowchart of FIG. 3, the process ends when the power is turned off or an interrupt for the end of the process occurs.

FIG. 4 is a flowchart showing an example of details of the parameter acquisition process (step S104) using the learner in the flowchart of FIG. In the flowchart of FIG. 4, it is assumed that the learner of (1) above is used.

(Step S201) The set generation unit 16 generates a plurality of sets including one or more parameters by using the conditions stored in the condition storage unit 15. The generated plurality of sets may be stored in a recording medium (not shown).

(Step S202) The score acquisition means 21 sets the counter i to 1.

(Step S203) The score acquisition means 21 includes one or more parameters included in the i-th set, one or more molding-related information received by the mounting reception unit 14, and one or more acquired by the sensor value acquisition unit 13. It is determined whether or not a molding result indicating normality is output from the learning device in which the normal sensor value of is input. Then, if the molding result indicating that it is normal is output, the process proceeds to step S204, and if not, the process proceeds to step S205.

(Step S204) The score acquisition means 21 acquires a score (confidence) according to the output of the molding result indicating that it is normal. The correspondence between the i-th set and the score may be stored in a recording medium (not shown).

(Step S205) The score acquisition means 21 increments the counter i by 1.

(Step S206) The score acquisition means 21 determines whether or not the i-th set exists. Then, if the i-th set exists, the process returns to step S203, and if not, the process proceeds to step S207.

(Step S207) The parameter acquisition means 22 acquires one or more parameters included in the set so that the score satisfies a predetermined condition. Then, the process returns to the flowchart of FIG.

FIG. 5 is a flowchart showing an example of details of the parameter acquisition process (step S104) using the learner in the flowchart of FIG. In the flowchart of FIG. 5, it is assumed that the learner of (2) above is used. Further, in the flowchart of FIG. 5, the processes other than steps S301 to S303 are the same as those of the flowchart of FIG. 4, and detailed description thereof will be omitted. If Yes in step S206, the process returns to step S301.

(Step S301) The score acquisition means 21 inputs one or more parameters included in the i-th set and one or more molding-related information received by the mounting reception unit 14 into the learner, so that one or more parameters are obtained. Get the output of the sensor value.

(Step S302) The score acquisition means 21 determines whether or not the one or more sensor values acquired from the learner match the one or more normal sensor values acquired by the sensor value acquisition unit 13. Then, if they match, the process proceeds to step S303, and if they do not match, the process proceeds to step S205. It should be noted that the judgment as to whether or not the one or more sensor values acquired from the learner and the one or more normal sensor values match is whether or not they are more similar than the predetermined criteria as described above. It may be a judgment.

(Step S303) The score acquisition means 21 acquires a score (confidence) according to the output of one or more sensor values. The correspondence between the i-th set and the score may be stored in a recording medium (not shown). Then, the process proceeds to step S205.

Next, the operation of the information processing apparatus 1 according to the present embodiment will be described with reference to specific examples. Needless to say, the information processing device 1 is not limited to this specific example. In this specific example, the case where the learning device is the one of the above (1) will be described. FIG. 6 is a schematic diagram for explaining each information in this specific example.

First, the person in charge of the mold maker attaches the mold with a sensor to the molding machine owned by the mold maker, sets a predetermined parameter in the molding machine, and molds the molded product. Then, result information indicating the quality of the molded product, one or more parameters set, one or more sensor values obtained at the time of molding, and molding-related information related to the molding (environmental information, material information, machine). Information etc.) and get. In this way, one learning information is acquired. In addition, the person in charge of the mold maker also acquires another learning information by setting various values as one or more parameters. It is preferable that this learning information is as much as possible. Further, it is preferable that the learning information including the result information indicating that it is normal and the learning information including the result information indicating that it is abnormal are acquired. The learning information acquired by the mold maker is referred to as learning information A1.

The person in charge at the shipping destination factory also acquires the learning information A2 in the same manner as the mold maker. In the acquisition of the learning information A2, a molding machine equipped with a mold with a sensor is used. The mold with a sensor may be the same mold as the mold with a sensor used when acquiring the learning information A1, or may be a different mold. Even in the latter case, the sensor-equipped mold used at the shipping destination factory can manufacture a mold having the same configuration as the sensor-equipped mold used by the mold maker (that is, the same molded product can be manufactured. It is preferable that the mold is the same as the mounting location of the sensor).

After that, machine learning is performed using the learning information A1 and A2, and the learning device L1 is configured. In the machine learning, the parameters included in the learning information A1 and A2, the sensor value, and the molding-related information are used as inputs, and the result information is used as an output. The learning device L1 is stored in the learning device storage unit 11 of the information processing device 1.

Next, the person in charge of the mold maker attaches the mold without a sensor to the molding machine owned by the mold maker, sets a predetermined parameter in the molding machine, and performs test molding of the molded product. A mold without a sensor is a mold manufactured by a mold maker and shipped to a factory to which it is shipped. If the molded product is a non-defective product, the person in charge acquires one or more normal parameters B1 set in the molding of the molded product and one or more molding-related information B2 related to the molding. If the molded product is defective, the parameters will be adjusted until the molded product becomes a non-defective product. Then, the person in charge transmits one or more normal parameters B1 and one or more molding-related information B2 to the information processing device 1 by operating a personal computer or the like.

One or more normal parameters B1 and one or more molding-related information B2 transmitted from the mold maker are received by the test reception unit 12 of the information processing device 1 and passed to the sensor value acquisition unit 13 (step S101). When the sensor value acquisition unit 13 receives one or more normal parameters B1 and one or more molding-related information B2, it generates two or more normal sensor value candidates F1, F2, ..., FK (K is an integer of 2 or more). By inputting one or more normal parameters B1, one or more molding-related information B2, and normal sensor value candidate F1 into the learner L1, an output indicating normal or abnormal is acquired. The sensor value acquisition unit 13 performs the processing in the same manner for each of the normal sensor value candidates F2 to FK. Then, the sensor value acquisition unit 13 acquires a score (confidence) at which the molding result is "normal", and one or more normal sensors which are one or more sensor values included in the normal sensor value candidate having the highest score. The value C1 is acquired and passed to the parameter acquisition unit 17 (step S102).

After that, the mold maker ships the mold without a sensor to the shipping destination factory. The person in charge at the shipping factory attaches the delivered mold without a sensor to the molding machine owned by the shipping factory. Then, the person in charge acquires one or more molding-related information D1 related to mounting molding for mass production of the molded product, and operates a personal computer or the like to transmit one or more molding-related information D1 to the information processing device. Send to 1.

One or more molding-related information D1 transmitted from the shipping destination factory is received by the mounting reception unit 14 of the information processing device 1 and passed to the parameter acquisition unit 17 (step S103). Then, the parameter acquisition unit 17 acquires parameters using one or more received molding-related information D1 (step S104). Specifically, first, the set generation unit 16 generates two or more sets including one or more parameters by using the conditions stored in the condition storage unit 15 (step S201). Next, the score acquisition means 21 of the parameter acquisition unit 17 is provided by one or more parameters included in the first set, one or more normal sensor values C1 acquired by the sensor value acquisition unit 13, and the mounting reception unit 14. By inputting one or more received molding-related information D1 to the learner L1, an output indicating normality or abnormality is acquired. Then, when the output is "normal", the score acquisition means 21 acquires the score (confidence) corresponding to the output "normal" (steps S202 to S204). The score acquisition means 21 performs the processing in the same manner for the other sets (steps S205, S206, S203, S204).

After that, the parameter acquisition means 22 acquires one or more parameters E1 included in the set having the highest score acquired by the score acquisition means 21, and passes them to the output unit 18 (step S207).

The output unit 18 transmits one or more parameters E1 to the shipping destination factory (step S105). As a result, one or more parameters E1 are set in the molding machine of the shipping destination factory, and mounting molding is performed, so that the molded product is mass-produced. Since one or more parameters E1 are acquired by using a learning device so that the result information becomes normal, a good product is manufactured by performing mounting molding using one or more parameters E1. It will be. Therefore, the person in charge at the shipping destination factory does not have to find the parameters for manufacturing a non-defective product by trial and error, and the burden on the person in charge is reduced.

As described above, according to the information processing apparatus 1 according to the present embodiment, it is possible to automatically acquire appropriate parameters used in mounting molding using a mold without a sensor. As a result, it is not necessary to find one or more parameters to be set in the molding machine by trial and error, and the labor for setting the parameters is reduced. Further, by using a learning device learned by using more learning information acquired by various molding machines, one or more parameters used in mounting molding can be acquired with higher accuracy. As a result, it becomes possible to reduce the possibility that the molded product manufactured in the mounting molding becomes a defective product by using one or more parameters acquired by the information processing device 1. Further, the process of acquiring normal parameters by trial and error is a heavy burden, but in the information processing apparatus 1, even if only one set of one or more normal parameters exists, the parameters can be acquired. It is possible to reduce the burden of acquiring normal parameters.

In the present embodiment, the case where the set generation unit 16 generates a plurality of sets has been described, but it is not necessary. Two or more sets generated in advance are stored in a recording medium (not shown), and the parameter acquisition unit 17 may acquire parameters by using the two or more sets. In that case, the information processing device 1 does not have to include the condition storage unit 15 and the set generation unit 16.

Further, in the present embodiment, the case where one learning device is stored in the learning device storage unit 11 has been mainly described, but two or more learning devices may be stored. In that case, in the learning device storage unit 11, for example, the learning device may be stored for each of two or more material information. The material information may be information indicating the material. Specifically, a plurality of learning devices are stored in the learning device storage unit 11 for each molding material, such as a learning device corresponding to the molding material "polyethylene" and a learning device corresponding to the molding material "polyvinyl chloride". You may be. Further, in the learning device storage unit 11, for example, the learning device may be stored for each of two or more product numbers of the molding machine. Specifically, a plurality of learning devices are stored in the learning device storage unit 11 for each product number, such as a learning device corresponding to the product number "A123" of the molding machine and a learning device corresponding to the product number "A987" of the molding machine. It may have been done. The product number of the molding machine is information for identifying the type of the molding machine, and may be included in the machine information included in the molding-related information.

In this way, when a plurality of learners are stored in the learner storage unit 11, the parameter acquisition unit 17 uses the information included in one or more molding-related information received by the mounting reception unit 14. A learning device may be selected, and one or more parameters for which the molding result is normal may be acquired using the selected learning device. In this way, in the process of acquiring one or more parameters by applying one or more molding-related information received by the mounting reception unit 14 and one or more normal sensor values to the learner, one or more moldings are performed. The process of selecting a learner using the information contained in the related information may be included.

More specifically, the parameter acquisition unit 17 selects a learning device corresponding to the material information included in one or more molding-related information received by the mounting reception unit 14, and the molding result is obtained using the selected learning device. May be acquired with one or more parameters for which is normal. In this case, it is preferable that the molding-related information includes at least material information. Further, the molding-related information may further include environmental information and / or machine information. Further, the parameter acquisition unit 17 selects a learning device corresponding to the product number indicated by the machine information included in one or more molding-related information received by the mounting reception unit 14, and the molding result is obtained using the selected learning device. One or more parameters that become normal may be acquired. In this case, it is preferable that the molding-related information includes at least machine information. Further, the molding-related information may further include environmental information and / or material information.

Further, in the present embodiment, the case where the sensor value acquisition unit 13 uses the score (confidence) when acquiring one or more normal sensor values by using the learning device of the above (1) has been mainly described. But it doesn't have to be. The sensor value acquisition unit 13 is "normal" in the output from the learner obtained by inputting one or more normal parameters, one or more molding-related information, and one normal sensor value candidate into the learner. If, one or more sensor values included in the normal sensor value candidates may be acquired as one or more normal sensor values. Therefore, the sensor value acquisition unit 13 repeatedly executes a process of applying one or more normal parameters or the like to the learner for different normal sensor value candidates until a “normal” output is obtained, and the “normal” sensor value acquisition unit 13 repeatedly executes the process. It may end when the output is obtained.

Further, in the present embodiment, the case where the parameter acquisition unit 17 acquires one or more parameters by using the score (confidence) has been described, but it is not necessary. As described below, when the parameters are acquired without using the score, the parameter acquisition unit 17 may not include the score acquisition means 21 and the parameter acquisition means 22.

When the learner of the above (1) is used, the parameter acquisition unit 17 learns one or more molding-related information, one or more normal sensor values, and one or more parameters included in one set. When the output from the learner obtained by inputting to is "normal", one or more parameters included in the set may be acquired as one or more parameters to be output. Therefore, the parameter acquisition unit 17 repeatedly executes the process of applying one or more molding-related information to the learner until a "normal" output is obtained for different sets, and a "normal" output is obtained. It may end at the time when it is done.

Further, when the learning device of the above (2) is used, the parameter acquisition unit 17 obtains by inputting one or more molding-related information and one or more parameters included in one set into the learning device. When the obtained one or more sensor values match one or more normal sensor values, one or more parameters included in the set may be acquired as one or more parameters to be output. Therefore, the parameter acquisition unit 17 repeats the process of applying one or more molding-related information or the like to the learner for different sets until an output of one or more sensor values matching one or more normal sensor values is obtained. And may end when an output that matches one or more normal sensor values is obtained.

Further, when the set generation unit 16 generates a set including one or more parameters, it is preferable that the one or more parameters included in the set are one or more parameters to be set in mounting molding. Therefore, for example, the mounting receiving unit 14 receives one or more molding-related information as well as information indicating one or more types of parameters set in mounting molding, and one or more of the types indicated by the received information. A set containing parameters may be generated by the set generation unit 16.

Further, in the present embodiment, the case where the molding machine is an injection molding molding machine has been mainly described, but the molding machine may be another molding machine. The molding machine may be, for example, a press molding molding machine, a blow molding molding machine, or the like.

Further, in the above embodiment, each process or each function may be realized by centralized processing by a single device or a single system, or distributed processing by a plurality of devices or a plurality of systems. It may be realized by.

Further, in the above embodiment, the transfer of information performed between the respective components depends on, for example, one of the components when the two components that transfer the information are physically different. It may be performed by outputting information and accepting information by the other component, or if the two components that pass the information are physically the same, one component. It may be performed by moving from the processing phase corresponding to the above to the processing phase corresponding to the other component.

Further, in the above embodiment, information related to the processing executed by each component, for example, information received, acquired, selected, generated, transmitted, or received by each component. In addition, information such as threshold values, mathematical formulas, and addresses used by each component in processing may be temporarily or for a long period of time in a recording medium (not shown) even if it is not specified in the above description. In addition, each component or a storage unit (not shown) may store information on a recording medium (not shown). Further, the information may be read from the recording medium (not shown) by each component or a reading unit (not shown).

Further, in the above embodiment, when the information used in each component or the like, for example, the information such as the threshold value and the address used in the processing by each component and various setting values may be changed by the user, the above The information may or may not be changed as appropriate by the user, even if it is not specified in the description. When the information can be changed by the user, the change is realized by, for example, a reception unit (not shown) that receives a change instruction from the user and a change unit (not shown) that changes the information in response to the change instruction. You may. The reception unit (not shown) may accept the change instruction from, for example, an input device, information transmitted via a communication line, or information read from a predetermined recording medium. ..

Further, in the above embodiment, when two or more components included in the information processing device 1 have a communication device, an input device, or the like, even if the two or more components physically have a single device. Well, or may have separate devices.

Further, in the above-described embodiment, each component may be configured by dedicated hardware, or a component that can be realized by software may be realized by executing a program. For example, each component can be realized by a program execution unit such as a CPU reading and executing a software program recorded on a recording medium such as a hard disk or a semiconductor memory. At the time of execution, the program execution unit may execute the program while accessing the storage unit or the recording medium. The software that realizes the information processing device 1 in the above embodiment is the following program. That is, this program includes one or more parameters to be set in the molding machine, one or more sensor values acquired by using the sensor-equipped mold in the molding machine in which one or more parameters are set, and the sensor-equipped metal. A computer that can access the learner storage unit that stores the learner that has learned two or more learning information with one or more molding-related information related to molding using a mold by a machine learning algorithm, without a sensor. Accepted by the test reception unit and the test reception unit that receive one or more normal parameters, which are one or more parameters that make the molding result in test molding using a mold normal, and one or more molding-related information related to test molding. A sensor value acquisition unit that acquires one or more normal sensor values, which are one or more sensor values that make the molding result normal by applying the obtained one or more normal parameters and one or more molding-related information to the learner. , A mounting reception unit that receives one or more molding-related information related to mounting molding using a mold without a sensor, one or more molding-related information received by the mounting reception unit, and one acquired by the sensor value acquisition unit. By applying the above normal sensor values to the learner, a parameter acquisition unit that acquires one or more parameters that make the molding result in mounting molding normal, and an output that outputs one or more parameters acquired by the parameter acquisition unit. It is a program to function as a department.

In the above program, the functions realized by the above program do not include functions that can be realized only by hardware. For example, the functions realized only by hardware such as a modem and an interface card in the reception unit for receiving information and the output unit for outputting information are not included at least in the functions realized by the above program.

Further, this program may be executed by being downloaded from a server or the like, and the program recorded on a predetermined recording medium (for example, an optical disk such as a CD-ROM, a magnetic disk, a semiconductor memory, etc.) is read out. May be performed by. Further, this program may be used as a program constituting a program product.

Further, the number of computers that execute this program may be singular or plural. That is, centralized processing may be performed, or distributed processing may be performed.

FIG. 8 is a schematic view showing an example of the appearance of a computer that executes the above program and realizes the information processing apparatus 1 according to the above embodiment. The above embodiment can be realized by computer hardware and a computer program executed on the computer hardware.

In FIG. 8, the computer system 900 includes a computer 901 including a CD-ROM drive 905, a keyboard 902, a mouse 903, and a monitor 904.

FIG. 9 is a diagram showing an internal configuration of the computer system 900. In FIG. 9, the computer 901 is connected to the MPU (Micro Processing Unit) 911, the ROM 912 for storing a program such as a bootup program, and the MPU 911 in addition to the CD-ROM drive 905, and issues an instruction of the application program. It includes a RAM 913 that temporarily stores and provides a temporary storage space, a hard disk 914 that stores application programs, system programs, and data, and a bus 915 that interconnects MPU 911, ROM 912, and the like. The computer 901 may include a network card (not shown) that provides a connection to a LAN, WAN, or the like.

The program for causing the computer system 900 to execute the function of the information processing apparatus 1 according to the above embodiment may be stored in the CD-ROM 921, inserted into the CD-ROM drive 905, and transferred to the hard disk 914. Alternatively, the program may be transmitted to the computer 901 via a network (not shown) and stored on the hard disk 914. The program is loaded into RAM 913 at run time. The program may be loaded directly from the CD-ROM921 or the network. Further, the program may be read into the computer system 900 via another recording medium (for example, DVD or the like) instead of the CD-ROM 921.

The program does not necessarily have to include an operating system (OS), a third-party program, or the like that causes the computer 901 to execute the functions of the information processing apparatus 1 according to the above embodiment. The program may contain only parts of instructions that call the appropriate functions or modules in a controlled manner to achieve the desired result. It is well known how the computer system 900 works, and detailed description thereof will be omitted.

Further, it goes without saying that the present invention is not limited to the above embodiments, and various modifications can be made, and these are also included in the scope of the present invention.

From the above, according to the information processing apparatus or the like according to the present invention, it is possible to obtain the effect that the molding result becomes normal and one or more parameters used in the mounting molding can be easily obtained, and one or more parameters in the mounting molding. It is useful as a device for acquiring information processing.

11 Learner storage unit 12 Exam reception unit 13 Sensor value acquisition unit 14 Mounting reception unit 15 Condition storage unit 16 Set generation unit 17 Parameter acquisition unit 18 Output unit 21 Score acquisition means 22 Parameter acquisition means

Claims (9)

One or more parameters to be set in the molding machine, one or more sensor values acquired by using the mold with a sensor in the molding machine in which the one or more parameters are set, and the mold with the sensor were used. A learning device storage unit that stores a learning device that has learned two or more learning information having one or more molding-related information related to molding by a machine learning algorithm, and a learning device storage unit.
A test reception unit that accepts one or more normal parameters, which are one or more parameters that normalize the molding result in test molding using a mold without a sensor, and one or more molding-related information related to the test molding.
By applying one or more normal parameters and one or more molding-related information received by the test reception unit to the learner, one or more normal sensor values, which are one or more sensor values that make the molding result normal, are one or more normal sensor values. And the sensor value acquisition unit to acquire
A mounting reception unit that receives one or more molding-related information related to mounting molding using the mold without a sensor, and a mounting reception unit.
By applying one or more molding-related information received by the mounting reception unit and one or more normal sensor values acquired by the sensor value acquisition unit to the learner, the molding result in the mounting molding is normal. A parameter acquisition unit that acquires one or more parameters
An information processing device including an output unit that outputs one or more parameters acquired by the parameter acquisition unit. The information processing apparatus according to claim 1, wherein the molding-related information includes at least one or more of environmental information regarding an environment at the time of molding, material information regarding a material to be molded, and machine information of a molding machine. The molding-related information includes environmental information regarding the environment at the time of molding and material information regarding the material to be molded.
A learning device is stored in the learning device storage unit for each of two or more material information.
The parameter acquisition unit selects a learner corresponding to the material information included in one or more molding-related information received by the mounting reception unit, and one or more that makes the molding result normal using the selected learning device. The information processing apparatus according to claim 1, wherein the parameters of the above are acquired. The information processing apparatus according to any one of claims 1 to 3, wherein the sensor value of 1 or more includes one or more of the flow velocity, displacement, pressure, and temperature of the material. The information processing apparatus according to any one of claims 1 to 4, wherein the learning information includes result information indicating that the molding result is normal or abnormal. A condition storage unit that stores the conditions of one or more parameters, and
Further, a set generation unit for generating two or more sets including one or more parameters by using the condition of each of the one or more parameters is further provided.
The parameter acquisition unit
A score acquisition means for applying two or more sets generated by the set generator to the learner and acquiring a score for which the molding result is normal for each set.
The information processing apparatus according to claim 5, further comprising a parameter acquisition means for acquiring one or more parameters constituting a set in which the score acquired by the score acquisition means is high enough to satisfy a predetermined condition. The information processing apparatus according to any one of claims 1 to 6, wherein the output unit sets one or more parameters acquired by the parameter acquisition unit in the molding machine. One or more parameters to be set in the molding machine, one or more sensor values acquired by using the sensor-equipped mold in the molding machine in which the one or more parameters are set, and the sensor-equipped mold were used. A learner storage unit that stores a learner that has learned two or more learning information having one or more molding-related information related to molding by a machine learning algorithm, a test reception unit, a sensor value acquisition unit, and implementation. It is an information processing method that is processed by using the reception unit, the parameter acquisition unit, and the output unit.
The test reception unit provides one or more normal parameters, which are one or more parameters for normal molding results in test molding using a mold without a sensor, and one or more molding-related information related to the test molding. Exam acceptance steps to accept and
The sensor value acquisition unit applies one or more normal parameters and one or more molding-related information received in the test acceptance step to the learner, so that the molding result becomes normal with one or more sensor values. A sensor value acquisition step for acquiring one or more normal sensor values, and
A mounting reception step in which the mounting reception unit receives one or more molding-related information related to mounting molding using the mold without the sensor, and a mounting reception step.
The parameter acquisition unit applies one or more molding-related information received in the mounting acceptance step and one or more normal sensor values acquired in the sensor value acquisition step to the learner, thereby mounting the mounting. A parameter acquisition step for acquiring one or more parameters for which the molding result in molding is normal, and
An information processing method including an output step in which the output unit outputs one or more parameters acquired in the parameter acquisition step. One or more parameters to be set in the molding machine, one or more sensor values acquired by using the mold with a sensor in the molding machine in which the one or more parameters are set, and the mold with the sensor were used. A computer that can access the learner storage unit that stores the learner that has learned two or more learning information with one or more molding-related information related to molding by a machine learning algorithm.
A test reception unit that accepts one or more normal parameters, which are one or more parameters for normal molding results in test molding using a mold without a sensor, and one or more molding-related information related to the test molding.
By applying one or more normal parameters and one or more molding-related information received by the test reception unit to the learner, one or more normal sensor values, which are one or more sensor values that make the molding result normal, are one or more normal sensor values. Sensor value acquisition unit,
A mounting reception unit that receives one or more molding-related information related to mounting molding using the mold without a sensor.
By applying one or more molding-related information received by the mounting reception unit and one or more normal sensor values acquired by the sensor value acquisition unit to the learner, the molding result in the mounting molding is normal. Parameter acquisition unit that acquires one or more parameters,
A program for functioning as an output unit that outputs one or more parameters acquired by the parameter acquisition unit.

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