Estimation method and estimation system

Abstract

To appropriately estimate information indicating a result of machining.SOLUTION: A method includes the steps of: performing an experiment of machining a device to acquire first-type information and second-type information each indicating conditions of the experiment of machining, and third-type information and fourth-type information each indicating a result of the experiment of machining (S401); deriving a first expression that receives the first-type information and the second-type information as inputs and outputs the third-type information, the first expression outputting the third information as a plurality of solutions, and a second expression that receives the first-type information and the second-type information as inputs and outputs the fourth-type information, and a plurality of third expressions that, using the first expression, receive the second-type information and the third-type information as inputs and outputs the first-type information (S402); and receiving the second-type information and the third-type information each measured in machining the device as inputs and outputting the fourth-type information indicating a result of machining the device using the second expression and the plurality of third expressions (S403).SELECTED DRAWING: Figure 18

Description

[Technical field]

[0001] The present invention relates to an estimation method and an estimation system. [Background technology]

[0002] Conventionally, models related to device processing have been used. Many cases have been reported in which the parameters of the objective variables (output variables) of such models are estimated from the parameters of the explanatory variables (input variables). When a physical model based on actual physical phenomena can be constructed, highly accurate estimation is possible by estimating the parameters of the objective variables using this physical model, and the measurement man-hours required for modeling can also be reduced.

[0003] On the other hand, when it is difficult to construct a physical model, a method is known in which the input-output relationship is assumed as a polynomial model using a large amount of accumulated measurement data, and estimation is performed by fitting. An estimation method that combines these two methods has also been proposed (see Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 4539619 Summary of the Invention [Problem to be solved by the invention]

[0005] However, all of the above-mentioned methods are premised on the assumption that measurement data of the parameters of the explanatory variables is collected in-line during device processing.

[0006] On the other hand, in order to obtain a model that can estimate the objective variable with high accuracy, it is assumed that the explanatory variables may include parameters for which measurement data is not collected inline. In this case, in order to make the model usable inline, it is necessary to devise a method such as combining another experimental design in which the parameter for which measurement data is not collected inline is used as the output variable.

[0007] However, even if such measures are taken, it is possible that the input variables of the other experimental plan may not be able to generate experimental points as planned due to the nature of the variables. Even if the experimental points can be generated as planned, it is necessary to carry out two experimental plans, which doubles the number of experiments required to generate the model.

[0008] The present invention has been made in consideration of the problems with the conventional technology, and has an object to provide an estimation method for appropriately estimating information indicating the results of processing. [Means for solving the problem]

[0009] An estimation method according to one embodiment of the present invention is an estimation method executed by a processor using a memory, which performs an experiment on processing a device, obtains first and second type information indicating conditions of the processing experiment, and third and fourth type information indicating results of the processing experiment, derives a first equation that takes the first type information and the second type information as input and outputs the third type information, the first equation outputting the third type information as a plurality of solutions, and a second equation that takes the first type information and the second type information as input and outputs the fourth type information, derives a plurality of third equations that take the second type information and the third type information as input and output the first type information using the first equation, and takes the second type information and the third type information measured during processing of the device as input, and outputs the fourth type information indicating results of processing of the device using the second equation and the plurality of third equations.

[0010] These comprehensive or specific aspects may be realized as a system, a method, an integrated circuit, a computer program, or a recording medium such as a computer-readable CD-ROM, or may be realized as any combination of a system, a method, an integrated circuit, a computer program, and a recording medium. Effect of the Invention

[0011] The generation method of the present invention can appropriately estimate information indicating the results of processing. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is an explanatory diagram showing a configuration of a system according to an embodiment. [Diagram 2] FIG. 2 is an explanatory diagram showing the processing of the system according to the embodiment. [Diagram 3] FIG. 3 is a block diagram illustrating a functional configuration of a generating device according to an embodiment. [Figure 4] FIG. 4 is an explanatory diagram showing parameters related to the laser welding process. [Diagram 5] FIG. 5 is an explanatory diagram showing the relationships between the first to fourth parameters in the experiment according to the embodiment. [Figure 6] FIG. 6 is an explanatory diagram showing the relationship between the first to fourth parameters and the first and second statistical model equations derived by the generating device according to the embodiment. [Figure 7] FIG. 7 is an explanatory diagram showing the relationship between the first to third parameters and the third statistical model equation derived by the generating device according to the embodiment. [Figure 8] FIG. 8 is an explanatory diagram illustrating an estimation model generated by a generating device according to an embodiment. [Figure 9] FIG. 9 is a flow diagram illustrating a process executed by a generating device according to an embodiment. [Figure 10] FIG. 10 is a flow diagram showing detailed processing executed by the generating device according to the embodiment. [Figure 11]FIG. 11 is a block diagram illustrating a hardware configuration of an estimation device according to an embodiment. [Figure 12] FIG. 12 is a block diagram illustrating a functional configuration of an estimation device according to an embodiment. [Figure 13] FIG. 13 is a flowchart showing the process executed by the estimation device according to the embodiment. [Figure 14] FIG. 14 is a flowchart showing detailed processing executed by the estimation device according to the embodiment. [Figure 15] FIG. 15 is an explanatory diagram illustrating the estimation accuracy of the estimation model according to the embodiment in comparison with the related art. [Figure 16] FIG. 16 is a first explanatory diagram illustrating the validity of a single selected first parameter according to an embodiment. [Figure 17] FIG. 17 is a second explanatory diagram illustrating the validity of a selected single first parameter according to an embodiment. [Figure 18] FIG. 18 is a flowchart showing a process executed by a system according to a modification of the embodiment. [Figure 19] FIG. 19 is a schematic diagram showing a configuration of a system according to a modified example of the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] An estimation method according to one embodiment of the present invention is an estimation method executed by a processor using a memory, which performs an experiment on processing a device, obtains first and second type information indicating conditions of the processing experiment, and third and fourth type information indicating results of the processing experiment, derives a first equation that takes the first type information and the second type information as input and outputs the third type information, the first equation outputting the third type information as a plurality of solutions, and a second equation that takes the first type information and the second type information as input and outputs the fourth type information, derives a plurality of third equations that take the second type information and the third type information as input and output the first type information using the first equation, and takes the second type information and the third type information measured during processing of the device as input, and outputs the fourth type information indicating results of processing of the device using the second equation and the plurality of third equations.

[0014] According to the above aspect, the fourth type information during processing can be estimated from the second type information and the third type information obtained during processing using the relational equation of the first type information, the second type information, the third type information, and the fourth type information obtained from the experiment. In the process, when the first equation outputs the third type information as a plurality of solutions, the fourth type information indicating the result of processing can be appropriately estimated using a plurality of third equations. In this way, the above estimation method can appropriately estimate information indicating the result of processing.

[0015] For example, in outputting the fourth type information, a single piece of first type information from among the multiple pieces of first type information indicating the conditions of processing of the device output using each of the multiple third equations may be selected, and the fourth type information indicating the results of processing of the device may be output using the selected single piece of first type information.

[0016] According to the above aspect, the single fourth information indicating the result of processing can be appropriately estimated by using a more appropriate single first information among one or more first information obtained by using a plurality of third formulas. Therefore, the estimation method can appropriately estimate the information indicating the result of processing.

[0017] For example, further, a fourth equation that takes the first type information and the second type information as input and outputs the third type information may be derived, the fourth equation being a linear equation for the third type information, and a fifth equation that takes the second type information and the third type information as input and outputs new first type information using the fourth equation. In outputting the fourth type information, the first type information that has the smallest difference from the new first type information output using the fifth equation using the second type information and the third type information measured during processing of the device as input may be selected as the single first type information.

[0018] According to the above aspect, the fourth type information indicating the result of processing can be appropriately estimated by using a single piece of first type information that is close to the new first type information outputted using the fifth formula among one or more pieces of first type information obtained using a plurality of third formulas. The new first type information outputted using the fifth formula generally has a relatively small difference from the true value. Therefore, when one or more pieces of first type information are obtained by a plurality of third formulas, the first type information that is the smallest difference from the new first type information can be selected to obtain first type information that is relatively close to the true value, and the fourth type information can be appropriately estimated using this. In this way, the above estimation method can appropriately estimate information indicating the result of processing.

[0019] For example, in outputting the fourth type of information, the first type of information that belongs to a normal range may be selected from the plurality of first type of information indicating the processing conditions of the device output using each of the plurality of third formulas, and the fourth type of information may be output using the selected first type of information.

[0020] According to the above aspect, the fourth type information indicating the result of processing can be appropriately estimated by using a single first type information that is within the normal range and is more appropriate among one or more first type information obtained by using a plurality of third formulas. Therefore, the estimation method can appropriately estimate the information indicating the result of processing.

[0021] For example, in outputting the fourth type information, if the multiple pieces of first type information indicating the processing conditions of the device output using the multiple third equations, respectively, are imaginary numbers, the imaginary parts of the imaginary numbers may be deleted, and the fourth type information may be output using the first type information with the imaginary parts deleted.

[0022] According to the above aspect, among one or more pieces of first type information obtained using a plurality of third formulas, imaginary numbers are converted to real numbers by removing imaginary parts, and the fourth type information indicating the result of processing can be appropriately estimated using a more appropriate single piece of first type information. Therefore, the estimation method can appropriately estimate the information indicating the result of processing.

[0023] For example, when deriving the multiple third equations, it may be determined whether the first equation is a polynomial of degree 2 or higher for the third type of information and is a polynomial that cannot be expressed in the form of an n-th power of (a×x+b) where x is the third type of information, and if it is determined that the first equation is a polynomial, the multiple third equations may be derived.

[0024] According to the above aspect, the fourth type information can be appropriately estimated using a plurality of third formulas by performing a determination based on a specific form of the first formula. Therefore, the estimation method can more easily appropriately estimate information indicating the result of processing.

[0025] For example, the first type of information and the fourth type of information may be information that is predetermined as information that is not measured during the processing, and the second type of information and the third type of information may be information that is predetermined as information that is measured during the processing.

[0026] According to the above aspect, when the information indicating the processing conditions includes information that is not measured and the information indicating the processing result includes information that is not measured, the information indicating the processing result can be appropriately estimated. Therefore, according to the above generation method, even when there is information that is not measured during processing, it is possible to generate a model that appropriately estimates the information indicating the processing result.

[0027] For example, the processing may be laser welding, the first type information may include a gap width between plate materials to be welded in the laser welding, the second type information may include a laser scanning speed in the laser welding, the third type information may include a surface weld width of a laser welded portion in the laser welding, and the fourth type information may include an interface weld width of a laser welded portion in the laser welding.

[0028] According to the above aspect, it is possible to more easily generate a model that appropriately estimates information indicating the results of processing in laser welding.

[0029] An estimation system according to one embodiment of the present invention includes an acquisition unit that conducts a processing experiment on a device and acquires first and second type information indicating conditions of the processing experiment and third and fourth type information indicating results of the processing experiment; a derivation unit that derives a first equation that takes the first type information and the second type information as input and outputs the third type information, the first equation outputting the third type information as a plurality of solutions and a second equation that takes the first type information and the second type information as input and outputs the fourth type information, and uses the first equation to derive a plurality of third equations that take the second type information and the third type information as input and output the first type information; and an estimation unit that takes the second type information and the third type information measured during processing of the device as input, and uses the second equation and the plurality of the third equations to output the fourth type information indicating results of the processing of the device.

[0030] This provides the same effects as the above estimation method.

[0031] These comprehensive or specific aspects may be realized as a system, an apparatus, an integrated circuit, a computer program, or a recording medium such as a computer-readable CD-ROM, or may be realized as any combination of a system, an apparatus, an integrated circuit, a computer program, or a recording medium.

[0032] Hereinafter, the embodiment will be specifically described with reference to the drawings.

[0033] The embodiments described below are all comprehensive or specific examples. The numerical values, shapes, materials, components, component arrangement and connection forms, steps, and order of steps shown in the following embodiments are merely examples and are not intended to limit the present invention. Furthermore, among the components in the following embodiments, components that are not described in an independent claim showing a top concept are described as optional components.

[0034] (Embodiment) In this embodiment, an estimation method for appropriately estimating information indicating the result of processing will be described.

[0035] First, the process of processing the device will be described. Here, a laser welding process in a manufacturing line will be described as an example of device processing, but application of this embodiment is not limited to this.

[0036] In general, in a process of processing a device, the quality of the process is evaluated. The quality is evaluated by evaluating information indicating the quality of the processing of the device, more specifically, a physical quantity related to the quality of the processing of the device. However, the physical quantity is not always measurable, and may not be measurable.

[0037] For example, in the laser welding process on a manufacturing line, the joint strength is one of the indicators for evaluating the process quality. If the joint strength could be measured in-line, it would be advantageous in that it could be linked to control to prevent defects in products produced through that process.

[0038] However, since it is practically difficult or impossible to measure the bonding strength in-line, the connection strength must be evaluated by a bonding strength evaluation test that is performed offline.

[0039] In addition, the joint strength is correlated with the interfacial fusion area between the plate materials to be welded, and the interfacial fusion area can be calculated from the interfacial weld width and the weld distance. Therefore, if the interfacial weld width between the plate materials can be estimated, it can be used to evaluate the joint strength. However, the interfacial weld width is not currently measured in-line.

[0040] The system of this embodiment (also referred to as an estimation system) enables evaluation of the quality of device processing by estimating information indicating the quality of device processing from measurable information among information indicating processing conditions and measurable information among information indicating processing results. According to this method, when information indicating the quality of device processing cannot be measured directly, the information can be obtained by estimation.

[0041] Hereinafter, we will explain a model generation method for generating a model that estimates information indicating the quality of device processing from information indicating the processing conditions and information indicating the results of the processing, and a method for estimating the information using the model.

[0042] FIG. 1 is an explanatory diagram showing the configuration of a system 1 according to the present embodiment.

[0043] 1, the system 1 is an estimation system including a generating device 10 and an estimating device 20. The estimating device 20 is connected to a processing device 29.

[0044] The generation device 10 is a device that generates a model that estimates information indicating a result of device processing. The generation device 10 generates a model (also called an estimation model) that estimates information indicating a result of device processing, based on information obtained by performing an experiment on device processing by a processing device 29. The generation device 10 provides the generated estimation model to the estimation device 20. The generation device 10 executes the above processing offline.

[0045] The estimation device 20 is a device that estimates information indicating the result of processing of a device. The estimation device 20 acquires information indicating the conditions of processing of the device and information indicating the result of processing of the device from the processing device 29, and inputs the acquired information into an estimation model, thereby estimating the information indicating the result of processing of the device. The estimation device 20 executes the above processing inline.

[0046] The processing device 29 is a device that processes devices. Specifically, the processing of devices includes laser welding, sputtering, and the like.

[0047] FIG. 2 is an explanatory diagram showing the processing of the system 1 according to the present embodiment.

[0048] 2, in step S1, the generation device 10 generates an estimation model offline that estimates information indicating a result of processing a device. At this time, the generation device 10 generates the estimation model using information obtained by performing a processing experiment.

[0049] In step S2, the generation device 10 stores the estimation model generated in step S1 in the estimation device 20.

[0050] In step S3, the estimation device 20 uses the estimation model stored in step S2 to in-line estimate and output information (parameters) indicating the results of processing. At this time, the estimation device 20 estimates the above information using information obtained as a result of actually processing the device.

[0051] The configuration and processing of the generating device 10 and the estimating device 20 will be described below.

[0052] (Generation device 10) FIG. 3 is a block diagram showing a functional configuration of generating device 10 according to the present embodiment.

[0053] 3, the generating device 10 includes, as functional units, an acquiring unit 11, a derivation unit 12, and a generating unit 13. The generating device 10 may be realized by a computer. The functional units included in the generating device 10 may be realized by a processor (e.g., a CPU (Central Processing Unit)) (not shown) included in the generating device 10 executing a program using a memory (not shown).

[0054] The acquisition unit 11 is a functional unit that performs an experiment on the processing of a device and acquires a first parameter and a second parameter indicating the processing conditions and a third parameter and a fourth parameter indicating the result of the processing. The first parameter, the second parameter, the third parameter, and the fourth parameter are also referred to as the first type information, the second type information, the third type information, and the fourth type information, respectively. The device processing experiment is an experiment performed before the processing of the device, assuming the processing of the device, and is performed offline. The device processing experiment includes, for example, an actual machine experiment in which a process similar to the processing process in the manufacturing line is actually performed in a different environment, or a simulation experiment in which a process simulating the processing process in the manufacturing line is performed by computer simulation.

[0055] In addition, when acquiring the above parameters by an actual machine experiment, the acquiring unit 11 may acquire the result of the actual machine experiment performed in an experimental device different from the generating device 10 from that device. In this case, the acquiring unit 11 may control the experimental device.

[0056] Furthermore, when the acquiring unit 11 acquires the above parameters through a simulation experiment, the acquiring unit 11 may execute the simulation experiment using computer resources (processor, memory, etc.) included in the generating device 10.

[0057] The acquiring unit 11 also acquires an experimental design model 105 including a set of parameters to be used in the experiment. The experimental design model 105 is used in the experiment to acquire the third parameter and the fourth parameter.

[0058] The derivation unit 12 is a functional unit that derives the relationship among the first parameter, the second parameter, the third parameter, and the fourth parameter. Specifically, the derivation unit 12 derives the relationship among the first parameter, the second parameter, and the third parameter (also referred to as the first relationship). In addition, the derivation unit 12 derives the relationship among the first parameter, the second parameter, and the fourth parameter (also referred to as the second relationship).

[0059] The first relationship is expressed by, for example, a first statistical model formula (also called a first formula) that receives the first and second parameters as inputs and outputs a third parameter, and the second relationship is expressed by a second statistical model formula (also called a second formula) that receives the first and second parameters as inputs and outputs a fourth parameter.

[0060] The generation unit 13 is a functional unit that generates and outputs an estimation model that is a model for estimating a fourth parameter indicating a processing result, using as input the second parameter and the third parameter measured in-line when the processing apparatus 29 actually processes the device. The generation unit 13 estimates the fourth information using the first relationship and the second relationship based on the estimation model.

[0061] When the first relationship is expressed by the first statistical model formula and the second relationship is expressed by the second statistical model formula, the estimation model includes a third statistical model formula (also called a third formula). The third statistical model formula is an formula derived from the first statistical model formula, which takes the second parameter and the third parameter as input and outputs the first parameter.

[0062] The estimation model includes a second statistical model formula together with a third statistical model formula, and the estimation model includes a model that acquires a first parameter output by the third statistical model formula using the second parameter and the third parameter measured during processing as input, and a fourth parameter output by the second statistical model formula using the second parameter measured during processing as input.

[0063] The first parameter and the second parameter may be information that is predetermined as information that is not measured during processing. The second parameter and the third parameter may be information that is predetermined as information that is measured during processing. Information that is not measured during processing may include, for example, information that is technically possible to measure during processing but is not actually measured due to constraints such as the cost or time required for measurement. Information that is not measured during processing may also include information that is technically difficult or impossible to measure during processing.

[0064] A method for generating an estimation model by the derivation unit 12 will be described below.

[0065] Fig. 4 is an explanatory diagram showing parameters related to a laser welding process. Fig. 5 is an explanatory diagram showing the relationship between the first parameter to the fourth parameter in an experiment according to the present embodiment. The first parameter to the fourth parameter will be described with reference to Figs. 4 and 5.

[0066] Fig. 4(a) shows a schematic diagram of a laser welding process in which the processing device 29 welds the plate material 9A and the plate material 9B by laser welding. As shown in Fig. 4(a), the plate material 9A and the plate material 9B are arranged so as to overlap each other. The processing device 29 irradiates the overlapping area of ​​the plate material 9A and the plate material 9B with a laser beam 91 while scanning the overlapping area.

[0067] FIG. 4(b) shows a schematic cross-sectional state of plate material 9A and plate material 9B welded by laser welding using processing device 29. As shown in FIG. 4(b), the portions of plate material 9A and plate material 9B irradiated with laser beam 91 are welded. In the welded portion between plate material 9A and plate material 9B, the width of the upper surface of plate material 9A (i.e., the surface viewed from the z-axis positive direction) is referred to as surface weld width 93, and the width of the interface between plate material 9A and plate material 9B is also referred to as interface weld width 95. In addition, there is a minute gap having gap width 94 between plate material 9A and plate material 9B.

[0068] Next, the first parameter 101 to the fourth parameter 104 and the experimental design model 105 used for generating the estimation model will be described with reference to FIG.

[0069] The first parameters 101 are parameters indicating processing conditions, and are parameters for which in-line measurement is not performed due to cost or time constraints, etc. The first parameters 101 are parameters necessary for estimating information indicating processing results with high accuracy.

[0070] The first parameter 101 includes, for example, a gap width 94 between plate materials 9A and 9B to be welded. The gap width 94 can be controlled by using a jig in an offline experiment, and can be controlled by setting simulation conditions in a simulation experiment.

[0071] The second parameters 102 are parameters indicating processing conditions and are parameters for which in-line measurement is performed. The second parameters 102 include, for example, the laser scan speed 92.

[0072] The experimental design model 105 is information including parameters (first parameter 101 and second parameter 102) used in an experiment. The experimental design model 105 is generated based on upper and lower limit values ​​that the first parameter 101 and the second parameter 102 can take in an experiment, which are set in advance. The experimental design model 105 includes setting information of values ​​(also called experimental point conditions) that the first parameter 101 and the second parameter 102 take in an experiment. As a result of conducting an experiment under the first parameter 101 and the second parameter 102 set according to the experimental point conditions shown in the experimental design model 105, a third parameter 103 and a fourth parameter 104 are output.

[0073] The third parameters 103 are information indicative of the results of the processing and are parameters for which in-line measurements are performed. The third parameters 103 include, for example, the surface weld width 93 of the laser weld.

[0074] The fourth parameter 104 is information indicating the result of processing, and is a parameter that is not measured in-line due to cost or time constraints, etc. The fourth parameter 104 is a characteristic parameter related to the quality of processing. The fourth parameter 104 includes, for example, an interface weld width 95 at the interface between the plate materials 9A and 9B of the laser weld. In order to directly measure the interface weld width 95, for example, there is a method of cutting the processed product offline and measuring the cut surface, but such a measurement is difficult or impossible in-line.

[0075] Next, the first to third statistical model formulas and the estimation model will be described.

[0076] Fig. 6 is an explanatory diagram showing the relationship between the first parameter to the fourth parameter and the first and second statistical model formulas derived by the generating device 10 according to the present embodiment. Fig. 7 is an explanatory diagram showing the relationship between the first parameter to the third parameter and the third statistical model formula derived by the generating device 10 according to the present embodiment. Fig. 8 is an explanatory diagram showing an estimation model generated by the generating device 10 according to the present embodiment.

[0077] The derivation unit 12 derives a first statistical model formula 111 and a second statistical model formula 112 by statistical modeling based on an experimental design model 105 using a set of a first parameter 101 to a fourth parameter 104 obtained by an experiment. Here, the first statistical model formula 111 is a model formula in which the first parameter 101 and the second parameter 102 are input variables (explanatory variables) and the third parameter 103 is an output variable (objective variable). In addition, the second statistical model formula 112 is a model formula in which the first parameter 101 and the second parameter 102 are input variables (explanatory variables) and the fourth parameter 104 is an output variable (objective variable).

[0078] That is, the first statistical model formula 111 and the second statistical model formula 112 can be expressed in the form shown in the following (Formula 1) (see FIG. 6).

[0079] First statistical model formula 111: Third parameter = f1 (first parameter, second parameter) Second statistical model formula 112: Fourth parameter = f2 (first parameter, second parameter) (Formula 1)

[0080] Incidentally, the fourth parameter 104, which is the objective variable, is used to evaluate the process. The fourth parameter 104 is a parameter that is not measured in-line and is estimated using the second statistical model formula 112.

[0081] However, since the first parameter 101, which is one of the input variables to the second statistical model formula 112, is also a parameter for which in-line measurement is not performed, the first parameter 101 also needs to be estimated.

[0082] Therefore, a first statistical model formula 111 is used as a method for estimating the first parameter 101. In the first statistical model formula 111, the first parameter 101 and the second parameter 102 are input variables, and the third parameter 103 is an output variable. By solving an algebraic equation with the first parameter 101 as an unknown, the first statistical model formula 111 can be converted into an equation with the second parameter 102 and the third parameter 103 as input variables and the first parameter 101 as an output variable.

[0083] The derivation unit 12 obtains the thus converted equation (corresponding to the third statistical model equation 113, see (Equation 2)) (see FIG. 7).

[0084] Third statistical model formula 113: First parameter = f1 -1 (2nd parameter, 3rd parameter) (Formula 2)

[0085] Here, the derivation unit 12 performs a determination on the first statistical model formula 111, and outputs a third statistical model formula 113 as follows.

[0086] When the derivation unit 12 determines that the first statistical model formula 111 is a linear expression for the first parameter, it derives the third parameter as a single solution and outputs the third statistical model formula 113, which is a single linear expression.

[0087] In addition, when the derivation unit 12 determines that the first statistical model formula 111 is a formula that can be expressed in the form of the nth power of (a×x+b) (where x is the first parameter, and n is an integer greater than or equal to 2, and so forth), it also derives the third parameter as a single solution and outputs the third statistical model formula 113, which is a single linear formula.

[0088] On the other hand, when the derivation unit 12 determines that the first statistical model formula 111 is a quadratic or higher mathematical formula for the first parameter and cannot be expressed in the form of the nth power of (a×x+b), it derives the third parameter as a plurality of solutions and outputs a plurality of third statistical model formulas 113 which are linear expressions for the third parameter. In this case, the derivation unit 12 can also output a single third statistical model formula 113 which is a linear expression for the third parameter, in addition to a set including a plurality of third statistical model formulas 113 which are linear expressions for the third parameter. In that case, the value derived by the single third statistical model formula 113 can also be treated as the first parameter. The first parameter derived by the single third statistical model formula 113 has a relatively small difference from the true value, but may have a larger difference from the true value than the first parameter derived by the plurality of third statistical model formulas 113 included in the set.

[0089] In this manner, the first parameter 101 is calculated by the third statistical model formula 113 including the second parameter 102 and the third parameter 103 .

[0090] Then, by substituting (Equation 2) for the first parameter 101 in the second statistical model equation 112 of (Equation 1) (i.e., by using both the first parameter 101 and the second parameter 102 as input variables), the fourth parameter 104 can be estimated by the second statistical model equation 112.

[0091] That is, the fourth parameter 104 can be expressed in the form of the following (Equation 3) (see FIG. 8).

[0092] 4th parameter = f2(f1 -1 (2nd parameter, 3rd parameter), 2nd parameter) (Formula 3)

[0093] In this way, a model that can output the fourth parameter 104 when the second parameter 102 and the third parameter 103 are input is also called an estimated model 106.

[0094] Therefore, when the second parameter 102 and the third parameter 103 obtained by the in-line measurement are input to the estimation model 106, the fourth parameter 104 can be estimated as information indicating the result of the processing that was the subject of the in-line measurement.

[0095] The processing of the generating device 10 configured as above will be described.

[0096] Fig. 9 is a flow diagram showing processing executed by generating device 10 according to the present embodiment. Fig. 10 is a flow diagram showing detailed processing executed by generating device 10 according to the present embodiment.

[0097] The process shown in Fig. 9 is included in step S1 in Fig. 2. Moreover, Fig. 10 is included in step S107 in Fig. 9.

[0098] As shown in FIG. 9, in step S101, the acquisition unit 11 acquires the experimental design model 105.

[0099] In step S102, the acquisition unit 11 sets a first parameter and a second parameter to be used in the experiment, based on the experimental design model 105 acquired in step S101.

[0100] In step S103, the acquisition unit 11 executes an experiment using the first and second parameters set in step S102.

[0101] In step S104, the acquisition unit 11 acquires the third parameter and the fourth parameter outputted as the results of the experiment executed in step S103.

[0102] In step S105, the derivation unit 12 derives a first statistical model formula using the first and second parameters set in step S102 and the third parameter acquired in step S104.

[0103] In step S106, the derivation unit 12 derives a second statistical model formula using the first parameter and the second parameter set in step S102 and the fourth parameter acquired in step S104.

[0104] In step S107, the derivation unit 12 derives a third statistical model equation using the first statistical model equation derived in step S105 and the second statistical model equation derived in step S106.

[0105] At this time, the derivation unit 12 executes different processing depending on whether the first statistical model equation is an equation that derives the third parameter as a single solution or an equation that derives the third parameter as a plurality of solutions.

[0106] That is, in step S111 (see FIG. 10), the derivation unit 12 judges whether the first statistical model formula is a formula that outputs the third parameter as a plurality of solutions. If it is judged that the first statistical model formula is a formula that outputs the third parameter as a plurality of solutions (Yes in step S111), the process proceeds to step S112, and if not (No in step S111), the process proceeds to step S113.

[0107] In step S112, the derivation unit 12 derives a plurality of third statistical model formulas by modifying the first statistical model formula.

[0108] In step S113, the derivation unit 12 transforms the first statistical model formula to derive a single third statistical model formula.

[0109] The third statistical model equation derived by the derivation unit 12 in step S112 or step S113 in this manner becomes the third statistical model equation derived in step S107 (see FIG. 9).

[0110] (Estimation device 20) Next, the estimation device 20 will be described.

[0111] FIG. 11 is a block diagram showing a hardware configuration of an estimation device 20 according to this embodiment.

[0112] The estimation device 20 is realized by, for example, a computer, and includes a processor 21, a memory 22, an input / output IF 23, a sensor 24, an input device 25, and a display device 26.

[0113] The processor 21 is a calculation device that performs parameter estimation processing, and is, for example, a CPU.

[0114] The memory 22 is a storage device that stores programs or data, and is, for example, a random access memory (RAM). The memory 22 stores the estimation model 106 generated by the generation device 10.

[0115] The input / output IF 23 is an interface device that exchanges data between the processor 21, the memory 22, the sensor 24, the input device 25, and the display device 26. The input / output IF 23 is connected to each of the above devices. The connection may be wired or wireless, or a combination of both.

[0116] The sensor 24 is installed in a processing device 29 that is the object of in-line measurement. The processing device 29 is, for example, a laser welding device. The sensor 24 is, for example, a laser displacement meter that measures a surface weld width 93 (see FIG. 4(b)) of a plate material that is the object of welding.

[0117] The input device 25 is a device for receiving input of information relating to the first to fourth parameters, and is, for example, a keyboard or a touch panel.

[0118] The display device 26 is a device that displays information relating to the first to fourth parameters, and is, for example, an LCD (Liquid Crystal Display) monitor.

[0119] FIG. 12 is a block diagram showing a functional configuration of an estimation device 20 according to this embodiment.

[0120] As shown in FIG. 12, the estimation device 20 includes, as its functional components, an input unit 31, a sensor data acquisition unit 32, a parameter estimation unit 33, an output unit 34, and a storage unit 35.

[0121] The input unit 31 is a functional unit that receives input of judgment value information such as standard values ​​for the first parameter 101, the second parameter 102, the third parameter 103, and the fourth parameter 104 from a user via the input device 25. The timing of the input is, for example, when the model of the processing device 29 is switched, but is not limited to this. The values ​​input here are registered in an input value storage unit 36 ​​of the storage unit 35.

[0122] The sensor data acquisition unit 32 is a functional unit that acquires measurement data of a second parameter 102 and a third parameter 103 from the sensor 24 connected to the processing device 29. The second parameter 102 is, for example, a scan speed 92 (see FIG. 4(a)), and the third parameter 103 is, for example, a surface weld width 93 (see FIG. 4(b)) of the plate material to be welded. The frequency of data acquisition can be set arbitrarily, but in the subsequent parameter estimation, sequentially acquired data may be used each time, or an average value may be calculated from a plurality of pieces of data acquired for one workpiece, and the average value may be used as a representative value of the workpiece. The acquired data is recorded in the sensor data storage unit 37. In addition, the acquired data is judged as to whether it conforms to the judgment conditions stored in the input value storage unit 36, and if it does not conform, quality information indicating a defect (NG) is output. The judgment conditions are, for example, conditions indicating standard values ​​or conditions indicating normal ranges.

[0123] The parameter estimation unit 33 estimates the fourth parameter 104 by inputting the second parameter 102 and the third parameter 103 recorded in the sensor data storage unit 37 to the estimation model 106 (i.e., by using the above (Equation 3)) and calculating and outputting the fourth parameter 104. At this time, when the first statistical model formula is a formula that derives the third parameter as a single solution, the parameter estimation unit 33 inputs the second parameter 102 and the single third parameter 103, and outputs the fourth parameter 104 by using the second statistical model formula and the single third statistical model formula.

[0124] Furthermore, when the first statistical model formula is a formula that derives the third parameter as a plurality of solutions, the parameter estimation unit 33 receives the second parameter 102 and the plurality of third parameters 103 as input, and outputs the fourth parameter 104 (described later) by using the second statistical model formula and the plurality of third statistical model formulas. The fourth parameter 104 is, for example, an interface weld width 95 between plate materials (see FIG. 4(b)). The output estimate value of the fourth parameter 104 is recorded in the parameter estimate value storage unit 38.

[0125] In outputting the fourth parameter 104, the parameter estimation unit 33 may select a single first parameter 101 from the multiple first parameters 101 indicating the conditions of device processing output using each of the multiple third statistical model equations, and output a fourth parameter indicating the result of device processing using the selected single first parameter 101.

[0126] Furthermore, in outputting the fourth parameter 104, among the multiple first parameters 101 indicating the device processing conditions output using each of the multiple third statistical model formulas, the first parameter 101 having the smallest difference from the new first parameter 101 output using the fifth statistical model formula with the second parameter 102 and the third parameter measured during device processing as input may be selected as the single first parameter 101. Here, it is assumed that a statistical model formula (also referred to as a fourth statistical model formula or a fourth formula) that is a linear formula for the third parameter 103 is derived using the first parameter 101 and the second parameter 102 as input and outputs the third parameter 103, and a statistical model formula (also referred to as a fifth statistical model formula or a fifth formula) that is an input of the second parameter 102 and the third parameter is used to derive the new first parameter 101.

[0127] In addition, when outputting the fourth parameter 104, a first parameter 101 that belongs to a normal range may be selected from among a plurality of first parameters 101 that indicate the device processing conditions output using each of a plurality of third statistical model formulas, and the fourth parameter 104 may be output using the selected first parameter 101.

[0128] In addition, when outputting the fourth parameter 104, if the multiple first parameters 101 indicating the device processing conditions output using each of the multiple third statistical model equations are imaginary numbers, the imaginary parts of the imaginary numbers may be deleted, and the fourth parameter 104 may be output using the first parameters 101 with the imaginary parts deleted.

[0129] In addition, the output estimated value of the fourth parameter 104 is judged as to whether it conforms to the judgment conditions stored in the input value storage unit 36, and if it does not conform, quality information indicating a defect (NG) is output. The judgment conditions are, for example, conditions indicating a standard value or conditions indicating a normal range.

[0130] The output unit 34 is a functional unit that outputs the data recorded in the storage unit 35 or the determination result. The output unit 34 outputs the data, for example, by displaying the data on the display device 26. The output unit 34 may output the data by voice, or may output the data by transmitting it to another device via communication.

[0131] The storage unit 35 is a functional unit that stores various values ​​and various data. The storage unit 35 has an input value storage unit 36, a sensor data storage unit 37, and a parameter estimate value storage unit 38. Values ​​or data are stored in or read from the storage unit 35 by the functional units.

[0132] 13 is a flow diagram showing the process executed by the estimation device 20 according to the present embodiment. The process shown in FIG. 13 is included in step S3 in FIG.

[0133] In step S301, the sensor data acquisition unit 32 acquires the measurement data of the second parameter 102 and the third parameter 103 from the sensor 24.

[0134] In step S302, the sensor data acquisition unit 32 stores in the sensor data storage unit 37 the measurement data acquired in step S301.

[0135] In step S303, the sensor data acquisition unit 32 determines whether the measurement data acquired in step S301 meets the determination condition. If the measurement data meets the determination condition (Yes in step S303), step S304 is executed, and if not (No in step S303), step S311 is executed.

[0136] In step S304, the parameter estimation unit 33 estimates the fourth parameter 104 by inputting the second parameter 102 and the third parameter 103, which are the measurement data recorded in the sensor data storage unit 37, to the estimation model 106 (i.e., by using the above (Equation 3)). The process of step S304 will be described in detail later.

[0137] In step S305, the parameter estimation unit 33 stores the fourth parameter 104 estimated in step S304 in the parameter estimate value storage unit .

[0138] In step S306, the parameter estimation unit 33 judges whether the fourth parameter 104 estimated in step S304 satisfies the judgment condition. If the judgment condition is satisfied (Yes in step S306), step S307 is executed, and if not (No in step S306), step S312 is executed.

[0139] In step S307, the output unit 34 outputs quality information indicating that the product is a non-defective product (OK).

[0140] In step S311, the output unit 34 outputs quality information indicating that the second parameter 102 or the third parameter 103 does not meet the judgment condition and is therefore defective (NG).

[0141] In step S312, the output section 34 outputs quality information indicating that the fourth parameter 104 does not meet the judgment condition and is therefore defective (NG).

[0142] When the process of step S307, S311, or S312 is completed, the series of processes shown in FIG. 13 ends.

[0143] The process in step S304 will be described in detail below.

[0144] FIG. 14 is a flow chart showing detailed processing executed by the estimation device 20 according to the present embodiment.

[0145] In step S321, the parameter estimation unit 33 judges whether the first statistical model formula is a formula that derives the third parameter as a plurality of solutions. If it is judged that the first statistical model formula is a formula that outputs the third parameter as a plurality of solutions (Yes in step S321), the process proceeds to step S322, and if not (No in step S321), the process proceeds to step S341.

[0146] When it is determined that the first statistical model formula is a formula that outputs the third parameter as a plurality of solutions, a plurality of third statistical model formulas are derived in advance by the derivation unit 12. When it is determined that the first statistical model formula is not a formula that outputs the third parameter as a plurality of solutions, a single third statistical model formula is derived in advance by the derivation unit 12.

[0147] In step S322, the parameter estimation unit 33 assigns the second parameters 102 and the third parameters 103, which are the measurement data recorded in the sensor data storage unit 37, to each of the third statistical model equations to calculate a plurality of first parameters.

[0148] In step S323, the parameter estimation unit 33 determines whether each of the first parameters calculated in step S322 is an imaginary number, and if it is determined that the first parameters are imaginary numbers, it obtains a real number by deleting the imaginary part of the imaginary number. Note that deleting the imaginary parts of the first parameters may result in the same number. Therefore, after the process of step S323 is performed, one or more first parameters exist.

[0149] In step S324, the parameter estimation unit 33 judges whether the number of first parameters belonging to the normal range among the multiple first parameters calculated in step S322 (if the imaginary part is deleted in step S323, one or multiple first parameters after the imaginary part is deleted) is multiple, one, or zero, and branches the subsequent processing according to the judgment result. If it is judged that there are multiple first parameters belonging to the normal range ("multiple" in step S324), the process proceeds to step S325, if it is judged that there is one first parameter belonging to the normal range ("one" in step S324), the process proceeds to step S331, and if it is judged that there are zero first parameters belonging to the normal range ("zero" in step S324), the process proceeds to step S335.

[0150] In step S325, the parameter estimation unit 33 substitutes the second parameter and the third parameter into a single third statistical model equation obtained from the first-order first statistical model equation for the third parameter, to calculate a new first parameter.

[0151] In step S326, the parameter estimation unit 33 selects a single first parameter from among the multiple first parameters calculated in step S322 (if the imaginary parts are deleted in step S323, the multiple first parameters after the imaginary parts are deleted), which is closer to the new first parameter calculated in step S325.

[0152] In step S331, the parameter estimation unit 33 selects one first parameter that belongs to the normal range. After completing step S331, the process proceeds to step S327.

[0153] In step S335, the parameter estimation unit 33 sets a predetermined value as the first parameter.

[0154] In step S336, the parameter estimation unit 33 may notify the user. This notification may be a notification indicating that there is no first parameter that belongs to the normal range, or a notification indicating that a predetermined value has been set as the first parameter. After completing step S336, the process proceeds to step S327.

[0155] In step S341, the parameter estimation unit 33 substitutes the second parameter 102 and the third parameter 103, which are the measurement data recorded in the sensor data storage unit 37, into the single third statistical model equation to calculate a single first parameter.

[0156] In step S327, the parameter estimation unit 33 calculates the fourth parameter by the second statistical model formula 112 using the first parameter and the second parameter 102, which is the measurement data recorded in the sensor data storage unit 37. The first parameter is the single first parameter selected in step S326 or S331, the first parameter set in step S335, or the first parameter calculated in step S341.

[0157] In the above description, a single first parameter is calculated when the first statistical model formula is a formula that outputs the third parameter as multiple solutions, and a single fourth parameter is calculated from the calculated first parameter. However, in the above case, multiple first parameters may be used to calculate multiple fourth parameters instead of calculating a single first parameter. This corresponds to performing the processes shown in FIG. 14 except for steps S323 to S326, S331, and S335 to S336 (i.e., the processes surrounded by the dashed line frame).

[0158] 13 and 14, for example, in a laser welding process, it becomes possible to estimate the interface weld width 95 between plate materials in-line based on measurement data such as the laser scan speed 92 that is measured in-line or the surface weld width 93 of the laser weld. If it were to actually measure the interface weld width 95, it would be necessary to observe the cross-sectional shape offline, but there is an advantage in that it becomes possible to estimate the interface weld width 95 in-line.

[0159] An example of the estimation accuracy of the estimation model according to the present embodiment will be described below. Specifically, (1) the estimation accuracy of the estimation model according to the present embodiment and (2) the validity of a single parameter selected from a plurality of first parameters will be described.

[0160] (1) Estimation Accuracy of the Estimation Model According to the Present Embodiment FIG. 15 is an explanatory diagram showing the estimation accuracy of the estimation model according to the present embodiment in comparison with the related art.

[0161] 15(a) is a graph in which the true value is plotted on the horizontal axis and the estimated value is plotted on the vertical axis for the fourth parameter estimated by the estimation model of the related technology. Here, the related technology is a technology that uses an estimation model that estimates the fourth parameter by inputting a fixed value corresponding to the first parameter and the second parameter acquired by inline measurement into a second statistical model formula, unlike the estimation model 106 in the present embodiment.

[0162] FIG. 15(b) is a graph in which the true value is plotted on the horizontal axis and the estimated value is plotted on the vertical axis for the fourth parameter estimated by the estimation model 106 in this embodiment.

[0163] The RMSE (Root Mean Squared Error) between the true value and the estimated value is 0.0625 in the related art and 0.0415 in this embodiment. It can be confirmed that the estimation accuracy in this embodiment is 30% or more higher than that of the related art.

[0164] In this way, the estimation according to this embodiment makes it possible to estimate the parameters of the objective variable with a small number of experiments, even if the explanatory variables include parameters for which measurement data are not collected inline.

[0165] (2) Validity of a single parameter selected from multiple first parameters 16 and 17 are diagrams illustrating the validity of a single selected first parameter according to an embodiment.

[0166] Here, a process of selecting a single first parameter from one or more first parameters calculated using the second and third parameters measured for evaluation and the third statistical model formula when the first statistical model formula is a quadratic expression for the first parameter is described using specific values.

[0167] In FIG. 16, two first parameters calculated for each of the 21 cases using the second and third parameters measured for evaluation and the third statistical model equation are shown as first parameters A and first parameters B (see step S322 in FIG. 14).

[0168] The first parameter A and the first parameter B may be real numbers (cases other than 13, 19, and 20) or imaginary numbers (cases 13, 19, and 20).

[0169] The parameter estimation unit 33 obtains one first parameter that is a real number by deleting the imaginary part in each case where the first parameter A and the first parameter B are imaginary numbers (step S323 in FIG. 14).

[0170] The first parameters for each of the 21 cases after step S323 are shown in Fig. 17 as first parameter A and first parameter B. Note that the one first parameter that is a real number obtained by deleting the imaginary part in step S323 is shown as first parameter A. In such cases, "·" is shown in the column for first parameter B.

[0171] The parameter estimation unit 33 obtains the number of first parameters that belong to the normal range among the first parameters A and B. In cases 13, 19, and 20, the number of first parameters that belong to the normal range is 1. If the normal range is a range greater than 0 and less than 100, in cases 8 and 9, the first parameters are negative values, so the number of first parameters that belong to the normal range is 1. If the number of first parameters that belong to the normal range is 1, the parameter estimation unit 33 selects that one first parameter (step S331 in FIG. 14). The first parameter selected in this way is shown in the "selected first parameter" column.

[0172] In cases other than those mentioned above (i.e., cases 1 to 7, 10 to 12, 14 to 18, and 21), the number of first parameters belonging to the normal range is two.

[0173] When the number of first parameters belonging to the normal range is 1, the parameter estimation unit 33 calculates a new first parameter by substituting the second parameter and the third parameter measured for evaluation into a single third statistical model equation obtained from the first-order first statistical model equation (see step S325 in FIG. 14). The new first parameter is shown as a first parameter C.

[0174] The parameter estimation unit 33 selects, from the first parameter A and the first parameter B, one that is closer to the first parameter C. The first parameter selected in this manner is shown in the "Selected first parameter" column.

[0175] The validity of the first parameters selected in this way will be described with reference to FIG. 17, with the results of a comparison with true values ​​for evaluation.

[0176] In both cases where the number of first parameters belonging to the normal range is 1 and where it is 2, it can be seen that, as an overall trend, the ratio of the difference between the selected first parameter and the true value to the true value (i.e., |selected first parameter-true value| / true value) is generally within about 15%.

[0177] Also, in the case where the number of first parameters belonging to the normal range is two, it can be seen that one first parameter closer to the true value is selected.

[0178] In this way, the system 1 can appropriately estimate information indicating the results of processing.

[0179] (Modification) In this modified example, another embodiment of the estimation method for appropriately estimating information indicating the result of processing will be described.

[0180] 18 is a flow diagram showing a process (that is, an estimation method) executed by a system (also called an estimation system) 2 according to this modification. The process shown in FIG. 18 is another example of the process shown in FIG.

[0181] As shown in FIG. 18, in step S401, the generation device 10 conducts a device processing experiment and obtains first and second types of information indicating the conditions of the processing experiment, and third and fourth types of information indicating the results of the processing experiment.

[0182] In step S402, the generating device 10 derives a first equation that receives the first and second information as inputs and outputs third information, the first equation outputting the third information as a plurality of solutions, and a second equation that receives the first and second information as inputs and outputs fourth information. Furthermore, the generating device 10 uses the first equation to derive a plurality of third equations that receive the second and third information as inputs and calculate the first information.

[0183] In step S403, the estimation device 20 receives as input the second type of information and the third type of information measured during processing of the device, and calculates and outputs a fourth type of information indicating the results of processing the device using the second equation and multiple third equations.

[0184] This enables the system 2 to appropriately estimate information indicating the result of processing.

[0185] 19 is a schematic diagram showing the configuration of a system 2 according to this modification. The process shown in FIG. 19 is an example of another configuration of the system 1 shown in FIG.

[0186] As shown in FIG. 19, the system 2 includes an acquisition unit 2A, a derivation unit 2B, and an estimation unit 2C.

[0187] The acquisition unit 2A performs a device processing experiment, and acquires first and second types of information indicating conditions of the processing experiment, and third and fourth types of information indicating results of the processing experiment.

[0188] The derivation unit 2B derives a first equation that takes first type information and second type information as input and outputs third type information, the first equation outputting third type information as multiple solutions, and a second equation that takes first type information and second type information as input and outputs fourth type information, and also derives multiple third equations that take second type information and third type information as input using the first equation to calculate the first type information.

[0189] The estimation unit 2C receives as input the second type information and the third type information measured during processing of the device, and calculates and outputs a fourth type information indicating the results of processing the device using the second equation and a plurality of third equations.

[0190] This enables the system 2 to appropriately estimate information indicating the result of processing.

[0191] As described above, according to the estimation method of the present embodiment, the fourth type information during processing can be estimated from the second type information and the third type information obtained during processing using the relational expressions of the first type information, the second type information, the third type information, and the fourth type information obtained from the experiment. In the process, when the first formula outputs the third type information as a plurality of solutions, the fourth type information indicating the result of processing can be appropriately estimated using a plurality of third formulas. In this way, the above estimation method can appropriately estimate information indicating the result of processing.

[0192] In addition, the single fourth information indicating the result of processing can be appropriately estimated by using a more appropriate single first information among one or more first information obtained by using a plurality of third formulas. Therefore, the estimation method can appropriately estimate the information indicating the result of processing.

[0193] In addition, among one or more pieces of first information obtained using a plurality of third formulas, a single piece of first information close to the new first information output using the fifth formula can be used to appropriately estimate the fourth information indicating the result of processing. The new first information output using the fifth formula generally has a relatively small difference from the true value. Therefore, when one or more pieces of first information are obtained using a plurality of third formulas, first information relatively close to the true value can be obtained by selecting the first information having the smallest difference from the new first information, and the fourth information can be appropriately estimated using this. In this way, the estimation method can appropriately estimate information indicating the result of processing.

[0194] In addition, the fourth type information indicating the result of processing can be appropriately estimated by using a single first type information that is more appropriate and falls within the normal range among one or more first type information obtained by using a plurality of third formulas. Therefore, the above estimation method can appropriately estimate the information indicating the result of processing.

[0195] In addition, among one or more pieces of first type information obtained using a plurality of third formulas, imaginary numbers are converted to real numbers by removing the imaginary parts, and the fourth type information indicating the result of processing can be appropriately estimated using a more appropriate single piece of first type information. Therefore, the above estimation method can appropriately estimate the information indicating the result of processing.

[0196] In addition, by making a judgment based on the specific form of the first formula, the fourth type information can be appropriately estimated using a plurality of the third formulas. Therefore, the above estimation method can more easily appropriately estimate the information indicating the result of processing.

[0197] Furthermore, when the information indicating the processing conditions includes information that is not measured and the information indicating the processing results includes information that is not measured, the information indicating the processing results can be appropriately estimated. Therefore, according to the generation method, even when there is information that is not measured during processing, it is possible to generate a model that appropriately estimates the information indicating the processing results.

[0198] In addition, it is possible to more easily generate a model that appropriately estimates information indicating the results of processing in laser welding.

[0199] In the above-described embodiment, each component may be configured with dedicated hardware, or may be realized by executing a software program suitable for each component. Each component may be realized by a program execution unit such as a CPU or a processor reading and executing a software program recorded on a recording medium such as a hard disk or a semiconductor memory. Here, the software for realizing the generating device and the estimating device of the above-described embodiment is a program such as the following.

[0200] In other words, this program causes a computer to execute an estimation method executed by a processor using a memory, comprising: conducting a device processing experiment; acquiring first and second type information indicating conditions of the processing experiment and third and fourth type information indicating results of the processing experiment; deriving a first equation that takes the first type information and the second type information as input and outputs the third type information, the first equation outputting the third type information as multiple solutions; and a second equation that takes the first type information and the second type information as input and outputs the fourth type information; deriving multiple third equations that take the second type information and the third type information as input and output the first type information using the first equation; and taking the second type information and the third type information measured during processing of the device as input, and using the second equation and the multiple third equations to output the fourth type information indicating results of the device processing.

[0201] Although the estimation device according to one or more aspects has been described based on the embodiment, the present invention is not limited to this embodiment. As long as it does not deviate from the spirit of the present invention, various modifications conceived by a person skilled in the art to this embodiment and forms constructed by combining components in different embodiments may also be included within the scope of one or more aspects. [Industrial Applicability]

[0202] The model generation method, parameter estimation method, and system according to the present invention enable parameter estimation of a response variable with a small number of experiments, even when the explanatory variables include a parameter for which measurement data is not collected inline, and are useful as a model generation method, a parameter estimation method, and a system. [Explanation of symbols]

[0203] 1, 2 System 2A, 11 Acquisition Department 2B, 12 Derivation part 2C Estimation Department 9A, 9B plate material 10 Generator 13 Generation part 20 Estimation device 21 Processors 22 Memory 23 Input / Output Interface 24 Sensors 25 Input Devices 26 Display device 29 Processing equipment 31 Input section 32 Sensor data acquisition section 33 Parameter Estimation Section 34 Output section 35 Storage section 36 Input value storage section 37 Sensor data storage unit 38 Parameter estimates storage unit 91 Laser Beam 92 Scan Speed 93 Surface Weld Width 94 Gap width 95 Interface Weld Width 101 First Parameter 102 Second Parameter 103 Third Parameter 104 Fourth Parameter 105 Experimental Design Model 106 Estimation Model 111 First statistical model formula 112 Second statistical model formula 113 Third statistical model formula

Claims

1. A method for estimating executed by a processor using a memory, comprising: conducting an experiment on processing a device, and acquiring first and second types of information indicating conditions of the experiment, and third and fourth types of information indicating results of the experiment; deriving a first equation that receives the first type information and the second type information as input and outputs the third type information, the first equation outputting the third type information as a plurality of solutions, and a second equation that receives the first type information and the second type information as input and outputs the fourth type information; deriving a plurality of third equations that output the first information by inputting the second information and the third information using the first equation; The second type information and the third type information measured during processing of the device are input, and the second equation and a plurality of the third equations are used to output the fourth type information indicating the result of processing of the device. Estimation method.

2. In the output of the fourth type of information, selecting a single piece of first information from the plurality of pieces of first information indicating conditions for processing the device, the single piece of first information being output using each of the plurality of third equations; and outputting the fourth type of information indicating a result of processing the device using the selected single first type of information. The estimation method according to claim 1 .

3. Furthermore, a fourth equation is derived that receives the first information and the second information as input and outputs the third information, the fourth equation being a linear equation for the third information; deriving a fifth equation that receives the second type information and the third type information as input and outputs new first type information using the fourth equation; In the output of the fourth type of information, Among the plurality of first type information indicating conditions for processing the device outputted using the plurality of third equations, the first type information having the smallest difference from the new first type information outputted using the fifth equation using the second type information and the third type information measured during processing of the device as input is selected as the single first type information. The estimation method according to claim 2.

4. In the output of the fourth type of information, Selecting the first type information belonging to a normal range from among the plurality of first type information indicating the processing conditions of the device outputted using the plurality of third formulas, The fourth information is output using the selected first information. The estimation method according to claim 2 or 3.

5. In the output of the fourth type of information, When the plurality of pieces of first type information indicating the processing conditions of the device outputted using the plurality of third expressions are imaginary numbers, an imaginary part of the imaginary number is deleted; The fourth type information is output using the first type information from which the imaginary part has been deleted. The estimation method according to any one of claims 1 to 4.

6. When deriving the third formula, determining whether or not the first formula is a polynomial of degree 2 or higher for the third kind of information and is a polynomial that cannot be expressed in the form of an n-th power of (a×x+b) where x is the third kind of information; deriving a plurality of said third expressions when said first expression is determined to be said polynomial expression; The estimation method according to any one of claims 1 to 5.

7. the first type information and the fourth type information are predetermined information that are not measured during the processing, The second type information and the third type information are predetermined information to be measured during the processing. The estimation method according to any one of claims 1 to 6.

8. The processing is laser welding, The first type information includes a gap width between plate materials to be welded in the laser welding, The second type of information includes a scanning speed of a laser in the laser welding, The third type information includes a surface weld width of the laser welded portion in the laser welding, The fourth type information includes an interface weld width of the laser welded portion in the laser welding. The estimation method according to any one of claims 1 to 7.

9. an acquisition unit that performs an experiment on processing a device and acquires first and second types of information indicating conditions of the experiment and third and fourth types of information indicating results of the experiment; a derivation unit that derives a first equation that receives the first type information and the second type information as input and outputs the third type information, the first equation being a first equation that outputs the third type information as a plurality of solutions, and a second equation that receives the first type information and the second type information as input and outputs the fourth type information, and that uses the first equation to derive a plurality of third equations that receive the second type information and the third type information as input and output the first type information; an estimation unit that receives the second type information and the third type information measured during processing of the device as input, and uses the second equation and a plurality of the third equations to output the fourth type information indicating a result of processing the device. Estimation system.

Images