Method for detecting cracks and method for manufacturing welded member

Abstract

This method for detecting cracks includes: a step of detecting, by an acoustic emission (AE) sensor 6 or 7, an elastic wave corresponding to a burst-type AE generated in a welded part during or after welding thereof; a step of extracting, from detection waveform data of the elastic wave, an elastic wave caused by the crack in the welded part, by analyzing a feature amount of the detection waveform data; and a step of detecting the crack in the welded part on the basis of a result of extracting the elastic wave caused by the crack in the welded part.

Description

Crack detection method and welded component manufacturing method

[0001] The present disclosure relates to a method for detecting cracks that occur during welding and a method for manufacturing a crack-free welded component.

[0002] In recent years, in the automotive field, progress has been made in reducing the weight of vehicle bodies to improve fuel efficiency, and as a result, the use of high-strength steel sheets in automotive parts has increased. Furthermore, among automotive parts, plated steel sheets with rust prevention properties, such as zinc, i.e., surface-treated steel sheets, are used for parts that are exposed to rainwater from the viewpoint of corrosion resistance. Here, plated steel sheets are steel sheets having a metal plating layer on the surface of a base material, i.e., a steel substrate. Examples of metal plating layers include zinc plating such as electrogalvanizing or hot-dip galvanizing, including alloyed hot-dip galvanizing; zinc alloy plating containing elements such as aluminum or magnesium in addition to zinc; and aluminum-zinc alloy plating containing aluminum and zinc as the main components.

[0003] On the other hand, when joining overlapping steel sheets in automobile assembly, resistance spot welding, which is a type of lap resistance welding method, is often used from the viewpoint of cost or manufacturing efficiency. When resistance spot welding is applied to a sheet assembly in which multiple steel sheets including plated steel sheets, particularly plated steel sheets containing elements such as zinc or aluminum, are overlapped, there is a problem that cracks are likely to occur in the welded portion.

[0004] Generally, the melting point of a coating containing elements such as zinc or aluminum is lower than that of the base material. Therefore, during resistance spot welding of plated steel sheets, the low-melting-point metal coating layer on the surface of the steel sheet melts during welding. When the electrode pressure and tensile stress due to thermal expansion and contraction of the steel sheet are applied to the weld, the molten low-melting-point metal penetrates the grain boundaries of the base material of the plated steel sheet, reducing grain boundary strength and causing cracks. In other words, most cracks in welds that occur during resistance spot welding of plated steel sheets are thought to be caused by so-called liquid metal embrittlement (LME) cracking. Liquid metal embrittlement cracking is also referred to as LME cracking hereinafter.

[0005] To select appropriate welding conditions and steel plate manufacturing conditions that do not cause cracks in the weld, conventionally, experiments on welding steel plates under various conditions are conducted, and the resulting welded joints are then cut, polished, and etched to prepare observation samples of the weld cross section, which are then observed for cracks under a microscope. However, the more conditions under which experiments are conducted, the more work required to observe cracks increases, reducing the efficiency of the work of selecting appropriate welding conditions. Therefore, it is necessary to eliminate the work of observing cracks through cross-sectional observation and improve the efficiency of the work of selecting resistance spot welding conditions.

[0006] In order to address this problem, Patent Document 1 considers a method for evaluating LME cracking without observing a cross section. Patent Document 1 proposes a method for nondestructively inspecting for the presence or absence of LME cracking by performing an X-ray CT scan on a resistance welded joint, binarizing the obtained slice images, blackening the base material, the weld metal, and the heat-affected zone, and whitening other parts, thereby detecting minute cracks that are not whitened by the binarization process.

[0007] Japanese Patent Application Laid-Open No. 2020-12752

[0008] However, in Patent Document 1, it is necessary to perform an offline inspection after welding. Therefore, a time lag occurs between the inspection and the next welding, and it is thought that the efficiency of the work of selecting welding conditions cannot be sufficiently improved. In order to efficiently select welding conditions, it is required to be able to determine the occurrence of cracks without performing cross-sectional observation.

[0009] In order to solve the above problems, the present disclosure aims to provide a crack detection method that can detect cracks inline during welding, and a method for manufacturing a crack-free welded component.

[0010] In order to achieve the above object, the crack detection method according to the present disclosure is as follows.

[0011] (1) A crack detection method comprising the steps of: detecting elastic waves corresponding to sudden acoustic emissions (AE) generated in a welded portion during or after welding of the welded portion using an AE sensor; extracting elastic waves caused by cracks in the welded portion from detected waveform data of the elastic waves by analyzing feature quantities of the detected waveform data; and detecting cracks in the welded portion based on the results of extracting the elastic waves caused by cracks in the welded portion. (2) The crack detection method described in (1) above, wherein the feature quantities include at least one of the amplitude and frequency of the elastic waves. (3) The crack detection method described in (2) above, wherein the analysis of the feature quantities of the detected waveform data includes determining that elastic waves that satisfy at least one of a maximum amplitude of 100 mV or more or an average frequency of 200 Hz or more and 500 Hz or less are elastic waves caused by cracks in the welded portion. (4) The crack detection method according to any one of (1) to (3) above, wherein in the step of detecting the elastic waves, two or more AE sensors are installed at positions equidistant from the central axis of the welding electrode, and noise is separated from the detected waveform data by comparing the elastic waves detected by each of the two or more AE sensors. (5) The crack detection method according to (4) above, wherein elastic waves not detected by at least one of the two or more AE sensors, or elastic waves detected by each of the two or more AE sensors with a time difference of more than 10 microseconds, are separated as noise from the detected waveform data. (6) The crack detection method according to (4) above, wherein in the step of detecting cracks in the weld, cumulative elastic wave energy calculated as the sum of the squares of the maximum amplitudes of elastic waves extracted as elastic waves caused by cracks in the weld is 3(V ２(7) The crack detection method according to any one of (1) to (6), wherein the welding of the weld is resistance spot welding in which two or more steel plates are overlapped and joined, and one or more of the two or more steel plates has a tensile strength of 590 MPa or more, and one or more of the two or more steel plates is a surface-treated steel plate having a metal plating layer. (8) A method for manufacturing a welded component, comprising performing welding to manufacture the welded component based on welding conditions determined using the crack detection method according to any one of (1) to (7).

[0012] According to the crack detection method of the present disclosure, cracks are detected in-line during welding, and according to the welded component manufacturing method of the present disclosure, a welded component without cracks is manufactured.

[0013] 6A is a schematic diagram illustrating an overview of resistance spot welding; FIG. 6B is a flowchart illustrating an example procedure for a welding condition search method, including the procedure for a crack detection method; FIG. 6C is a block diagram illustrating an example configuration of a detection device according to the present disclosure; FIG. 6D is a graph illustrating detected waveform data generated by detecting elastic waves with an AE sensor when resistance spot welding is performed, and welding current and pressing force data; FIG. 6E is a graph illustrating an example of a waveform that is considered to be noise in the detected waveform data; FIG. 6F is a graph illustrating an example of a waveform of a sudden AE; FIG. 6G is a graph illustrating an example of a waveform of a continuous AE; FIG. 6H is a graph illustrating detected waveform data and welding current and pressing force data in welding according to an embodiment; FIG. 6I is a graph illustrating the maximum amplitude of elastic waves of events extracted from the detected waveform data of FIG. 5 using relational expressions (1) to (3); FIG. 6I is a graph illustrating the maximum amplitude of elastic waves of events further extracted from the events extracted in FIG. 6A using relational expression (4); FIG. 6I is a graph illustrating an example of the relationship between cumulative elastic wave energy and the presence or absence of cracks.

[0014] Hereinafter, embodiments of a method for detecting cracks in a weld according to the present disclosure will be described with reference to the drawings. Each drawing is a schematic diagram and may differ from the actual product. Furthermore, the following embodiments exemplify an apparatus or method for embodying the technical idea of ​​the present disclosure, and are not intended to limit the configuration to that described below. In other words, the technical idea of ​​the present disclosure can be modified in various ways within the technical scope described in the claims.

[0015] The method for detecting cracks in a weld according to the present disclosure can detect cracks that occur in a weld, for example, during resistance spot welding. Cracks that can be detected by the method according to the present disclosure may include liquid metal embrittlement (LME) cracks. The method for detecting cracks according to the present disclosure is not limited to resistance spot welding, and may be applied to various other welding methods, such as arc welding. Detection of cracks that occur during resistance spot welding will be described below.

[0016] Resistance spot welding, to which the crack detection method according to the present disclosure is applied, is a method of joining two overlapping steel sheets, a lower electrode 3 and an upper electrode 4, by sandwiching and pressurizing a pair of steel sheets, a lower electrode 3 and an upper electrode 4, as shown in FIG. 1 , while passing a welding current between the lower electrode 3 and the upper electrode 4. In resistance spot welding, the steel sheets melt due to resistance heat generated by the flow of the welding current, thereby forming a point-like weld, i.e., a nugget 5. In other words, the nugget 5 is a portion of the steel sheets that melts and solidifies at the contact point of the overlapping steel sheets when a current is passed through them. The nugget 5 joins the steel sheets 1 and 2 at a point.

[0017] The crack detection method according to the present disclosure is a method for detecting elastic waves generated during the resistance spot welding described above using an acoustic emission (AE) sensor installed on or near the steel plate 1 or 2, and detecting the occurrence of cracks in the weld based on the results of the elastic wave detection.

[0018] The crack detection method according to the present disclosure is executed as part of the welding condition search method shown in Fig. 2. The welding condition search method includes a provisional setting step of provisionally setting welding conditions, a measurement step of performing resistance spot welding using the provisionally set welding conditions and detecting elastic waves generated during welding using an AE sensor, an analysis step of analyzing feature quantities of elastic wave data acquired in the measurement step and extracting elastic waves generated by cracks, a determination step of determining the presence or absence of cracks in the weld based on the feature quantities of the elastic waves extracted in the analysis step, and a step of determining the welding conditions by adopting the welding conditions that were provisionally set when it is determined that no cracks are present as search results for welding conditions. The crack detection method according to the present disclosure includes the measurement step, analysis step, and determination step of the welding condition search method.

[0019] In the measurement process, an AE sensor detects elastic waves generated during resistance spot welding, in which a sheet set made up of multiple overlapping steel sheets 1 and 2 is sandwiched between a pair of lower and upper electrodes 3 and 4, as shown in Figure 1, and joined by applying pressure and passing current through the electrodes.

[0020] At least one steel sheet constituting a sheet set of steel sheets for which welding conditions for resistance spot welding are searched for by performing a welding condition search method including a crack detection method according to the present disclosure may be a surface-treated steel sheet having a metal plating layer on its surface. It is preferable that at least one steel sheet constituting the sheet set is a steel sheet having a metal plating layer with a melting point lower than that of the base material of the surface-treated steel sheet. A typical metal plating layer has a lower melting point than the steel sheet. For example, the melting point of the base material, i.e., the base steel sheet, is 1400°C to 1570°C, and the melting point of the metal plating layer is 300°C to 1200°C. The metal plating layer is not particularly limited, but may include, for example, a Zn-based plating layer or an Al-based plating layer. For components requiring corrosion resistance, a Zn-based plating layer is superior to an Al-based plating layer. This is because the sacrificial corrosion protection effect of zinc (Zn) can slow the corrosion rate of the base steel sheet. Examples of the Zn-based plating layer include general hot-dip galvanizing (GI), galvannealed hot-dip plating (GA), and electrogalvanizing (EG). For example, Zn-Ni-based plating containing 10% by mass to 25% by mass of Ni, Zn-Al-based plating, Zn-Mg-based plating, or Zn-Al-Mg-based plating. Examples of the Al-based plating layer include Al-Si-based plating containing 10% by mass to 20% by mass of Si. The metal plating layer may be formed on one side of the surface-treated steel sheet, but may also be formed on both sides. The metal plating layer may be formed on the joining surface between the steel sheets, i.e., the surface that will become the mating surface. The metal plating layer may be formed on the surface that will come into contact with the electrode. Furthermore, the metal plating layer may be formed on both the surface that will become the joining surface between the steel sheets and the surface that will come into contact with the electrode. The coating weight of the metal plating layer is also optional, but from the viewpoint of weldability, a coating weight of 120 g / m per side is recommended. ２ It is preferable that:

[0021] In the present disclosure, the steel type, i.e., the chemical composition, of the steel sheets to be joined by resistance spot welding is not particularly limited. The manufacturing method of the steel sheets to be joined by resistance spot welding may be any method, such as cold rolling or hot rolling. Similarly, the structure of the steel sheets to be joined by resistance spot welding may be any method. Hot-pressed steel sheets may also be used as the steel sheets to be joined by resistance spot welding. The thickness of the steel sheets is not particularly limited, but is preferably within the range that can be used for a typical automobile body, i.e., approximately 0.5 mm to 4.0 mm.

[0022] Furthermore, at least one steel sheet in a sheet pair of steel sheets joined by resistance spot welding is preferably a high-strength steel sheet having a tensile strength of 590 MPa or more. Furthermore, the steel sheet is more preferably a high-strength steel sheet having a tensile strength of 780 MPa or more. LME cracking occurs when a low-melting-point metal plating layer on the surface of a steel sheet melts during welding, and tensile stress due to thermal expansion and contraction of the steel sheet is applied to the weld. The molten low-melting-point metal penetrates the grain boundaries of the base material of the plated steel sheet, reducing grain boundary strength. Therefore, as the tensile strength of the base material increases, the constraint on the weld increases, and the tensile stress generated in the weld also increases, making welding cracks more likely to occur. Therefore, by applying the crack detection method according to the present disclosure to resistance spot welding of a sheet pair including at least one steel sheet having a tensile strength of 590 MPa or more, the effects of the crack detection method according to the present disclosure can be significantly achieved. In particular, by applying the crack detection method according to the present disclosure to resistance spot welding of a sheet pair including at least one steel sheet having a tensile strength of 780 MPa or more, even greater benefits can be obtained. Furthermore, by applying the crack detection method according to the present disclosure to resistance spot welding of a sheet pair including at least one steel sheet having a tensile strength of 590 MPa or more and at least one steel sheet being a surface-treated steel sheet having a metal plating layer, even greater benefits can be obtained.

[0023] The multiple steel sheets to be joined by resistance spot welding may be steel sheets of the same type and thickness, or may be steel sheets of different types or thicknesses. Furthermore, a surface-treated steel sheet having a metal plating layer and a steel sheet not having a metal plating layer may be overlapped. Surface-treated steel sheets having a metal plating layer may also be overlapped. The number of overlapping steel sheets may be two, as exemplified in FIG. 1 , or may be three or more.

[0024] In the measurement process, a sheet assembly including a plurality of overlapping steel sheets is sandwiched between a pair of welding electrodes including a lower electrode 3 and an upper electrode 4 and pressurized while current is passed between the lower electrode 3 and the upper electrode 4 to allow a welding current to flow. The welding electrodes are then released from the sheet assembly. A welding device that includes a pair of upper and lower electrodes and is capable of freely controlling the welding pressure and welding current during welding may be used as an apparatus for performing the resistance spot welding method. The welding device may be equipped with a pressure mechanism such as an air cylinder or a servo motor, a stationary or robotic gun, or the shape of the electrodes. The welding device may be powered by either direct current or alternating current. The type of power source for the welding device is not particularly limited, such as a single-phase alternating current, an AC inverter, or a DC inverter. When the welding device is powered by alternating current, the welding current is set as an effective current in the welding conditions. The shape of the electrodes for the welding device is also not particularly limited. The electrode tip shape may be, for example, a dome radius (DR type), a radius (R type), or a dome (D type) as described in JIS C 9304:1999. The electrode tip diameter may be, for example, 4 mm to 16 mm. The electrode tip curvature radius may be, for example, 10 mm to 100 mm. Resistance spot welding is performed while the electrode is constantly water-cooled.

[0025] Elastic waves are measured by an AE sensor during and after welding of the sheet set in which the steel sheets 1 and 2 are overlapped. A method for measuring elastic waves by an AE sensor during and after resistance spot welding will be specifically described below.

[0026] The AE sensor may be installed at various locations, such as on the steel plates of the plate assembly, on a jig near the plate assembly, or on the electrode. The number of AE sensors may be one or more. However, because electrical noise is likely to occur during welding due to servo operation or high current, it is important to distinguish between elastic waves caused by welding phenomena such as crack initiation or propagation or shear deformation of transformation. Elastic waves generated in the weld theoretically propagate in all directions. Therefore, if two or more AE sensors are installed equidistant from the elastic wave generation position, i.e., the electrode position, it is believed that the elastic waves generated in the weld will be detected by each of the two or more AE sensors. Therefore, it is preferable to install two or more AE sensors equidistant from the electrode. For example, as shown in Figure 3, two AE sensors 6 and 7 are installed so as to contact the steel plates at equidistant positions from the position where the plate assembly is sandwiched between the electrodes. The AE sensors 6 and 7 may be installed in opposite directions from the electrode, i.e., at equal distances in two directions with an angle of 180 degrees, or may be installed at equal distances in two directions with an angle of less than 180 degrees from the electrode.

[0027] The signals detected by the AE sensors 6 and 7 are stored in the data storage unit 11 of the detection device 10 via signal lines. The data display unit 12 of the detection device 10 may display the signals detected by the AE sensors 6 and 7. The data analysis unit 13 of the detection device 10 analyzes feature quantities of the signals detected by the AE sensors 6 and 7 and determines whether or not cracks have occurred in the weld. The detection device 10 may be configured using multiple computers as its hardware configuration. Specifically, the computer includes a control device, a central processing unit, a storage medium storing various programs, an interface through which a user can input and output, and the like. The data storage unit 11 may be a semiconductor memory, an electromagnetic storage medium, or the like. The data display unit 12 may be various displays. The data analysis unit 13 may be a general-purpose processor such as a CPU (Central Processing Unit) or a dedicated processor.

[0028] With the AE sensors 6 and 7 installed on the steel plate 1, the detection device 10 starts measuring elastic waves. Specifically, the detection device 10 measures elastic waves by acquiring the results of detection of elastic waves generated at the welded portion by the AE sensors 6 and 7. The AE sensors 6 and 7 detect the elastic waves generated at the welded portion and generate detected waveform data of the elastic waves.

[0029] Resistance spot welding is performed while the detection device 10 continues measuring elastic waves. Resistance spot welding is performed by passing current between the steel sheets 1 and 2 while sandwiching and applying pressure between the lower electrode 3 and the upper electrode 4 to form a molten zone. During resistance spot welding, the passage of current between the lower electrode 3 and the upper electrode 4 is terminated after a current application time set as a welding condition has elapsed, and the pressure applied by sandwiching the steel sheets between the lower electrode 3 and the upper electrode 4 is released. The detection device 10 measures elastic waves generated in the nugget 5, i.e., the welded portion, during resistance spot welding by detecting the elastic waves generated in the welded portion with the AE sensors 6 and 7. The detection device 10 continues measuring elastic waves after the electrode pressure is released during resistance spot welding, i.e., for at least 3 seconds or more after welding. The detection device 10 ends elastic wave measurement when a predetermined time has elapsed after welding. The predetermined time is set to at least 3 seconds or more. The detection device 10 may also perform waveform filtering or amplification processing as appropriate when measuring the elastic waves.

[0030] The detection device 10 acquires the detected waveform data generated by the AE sensors 6 and 7, stores it in the data storage unit 11, and displays it on the data display unit 12. The display format of the detected waveform data is not particularly limited, but the detected waveform data may be displayed as an RMS waveform that continuously displays the root mean square (RMS) value of the voltage detection value of the elastic wave over a certain period, i.e., an effective value waveform that continuously displays the effective value of the voltage. The root mean square value of the voltage detection value of the elastic wave is also referred to as the AE RMS value. Furthermore, the detected waveform data may be displayed by determining, as a single elastic wave, a waveform during a period in which the amplitude of the voltage value of the detected waveform data is equal to or greater than a certain voltage threshold, and calculating and plotting the maximum amplitude of the voltage value of the elastic wave or the frequency of the elastic wave as a feature value.

[0031] The detection device 10 uses a data analysis unit 13 to analyze the feature quantities of the data stored in the data storage unit 11. The raw data of the detected waveform data measured by the AE sensors 6 and 7 includes electrical noise generated due to servo operation, high current, etc. during welding. Therefore, in the detected waveform data, it is necessary to distinguish between electrical noise components and elastic wave components caused by welding phenomena, such as elastic waves caused by the occurrence or propagation of cracks in the weld or elastic waves caused by shear deformation of transformation.

[0032] For example, as shown in Figure 4A, assume that the AE RMS value during the measurement time, i.e., the effective value of the detected voltage value of the elastic wave, the current flowing between the electrodes of the resistance spot welding, and the applied pressure between the electrodes are measured. The horizontal axis of the graph in Figure 4A represents the measurement time. The unit of measurement time is seconds (s). The vertical axis on the left represents the AE RMS value. The unit of the AE RMS value is millivolts (mV). The vertical axis on the right represents the current and the applied pressure. The unit of the current is kiloamperes (kA). The unit of the applied pressure is kilonewtons (kN).

[0033] Comparison of the AE RMS value with the current and the applied pressure revealed that the waveforms shown in FIG. 4B , in which the AE RMS value irregularly changes and is considered to be noise, were detected during the period from 0 second to approximately 0.2 seconds when servo operation for clamping and applying pressure between the electrodes begins, the period from approximately 0.4 seconds to approximately 0.8 seconds when the measurement time is current flowing, and the period from approximately 0.9 seconds to approximately 0.95 seconds when the pressure applied by the electrodes is released.

[0034] On the other hand, a sudden AE waveform as exemplified in FIG. 4C was detected in the measurement period from about 1.7 seconds to about 2.1 seconds, approximately one second after the release of the electrode pressure, in which the AE RMS value rapidly increased and then decayed in a short time. Furthermore, a continuous AE waveform as exemplified in FIG. 4D was detected in the measurement period from about 1.7 seconds to about 3 seconds, in which the AE RMS value remained substantially constant at a lower value compared to the sudden AE waveform and showed almost no decay. The sudden AE waveform is known as the waveform of an elastic wave caused by a crack in a weld. Furthermore, the continuous AE waveform is known as the waveform of an elastic wave caused by shear deformation in a weld.

[0035] The data analyzer 13 analyzes the detected waveform data to extract feature quantities in order to separate waveforms resulting from phenomena such as cracking or shear deformation occurring in the weld as described above from waveforms resulting from electrical noise. The feature quantities of the detected waveform data are not limited to, but may include, for example, mathematically describable calculated values ​​such as the maximum AE RMS value of the detected waveform data, the rise time from when the AE RMS value starts to increase toward the maximum value until it reaches the maximum value, or the average or maximum value of the frequency of the elastic wave. The feature quantities of the detected waveform data may also include, when the same elastic wave is detected by each of the two AE sensors 6 and 7, the difference in the time it takes for the elastic wave to reach each of the AE sensors 6 and 7.

[0036] The data analysis unit 13 separates the component caused by electrical noise from the detected waveform data by setting conditions that can capture the difference between the features of the component caused by electrical noise in the detected waveform data and the features of the component caused by welding phenomena such as cracking or shear deformation, based on the feature amounts extracted from the detected waveform data. The conditions may be set differently depending on the material to be measured.

[0037] For example, the following relational expressions (1) to (3) are preferably used as conditions for extracting elastic waves caused by cracks that occur when welding steel plates containing 50% or more iron (Fe): Vmax (mV) ≧ 100 mV (1), where Vmax is the maximum AE RMS value; 200 Hz ≦ Fmean (Hz) ≦ 500 Hz (2), where Fmean is the average frequency of elastic waves within a predetermined period; and Terr (μs) ≦ 10 μs (3), where Terr is the difference in time between the arrival of the same elastic wave at each of the two AE sensors 6 and 7, i.e., the difference in time between the detection of the same elastic wave by each of the two AE sensors 6 and 7. When the number of AE sensors is three or more, Terr may be the difference between the first and last detection times of the same elastic wave. An elastic wave that is not detected by at least one of the two or more AE sensors is determined to be an elastic wave that does not satisfy the relational expression (3).

[0038] The data analysis unit 13 extracts a waveform that satisfies at least one of the above relational expressions (1) to (3) as a waveform caused by a crack in the weld. Conversely, a waveform that does not satisfy all of the above relational expressions (1) to (3) may be separated from the detected waveform data as noise.

[0039] When at least one waveform is extracted as a waveform resulting from a crack in a weld, the data analysis unit 13 may calculate the sum of squares of the Vmax values ​​of each extracted waveform as the cumulative elastic wave energy Eaccum. Specifically, when n waveforms are extracted as waveforms resulting from a crack in a weld, Eaccum is calculated by the following formula using Vmax(i), which is the maximum value of the AE RMS value of each waveform. Here, i is a natural number equal to or less than n. The unit of Eaccum is the square of the volt (V ２ )

[0040] As a condition for determining that the extracted waveform is a waveform caused by cracking of the weld, it is preferable to use the following relational expression (4) regarding the value of Eaccum calculated by the above formula: Eaccum (V ２ ) ≧ 3V ２ ...(4)

[0041] The data analysis unit 13 can improve the accuracy of detecting cracks in the weld by confirming that the above relational expression (4) is further satisfied.

[0042] As described above, the crack detection method according to the present disclosure detects cracks in a joint joined by resistance spot welding during welding, i.e., in-line. Specifically, a measurement process using an AE sensor to detect elastic waves generated during welding and an analysis process analyzing feature quantities of elastic wave data acquired in the measurement process to extract elastic waves generated by cracks are performed to detect cracks in the weld. In other words, the presence or absence of cracks in the weld is determined. By performing the above-described crack detection method during welding under various welding conditions, welding conditions that do not cause cracks in resistance spot welding can be efficiently selected. Note that the LME cracks to be detected in the present disclosure can be detected not only during welding but also after welding. Therefore, LME cracks can be detected regardless of the timing of their occurrence.

[0043] (Summary) As described above, according to the crack detection method and detection device 10 disclosed herein, cracks are detected inline during welding. Inline crack detection reduces the time lag until it is determined whether or not a crack has occurred in a welding test that is performed by changing welding conditions. As a result, the efficiency of the work of selecting welding conditions is improved.

[0044] EXAMPLES Hereinafter, the present disclosure will be described using examples to further understand the present disclosure, but the present disclosure is not limited to the examples in any way.

[0045] As described above, the crack detection method according to the present disclosure can detect the occurrence of cracks in a welded portion of a plate assembly in which two or more steel plates are overlapped.

[0046] In this example, as shown in Fig. 1, a welded joint was created by sandwiching a sheet set consisting of two overlapping steel sheets 1 and 2 between a pair of lower and upper electrodes 3 and 4, and performing resistance spot welding under predetermined welding conditions. Steel sheet 1 was a galvannealed steel sheet with a surface layer treated with galvannealing (GA), a tensile strength (TS) of 980 MPa, and a thickness of 1.4 mm. Steel sheet 2 was an ungalvanized steel sheet with a TS of 1470 MPa and a thickness of 1.4 mm.

[0047] An inverter DC resistance spot welding machine was used as the welding machine. Two electrodes, the lower electrode 3 and the upper electrode 4, were identically shaped. The lower electrode 3 and the upper electrode 4 were DR-type chromium copper electrodes with a tip diameter of 6 mm and a curvature radius of 40 mm. Resistance spot welding was performed in an environment with a room temperature of 20°C, with the electrodes constantly water-cooled. The welding force (kN) was maintained constant throughout the energization and de-energization steps. The holding time after the end of the energization step was set to between 20 ms and 300 ms.

[0048] As an example of the method of installing the AE sensors, two AE sensors 6 and 7 were installed at positions 60 mm apart in the left-right direction from the electrode pressure position, as shown in Fig. 3. In other words, the AE sensors 6 and 7 were installed at positions equidistant from the electrode in opposite directions.

[0049] The AE sensors 6 and 7 detected elastic waves at their installed positions and generated detected waveform data of the elastic waves. In this example, the AE RMS value was recorded every 2 ms as the detected waveform data. Furthermore, an event corresponding to the AE RMS value reaching 100 mV or more was recorded as the detected waveform data. The data storage unit 11 of the detection device 10 acquired and stored the detected waveform data from the AE sensors 6 and 7.

[0050] Figure 5 shows a graph illustrating the relationship between the AE RMS value detected by the AE sensor 6, the current, and the applied pressure when welding was performed under welding conditions in which the applied pressure was maintained for 20 ms after the end of current flow. The horizontal axis of the graph in Figure 5 represents measurement time, measured in seconds (s). The vertical axis on the left represents the AE RMS value, measured in millivolts (mV). The vertical axis on the right represents the current and applied pressure. The current is measured in kiloamperes (kA). The applied pressure is measured in kilonewtons (kN).

[0051] 6A also shows the maximum AE RMS value of an event corresponding to an AE RMS value of 100 mV or more in the AE RMS waveform of FIG. 5, i.e., the maximum amplitude of the elastic wave. The horizontal axis of the graph in FIG. 6A represents measurement time, measured in seconds (s). The measurement time in FIG. 6A corresponds to the measurement time in FIG. 5. The vertical axis represents the maximum amplitude of the elastic wave of the event, measured in millivolts (mV). According to the graph in FIG. 6A, multiple events occurred after the pressure on the welding electrode was released, a measurement time of approximately 0.9 seconds.

[0052] From the multiple events extracted in the graph of FIG. 6A, elastic waves satisfying any of the above-described relational expressions (1) to (3) were extracted as elastic waves caused by cracks. FIG. 6B shows the maximum amplitude of the elastic waves of the events extracted using relational expressions (1) to (3). The horizontal and vertical axes of the graph of FIG. 6B are the same as the horizontal and vertical axes of the graph of FIG. 6A. According to the graph of FIG. 6B, multiple elastic waves were extracted as events satisfying any of relational expressions (1) to (3) after the pressure of the welding electrode was released, which was measured for approximately 0.9 seconds. The elastic waves extracted in the graph of FIG. 6B are believed to be elastic waves caused by cracks in the weld. In other words, it is believed that cracks were detected in the weld. In fact, when a cross-section of the weld was observed after welding, LME cracks of approximately several hundred microns were observed. Therefore, the observation results confirmed that the crack detection method disclosed herein can detect the occurrence of cracks in the weld.

[0053] Furthermore, welding was performed under a plurality of welding conditions in which the hold time after energization was set to various times between 20 ms and 300 ms, and elastic waves were detected by AE sensors 6 and 7, and detected waveform data for each welding condition was generated. Events were extracted from the events recorded in the detected waveform data for each welding condition by performing a similar analysis process using relational expressions (1) to (3), and then the cumulative elastic energy Eaccum for each welding condition was calculated using the maximum amplitude of the extracted events, i.e., the maximum AE RMS value. Then, relational expression (4) was used to determine whether the detected waveform data for each welding condition contained elastic waves due to cracks.

[0054] The graph in Figure 7 shows the relationship between sample numbers assigned to distinguish welding conditions and the cumulative elastic energy Eaccum calculated from the detected waveform data for each welding condition, with the presence or absence of cracks confirmed by actual cross-sectional observation after welding being distinguished. The horizontal axis of the graph in Figure 7 represents the sample number, and the vertical axis represents the cumulative elastic energy Eaccum. The unit of Eaccum is volt squared (V ２ )

[0055] According to the results shown in the graph of Figure 7, it was confirmed that cracks occurred in actual cross-sectional observation under welding conditions where Eaccum was 3 or more, i.e., welding conditions where the above-mentioned relational expression (4) was satisfied. On the other hand, it was confirmed that cracks did not occur in actual cross-sectional observation under welding conditions where Eaccum was less than 3, i.e., welding conditions where the above-mentioned relational expression (4) was not satisfied. From the above results, it was found that the value of the cumulative elastic wave energy Eaccum differs greatly depending on whether or not cracks were present. Furthermore, the presence or absence of cracks was determined with high accuracy by determining using relational expression (4).

[0056] (Method for manufacturing welded component) As described above, the crack detection method according to the present disclosure is executed as part of the welding condition search method shown in Fig. 2. By executing the welding condition search method including the crack detection method according to the present disclosure, crack-free welding conditions are determined. By welding components using the crack-free welding conditions, a crack-free welded component is obtained.

[0057] The method for manufacturing a welded component according to the present disclosure includes performing welding to manufacture the welded component based on the welding conditions determined using the crack detection method described above. That is, the method for manufacturing a welded component according to the present disclosure controls the welding conditions so that cracks are not generated by the crack detection method described above. The method for manufacturing a welded component according to the present disclosure manufactures a welded component without cracks.

[0058] Although the embodiments of the present disclosure have been described based on the drawings and examples, it should be noted that those skilled in the art could make various modifications or alterations based on the present disclosure. Therefore, it should be noted that these modifications and alterations are included within the scope of the present disclosure. For example, the functions included in each component or step can be rearranged so as not to cause logical inconsistencies, and multiple components or steps can be combined or divided into one. The embodiments of the present disclosure can also be realized as a program executed by a processor included in an apparatus or a storage medium on which a program is recorded. It should be understood that these are also included within the scope of the present disclosure.

[0059] 1, 2: Steel plate; 3: Lower electrode; 4: Upper electrode; 5: Nugget; 6, 7: AE sensor; 10: Detection device (11: Data storage unit; 12: Data display unit; 13: Data analysis unit)

Claims

1. A step of detecting elastic waves corresponding to sudden acoustic emissions (AE) generated in the welded area during or after welding of the welded area using an AE sensor, A step of extracting elastic waves caused by cracks in the welded joint from the detected elastic wave waveform data by analyzing the feature quantities of the detected waveform data, A step of detecting cracks in the welded portion based on the results of extracting elastic waves caused by cracks in the welded portion. A method for detecting cracks, including the method described above.

2. The crack detection method according to claim 1, wherein the feature quantity includes at least one of the amplitude or frequency of the elastic wave.

3. The crack detection method according to claim 2, wherein the analysis of the feature quantities of the detected waveform data includes determining that an elastic wave satisfying at least one of the following conditions is an elastic wave caused by a crack in the welded part: the maximum amplitude of the elastic wave is 100 mV or more, or the average frequency of the elastic wave is 200 Hz or more and 500 Hz or less.

4. The crack detection method according to any one of claims 1 to 3, wherein in the step of detecting elastic waves, two or more AE sensors are installed at positions at equal distances from the central axis of the welding electrode, and noise is separated from the detected waveform data by comparing the elastic waves detected by each of the two or more AE sensors.

5. The crack detection method according to claim 4, wherein elastic waves not detected by at least one of the two or more AE sensors, or elastic waves detected by each of the two or more AE sensors with a time difference of more than 10 microseconds, are separated as noise from the detected waveform data.

6. In the step of detecting cracks in the welded portion, the cumulative elastic wave energy calculated as the sum of the squares of the maximum amplitudes of the elastic waves extracted as elastic waves caused by the cracks in the welded portion is 3(V) ２ A crack detection method according to any one of claims 1 to 3, wherein if the value is greater than or equal to the value, a crack has occurred in the welded joint.

7. The welding of the aforementioned welded joint is a resistance spot welding method that joins two or more overlapping steel plates. The tensile strength of one or more of the two or more steel plates is 590 MPa or more. Of the two or more steel plates, one or more are surface-treated steel plates having a metal plating layer. A method for detecting cracks according to any one of claims 1 to 3.

8. A method for manufacturing a welded member, comprising performing welding for manufacturing a welded member based on welding conditions determined using the crack detection method described in any one of claims 1 to 3.