JPH0215022B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention provides acoustic transmission (AE)
The present invention relates to a welding defect detection method for detecting defects in welds in real time using More specifically, in the present invention, an acoustic emission sensor (referred to as an AE sensor) is fixedly installed, a signal obtained from the sensor is processed, and after the weld metal solidifies,
The present invention relates to a welding defect detection method that allows defects in welded parts to be detected as early as possible.

For example, when welding steel, the method of fixing multiple AE sensors to the target object, detecting the sound emitted by welding defects, and estimating the defect location from the time difference is as follows.
Various types have conventionally existed. However, a common fundamental drawback of this type of conventional method is that it does not have a robust noise removal technique, which may lead to incorrect defect location. Here, the noise associated with welding is:
As is well known, there are arc noises, vibration noises caused by collisions between droplets and molten pools, and noises caused by cracking or peeling of slag or scale. These various types of noise can be sources of acoustic emission (hereinafter abbreviated as AE).

As a countermeasure against this type of noise, a method has been proposed that uses a filter that catches the frequency of the person emitting the defect and sets an appropriate threshold value, but this method is not perfect.

Here, the present invention attempts to provide a method that is not affected by various noises and can detect weld defects as early as possible after solidification of weld metal.

The method according to the present invention is characterized in that it mainly processes surface wave signals above a certain level emitted by weld metal in a temperature range of 450 to 750°C.

Based on various experimental results, the present invention has found that defects such as hot cracks do not always emit AE, and that defects such as hot cracks do not always emit AE, but almost always occur at the temperature at which the weld metal begins to bear the force.
Although there are some differences depending on the type of steel and the fact that sound starts to be emitted from below 750℃, if the weld metal temperature is kept within the monitoring range of 450℃ or higher, martensitic transformation, slag, scale cracking, etc.
This is based on the fact that the sound associated with peeling, ie, the noise for defect detection, can be eliminated.

FIG. 1 shows experimental results showing an example of the above-mentioned fact. This figure shows the CCT (Continuous Cooling Transfer) curve of the weld metal equivalent to ASTM-A387 Grade 22 in submerged arc welding, the temperature curve (Temp) of the weld metal, and the AE ring of the hot cracking area in the same weld bead. The cumulative value of the down count (#2163) and the cumulative value of the AE ring down count (#2161) of a sound weld are shown superimposed. In this CCT curve, Ac ₃ is the temperature at which ferrite changes to austenite when heated while maintaining equilibrium when the carbon content is 0, F is the region where ferrite precipitates, A is the austenite region, and Zw is the temperature where ferrite changes to austenite. The mesostructure region, M, is the martensite region. In the case of this steel type, due to switching
AE mainly occurs in the temperature range of approximately 700°C or lower and 500°C or higher, and it is also recognized that AE associated with the generation of intermediate structures is extremely rare compared to AE associated with defects.

Figure 1 shows the properties of steel equivalent to ASTM-387 Grada 22, but in general, steel exhibits properties as an elastic solid and has the ability to generate "sound" due to localized large plastic deformation. The temperature is approximately 700°C or lower, and slag peeling may cause noise at approximately 500°C or lower. Therefore,
Approximately 750℃ to detect defects in steel using AE waves
It is sufficient to measure within a temperature range of 450℃.

In this way, generally weld metal temperature 750~450
If an AE measurement method with a monitoring range of °C is adopted, welding defects such as hot cracking can be detected in almost real time for almost all welding methods and for almost all steel types.

Furthermore, in the case of multilayer welding, when the welding arc approaches a defect that has occurred in the previous layer, a sudden change in thermal stress may generate a sound that far exceeds the level of arc noise. Therefore, if the vicinity of the welding torch is also included in the monitoring range, defects missed in the previous layer can be detected, and a high detection rate can be obtained.

FIG. 2 is a block diagram showing an example of an apparatus for implementing the method of the present invention.

In this figure, 11, 12, 13... are AE
The sensors are arranged one-dimensionally or two-dimensionally on the object to be welded at predetermined intervals, and information on these arrangement positions is input in advance to a computer 6 having a storage means. 21, 22, 23... are preamplifiers that amplify each signal from the AE sensor, 31, 32, 33
... is a filter that inputs the signal from each preamplifier, and a 300-700KHz bandpass filter is optimal. The output signals from each of these filters are sent to main amplifiers 41, 42, 43, respectively.
It is applied to the signal processing circuit 5 via... This signal processing circuit 5 includes a time difference measuring device and a ring-down count counter or an AE wave energy measuring device. Here, the time difference measuring device uses two or more sensors, and in the case of a one-dimensional sensor arrangement, measures the time difference between when one sensor receives a signal and when the second sensor receives a signal, Furthermore, in the case of a two-dimensional sensor arrangement using three or more sensors, the time difference between the two sets is sequentially measured after one sensor receives a signal until the third sensor receives a signal. The ring-down count counter counts the number of times the first arrival signal crosses a certain set threshold with a positive (or negative) slope, and configures a set of data with the first arrival sensor number, the time difference measurement result, and the counting result. , outputs this data to the computer 6. In addition, when an AE wave energy measuring device is installed, the energy value of the AE wave is determined.
(t) is a concept expressed as ∫{V(t)} ² dt. For example, as shown in FIG. 3, main amplifiers 41, 42...
The AE wave (3rd a) after being amplified is subjected to double detection to obtain a double detected signal (3rd b). Then, the double detection signal is subjected to envelope detection, and the envelope detection signal (third
c) is discriminated using a predetermined threshold, the discriminated envelope detection signal is subjected to voltage-frequency conversion, and then the frequency-converted signal (3rd d) is integrated, and the integrated signal (3rd e) is output as an energy value indicating the relative magnitude of energy. The energy value obtained in the above manner constitutes a set of data together with the first arrival sensor number and the time difference measurement result, as in the case of the ringdown count counter,
This data is output to the computer 6.

The calculator 6 inputs and stores the torch speed, welding start position, and welding start time set according to the welding conditions, and calculates the torch position from time to time based on this information.In the case of a one-dimensional sensor arrangement, the torch position ± 20mm, in the case of a two-dimensional sensor arrangement, calculate the set of time difference ranges corresponding to a circle with a radius of 20mm centered on the torch position and the first arrival sensor number that should be there, and store it for a certain period of time (this time is (Usually 0.5 to 1 sec is appropriate, but it is optimally selected depending on torch speed and required accuracy) After memory purge, new data is stored and this operation is repeated.

In addition, this calculator 6 includes a welding torch that is determined in advance based on welding heat input, the shape of the workpiece, etc., and a 450℃
The distance range between weld metals at ~750°C has been entered. Thereby, the calculator 6 calculates the set of the first arrival sensor number and the time difference range that should be the momentary torch position for the weld metal that is 450 to 750°C from the distance range, and stores it for a certain period of time. . Memory purge and new data storage are performed in the same manner as described above.

If the set of data output from the signal processing circuit 5 meets any of the first arrival sensor numbers and time difference conditions described above, the computer 6 regards this as a correct signal and sets it as the first arrival sensor number. The position calculated from the time difference data is displayed on the display device (or printing device) 8 via the interface 7.
Display or print the ring down count result (or energy value).

In addition, in this device, if the sensor arrangement is left as is and monitoring is performed using a conventional method after welding is completed, it is also possible to monitor cold cracking.

As explained above, according to the present invention, a plurality of AE sensors are fixedly arranged on the object to be welded, and AE waves are detected based on the output of the AE sensors to detect welding defects. Received AE
Since the defect location is detected based on the reception time difference between the sensor and each AE sensor, there is no need to move the AE sensor, and it is not affected by various noises, making it possible to reliably detect welding defects in real time. We can provide a method that can do this.

[Brief explanation of drawings]

FIG. 1 is a diagram showing an example of the experimental results that form the basis of the present invention, and FIG. 2 is a block diagram showing an example of an apparatus for implementing the method according to the present invention. FIG. 3 is a time chart showing the waveforms of each signal in the processing process for determining the energy value. 11, 12, 13, 14...AE sensor, 21,
22, 23, 24... preamplifier, 31, 32,
33, 34... Filter, 41, 42, 43,
44... Main amplifier, 5... Signal processing circuit, 6
...Calculator, 7...Interface, 8...Display device.

Claims (1)

[Claims] 1. Install multiple acoustic emission sensors on the object to be welded, and select from the signals received by the acoustic emission sensors at 450°C.
A reception signal of a certain level or higher emitted by weld metal in the temperature range of ~750℃ is detected, and defects in the weld metal are detected based on the reception signal, and the acoustic emission sensor and each acoustic A welding defect detection method characterized by determining a welding defect position based on the time difference between when a transmission sensor receives a signal.