Patent ID: 12202184

DETAILED DESCRIPTION OF EMBODIMENTS

A molding condition determination assisting device50(hereinafter referred to simply as the assisting device50) according a first embodiment is used with a molding method that molds an article by feeding molten material into a mold of a molding apparatus. For example, the molding method may be injection molding of resin, rubber, or the like, or may be metal casting such as die casting. In the description below, injection molding is mainly taken as an example of the molding method to describe the assisting device50.

An injection molding apparatus1for performing injection molding is described with reference toFIG.1. The assisting device50may be either included in the injection molding apparatus1or separated from the injection molding apparatus1. The injection molding apparatus1includes a bed2, an injection device3, a clamping device4, and a control device5. The injection device3is mounted on the bed2. The injection device3heats and melts molding material and then injects the molding material under high pressure into a cavity C of a mold6. The molding material that has been heated and molten is hereinafter referred to as molten material.

The injection device3includes a hopper31, a heating cylinder32, a screw33, a nozzle34, a heater35, a drive device36, and an injection device sensor37. Pellets (molding material in the form of particles) are fed into the hopper31. The heating cylinder32heats and melts the pellets in the hopper31into molten material and pressurizes the molten material. The heating cylinder32is axially movable relative to the bed2. The screw33is mounted inside the heating cylinder32in a manner such that the screw33is rotatable and axially movable in the heating cylinder32.

The nozzle34is an injection opening provided at the tip of the heating cylinder32and feeds the molten material in the heating cylinder32into the cavity C of the mold6in accordance with the axial movement of the screw33. The heater35is mounted, for example, to the outside of the heating cylinder32and heats the pellets in the heating cylinder32. The drive device36performs the axial movement of the heating cylinder32and also performs the rotation and axial movement of the screw33. The injection device sensor37is a general term for sensors that obtain various types of information relating to the injection device3, including: the amount of stored molten material; a dwell pressure; a dwell time; an injection speed; and the condition of the drive device36. The injection device sensor37may obtain information other than that described above.

The clamping device4is mounted on the bed2and faces the injection device3. The clamping device4opens and closes the mold6attached thereto, and also clamps the mold6such that the mold6is not opened by pressure of the molten material injected into the cavity C of the mold6.

The clamping device4includes a fixed platen41, a movable platen42, a tie bar43, a drive device44, and a clamping device sensor45. A first mold6aas a fixed part of the mold6is fixed to the fixed platen41. The fixed platen41is abutable with the nozzle34of the injection device3and guides the molten material injected from the nozzle34into the cavity C of the mold6. The cavity C is a space formed between the first mold6aand a second mold6band has a shape corresponding to the shape of an article to be molded. The second mold6bas a movable part of the mold6is fixed to the movable platen42. The movable platen42is movable toward and away from the fixed platen41. The tie bar43supports the movement of the movable platen42. The drive device44moves the movable platen42. For example, the drive device44may be structured as a cylinder device. The clamping device sensor45is a general term for sensors that obtain various types of information relating to the clamping device4, including: a mold clamping force; a mold temperature; and the condition of the drive device44.

The cavity C of the mold6is formed between the first mold6aand the second mold6b. The first mold6ahas a feed channel6c(a sprue, a runner, and a gate) from the nozzle34to the cavity C. The first mold6aor the second mold6bincludes first and second pressure sensors6dand6e. The pressure sensors6dand6edetect pressure applied from the molten material.

The control device5controls both the drive device36of the injection device3and the drive device44of the clamping device4on the basis of a command value for a molding condition. Specifically, the control device5obtains various types of information from the injection device sensor37, the clamping device sensor45, and the pressure sensors6dand6e, and controls the drive device36of the injection device3and the drive device44of the clamping device4so as to cause the injection device3and the clamping device4to operate in accordance with the command value.

Below is a description of a method of injection molding performed by the injection molding apparatus1. The injection molding method includes the following successive steps: a measuring step; a clamping step; an injection filling step; a dwell step; a cooling step; and a removing step. In the measuring step, pellets are melted into molten material by heat from the heater35and by shear frictional heat generated by rotation of the screw33, and the molten material is stored between the tip of the heating cylinder32and the nozzle34. As the amount of the stored molten material increases, the screw33retracts. Thus, the amount of the stored molten material is measured from a retracted position of the screw33.

Then, in the clamping step, by moving the movable platen42, the first mold6aand the second mold6bare brought together to clamp the mold6. Further, the nozzle34is connected to the fixed platen41of the clamping device4. Next, in the injection filling step, by moving the screw33that stops rotating toward the nozzle34by a predetermined pressing force, the molten material is injected at high pressure into the cavity C of the mold6and fills the cavity C.

In the dwell step after the injection filling step, the molten material is further pressed into the cavity C filled with the molten material, thereby performing a dwell process that applies a predetermined dwell pressure to the molten material inside the cavity C for a predetermined period of time. Specifically, the predetermined dwell pressure is applied to the molten material by applying a constant pressing force to the screw33. The pressure of the molten material in the cavity C varies depending on the location inside the cavity C.

After the dwell process at the predetermined dwell pressure is finished, the process proceeds to the cooling step. In the cooling step, the pressing of the molten material is stopped to reduce the dwell pressure (this process is hereinafter referred to as a dwell pressure reduction process), and also the mold6is cooled. As the mold6cools, the molten material in the cavity C of the mold6solidifies. Finally, in the removing step, a molded article is removed by separating the first mold6aand the second mold6bfrom each other.

Next, the structure of the assisting device50according to the first embodiment is described with reference toFIGS.2to5. The assisting device50includes a portion functioning in a learning phase of machine learning and a portion functioning in an inference phase of the machine learning.

Specifically, as illustrated inFIG.2, the assisting device50includes the following, as the portion functioning in the learning phase: a molding condition database (DB)51; a molding state database (DB)52; a molded article quality database (DB)53; a first learning model generating unit54; a first learning model storage unit55; a second learning model generating unit56; a second learning model storage unit57. Further, the assisting device50includes the following, as the portion functioning in the inference phase: the first learning model storage unit55; the second learning model storage unit57; a molding state data adjustment amount obtaining unit60; a molding condition element adjustment amount obtaining unit71; and a condition changing unit72.

Molding condition elements for a large number of articles to be molded that are input as command values to the control device5are stored in the molding condition database51in association with the respective articles. For example, the molding condition element includes the following: a mold temperature; a dwell pressure; an injection speed; a dwell time; a mold clamping force; and the amount of molten material stored in the heating cylinder32. The molding condition database51stores such molding condition elements relating to a large number of articles to be molded. That is, the molding condition database51stores molding condition elements regarding the shapes and materials of a large number of articles to be molded.

Molding state data that is detected during molding of articles by the injection device sensor37, the clamping device sensor45, and the pressure sensors6dand6eattached to the injection molding apparatus1is stored in the molding state database52in association with the respective molded articles. The molding state data is data obtained during molding of an article.

The molding state data may be information about how a subject data item behaves over time or may be a predetermined statistical amount obtained from the behavior information. For example, as illustrated inFIG.3, the molding state data may be information about how dwell pressure data behaves over time during molding of one article or may be a statistical amount obtained from the behavior information.

There are behaviors of a subject data item contained in the behavior information in the same number as the number of the sampling time values. Examples of the statistical amount include the following: an integral value over the whole period (from the start to end of molding of one article); an integral value over a predetermined part of the whole period; a derivative value at a predetermined time; a maximum value; and a maximum derivative value. In addition to the dwell pressure data, the molding state data may include, for example, mold temperature data, injection speed data, mold clamping force data, and the amount of molten material stored in the heating cylinder32.

The dwell pressure data may be data about the pressing force applied to the screw33and detected by the injection device sensor37or may be data about pressures in the mold6detected by the pressure sensors6dand6e. Although the pressing force applied to the screw33is a parameter controllable by the control device5, the pressure detected by the pressure sensors6dand6eis not a parameter controllable by the control device5.

The molded article quality database53stores quality elements of a large number of molded articles in association with the respective molded articles. The quality element may include the following items: the mass of a molded article; the shape of the molded article; the condition of voids in the molded article; and the condition of burns on the molded article. The quality element is information obtained after molding of the article through inspection by an inspection apparatus (not illustrated) or any other suitable method. The quality element may be an inspection value of each item directly obtained by the inspection or may be an evaluation value derived from the inspection value.

According to the first embodiment, the molding condition database51, the molding state database52, and the molded article quality database53are separate databases. Alternatively, these databases51,52, and53may be an integrated database. In the case of an integrated database, the molding condition element, the molding state data, and the quality element are stored in association with individual molded articles.

As illustrated inFIG.4, the first learning model generating unit54performs machine learning in which at least the molding state data stored in the molding state database52is used as first learning data. According to the first embodiment, not only the molding state data but also the quality element stored in the molded article quality database53are used as the first learning data in the machine learning performed by the first learning model generating unit54. Through the machine learning, the first learning model generating unit54generates a first learning model relating to the molding state data and the quality element. According to the first embodiment, supervised learning is used to create the first learning model. Alternatively, any other suitable machine learning algorithm may be used to create the first learning model. For example, the first learning model may be a regression model. The first learning model created by the first learning model generating unit54is stored in the first learning model storage unit55.

As illustrated inFIG.5, the second learning model generating unit56performs machine learning in which the molding state data stored in the molding state database52and the molding condition element stored in the molding condition database51are used as second learning data. Through the machine learning, the second learning model generating unit56generates a second learning model relating to the molding state data and the molding condition element. For example, the second learning model may be a regression model or the like. The second learning model created by the second learning model generating unit56is stored in the second learning model storage unit57.

The molding state data adjustment amount obtaining unit60obtains, using the first learning model, a molding state data adjustment amount during molding of a subject article. Specifically, the molding state data adjustment amount obtaining unit60obtains the molding state data adjustment amount on the basis of the molding state data detected by the injection device sensor37and the clamping device sensor45. The molding state data adjustment amount is a value equivalent to the difference between the molding state data detected by the sensors37and45and a molding state data target value.

The molding state data adjustment amount obtaining unit60includes a quality element target value obtaining unit61, a molding state data target value obtaining unit62, a molding state data obtaining unit63, and a second molding state data adjustment amount obtaining unit64.

The quality element target value obtaining unit61obtains a quality element target value for the subject article. For example, the quality element target value may be a threshold value for determining whether or not the subject article is a conforming article in terms of the quality element. The subject article is an article being molded now or to be molded from now. The quality element target value is preset information. The quality element target value obtaining unit61obtains the quality element target value for the subject article by input from an operator. Examples of the quality element target value include the following: a target mass value expressed in units of kilograms (kg); a target shape value expressed in units of meters (m); an index of the condition of voids; and an index of the condition of burns.

The molding state data target value obtaining unit62obtains, using the first learning model stored in the first learning model storage unit55, a molding state data target value corresponding to the quality element target value obtained by the quality element target value obtaining unit61. As already mentioned, the first learning model is a model relating to the molding state data and the quality element. Thus, in response to input of the quality element target value, the first learning model outputs information regarding the molding state data, specifically, the molding state data target value. The molding state data target value is of the same type as the molding state data stored in the molding state database52. That is, the molding state data target value may be information about how a corresponding data item behaves over time or may be a predetermined statistical amount obtained from the behavior information.

During molding of a subject article, the molding state data obtaining unit63obtains molding state data detected by the injection device sensor37, the clamping device sensor45, and the pressure sensors6dand6e. The molding state data obtained by the molding state data obtaining unit63is of the same type as the molding state data stored in the molding state database52. That is, the molding state data may be information about how a data item being detected behaves over time or may be a predetermined statistical amount obtained from the behavior information.

The second molding state data adjustment amount obtaining unit64calculates the difference between the molding state data obtained by the molding state data obtaining unit63and the molding state data target value obtained by the molding state data target value obtaining unit62, and obtains the calculated difference as the molding state data adjustment amount.

The molding condition element adjustment amount obtaining unit71obtains, using the second learning model stored in the second learning model storage unit57, a molding condition element adjustment amount corresponding to the molding state data adjustment amount obtained by the second molding state data adjustment amount obtaining unit64. As already mentioned, the second learning model is a model relating to the molding state data and the molding condition element. Thus, in response to input of the molding state data adjustment amount using the second learning model, information regarding the molding condition element, specifically, the molding condition element adjustment amount, is output. The molding condition element adjustment amount is an adjustment amount for the molding condition element and may be, for example, an increase of five degrees in command value for a mold temperature.

On the basis of the molding condition element adjustment amount obtained by the molding condition element adjustment amount obtaining unit71, the condition changing unit72changes the value of the molding condition element (the command value) in the control device5of the injection molding apparatus1. The condition changing unit72automatically executes the change process, without receiving an execution signal from an operator. Alternatively, the condition changing unit72may execute the change process upon receipt of the execution signal from an operator.

Referring toFIG.6, an assist process performed by the assisting device50according to the first embodiment is described. The assist process has a learning phase and an inference phase. For the sake of brevity, only the inference phase of the assist process is described here. That is, the following description refers to the assist process to be performed after the first learning model and the second learning model are created.

First, the quality element target value obtaining unit61obtains a quality element target value (S1). Next, the molding state data target value obtaining unit62obtains, using the first learning model, a molding state data target value corresponding to the quality element target value (S2). Then, the molding state data obtaining unit63obtains molding state data (S3) during molding of an article. After that, on the basis of the molding state data and the molding state data target value, the second molding state data adjustment amount obtaining unit64obtains a molding state data adjustment amount (S4). The molding state data adjustment amount is the difference between the molding state data and the molding state data target value.

Then, the molding condition element adjustment amount obtaining unit71determines whether the molding state data adjustment amount is greater than a predetermined value (S5). If the adjustment amount is not greater than the predetermined value (S5: No), the assist process returns to step S3and repeats the above procedures. In contrast, if the adjustment amount is greater than the predetermined value (S5: Yes), the molding condition element adjustment amount obtaining unit71obtains, using the second learning model, a molding condition element adjustment amount (S6).

Next, the condition changing unit72changes the value of a molding condition element in the control device5, on the basis of the molding condition element adjustment amount (S7). If it is not determined that there is not a next article to be molded that is of the same type as the molded article (S8: No), the assist process returns to step S3and repeats the same procedures for the next article. In contrast, if it is determined that there is not a next article to be molded that is of the same type (S8: Yes), the assist process ends. If there is a next article to be molded that is of a different type from the molded article, the assist process restarts from step S1.

As described above, in the inference phase of the machine learning, molding state data is obtained, and a molding condition element adjustment amount is obtained on the basis of the molding state data. The molding state data is detected by the sensors37and45attached to the injection molding apparatus1. That is, the molding state data is information obtainable before the quality element of the molded article is obtained in an inspection process. This makes it possible to predict the likelihood of defects occurring in the molded article before the inspection process, thus curbing production of defective molded articles.

However, the quality element of the molded article is an important element. For this reason, the assisting device50creates the first learning model that indicates the relationship between the molding state data and the quality element of the molded article. The molding state data adjustment amount is obtained taking into account the first learning model and the molding state data so as to allow the quality element of the molded article to meet a predetermined value.

The assisting device50further creates the second learning model that indicates the relationship between the molding state data and the molding condition element. The molding condition element adjustment amount is obtained taking into account the second learning model and the molding state data adjustment amount. Thus, the molding condition element is adjusted in accordance with the obtained molding condition element adjustment amount so as to improve the quality element of the molded article.

Specifically, in the molding state data adjustment amount obtaining unit60, the molding state data target value obtaining unit62obtains, using the first learning model, a molding state data target value corresponding to a quality element target value that is preset. Then, the second molding state data adjustment amount obtaining unit64obtains the difference between the molding state data target value and the molding state data detected by the sensors37and45, thereby obtaining the molding state data adjustment amount. In this way, using the first learning model makes it possible to obtain the molding state data target value and consequently to obtain the molding state data adjustment amount. Thus, the molding state data adjustment amount is reliably obtainable.

As described above, the quality element target value is a preset value. Thus, in the assist process, the molding state data target value can be obtained in advance. That is, in the assist process, the procedure of obtaining the molding state data target using the first learning model is not performed every time when molding of a new article is performed. This leads to a reduction in the execution time of the assist process.

The second learning model generating unit56needs to create the second learning model before the value of the molding condition element is changed. For this reason, as described above, the second learning model generating unit56creates the second learning model in advance.

One concern with this approach is that the relationship between the molding condition element and the molding state data may change with the age deterioration of the injection molding apparatus1. For example, due to worn parts or other causes, the actual dwell pressure may differ between the initial pressure and the pressure after the age deterioration even when the command value used to control the dwell pressure remains unchanged. Therefore, the second learning model generating unit56preferably update the second learning model in accordance with the age deterioration of the injection molding apparatus1. This allows the age deterioration of the injection molding apparatus1to be taken into account when obtaining the molding condition element adjustment amount using the second learning model. Thus, more accurate molding condition element adjustment amount is obtainable.

Below is a description of a first example of the first learning model used in the assisting device50according to the first embodiment. In this example, the first learning model is a learning model relating to pressure data inside the mold6as the molding state data and a value indicative of the shape of a molded article as the quality element.

The detailed structure of the mold6is described with reference toFIGS.7and8. The mold6has at least one cavity C. In this example, an article to be molded by the injection molding apparatus1is a cage used in a constant-velocity joint. That is, the molded article has an annular shape, in particular, a circular annular shape. The cavity C also has an annular shape, in particular, a circular annular shape. Alternatively, the molded article may have a shape other than an annular shape, such as a C-shape or a rectangular frame shape. The cavity C has a shape corresponding to the shape of the molded article.

The first mold6ahas the feed channel6cfor feeding molten material. The feed channel6cis located between the nozzle34and the cavity C. The first mold6aincludes, as the feed channel6c, one sprue6c1, one or a plurality of runners6c2, and one or a plurality of gates6c3. In this example, it is assumed that the first mold6aincludes one sprue6c1, one runner6c2, and one gate6c3.

The sprue6c1is a channel for receiving molten material fed from the nozzle34. The runner6c2is a channel branching from the sprue6c1, and the molten material fed to the sprue6c1flows into the runner6c2. The gate6c3is a channel for guiding the molten material in the runner6c2to the cavity C and has a flow cross-sectional area smaller than the flow cross-sectional area of the runner6c2.

When the cavity C has an annular shape and the first mold6ahas one gate6c3, a flow path for the molten material flowing inside the cavity C extends from the gate6c3in the circumferential direction of the annular shape of the cavity C. That is, inside the cavity C, the molten material starts to flow from a location adjacent to the gate6c3and finally reaches a location farthest from the gate6c3.

The mold6may has a plurality of cavities C. In this case, the first mold6ahas runners6c2and gates6c3in the same number as the cavities C, and the molten material fed to the sprue6c1is fed to the cavities C via the corresponding runners6c2and gates6c3.

As already described, the mold6includes the first pressure sensor6d. Specifically, the mold6includes, as the first pressure sensor6d, six first pressure sensors6d1to6d6for detecting pressure applied from the molten material in the cavity C. The six first pressure sensors6d1to6d6are disposed in the flow path inside the cavity C and are each at a different distance from the gate6c3.

In the flow path, some of the six first pressure sensors6d1to6d6are located closer to the gate6c3than to the location farthest from the gate6c3. In the flow path, the others of the six first pressure sensors6d1to6d6are located closer to the location farthest from the gate6c3than to the gate6c3.

The first pressure sensor6d1is located farthest from the gate6c3in the flow path inside the cavity C. The first pressure sensor6d2is located the second farthest from the gate6c3. The first pressure sensors6d3,6d4, and6d5are respectively located the third, fourth, and fifth farthest from the gate6c3. The first pressure sensor6d6is located closest to the gate6c3.

Specifically, the first pressure sensor6d1is located in a region where the molten material flowing into the cavity C from the gate6c3finally reaches. On the other hand, the first pressure sensor6d6is located in a region that lies on an extension of the gate6c3and where the molten material flowing into the cavity C from the gate6c3first reaches.

As already described, the mold6includes the second pressure sensor6e. Specifically, the first mold6aincludes at least one second pressure sensor6e. In this example, it is assumed that the first mold6aincludes one second pressure sensor6e. The second pressure sensor6eis located in any of the sprue6c1, the runner6c2, and the gate6c3, and detects pressure applied from the molten material in the feed channel6cbetween the nozzle34and the cavity C. In this example, the second pressure sensor6eis located in the runner6c2and detects pressure that the first mold6areceives from the molten material in the runner6c2.

Pressure data detected by the six first pressure sensors6d1to6d6during successive steps including the injection filling step, the dwell step, and the cooling step is described with reference toFIGS.9A and9B.FIG.9Aillustrates graphs of change in the pressure data over time from the injection filling step to the cooling step during molding under a predetermined molding condition X.FIG.9Billustrates graphs of change in the pressure data over time from the injection filling step to the cooling step during molding under a predetermined molding condition Y that is different from the molding condition X.

The roundness of the article molded under the molding condition X is greater than the roundness of the article molded under the molding condition Y. In other words, the form accuracy of the article molded under the molding condition X is less (in particular, in terms of roundness) than the form accuracy of the article molded under the molding condition Y. The relationship between the change in the pressure data and the form accuracy (in particular, in terms of roundness) is discussed below.

InFIGS.9A and9B, a period of time from T1 to T2 represents the injection filling step, a period of time form T2 to T3 represents the dwell step, and a period of time after T3 represents the cooling step. The dwell process in the dwell step starts from when the cavity C is filled. That is, the dwell process starts from when all the pressure data detected by the first pressure sensors6d1to6d6becomes a value that is not zero (a value that is greater than a predetermined very small value). The dwell process ends when the pressing force being applied to the screw33is removed. In other words, the dwell pressure reduction process in the dwell step starts from when the pressing force being applied to the screw33is removed. The change in the pressure data during the dwell process is hereinafter referred to as “dwell process change data”, and the change in the pressure data during the dwell pressure reduction process is hereinafter referred to as “reduction process change data”.

If the molten material in the cavity C shrinks uniformly over the whole area of the cavity C upon start of the dwell pressure reduction process, the form accuracy of a molded article after solidification will be improved. Further, if the molten material filling the cavity C shrinks uniformly over the whole area of the cavity C after the start of the dwell pressure reduction process, the values of the reduction process change data detected by the six first pressure sensors6d1to6d6will become close to each other. In contrast, if the degree of shrinkage of the molten material after the start of the dwell pressure reduction process varies greatly depending on the location of the molten material inside the cavity C, the values of the reduction process change data detected by the six first pressure sensors6d1to6d6will vary greatly from each other.

As can be seen from comparing the graphs illustrated inFIG.9Aand the graphs illustrated inFIG.9B, the variation in behavior of the reduction process change data among the six first pressure sensors6d1to6d6is greater when the molding condition X is used than when the molding condition Y is used. In particular, when the molding condition X is used, there is a large variation in behavior of the reduction process change data between the first pressure sensor6d1and the first pressure sensors6d6.

This indicates that there is a variation in the degree of shrinkage of the molten material filling the cavity C after the start of the dwell pressure reduction process, between the molten material located close to the gate6c3and the molten material located far from the gate6c3. Such a variation in the degree of shrinkage of the molten material causes a variation in the degree of shrinkage of a molded article. Therefore, compared to the article molded under the molding condition Y, the article molded under the molding condition X has a low form accuracy and a great roundness. In summary, the difference and variation in the reduction process change data among the six first pressure sensors6d1to6d6have a high correlation with the form accuracy of a molded article.

Next, the first example of the first learning model is described in detail. The first example of the first learning model is a learning model relating to pressure data inside the mold6as the molding state data and a value indicative of the shape of a molded article as the quality element. The first learning model is created through machine learning in which at least the pressure data inside the mold6as the molding state data is used as the first learning data. In this example, the value indicative of the shape of a molded article is the roundness of the outer or inner circumferential surface of a molded article having a circular annular shape. The first learning data used to create the first learning model may include the value indicative of the shape of a molded article as the quality element. The first learning data may further include other types of molding state data in addition to the pressure data.

The pressure data is data on pressures inside the mold6detected by the pressure sensors6dand6eduring the dwell pressure reduction process. The pressure data during the dwell pressure reduction process is defined here as “reduction process pressure data”, and the relationship between the reduction process pressure data and an elapsed time since the start of the dwell pressure reduction process is defined here as “reduction process change data”.

The reduction process pressure data inside the mold6includes pressure data detected by the first pressure sensors6d1to6d6during the dwell pressure reduction process. The reduction process pressure data may further include pressure data detected by the second pressure sensor6e. The pressure data inside the mold6may be detected by only one or some of the first pressure sensors6d1to6d6.

Preferably, the first learning data include the value indicative of a variation in the reduction process pressure data. As illustrated inFIGS.9A and9B, the roundness of a molded article increases with an increasing variation in the reduction process pressure data. For this reason, when the value indicative of the variation is included in the first learning data, the first learning model has a higher correlation with the form accuracy, particularly the roundness of a molded article.

Examples of the value indicative of the variation include the following: differences in reduction process pressure data among the pressure sensors6dand6e; variances among the reduction process pressure data; differences in time integral value among the reduction process change data; variances in time integral value among the reduction process pressure data; differences in mean value of time integral values among the reduction process pressure data; and variances in mean value of time integral values among the reduction process pressure data. As another example, differences in dwell pressure reduction time among the first pressure sensors6d1to6d6may be used as the value indicative of the variation. The dwell pressure reduction time is the time taken by the pressure data to drop to or below a predetermined value close to zero after the start of the dwell pressure reduction process.

In this example, the assist process, illustrated inFIG.6, according to the first embodiment is performed as follows. First, the quality element target value obtaining unit61obtains, as a quality element target value, a target value for the roundness of the outer or inner annular circumferential surface of a molded article (S1). Next, the molding state data target value obtaining unit62obtains, using the first learning model, a molding state data target value corresponding to at least the roundness target value as the quality element target value (S2). In this example, the molding state data target value obtaining unit62obtains, as the molding state data target value, a target value for a value related to reduction process pressure data inside the mold6.

Then, the molding state data obtaining unit63obtains at least the reduction process pressure data as molding state data (S3). Next, on the basis of the molding state data and the molding state data target value, the second molding state data adjustment amount obtaining unit64obtains a molding state data adjustment amount (S4). The molding state data adjustment amount is the difference between the molding state data and the molding state data target value. After that, the subsequent procedures (S5, S6) are performed in the same manner as already described, and then the value of the molding condition element is changed (S7). Thus, by performing the assist process using the first example of the first learning model, a molding condition that improves the roundness of a molded article as the quality element can be determined.

Next, a second example of the first learning model used in the assisting device50according to the first embodiment is described. In this example, the first learning model is a learning model relating to pressure data inside the mold6as the molding state data and the mass of a molded article as the quality element.

Pressure data detected by the first pressure sensor6dand the second pressure sensor6eduring successive steps including the injection filling step, the dwell step, and the cooling step is described with reference toFIG.10.FIG.10illustrates graphs of change in the pressure data over time from the injection filling step to the cooling step during molding of a molded article under a predetermined molding condition. T1, T2, and T3 inFIG.10respectively represent the same time points as those inFIGS.9A and9B. The pressure data during the dwell process is defined here as “dwell process pressure data”, and the relationship between the dwell process pressure data and an elapsed time since the start of the dwell process is defined here as “dwell process change data”.

The inventors of the present invention have found that the mass of the molded article has correlations with the dwell process pressure data. Specifically, as the dwell process is performed for a longer time, the molded article has a larger mass. Further, as the dwell process is performed under high dwell pressure, the molded article has a larger mass. In addition, as the dwell process change data has a larger variation, the molded article has a smaller mass.

Compared to the pressure of the molten material in the cavity C, the pressure of the molten material in the feed channel6cof the mold6has a high correlation with the pressure applied from the injection device3during the dwell process. This is because the feed channel6cis located closer to the nozzle34than the cavity C. Since the first pressure sensor6d1is located farthest from the gate6c3in the flow path inside the cavity C, a loss of pressure applied to the first pressure sensor6d1by the molten material filled inside the cavity C becomes maximum.

As a result, the pressure data detected by the first pressure sensor6d1becomes smaller than the pressure data detected by the second pressure sensor6e. A larger difference in the pressure data between the first and second pressure sensors6d1and6eindicates a larger loss of pressure, which results in a smaller mass.

Next, the second example of the first learning model is described in detail. In the second example, the first learning model is a learning model relating to pressure data inside the mold6as the molding state data and the mass of a molded article as the quality element. The first learning model is created through machine learning in which at least the pressure data inside the mold6as the molding state data is used as the first learning data. The first learning data used to create the first learning model may include the mass of a molded article as the quality element. The first learning data may further include other types of molding state data in addition to the pressure data.

The pressure data is data on pressure inside the mold6that is detected by the pressure sensors6dand6eduring the dwell process. The dwell process pressure data inside the mold6includes pressure data detected by the first pressure sensors6d1to6d6during the dwell process and pressure data detected by the second pressure sensor6eduring the dwell process. The pressure data inside the mold6may be detected by only one or some of the first pressure sensors6d1to6d6.

Preferably, the first learning data include the time integral value of the dwell process change data representing the duration of the dwell process and a dwell pressure during the dwell process. It is noted that the duration of the dwell process and the dwell pressure during the dwell process have different degrees of influence on the mass of a molded article. For this reason, it is preferable that the first learning data include at least one of the duration of the dwell process itself and the pressure data itself. For example, the first learning data may include, as the pressure data itself, at least one of the maximum value and the mean value of the pressure data detected by the first pressure sensors6d1to6d6during the dwell process.

Further, it is preferable that the first learning data include a variation in the dwell process pressure data among the pressure sensors6dand6e. As already described, as the dwell process pressure data has a larger variation, the molded article has a smaller mass. For this reason, when a value indicative of the variation is included in the first learning data, the first learning model has a higher correlation with the mass of a molded article.

In particular, as the difference between the pressure data at the location farthest from the gate6c3and the pressure data in the feed channel6cbecomes larger, the mass of a molded article becomes smaller. Therefore, it is preferable that the dwell process pressure data inside the mold6includes pressure data detected during the dwell process by the first pressure sensor6d1located farthest from the gate6c3and pressure data detected during the dwell process by the second pressure sensor6elocated in the feed channel6c.

Preferably, the first learning data include at least one of the following, as the value indicative of the variation: differences in dwell process pressure data among the pressure sensors6dand6e; variances among the dwell process pressure data; differences in time integral value among the dwell process change data; variances in time integral value among the dwell process change data; differences in mean value of time integral values among the dwell process change data; and variances in mean value of time integral values among the dwell process change data.

In this example, the assist process, illustrated inFIG.6, according to the first embodiment is performed as follows. First, the quality element target value obtaining unit61obtains, as the quality element target value, a target value for the mass of a molded article (S1). Next, the molding state data target value obtaining unit62obtains, using the first learning model, a molding state data target value corresponding to at least the mass target value as the quality element target value (S2). In this example, the molding state data target value obtaining unit62obtains, as the molding state data target value, a target value for a value related to dwell process pressure data inside the mold6.

Then, the molding state data obtaining unit63obtains at least the dwell process pressure data as the molding state data (S3). Next, on the basis of the molding state data and the molding state data target value, the second molding state data adjustment amount obtaining unit64obtains a molding state data adjustment amount (S4). The molding state data adjustment amount is the difference between the molding state data and the molding state data target value. After that, the subsequent procedures (S5, S6) are performed in the same manner as already described, and then the value of the molding condition element is changed (S7). Thus, by performing the assist process using the second example of the first learning model, a molding condition that allows the mass of a molded article as the quality element can be determined to meet a desired value.

Next, the structure of a molding condition determination assisting device150(hereinafter referred to simply as the assisting device150) according to a second embodiment is described with reference toFIG.11. The assisting device150has some common structural features with the assisting device50of the first embodiment. The common structural features are denoted by the same reference symbols, and their description is omitted for the sake of brevity.

The assisting device150includes a portion functioning in a learning phase of machine learning and a portion functioning in an inference phase of the machine learning. Specifically, as illustrated inFIG.11, the assisting device150includes the following, as the portion functioning in the learning phase: the molding condition database (DB)51; the molding state database (DB)52; the molded article quality database (DB)53; the first learning model generating unit54; the first learning model storage unit55; the second learning model generating unit56; and the second learning model storage unit57. Further, the assisting device150includes the following, as the portion functioning in the inference phase: the first learning model storage unit55; the second learning model storage unit57; a molding state data adjustment amount obtaining unit160; the molding condition element adjustment amount obtaining unit71; and the condition changing unit72.

That is, the molding state data adjustment amount obtaining unit160is the only difference of the assisting device150according to the second embodiment from the assisting device50according to the first embodiment. Below is a description of the molding state data adjustment amount obtaining unit160.

As with the molding state data adjustment amount obtaining unit60of the first embodiment, the molding state data adjustment amount obtaining unit160obtains, using the first learning model, the molding state data adjustment amount during molding of a subject article. Specifically, the molding state data adjustment amount obtaining unit160obtains the molding state data adjustment amount on the basis of the molding state data detected by the injection device sensor37and the clamping device sensor45.

The molding state data adjustment amount obtaining unit160includes a molding state data obtaining unit161, a quality element obtaining unit162, a quality element target value obtaining unit163, a quality element adjustment amount obtaining unit164, and a second molding state data adjustment amount obtaining unit165.

During molding of a subject article, the molding state data obtaining unit161obtains molding state data detected by the injection device sensor37, the clamping device sensor45, and the pressure sensors6dand6e. The molding state data obtained by the molding state data obtaining unit161is of the same type as the molding state data stored in the molding state database52. That is, the molding state data may be information about how a target data item being detected behaves over time or may be a predetermined statistical amount obtained from the behavior information.

The quality element obtaining unit162obtains, using the first learning model, the value of a quality element corresponding to the molding state data obtained by the molding state data obtaining unit161. The quality element target value obtaining unit163obtains a quality element target value for the subject article. For example, the quality element target value may be a threshold value for determining whether or not the subject article is a conforming article in terms of the quality element. The quality element target value is preset information.

The quality element adjustment amount obtaining unit164calculates the difference between the quality element target value obtained by the quality element target value obtaining unit163and the value of the quality element obtained by the quality element obtaining unit162, and obtains the calculated difference as a quality element adjustment amount.

The second molding state data adjustment amount obtaining unit165obtains, using the first learning model stored in the first learning model storage unit55, a molding state data adjustment amount corresponding to the quality element adjustment amount obtained by the quality element adjustment amount obtaining unit164. The molding state data adjustment amount obtained by the second molding state data adjustment amount obtaining unit165has a value equivalent to the difference between a molding state data target value and the molding state data detected by the sensors37and45.

Referring toFIG.12, an assist process performed by the assisting device150according to the second embodiment is described. The assist process has a learning phase and an inference phase. For the sake of brevity, only the inference phase of the assist process is described here. That is, the following description refers to the assist process to be performed after the first learning model and the second learning model are created.

First, the molding state data obtaining unit161obtains molding state data (S11). Next, the quality element obtaining unit162obtains, using the first learning model, the value of a quality element corresponding to the molding state data (S12). Then, the quality element target value obtaining unit163obtains a quality element target value (S13). After that, on the basis of the value of the quality element and the quality element target value, the quality element adjustment amount obtaining unit164obtains a quality element adjustment amount (S14). The quality element adjustment amount is the difference between the value of the quality element and the quality element target value.

Then, the second molding state data adjustment amount obtaining unit165determines whether the quality element adjustment amount is greater than a predetermined value (S15). If the quality element adjustment amount is not greater than the predetermined value (S15: No), the assist process returns to step S11and repeats the above procedures. In contrast, if the quality element adjustment amount is greater than the predetermined value (S15: Yes), the second molding state data adjustment amount obtaining unit165obtains, using the first learning model, a molding state data adjustment amount (S16). Next, the molding condition element adjustment amount obtaining unit71obtains, using the second learning model, a molding condition element adjustment amount (S17).

Then, the condition changing unit72changes the value of a molding condition element in the control device5, on the basis of the molding condition element adjustment amount (S18). If it is not determined that there is not a next article to be molded that is of the same type as the molded article (S19: No), the assist process returns to step S11and repeats the same procedures for the next article to be molded. In contrast, if it is determined that there is not a next article to be molded that is of the same type (S19: Yes), the assist process ends. If there is a next article to be molded that is of a different type from the molded article, the assist process restarts from step S11.

As described above, in the molding state data adjustment amount obtaining unit160, the quality element obtaining unit162uses the first learning model to infer the value of the quality element corresponding to the molding state data. Then, the quality element adjustment amount obtaining unit164obtains the quality element adjustment amount. After that, the second molding state data adjustment amount obtaining unit165uses the first learning model to obtain the molding state data adjustment amount. In this way, the first learning model is used in two processes. Also in the second embodiment, the molding state data adjustment amount is reliably obtainable.

Next, a first example of the first learning model used in the assisting device150according to the second embodiment is described. In this example, the first learning model is a learning model relating to pressure data inside the mold6as the molding state data and a value indicative of the shape of a molded article as the quality element. The first example of the first learning model used in the assisting device50according to the first embodiment may be used as the first example of the first learning model used in the assisting device150according to the second embodiment.

In this example, the assist process, illustrated inFIG.12, according to the second embodiment is performed as follows. First, the molding state data obtaining unit161obtains at least reduction process pressure data (S11). Next, the quality element obtaining unit162obtains, using the first learning model, the value of a quality element corresponding to the reduction process pressure data (S12). In this example, the value of the quality element is the roundness of the outer or inner circumferential surface of a molded article. Then, the quality element target value obtaining unit163obtains, as the quality element target value, a target value for the roundness of the molded article (S13). Next, on the basis of the roundness of the molded article as the value of the quality element and the target value for the roundness of the molded article as the quality element target value, the quality element adjustment amount obtaining unit164obtains a quality element adjustment amount (S14). The quality element adjustment amount is the difference between the value of the quality element and the quality element target value.

After that, the subsequent procedures (S15, S16, S17) are performed in the same manner as already described, and then the value of the molding condition element is changed (S18). Thus, by performing the assist process using the first example of the first learning, a molding condition that improves the roundness of a molded article as the quality element can be determined.

Next, a second example of the first learning model used in the assisting device150according to the second embodiment is described. The second example of the first learning model is a learning model relating to pressure data inside the mold6as molding state data and the mass of a molded article as a quality element. The second example of the first learning model used in the assisting device50according to the first embodiment may be used as the second example of the first learning model used in the assisting device150according to the second embodiment.

In this example, the assist process, illustrated inFIG.12, according to the second embodiment is performed as follows. First, the molding state data obtaining unit161obtains, as molding state data, at least dwell process pressure data (S11). Next, the quality element obtaining unit162obtains, using the first learning model, the value of a quality element corresponding to the dwell process pressure data (S12) In this example, the value of the quality element is the mass of a molded article. Then, the quality element target value obtaining unit163obtains, as a quality element target value, a target value for the mass of the molded article (S13). Next, on the basis of the mass of the molded article as the value of the quality element and the target value for the mass of the molded article as the quality element target value, the quality element adjustment amount obtaining unit164obtains a quality element adjustment amount (S14). The quality element adjustment value is the difference between the value of the quality element and the quality element target value.

After that, the subsequent procedures (S15, S16, S17) are performed in the same manner as already described, and then the value of the molding condition element is changed (S18). Thus, by performing the assist process using the second example of the first learning model, a molding condition that allows the mass of a molded article as the quality element can be determined to meet a desired value.