Apparatus, method and program for inferring operating conditions of a blow molding machine, and learning apparatus

Abstract

Assists with various settings on blow molding machines. [Solution] A trained model 73 is prepared, which uses as input data the physical property information of the preform, the conditions at the time of molding the bottle, the measured values ​​of the physical property information of the bottle, and the measured values ​​of the environmental information at the time of manufacturing the bottle, and outputs as output data the operating conditions of a blow molding machine. By inputting target values ​​81a of the physical property information that the molded bottle should satisfy, as well as the physical property information of the preform, the conditions at the time of molding the bottle, and the measured values ​​81b of the environmental information, into the trained model 73, the operating conditions of the blow molding machine (heater output, blow pressure, blow timing, rod speed) that should be set in the blow molding machine to mold a bottle that satisfies the target values ​​are inferred.

Description

[Technical field]

[0001] The present invention relates to an operating condition inference device for a blow molding machine, an operating condition inference method and program for a blow molding machine, and a learning device. [Background technology]

[0002] Many plastic bottles, such as PET bottles, are made by first manufacturing a preform similar in shape to a test tube, then stretch-blow molding it. A blow molding machine for stretch-blow molding is used to stretch the preform and shape the bottle (see Patent Document 1 for blow molding machines).

[0003] A wide variety of settings are set for blow molding machines. In recent years, the variety of bottles has increased, and with the demand for lighter bottles and the use of recycled materials, the variety of preforms has also increased. If the type of bottle or the type of preform used to mold the bottles is different, it is natural that the settings for the blow molding machine must also be adjusted (changed). Setting up a blow molding machine often relies on the intuition and experience of a skilled operator. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Special Publication No. 2020-533195 Summary of the Invention [Problem to be solved by the invention]

[0005] The object of this invention is to assist in various settings of a blow molding machine.

[0006] The operating condition inference device for a blow molding machine according to the present invention is an inference device that infers the operating conditions of a blow molding machine that heats a preform with a heater, moves the heated preform to a mold, stretches the preform in the axial direction by inserting a stretch rod into the preform, and stretches the preform outward by supplying pressurized fluid inside, thereby molding a hollow bottle, and is characterized in that it is equipped with a trained model in which parameters are trained so that physical property information of the preform is used as input data and the operating conditions of the blow molding machine are used as output data, and an inference means for inferring the operating conditions of the blow molding machine that should be set on the blow molding machine in order to mold a bottle that satisfies the target value, which is inferred by inputting target values ​​of physical property information that the molded bottle should satisfy into the trained model.

[0007] According to this invention, the operating conditions of the blow molding machine that should be set in the blow molding machine in order to mold a bottle that satisfies the target values ​​are inferred. The inferred operating conditions are based on a trained model in which parameters have been trained so that the physical property information of the bottle is used as input data and the operating conditions of the blow molding machine are used as output data. The operating conditions of the blow molding machine for molding a bottle with the target physical property information can be inferred in a short period of time without relying on the experience of the operator.

[0008] Preferably, the input data further includes at least one of physical property information of the preform, molding conditions of the bottle, and environmental information during the manufacture of the bottle. This increases the possibility of more accurately and precisely inferring the operating conditions of the blow molding machine for molding a bottle having target physical property information.

[0009] In one embodiment, the inferred operating conditions of the blow molding machine are the heater output conditions for heating the preform and the pressure conditions of the fluid supplied to the preform. This is because the heater output and fluid pressure (blow pressure) have a significant effect on the physical properties of the bottle molded by the blow molding machine. The learned model infers the heater output and fluid pressure that should be set in the blow molding machine in order to mold a bottle that satisfies the target physical property values. This increases the certainty that a bottle that satisfies the specified physical property target values ​​can be molded without relying on the experience of the operator.

[0010] The inferred operating conditions of the blow molding machine may further include, in addition to the heater power and fluid pressure (blow pressure) mentioned above, the timing conditions for supplying fluid to the preform and / or the speed conditions of the stretch rod inserted into the preform.

[0011] Preferably, the input data includes information regarding the presence or absence of an abnormality in the appearance of the bottle, and the inference means infers the operating conditions of the blow molding machine that should be set in the blow molding machine in order to mold a bottle that satisfies the target value and has no abnormality in appearance, thereby making it possible to infer the operating conditions for molding a bottle of higher quality.

[0012] In one embodiment, the preform physical property information includes data regarding the material and weight of the preform.

[0013] In another embodiment, the bottle forming conditions include data regarding the volume and shape of the bottle.

[0014] In another embodiment, the physical property information of the bottle includes at least one of the bottle's full capacity, buckling strength, overall height, wall thickness, and section weight.

[0015] In another embodiment, the environmental information includes at least one of the temperature and humidity surrounding the blow molding machine.

[0016] The present invention also provides a learning device that generates a trained model suitable for use in a blow operation condition inference device. The learning device according to the present invention includes a learning data acquisition means that acquires learning data including operating conditions of a blow molding machine that heats a preform with a heater, moves the heated preform to a mold, stretches the preform in the axial direction by inserting a stretch rod into the preform, and stretches the preform outward by supplying a pressurized fluid inside to thereby mold a hollow bottle, and physical property information of the bottle, and a model generation means that uses the learning data to generate a trained model for inferring, from target values ​​of physical property information that the molded bottle should satisfy, operating conditions of the blow molding machine that should be set on the blow molding machine in order to mold a bottle that satisfies the target values.

[0017] The present invention further provides a method for inferring operating conditions of a blow molding machine and a program for causing a computer to execute this method. [Brief description of the drawings]

[0018] [Figure 1] FIG. 1 is a schematic plan view of a blow molding machine. [Diagram 2] The positional relationship between the preform and the heater zone is shown. [Diagram 3] FIG. 2 is a vertical cross-sectional view of a mold in which a preform is set. [Figure 4] FIG. 2 is a functional block diagram of the blow molding machine. [Diagram 5] FIG. 2 is a block diagram of a learning device. [Figure 6] FIG. 2 is a block diagram of an inference device. [Figure 7] 4 shows the processing results of the inference device. [Figure 8] 13 is a graph showing the relationship between the target value and the actual measured value of the full filling capacity. EXAMPLES

[0019] FIG. 1 is a plan view showing a schematic diagram of a blow molding machine.

[0020] A large number of preforms P made of resin, for example PET (polyethylene terephthalate), are prepared in the preform supply station 1. The preforms P are supplied from the preform supply station 1 along a transport path to the heating station 2. The heating station 2 has one or more heater zones 5 (six heater zones 5 are shown in FIG. 1), and the preforms P are heated to a temperature at which they can be blow molded by passing through the heater zones 5. The number of heater zones 5 can be designed as desired.

[0021] The preforms P heated in the heating station 2 proceed to the molding station 3. The molding station 3 is equipped with a rotating wheel 6 and a number of dies 7 that are arranged along the rotating wheel 6 and revolve around the molding station 3 at a constant speed in accordance with the orbital motion of the rotating wheel 6. The preforms P supplied to the molding station 3 are each set in the dies 7. Note that the molding station 3 does not necessarily need to be equipped with a rotating wheel 6, and the dies 7 may be transported in a straight line. The number of dies 7 that the molding station 3 is equipped with is also arbitrary.

[0022] A stretch rod is inserted into the preform P from the mouth of the preform P set in the mold 7, thereby stretching the preform P in the axial direction (longitudinal direction). A fluid, typically compressed air, is blown into the preform P from the mouth of the axially stretched preform P (air blow). The preform P is stretched outward (axial and circumferential (radial) directions) by the air blow. The air blow is performed in two stages, first by blowing in low pressure air (hereinafter referred to as first air) to inflate the preform P, and then by blowing in high pressure air (hereinafter referred to as second air) to make the preform P stick to the inner surface of the mold, thereby making a bottle B having a shape according to the shape of the mold 7. After the compressed air and stretch rod are removed from the bottle B, the bottle B is taken out of the mold 7. The bottle B that leaves the molding station 3 proceeds to subsequent processes (not shown), such as a sterilization process, a beverage filling process, a capping process, etc., and a beverage-filled bottle is completed.

[0023] Fig. 2 is a schematic diagram showing the positional relationship between the preform P and the heater zone 5 when the preform P is transported through the heating station 2, and shows a schematic cross section of the heater zone 5. Fig. 2 also shows a spindle 8 for holding and rotating the preform P. In the heating station 2, the preform P is transported through the heating station 2 while being rotated by the spindle 8 with its axial direction as the rotation axis.

[0024] The heating station 2 of the blow molding machine is provided with the heater zone 5 as described above, where the preform P is softened so that the preform P can be stretched by the stretching rod and air blow. When multiple heater zones 5 are provided, the heater zones 5 include a type that heats the entire preform P, a type that concentrates heating around the support ring of the preform P (focus heater), a type that does not have a heater for heating and blows air onto the entire preform P (surface cleaning), and a type that blows air onto a part of the preform P (air knife), and various types can be used, which allows for fine control of the heating of the preform P. The heater zone 5 illustrated in FIG. 2 shows a type that heats the entire preform P. The multiple heater zones 5 can be controlled independently of each other.

[0025] The heater zone 5 shown in Fig. 2 is provided with a plurality of heaters 5a (e.g., halogen lamps) (seven in Fig. 2) arranged at intervals along the axial direction of the preform P. Each of the seven heaters 5a provided in the heater zone 5 can be controlled independently of the others. The number of heaters 5a provided in the heater zone 5 can also be any number.

[0026] For example, if it is desired to thicken the wall of a bottle B molded by a blow molding machine in a certain area, it is possible to lower the heating temperature of the part of the preform P that corresponds to that area. For example, by using a heater zone 5 of the type shown in Figure 2 and lowering the output of the heater 5a that heats that part, it is possible to lower the heating temperature of a part of the preform P, and it becomes possible to mold a bottle B that is thicker in some areas.

[0027] FIG. 3 is a schematic diagram showing the blow molding process, showing a vertical cross section of a mold 7 in which a preform P is set.

[0028] The preform P, which has been heated in the heating station 2 and delivered to the molding station 3, has its sides surrounded by the split dies 7a and 7b that constitute the metal mold 7. The bottom surfaces of the split dies 7a and 7b are closed by a bottom die 7c, and a valve block 13 is joined to the mouth of the preform P. A stretch rod 11 is inserted into the preform P through a hole formed in the valve block 13. The stretch rod 11 stretches the preform P in the axial direction.

[0029] The valve block 13 has two valves V1 and V2 inside. Compressed air from the air pump 12 is sent into the preform P through the valve V1. The first air and the second air described above are sequentially blown into the preform P from the valve V1, which causes the preform P to expand outward and form the bottle B.

[0030] After the bottle B is molded, the compressed air inside the bottle B is exhausted to the outside through the valve V2 of the valve block 13. The stretch rod 11 is removed from the bottle B, and the bottom mold 7c and the split molds 7a, 7b are opened, and the completed bottle B is transported out of the blow molding machine.

[0031] In the molding station 3, it is possible to control the rod speed (advancement speed) of the stretch rod 11, the timing to start blowing the first air, the pressure of the first air, the timing to switch from the first air to the second air, the pressure of the second air, and the like.

[0032] For example, if you want to thin the wall thickness near the bottom of bottle B molded by a blow molding machine, you can start blowing the first air earlier. Since the first air is supplied before the stretch rod is fully extended (before the preform P is fully stretched vertically), the wall thickness of the bottom of bottle B becomes thin, and instead, the wall thickness from the neck to the shoulder area of ​​bottle B can be made thicker.

[0033] FIG. 4 shows a functional block diagram of a blow molding machine.

[0034] Functionally, the blow molding machine is roughly divided into a heater function 20 and a blow function 30. The heater function 20 is realized by the multiple heater zones 5 provided in the heating station 2. The blow function 30 is realized by the stretch rod 11, air pump 12, valve block 13, etc. provided in the molding station 3.

[0035] As described above, each of the multiple heater zones 5 in the heating station 2, and further each of the multiple heaters 5a in the heater zone 5, can be controlled individually. The pressure of the air supplied from the air pump 12 in the molding station 3, the timing of the air supply, and the rod speed (advance speed) of the stretch rod 11 can also be controlled. The heater function 20 and the blow function 30 are centrally managed by the control device 40.

[0036] A setting device 50 is connected to the control device 40. The setting device 50 has a display device (not shown), and a number of setting items are displayed on a setting screen displayed on the display device. The control device 40 controls the heater function 20 and the blow function 30 according to the setting of the number of setting items using the setting screen.

[0037] Setting values ​​for many setting items can be set in the setting device 50, and the heater function 20 and blow function 30 of the blow molding machine are precisely controlled according to these many setting values. By changing the setting values, as described above, for example, it is possible to make the wall thickness of a specific portion of the bottle B that is finally molded thicker or thinner.

[0038] Specifically, the setting device 50 is set with preform physical property information such as the material and weight of the preform P, bottle molding conditions such as the volume and shape of the bottle B to be molded, heater setting information such as the output of the heater 5a in the heater zone 5, and blow setting information such as the rod speed (progress (insertion) speed) of the stretch rod 11, the timing to start the first air, the pressure of the first air, the timing to switch from the first air to the second air, and the pressure of the second air. There are many setting items for the blow molding machine, and on-site, a skilled operator finely adjusts the setting values ​​of many setting items. This is because a skilled operator has experience in knowing which of the many setting items should be adjusted and how the results will appear in the bottle B.

[0039] In this embodiment, machine learning is performed as described below in order to mechanically realize detailed adjustments of setting items made by a skilled operator, reduce the time required for adjustment work, and stabilize the quality of the molded bottle B. Target values ​​of physical property information that the molded bottle B should satisfy are given, and the operating conditions to be set in the blow molding machine in order to mold the bottle B that satisfies the target values ​​are inferred as the results of machine learning processing.

[0040] As described below, in this embodiment, the operating conditions inferred using machine learning processing are the heater power and blow pressure of the blow molding machine. In addition to these, blow timing and rod speed may also be added to the inferred operating conditions. This is because the heater power, blow pressure, blow timing and rod speed have a correlation with the physical properties of the bottle B molded from the preform P by the blow molding machine.

[0041] 5 shows a block diagram of the learning device 70. The learning device 70 performs machine learning related to a blow molding machine, and includes a learning data acquisition unit 71 that acquires learning data, which is data used for learning, a model generation unit 72 that uses the learning data to generate a learning model used to infer the heater output, blow output, blow timing, and rod speed of the blow molding machine, and a generated trained model 73 (a storage unit for the model). The learning device 70 is realized by, for example, a computer device, and operates according to a program that causes the computer device to realize the functions of the learning device 70.

[0042] The learning data acquisition unit 71 acquires (receives) data set or measured when the bottle B is actually formed as learning data from a plurality of bottle molding factories (bottled beverage manufacturing factories) where blow molding machines are installed, via a network (e.g., the Internet). The learning data includes the following data:

[0043] (1) Heater output This is the output of the multiple heater zones 5 equipped in the heating station 2 of the blow molding machine. The data representing the heater output may be the output ratio (total output) (e.g., 80%) of the maximum output of the entire heating station 2, each heater zone 5, or each heater 5a, or may be on / off data of the multiple heaters 5a equipped in the heater zone 5. (2) Blow pressure These are the pressure values ​​of the first air and the second air blown into the preform P.

[0044] (3) Blow timing These are the start timing of the first air and the timing when the first air is switched to the second air (switching timing). Instead of the switching timing, a combination of the end timing of the first air and the start timing of the second air may be used. The data representing the blow timing may be the rotation angle of the rotating wheel 6, with the position where the preform P is handed over to the molding station 3 as the reference (0°). For example, if the start timing of the first air is "33°", this means that the first air will start at the timing when the preform P is handed over to the molding station 3 and rotated 33° by the rotating wheel 6.

[0045] (4) Rod speed This is the advancement (insertion) speed of the stretch rod 11 inserted into the preform P.

[0046] (5) Preform physical property information These include the material, weight, and dimensions of the preform P. The dimensions generally include the diameter of the opening of the preform P, the diameter of the body, and the length from the support ring to the bottom end (body length).

[0047] (6) Bottle molding conditions The capacity and shape of bottle B. The shape is generally classified as round or square.

[0048] (7)Environmental information This is a measurement of the temperature and humidity surrounding the blow molding machine.

[0049] (8) Bottle physical properties information These are the measured values ​​of the full capacity, buckling strength, overall height, wall thickness and section weight (the weight of each part divided by cutting bottle B horizontally (cutting perpendicular to the axial direction) at one or more specified points) of the molded bottle B. For wall thickness and section weight, the respective values ​​(or more detailed values) of multiple points of bottle B, such as the neck, shoulder, body and bottom, are generally used.

[0050] (9) Bottle appearance information Bottle B molded from preform P may become cloudy (whiten). The presence or absence of this whitening is included in the bottle appearance information. The presence or absence of structural defects in bottle B may also be included in the bottle appearance information. There are two types of whitening: whitening due to overstretching and whitening due to crystallization. Whitening due to overstretching is likely to occur when preform P is not heated enough, while whitening due to crystallization occurs when preform P is heated too much and the material of preform P crystallizes.

[0051] The model generation unit 72 learns the learning data from the learning data acquisition unit 71 and generates a learned model for inferring the operating conditions that should be set in the blow molding machine in order to mold a bottle B that meets the target, specifically, the heater output, blow pressure, blow timing, and rod speed.

[0052] The learning algorithm used by the model generation unit 72 may be any known algorithm, such as supervised learning, unsupervised learning, or reinforcement learning.

[0053] For example, it is possible to use LightGBM, a learning algorithm with high prediction accuracy and short training time. The trained model 73 represents the relationship between explanatory variables (specifically, bottle physical property information, preform physical property information, bottle molding conditions and environmental information) and objective variables (heater output of the blow molding machine, blow pressure, blow timing and rod speed), and has learned parameters for calculating the values ​​of the objective variables. External setting variables (hyperparameters) (such as the number of branches of the decision tree) for managing the training of the learning model are optimized by, for example, a Bayesian optimization library.

[0054] The output data (objective variables) of the trained model 73 may be preferentially selected from those that have a large effect on the physical properties of the bottle B molded by the blow molding machine. Here, an example in which four pieces of data, the heater output, blow pressure, blow timing, and rod speed of the blow molding machine, are treated as output data will be described in particular, but for example, two pieces of data, the heater output and blow pressure (particularly the pressure of the first air), may be used as output data (objective variables). In LightGBM, the importance (degree of influence) of explanatory variables that affect the objective variable is calculated for each explanatory variable, so when focusing on a bottle property of interest (for example, full fill capacity) (one of the explanatory variables), data that has a large effect on the bottle property of interest (objective variables) can be selected as the output data of the trained model 73.

[0055] It is of course also possible to limit the input data (explanatory variables) of the trained model 73 to, for example, physical property information of the bottle B molded by the blow molding machine (for example, the full order capacity, which is one of the physical property information). However, if multiple types of input data are used, the possibility of inferring setting items and setting values ​​that the operator does not notice increases.

[0056] FIG. 6 is a block diagram of an inference device for inferring operating conditions to be set in a blow molding machine.

[0057] The inference device 80 comprises an inference data acquisition unit 82 and an inference unit 83. The inference device 80 is also realized by, for example, a computer device, and operates according to a program that causes the computer device to realize the functions of the inference device 80.

[0058] The inference data acquisition unit 82 is provided with at least target values ​​(design values) 81a of physical property information of the bottle B to be molded (at least one of full capacity, buckling strength, overall height, wall thickness, and section weight), and preferably is provided with physical property information of the preform P used to mold the bottle B (at least one of the material and weight of the preform P), bottle molding conditions (at least one of the bottle capacity and shape), and environmental information (at least one of the temperature and humidity) 81b.

[0059] The inference unit 83 infers the operating conditions of the blow molding machine, here the heater output, blow pressure, blow timing and rod speed, by utilizing the trained model 73. That is, the inference unit 83 inputs the inference data acquired by the inference data acquisition unit 82 to the trained model 73, and outputs the heater output, blow pressure, blow timing and rod speed such that the bottle to be produced will have the targeted physical property information.

[0060] The target (value) of the physical property information of the bottle to be molded may be a specific numerical value or a numerical range with a predetermined width. For the wall thickness and section weight, values ​​are generally set for multiple locations of the bottle B, such as the neck, shoulder, body, and bottom.

[0061] By using the trained model 73 that has been trained using a large amount of training data, the heater output, blow pressure, blow timing, and rod speed to be set in the setting device 50 of the blow molding machine in order to generate a bottle B having physical properties that satisfy the target or fall within the target range can be output to the inference device 80. By setting the heater output, blow pressure, blow timing, and rod speed output from the inference device 80 in the setting device 50 of the blow molding machine, a bottle B that satisfies the target physical properties can be manufactured as if it had been set by a skilled operator.

[0062] As described above, since the learning data includes bottle appearance information, it is also possible to have the inference device 80 output the heater output, blow pressure, blow timing, and rod speed to be set in the setting device 50 of the blow molding machine in order to generate a bottle B that has the target physical properties and is free of abnormalities in appearance (e.g., no whitening). A bottle B of higher quality can be molded.

[0063] Fig. 7 shows the processing results of the inference device 80 using the trained model 73, in which "bottle capacity (600 ml)" and "bottle shape (round)" are the bottle molding conditions, "full capacity" (ml) is the target value (explanatory variable, input data) of the physical property information of the bottle to be molded, and two operating conditions of the blow molding machine, "heater output" (%) (total output) and "blow pressure" (pressure of the first air) (bar), are the inferred values ​​(objective variable, output data). Fig. 7 also shows the actual measured full capacity values ​​of five bottles B actually molded from five preforms P of the same type by setting the inferred heater output and blow pressure when the target value of full capacity was changed in the blow molding machine, and the error between the target value and the actual value. The "error between target value and actual value" also shows the "error (= actual value - target value)," "error rate (= error ÷ actual value)," "absolute error rate (= absolute value of error rate)," and "average absolute error rate (= total absolute error rate ÷ number of data)." Figure 8 is a graph with the target value for full capacity on the horizontal axis and the actual value for full capacity on the vertical axis, with the target value and actual value for each of the five data plotted with a circle. The dashed line in the graph in Figure 8 is an auxiliary line that shows when the target value and actual value match.

[0064] The average absolute error rate of the five data was small at 0.124%, and it was confirmed that by molding bottle B using the heater output and blow pressure estimated using the trained model 73, it is possible to mold bottle B with a full filling capacity that is quite close to the target value. [Explanation of symbols]

[0065] 1 Preform supply station 2 Heating Station 3 Forming Station 5 Heater Zone 5a Heater 7. Mold 11 Extension rod 12 Air Pump 13 Valve block 20 Heater function 30 Blow function 40 Control device 50 Setting device 70 Learning Device 71 Learning data acquisition unit 72 Model Generation Unit 73 trained models 80 Reasoning device 82 Data acquisition unit for inference 83 Reasoning part B Bottle P Preform

Claims

1. 1. An inference device for inferring operating conditions of a blow molding machine which heats a preform with a heater, moves the heated preform to a mold, inserts a stretch rod into the preform to stretch the preform in an axial direction, and supplies a pressurized fluid to the inside of the preform to stretch the preform outward, thereby forming a hollow bottle, A trained model in which parameters are trained to use the physical property information of a bottle as input data and the operating conditions of a blow molding machine as output data; and an inference means for inferring operating conditions of the blow molding machine that should be set on the blow molding machine in order to mold a bottle that satisfies a target value, the operating conditions being inferred by inputting a target value of physical property information that a molded bottle should satisfy into the trained model; The physical property information of the bottle used as the input data includes the full capacity of the bottle, An operating condition inference device for blow molding machines.

2. 1. An inference device for inferring operating conditions of a blow molding machine which heats a preform with a heater, moves the heated preform to a mold, inserts a stretch rod into the preform to stretch the preform in an axial direction, and supplies a pressurized fluid to the inside of the preform to stretch the preform outward, thereby forming a hollow bottle, A trained model in which parameters are trained to use the physical property information of a bottle as input data and the operating conditions of a blow molding machine as output data; and an inference means for inferring operating conditions of the blow molding machine that should be set on the blow molding machine in order to mold a bottle that satisfies a target value, the operating conditions being inferred by inputting a target value of physical property information that a molded bottle should satisfy into the trained model; The input data further includes physical property information of the preform, and the physical property information of the preform includes data on the material and weight of the preform. An operating condition inference device for blow molding machines.

3. 1. An inference device for inferring operating conditions of a blow molding machine which heats a preform with a heater, moves the heated preform to a mold, inserts a stretch rod into the preform to stretch the preform in an axial direction, and supplies a pressurized fluid to the inside of the preform to stretch the preform outward, thereby forming a hollow bottle, A trained model in which parameters are trained to use the physical property information of a bottle as input data and the operating conditions of a blow molding machine as output data; and an inference means for inferring operating conditions of the blow molding machine that should be set on the blow molding machine in order to mold a bottle that satisfies a target value, the operating conditions being inferred by inputting a target value of physical property information that a molded bottle should satisfy into the trained model; The input data further includes environmental information during the manufacture of the bottle, the environmental information including at least one of the temperature and humidity of the surroundings of the blow molding machine. An operating condition inference device for blow molding machines.

4. The input data further includes the molding conditions of the bottle. The operating condition inference device for a blow molding machine according to any one of claims 1 to 3.

5. The operating conditions of the blow molding machine are a heater output condition for heating the preform and a pressure condition of a fluid supplied to the preform. The operating condition inference device for a blow molding machine according to any one of claims 1 to 3.

6. The operating conditions of the blow molding machine further include timing conditions for supplying fluid to the preform; The operating condition inference device for a blow molding machine according to any one of claims 1 to 3.

7. The operating conditions of the blow molding machine further include a speed condition of a stretch rod inserted into the preform; The operating condition inference device for a blow molding machine according to any one of claims 1 to 3.

8. The input data includes information regarding the presence or absence of an appearance abnormality of the bottle, The inference means is Inferring the operating conditions of the blow molding machine that should be set for the blow molding machine in order to mold a bottle that satisfies the target value and has no abnormality in appearance. The operating condition inference device for a blow molding machine according to any one of claims 1 to 3.

9. The above bottle molding conditions include data on the capacity and shape of the bottle. The operating condition inference device for a blow molding machine according to claim 2.

10. The physical property information of the bottle further includes at least one of buckling strength, total height, wall thickness, and section weight. The operating condition inference device for a blow molding machine according to any one of claims 1 to 3.

11. a learning data acquisition means for acquiring learning data including operating conditions of a blow molding machine that heats a preform with a heater, moves the heated preform to a mold, inserts a stretching rod into the preform to stretch the preform in the axial direction, and supplies a pressurized fluid to the inside to stretch the preform outwardly, thereby forming a hollow bottle, and physical property information of the molded bottle; and a model generation means for generating a trained model for inferring, from target values ​​of physical property information that a bottle after molding should satisfy, operating conditions of the blow molding machine that should be set in the blow molding machine in order to mold a bottle that satisfies the target values, using the training data; The physical properties of the bottle include the full capacity of the bottle, Learning device.

12. a learning data acquisition means for acquiring learning data including operating conditions of a blow molding machine that heats a preform with a heater, moves the heated preform to a mold, inserts a stretching rod into the preform to stretch the preform in the axial direction, and supplies a pressurized fluid to the inside to stretch the preform outwardly, thereby forming a hollow bottle, and physical property information of the molded bottle; and a model generation means for generating a trained model for inferring, from target values ​​of physical property information that a bottle after molding should satisfy, operating conditions of the blow molding machine that should be set in the blow molding machine in order to mold a bottle that satisfies the target values, using the training data; The learning data further includes physical property information of the preform, and the physical property information of the preform includes data on the material and weight of the preform. Learning device.

13. a learning data acquisition means for acquiring learning data including operating conditions of a blow molding machine that heats a preform with a heater, moves the heated preform to a mold, inserts a stretching rod into the preform to stretch the preform in the axial direction, and supplies a pressurized fluid to the inside to stretch the preform outwardly, thereby forming a hollow bottle, and physical property information of the molded bottle; and a model generation means for generating a trained model for inferring, from target values ​​of physical property information that a bottle after molding should satisfy, operating conditions of the blow molding machine that should be set in the blow molding machine in order to mold a bottle that satisfies the target values, using the training data; The learning data further includes environmental information at the time of manufacturing the bottle, and the environmental information includes at least one of the temperature and humidity around the blow molding machine. Learning device.

14. 1. A method for predicting operating conditions of a blow molding machine which heats a preform with a heater, moves the heated preform to a mold, inserts a stretch rod into the preform to stretch the preform in an axial direction, and supplies a pressurized fluid therein to stretch the preform outwardly, thereby forming a hollow bottle, comprising: A trained model is prepared in which parameters are trained so that the physical property information of the bottle is used as input data and the operating conditions of the blow molding machine are used as output data. Inferring operating conditions of the blow molding machine that should be set on the blow molding machine in order to mold a bottle that satisfies the target values, which are inferred by inputting target values ​​of physical property information that the molded bottle should satisfy into the trained model; The physical property information of the bottle used as the input data includes the full capacity of the bottle, A method for inferring operating conditions of a blow molding machine.

15. 1. A method for predicting operating conditions of a blow molding machine which heats a preform with a heater, moves the heated preform to a mold, inserts a stretch rod into the preform to stretch the preform in an axial direction, and supplies a pressurized fluid therein to stretch the preform outwardly, thereby forming a hollow bottle, comprising: A trained model is prepared in which parameters are trained so that the physical property information of the bottle is used as input data and the operating conditions of the blow molding machine are used as output data. Inferring operating conditions of the blow molding machine that should be set on the blow molding machine in order to mold a bottle that satisfies the target values, which are inferred by inputting target values ​​of physical property information that the molded bottle should satisfy into the trained model; The input data further includes physical property information of the preform, and the physical property information of the preform includes data on the material and weight of the preform. A method for inferring operating conditions of a blow molding machine.

16. 1. A method for predicting operating conditions of a blow molding machine which heats a preform with a heater, moves the heated preform to a mold, inserts a stretch rod into the preform to stretch the preform in an axial direction, and supplies a pressurized fluid therein to stretch the preform outwardly, thereby forming a hollow bottle, comprising: A trained model is prepared in which parameters are trained so that the physical property information of the bottle is used as input data and the operating conditions of the blow molding machine are used as output data. Inferring operating conditions of the blow molding machine that should be set on the blow molding machine in order to mold a bottle that satisfies the target values, which are inferred by inputting target values ​​of physical property information that the molded bottle should satisfy into the trained model; The input data further includes environmental information during the manufacture of the bottle, the environmental information including at least one of the temperature and humidity of the surroundings of the blow molding machine. A method for inferring operating conditions of a blow molding machine.

17. A program for causing a computer to carry out the method according to any one of claims 14 to 16.

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