Learning device for processing conditions, processing device with learning device, and processing system with learning device

Abstract

To provide a learning device for learning processing conditions for joining and cutting a part, a processing device with a learning device, and a processing system with a learning device.SOLUTION: A learning device 100 comprises: processing result measuring means for measuring a processing result; a processing result evaluation means for evaluating the processing result as a reward; learning storage means for storing the processing result and the reward; processing condition learning means for reading the processing conditions from the learning result obtained by learning processing results and reward; processing specification monitoring means for monitoring processing specifications; status monitoring means; and processing condition reading means for reading processing conditions from the learning storage means. The learning device measures the processing results, evaluates the processing result as a reward, stores the processing results and reward, prepares to read the processing conditions from the learning results learned by the processing condition learning means and reads the processing conditions that satisfy the latest and current processing specifications from the learning storage means to output the read processing conditions to the processing means. The processing means performs joining or fusion cutting of a part under the processing conditions.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a learning device for processing conditions, a processing device with a learning device, and a processing system with a learning device. More specifically, the present invention relates to a learning device for joining conditions of two or more parts, a joining device with a learning device, and a joining system with a learning device, a learning device for fusing conditions of one or more parts, a fusing device with a learning device, and a fusing system with a learning device.

Background Art

[0002] Conventionally, as methods for joining two or more parts such as plastic and metal, (1) a high-frequency welding method in which a high-frequency current is applied to two or more parts to perform welding by dielectric heating, (2) a non-contact hot plate joining method in which a hot plate is brought close to the surfaces of two or more parts without contact for joining, (3) a vibration welding method in which two or more parts are pressed together while applying a low-frequency vibration of about 100 Hz for welding, (4) a laser joining method in which two or more parts are irradiated with a laser beam for joining, (5) an ultrasonic welding method for welding two or more plastic parts, (6) an ultrasonic metal joining method for solid-phase joining of two or more metal sheets, (7) an ultrasonic joining method for joining a metal electrode to the surface of a glass plate, etc. have been used.

[0003] Also, as methods for fusing one or more parts such as plastic, food, and rubber sheets, (a) a laser fusing method in which a laser beam is irradiated for fusing, (b) an ultrasonic fusing method in which a cutter vibrating ultrasonically is pressed against one or more parts for fusing, etc. have been used.

[0004] In the above (1) to (7), (a) and (b), energy such as vibration energy and heat energy is applied to the parts to cause the parts to generate heat, melt, or perform solid-phase bonding to perform joining and fusing processes to produce products. Therefore, by changing the magnitude of the energy applied to the parts and the supply time of the energy, the joining conditions and fusing conditions can be found.

[0005] However, the bonding conditions and fusing conditions vary depending on the material, shape, and dimensions of the parts to be bonded or the parts to be fused. Conventionally, when bonding or fusing new parts, new bonding conditions and new fusing conditions that meet the bonding specifications and fusing specifications have been experimentally determined each time.

[0006] On the other hand, processing apparatuses such as these bonding apparatuses and fusing apparatuses are required to have versatility. These processing apparatuses have a wide range of processing targets and a wide variety of processing contents. Therefore, one state is selected from the multiple states of energy that can be supplied, and the energy supply time is determined.

[0007] The following describes how to determine processing conditions such as conventional bonding conditions and fusing conditions, taking the conventional high-frequency welding method (1) and the high-frequency welding apparatus using the same as representative examples. Note that in the conventional bonding methods (2) to (7) and the conventional fusing methods (a) and (b), many aspects, such as how to determine processing conditions such as desired bonding conditions and fusing conditions by changing the magnitude of the applied energy and the supply time, are the same as or similar to those in (1), and thus the description is omitted.

[0008] In the high-frequency welding method, when a strong high-frequency electric field is applied to a plastic material, film, sheet, etc., the polarity of the electrodes continuously changes at the molecular level, and collisions, vibrations, and frictions at the molecular level occur inside the material, generating self-heat and causing the plastic material, film, sheet, etc. to fuse and weld.

[0009] As a method for controlling the high-frequency current, it is common to set the supply time of the high-frequency current supplied to the object to be welded (hereinafter referred to as the "welding time") to a predetermined value. However, if the object to be welded has different dimensions and shapes, controlling it with a uniform welding time may result in excessive welding, spark generation, or insufficient welding.

[0010] Figure 42 shows the relationship between the welding time (S) and the current (A) when high-frequency welding is controlled only by the conventional welding time and plastic materials, which are workpieces with different optimal welding times, are welded. In Figure 42, the welding time (S) is taken on the X-axis and the current (A) is taken on the Y-axis.

[0011] In Figure 42, for three types of plastic materials (Material A, Material B, and Material C), which are workpieces with different optimal welding times, when a uniform welding time, that is, when the workpiece is welded until the welding end time T2, (1) for Material A, the workpiece is over-welded, and (2) for Material C, the workpiece is under-welded, are exemplified. In Figure 42, Material B exemplifies normal welding.

[0012] In Figure 42, the high-frequency current is flowing uniformly until the welding end time (T2). However, for Material A (the graph of the dashed-dotted line in Figure 42), the welding of the workpiece is already completed at the welding time (T1) before the welding end time (T2) from the welding start time (T0). From the welding time (T1) to the welding end time (T2), an excessive high-frequency current is applied to the workpiece. As a result, the plastic material (Material A) is over-welded. This not only wastes energy but also damages the workpiece.

[0013] Conversely, for Material C (the graph of the dotted line in Figure 42), although the welding time is originally required until T3, the high-frequency current is stopped at the welding end time T2 even though the welding is not yet completed, resulting in under-welding.

[0014] Therefore, as another conventional control method, it is known to detect the change in the distance between two molds sandwiching workpieces with different optimal welding times by the current value supplied to the high-frequency welding processing unit of the high-frequency welding apparatus, and control the amount of high-frequency energy supplied to the high-frequency welding processing unit by detecting the current value (see, for example, Patent Document 1).

[0015] In this other conventional control method, welding is started with the point where the workpiece contacts the molds on both sides as the welding start point. As the welding progresses and the distance between the molds decreases, the current value supplied to the high-frequency welding processing unit increases. The inflection point at which the current value converges after reaching its maximum is defined as the welding completion point. In other words, the point when the current value passes through the current value (L reference value) at the welding start point, reaches its maximum, and then reaches the current value (H reference value) at the inflection point is used as the welding completion point.

[0016] Fig. 43 shows the relationship between the welding time (S) and the current (A) when welding workpieces A, B, and C made of plastic material by another conventional high-frequency welding control method. Similar to Fig. 42, the welding time (S) is taken on the X-axis and the current (A) is taken on the Y-axis.

[0017] In the example shown in Fig. 43, the three types of workpieces A, B, and C have the same welding start point TS, but different welding completion points TF’, TF’’, and TF’’’, and the optimal welding times TA, TB, and TC are assigned respectively. This reduces (1) excessive welding in the case of workpieces with a short optimal welding time and (2) insufficient welding in the case of workpieces with a long optimal welding time compared to the conventional example shown in Fig. 42.

[0018] In Fig. 43, for workpieces with predetermined dimensions and shapes in advance, the H reference value, which is the current value at the welding completion point, is measured and investigated, and then set in the welding apparatus. During operation, the change in the current is detected, and when the current exceeds the H reference value once, reaches its maximum, and then reaches the H reference value again, the high-frequency output is controlled to stop, and the workpiece is being welded.

[0019] However, the work of measuring and investigating the welding conditions for workpieces with predetermined dimensions and shapes in advance was time-consuming and involved repeated trial and error even for skilled workers. To weld new workpieces, it was necessary to measure and investigate the welding conditions each time.

[0020] Recently, artificial intelligence technology (hereinafter abbreviated as AI technology) has attracted attention. AI technology learns from data accumulated in the past. By using AI technology, it is expected that there is a possibility that new joining conditions and welding conditions do not have to be found every time new joining or welding is performed.

[0021] However, in the conventional joining methods for two or more parts and welding methods for one or more parts, no practical and specific AI technology has been disclosed, nor is there any suggestion. There is a learning device that learns the arc welding conditions of metals using AI technology. In this document, the update formula of the action value function Q(a, s) shown in FIG. 44, which is known in Q-learning Q(s,a)← Q(s,a)+α(r+γmaxQ(Snext、anext)―Q(s,a)) is only conceptually disclosed for applying to one state of arc welding and performing arc welding, and the detailed content of practical and specific technology is not disclosed (see Patent Document 2).

[0022] Regarding conventional AI technologies, as shown in FIG. 45, research on reinforcement learning, Q-learning, statistical learning (neural network), deep learning (deep learning), and deep reinforcement learning that combines deep learning and reinforcement learning is progressing (see Known Document 1).

[0023] Reinforcement learning learns, in a state S in a certain environment, by trial and error of an action a, which action a should be taken to maximize the action value as a reward. A simple example will be described with reference to FIG. 46. In FIG. 46, in the state (St), when four actions (at1), (at2), (at3), and (at4) are performed, Q values (action values) obtained by evaluating the results of the actions as rewards, Q(St, at1), Q(St, at2), Q(St, at3), and Q(St, at4) are obtained respectively. Reinforcement learning is the idea of selecting the action (a) that gives the maximum reward by calculating backward from the magnitude of the reward. In FIG. 46, among the four rewards, Q(St, at3) is the maximum value, so by using the action (at3), the maximum reward (Q value) can be obtained.

[0024] In Q-learning, as shown in FIG. 44, using the action value function Q(a, s) as the update formula, online learning is continued in a state S in a certain environment, where a new action a is taken to obtain a reward. As the learning experience increases, the action value function Q(a, s) is updated, and it quickly reaches an action that can obtain a large reward (see Non-Patent Documents 1 and 2).

[0025] In deep learning, a multi-layer neural network as shown in FIG. 47(b) with multiple stacked perceptrons as shown in FIG. 47(a) is created. When an action is input to the input layer of the multi-layer neural network, each input is weighted, learning is performed in the hidden layer, and a policy obtained by multiplying the maximum reward by a probability (the probability to be adopted) is output from the output layer.

[0026] In deep reinforcement learning, as shown in FIG. 48, when an action is input to the input layer of a multi-layer neural network in a certain state, each input is weighted, and a policy obtained by multiplying the action by a probability is output from the output layer through the hidden layer. Then, the probability of an action leading to a high reward is increased, and learning is repeated with the multi-layer neural network to reach an action that can obtain a large reward. When the policy obtained by multiplying the action by a probability changes and the environment or state changes, learning is performed taking these into account.

[0027] However, in the conventional AI technology, (1) in processing methods such as the joining method of two or more parts or the fusing method of one or more parts, no practical and specific AI technology using a learning device is disclosed. Also, (2) in a general-purpose processing device that can perform processing in multiple states, when starting processing, the state of the energy applied to the workpiece is confirmed, and no AI technology is disclosed for selecting specific processing conditions as an action that can obtain the maximum reward in that state, that is, an action that satisfies the so-called processing specifications, and performing processing. (3) No AI technology is disclosed for monitoring the state of energy supply during processing of a processing device, determining whether the state of processing using a learning device is normal or abnormal using a learning device, and taking appropriate measures in the case of an abnormality.

Prior Art Documents

Patent Documents

[0028] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-370283 [Patent Document 2] Japanese Patent Application Laid-Open No. 2017-30014 [Non-Patent Document]

[0029] [Non-Patent Document 1] "An Introduction to Artificial Intelligence Learned through Illustrations" by Tadao Taniguchi, Kodansha, December 2020 [Non-Patent Document 2] "Reinforcement Learning and Deep Learning: Simulation in C Language" by Tomohiro Odaka, Ohmsha, October 2017 [Summary of the Invention] [Problems to be Solved by the Invention]

[0030] In conventional processing methods such as joining methods and cutting methods, particularly in joining methods for two or more parts and cutting methods for one or more parts, when joining or cutting new parts, new joining conditions and new cutting conditions that satisfy the joining specifications and cutting specifications have been determined through experiments each time. New parts often have different materials, dimensions, shapes, etc. from conventional joining or cutting target parts, and it has been rare to directly use the joining conditions and cutting conditions obtained in the past. Each time, it has been necessary for workers to conduct experiments to obtain desirable joining conditions and cutting conditions, which has been a burden.

[0031] The first object of the present invention is to provide a learning device that learns processing conditions when processing parts, a processing device with a learning device, and a processing system with a learning device. More specifically, it is to provide a learning device that learns joining conditions when joining two or more parts, a joining device with a learning device, and a joining system with a learning device, and a learning device that learns cutting conditions for one or more parts, a cutting device with a learning device, and a cutting system with a learning device.

[0032] Since conventional general-purpose processing devices have a wide range of processing targets and processing contents, the processing conditions frequently change. It would be convenient if it could be guaranteed that connecting a learning device that learns the processing conditions always supports processing under the processing conditions that meet the processing specifications.

[0033] The second problem of the present invention is to provide a learning device, a processing device with a learning device, and a processing system with a learning device that monitor the processing specifications during processing from the start of processing under the processing conditions that meet the processing specifications and always support processing under the processing conditions that meet the processing specifications. For example, even when the processing specifications are updated from the previous processing operation or changed during processing, the processing operation is performed under the processing conditions that meet the latest and current processing specifications.

[0034] Also, even if the materials, shapes, and dimensions of the parts to be processed, such as joining and welding, are the same, if there are variations within the tolerance, the amount of energy supplied to the parts will change slightly. Therefore, even if processing conditions such as joining conditions and welding conditions that are considered to be optimal obtained through experiments in advance are used, if the work is done with only one processing condition, variations in the adhesion force and variations in the appearance of the cut surface will occur due to variations within the tolerance of the parts to be processed. There was a problem that the quality after processing was not stable.

[0035] Also, in conventional joining devices, even when a part outside the tolerance is mistakenly set as the object to be welded, joining is performed, so there was a possibility of producing defective products with parts outside the tolerance joined.

[0036] The third problem of the present invention is to determine the state when energy supply is started even if the materials, shapes, and dimensions of the parts to be processed, such as joining and welding, are substantially the same and there are variations within the tolerance, and read the energy supply time that meets the processing specifications such as joining and welding under the processing conditions such as joining conditions and welding conditions from the learning device. And when a part outside the tolerance is set, joining is not performed so as not to produce defective products with parts outside the tolerance joined.

[0037] Furthermore, it would be convenient if the learning device itself could indicate the need for updated learning when it fails to find processing conditions such as joining conditions and fusing conditions that meet the processing specifications from the learning device that has learned the processing conditions such as joining conditions and fusing conditions.

[0038] A fourth problem of the present invention is to determine whether or not processing conditions such as joining conditions and fusing conditions that meet the processing specifications can be found from the learned learning device, and to indicate that updated learning is necessary when the processing conditions such as joining conditions and fusing conditions that meet the processing specifications cannot be found.

[0039] A fifth problem of the present invention is to perform updated learning on the processing conditions such as joining conditions and fusing conditions that meet the processing specifications when it is determined that the processing conditions such as joining conditions and fusing conditions that meet the processing specifications cannot be found from the learned learning device, and to provide a processing device with a learning device that can perform processing such as joining two or more parts or fusing one or more parts using the updated processing conditions such as joining conditions and fusing conditions.

[0040] A sixth problem of the present invention is to check the energy state given when a general-purpose joining device or fusing device starts processing such as joining or fusing, read the energy supply time that meets the processing specifications for joining or fusing in that state from the learning device as processing conditions such as joining conditions and fusing conditions, and perform processing such as joining work or fusing work.

[0041] A seventh problem of the present invention is to monitor the energy state during processing of the processing device, indicate when the energy state has changed to an abnormal state, stop the processing, and ensure the safety of the processing device.

Means for Solving the Problems

[0042] To achieve the above object, the learning device of the present invention includes at least (1) "processing result measuring means" for measuring the processing result, (2) "processing result evaluating means" for evaluating the processing result as a reward, (3) "learning storage means" for storing the processing result and the reward obtained by evaluating the processing result, (4) "processing condition learning means" for organizing the learning result evaluated based on the processing result so that the processing conditions can be read out, (5) "processing specification monitoring means" for monitoring the processing specification, and (6) "processing condition reading means" for reading out the processing conditions from the learning storage means. In advance, the processing result is measured by the processing result measuring means in (1), the processing result is evaluated as a reward by the processing result evaluating means in (2), the processing result and the reward are stored in the learning storage means in (3), the learning result evaluated based on the processing result is organized by the processing condition learning means in (4) so that the processing conditions can be read out. When a processing specification is input to the processing means connected to the learning device, the processing specification is monitored by the processing specification monitoring means in (5), and the processing conditions that satisfy the latest and current processing specification are read out from the learning storage means by the processing condition reading means in (6), and the read processing conditions are output to the processing means, which is configured as a processing condition learning device.

[0043] As a result, when a processing specification is input to the processing specification input means, the processing conditions such as the joining conditions or cutting conditions that satisfy the processing specification are read out from the learning storage means and output to the processing means. The processing means stores the processing conditions in the "main storage means" and performs processing using the stored processing conditions.

[0044] Also, after the processing starts under the processing conditions that satisfy the processing specification, during the processing, the processing specification is monitored, and it always supports processing under the processing conditions that satisfy the processing specification.

[0045] In addition, when there are variations in the material, shape, and dimensions of the parts to be processed, it is configured to learn the processing conditions corresponding to the variations and read out the processing conditions that satisfy the processing specification.

[0046] Furthermore, it is configured to determine whether processing conditions that satisfy the processing specifications can be found in the learning memory means, and when processing conditions that satisfy the processing specifications cannot be found, to present that update learning is necessary.

[0047] And when it is determined that processing conditions that satisfy the processing specifications cannot be found in the learning memory means, it is configured to perform update learning on the processing conditions that satisfy the processing specifications and read out the updated processing conditions.

[0048] Furthermore, a "state monitoring means" for monitoring the state of the energy applied to the workpiece of the processing device connected to the learning device is provided, and the actions and rewards in the energy state when the processing device starts processing are learned in advance. When the state when the processing device starts processing is confirmed, it is configured to read out the processing conditions that satisfy the processing specifications in that state.

[0049] And when the state monitoring means monitors the state of the energy applied to the workpiece of the processing device after processing starts, and when the state of the processing device becomes abnormal compared to the state assumed by the learning device, it is configured to present that fact and stop the processing of the processing device.

[0050] Also, a learning device is configured in which the processing conditions to be learned are the joining conditions of two or more parts.

[0051] Also, a learning device is configured in which the processing conditions to be learned are the fusing conditions of one or more parts.

[0052] And in the processing device with a learning device that connects any one of the above learning devices via a communication line, at least (a) a "processing specification input / output means" for inputting processing specifications, (b) a "main memory means", and (c) a "main control means" are provided. The processing conditions read from the learning memory means of the connected learning device are stored in the main memory means, and the processing operation using the stored processing conditions is performed by the main control means, thereby configuring the processing device with a learning device.

[0053] In addition, the processing means constituting the processing system is provided with at least (a) "processing specification input / output means" for inputting processing specifications, (b) "main storage means", and (c) "main control means", and stores the processing conditions read from the learning storage means of the connected learning device in the main storage means, and causes the main control means to perform a processing operation using the stored processing conditions, thereby configuring a processing system with a learning device.

Effect of the Invention

[0054] In the present invention, a method and a learning device for learning processing conditions when joining two or more parts, cutting one or more parts, etc. are used to perform processing such as joining two or more parts or cutting one or more parts.

[0055] And the present invention monitors the processing specification during processing from the start of processing under processing conditions that satisfy the processing specification, and constantly supports processing under processing conditions that satisfy the processing specification. Therefore, for example, even when the processing specification is updated from the previous processing operation or changed during processing, the processing operation can be performed under processing conditions that satisfy the latest and current processing specification.

[0056] Further, the present invention uses a method and a learning device for learning processing conditions such as joining conditions and cutting conditions that satisfy the processing specification when the materials, shapes, and dimensions of the parts to be joined are substantially the same and there are variations within the tolerance, and performs processing such as joining two or more parts or cutting one or more parts. Also, when a part outside the tolerance is set as the object to be welded, joining can be prevented, and defective products caused by joining parts outside the tolerance can be avoided.

[0057] Furthermore, the present invention can determine whether joining conditions or cutting conditions that satisfy the processing specification are found in the learned learning device, and can present that update learning is necessary when joining conditions or cutting conditions that satisfy the processing specification are not found.

[0058] When the processing conditions such as bonding conditions and fusing conditions that satisfy the processing specifications cannot be found in the learned learning device, the present invention updates and learns the processing conditions such as bonding conditions and fusing conditions that satisfy the processing specifications, and uses the updated processing conditions such as bonding conditions and fusing conditions to perform processing such as bonding two or more parts or fusing one or more parts.

[0059] In addition, when starting processing such as bonding or fusing, the present invention checks the energy state provided by a general-purpose processing device such as a bonding device or a fusing device, and performs processing operations such as bonding operations and fusing operations under processing conditions that satisfy the processing specifications in that state.

[0060] Furthermore, the present invention monitors the energy state during the processing of the processing device. When the energy state changes to an abnormal state, it presents that the state has changed to an abnormal state, stops the processing, and can ensure the safety of the processing device.

Brief Description of the Drawings

[0061]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

Figure 12

Figure 13

Figure 14

Figure 15

Figure 16

Figure 17

Figure 18

Figure 19

Figure 20

Figure 21

Figure 22

Figure 23

Figure 24

Figure 25

Figure 26

Figure 27

Figure 28

Figure 29

Figure 30

Figure 31

Figure 32

Figure 33

Figure 34

Figure 35

Figure 36

Figure 37

Figure 38

Figure 39

Figure 40

Figure 41

Figure 42

Figure 43

Figure 44

Figure 45

Figure 46

Figure 47

Figure 48

Modes for Carrying Out the Invention

[0062] Regarding the modes for implementing the learning device for processing conditions, the processing device with a learning device, and the processing system with a learning device of the present invention, in the first to sixth embodiments, two or more parts of After explaining the learning device for joining conditions, the joining device with a learning device, and the joining system with a learning device, in the seventh embodiment, one or more parts of The mode for implementing the learning device for fusing conditions, the fusing device with a learning device, and the fusing system with a learning device will be described.

[0063] (First Embodiment) In the first embodiment, a high-frequency welding method using a learning device for learning the joining conditions of two or more parts and a high-frequency welding device 301 with a learning device will be described. FIG. 1 shows an external view of the high-frequency welding device 301 with a learning device. FIG. 2 shows the internal configuration of the high-frequency welding device 301 with a learning device. The high-frequency welding device 301 with a learning device has a learning device 100 arranged on the right side of the high-frequency welding means 1, and the two are connected by a communication line 120 and are integrally controlled.

[0064] In FIG. 1, the high-frequency welding means 1 includes a table 70, an upper mold 2 and a lower mold 3 that sandwich the workpiece 4 in the vertical direction, a high-frequency current supply means 6 that passes a high-frequency current through the workpiece 4 sandwiched between the upper mold 2 and the lower mold 3, a state output means 13 that outputs to the learning device 100 the state in which the high-frequency current is supplied by the high-frequency current supply means 6, a main memory means 11 that stores information necessary for controlling welding conditions and the like, a welding specification input / output means 12 that inputs and outputs welding specifications, a main control means 10 that controls the entire high-frequency welding means 1, an air cylinder 5, and a lever 71 that operates the air cylinder 5.

[0065] In the high-frequency welding means 1, when the lever 71 is pushed down, the upper surface of the workpiece 4 placed on the lower mold 3 on the table 70 is pressed by the upper mold 2 with the force of the air cylinder 5. Then, a high-frequency current is passed through the workpiece 4 sandwiched between the upper mold 2 and the lower mold 3 by the high-frequency current supply means 6, and the workpiece 4 is heated by dielectric heating to be welded.

[0066] As shown in FIG. 2, the learning device 100 is provided with a welding result measuring means 130 such as a thickness measuring means 101, an appearance inspection means 102, a weld strength measuring means 103, etc. And a welding result evaluation means 104 that evaluates the value measured by the welding result measuring means 130 as a reward, a learning memory means 105 that stores the welding result and the reward for evaluating the welding result, a welding condition learning means 106 that organizes the learning result evaluated based on the welding result so that the welding conditions can be read out, an I / O means 107 that inputs and outputs the learning plan of AI technology, specifically the software of the learning program, a welding condition reading means 108 that reads out the welding conditions that satisfy the welding specifications from the learning memory means 105, and a specification monitoring means 109A and a state monitoring means 109B.

[0067] As shown by the two-dot chain line frame in Fig. 2, the learning device 100 is divided into two parts: a lower part 100a and an upper part 100b. The lower part 100a includes a thickness measurement means 101, which is a welding result measurement means 130, an appearance inspection means 102, a welding strength measurement means 103, etc. The upper part 100b includes a welding result evaluation means 104, a learning memory means 105, a welding condition learning means 106, an I / O means 107, a welding condition reading means 108, a specification monitoring means 109A, and a state monitoring means 109B. The black dot ● on the communication line 120 connecting each means in Fig. 2 indicates that they are connected by connectors 101a, 102a, 103a, 110a, and 110b. The connectors 101a, 102a, 103a, 110a, and 110b are detachable. For example, the configuration of the welding result measurement means 130 can be arbitrarily reorganized.

[0068] Also, by connecting the high-frequency welding means 1 connected to the learning device 100 via the connector 110b to other processing devices, it can be configured as another processing device with a learning device. As will be described later with reference to Fig. 24, instead of the connectors 101a, etc., a configuration in which the learning device and the processing means are spatially separated can also be adopted using wireless transmission and reception means 601 to 609.

[0069] A learning plan is input to the learning device 100 from the I / O means 107. As the learning plan to be input, software of learning methods such as reinforcement learning and Q-learning, which are AI technologies, is selectively used as necessary, and the software to be used is stored in the learning memory means 105. In the first embodiment, for the sake of understanding the invention, it will be described assuming that software of a simple reinforcement learning method is stored.

[0070] In the learning device 100, the welding result evaluation means 104 evaluates the welding result measured by the welding result measurement means 130 as a reward, and stores the information obtained by evaluating the welding result in the learning memory means 105. The welding condition learning means 106 learns the welding conditions from the stored information in the learning memory means 105, and generates the learning result as a learned model. Then, the welding condition reading means 108 is configured to be able to read the welding conditions that satisfy the welding specifications from the learned model of the learning memory means 105.

[0071] The specification monitoring means 109A monitors the latest and current welding specifications, and reads the welding conditions that satisfy the latest and current welding specifications from the learned model of the learning memory means 105, even when the welding specifications are updated from the previous welding operation or changed during welding.

[0072] The state monitoring means 109B obtains information on the state of the high-frequency current that the high-frequency welding means 1 has started to flow from the state output means 13 of the high-frequency welding means 1, and transmits it to the welding condition reading means 108. The welding condition reading means 108 is configured to read the welding conditions that satisfy the welding specifications in the state of the high-frequency current that has started to flow from the learned model of the learning memory means 105. In addition, the state monitoring means 109B monitors whether the state of the high-frequency current during high-frequency welding is normal or abnormal.

[0073] Fig. 3 shows the relationship between the transition of the anode current of the high-frequency welding apparatus 301 with a learning device according to the first embodiment and the welding strength after welding. The left vertical axis (left Y-axis) in Fig. 3 indicates the value of the high-frequency current flowing through the workpiece 4 sandwiched between the upper mold 2 and the lower mold 3, that is, the anode current value (A), and the right vertical axis (right Y-axis) indicates the welding strength (F) after welding, in other words, the force required to peel off the workpiece (peeling force). When the high-frequency current starts to flow from the high-frequency current supply means 6, the anode current starts to flow through the workpiece 4 sandwiched between the upper mold 2 and the lower mold 3. As the anode current flows through the workpiece 4, the current value gradually increases little by little. In other words, the current gradually rises. Even when the anode current flows through the workpiece 4, the amount of heat generation is not sufficient until a certain measured reference current value (It), so the workpiece 4 does not melt and fuse, and no welding occurs. After that, when the anode current continues to flow, the workpiece 4 melts and fuses, and welding starts. Then, as the welding time changes to TA1, TA2, TA3, TA4, the welding strength of the welded workpiece 4 increases as FA1, FA2, FA3, FA4 respectively.

[0074] As shown in Fig. 3, the curve of the current that supplies the high-frequency current up to the target current value (IWA) after the high-frequency current, that is, the anode current, rises can be said to be in one state, "state A". In "state A", welding is performed along the curve of the current in "state A" with a small amount of energy, so-called power.

[0075] Fig. 4 shows the current curve "state D" when the target current value (IWD) of the high-frequency current is made larger than that in Fig. 3. In "state D", welding is performed along the curve of the current in "state D" with a large amount of energy, so-called power. In the case of "state D", compared with the case of "state A", the rise time (tD) is short, and the welding strength of the workpiece 4 becomes a large value such as FD1, FD2, FD3, FD4.

[0076] For the present invention, for example, regarding "State A", as shown in FIGS. 5(a) to (d), welding is performed for each welding time (TA1, TA2, TA3, TA4), and the welding strength (FA1, FA2, FA3, FA4) after welding is individually measured using the welding result measuring means 130. Then, the welding result evaluation means 104 evaluates the welding result as a reward and stores it in the learning memory means 105. And the welding condition learning means 106 learns the welding conditions from the welding results evaluated as rewards and generates the learning results as a learned model. And when a welding specification is input, the welding time can be read out from the learned model as the welding condition that satisfies the welding specification.

[0077] If the welding strength FA1 is input as the welding specification to the welding specification input / output means 12 of the high-frequency welding means 1, the learning device 100 calculates the welding time TA1 as an action by inverse calculation from the welding strength FA1 of the learned model and reads it out. If the welding strength FA2 is input as the welding specification, the welding time TA2 as an action is read out by inverse calculation from the welding strength FA2 of the learned model. If the welding strength FA3 is input as the welding specification, the welding time TA3 as an action is read out by inverse calculation from the welding strength FA3 of the learned model. The same applies when the welding strength FA4 is input as the welding specification.

[0078] In addition, when values between the above four welding strengths FA1, FA2, FA3, and FA4 are input as the welding specification, by performing regression analysis, which is often used in AI technology for the relationship between the welding time and the welding strength, and formulating it into a mathematical formula, the welding time corresponding to the input welding strength is calculated and read out. Also, by increasing the number of data for learning, the welding time that satisfies the specification can be read out even for a detailed welding specification.

[0079] For better understanding of the invention, FIG. 6 is shown as a simple example of reinforcement learning. In FIG. 6(a), a tree is shown when the curve of the anode current flowing through the high-frequency welding device 301 with a learning device according to the first embodiment is regarded as one "state", the welding time is regarded as an "action", and the obtained welding strength is regarded as the Q value (action value) as a "reward".

[0080] In Fig. 6(a), the states of the high-frequency welding apparatus are "state A" with low energy, i.e., so-called power, and "state D" with high power. In "state A", if the welding times are TA1, TA2, TA3, and TA4 as "actions", Q values, Q(A, TA1), Q(A, TA2), Q(A, TA3), and Q(A, TA4) are obtained as "rewards". Here, the Q value is shown as Q(A, TA1). A in the parentheses indicates that the state is "state A". TA1 indicates that the action is "welding time TA1".

[0081] Similarly, in "state D", it is shown that if the welding times are TD1, TD2, TD3, and TD4 as "actions", Q values, Q(D, TD1), Q(D, TD2), Q(D, TD3), and Q(D, TD4) are obtained as "rewards".

[0082] If the welding times as the respective "actions" and the Q values as the "rewards" in "state A" and "state D" in Fig. 6(a) are measured in correspondence, then as shown in Fig. 6(b), when it is desired to obtain a certain "reward" in a certain "state", the "action" can be found by back-calculating from the "reward".

[0083] Figs. 7(a) to (d) show an image of measuring the welding strengths (FA1, FA2, FA3, FA4) when welding is performed by randomly setting the welding times (TA1, TA2, TA3, TA4) for four samples with the high-frequency welding apparatus 301 with a learning device according to the first embodiment of the present invention.

[0084] In FIGS. 7(a) to (d), the upper mold 2 with a ring-shaped end face is pressed against the upper surfaces of the elongated plastic sheets 4a and 4b such as two overlapping vinyl chloride sheets, and welded in a ring shape with random welding times. For example, in FIG. 7(a), four ring-shaped welded portions are respectively formed with the welding times being TA1, TA2, TA3, and TA4 from the right. In FIG. 7(b), the welding times are TA4, TA3, TA2, and TA1 from the right as well. In FIG. 7(c), the welding times are TA2, TA4, TA1, and TA3 from the right as well. In FIG. 7(d), the welding times are TA3, TA1, TA4, and TA2 from the right as well, and four ring-shaped welded portions are respectively welded. Then, after welding, the upper plastic sheet 4a is vertically lifted and peeled off from the lower plastic sheet 4b to measure the welding strength. The measurement data of the welding results is stored in the learning memory means 105.

[0085] As shown in FIGS. 7(a) to (d), the learning device 100 stores the measurement results measured in the learning memory means 105, evaluates them as rewards by the welding result evaluation means 104, and the welding condition learning means 106 learns the relationship between the welding time as an action and the reward, summarizes the learning results in a table, and uses the learning results as a learned model. And when the welding specification is input, the welding condition reading means 108 can read out the welding conditions that satisfy the welding specification.

[0086] FIGS. 8(a) to (c) show the stored contents in the learning memory means 105. In FIG. 8, the welding time and the welding strength are stored as a pair. Also, in FIGS. 8(a) and (b), trapezoidal, round, and square frames are added to each pair for easy understanding.

[0087] In FIG. 8(a), the measured data is stored in the order of measurement. For example, in data 1, it shows that from the right, the pair of welding strength FA11 and Q value, Q(A, TA11), the pair of welding strength FA21 and Q value, Q(A, TA21), the pair of welding strength FA31 and Q value, Q(A, TA31), and the pair of welding strength FA41 and Q value, Q(A, TA41) are stored. From data 2 to data 4, each pair is randomly arranged in the order of measurement.

[0088] In FIG. 8(b), the data that is the stored content of the learning storage means 105 by the welding condition learning means 106 is arranged in a form where the welding force magnitudes are sorted in ascending order from a random form. For example, in Data 1, compared with FIG. 8(a), the order is reversed, and from the left, the pairs of welding force FA11 and Q value (A, TA11), the pair of welding force FA21 and Q value (A, TA21), the pair of welding force FA31 and Q value (A, TA31), and the pair of welding force FA41 and Q value (A, TA41) are arranged. For Data 2 to Data 4, each pair is arranged in the same order as Data 1.

[0089] And in FIG. 8(c), the welding condition learning means 106 obtains the average value of the welding force for each welding time for the data arranged in FIG. 8(b), and creates a table from which the welding time can be read by inverse calculation from the welding force. In the present invention, the content of FIG. 8(c), which is the learning result learned from Data 1 to Data 4, is used as the learned model. And the welding time can be read out as the welding condition that satisfies the welding force of the welding specification.

[0090] Figure 9 shows the details of the learning procedure in the learning device 100 as a flowchart. In the flowchart of Figure 9, a learning plan is input by the I / O means 107, stored in the learning storage means 105, and then learning is started (step ST1). A target current value is set so that the input welding specification is satisfied. Note that since a learning device that has not learned at all is ignorant, it is advisable to include the target current value expected to satisfy the welding specification as an initial value in the welding specification and input it (step ST2). The anode current starts to flow (step ST3). Here, it is asked whether to check the state during welding (step ST4). This is because when the high-frequency welding means 1 has only a single energy supply state, there is only one energy state, so there is no need to check the state during welding. In that case, the next step ST5 is skipped (NO in step ST4). When the high-frequency welding means 1 is a general-purpose processing device that can select one state from a plurality of energy supply states, step ST5 is performed. In step ST5, the rise time until the anode current reaches the measurement reference current value is measured and stored in the learning storage means 105. Then, the anode current is passed for each welding time to perform welding (step ST6). The weld strength as the reward for the welding time as an action is measured, the corresponding data of the welding time and the weld strength is created, and stored in the learning storage means 105 (step ST7). The welding condition learning means 106 learns the welding conditions from the information stored in the learning storage means 105, generates the learning result as a learned model, and stores it in the learning storage means 105 in a form where the welding conditions can be read from the learned model (step ST8).

[0091] Then, it is asked whether data on the welding force corresponding to a predetermined number of welding times has been obtained for one state (i.e., one current curve) (step ST9). If the predetermined number of data has not been obtained, the process returns to step ST3 (NO in step ST9). If the predetermined number of data has been obtained (YES in step ST9), it is asked whether to update the target current value and learn about other states (step ST10). When updating the target current value based on the input learning plan, the process returns to step ST2 (YES in step ST10). When not updating the target current value (NO in step ST10), it is asked whether learning has been done as planned (step ST11). If learning has not been done as per the learning plan, the process returns to step ST2 (NO in step ST11), and if learning as per the plan has been completed (YES in step ST11), the process proceeds to step ST12 and learning is terminated.

[0092] Note that steps ST8 and ST9 may be interchanged so that after obtaining a predetermined number of data, learning is performed and the learning result is stored in the learning storage means 105.

[0093] When performing update learning, starting from step ST34, the process enters step ST1 and performs the steps below step ST1. By performing the procedure of the flowchart in FIG. 9, for one or more states, data on the welding force for each welding time, which is the respective action, is obtained, the data on the welding force is stored as a reward, the stored data is learned, the learning result is made in a form that can be read out as welding conditions, and it is stored in the learning storage means 105.

[0094] Figure 10 shows the procedure of the welding operation as a flow chart. In Figure 10, first, welding specifications are input and the welding operation is started (step ST20). The workpieces to be welded are set (step ST21). It is asked whether to check the state (step ST22). As also explained in the flow chart of Figure 9, when the high-frequency welding means 1 has only a single energy supply state, since there is only one energy state, it is not necessary to check the state during welding. At that time, it is to skip the next steps ST23 and ST24 (NO in step ST22). When the high-frequency welding means 1 can select one state from a plurality of energy supply states, the next steps ST23 and ST24 are performed. In step ST23, the rise time of the anode current is measured. And the state of the energy that the high-frequency welding means 1 has started to supply is checked (step ST24). Then, in the state of the energy that has started to be supplied, it is asked whether there are welding conditions that satisfy the latest and current welding specifications confirmed by the specification monitoring means 109A among the learned models of the learning storage means 105 (step ST25). If there are welding conditions, they are read from the learning storage means 105 (step ST26), stored in the main storage means 11, and welding is performed by the high-frequency welding means 1 (step ST27). The state monitoring means 109B monitors whether the state during welding is abnormal (step ST28). When the state is normal (YES in step ST28), it is confirmed that the welding time for ending the welding operation has been reached (step ST29), and the welding operation is ended (step ST31). In addition, when the state is abnormal in step ST28, an abnormality is presented and welding is stopped (step ST30). Then, the welding operation is ended (step ST31).

[0095] Also, when no welding conditions are found in the learned models of the learning storage means 105 in step ST25 (NO in step ST25), it is presented that "update learning is necessary" (step ST32). Then, it is asked whether to perform update learning (step ST33). When performing update learning, starting from step ST34, it jumps to the flow chart of Figure 9, and update learning is performed according to the procedure below step ST1 of Figure 9.

[0096] The content of the learning memory means 105 when the welding conditions are read in step ST26 is shown in FIG. 11. Note that FIG. 11 is shown in a simplified manner so that an image can be grasped for understanding the invention.

[0097] The case of FIG. 11(a) will be described. First, when the welding force is input as "FA2" as the welding specification (step ST20). When the rise time is measured (step ST23), since the rise time is "TA", it is confirmed that the state is "state A" (step ST24). Since the welding force that satisfies the welding specification in "state A" is "FA2" in the table, the welding time "TA2" paired with the welding force "FA2" is read as the action for obtaining the welding force "FA2" of the welding specification, that is, the welding time of the welding conditions.

[0098] Similarly, in the case of FIG. 11(b), when the welding force is input as "FD3" as the welding specification (step ST20). When the rise time is measured (step ST23), since the rise time is "TD", it is confirmed that the state is "state D" (step ST24). Since the welding force that satisfies the welding specification in "state D" is "FD3" in the table, the welding time "TD3" paired with the welding force "FD3" is read as the action for obtaining the welding force "FD3" of the welding specification, that is, the welding time of the welding conditions.

[0099] In the case of Fig. 11(c), when the welding force is input with the upper limit value "FA3" and the lower limit value "FA2" as the welding specification (step ST20), when the rise time is measured (step ST23), since the rise time is "TA", it is confirmed that the state is "state A" (step ST24). Since the welding forces that satisfy the welding specification in "state A" are "FA3" and "FA2" in the table, the welding time "TA3" paired with the welding force "FA3" and the welding time "TA2" paired with the welding force "FA2" are read as the actions for obtaining the welding forces "FA3" and "FA2" of the welding specification, that is, the upper limit welding time "TA3" and the lower limit welding time "TA2" of the welding conditions. When the upper limit welding time "TA3" and the lower limit welding time "TA2" are determined, the welding time within the range sandwiched between the upper limit welding time "TA3" and the lower limit welding time "TA2" is used as the welding condition. Fig. 12 shows the range (TA2~TA3) of the welding time specified as the welding condition when the upper limit welding force and the lower limit welding force are input as the welding specification in the case of Fig. 11(c).

[0100] The update learning will be described with reference to Fig. 13. For example, when learning has already been performed from the welding forces FA1 to FA4 and FD1 to FD4, when the welding force input in the welding specification is a value smaller than FA1 or a value larger than FD4, the welding conditions that satisfy the specification cannot be read from the learned model. When the welding force input as the welding specification is not in the learning storage means 105 in step ST25 of Fig. 10 (NO in step ST25), "update learning is required" is displayed in step ST32. And when performing update learning (YES in step ST33), it jumps from step ST34 of Fig. 10 to step ST1 of Fig. 9.

[0101] In step ST1 of Fig. 9, since the learning plan has already been input, update learning is started and it proceeds to step ST2. At this time, as the learning device 100, in light of the welding specification, the target current value is set in the direction in which the welding force that satisfies the welding specification can be obtained (step ST2). Then, according to the flowchart of Fig. 9, steps ST3 and below are performed to perform a learning operation to obtain the welding conditions under which the welding force that satisfies the processing specification can be obtained.

[0102] In FIG. 13, as shown by FH1 to FH4, an example of updated learning of welding conditions for obtaining a welding force smaller than FA1 was shown. From the result of the updated learning in FIG. 13, the welding time as the welding condition for obtaining welding forces FH1 to FH4 smaller than FA1 is obtained.

[0103] After the updated learning, if returning to step ST20 of the flowchart in FIG. 10 and restarting the welding operation, assuming that there are welding conditions (YES in step ST25), the welding conditions updated by learning are read from the learned model of the learning storage means 105 (step ST26). Then, a new welding condition that did not exist before the updated learning, for example, the welding time TH1 corresponding to FH1, is read and welding is performed (step ST27), and the welding operation is terminated (step ST31).

[0104] Thus, in the first embodiment of the present invention, learning is performed using a learning device for joining conditions when joining two or more parts. When a welding specification is input, the welding conditions that satisfy the input welding specification are read from the learned model of the learning device, and two or more parts are joined under the read welding conditions.

[0105] Thereby, the first problem of the present invention, that is, a method and a learning device for learning processing conditions when joining two or more parts, fusing or processing one or more parts, are used to perform processing such as joining two or more parts or fusing one or more parts, is solved.

[0106] And the second problem of the present invention, that is, starting processing under processing conditions that satisfy the processing specification and also monitoring the processing specification during processing to always support processing under processing conditions that satisfy the processing specification, is realized.

[0107] Also, the fourth problem of the present invention, that is, determining whether joining conditions or fusing conditions that satisfy the processing specification can be found from the learned learning device, and presenting that updated learning is necessary when joining conditions or fusing conditions that satisfy the processing specification cannot be found, is solved.

[0108] And, with respect to the fifth problem of the present invention, that is, when processing conditions such as joining conditions and fusing conditions that satisfy the processing specifications cannot be found from the learned learning device, the processing conditions such as joining conditions and fusing conditions that satisfy the processing specifications are updated and learned, and two or more parts are joined or one or more parts are fused using the updated processing conditions such as joining conditions and fusing conditions. This solves the problem of performing processing such as fusing.

[0109] And, with respect to the sixth problem of the present invention, that is, a general-purpose processing device such as a joining device or a fusing device checks the energy state when starting processing such as joining or fusing, and performs processing operations such as joining work or fusing work using processing conditions that satisfy the processing specifications in that state. This solves the problem.

[0110] Furthermore, with respect to the seventh problem of the present invention, that is, the energy state during processing of the processing device is monitored, and when the energy state changes to an abnormal state, it notifies that the change to the abnormal state has occurred and stops the processing. This solves the problem.

[0111] Regarding the solution to the third problem of the present invention, it will be described in the second embodiment and the third embodiment.

[0112] (Second Embodiment) In the second embodiment of the present invention, a high-frequency welding device with a learning device for welding a synthetic resin sheet such as a vinyl chloride sheet will be described.

[0113] Generally, when welding parts 4a and 4b to produce a welded object 4 having a certain thickness, a tolerance is defined for the thickness of each of parts 4a and 4b. Ideally, if parts 4a and 4b are within the tolerance, a product that satisfies the welding specifications can be produced when welded under one welding condition.

[0114] However, when the thickness of each of parts 4a and 4b is the combination of the maximum thickness within the tolerance or the combination of the minimum thickness within the tolerance, the flow of the anode current changes due to the difference in thickness.

[0115] For understanding the invention, Fig. 14 illustrates the thickness when the components 4a and 4b of the object to be welded 4 are combined. In Fig. 14, when the minimum value of the thickness within the tolerances of components 4a and 4b is D1 and the maximum value is D2, the intermediate combination of the minimum and maximum values is Da (= D1 + D2). The combination of the thinnest minimum values is Db (= 2 × D1). The combination of the thickest maximum values is Dc (= 2 × D2).

[0116] Fig. 15 shows the transition of the anode current of the high-frequency welding apparatus with a learning device according to the second embodiment of the present invention. At the median value Da (= D1 + D2) within the tolerance of the thickness of the object to be welded, the anode current flows as in "State A". When it is the minimum value Db (= D1 + D1) within the tolerance of the thickness, the anode current flows as "State B". And when it is the maximum value Dc (= D2 + D2) within the tolerance of the thickness, the anode current flows as "State C".

[0117] In Fig. 15, "State B" where the thickness of the object to be welded is the minimum value Db within the tolerance rises earlier than "State A" which is the median value Da within the tolerance. Also, "State C" which is the maximum value Dc within the tolerance rises later than "State A".

[0118] Fig. 16 shows a comparison of the transition of the anode current and the welding strength after welding in the cases of "State A" and "State B". In Fig. 16, when in "State A", the line connecting the anode current flowing through components 4a and 4b and the welding strengths FA1, FA2, FA3, FA4 is shown as a solid line. Also, when in "State B", the line connecting the anode current flowing through components 4a and 4b and the welding strengths FB1, FB2, FB3, FB4 after welding is shown as a dashed line. Looking at Fig. 16, it can be seen that the welding strengths FB1, FB2, FB3, FB4 in "State B" increase earlier than the welding strengths in "State A" and are shifted to the left.

[0119] Fig. 17 shows a comparison of the transition of the anode current and the relationship of the welding strength after welding for "State A" and "State C". In Fig. 17, for "State A", the line connecting the anode current flowing through parts 4a and 4b and the welding strengths FA1, FA2, FA3, and FA4 is shown as a solid line. Also, for "State C", the line connecting the anode current flowing through parts 4a and 4b and the welding strengths FC1, FC2, FC3, and FC4 after welding is shown as a dashed line. Looking at Fig. 17, it can be seen that the welding strengths FC1, FC2, FC3, and FC4 in "State C" increase more slowly than the welding strengths in "State A" and are shifted to the right.

[0120] If the welding time in State A is directly used for State B or State C, problems such as excessive welding or insufficient welding will occur as in the conventional example shown in Fig. 42, and the welding specifications will not be met.

[0121] In the second embodiment of the present invention, for the "State A", "State B", and "State C" of the tree shown in Fig. 18, the welding strength after welding when welding is performed at a plurality of welding times is measured as the Q value, and learning is performed by a learning device.

[0122] The tree in Fig. 18 shows that there are three states in a high-frequency welding device with a learning device: "State A" in which an anode current is passed through the median value Da (= D1 + D2) within the tolerance of the workpiece to be welded, "State B" in which an anode current is passed through the minimum value Db (= D1 + D1) within the tolerance of the workpiece to be welded, and "State C" in which an anode current is passed through the maximum value Dc (= D2 + D2) within the tolerance of the workpiece to be welded. And Fig. 18 shows that a plurality of actions and the Q values as rewards corresponding to the actions are connected to each of the three states.

[0123] The learning procedure is the same as the procedure shown in the flowchart of Fig. 9 of the first embodiment. Since the explanation of the learning procedure is repetitive, it is omitted. When learning is executed according to the flowchart of Fig. 9, the learning result shown in Fig. 19 is obtained as a learned model. In Fig. 19, the materials of parts 4a and 4b, the rise time, and the pairs of the target welding current value, the welding strength, and the welding time are stored in the learning storage means 105.

[0124] The welding procedure is the same as the procedure shown in the flowchart of FIG. 10 of the first embodiment. Although the description of the overlapping part of the welding procedure is omitted, when welding along the flowchart of FIG. 10, as the main procedure, at step ST23 of FIG. 10, the rise time of the anode current is measured, and at step ST24, it is confirmed whether it is in any of "state A", "state B", or "state C". If "state A" is confirmed, then at step ST25, it is asked whether there are welding conditions that satisfy the welding specifications in state A. Similarly, at step ST24, when "state B" is confirmed based on the rise time of the anode current, at step ST25, it is asked whether there are welding conditions that satisfy the welding specifications in state B. When "state C" is confirmed at step ST24 based on the rise time of the anode current, at step ST25, it is asked whether there are welding conditions that satisfy the welding specifications in state C. And if there are welding conditions in the learned model stored in the learning storage means 105 of the learning device, then at step ST26, they are read out from the welding storage means 105, stored in the main storage means 11 of the high-frequency welding means 1, and the parts 4a and 4b are welded under the welding conditions stored in the main storage means 11 to produce the product which is the welded object 4.

[0125] In this way, even if the thickness of the welded object fluctuates within the tolerance, after confirming the state of the anode current flow from the rise time, the welding time as the action that satisfies the welding specifications in that state is read out as the welding condition and welding is performed by the high-frequency welding means 1.

[0126] Using FIG. 20, the state of reading out the welding time as the welding condition that satisfies the welding specifications will be described. In FIG. 20(a), when "state A" is confirmed based on the rise time (TA), the welding time (TA3) is read out from the learned model of the learning storage means 105 by inverse calculation from the welding force (FA3) of the welding specifications as the action, that is, the welding condition.

[0127] In FIG. 20(b), when "state B" is confirmed based on the rise time (TB), the welding time (TB3) is read out from the learned model of the learning storage means 105 by inverse calculation from the welding force (FB3) of the welding specifications as the action, that is, the welding condition.

[0128] In FIG. 20(c), when "State C" is confirmed based on the rise time (TC), the welding time (TC3) as an operation, i.e., welding condition, is read from the learned model of the learning storage means 105 by back-calculating from the welding strength (FC3) of the welding specification.

[0129] In the present invention, the rise time when starting to flow the anode current is measured to confirm the state of the anode current that has started to flow in the high-frequency welding apparatus. Then, the welding time as a welding condition that provides a welding strength satisfying the welding specification in the confirmed state is found from the learned model of the learning storage means 105 of the learning apparatus 100. If found, the welding condition is read from the learning storage means 105 of the learning apparatus 100 and stored in the main storage means of the high-frequency welding means 1, and welding is performed by the main control means.

[0130] Thus, even if the thickness of the workpiece varies within the tolerance, it is possible to read and weld the welding conditions that satisfy the welding specification in each state from among the welding conditions learned by the learning apparatus.

[0131] In the second embodiment, when the materials, shapes, and dimensions of the parts to be welded in the third problem of the present invention are substantially the same and there are variations within the tolerance, it was explained that the state when starting to apply the anode current is confirmed from the rise time when starting to apply the anode current, the welding time that satisfies the welding specification in the confirmed state is learned as a welding condition, read from the learning storage means, and welded.

[0132] Note that since the second embodiment of the present invention is based on the first embodiment, as already described in the first embodiment, it goes without saying that the first problem, the second problem, the fourth problem to the seventh problem of the present invention are solved.

[0133] (Third Embodiment) In the third embodiment, the latter half of the third problem of the present invention, i.e., "when a part with a thickness outside the tolerance is set, no joining is performed so as not to produce a defective product with a part outside the tolerance joined" is explained.

[0134] Note that the configuration of the high-frequency welding apparatus 301 with a learning device according to the third embodiment is basically the same as that of the first and second embodiments, and thus the description thereof is omitted.

[0135] Fig. 21 shows the transition of the anode current of the high-frequency welding apparatus with a learning device according to the third embodiment. In the third embodiment, the anode current flowing through the parts 4a and 4b sandwiched between the upper mold 2 and the lower mold 3 flows in five states, namely, "state B", "state E", "state A", "state F", and "state C" from the left in Fig. 21. In each state, when welding is performed at a plurality of welding times, the welding strength after welding is measured and stored in the learning storage means 105 of the learning device 100, and learning is performed so that the welding conditions satisfying the welding specifications can be read out. This is the same as in the first and second embodiments described above.

[0136] In the third embodiment, regarding the range of the rise time from the start of energization of the anode current to reaching a predetermined current value (measurement reference current value), the rise time ranges (tB~tE), (tE~tA), (tA~tF), and (tF~tC) are set. The state of the anode current is determined based on which rise time range the rise time (t) during the welding operation is included in. The welding conditions satisfying the welding specifications are read out from the learned model of the learning storage means 105 based on the determined state. Then, the parts 4a and 4b sandwiched between the upper mold 2 and the lower mold 3 are welded under the read welding conditions.

[0137] Fig. 22 shows a flowchart of the welding procedure according to the third embodiment. In Fig. 22, the welding specifications are input and the welding operation is started (step ST20). The workpiece to be welded is set (step ST21). In the third embodiment, since the state is always confirmed, the rise time (t) of the anode current is measured (step ST23). Therefore, step ST22 in Fig. 10 is not present in Fig. 22. The state is confirmed based on which rise time range the rise time (t) is included in (step ST24).

[0138] In the third embodiment, detailed procedures are provided in step ST24. In Fig. 22, the detailed procedures of step ST24 are shown within the dashed frame.

[0139] As the detailed procedure of step ST24, it is determined whether the rise time (t) is equal to or greater than (tB) and equal to or less than (tE) (step ST40). If it is YES (YES in step ST40), it is determined that the state is E (step ST41). Then, the welding time as the welding condition satisfying the welding specification in state E is read out (step ST26), and welding is performed (step ST27). If it is NO in step ST40, it is determined whether the rise time (t) is greater than (tE) and equal to or less than (tA) (step ST42). If it is YES (YES in step ST42), it is determined that the state is A (step ST43). Then, the welding time as the welding condition satisfying the welding specification in state A is read out (step ST26), and welding is performed (step ST27). If it is NO in step ST42, it is determined whether the rise time (t) is greater than (tA) and equal to or less than (tF) (step ST44). If it is YES (YES in step ST44), it is determined that the state is F (step ST45). Then, the welding time as the welding condition satisfying the welding specification in state F is read out (step ST26), and welding is performed (step ST27). If it is NO in step ST44, it is determined whether the rise time (t) is greater than (tF) and equal to or less than (tC) (step ST46). If it is YES (YES in step ST46), it is determined that the state is C (step ST47). Then, the welding time as the welding condition satisfying the welding specification in state C is read out (step ST26), and welding is performed (step ST27).

[0140] The procedure for ending the welding operation from step ST27 to step ST31 after step ST26 is the same as that in FIG. 10 described in the first embodiment, so the description is omitted.

[0141] When the answer in step ST46 is NO, that is, when the state is not "state E", "state A", "state F", or "state C", it means that the thickness when parts 4a and 4b are overlapped is not within the tolerance range, i.e., outside the tolerance. At this time, proceed to step ST48 (NO in step ST46). Then, it is determined whether the rise time (t) is less than (tB) (step ST48). If YES (YES in step ST48), it is indicated that the rise time is too short (step ST49). If the answer in step ST48 is NO, it is determined whether the rise time (t) is greater than (tC) (step ST50). If YES (YES in step ST50), it is indicated that the rise time is too long (step ST51). If the answer in step ST50 is NO, proceed to step ST32. The procedure from step ST32 until the end of the welding operation (step ST31) is the same as that in FIG. 10 described in the first embodiment, so the description is omitted.

[0142] By performing the procedure shown in FIG. 22, when starting to flow the anode current, the rise time of the anode current is measured to confirm the state in which the high-frequency welding apparatus is about to weld. Then, welding conditions that satisfy the welding specifications are read out in the confirmed state.

[0143] By performing the above procedure, welding is performed only when the rise time of the anode current is within a certain range, that is, only when the thicknesses of parts 4a and 4b are both within the tolerance range, and no welding is performed when outside the certain range, that is, when the thicknesses of parts 4a and 4b are both outside the tolerance range. In the third embodiment, when the thickness of the parts of the workpiece to be welded is determined by the tolerance, only the workpieces within the tolerance are welded to produce good products, and the workpieces outside the tolerance are not welded. It has been explained that no defective products are produced.

[0144] Note that, as described in the second embodiment, providing a learning device that determines the state of energy supply according to the variation when the materials, shapes, and dimensions of the parts to be joined are substantially the same and there is a variation within the tolerance, which is the first half of the third problem of the present invention, and learns the energy supply time at which the reward is maximized in that state as the welding condition or the fusing condition.

[0145] Also, since the third embodiment of the present invention is already based on the first embodiment, solving the first, second, fourth to seventh problems of the present invention is as described in the first embodiment.

[0146] (Fourth Embodiment) In the fourth embodiment of the present invention, a learning method using a neural network will be described. In the learning method using a neural network, a multi-layer neural network with multiple stacked perceptrons is created. When an action is input to the input layer of the multi-layer neural network, each input is weighted, learning is performed in the hidden layer, and a policy obtained by multiplying the maximum reward by a probability (probability to be adopted) is output from the output layer.

[0147] In the fourth embodiment, as shown in FIG. 23, learning is performed in a form where "material" is added to "thickness" and "adhesion strength". When "thickness", "adhesion strength", and "material" are input on the input side, "anode current state" and "welding time" are output as welding conditions on the output side.

[0148] In the fourth embodiment of the present invention, a learning plan using the multi-layer neural network shown in FIG. 23 is input from the I / O means of the learning device to the learning storage means 105 in advance and stored. The welding condition learning means 107 performs learning considering "thickness", "adhesion strength", and "material" in a composite manner, and outputs "anode current state" and "welding time" to generate a learned model. Then, the welding conditions that satisfy the welding specification are read from the learned model of the learning storage means 105. After reading the welding conditions that satisfy the welding specification, they are stored in the main storage means 11 of the high-frequency welding means 1, and the object to be welded 4 is welded by the main control means 10.

[0149] As described above, in the first to fourth embodiments, a learning device for joining conditions of two or more parts, a joining device using the same, and a joining system using the same have been described.

[0150] (Fifth Embodiment) In the fifth embodiment of the present invention, a learning device for processing conditions, a processing device using the same, and a processing system using the same will be described. Note that in this specification, processing is described as a superordinate concept such as joining and cutting.

[0151] FIG. 24 shows a schematic configuration diagram of the fifth embodiment of the present invention. In FIG. 24, the first processing device 501, the second processing device 502, and the third processing device 503, which are three processing devices, are arranged, and transmission / reception means 601, 602, and 603 are respectively attached to the three processing devices, and transmission / reception means 604 that communicates with these transmission / reception means is attached to the learning device 200, showing a system in which one learning device 200 operates the three processing devices as processing devices with a learning device.

[0152] The transmission / reception means 605 of the learning device 200 takes in and learns the processing result measurement data by communicating with the transmission / reception means 606, 607, 608, and 609 to which a thickness measurement means 201, an appearance inspection means 202, an adhesive force measurement means 203, and a cutting depth measurement means 204 are respectively attached.

[0153] For example, one learning device 200 can be installed in one factory, and a plurality of processing devices 501, 502, 503, etc. and the learning device 200 can be installed in other factories within the same site so as to be individually communicable, and the learning device 200 can be shared. It can be operated as a joining system in which the learning data increases and the learning progresses.

[0154] Instead of the first processing device 501, the second processing device 502, and the third processing device 503, a system in which transmission / reception means 601, 602, and 603 are respectively attached to the first welding device 371, the second welding device 372, and the third welding device 373 may be used. Also, a system in which transmission / reception means 601, 602, and 603 are respectively attached to the first cutting device 471, the second cutting device 472, and the third cutting device 473 may be used.

[0155] Also, the Internet may be used as the communication means and it may be used as an IOT processing system.

[0156] (Sixth Embodiment) Using FIG. 25, an example of a bonding apparatus 300 with a learning device 150 of the present invention attached thereto and a fusing apparatus 400 with a learning device 150 attached thereto is shown as a system diagram. Note that a learning device for a fusing method of one or more components, a fusing apparatus using the same, and a fusing system using the same will be described later as a seventh embodiment of the present invention.

[0157] Examples of the bonding apparatus 350 include a "high-frequency welding apparatus", a "non-contact hot plate welding apparatus", a "vibration welding apparatus", a "laser welding apparatus", a "ultrasonic welding apparatus", a "ultrasonic metal bonding apparatus", and a "ultrasonic bonding apparatus". These bonding apparatuses apply energy such as vibration energy and heat energy to components to heat, melt, or solid-phase bond the components to perform bonding processing to produce products. Therefore, they are common in that by changing the magnitude of the energy applied to the components and the supply time of the energy, bonding conditions that satisfy the processing specifications can be found.

[0158] Therefore, those obtained by attaching a learning device to these bonding apparatuses can perform the bonding method using the learning device of the present invention as a "high-frequency welding apparatus with a learning device 301", a "non-contact hot plate welding apparatus with a learning device 302", a "vibration welding apparatus with a learning device 303", a "laser welding apparatus with a learning device 304", a "ultrasonic welding apparatus with a learning device 305", a "ultrasonic metal bonding apparatus with a learning device 306", and a "ultrasonic bonding apparatus with a learning device 307", respectively.

[0159] And those obtained by attaching the learning device 150 to the fusing apparatus 450 can perform the fusing method using the learning device of the present invention as a "laser fusing apparatus with a learning device 401" and a "ultrasonic fusing apparatus with a learning device 402", respectively.

[0160] Although not shown in FIG. 25, it can be applied to a "spin welder" and an "impulse welder". These will be described later with reference to FIGS. 30 and 31.

[0161] Fig. 26 shows an external view of the non-contact hot plate welding apparatus 302 with a learning device according to the present invention. In the non-contact hot plate welding apparatus 352, welding conditions are determined by the magnitude of the thermal energy heated to a predetermined temperature of the hot plates 352a and 352b and the time for applying the thermal energy.

[0162] For example, by taking the surface temperature for holding the hot plates 352a and 352b as the "state" of the apparatus, the heating time for heating while approaching the workpiece 4W1 as the "action", and the welding strength or the aesthetic level as the "reward" for learning, welding can be performed.

[0163] Note that the learning procedure and the welding procedure are the same as those in the first embodiment. Therefore, by applying the bonding method of the present invention already described in the first to fifth embodiments, the learning device 150 can be attached and welding can be performed using the learning device of the present invention as the "non-contact hot plate welding apparatus 302 with a learning device".

[0164] Fig. 27 shows an external view of the vibration welding apparatus 303 with a learning device according to the present invention. In the vibration welding apparatus 353, low-frequency vibration of about 100 Hz is applied to one component 4W2a with the upper mold 353a, and the surface of the other component 4W2b fixed to the lower mold 353b is rubbed to generate frictional heat, and it is a bonding apparatus for welding the contact surfaces of the components 4W2a and 4W2b with the frictional heat. The welding conditions are determined by the generated frictional force and the time for applying the frictional force.

[0165] For example, by taking the magnitude of the frictional force applied to the components 4W2a and 4W2b of the workpiece as the "state", the heating time for heating by rubbing the components 4W2a and 4W2b of the workpiece, that is, the time for applying the frictional force as the "action", and the welding strength or the aesthetic level as the "reward" for learning, welding can be performed.

[0166] Note that the learning procedure and the bonding / welding procedure are the same as those in the first embodiment. Therefore, by applying the bonding method of the present invention already described in the first to fifth embodiments, the learning device 150 can be attached and welding can be performed using the learning device of the present invention as the "vibration welding apparatus 303 with a learning device".

[0167] Fig. 28 shows an external view of the laser welding apparatus 304 with a learning device according to the present invention. In the laser welding apparatus 354, the welding conditions are determined by the magnitude of the energy of the laser beam 354a and the time for applying the energy.

[0168] For example, by taking the magnitude of the laser light output as the "state", the laser light irradiation time for irradiating the pair of workpieces 4W3 with the laser light as the "action", and the welding strength or the aesthetic level as the "reward" for learning, welding can be performed.

[0169] Note that the learning procedure and the joining / welding procedure are the same as those in the first embodiment. Therefore, by applying the joining method of the present invention already described in the first to fifth embodiments, the learning device 150 can be attached and welding can be performed using the learning device of the present invention as the "laser welding apparatus 304 with a learning device".

[0170] Fig. 29 shows an external view of the ultrasonic welding apparatus 305 with a learning device. In the ultrasonic welding apparatus 355, the tool horn 355a that vibrates ultrasonically in the vertical direction is pressed against the pair of workpieces 4W4 for welding.

[0171] The welding conditions are determined by the magnitude of the energy of the ultrasonic vibration and the time for applying the energy.

[0172] For example, by taking the frequency and amplitude of the ultrasonic vibration as the "state", the time for applying the ultrasonic vibration as the "action", and the welding strength or the aesthetic level as the "reward" for learning, welding can be performed.

[0173] Note that the learning procedure and the joining / welding procedure are the same as those in the first embodiment. Therefore, by applying the joining method of the present invention already described in the first to fifth embodiments, the learning device 150 can be attached and welding can be performed using the learning device of the present invention as the "ultrasonic welding apparatus 305 with a learning device".

[0174] Although the appearance of the device is not illustrated, the ultrasonic metal bonding device 306 is a bonding device that applies ultrasonic vibration parallel to the contact surface between metals to solid-phase bond the contact surfaces between metals. The welding conditions are determined by the magnitude of the energy of the ultrasonic vibration and the time for applying the energy.

[0175] Note that the learning procedure and the bonding / welding procedure are the same as those in the first embodiment. Therefore, by applying the bonding method of the present invention already described in the first to fifth embodiments, the learning device 150 can be attached and bonding can be performed using the learning device of the present invention as an "ultrasonic metal bonding device 306 with a learning device".

[0176] Similarly, although the appearance of the device is not illustrated, the ultrasonic bonding device 307 places a metal sheet on the surface of the glass, each other and is a bonding device that applies ultrasonic vibration parallel to the contact surface between them to solid-phase bond the metal sheet to the surface of the glass. The welding conditions are determined by the magnitude of the energy of the ultrasonic vibration and the time for applying the energy.

[0177] Note that the learning procedure and the bonding / welding procedure are the same as those in the first embodiment. Therefore, by applying the bonding method of the present invention already described in the first to fifth embodiments, the learning device 150 can be attached and bonding can be performed using the learning device of the present invention as an "ultrasonic bonding device 307 with a learning device".

[0178] Figures 30(a) through (c) are cross-sectional views showing the welding process of a spin welder. As shown in Figure 30(a), the spin welder has the end faces of two cylindrical plastic parts 4W6a and 4W6b facing each other and in contact. With the lower part 4W6b fixed as shown in Figure 30(b), the upper part 4W6a is rotated at high speed, and as shown in Figure 30(c), it is a welding device that welds the contact surfaces together by frictional heat. The upper part 4W6a is rotated at high speed to impart kinetic energy, generating frictional heat at the contact surface with the lower part 4W6b to melt and join the parts together. Therefore, the welding conditions are determined by the magnitude of the rotational kinetic energy imparted to the upper part 4W6a and the time for imparting the rotational kinetic energy. The present invention welds by learning these welding conditions with a learning device.

[0179] Note that the learning procedure and the joining / welding procedure are the same as those in the first embodiment. Therefore, the joining method of the present invention already described in the first to fifth embodiments can be applied to join two parts as a spin welder with a learning device.

[0180] Figure 31 is a diagram showing the change in temperature of the workpiece when a large current is passed through an impulse welder. The impulse welder is a welding device that instantaneously passes a large current through two or more parts to generate heat and then cools, melting and joining the parts together. As shown in Figure 31, the welding conditions are determined by the amount of heat generated by passing a large current for heating and cooling and the heating / cooling time. The present invention welds by learning these welding conditions with a learning device.

[0181] Note that the learning procedure and the joining / welding procedure are the same as those in the first embodiment. Therefore, the joining method of the present invention already described in the first to fifth embodiments can be applied to join two parts as an impulse welder with a learning device.

[0182] (Seventh Embodiment) As the seventh embodiment of the present invention, a learning device for a melting method of one or more parts, a melting device with a learning device, and a melting system with a learning device will be described.

[0183] The fusing devices 450 include "laser fusing devices", "ultrasonic fusing devices", etc. These fusing devices irradiate the object to be fused with a laser beam or press a cutter vibrating ultrasonically against the object to be fused, thereby fusing one or more components. These fusing devices determine the fusing conditions based on the magnitude of the energy of the laser beam or the energy of the ultrasonic vibration and the time for applying the energy. In the tree of FIG. 25, "laser fusing device 401 with a learning device" and "ultrasonic fusing device 402 with a learning device" are shown at the lower right.

[0184] Although the "laser fusing device 401 with a learning device" is not shown, it has a similar appearance to the laser welding device of FIG. 28. The laser fusing means of the "laser fusing device 401 with a learning device" irradiates the object to be fused with a laser beam for fusing. The learning procedure and the fusing procedure of the fusing conditions are substantially the same as those of the first embodiment. Therefore, by applying the method of the present invention already described in the first to fifth embodiments and attaching the learning device 150, fusing using the learning device of the present invention can be performed as the "laser fusing device 401 with a learning device" and the "ultrasonic fusing device 402 with a learning device", respectively.

[0185] The ultrasonic fusing device 402 with a learning device will be described in detail with reference to FIGS. 32 to 41. FIG. 32 shows an external perspective view of the ultrasonic fusing device 402 with a learning device for fusing one or more components of the present invention. The ultrasonic fusing means 452 shown in FIG. 32 has an ultrasonic cutter 412 attached to the lower end of an ultrasonic vibrator 411 that vibrates ultrasonically up and down, and presses the ultrasonic cutter 412 that is vibrating ultrasonically by an air cylinder 414 attached to a support column 410 against the object to be fused 4W5 placed on a table 413 for fusing.

[0186] As the material to be severed 4W5, as shown in Fig. 33, relatively soft foods such as sponge cake, strawberry cake, mochi, and relatively hard foods such as senbei, sanshoku hishi mochi, rubber sheet, and old tire are used as the materials to be severed. Fig. 33 shows the types of materials to be severed, the necessity of aesthetics after severance, and the magnitude of the pressing force. Some of the materials to be severed require aesthetics after severance, while others do not. Also, some materials can be severed with a small pressing force, while others require a large pressing force.

[0187] Among them in Fig. 33, for foods, it is required that they do not lose their shape even after being severed. On the other hand, for rubber sheets and old tires, etc., it is sufficient if they can be cut, and there are few problems even if the surface state changes or the shape collapses.

[0188] The ultrasonic severing device 402 with a learning device determines the severing conditions according to the severing specifications of the material to be severed, based on the magnitude of the ultrasonic energy and the severing time. Although there is a difference between "severing" and "welding" in the first embodiment, they are common in that they control the magnitude of the ultrasonic energy and the energy supply time. Therefore, the content of the present invention described in the first to fifth embodiments can be used as a severing device with a learning device.

[0189] In the following description, the parts that overlap with the descriptions of the first to fifth embodiments already described will be omitted, and the description will focus on the key points.

[0190] Fig. 34 is a schematic configuration diagram of the ultrasonic severing device 402 with a learning device according to the seventh embodiment of the present invention. In terms of content, it is similar to the schematic configuration diagram of Fig. 2. Instead of the high-frequency welding means 1, an ultrasonic severing means 452 is connected to the learning device 150 by a communication line 170 and is integrally controlled.

[0191] Regarding each means of the learning device 150, although the names are different from those of the means of the learning device 100 described in the first embodiment, the operations are the same or similar. That is, the learning device 150 is provided with a fuse result measuring means 180, such as a thickness measuring means 151, an appearance inspection means 152, and a fuse depth measuring means 153. And there are a fuse result evaluation means 154 for evaluating the value measured by the fuse result measuring means 180 as a reward, a fuse learning storage means 155 for storing the information obtained by evaluating the fuse result, a fuse condition learning means 156 for learning the fuse conditions from the information stored in the fuse learning storage means 155, an I / O means 157 for inputting and outputting the learning plan of the AI technology, a fuse condition reading means 158 for reading out the fuse conditions that satisfy the fuse specifications from the fuse learning storage means 155, and a specification monitoring means 159A and a state monitoring means 159B.

[0192] The specification monitoring means 159A monitors the latest and current fuse specifications by monitoring whether the processing specifications have been updated from the previous fuse operation or changed during the fuse operation, so as to read out the fuse conditions that satisfy the latest and current fuse specifications from the learned model of the fuse learning storage means 155.

[0193] The state monitoring means 159B obtains information on the state of the current that the ultrasonic fusing means 452 has started to flow from the state output means 463 of the ultrasonic fusing means 452 and transmits it to the fuse condition reading means 158. The fuse condition reading means 158 is configured to read out the fuse conditions that satisfy the fuse specifications in the state of the started current from the learned model of the fuse learning storage means 155. In addition, the state monitoring means 159B monitors whether the state of the current during the ultrasonic fusing operation is normal or abnormal.

[0194] Fig. 35 shows the transition of the current value and the value of the fuse depth when ultrasonic energy is applied by the fusing device with a learning device according to the seventh embodiment of the present invention. The X-axis is the time axis of the fusing operation, the left Y-axis is the current value flowing through the ultrasonic driving means, and the right Y-axis is the value of the fuse depth. When the tip of the cutting edge of the ultrasonic cutter is pressed against the object to be fused and the current starts to flow, fusing starts after a relatively short rising time.

[0195] In FIG. 36, the melting and cutting device according to the seventh embodiment of the present invention individually shows the melting and cutting time as several actions (TG) in one state (G), and the reward when melting and cutting at that melting and cutting time, and the melting and cutting depth as the Q value (Q(G, TG)).

[0196] Here, for the "state G", as shown in FIGS. 36(a) to (d), the learning device 150 melts for each melting and cutting time (TG1, TG2, TG3, TG4), and individually measures the values (LG1, LG2, LG3, LG4) of the melting and cutting depth after melting using the melting and cutting depth measuring means 153 of the melting and cutting result measuring means 180. Then, the melting and cutting result evaluation means 154 evaluates the melting and cutting result as a reward and stores it in the melting and cutting learning storage means 155. And the melting and cutting condition learning means 156 learns the melting and cutting conditions from the melting and cutting results evaluated as rewards, so that when the melting and cutting specifications are input, the melting and cutting time can be read out as the melting and cutting conditions that satisfy the melting and cutting specifications.

[0197] For example, if the melting and cutting depth LG1 is input as the melting and cutting specification to the melting and cutting specification input / output means 462 that inputs the melting and cutting specification of the ultrasonic melting and cutting means 452, the learning device 150 calculates the melting and cutting time TG1 as an action by inverse calculation from the melting and cutting depth LG1 and reads it out. If the melting and cutting depth LG2 is input as the melting and cutting specification, the melting and cutting time TG2 as an action is calculated by inverse calculation from the melting and cutting depth LG2 and read out. If the melting and cutting depth LG3 is input as the melting and cutting specification, the melting and cutting time TG3 as an action is calculated by inverse calculation from the melting and cutting depth LG3 and read out. The same applies when the melting and cutting depth LG4 is input as the melting and cutting specification.

[0198] In addition, when values other than the above four melting and cutting depths LG1, LG2, LG3, LG4 are input as the melting and cutting specification, by performing regression analysis, which is often used in AI technology, on the relationship between the melting and cutting time and the melting and cutting depth and formulating it into a mathematical formula, the melting and cutting time corresponding to the input melting and cutting depth is calculated and read out. Also, by increasing the number of data to be learned, the melting and cutting time that satisfies the specification can be read out even for fine melting and cutting specifications. This is the same as in the case of the welding device of the first embodiment.

[0199] FIG. 37 shows, as an image, the procedure for measuring the cutting depth when the cutting times (TG1, TG2, TG3, TG4) are randomly set for four samples with the ultrasonic cutting device 402 with a learning device according to the seventh embodiment of the present invention.

[0200] In FIG. 37, a rubber sheet is used as the material to be cut 4W5, and an ultrasonic cutter 412 with a straight blade at the end face (not shown) is pressed against the upper surface of the rubber sheet, showing the state of cutting with random cutting times. FIG. 37 shows cases from those where the cutting depth L is shallow and cutting is not possible to those where the cutting depth L becomes deeper and those where complete cutting is possible.

[0201] In FIG. 37(a), the cutting times are TG1, TG2, TG3, TG4 from the right; in FIG. 37(b), the cutting times are TG4, TG3, TG2, TG1 from the right; in FIG. 37(c), the cutting times are TG2, TG4, TG1, TG3 from the right; and in FIG. 37(d), the cutting times are TG3, TG1, TG4, TG2 from the right for cutting. The measurement of the cutting depth L uses the value measured by the cutting depth measuring means 153 for the descending amount of the ultrasonic cutter 411. The cutting time that satisfies the cutting specifications is stored in the cutting learning storage means 155 as the cutting conditions that satisfy the cutting specifications.

[0202] Fig. 38 shows a flowchart of the learning procedure according to the seventh embodiment of the present invention. In the flowchart of Fig. 38, the learning procedure of reinforcement learning is used as a learning plan, input through the I / O means 157, stored in the memory means 155 for fusion cutting learning, and then learning is started (step ST51). Based on the fusion cutting specifications, a target current value is set so as to satisfy the fusion cutting specifications. Note that since a learning device that has not learned at all is ignorant, it is advisable to include, as an initial value, a target current value that is expected to satisfy the fusion cutting specifications in the welding specifications and input it (step ST52). A current is passed through the ultrasonic driving means (step ST53). Here, it is asked whether to check the state during fusion cutting (step ST54). This is because when the ultrasonic fusion cutting means 452 has only a single energy supply state, since there is only one energy state, there is no need to check the state during fusion cutting. In that case, the next step ST55 is skipped (NO in step ST54). When the ultrasonic fusion cutting means 452 is a general-purpose processing device and can select one state from a plurality of energy supply states, step ST55 is performed. In step ST55, the rise time until the reference current value is reached is measured and stored in the memory means 155 for fusion cutting learning. Then, a current is passed for each fusion cutting time to perform fusion cutting (step ST56). The fusion cutting depth L as a reward for the action is measured, correspondence data with the fusion cutting time is created, and stored in the memory means 155 for fusion cutting learning (step ST57). The fusion cutting condition learning means 156 learns the fusion cutting conditions from the information stored in the memory means 155 for fusion cutting learning, and stores the learning result as a learned model in the memory means 155 for fusion cutting learning in a form that can be read out as the fusion cutting conditions (step ST58).

[0203] Then, it is asked whether data corresponding to a predetermined number of fusing times has been obtained for one state (i.e., one current curve) (step ST59). If the predetermined number of data has not been obtained, the process returns to step ST53 (NO in step ST59). If the predetermined number of data has been obtained (YES in step ST59), it is asked whether to update the target current value and learn about other states (step ST60). When updating the target current value, the process returns to step ST52 (YES in step ST60). When not learning about other states (NO in step ST60), it is asked whether learning has been performed as planned (step ST61). If learning has not been performed as per the learning plan, the process returns to step ST52 (NO in step ST61). If learning as per the plan has been completed (YES in step ST61), the process proceeds to step ST62 to end the learning. Note that steps ST58 and ST59 may be swapped so that after obtaining the predetermined number of data, learning is performed and the learning result is stored in the fusing learning storage means 155.

[0204] Note that when performing update learning, starting from step ST84, the process enters step ST51 and performs the procedures below step ST51. By performing the procedures in the flowchart of FIG. 38, for the state where the ultrasonic cutter 412 is vibrating, data on the fusing depth L for each fusing time, which is each action, is obtained, the data is stored as a reward, the stored data is learned, the learning result is made into a form that can be read out as fusing conditions, and the result is stored in the fusing learning storage means 155.

[0205] Thus, if the fusing depth L is input according to the fusing specification, the fusing time as the fusing condition that satisfies the fusing specification can be read out from the fusing learning storage means 155.

[0206] When performing the fusing operation, the fusing time as the fusing condition that satisfies the fusing specification is read out from the learned model of the learning device, stored in the main storage means of the ultrasonic fusing means 452, and fusing is performed.

[0207] Fig. 39 shows the content of the learning results stored in the storage means according to the seventh embodiment of the present invention. In Fig. 39, the welding time and the welding depth L are stored as a pair. Also, trapezoidal, round, and square frames are added to each pair for easy understanding.

[0208] Fig. 39(a) stores the measured data in the order in which they were measured. For example, in Data 1, from the right, it shows that the pairs of the welding depth LG11 and the Q value (G, TG11), the welding depth LG21 and the Q value (G, TG21), the welding depth LG31 and the Q value (G, TG31), and the welding depth LG41 and the Q value (G, TG41) are stored. For Data 2 to Data 4, each pair is arranged randomly.

[0209] In Fig. 39(b), the data, which is the stored content of the welding learning storage means 155 by the welding condition learning means 156, is arranged in a form sorted by the welding depth L. For example, in Data 1, compared with Fig. 39(a), the order is reversed, and from the left, the pairs of the welding depth LG11 and the Q value (G, TG11), the welding depth LG21 and the Q value (G, TG21), the welding depth LG31 and the Q value (G, TG31), and the welding depth LG41 and the Q value (G, TG41) are arranged. For Data 2 to Data 4, each pair is arranged in the same order as Data 1.

[0210] And in Fig. 39(c), the welding condition learning means 156 calculates the average value of the welding depth L for each welding time for the data arranged in Fig. 39(b), and creates a table from which the welding time can be read by inverse calculation from the welding depth L. And this is used as the learned model. Since Data 1 to Data 4 are arranged, when the welding depth L is input as the welding specification, the welding time as the welding condition satisfying the welding specification can be read from Fig. 39(c).

[0211] In the fusing operation, the fusing operation is performed according to the fusing operation procedure shown in the flowchart of FIG. 40. In FIG. 40, first, the fusing specifications are input to start the fusing operation (step ST70). The object to be fused is set (step ST71). It is asked whether to check the state (step ST72). As also described in the flowchart of FIG. 38, when the ultrasonic fusing means 452 has only a single energy supply state, since there is only one energy state, it is not necessary to check the state during fusing. At that time, steps ST73 and ST74 are skipped (NO in step ST72). When the ultrasonic fusing means 452 can select one state from a plurality of energy supply states, steps ST73 and ST74 are performed. In step ST73, the rise time is measured. Then, the state of the energy that the ultrasonic fusing means 452 has started to supply is checked (step ST74). It is asked whether there are fusing conditions that satisfy the latest and current fusing specifications in the confirmed state (step ST75). If there are fusing conditions, they are read from the fusing learning storage means 155 (step ST76) and stored in the main storage means 461. Fusing is performed by the ultrasonic fusing means 452 (step ST77). The state monitoring means 159B monitors whether the state during fusing is abnormal (step ST78). When the state is normal (YES in step ST78), it is confirmed that the fusing time for ending the fusing operation has been reached (step ST79), and the fusing operation is ended (step ST81). When the state is abnormal in step ST78, an abnormality is presented and fusing is stopped (step ST80). Then, the fusing operation is ended (step ST81).

[0212] Also, when no fusing conditions that satisfy the fusing specifications are found in the fusing learning storage means 155 in step ST75 (NO in step ST75), "Update learning is required" is presented (step ST82). Then, it is asked whether to perform update learning (step ST83). When performing update learning, starting from step ST84, it jumps to the flowchart of FIG. 38, and update learning is performed according to the procedure of step ST50 and below in FIG. 38.

[0213] The content of the welding learning memory means 155 when reading the welding conditions in step ST76 is shown in FIG. 41. In the case of FIG. 41(a), "LG2" is input as the welding depth L of the welding specification, and the welding operation is started (step ST70). When measuring the rise time (step ST73), since the rise time is "TG", it is confirmed that the state is "state G" (step ST74). Since the welding depth L that satisfies the welding specification in "state G" is "LG2" in the table, the welding time "TG2" paired with the welding depth "LG2" is read as the action for obtaining the welding depth "FD3" of the welding specification, that is, the welding time as the welding condition is "TG2".

[0214] Similarly, in the case of FIG. 41(b), "LH3" is input as the welding depth L of the welding specification, and the welding operation is started (step ST70). When measuring the rise time (step ST73), since the rise time is "TH", it is confirmed that the state is "state H" (step ST74). Since the welding depth L that satisfies the welding specification in "state H" is "LH3" in the table, the welding time "TH3" paired with the welding depth "LH3" is read as the action for obtaining the welding depth "FD3" of the welding specification, that is, the welding time as the welding condition is "TH3". Regarding the update learning, since it is the same as the content described in the first embodiment, the description is omitted.

[0215] As described above, in the present invention, processing conditions such as joining conditions or welding conditions are learned by learning means using AI technology, and welding conditions that realize processing specifications such as welding specifications or welding specifications that satisfy the processing specifications are extracted from the learning results, and the workpieces are welded or processed such as welded under the extracted processing conditions such as welding conditions and welding conditions.

[0216] By using the learning device of the processing method of the present invention and the processing device using the same, in the present invention, it is possible to learn processing conditions such as joining conditions or welding conditions when joining two or more parts or processing one or more parts such as welding, and to join two or more parts or process one or more parts such as welding using the learning method and learning device.

[0217] In addition, even when the processing specifications are updated from the immediately preceding processing operation or changed during processing, the present invention can monitor the latest and current processing specifications by the specification monitoring means, read out the processing conditions such as joining conditions or cutting conditions that satisfy the latest and current processing specifications from the learning storage means, and perform processing operations such as joining operations and cutting operations. Therefore, for example, when the hardness of the material to be cut changes midway, such as in an old tire containing steel wires, or when a hard fruit is contained in a soft sponge cake such as a strawberry cake, a cutting operation with complex cutting specifications can also be handled by switching the learned model after learning.

[0218] And when the materials, shapes, and dimensions of the parts to be joined are substantially the same and there are variations within the tolerance, the present invention can stably join two or more parts or cut one or more parts by using a method and device for learning joining conditions and cutting conditions.

[0219] Furthermore, the present invention can provide a method and device for determining whether joining conditions or cutting conditions that satisfy the processing specifications are found in the learning storage means, and when the joining conditions or cutting conditions that satisfy the processing specifications are not found, presenting that update learning is necessary.

[0220] And when it is determined that joining conditions or cutting conditions that satisfy the processing specifications are not found in the learning storage means, the present invention can perform update learning on the joining conditions or cutting conditions that satisfy the processing specifications, and join two or more parts or cut one or more parts with the updated joining conditions or cutting conditions.

[0221] In addition, when there are multiple energy level application states that a single joining device or a single cutting device applies to the workpiece, the present invention can confirm the state of the energy that has actually started to be supplied, read out the joining conditions or cutting conditions that are actions satisfying the processing specifications in the confirmed state from the learning device, and perform joining operations and cutting operations.

[0222] Furthermore, the present invention can monitor the state of the processing apparatus since the start of processing, and when the state of the processing apparatus changes and becomes abnormal, it can present this fact and stop the processing of the processing apparatus.

[0223] Also, in the present invention, by checking the state of the actually supplied energy, reading out the processing conditions, which are actions that satisfy the processing specifications in the confirmed state, from the learning device and performing processing, and having the learning device monitor whether the state of the processing apparatus since the start of processing is normal or abnormal, is useful for a processing method with a learning device, a processing apparatus using this, and a processing system using this.

[0224] In the description of the present invention, for the sake of understanding the invention, as an AI technology, a simple example of reinforcement learning was shown. Since AI technology will continue to evolve in the future, in the learning device 100 of the present invention, using the I / O means 107, any one of complex reinforcement learning, Q-learning, statistical learning (neural network), deep learning (deep learning), and deep reinforcement learning combining deep learning and reinforcement learning can be used as a learning plan (learning algorithm, learning software), it is possible to provide a more advanced learning device for processing conditions, a processing apparatus using this, and a processing system using this.

Industrial Applicability

[0225] The present invention can be widely applied to high-frequency welding apparatuses such as vinyl chloride sheets, and of course, it can also be widely applied to learning devices for joining methods of two or more other parts, joining apparatuses using this, and joining systems using this, learning devices for melting methods of one or more parts, melting apparatuses using this, and melting systems using this.

Explanation of Signs

[0226] 1 High-frequency welding means 2 Upper mold 3 Lower mold 4 Welded object 4a, 4b Parts 5 Air cylinder 6 High-frequency current supply means 10 Main control means 11 Main memory means 12 Welding specification input / output means 13 Status output means 100 Learning device 101 Thickness measurement means 102 Appearance inspection means 103 Weld strength measurement means 104 Welding result evaluation means 105 Learning memory means 106 Welding condition learning means 107 I / O means 108 Welding condition reading means 109A Specification monitoring means 109B Status monitoring means 120 Communication line 130 Welding result measurement means

Claims

1. A learning device for learning processing conditions, comprising: at least: (1) "processing result measuring means" for measuring a processing result; (2) "processing result evaluating means" for evaluating the processing result as a reward; (3) "learning memory means" for storing the processing result and the reward obtained by evaluating the processing result; (4) "processing condition learning means" for organizing the learning result evaluated based on the processing result so as to be able to read out the processing conditions; (5) "processing specification monitoring means" for monitoring the processing specification; (6) "processing condition reading means" for reading out the processing conditions from the learning memory means; and configured such that: previously, the processing result is measured by the processing result measuring means of (1), the processing result is evaluated as a reward by the processing result evaluating means of (2), the processing result and the reward are stored in the learning memory means of (3), and the learning result evaluated based on the processing result by the processing condition learning means of (4) is organized so as to be able to read out the processing conditions; when a processing specification is input to a processing means connected to the learning device, the processing specification is monitored by the processing specification monitoring means of (5), and the processing condition reading means of (6) reads out from the learning memory means the processing conditions that satisfy the latest and current processing specification, and outputs the read processing conditions to the processing means; the processing condition learning means of (4) is configured to read out the processing conditions from the output of a learned model that has been learned to output, based on the information stored in the learning memory means of (3), the processing conditions that satisfy at least a desired processing specification when the desired processing specification is input; the processing conditions of (4) are processing conditions in a high-frequency welding device that applies a high-frequency current to two or more parts for welding by dielectric heating, and include the supply time of the high-frequency current supplied to the workpiece to be welded; the processing specification of (5) includes the welding strength; A learning device for processing conditions.

2. The learning device according to Claim 1, wherein the processing conditions of (4) further include the "state of the anode current" indicating the transition of the anode current flowing through the high-frequency welding device.

3. The learning device according to Claim 1, configured to learn processing conditions according to variations in the material, shape, and dimensions of the parts to be processed, and to read out the processing conditions that satisfy the processing specification when there are variations.

4. The learning device according to claim 1, configured to determine whether processing conditions that satisfy the processing specifications are found in the learning memory means, and to present that update learning is necessary when processing conditions that satisfy the processing specifications are not found.

5. The learning device according to claim 1, configured such that when it is determined that processing conditions that satisfy the processing specifications are not found in the learning memory means, the processing conditions that satisfy the processing specifications are updated and learned, and the processing conditions are read from the learning memory means after the update learning.

6. The learning device according to claim 1 is further provided with "state monitoring means" for monitoring the state of the energy applied to the workpiece of the processing device connected to the learning device. The learning device according to claim 1, configured to learn the actions and rewards in the energy state when the processing device starts processing, confirm the state when the processing device starts processing, and read the processing conditions that satisfy the processing specifications in that state.

7. The learning device according to claim 6, configured such that the state monitoring means monitors the state of the energy applied to the workpiece of the processing device after processing starts, and when the state of the processing device becomes abnormal compared to the state assumed by the learning device, this is presented and the processing of the processing device is stopped.

8. A processing device with a learning device that connects and integrally controls any one of the learning devices described in claims 1 to 7 via a communication line, having at least (a) "processing specification input / output means" for inputting processing specifications, (b) "main memory means", (c) "main control means", storing the processing conditions read from the learning memory means of the connected learning device in the main memory means, configuring the main control means to perform a processing operation using the stored processing conditions, and the processing device being a "high-frequency welding device".

9. A processing system with a learning device to which any one of the learning devices described in claims 1 to 7 is connected, wherein the processing means constituting the processing system has at least (a) "processing specification input / output means" for inputting processing specifications, (b) "main memory means", (c) "main control means", stores the processing conditions read from the learning memory means of the connected learning device in the main memory means, and configures the main control means to perform a processing operation using the stored processing conditions. A processing system with a learning device that uses a processing apparatus for the above processing system as a "high-frequency welding apparatus".

Images