Laser processing state determination method and determination device

Abstract

This determination method for a processing state comprises: a step for detecting, using an optical sensor, at least one of heat radiating light, visible light, and reflected light that generate in a welding portion formed on the surface of a workpiece due to the workpiece being irradiated with laser light; a step for acquiring, from the optical sensor, a signal indicating a change in one of heat radiation, visible light, and reflected light in a time period corresponding to a welding time for each workpiece; a step in which a feature amount including a signal intensity of a signal is input into a determination model for determining a processing state and then the positions and number of abnormal shaped welds within a welding area with a melted length and a melted width are determined as the processing state, the abnormal shaped welds occurring when a foreign substance is present between the overlapping surfaces in the workpiece; and a step for outputting the determination result. The determination model is established on the basis of training data including: feature amounts calculated under the condition that abnormal shaped welds are occurring; and processing states under the condition that abnormal shaped welds are occurring.

Description

[Technical Field]

[0001] The present disclosure relates to a method and an apparatus for determining a processing state in laser processing for lap welding. [Background technology]

[0002] Patent Document 1 discloses a laser welding condition determination method for determining the quality of welding, such as the quality of the workpiece, applied to a laser welding method in which a pulsed laser beam is irradiated onto the workpiece to perform welding. The method of Patent Document 1 detects the intensity of plasma light and reflected light emitted from the workpiece during laser welding as detected light intensity, and extracts a pulse-by-pulse feature value for each pulse of the laser beam based on the detected light intensity in a predetermined extraction section from one cycle of the detected light intensity corresponding to one pulse of the laser beam. The pulse-by-pulse feature value is calculated based on the average value of the detected light intensity, the amount of change due to differential processing, the amplitude due to differential processing, etc. The method of Patent Document 1 obtains the lower or upper limit of the pulse-by-pulse feature value as an extreme value, compares the extreme value with a predetermined threshold, and determines the occurrence of a welding defect as the welding condition for each workpiece. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2000-153379 Summary of the Invention

[0004] According to one aspect of the present disclosure, there is provided a method for determining a processing state in laser processing for overlap welding. The method includes the steps of using an optical sensor to detect at least one of thermal radiation, visible light, and reflected light generated at a weld formed on a surface of a workpiece by irradiating the workpiece with laser light, acquiring a signal indicating a change in one of the thermal radiation, visible light, and reflected light over a time period corresponding to the welding time for each workpiece from the optical sensor, inputting feature values ​​including signal intensities of the signals based on the detected signal into a determination model for determining the processing state, and determining the positions and number of melt shape abnormalities in a weld region having a melt length and a melt width, which may occur when a foreign object is present on the overlapping surfaces of the workpieces, as the processing state, and outputting the positions and number of the determined melt shape abnormalities as a determination result. The determination model is constructed based on training data including feature values ​​calculated under conditions in which the melt shape abnormality occurs and the processing state under conditions in which the melt shape abnormality occurs.

[0005] According to one aspect of the present disclosure, a processing state determination device for laser processing for overlap welding is provided. The determination device includes an arithmetic circuit and a communication circuit. The communication circuit receives a signal generated by an optical sensor detecting at least one of thermal radiation, visible light, and reflected light generated at a weld formed on the surface of a workpiece by irradiating the workpiece with laser light. The signal indicates a change in one of the thermal radiation, visible light, and reflected light over a time period corresponding to the welding time for each workpiece. The arithmetic circuit acquires the signal via the communication circuit and inputs feature values, including signal strength, based on the signal into a determination model for determining the processing state. The arithmetic circuit determines the position and number of melt shape abnormalities in a welded area having a melt length and a melt width, which occur when a foreign object is present on the overlapping surfaces of the workpieces, as the processing state. The determined position and number of melt shape abnormalities are output as a determination result via the communication circuit. The determination model is constructed based on training data including feature values ​​calculated under conditions in which the melt shape abnormality occurs and the processing state under conditions in which the melt shape abnormality occurs. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is a diagram illustrating an overview of a determination system according to a first embodiment of the present disclosure. [Figure 2] FIG. 1 is a diagram illustrating a configuration of a laser processing device in a determination system. [Figure 3] FIG. 1 is a diagram illustrating a configuration of a spectroscopic device in a determination system; [Figure 4] FIG. 1 is a block diagram illustrating a configuration of a determination device in a determination system. [Figure 5] 1 is a flowchart illustrating a determination process in a determination device; [Figure 6] FIG. 1 is a diagram for explaining a signal acquired by a determination device; [Figure 7] FIG. 10 is a diagram for explaining a process for calculating a feature amount in a determination device. [Figure 8] FIG. 1 is a diagram for explaining processing of a determination model in a determination device. [Figure 9] 1 is a flowchart illustrating a process for training a decision model. [Figure 10] FIG. 1 is a diagram for explaining a signal generated when a melt shape abnormality occurs. DETAILED DESCRIPTION OF THE INVENTION

[0007] In laser welding, for example, if there is dirt or foreign matter on the workpiece, an abnormal fusion shape, such as a hole in the weld, may occur during laser irradiation. While the method of determining the occurrence of welding defects using a threshold value can determine the presence or absence of such an abnormality, it has been difficult to determine the detailed processing state, such as the number and location of the abnormal fusion shape.

[0008] The present disclosure provides a determination method and a determination device that can determine in detail the processing state in laser processing for lap welding.

[0009] Hereinafter, embodiments will be described in detail with reference to the drawings as appropriate. However, more detailed description than necessary may be omitted. For example, detailed description of well-known matters or redundant description of substantially identical configurations may be omitted. This is to avoid unnecessary redundancy in the following description and to facilitate understanding by those skilled in the art. Note that the inventors provide the accompanying drawings and the following description to enable those skilled in the art to fully understand the present disclosure, and the subject matter described in the claims is not limited by them.

[0010] (Embodiment 1) In embodiment 1, as an example of using the judgment method and judgment device according to the present disclosure, a judgment system is described which detects components of light generated during laser processing for lap welding, acquires a signal based on the detected components, and judges the processing state.

[0011] 1. Configuration The determination system according to the first embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an overview of a determination system 100 according to the present embodiment.

[0012] 1-1. System Overview The determination system 100 includes a laser processing device 30 that performs laser processing for lap welding, a spectroscopic device 40 that detects light components, and a determination device 50. The determination device 50 is an example of a determination device according to the present disclosure. A workpiece 70 for lap welding is made of, for example, metal. When irradiated with laser light 6, thermal radiation light in the near-infrared region (also referred to as "thermal radiation") due to temperature rise and metal-specific light emission or plasma light emission, which is mainly visible light, are generated. Furthermore, a portion of the laser light 6 that does not contribute to processing is reflected as return light. Thus, when the laser processing device 30 irradiates the workpiece 70 with laser light 6, thermal radiation, visible light, and reflected light are generated in a fusion zone 27 formed in the workpiece 70. The fusion zone 27 is an example of a weld zone in this embodiment.

[0013] When the laser beam 6 is irradiated, if a foreign object 80 made of a carbon-based material such as resin or oil is present between the two members 70a and 70b that make up the workpiece 70, an abnormal fusion shape such as a hole or a protrusion will occur in the welded area. The welded area is an area that remains as a trace of the fusion zone 27 on the surface of the member 70a on the laser processing device 30 side after processing, and has a fusion length that is the length in the direction of welding progress and a fusion width that is the width in the direction perpendicular to the direction of welding progress. Melting of such foreign object 80 present on the overlapping surfaces of the workpieces 70 also causes light to be emitted in the fusion zone 27.

[0014] The light generated in the molten portion 27 is collected by the laser processing device 30 and transmitted to the spectroscopic device 40 through the optical fiber 13 connecting the laser processing device 30 and the spectroscopic device 40. The light transmitted to the spectroscopic device 40 is separated into thermal radiation, visible light, and reflected light, which are detected by the optical sensor 22 of the spectroscopic device 40 and converted into signals. Upon receiving a signal from the spectroscopic device 40, the determination device 50 of this embodiment determines the processing state, such as the position and number of melt shape abnormalities that appear in the form of holes, as well as the size of the melt shape abnormalities, and outputs the determination result.

[0015] 1-2.Configuration of laser processing equipment 2 is a diagram illustrating the configuration of a laser processing apparatus 30 according to this embodiment. The laser processing apparatus 30 includes a laser oscillator 1, a laser transmission fiber 2, a lens barrel 3, a collimating lens 4, focusing lenses 5 and 11, a first mirror 7, and a second mirror 8.

[0016] The laser oscillator 1 supplies light for generating pulsed laser light 6, for example, with a wavelength of approximately 1070 nanometers (nm). The light supplied from the laser oscillator 1 is amplified while being transmitted through the laser transmission fiber 2, passes through a collimating lens 4 to obtain a parallel beam, and forms the laser light 6, which then travels straight through the lens barrel 3. The lens barrel 3 constitutes the processing head of the laser processing device 30.

[0017] The laser beam 6 is reflected by the first mirror 7 except for a portion that passes through, and is then focused by the condenser lens 5 and irradiated onto the workpiece 70, which is fixed by a holding jig 26 on a scanning table (not shown), for example. This performs laser processing for overlap welding of the workpiece 70. The wavelength of the laser beam 6 is not limited to 1070 nm, and it is preferable to use a wavelength that is highly absorbed by the material.

[0018] When the laser beam 6 is irradiated, the molten zone 27 generates thermal radiation from the workpiece 70, visible light due to plasma emission, and reflected light of the laser beam 6. These beams pass through the first mirror 7, are reflected by the second mirror 8, and are collected by the collecting lens 11, and then transmitted to the spectrometer 40 through the optical fiber 13. The light that is partially transmitted through the second mirror 8 may be detected by a camera or a sensor.

[0019] 1-3.Configuration of spectrometer 3 is a diagram illustrating the configuration of a spectroscopic device 40 of this embodiment. The spectroscopic device 40 includes, inside a housing 28, a collimating lens 15, a third mirror 16, a fourth mirror 17, a fifth mirror 18, condenser lenses 19, 20, and 21, an optical sensor 22, a transmission cable 23, and a controller 24. The housing 28 prevents unwanted light from entering the inside of the spectroscopic device 40 from the outside and prevents light leakage from the inside.

[0020] The collimating lens 15 converts the light transmitted from the laser processing device 30 through the optical fiber 13 back into parallel light. The third mirror 16 transmits visible light with a wavelength of 400 nm to 700 nm, for example, and reflects other components. The fourth mirror 17 reflects the reflected light of the laser light 6 with a wavelength of approximately 1070 nm, for example, and transmits other components. The fifth mirror 18 reflects thermal radiation with a wavelength of 1300 nm to 1550 nm, for example.

[0021] The light that has passed through the collimating lens 15 is split into visible light, reflected light, and thermal radiation by the third mirror 16, fourth mirror 17, and fifth mirror 18, and each of these components is focused by the focusing lenses 19 to 21. Note that any band-pass filter may be placed in the optical path after the third mirror 16, fourth mirror 17, and fifth mirror 18, respectively, to select the wavelength to be passed.

[0022] The optical sensor 22 includes, for example, optical sensors 22a, 22b, and 22c, each highly sensitive to a different wavelength. The optical sensors 22a, 22b, and 22c detect visible light, reflected light, and thermal radiation collected by the respective collecting lenses 19 to 21, and generate electrical signals corresponding to the intensities of the detected light. Note that the optical sensor 22 may be configured with a single optical sensor capable of detecting the intensity for each wavelength.

[0023] The electrical signal generated by the optical sensor 22 is transmitted to the controller 24 via the transmission cable 23. The controller 24 is a hardware controller and controls the overall operation of the spectroscopic device 40. The controller 24 includes a CPU, a communication circuit, etc., and transmits the electrical signal received from the optical sensor 22 to the determination device 50. The controller 24 is equipped with, for example, an A / D converter, and converts the analog electrical signal into a digital signal (also simply referred to as a "signal"). Note that the sampling period for converting into a digital signal is preferably, for example, 1 / 100 or less of the time for controlling the output of the laser light 6, from the viewpoint of ensuring a sufficient number of samples for capturing the characteristics of the machining process and the trends in local values ​​of physical quantities in determining the machining state.

[0024] 1-4.Configuration of the judgment device 4 is a block diagram illustrating the configuration of a determination device 50 according to this embodiment. The determination device 50 is configured as an information processing device such as a computer. The determination device 50 includes a CPU 51 that performs calculation processing, a communication circuit 52 for communicating with other devices, and a storage device 53 that stores data and computer programs.

[0025] The CPU 51 is an example of an arithmetic circuit of the determination device in this embodiment. The CPU 51 executes a control program 56 stored in the storage device 53 to realize predetermined functions including training and execution of a determination model 57. The determination device 50 realizes the function of the determination device in this embodiment by the CPU 51 executing the control program 56. Note that the arithmetic circuit configured as the CPU 51 in this embodiment may be realized by various processors such as an MPU or a GPU, or may be configured by one or more processors.

[0026] The communication circuit 52 is a communication circuit that performs communication in accordance with a standard such as IEEE802.11, 4G, or 5G. The communication circuit 52 may perform wired communication in accordance with a standard such as Ethernet (registered trademark). The communication circuit 52 is connectable to a communication network such as the Internet. The determination device 50 may communicate directly with other devices via the communication circuit 52 or may communicate via an access point. The communication circuit 52 may be configured to be able to communicate with other devices without going through a communication network. For example, the communication circuit 52 may include connection terminals such as a USB (registered trademark) terminal and an HDMI (registered trademark) terminal.

[0027] The storage device 53 is a storage medium that stores computer programs and data necessary to realize the functions of the determination system 100, and stores a control program 56 executed by the CPU 51 and various data. After the determination model 57 is constructed, the storage device 53 stores the determination model 57. The determination model 57 is constructed based on training data including feature amounts calculated from signals under conditions in which a melt shape abnormality occurs and processing states when the melt shape abnormality occurs. Details of the determination model 57 will be described later.

[0028] The storage device 53 is configured with, for example, a magnetic storage device such as a hard disk drive (HDD), an optical storage device such as an optical disk drive, or a semiconductor storage device such as an SSD. The storage device 53 may include a temporary storage element configured with, for example, a RAM such as a DRAM or an SRAM, and may function as an internal memory of the CPU 51.

[0029] 2.Operation In the determination system 100 configured as above, as shown in Fig. 1, for example, the spectroscopic device 40 detects, by the optical sensor 22, the thermal radiation, visible light, and reflected light generated in the molten part 27 by the irradiation of the laser light 6. The spectroscopic device 40 transmits signals according to the intensities of the detected thermal radiation, visible light, and reflected light to the determination device 50. The operation of the determination device 50 in this system 100 will be described below.

[0030] 2-1. Judgment process The determination process for determining the position, number, and size of the melting shape abnormality in the determination device 50 will be described below with reference to FIGS.

[0031] 5 is a flowchart illustrating a determination process in the determination device 50 of this embodiment. Each process shown in this flowchart is executed by, for example, the CPU 51 of the determination device 50. This flowchart is started when, for example, a user of the determination system 100 inputs a predetermined operation for starting the determination process from an input device connected via the communication circuit 52.

[0032] First, the CPU 51 acquires signals corresponding to the thermal radiation, visible light, and reflected light detected by the optical sensor 22 of the spectrometer 40 through the communication circuit 52 (S1).

[0033] FIG. 6 is a diagram for explaining signals acquired by the determination device 50. (A) of FIG. 6 illustrates an example of a signal waveform corresponding to any of thermal radiation, visible light, and reflected light when a melt shape abnormality occurs during processing. (B) of FIG. 6 illustrates an example of a signal waveform corresponding to any of thermal radiation, visible light, and reflected light when a melt shape abnormality does not occur. (C) of FIG. 6 shows the output of the laser light 6 irradiated onto the workpiece 70. The signals of (A) and (B) of FIG. 6 correspond to any of thermal radiation, visible light, and reflected light generated by the laser output of (C) of FIG. 6.

[0034] In (A) to (C) of Figure 6, the horizontal axis represents time, and the vertical axis represents signal intensity ((A) and (B) of Figure 6) or laser output ((C) of Figure 6). Time T1 represents a time period corresponding to one pulse of the laser light 6, and time T2 represents a time period corresponding to the peak output excluding the rise and fall of the laser output. In the laser processing apparatus 30 of this embodiment, welding is performed for each workpiece 70 at time T1. In step S1, the CPU 51 acquires signals representing changes in thermal radiation, visible light, and reflected light at time T1, which corresponds to the welding time for each workpiece 70.

[0035] As shown in FIG. 6A, when a melting shape abnormality occurs, a waveform signal is acquired in which a peak in signal intensity temporarily increases compared to the normal state shown in FIG. 6B. The signal peak when a melting shape abnormality occurs is due to, for example, light emission from a foreign object 80 that causes the abnormality. Note that when a melting shape abnormality occurs, the light emission may be momentarily attenuated by the foreign object 80, resulting in a temporary attenuation peak. In this case, a waveform signal is acquired in which a peak in signal intensity temporarily decreases. Even in such a case, it is sufficient to extract a local minimum value and calculate an integral value obtained by subtracting the average value Sa from the signal intensity in the section Tp in the flow chart shown in FIG. 5 (described later). In the following flow, the flow will be described using, as an example, a waveform signal in which a peak in signal intensity temporarily increases.

[0036] 5, next, the CPU 51 calculates, from the acquired signal, a feature amount to be input to the determination model 57 (S2). In this embodiment, the CPU 51 calculates, as the feature amount, an intensity value based on the signal intensity at the peak (hereinafter referred to as a "peak intensity value") in addition to the signal intensity to which preprocessing such as normalization has been applied.

[0037] Fig. 7 is a diagram for explaining the process (S2) of calculating the feature amount in the determination device 50. Fig. 7(A) shows the time change in the signal intensity of the signal corresponding to thermal radiation, visible light, or reflected light when a melting shape abnormality occurs, similar to Fig. 6(A). The process of calculating the feature amount of the peak intensity value in step S2 of Fig. 5 will be described using Fig. 7.

[0038] The CPU 51 first performs a process of detecting peaks in the acquired signal. For example, the CPU 51 performs a calculation to compare signal strength values ​​for each sampling period, and extracts a point whose value is greater than those of adjacent points before and after it as a local maximum value. At this time, a threshold may be set to limit the value extracted as the local maximum value to a predetermined signal strength or greater. The CPU 51, for example, extracts a local minimum value of the signal strength in the same way as the local maximum value, and detects a peak as a region of the signal waveform in a section Tp sandwiched between the local maximum value and two adjacent points. The section Tp corresponds to the time at which the peak occurs. Figure 7(B) shows an example of detecting a peak in section Tp in the signal of Figure 7(A).

[0039] After detecting the peak, the CPU 51 calculates the average value Sa of the signal intensity excluding the peak. The average value Sa is calculated as the average value of the signal intensity over a time period (T2-Tp) excluding a section Tp from the time T2 of the peak output in one pulse of the laser light 6. (C) of Fig. 7 shows an example in which the average value Sa is calculated for the example of (B) of Fig. 7.

[0040] Next, the CPU 51 calculates an integral value for the section Tp corresponding to the time when the peak occurred, which is the peak intensity value obtained by subtracting the average signal intensity Sa excluding the peak from the signal intensity for the section Tp. Fig. 7D shows an example of calculating the integral value in the example of Fig. 7C. The integral value corresponds to the area of ​​the region Rp shown in Fig. 7D.

[0041] After calculating the above-described feature amounts (S2), the CPU 51 inputs the feature amounts to the determination model 57 and performs processing of the determination model (S3) to determine the position, number, and size of the melting shape abnormality. The feature amount of the signal intensity is input to the determination model 57 as, for example, the amplitude of the signal waveform for each sampling period in A / D conversion.

[0042] Fig. 8 is a diagram for explaining the processing (S3) of the judgment model. Fig. 8(A) shows a signal waveform when a melt shape abnormality occurs, similar to Fig. 6(A). Fig. 8(B) shows a schematic appearance of the member 70a of the workpiece 70 on the laser processing device 30 side after processing when the signal of Fig. 8(A) is generated. In Fig. 8(B), a hole 85 has occurred as an example of a melt shape abnormality in the welding region 270 having a melt length Wx and a melt width Wy.

[0043] The laser processing apparatus 30 of this embodiment performs welding over a fusion length Wx for each workpiece 70 in a time T1 corresponding to one pulse. In the example of Fig. 8(A), when the laser processing apparatus 30 proceeds with processing in the positive direction of the x-axis in Fig. 8(B), a hole 85 is generated, and a peak in section Tp occurs in response to this, and is detected in step S2.

[0044] In the example of FIG. 8, in the judgment model processing (S3), the CPU 51 inputs the feature quantities of the signal intensity and peak intensity value calculated from the signal of FIG. 8A into the judgment model 57 to judge the position, number, and size of the hole 85 of FIG. 8B. The position is judged as the coordinates of the center of gravity of the hole 85 in an orthogonal coordinate system with the welding start point on the member 70a as the origin. The size is judged as the area of ​​the hole 85, for example. The number is judged to be "1" because there are no molten shape abnormalities other than the hole 85 in FIG. 8B.

[0045] 5, the CPU 51 outputs the determination results of the positions, number, and sizes of the melt shape abnormalities such as holes 85 via the communication circuit 52 (S4). The determination results may be received and displayed by, for example, an external information processing device or display device. Alternatively, the determination device 50 may be provided with a display device (for example, a display) capable of communicating with the CPU 51, and the determination results may be displayed on the display device.

[0046] Thereafter, the CPU 51 ends the flowchart of Fig. 5. The flowchart of Fig. 5 is repeatedly executed, for example, every time welding is performed on each workpiece 70.

[0047] According to the above-described determination process, the determination device 50 of this embodiment acquires a signal generated by the optical sensor 22 of the spectrometer 40 (S1), calculates feature quantities from the signal (S2), and determines the position, number, and size of the fusion shape abnormality using the determination model 57 based on the feature quantities (S3). This allows the determination device 50 to determine in detail the processing state regarding the fusion shape abnormality in laser processing for lap welding.

[0048] 5, the feature amount may be calculated for all of thermal radiation, visible light, and reflected light, or may be calculated for only one of thermal radiation, visible light, and reflected light. Also, in the processing of the determination model (S3), the determination model 57 may determine, for example, only the position and number of melt shape abnormalities.

[0049] Furthermore, in step S2, the attenuation peaks described above may also be detected and the integral value of the signal intensity may be calculated. In this case, the value for the attenuation peak will be negative, while the peak intensity value calculated for the increase peak described in the example of FIG. 7 will be positive. In this way, peaks due to attenuation and peaks due to increase in signal intensity can be distinguished, and the change in luminescence due to the foreign matter 80 can be reflected in the feature amount. Note that even when attenuation peaks are detected, the integral value of the signal intensity for the peaks is not limited to this. For example, the absolute value of the peak may be used as the feature amount, focusing only on the existence and magnitude of the peak.

[0050] 2-2. Training process The training process for constructing the determination model 57 will be described below with reference to FIGS.

[0051] 9 is a flowchart illustrating a training process for the determination model 57. Each process in this flowchart is executed by the CPU 51 of the determination device 50, for example.

[0052] First, the CPU 51 acquires training data that is stored in advance in, for example, the storage device 53 (S11).

[0053] The training data is data that associates feature quantities, such as signal intensities and peak intensity values ​​of thermal radiation, visible light, and reflected light, with the position, number, and size of melt shape abnormalities as processing states. The training data is constructed by recording, under multiple conditions in which the processing state changes, feature quantities calculated from signals based on thermal radiation, visible light, and reflected light detected during laser processing, and processing states determined by appearance measurement of the welded area 270 after processing. The appearance measurement can be performed, for example, by observing the welded area 270 with an optical microscope or by measuring images of the welded area 270, but is not limited to these.

[0054] Fig. 10 is a diagram for explaining signals generated when an abnormal melt shape occurs. In constructing training data, feature quantities based on signals having various waveform patterns as exemplified in Fig. 10 and corresponding processing states are collected.

[0055] In Figure 10(A), a peak corresponding to one melt shape anomaly is detected in all of the signals Lt, Lv, and Lr, which are generated according to the intensities of thermal radiation, visible light, and reflected light, respectively. In Figure 10(B), a peak corresponding to one melt shape anomaly is detected in both the thermal radiation and visible light signals Lt and Lv. In Figure 10(C), a peak corresponding to one melt shape anomaly is detected only in the reflected light signal Lr. In Figure 10(D), two peaks corresponding to two melt shape anomalies are detected in each of the thermal radiation, visible light, and reflected light signals Lt, Lv, and Lr. As shown in Figures 10(A) and 10(D), the reflected light signal Lr tends to have peaks at earlier times than the thermal radiation and visible light signals Lt and Lv.

[0056] By including in the training data feature quantities based on signals with such diverse peak detection patterns and corresponding processing states, a determination model 57 can be generated that can determine the processing state in detail even when conditions such as the light, time, and number of peaks detected change, as described below. In this embodiment, by using feature quantities based on three signals, thermal radiation, visible light, and reflected light, the determination model 57 can reflect the tendency for melt shape abnormalities to occur even when peaks are detected in only one or two signals, thermal radiation, visible light, and reflected light. For example, data containing two or fewer peaks, which is the number of peaks expected during actual processing, is used as the training data. However, this is not limited to this, and data containing three or more peaks may also be used. Furthermore, a time interval considered to be one peak may be set in advance.

[0057] When the CPU 51 acquires training data (S1), it performs machine learning using the training data to generate a determination model 57 (S2). The determination model 57 is generated as a regression model based on, for example, a random forest or a neural network.

[0058] According to the above training process, a judgment model 57 can be generated as a trained model that judges the position, number, and size of molten shape abnormalities from features based on signals corresponding to thermal radiation, visible light, and reflected light detected during laser processing.

[0059] The training process for the determination model 57 may be executed in an information processing device separate from the determination device 50. The determination device 50 may acquire the constructed determination model by the communication circuit 52, for example, via a communication network.

[0060] Furthermore, the training data for the determination model 57 may include a feature value for when no melt shape abnormality occurs and a processing state for when no melt shape abnormality occurs. The feature value for when no melt shape abnormality occurs may be, for example, a peak intensity value of "0." The processing state for when no melt shape abnormality occurs may be, for example, the position, number, and size of the melt shape abnormality of "0."

[0061] 3. Effects etc. As described above, in this embodiment, the determination processes (S1 to S4) provide a method for determining a processing state in laser processing for overlap welding. This method includes the steps of: using an optical sensor 22 to detect at least one of thermal radiation (thermal radiation light), visible light, and reflected light generated in a molten zone 27 (an example of a welded zone) formed on the surface of the workpiece 70 when the workpiece 70 is irradiated with a laser beam 6; acquiring from the optical sensor 22 signals indicating changes in the thermal radiation, visible light, and reflected light over a time T1 (time interval) corresponding to the welding time for each workpiece 70; inputting feature quantities including signal intensities of the signals into a determination model 57 that determines the processing state, and determining, as the processing state, the positions and number of molten shape abnormalities in a welding region 270 having a molten length Wx and a molten width Wy that occur when a foreign object 80 is present on the overlapping surfaces of the workpieces 70; and outputting the determined positions and number of molten shape abnormalities as a determination result (S4). The determination model 57 is constructed based on training data including feature amounts calculated under conditions where the melt shape abnormality occurs and processing states under conditions where the melt shape abnormality occurs.

[0062] According to the above method, a signal based on one or more of thermal radiation, visible light, and reflected light generated and detected by irradiation with laser light 6 is acquired (S1), feature quantities including signal intensity are calculated, and the positions and number of fusion shape abnormalities are determined as the processing state (S2, S3). This makes it possible to determine the processing state related to fusion shape abnormalities in detail based on the signal intensity of at least one of thermal radiation, visible light, and reflected light detected during laser processing for lap welding.

[0063] In this embodiment, the determination steps (S2, S3) include detecting a signal peak and determining the size of the melt shape abnormality as the processing state. The output step (S4) further includes outputting the determined size of the melt shape abnormality as the determination result. The feature value includes a peak intensity value, which is an example of an intensity value based on the signal intensity of the signal at the peak. This allows the processing state, including the size of the melt shape abnormality, to be determined in more detail based on the peak intensity value.

[0064] In this embodiment, the intensity value is an integral value obtained by subtracting the average value Sa of the signal intensities of the signals excluding the peak from the signal intensity of the peak, and integrating the result over the section Tp (the time when the peak occurs) (see FIG. 7). This allows the intensity of the light emitted due to the occurrence of the melt shape abnormality caused by the foreign matter 80 to be reflected in the feature amount, making it easier to determine the processing state, such as the size of the melt shape abnormality, in detail.

[0065] In this embodiment, the determination model 57 includes a trained model generated by machine learning using training data that associates, under each of a plurality of conditions under which the processing state changes, feature amounts calculated from signals based on at least one of thermal radiation, visible light, and reflected light detected during laser processing with the processing state determined by appearance measurement of the welding area 270. In this way, the determination model 57 that determines the processing state is obtained from feature amounts based on at least one of thermal radiation, visible light, and reflected light.

[0066] In the determination system 100 of this embodiment, the determination device 50 is an example of a device for determining the processing state in laser processing for lap welding. The determination device 50 includes a CPU 51 as an example of an arithmetic circuit, and a communication circuit 52. The communication circuit 52 receives a signal generated by an optical sensor 22 detecting at least one of thermal radiation (thermal radiation light), visible light, and reflected light generated in a molten zone 27 (an example of a welded zone) formed on the surface of the workpiece 70 when the laser beam 6 is irradiated onto the workpiece 70. The signal indicates a change in at least one of the thermal radiation, visible light, and reflected light during time T1 as an example of a time interval corresponding to the welding time for each workpiece 70. The CPU 51 acquires a signal via the communication circuit 52 (S1), inputs feature quantities including the signal intensity of the signal based on the signal into a determination model 57 that determines the processing state, determines the positions and number of molten shape abnormalities in the welding area 270 having the molten length Wx and the molten width Wy that occur when a foreign object 80 is present on the overlapping surface of the workpiece 70, as the processing state (S2, S3), and outputs the determined positions and number of molten shape abnormalities as the determination result via the communication circuit 52 (S4). The determination model 57 is constructed based on training data including feature quantities calculated under conditions in which molten shape abnormalities occur and the processing state under conditions in which molten shape abnormalities occur.

[0067] According to the above-described determination device 50, the above-described determination method can be executed to determine in detail the processing state in laser processing for lap welding.

[0068] (Other embodiments) As described above, the above embodiments have been described as examples of the technology disclosed in this application. However, the technology in this disclosure is not limited to these, and can be applied to embodiments in which appropriate modifications, substitutions, additions, omissions, etc. are made. Furthermore, it is also possible to combine the components described in each of the above embodiments to create new embodiments.

[0069] In the above-described first embodiment, the determination device 50 calculates the feature quantities of the signal intensity and the peak intensity value in the determination process (S2 in FIG. 5). In the present embodiment, in step S2, the peak intensity value may not be calculated, and only the signal intensity may be used as the feature quantity.

[0070] In the above-described first embodiment, the determination device 50 acquires signals corresponding to thermal radiation, visible light, and reflected light detected by the optical sensor 22 of the spectrometer 40 (S1). In the present embodiment, the determination device 50 may acquire signals for only one or two of the thermal radiation, visible light, and reflected light. In this case, in steps S2 and S3, feature amounts are calculated for only one or two of the signals of the thermal radiation, visible light, and reflected light, and input to the determination model 57. Furthermore, in the present embodiment, the determination model 57 may be constructed using training data of feature amounts and processing states based on only one or two of the signals of the thermal radiation, visible light, and reflected light.

[0071] In the first embodiment, the determination model 57 is constructed using the feature quantities such as signal intensity and the positions, number, and size of the melt shape abnormality as training data (S11 to S12). In the present embodiment, the determination model 57 may be constructed using the feature quantities and the positions and number of the melt shape abnormality as training data. In this case, the determination device 50 determines the positions and number of the melt shape abnormality as the processing state in the determination process (S1 to S4).

[0072] According to the determination method and determination device of the present disclosure, the processing state can be determined in detail in laser processing for lap welding, particularly with regard to molten shape abnormalities occurring in the welding area.

[0073] The present disclosure is not limited to the above-described embodiments, and various modifications are possible. In other words, embodiments obtained by combining technical means modified as appropriate by a person skilled in the art are also within the scope of the present disclosure. [Industrial Applicability]

[0074] The present disclosure is applicable to a system for determining the processing state in laser processing for overlap welding, and in particular to a method and apparatus for determining an abnormal fusion shape of a weld. [Explanation of symbols]

[0075] 1 Laser oscillator 2. Laser transmission fiber 3 Telescope 4 Collimating Lenses 5, 11 Condenser lens 6. Laser light 7. Mirror No. 1 8 Second Mirror 13 Optical Fiber 15 Collimating Lens 16 Third Mirror 17 4th mirror 18 5th Mirror 19, 20, 21 Condenser lenses 22 Optical Sensor 23 Transmission cable 24 Controller 26 Holding jig 27 Welding section 30 Laser processing equipment 40 spectroscopic equipment 50 Judgment device 51 CPU 52 Communication Circuit 53 Storage device 56 Control Program 57 Decision Model 70 Workpiece 70a, 70b members 85 holes 100 Judgment System 270 Welding Area

Claims

1. A method for determining a processing state in laser processing for lap welding, comprising: using an optical sensor to detect at least one of thermal radiation light, visible light, and reflected light generated at a weld formed on a surface of a workpiece by irradiating the workpiece with laser light; acquiring, from the optical sensor, a signal indicating a change in at least one of the thermal radiation light, the visible light, and the reflected light during a time interval corresponding to a welding time for each of the workpieces; a step of inputting feature quantities including a signal intensity of the signal based on the signal and an intensity value based on the signal intensity of the signal at a peak of the signal into a judgment model for judging the processing state, and judging, as the processing state, the position, number, and size in a welding region having a fusion length and a fusion width of a fusion shape abnormality that occurs when a foreign object is present on the overlapping surfaces of the workpieces; a step of outputting the determined position, number, and size of the melt shape abnormality as a determination result; Including, The judgment model is constructed based on training data including the feature amount calculated under the condition in which the melt shape abnormality occurs and the processing state under the condition in which the melt shape abnormality occurs. Judgment method.

2. The intensity value is an integral value obtained by subtracting the average value of the signal intensities of the signals excluding the peak from the signal intensity of the peak, and integrating the result over the time when the peak occurs. The determination method according to claim 1 .

3. The determination model includes a trained model generated by machine learning using training data that associates a feature calculated from a signal based on at least one of the thermal radiation light, the visible light, and the reflected light detected by performing the laser processing under each of a plurality of conditions under which the processing state changes with the processing state determined by appearance measurement of the welding region. The determination method according to claim 1 or 2.

4. A device for determining a processing state in laser processing for lap welding, an arithmetic circuit; a communication circuit that receives a signal generated by an optical sensor detecting at least one of thermal radiation light, visible light, and reflected light generated at a weld formed on a surface of a workpiece by irradiating the workpiece with a laser beam; and Equipped with the signal is a signal indicating a change in at least one of the thermal radiation light, the visible light, and the reflected light during a time interval corresponding to a welding time for each of the workpieces; The arithmetic circuit comprises: The communication circuit acquires the signal; inputting feature quantities including a signal intensity of the signal based on the signal and an intensity value based on the signal intensity of the signal at a peak of the signal into a judgment model for judging the processing state, and judging, as the processing state, the position, number, and size of a fusion shape abnormality occurring when a foreign object is present on the overlapping surfaces of the workpieces in a welding region having a fusion length and a fusion width; The determined position, number, and size of the melt shape abnormality are output as a determination result by the communication circuit; The judgment model is constructed based on training data including the feature amount calculated under the condition in which the melt shape abnormality occurs and the processing state under the condition in which the melt shape abnormality occurs. Judgment device.

5. The intensity value is an integral value obtained by subtracting the average value of the signal intensities of the signals excluding the peak from the signal intensity of the peak, and integrating the result over the time when the peak occurs. The determination device according to claim 4 .

6. The determination model includes a trained model generated by machine learning using training data that associates a feature calculated from a signal based on at least one of the thermal radiation light, the visible light, and the reflected light detected by performing the laser processing under each of a plurality of conditions under which the processing state changes with the processing state determined by appearance measurement of the welding region. The determination device according to claim 4 or 5.

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