Welding system and welding monitor device

Abstract

To easily search for a processing condition suitable for welding with a simple structure in a short time and estimate a welded result of a welded part by welding under the suitable processing condition in a non-destructive manner.SOLUTION: A welding system according to one aspect comprises: a welding device that welds a work-piece; a learning device that inputs, as teacher data, processing condition information including a processing condition for welding previously executed by the welding device and welded-result information relevant to a welded result obtained after welding based on the processing condition, and generates and outputs a welded-result estimation model, on the basis of the teacher data; and an estimating device that inputs, as data for estimation, processing condition information including a processing condition set on the welding device in the welded-result estimation model generated by the learning device, and estimates and outputs a welded result of welding performed by the welding device on the basis of the processing condition.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a welding system and a welding monitoring device. [Background technology]

[0002] In conventional welding, when determining appropriate processing conditions corresponding to the required welding specifications, appropriate processing conditions have been searched for by automatically determining what processing conditions should be used to conduct processing experiments as needed with as few trials as possible (see Patent Document 1), or by repeating search experiments by trying all possible combinations of numerous elements such as the materials that make up the weld and the welding time.

[0003] Furthermore, after welding is completed, non-destructive testing using ultrasonic waves (see Patent Document 2) or destructive testing, in which the weld is actually destroyed to check its condition, has been carried out to check whether the weld is properly welded or whether the required weld strength has been achieved. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2012-236267 [Patent Document 2] Japanese Patent Application Laid-Open No. 2009-156701 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the device disclosed in Patent Document 1 requires multiple processing experiments, and exploratory experiments to try all possible combinations rely heavily on the intuition and experience of the operator, resulting in the problem that it takes a long time to find appropriate processing conditions.

[0006] Furthermore, in order to confirm the weld strength of a welded portion by non-destructive testing using the device disclosed in Patent Document 2, components such as sensors and probes are required in the device, making it difficult to minimize the device configuration. Furthermore, there is a problem in that non-destructive testing is unavoidable in order to confirm whether the weld strength of a welded portion welded under certain processing conditions is comparable between welds that have been subjected to destructive testing and those that have not.

[0007] One aspect of the present invention is a welding system and welding monitoring device that can easily search for appropriate welding processing conditions in a short time with a simple configuration and can non-destructively estimate the welding results of a welded portion welded under the appropriate processing conditions. [Means for solving the problem]

[0008] A welding system according to one aspect of the present invention includes a welding device for welding workpieces, a learning device that inputs, as training data, processing condition information including processing conditions for welding previously performed by the welding device and welding result information related to the welding result obtained after welding based on the processing conditions, and creates and outputs a welding result estimation model based on the training data, and an estimation device that inputs, as estimation data, processing condition information including the processing conditions set for the welding device to the welding result estimation model created by the learning device, and estimates and outputs the welding result of welding performed by the welding device based on the processing conditions.

[0009] According to one aspect of the present invention, a welding result estimation model is created based on training data of processing condition information for welding previously performed by a welding device and welding result information obtained after welding based on the processing condition information. Furthermore, by inputting processing condition information set in the welding device as estimation data into the welding result estimation model, the welding result of welding performed by the welding device based on the processing conditions is estimated and output, thereby making it possible to non-destructively estimate the welding result of a welded portion welded under appropriate processing conditions. Furthermore, since the welding result can be estimated by inputting processing condition information set in the welding device into the welding result estimation model, a simple configuration can be used to reduce the number of processing condition searches and the amount of searches, thereby making it possible to easily search for appropriate welding processing conditions in a short amount of time. [Effects of the Invention]

[0010] According to one aspect of the present invention, it is possible to easily search for suitable welding processing conditions in a short time with a simple configuration, and to non-destructively estimate the welding results of a welded portion welded under suitable processing conditions. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is an explanatory diagram showing the basic configuration of a welding system according to a first embodiment of the present invention. [Figure 2] FIG. 2 is an explanatory diagram showing the basic hardware configuration of a learning device and / or an estimation device of a welding system. [Figure 3] FIG. 3 is an explanatory diagram showing the basic configuration of a welding system according to a second embodiment of the present invention. [Figure 4] FIG. 4 is an explanatory diagram showing the basic configuration of a welding system according to a third embodiment of the present invention. [Figure 5] FIG. 5 is a graph showing an example of time-series data. [Figure 6] FIG. 6 is an explanatory diagram showing the basic configuration of a welding system according to a fourth embodiment of the present invention. [Figure 7] FIG. 7 is an explanatory diagram showing the basic configuration of a welding system according to a fifth embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0012] A welding system and a welding monitoring device according to embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, the following embodiments do not limit the invention according to each claim, and not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention. Furthermore, in the following embodiments, the arrangement, scale, dimensions, etc. of each component may be shown exaggerated or minimized, and some components may be omitted.

[0013] [First embodiment] Fig. 1 is an explanatory diagram showing the basic configuration of a welding system according to a first embodiment of the present invention, and Fig. 2 is an explanatory diagram showing the basic hardware configuration of a learning device and / or an estimation device of the welding system.

[0014] As shown in Fig. 1, a welding system 100 according to a first embodiment is a system capable of estimating and outputting a welding result. The welding system 100 includes a welding device (laser welder 40) that welds workpieces; a learning device 10 that receives, as training data, processing condition information 2 including processing conditions for welding previously performed by the welding device (laser welder 40) and welding result information 1 related to the welding result obtained after welding based on the processing conditions, and creates and outputs a welding result estimation model 3 based on the training data; and an estimation device 30 that receives, as estimation data, processing condition information 4 including processing conditions set for the welding device (laser welder 40) into the welding result estimation model 3 created by the learning device 10, and estimates and outputs the welding result of welding performed by the welding device (laser welder 40) based on the processing conditions. The estimation device 30 is included in a welding result determination device 20 that determines the welding result based on the estimated welding result.

[0015] In this welding system 100, for example, the training data of the objective variable input to the learning device 10 is the welding strength F (N / mm 2) and the teacher data for the explanatory variables includes processing condition information 2 including various processing conditions (laser power (output value), laser irradiation time, etc.). Note that the objective variables and explanatory variables used in welding system 100 are not limited to these examples.

[0016] In the welding system 100 of the first embodiment, the welding device is a laser welder 40, and the processing condition information 2, 4 includes at least one processing condition: material of the workpiece, thickness of the workpiece, laser power, laser irradiation time, time from the start of laser irradiation to reach peak power, time from the peak power to the end of laser irradiation, fiber diameter, lens focal length, focal position, and laser diameter at the irradiation point. Note that the spot diameter calculated from the combination of lens, fiber, and focal position is used for machine learning in the welding result learning unit 11, for example.

[0017] The laser welder 40 is a device that irradiates a workpiece with a laser beam under processing conditions designated by, for example, an external input, to perform welding. The laser welder 40 is configured to be able to acquire or transmit / receive various conditions for irradiating the laser beam through various communications (network communications or direct connection communications). The welding results include the welding strength, spot diameter, welding quality, etc.

[0018] In this way, welding system 100 of the first embodiment is used, for example, as a system for estimating the weld strength included in the welding result of laser welding by laser welder 40. Note that welding result information 1 may include the spot diameter, or may be an index that evaluates the metal structure based on the appearance or cross section as the welding quality, or an index that evaluates the corrosion resistance. Therefore, welding system 100 can also be used as a system for estimating the spot diameter and weld quality included in the welding result of laser welding.

[0019] The learning device 10 includes a welding result learning unit 11. The welding result learning unit 11 performs machine learning using, for example, a predetermined machine learning algorithm based on training data including processing condition information 2 input to the learning device 10 and welding result information 1 related to the welding results of the welded portion obtained, for example, by destructive testing after welding based on the processing conditions. The destructive testing may be any of the destructive tests listed on the website of the Welding Information Center of the Japan Welding Engineering Society, such as JIS Z 2241 Tensile Test Method for Metallic Materials, or other listed JIS standards applicable to welding tests and inspections. The experimental and evaluation methods are also those specified in the JIS standards. The trained welding result estimation model 3 is implemented, for example, by software run on a computer. In this example, a program may be used to input processing condition information 4 as estimation data and output a welding result estimate 8, including weld strength, for example.

[0020] The learning method includes, but is not limited to, algorithms such as regression, classification, clustering, discrimination, interpolation, feature extraction, and time series modeling. The welding result estimation model 3 learned by the welding result learning unit 11 includes various parameters, various formulas, and / or algorithms for prediction and estimation that characterize the learned model. The learning device 10 then outputs the welding result estimation model 3 to the welding result determination device 20 that includes the estimation device 30.

[0021] On the other hand, estimation device 30 is included in welding result determination device 20 and includes welding result estimation unit 31. Welding result determination device 20 includes, together with estimation device 30, welding result estimation model 3 output from learning device 10. Welding result estimation unit 31 inputs processing condition information 4 set in laser welder 40 that has been input to estimation device 30 as estimation data to welding result estimation model 3 included in welding result determination device 20, estimates the welding result of welding performed by laser welder 40 based on the processing conditions, and outputs welding result estimation value 8.

[0022] Welding result determination device 20 can determine, for example, the weld strength and / or spot diameter (hereinafter referred to as "weld strength, etc.") of the weld based on welding result estimation value 8 output from welding result estimation unit 31 of estimation device 30. Note that, with regard to weld strength, welding result estimation value 8 refers to information that can numerically express, for example, the tensile strength (unit: N) and strength distribution range of a certain weld point (welded portion). With regard to spot diameter, welding result estimation value 8 refers to information that can numerically express, for example, the size of the irradiation area of ​​the laser light on the surface of a certain weld point (welded portion) or the size of the melted portion of the weld.

[0023] Note that when performing machine learning and estimation in the learning device 10 and the estimation device 30, various algorithms can be used, such as regression analysis (RA), principal component analysis (PCA), singular value decomposition (SVD), linear discriminant analysis (LDA), independent component analysis (ICA), Gaussian process latent variable model (GPLVM), logistic regression (LR), support vector machine (SVM), discriminant analysis (DA), random forest (RF), ranking support vector machine (RSVM), gradient boosting (GB), naive Bayes (NB), and k-nearest neighbor algorithm (K-NN).

[0024] As described above, in welding system 100 of the first embodiment, welding result learning unit 11 of learning device 10 performs, for example, machine learning based on processing condition information 2 and welding result information 1 to create welding result estimation model 3. Then, welding result estimation unit 31 of estimation device 30 inputs processing condition information 4 set in laser welder 40 to welding result estimation model 3, thereby estimating the welding result (weld strength, spot diameter) of welding performed by laser welder 40 and outputting welding result estimation value 8.

[0025] Welding system 100 can obtain a welding result estimate 8 simply by inputting processing condition information 4 set in laser welder 40 into learned welding result estimation model 3, thereby eliminating the cumbersome task of actually irradiating a laser beam using multiple pieces of processing condition information 4 and measuring the results, and making it possible to reduce the number of combinations of processing condition information used to search for processing conditions that will achieve a desired weld strength. This makes it possible to easily search for appropriate processing conditions for laser welding in a short amount of time with a simple configuration, and to non-destructively estimate the welding result of a welded part obtained by laser welding under appropriate processing conditions.

[0026] Each piece of data, welding result information (teaching data of objective variables) 1 and processing condition information (teaching data of explanatory variables) 2 in welding system 100, may be composed of one or more variables or parameters, and may be stored in, for example, a stationary or portable storage device or storage medium (not shown). Each piece of data may be transmitted and received via an information and communication medium such as the Internet, and may be raw data acquired by a measuring device such as a sensor (not shown). Furthermore, welding result estimation model 3 may be input and output between learning device 10 and estimation device 30 via the storage medium or information and communication medium.

[0027] As shown in FIG. 2, the learning device 10 and / or the estimation device 30 of the welding system 100 includes, as basic hardware configuration, for example, a CPU (central processing unit) 201, a RAM (random access memory) 202, a ROM (read only memory) 203, an HDD (hard disk drive) 204 and / or an SSD (solid state drive) 205, and a memory card 206.

[0028] The learning device 10 and / or the estimation device 30 further includes, for example, an input I / F (interface) 207, an output I / F (interface) 208, and a communication I / F (interface) 209. The components 201 to 209 are connected to one another via a bus 200.

[0029] The CPU 201 controls the learning device 10 and / or the estimation device 30 by executing various programs stored in the RAM 202, the ROM 203, the HDD 204, the SSD 205, etc. In the learning device 10, the CPU 201 executes the learning program to realize the functions of each part of the learning device 10, including the function of the welding result learning unit 11.

[0030] Furthermore, CPU 201 executes an estimation program in estimation device 30, thereby realizing the functions of each unit of estimation device 30, including the function of welding result estimation unit 31. Note that the CPUs 201 of learning device 10 and estimation device 30 may be configured to cooperate with each other to control the entire welding system 100.

[0031] The RAM 202 can be used as a working area for the arithmetic processing of the CPU 201. The ROM 203 stores the above-mentioned various programs in a readable manner. The HDD 204 and SSD 205 store the above-mentioned various data in a readable and writable manner. The memory card 206 stores the above-mentioned various data in a readable and writable manner and constitutes a storage medium that is detachable from each of the devices 10 and 30. The HDD 204, SSD 205, and memory card 206 realize the functions of the above-mentioned storage devices or storage media.

[0032] To input I / F 207, for example, sensor 212 is connected to acquire detected information. Sensor 212 includes various sensors such as a temperature sensor, an optical sensor, an acoustic sensor, and an image sensor. Note that input I / F 207 is connected to touch panel 211 that functions as an operation unit or input unit of learning device 10 and / or estimation device 30, and receives information associated with operation input from a user of welding system 100. Various input devices such as a keyboard and a mouse (including a trackball mouse), not shown, may also be connected to input I / F 207.

[0033] Output I / F 208 is connected to, for example, a display 210 as a display device, and outputs various information to be displayed on a monitor of learning device 10 and / or estimation device 30. Touch panel 211 may be provided on display 210. Furthermore, learning device 10 and / or estimation device 30 may be indirectly or directly connected via communication I / F 209 to a server device, external device, etc. connected to a network such as the Internet (not shown).

[0034] [Second embodiment] Fig. 3 is an explanatory diagram showing the basic configuration of a welding system according to a second embodiment of the present invention. In the following description, including Fig. 3, the same components as those in the first embodiment and its modified examples are denoted by the same reference numerals, and therefore, redundant description will be omitted below.

[0035] 3, welding system 100A according to the second embodiment includes welding monitoring device 50 that outputs monitoring information 5 and 6 obtained by monitoring (status observation) the progress of welding by welding equipment (laser welder 40) using sensor 212, learning device 10 calculates feature amount information as training data for auxiliary variables from the training data of monitoring information 5 output from welding monitoring device 50, creates and outputs welding result estimation model 3 based on the training data to which the auxiliary variables have been added, and estimation device 30 calculates feature amount information as estimation data for auxiliary variables from the estimation data of monitoring information 6 output from welding monitoring device 50, and estimates and outputs the welding result using the estimation data to which the auxiliary variables have been added. Note that welding monitoring device 50 is a device that monitors the welded portion of a workpiece with sensor 212 and is capable of acquiring and outputting output information from sensor 212 as monitoring information 5 and 6.

[0036] That is, welding system 100A of the second embodiment is a system applied to laser welding that uses monitoring information 5, 6 from welding monitor device 50 as auxiliary variables (teaching data and estimation data) that are correlated with processing condition information 2, 4. Note that examples of auxiliary variables that are correlated with explanatory variables (processing condition information) include physical quantities (welding temperature, sound, light, color, etc.) observed during or after laser welding. However, the auxiliary variables used in welding system 100A are not limited to those exemplified above.

[0037] In order to use auxiliary variables, specifically, the learning device 10 of the welding system 100A includes a first auxiliary variable calculation unit 12 that calculates feature information as training data for the auxiliary variables from the training data of the monitoring information 5, and a welding result learning unit 11 that creates and outputs a welding result estimation model 3, for example by performing machine learning, based on the training data of the welding result information 1, the training data of the processing condition information 2, and the training data (feature information) for the auxiliary variables calculated by the first auxiliary variable calculation unit 12.

[0038] The estimation device 30 includes a second auxiliary variable calculation unit 32 that calculates feature information from the estimation data of the monitoring information 6 as estimation data of the auxiliary variables, and a welding result estimation unit 31 that inputs the estimation data (feature information) of the auxiliary variables calculated by the second auxiliary variable calculation unit 32 and the estimation data of the processing condition information 4 into a welding result estimation model 3, and estimates and outputs the welding result.

[0039] The monitoring information 5, 6 output from welding monitoring device 50 may be, for example, information including time-series data of physical quantities (welding temperature, sound, light, color, etc.) observed during laser welding. The feature amount information calculated based on monitoring information 5 is used as teacher data for auxiliary variables provided when learning device 10 learns welding result estimation model 3, and the feature amount information calculated based on monitoring information 6 is used as estimation data for auxiliary variables provided when estimation device 30 estimates welding result estimated value 8.

[0040] In the welding system 100A of the second embodiment, the welding device is a laser welder 40. The processing condition information 2 and 4 includes at least one processing condition: the material of the workpiece, the thickness of the workpiece, the laser power, the laser irradiation time, the time from the start of laser irradiation to reaching peak power, the time from the peak power to the end of laser irradiation, the fiber diameter, the lens focal length, the focal position, and the laser diameter at the irradiation point. The spot diameter calculated from the combination of the lens, fiber, and focal position is used for machine learning in the welding result learning unit 11. The monitoring information 5 and 6 includes time-series fluctuation data of the laser power and time-series fluctuation data of the radiated near-infrared light intensity at the irradiation point.

[0041] In welding system 100A of the second embodiment, first auxiliary variable calculation unit 12 of learning device 10 calculates feature amount information from the training data of monitoring information 5 as training data for the auxiliary variables, and welding result learning unit 11 uses data including this feature amount information and the training data of welding result information 1 and processing condition information 2 to learn and create welding result estimation model 3. Then, welding result estimation model 3 created by learning device 10 is provided to estimation device 30 of welding result determination device 20.

[0042] In estimation device 30 provided with welding result estimation model 3, second auxiliary variable calculation unit 32 calculates feature information as estimation data for auxiliary variables from estimation data of monitoring information 6 using, for example, the same algorithm as that used by first auxiliary variable calculation unit 12, and inputs data including this feature information and estimation data of processing condition information 4 to welding result estimation model 3, and welding result estimation unit 31 estimates the welding result and outputs welding result estimation value 8.

[0043] The first auxiliary variable calculation unit 12 may detect a plurality of predetermined features from the teacher data of the monitoring information 5, which is time-series data, and calculate the value of each detected feature as feature amount information. The second auxiliary variable calculation unit 32 may detect a plurality of predetermined features from the estimation data of the monitoring information 6, which is time-series data, and calculate the value of each detected feature as feature amount information. The calculation of this feature amount information will be described later.

[0044] As a result, welding system 100A of the second embodiment can achieve the same effects as those of the first embodiment. Furthermore, by using monitoring information 5 and 6 as auxiliary variables, it is possible to estimate physical phenomena caused by welding as events that occurred during welding, including fluctuations in the welding state. This allows for more precise estimation, improving the accuracy of the estimation.

[0045] [Third embodiment] FIG. 4 is an explanatory diagram showing the basic configuration of a welding system according to a third embodiment of the present invention. As shown in FIG. 4, in welding system 100B according to the third embodiment, learning device 10 includes first latent variable calculation unit 13 that calculates teacher data (not shown) of a latent variable that can abstractly express the correlation between the teacher data of the processing condition information 2 from the teacher data of the auxiliary variables (feature amount information) by a technique such as dimension reduction or dimension compression, based on the teacher data of the processing condition information 2 and the teacher data of the auxiliary variables (feature amount information), and outputs latent variable calculation information 7, and welding result learning unit 11 performs, for example, machine learning based on the teacher data of the processing condition information 2, the teacher data of welding result information 1, and the teacher data of the latent variables, to create and output a welding result estimation model 3.

[0046] The estimation device 30 includes a second latent variable calculation unit 33 that calculates estimation data (not shown) of a latent variable that can abstractly express the correlation between the estimation data of the processing condition information 4 from the estimation data of the auxiliary variable (feature information) by a technique such as dimension reduction or dimension compression, based on the estimation data of the processing condition information 4, the estimation data of the auxiliary variable (feature information), and the latent variable calculation information 7, and the welding result estimation unit 31 inputs the estimation data of the processing condition information 4 and the estimation data of the latent variable to the welding result estimation model 3, and estimates and outputs the welding result.

[0047] That is, the welding system 100B of the third embodiment is a system applied to laser welding using latent variables (teaching data and estimation data) that abstractly express the correlation between auxiliary variables (feature information) calculated based on monitoring information 5 and 6 and processing condition information 2 and 4 using techniques such as dimension reduction or dimension compression.

[0048] The first latent variable calculation unit 13 outputs the latent variable calculation information 7 determined when calculating the teacher data of the latent variables to the estimation device 30 of the welding result determination device 20 so that it can be used. The first latent variable calculation unit 13 may calculate one-dimensional or two-dimensional information obtained by dimension-reducing or dimension-compressing the teacher data of the machining condition information 2 and the teacher data (feature amount information) of the auxiliary variables as the teacher data of the latent information. The second latent variable calculation unit 33 may calculate one-dimensional or two-dimensional information obtained by dimension-reducing or dimension-compressing the estimation data of the machining condition information 4 and the estimation data (feature amount information) of the auxiliary variables as the estimation data of the latent information.

[0049] The training data and estimation data for latent variables are obtained by, for example, reducing or compressing the dimensionality of the auxiliary variables, or the auxiliary variables and explanatory variables, respectively, so as to reduce the correlation between the auxiliary variables and the explanatory variables relative to the auxiliary variables. In this embodiment, the latent variables, like the explanatory variables, characterize the dependent variable and are variables that are not directly observed but are estimated from observed variables (auxiliary variables). In other words, the latent variables refer to variables that characterize observed variables and have been dimensionally reduced or compressed from at least the auxiliary variables. The explanatory variables, dependent variables, latent variables, and latent variable calculation information 7 used in the welding system 100B may each be composed of one or more variables or parameters. In this specification, the term "prediction / estimation of dependent variables" means "prediction of dependent variables" or "estimation of dependent variables." "Prediction of dependent variables" refers to assuming a dependent variable that is expected to be realized in the future, and "estimation of dependent variables" refers to inferring a dependent variable that is currently realized but cannot be directly observed. Note that "correlation" is not limited to a linear relationship between multiple variables, but refers to a relationship between multiple variables in which a change in one variable causes a change in the other variables, regardless of whether the relationship is linear or nonlinear.

[0050] The latent variable calculation information 7 refers to various information that is determined when the first latent variable calculation unit 13 calculates the teacher data for the latent variables and is used when calculating additional latent variables, and includes, for example, various parameters, various formulas, and / or algorithms. For example, if the latent variable calculation information 7 includes an algorithm, the second latent variable calculation unit 33 can calculate the estimation data for the latent variables using the same algorithm as the algorithm used to calculate the teacher data for the latent variables in the first latent variable calculation unit 13 of the learning device 10. In this way, by calculating the latent variables using the same algorithm as during learning, even during estimation, estimation using the same welding result estimation model 3 becomes possible.

[0051] Furthermore, the latent variable calculation information 7 passed from the learning device 10 to the estimation device 30 can be selected as appropriate for calculating latent variables in each device 10, 30. For example, when the learning device 10 calculates the teacher data for latent variables using principal component analysis, the latent variable parameters may be principal component vectors and fixed values, or in addition, latent variable parameters may be selected that exclude principal component vectors whose teacher data contribution rate for a specific auxiliary variable (monitoring information 5) is higher than a predetermined value. This allows, for example, latent variables that have a smaller correlation with explanatory variables than auxiliary variables to be used as teacher data and estimation data together with explanatory variables, thereby avoiding deterioration in estimation accuracy due to multicollinearity.

[0052] The first auxiliary variable calculation unit 12 and the second auxiliary variable calculation unit 32 detect multiple features that have been automatically extracted in advance by computer processing, etc., based on selection conditions that have been confirmed in advance through experiments, etc., from the teacher data of the monitoring information 5, which is, for example, time-series data, and the estimation data of the monitoring information 6, and calculate the value of each detected feature as feature information.The multiple features that can be detected at this time include, for example, the following:

[0053] FIG. 5 is a graph showing an example of time-series data. Examples of the time-series data included in the monitoring information 5 and 6 include time-series fluctuation data D1 of laser power and time-series fluctuation data D2 of the intensity of radiated near-infrared light at the irradiation point, as shown in Fig. 5. In Fig. 5, the horizontal axis represents time (seconds) and the vertical axis represents temperature (×100°C), and multiple features from which feature amount information in the data can be calculated are shown.

[0054] As one of the multiple feature values ​​in Figure 5, "rise_period" is a value that indicates the time from when the laser is turned on to the inflection point (predicted (70%)). This "rise_period" value is calculated from the number of measurement points (time x 5 μs) from the time the laser is turned on until it reaches 70% of the measured value at the time the laser rises.

[0055] Furthermore, "inflection1_period" is a value that indicates the time from when the laser is turned on until melting begins. The value of this "inflection1_period" is calculated from the number of measurement points (time x 5 μs) from the time the laser is turned on to inflection point 1 at the completion of the laser rise. "rise_value" is a value that indicates the inflection point (predicted (70%)). The value of this "rise_value" is calculated as 70% of the value measured at the time the laser rise is completed. "inflection1_value" is a value that indicates the temperature at the start of melting (melting point). The value of this "inflection1_value" is calculated as the measurement value at inflection point 1.

[0056] Additionally, "inflection3_value" is a value that indicates the temperature at which solidification begins (melting point). This "inflection3_value" value is calculated as the measurement value at inflection point 3, where solidification begins after the laser is stopped. "laseroff_inf3_period" is a value that indicates the time from when the laser is turned off to when solidification begins. This "laseroff_inf3_period" value is calculated using the number of measurement points from when the laser is turned off to inflection point 3 (time x 5 μs). "inflection3_5ms_value" is a value that indicates the first 5 ms of solidification (speed of cooling (hardness of solidified metal) x (related to molten volume)). This "inflection3_5ms_value" value is calculated as the measurement value 5 ms after inflection point 3.

[0057] Furthermore, "inflection2_value" is a value that indicates the temperature at which the temperature begins to drop. This "inflection2_value" value is calculated as the measurement value at inflection point 2, where the measurement value begins to drop after the laser is turned off. "inf2_inf3_period" is a value that indicates the time from when the temperature begins to drop to when solidification begins. This "inf2_inf3_period" value is calculated using the number of measurement points from inflection point 2 to inflection point 3 (time x 5 μs). "laseroff_inf2_period" is a value that indicates the time from when the laser is turned off to when the temperature begins to drop. This "laseroff_inf2_period" value is calculated using the number of measurement points from when the laser is turned off to when the temperature begins to drop.

[0058] Furthermore, "trend_start_value" refers to the first value (minimum) of the regression line interval. This "trend_start_value" value is calculated as the value at the time of inflection point 1 of the linear regression equation of the measured values ​​for the period from the time of inflection point 1 to the time of inflection point 2 (hereinafter referred to as the "specific period"). "trend_finish_value" refers to the last value (maximum) of the regression line interval. This "trend_finish_value" value is calculated as the value at the time of inflection point 2 of the linear regression equation of the measured values ​​for the specific period. "trend_period" refers to the time from the start of melting to the start of the temperature drop (time calculated). This "trend_period" value is calculated at the time (μs) of the specific period. "trend_slope" refers to the rate of temperature increase during melting. This "trend_slope" value is calculated as the slope of the linear regression equation of the measured values ​​for the specific period.

[0059] Other item elements, not shown in Figure 5, include "rise_value_sum," "laserontime_value_sum," "count," "mean," "std," "min," "25%," "50%," "75%," "max," and "dence1-5." "rise_value_sum" is a value that indicates the total temperature rise. The value of this "rise_value_sum" is calculated by integrating the measured values ​​from the time the laser is turned on to the "rise_period." "laserontime_value_sum" is a value that indicates the total temperature during laser output. The value of this "laserontime_value_sum" is calculated by integrating the measured values ​​from the time the laser is turned on to the time the laser is turned off.

[0060] Additionally, "count" is a value that indicates the time from the start of melting until the temperature begins to drop. This "count" value is calculated using the number of measurement points in a specific period (time x 5 μs). "Mean" is a value that indicates the degree of deviation from a linear trend (waviness). This "mean" value is calculated as the average value of the measured values ​​excluding the linear trend (linear regression) for a specific period. Similarly to "mean", "std" is a value that indicates the degree of deviation from a linear trend (waviness). This "std" value is calculated as the standard deviation of the measured values ​​for a specific period.

[0061] Furthermore, "min" is a value that indicates the minimum temperature during melting. This "min" value is calculated as the minimum value of the measured values ​​over a specific period. "25%" means the 25th / 100th measured value in ascending order. This "25%" value is calculated as the 25th percentile of the measured values ​​over a specific period. "50%" means the 50th / 100th measured value in descending order. This "50%" value is calculated as the 50th percentile of the measured values ​​over a specific period. "75%" means the 75th / 100th measured value in descending order. This "75%" value is calculated as the 75th percentile of the measured values ​​over a specific period. "max" is a value that indicates the maximum temperature during melting. This "max" value is calculated as the maximum value of the measured values ​​over a specific period.

[0062] Furthermore, "dence1" is a value that indicates the (rough) degree of temperature fluctuation (jaggedness) based on the time-series fluctuation data D2. This "dence1" value is calculated using the spectral density of 195-585 Hz (the sum of the spectral densities of three specified frequencies). "dence2" is a value that indicates the (slightly coarser) degree of temperature fluctuation (jaggedness) based on the time-series fluctuation data D2. This "dence2" value is calculated using the spectral density of 781-1171 Hz (the sum of the spectral densities of three specified frequencies).

[0063] "dence3" is a value that indicates the (medium) degree of temperature fluctuation (jaggedness) based on the time-series fluctuation data D2. The value of "dence3" is calculated using the spectral density of 1367-1757 Hz (the sum of the spectral densities of three specified frequencies). "dence4" is a value that indicates the (slightly finer) degree of temperature fluctuation (jaggedness) based on the time-series fluctuation data D2. The value of "dence4" is calculated using the spectral density of 1953-2343 Hz (the sum of the spectral densities of three specified frequencies). "dence5" is a value that indicates the (finer) degree of temperature fluctuation (jaggedness) based on the time-series fluctuation data D2. The value of "dence5" is calculated using the spectral density of 2539-2929 Hz (the sum of the spectral densities of three specified frequencies).

[0064] The first auxiliary variable calculation unit 12 and the second auxiliary variable calculation unit 32 may detect, for example, automatically extracted features from among the multiple features described above and calculate their values ​​as feature information, but this is not limiting. For example, the first auxiliary variable calculation unit 12 may detect the automatically extracted features based on the magnitude of their contribution to the estimation of the objective variable. The contribution is weighted by, for example, a predetermined machine learning algorithm used when the welding result learning unit 11 created the welding result estimation model 3, for multiple features included in the time-series data of the training data of the monitoring information 5 as their contribution to the estimation of various physical phenomena related to welding quality, such as spatter and porosity. The first auxiliary variable calculation unit 12 may detect the automatically extracted features based on their contribution and calculate their values ​​as feature information.

[0065] According to the welding system 100B of the third embodiment, it is possible to more precisely and accurately estimate the welding result, including fluctuations in the welding situation, by using the teacher data and estimation data of the auxiliary variables calculated by the first and second auxiliary variable calculation units 12 and 32 based on the monitoring information 5 and 6, as well as the teacher data and estimation data of the latent variables calculated by the first and second latent variable calculation units 13 and 33. Furthermore, it is possible to more effectively avoid deterioration in estimation accuracy due to the correlation between the monitoring information 5 and 6 and the machining condition information 2 and 4.

[0066] [Welding system operation] In welding system 100B, welding result information 1 and processing condition information 2 stored in, for example, an external storage device or storage medium, and monitoring information 5 output from welding monitor device 50 are input to learning device 10 as, for example, teacher data for the objective variable, teacher data for the explanatory variable, and teacher data for the auxiliary variable, respectively, and stored in a memory unit not shown.

[0067] The learning device 10 calculates feature amount information (teaching data of auxiliary variables) in a first auxiliary variable calculation unit 12 from the input data, including the teaching data of the monitoring information 5 from the welding monitor device 50. Furthermore, based on the feature amount information calculated in the first auxiliary variable calculation unit 12 and the teaching data of the machining condition information 2, a first latent variable calculation unit 13 calculates teaching data of latent variables that are abstractly expressed (for example, with small correlation) by performing dimensional reduction or dimensional compression on the correlation between the feature amount information and the teaching data of the machining condition information 2, and outputs latent variable calculation information 7.

[0068] Furthermore, learning device 10 performs, for example, machine learning in welding result learning unit 11 based on the teacher data of welding result information 1, the teacher data of processing condition information 2, and the teacher data of the latent variables calculated by first latent variable calculation unit 13, to create a welding result estimation model 3 that estimates the welding result including the welding strength, etc. Learning device 10 stores latent variable calculation information 7 used to calculate the teacher data of the latent variables and the created welding result estimation model 3 in a storage unit within the device, and outputs them to estimation device 30 of welding result determination device 20 so that they can be used.

[0069] Learning device 10 may output latent variable calculation information 7 and welding result estimation model 3 to an external storage device (not shown), for example, via a connected network. In this case, the external storage device temporarily or permanently stores the input latent variable calculation information 7 and welding result estimation model 3. Various data may be stored in a storage device (not shown) that constitutes a part of welding system 100B, instead of in the above-mentioned external storage device or storage medium.

[0070] Meanwhile, in laser welding machine 40, laser welding is performed based on set processing condition information 4. Monitoring information 6, which is state observation information during laser welding of an estimation target (e.g., a welded portion of a workpiece), is acquired by welding monitoring device 50 using sensor 212 attached to laser welding machine 40. Processing condition information 4 set in laser welding machine 40 and monitoring information 6 of welding monitoring device 50 are input so as to be usable as estimation data for explanatory variables and estimation data for auxiliary variables, respectively, to estimation device 30 of welding result determination device 20.

[0071] The estimation device 30 stores various data input together with the latent variable calculation information 7 and the welding result estimation model 3 in a storage unit (not shown), and calculates feature amount information (estimation data of auxiliary variables) from data including estimation data of the monitoring information 6 from the welding monitoring device 50 in a second auxiliary variable calculation unit 32. Furthermore, based on the feature amount information calculated in the second auxiliary variable calculation unit 32, the estimation data of the machining condition information 4, and the latent variable calculation information 7, a second latent variable calculation unit 33 calculates estimation data of latent variables that are abstractly expressed (for example, with low correlation) by performing dimensional reduction or dimensional compression on the correlation between the feature amount information and the estimation data of the machining condition information 4.

[0072] Furthermore, estimation device 30 inputs the estimation data of the latent variables calculated by second latent variable calculation unit 33 in welding result estimation unit 31 and the estimation data of processing condition information 4 to welding result estimation model 3, estimates the welding result including the welding strength and the like, and outputs welding result estimation value 8. Welding result estimation value 8 is stored in the storage unit, and is also output from estimation device 30 in an appropriately usable form such as display or print, and can be used by welding result determination device 20.

[0073] Note that when calculating the feature amount information, calculating the latent variables, and creating the welding result estimation model 3, the learning device 10 may add the estimation data of the processing condition information 4 set in the laser welding machine 40 and the estimation data of the monitoring information 6 from the welding monitor device 50 as teacher data for the explanatory variables and auxiliary variables to the teacher data of the processing condition information 2 and the teacher data of the monitoring information 5, or may refer to and use the same as teacher data for the explanatory variables and auxiliary variables. In this way, it is possible to use more data and contribute to improving the accuracy of estimation.

[0074] In welding system 100B of the third embodiment, feature amount information and latent variables are calculated based on monitoring information 5 as teacher data for auxiliary variables of welding result information 1, and then welding result estimation model 3 is created using this information. Furthermore, feature amount information and latent variables are calculated based on monitoring information 6 as estimation data for auxiliary variables using latent variable calculation information 7, and then this information and processing condition information 4, which is estimation data for explanatory variables, are input to welding result estimation model 3, and welding result estimated value 8 including weld strength and the like is output.

[0075] Therefore, according to the welding system 100B of the third embodiment, the welding result of the estimation target (the welded portion of the workpiece) can be estimated non-destructively from data related to the laser welding processing conditions and data monitored during welding, making it possible to estimate the weld strength contained in the welding result and the spot diameter resulting from the combination of the lens, fiber, and focal position.

[0076] Generally, in laser welding, even when laser welding is performed under the same processing conditions, the weld strength varies depending on factors such as the material of the workpiece and variations in the welding environment, so there is a problem that the weld strength cannot be measured without actually destroying the product. Another problem is that it is impossible to know the various processing conditions required to obtain the desired welding results unless an experiment is conducted in which actual laser light is irradiated.

[0077] However, welding system 100B of the third embodiment employs a configuration that can improve estimation accuracy by using feature amount information from monitoring information as an auxiliary variable and can avoid deterioration of estimation accuracy due to correlation, and a configuration that can estimate physical phenomena caused by welding, including fluctuations in the welding situation, as events that occurred during welding, thereby solving such problems. At the same time, not only can the same effects as those of the first and second embodiments be achieved, but also more precise estimation can be made, improving estimation accuracy and avoiding deterioration of accuracy due to correlation.

[0078] Although the welding system 100B of the third embodiment is intended to estimate the welding result including the weld strength of laser welding, the present invention is not limited to this, and it is also possible to estimate the welding result including the quality of welding based on various physical phenomena related to the welding quality, such as spatter, porosity, etc. In this case, the objective variable in the learning device 10 can be set to the welding quality of laser welding.

[0079] The data relating to welding quality includes at least one of data indicating good welding and data indicating poor welding, and the processing condition information 2 and monitoring information 5 at that time are used as teacher data for explanatory variables and auxiliary variables, and machine learning is performed in a learning device 10 to create a welding result estimation model 3. If an estimation device 30 uses this welding result estimation model 3 to perform estimation based on the estimation data of the processing condition information 4 and monitoring information 6, it is possible to estimate the welding result, including whether the welding is good or bad, even during laser welding.

[0080] Furthermore, in welding system 100B, learning device 10 and estimation device 30 can be centrally installed at the same site as laser welder 40, or separately installed in remote locations. This enables a wide variety of operations, even for global deployment, regarding estimation of the welding strength of laser welding.

[0081] [Fourth embodiment] Fig. 6 is an explanatory diagram showing the basic configuration of a welding system according to a fourth embodiment of the present invention. As shown in Fig. 6, welding system 100C according to the fourth embodiment differs from welding system 100B according to the third embodiment in that an estimation device 30 is provided inside welding monitor device 50A.

[0082] That is, welding monitoring device 50A includes a first memory unit (not shown) that stores monitoring information 5 and 6 obtained by monitoring, by a sensor, the progress of welding by a welding device (laser welder 40) that welds workpieces; a second memory unit (not shown) that stores a welding result estimation model 3 created based on processing condition information 2 including processing conditions for welding previously performed by the welding device (laser welder 40), welding result information 1 related to the welding result obtained after welding based on the processing condition information 2, and monitoring information 5 output from the first memory unit (not shown); and a welding result estimation unit 31 (estimation device 30) that inputs processing condition information 4 including the processing conditions set for the welding device (laser welder 40) and the monitoring information 6 stored in the first memory unit (not shown) into welding result estimation model 3 stored in the second memory unit (not shown), and estimates and outputs the welding result of welding performed by the welding device (laser welder 40).

[0083] Therefore, welding monitoring device 50A is configured to be able to input latent variable calculation information 7 and welding result estimation model 3 acquired from learning device 10 to internal estimation device 30. Then, in welding monitoring device 50A, this internal estimation device 30 inputs monitoring information 5 and 6 acquired by sensor 212 and stored in a first memory unit, and processing condition information 4 set in laser welder 40, into welding result estimation model 3 stored in a second memory unit, to estimate the welding result.

[0084] This allows welding monitor device 50A alone to estimate the welding result including the welding strength and the like and output welding result estimation value 8. Welding system 100C of the fourth embodiment can achieve the same effects as those of the third embodiment, and can also achieve the function of estimation device 30 with welding monitor device 50A alone.

[0085] [Fifth embodiment] Fig. 7 is an explanatory diagram showing the basic configuration of a welding system according to a fifth embodiment of the present invention. As shown in Fig. 7, a welding system 100D according to the fifth embodiment is similar to the welding system 100B of the third embodiment in that it uses monitoring information 5 and 6 from a welding monitor device (resistance welding checker) 51 as auxiliary variables (teaching data and estimation target data) correlated with processing condition information 2 and 4. However, it differs from the welding system 100B of the third embodiment in that it is applied to resistance welding.

[0086] That is, in the welding system 100D of the fifth embodiment, the welding device is a resistance welding machine (resistance welding power supply / head 41). The processing condition information 2 and 4 includes at least one processing condition: welding current, welding pressure, current application time, welding method, workpiece material, workpiece thickness, workpiece surface treatment, workpiece combination conditions, sampling period, displacement detection resolution, and nugget diameter. The monitoring information 5 and 6 includes time-series fluctuation data of the welding current and voltage and time-series fluctuation data of the welding pressure and displacement at the welding point.

[0087] In this welding system 100D of the fifth embodiment, the first auxiliary variable calculation unit 12 of the learning device 10 calculates feature information (teaching data of the auxiliary variables) based on the monitoring information 5, the first latent variable calculation unit 13 calculates teaching data of the latent variables based on the feature information and outputs latent variable calculation information 7, the welding result learning unit 11 creates a welding result estimation model 3, and the welding result estimation model 3 and the latent variable calculation information 7 are provided to the estimation device 30 of the welding result judgment device 20.

[0088] Second auxiliary variable calculation unit 32 of estimation device 30 of welding result determination device 20 uses latent variable calculation information 7 to calculate feature amount information (data for estimating auxiliary variables) based on monitoring information 6, second latent variable calculation unit 33 calculates data for estimating latent variables based on the feature amount information, and welding result estimation unit 31 inputs data including the data for estimating latent variables into welding result estimation model 3 to estimate the welding result. The obtained welding result estimated value 8 can then be used to estimate the welding strength, determine the welding result, or output welding result estimated value 8 to the outside. In this way, welding system 100D of the fifth embodiment can also achieve the same effects as welding system 100B of the third embodiment.

[0089] Although several embodiments of the present invention have been described above, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, and are also included in the inventions and their equivalents as defined in the claims. [Explanation of symbols]

[0090] 1. Welding result information (training data for objective variables) 2. Processing condition information (teaching data for explanatory variables) 3 Welding result estimation model 4. Processing condition information (data for estimating explanatory variables) 5. Monitoring information (teaching data for auxiliary variables) 6 Monitoring information (data for estimating auxiliary variables) 7 Latent variable calculation information 8 Estimated welding results 10 Learning Device 11 Welding Results Learning Section 12 First auxiliary variable calculation unit 13 First latent variable calculation section 20 Welding result judgment device 30 Estimation device 31 Welding result estimation section 32 Second auxiliary variable calculation unit 33 Second latent variable calculation section 40 Laser Welder 41 Resistance welding power supply head 50,50A welding monitor device 51 Resistance welding checker 100 Welding System

Claims

1. a welding device for welding the workpiece; a welding monitor device that monitors the progress of welding by the welding device using a sensor and outputs the obtained monitoring information; a learning device that receives as input, as training data, processing condition information including processing conditions for welding previously performed by the welding device and welding result information related to the welding result obtained after welding based on the processing conditions, and creates and outputs a welding result estimation model based on the training data; an estimation device that inputs processing condition information including processing conditions set in the welding device as estimation data into the welding result estimation model created by the learning device, and estimates and outputs a welding result of welding performed by the welding device based on the processing conditions, The learning device a first auxiliary variable calculation unit that calculates feature amount information from the training data of the monitoring information as training data of an auxiliary variable; a first latent variable calculation unit that calculates teacher data of latent variables that can be abstractly expressed by performing dimensional reduction or dimensional compression on the correlation between the teacher data of the auxiliary variables and the teacher data of the machining condition information based on the teacher data of the machining condition information and the teacher data of the auxiliary variables calculated by the first auxiliary variable calculation unit, and outputs latent variable calculation information; a welding result learning unit that creates and outputs the welding result estimation model based on teacher data of the processing condition information, teacher data of the welding result information, and teacher data of the latent variables, The estimation device includes: a second auxiliary variable calculation unit that calculates the feature amount information from the estimation data of the monitoring information as estimation data of an auxiliary variable; a second latent variable calculation unit that calculates estimation data of a latent variable that can be abstractly expressed by performing dimensional reduction or dimensional compression on the correlation between the estimation data of the auxiliary variable and the estimation data of the machining condition information, based on the estimation data of the machining condition information, the estimation data of the auxiliary variable calculated by the second auxiliary variable calculation unit, and the latent variable calculation information; a welding result estimation unit that inputs the estimation data of the processing condition information and the estimation data of the latent variables into the welding result estimation model, and estimates and outputs the welding result. Welding system.

2. a welding device for welding the workpiece; a welding monitor device that monitors the progress of welding by the welding device using a sensor and outputs the obtained monitoring information; a learning device that receives as input, as training data, processing condition information including processing conditions for welding previously performed by the welding device and welding result information related to the welding result obtained after welding based on the processing conditions, and creates and outputs a welding result estimation model based on the training data; an estimation device that inputs processing condition information including processing conditions set in the welding device as estimation data into the welding result estimation model created by the learning device, and estimates and outputs a welding result of welding performed by the welding device based on the processing conditions, The learning device a first auxiliary variable calculation unit that calculates feature amount information from the training data of the monitoring information as training data of an auxiliary variable; a first latent variable calculation unit that calculates one-dimensional or two-dimensional information by dimension-reducing or dimension-compressing the teacher data of the processing condition information and the teacher data of the auxiliary variables calculated by the first auxiliary variable calculation unit as teacher data of latent variables, and outputs latent variable calculation information; a welding result learning unit that creates and outputs the welding result estimation model based on teacher data of the processing condition information, teacher data of the welding result information, and teacher data of the latent variables, The estimation device includes: a second auxiliary variable calculation unit that calculates the feature amount information from the estimation data of the monitoring information as estimation data of an auxiliary variable; a second latent variable calculation unit that calculates, based on the latent variable calculation information, one-dimensional or two-dimensional information obtained by dimension reduction or dimension compression of the estimation data of the machining condition information and the estimation data of the auxiliary variables calculated by the second auxiliary variable calculation unit, as estimation data of latent variables; a welding result estimation unit that inputs the estimation data of the processing condition information and the estimation data of the latent variables into the welding result estimation model, and estimates and outputs the welding result. Welding system.

3. the first auxiliary variable calculation unit detects a plurality of predetermined features from teacher data of the monitoring information, which is time-series data, and calculates values ​​of each of the detected features as the feature amount information; The second auxiliary variable calculation unit detects a plurality of predetermined features from the estimation data of the monitoring information, which is time-series data, and calculates the value of each detected feature as the feature amount information. The welding system according to claim 1 or 2.

4. the welding device is a laser welder; The processing condition information includes at least one processing condition of the material of the workpiece, the thickness of the workpiece, the laser power, the laser irradiation time, the time from the start of laser irradiation to reach the peak power, the time from the peak power to the end of laser irradiation, the fiber diameter, the lens focal length, the focal position, and the laser diameter at the irradiation point. The welding system according to any one of claims 1 to 3.

5. The monitoring information includes time-series fluctuation data of laser power and time-series fluctuation data of radiated near-infrared light intensity at the irradiation point. The welding system of claim 4.

6. the welding device is a resistance welder; The processing condition information includes at least one processing condition of a welding current, a welding pressure, a current application time, a welding method, a material of the workpiece, a plate thickness of the workpiece, a surface treatment of the workpiece, a combination condition of the workpiece, a sampling period, a displacement amount detection resolution, and a nugget diameter. The welding system according to any one of claims 1 to 3.

7. a first storage unit that stores monitoring information obtained by monitoring the progress of welding by a welding device that welds workpieces using a sensor; a second storage unit that stores latent variable calculation information and a welding result estimation model created based on teacher data of processing condition information including processing conditions for welding previously performed by the welding device, teacher data of welding result information related to a welding result obtained after welding based on the processing condition information, and teacher data of latent variables calculated so as to be abstractly expressible by dimensionally reducing or dimensionally compressing a correlation between the teacher data of the auxiliary variables and the teacher data of the processing condition information based on teacher data of the auxiliary variables calculated as feature amount information from the teacher data of the processing condition information and the teacher data of the monitoring information output from the first storage unit; a welding result estimation unit that inputs estimation data of latent variables calculated from the estimation data of the auxiliary variables to perform dimension reduction or dimension compression on a correlation between the estimation data of the machining condition information and the estimation data of the machining condition information so as to be abstractly expressible, based on estimation data of machining condition information including machining conditions set in the welding device, estimation data of auxiliary variables calculated as the feature amount information from the estimation data of the monitoring information stored in the first storage unit, and the latent variable calculation information stored in the second storage unit, and the estimation data of the machining condition information into the welding result estimation model stored in the second storage unit, and estimates and outputs a welding result of welding performed at the welding device. Welding monitoring device.

8. a first storage unit that stores monitoring information obtained by monitoring the progress of welding by a welding device that welds workpieces using a sensor; a second storage unit that stores latent variable calculation information and a welding result estimation model created based on teacher data of processing condition information including processing conditions for welding previously performed by the welding device, teacher data of welding result information related to a welding result obtained after welding based on the processing condition information, and teacher data of latent variables calculated as one-dimensional or two-dimensional information obtained by dimension-reducing or dimension-compressing teacher data of auxiliary variables calculated as feature amount information from the teacher data of the processing condition information and the teacher data of the monitoring information output from the first storage unit; a welding result estimation unit that inputs estimation data of latent variables calculated as one-dimensional or two-dimensional information obtained by dimension-reducing or dimension-compressing estimation data of machining condition information including machining conditions set in the welding device and estimation data of auxiliary variables calculated as the feature amount information from the estimation data of the monitoring information stored in the first storage unit based on the latent variable calculation information stored in the second storage unit, and the estimation data of the machining condition information into the welding result estimation model stored in the second storage unit, and estimates and outputs a welding result of welding performed at the welding device. Welding monitoring device.

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