A METHOD AND SYSTEM FOR ROBOTIC WELDING

Abstract

A method and a system for controlling a welding operation is provided by a welding machine controlled by an automatic motion generating mechanism. The method includes the steps of acquiring a set of welding data during the welding operation; computing at least a first part of the set of welding data and at least a second part of the set of welding data providing computed data, wherein the computed data indicate an abnormality; and transferring an abnormality output to a robot controller, which is controlling the welding machine and the automatic motion generating mechanism.

Claims

1.-16. (canceled)

17. A method of controlling a welding operation provided by a welding machine controlled by an automatic motion generating mechanism, the method comprising the steps of: acquiring a set of welding data during the welding operation; computing at least a first part of the set of welding data and at least a second part of the set of welding data providing computed data, wherein the computed data indicate an abnormality; transferring an abnormality output to a robot controller, which is controlling the welding machine and the automatic motion generating mechanism.

18. The method according to claim 17, wherein the step of computing the at least first part and the at least second part is performed by a neural network.

19. The method according to claim 17, wherein the welding operation is an arc-welding operation, or an automatic arc-welding operation, or a resistance welding operation.

20. The method according to claim 17, wherein the welding data comprises the welding current, the welding voltage, the energy used for the welding, flow of gas, arc-sensor signals, or arc-sensor signals relating to Through Arc-sensor Seam Tracking (TAST).

21. The method according to claim 18, wherein the method comprises the step of preparing the acquired welding data for the neural network.

22. The method according to claim 17, wherein the robot controller, when the abnormality output is received, controls the automatic motion generating mechanism and the welding machine to redo at least a part of the welding operation.

23. The method according to claim 17, wherein the robot controller receives a normality output as long as no abnormality is detected when computing the at least first part and the at least second part.

24. The method according to claim 18, wherein the neural network provides a neural network output indicating abnormality based on the step of computing the at least first part and the at least second part performed by the neural network, wherein the provision of the neural network output indicating abnormality initiates the transferring of the abnormality output to the robot controller.

25. The method according to claim 24, wherein a plurality of neural network outputs are buffered, and the plurality of buffered neural network outputs are processed together for providing the abnormality output.

26. The method according to claim 25, wherein the neural network output is squared and then recorded in a short memory queue, whereafter the average of the buffered neural network outputs is calculated and compared with welding parameters to produce a decision signal, said detection signal is a binary signal representing either abnormality or no abnormality detected.

27. A system for controlling a welding operation by automatic detection of a welding abnormality, said system comprising: a welding machine with a welding gun configured for performing a welding operation; an automatic motion generating mechanism configured for moving the welding gun along a welding path during the welding operation; a robot controller configured for controlling the welding operation performed by the welding machine and the movements of the automatic motion generating mechanism; a processor unit; wherein the processor unit is configured for: receiving a set of welding data characterizing the welding operation, computing an output based on at least a first part of the set of welding data and at least a second part of the set of welding data providing computed data, wherein the computed data indicate an abnormality, providing an abnormality output, and transferring the abnormality output to the robot controller.

28. The system according to claim 27, wherein the processor unit comprises a neural network, wherein the neural network is configured for computing the output based on the at least first part and the at least second part for detecting abnormalities in the welding operation.

29. The system according to claim 27, wherein the welding operation is an arc-welding operation, or an automatic arc-welding operation, or a resistance welding operation, or a gas welding operation.

30. The system according to claim 27, wherein the welding data comprises welding current, welding voltage, energy used for the welding operation, flow of a welding gas, flow of an inert shielding gas, arc-sensor signals, or arc-sensor signals relating to Through Arc-sensor Seam Tracking (TAST).

31. The system according to claim 27, wherein the processing unit comprises a pre-processor for preparing the collected data for the neural network.

32. The system according to claim 27, wherein the neural network is a the Long Short-Term memory (LSTM) network comprising at least 600 neurons or cells.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In the following the invention is described in more detail with reference to the accompanying drawings, in which:

[0053] FIG. 1 is a diagram of the process components in a system according to the invention;

[0054] FIG. 2 is a schematic perspective view of an example of a welding operation including abnormalities;

[0055] FIG. 3 is a graph of the standard deviation of welding current as a function of time and the corresponding abnormality output;

[0056] FIG. 4 is a graph of the derivative of the standard deviation of welding current as a function of time and the corresponding abnormality output.

DETAILED DESCRIPTION OF THE INVENTION

[0057] In the system according to the invention and as exemplified in the diagram of FIG. 1, the robot can automatically detect an abnormality in the welding process and based on the feed-back data the system is capable of signalling, stopping, pausing or changing a welding task accordingly. An abnormality can be missing of material due to the presence of a water hole (i.e. drain hole) or other type of cut-out in the welding path. It can also be prior welding marks, such as tacks, gaps between elements or simply be unexpected change of the welding seam.

[0058] The system comprises a welding machine used to weld material together in an automatic or semi-automatic manner. A robotic or similar automated motion generating mechanism (hereafter referred to as a robot) moves the welding gun of the welding machine while welding the material. The welding machine and the robot are controlled by a robot controller. During welding welding data on how the process is running is collected. The welding data may be collected from the welding machine or by a number of sensors, like e.g. a gas flow sensor, or a microbolometer. In a detection unit or a processing unit, such as a PC, the collected data is analysed and upon detection of an abnormality, the signal thereof is transferred to handle the detection.

[0059] The collected data can be process parameters, such as but not limited to the welding current, the welding voltage, air flow, gas flow, welding material consumption, the energy used for the welding and Arc-sensor signals, such as Through Arc-sensor Seam Tracking (TAST). It is by the invention realised that other types of data could also be collected in addition to or instead of one or more of the here mentioned types of data. The collected data is then passed on to the detection unit or the processing unit.

[0060] The detection unit or the processing unit can comprise any or all of a pre-processor, a neural network and a post-processor. The signal pre-processor receives the collected data receives the collected data and prepares it to the input structure of the neural network.

[0061] The neural network is built up as a sequential model comprising a Long Short-Term Memory (LSTM) network with e.g. 600 neurons or cells. The model consists of a dense layer that collects the network to a single output, using a sigmoid activation function. A random neural network can also be used.

[0062] The output from the network is passed through to the post-processing segment to determine if the robot should stop or not. This process begins by squaring the output from the network, this value is added to a short memory queue. The average of this buffer is then compared to a threshold, which is adjusted based on welding parameters.

[0063] This comparison then determines if a missing material has been detected or not. To avoid hysteresis in the detections, the decision is added to a buffer of the past 10 decisions, and if this buffer has more than 5 votes for a missing material existing, the post-processor issues a positive detection, and a signal is sent to further processing to handle the detections. Often, this results in the robot controller or program logic will stop the welding and search for a new start position.

[0064] In FIG. 2, a schematic illustration of a welding job with abnormalities is shown. Two steel plates 1, 2 are positioned relative to each other. The second steel plate 2 is positioned abutting the first steel plate 1. To hold the second steel plate 2 in position it is tack welded 10 at some locations as a preliminary fixation to the first steel plate 1. As shown, the abutting plate 2 is provided with drain holes 11 so that water can be drained in the finished work piece. The welding gun 21 is controlled by the welding machine (not shown) and moved along the welding path 20 by a robot (not shown). As the welding process takes place the abnormalities, which in the illustrated example are drain holes 11 and weld tacks 10 are detected and processed so the welding machine is properly corrected so that the quality of the welding operation is ensured.

[0065] FIG. 3 shows a signal 50 of a standard deviation of a welding current versus time from an arc-welding operation. In the presented time window the standard deviation increases five times shown as five peaks (52,54,56,58,60). In between the peaks, the standard deviation is relatively stable indicating a stable welding operation. The increased standard deviation at the five peaks indicates that something has happened that has influenced the welding operation so that the welding operation is not optimal or normal.

[0066] The welding data, here in the form of the welding current, are computed to achieve the standard deviation. In this example, when several subsequent calculated standard deviations are above a predefined threshold 62, a first abnormality output 64 is set to one. When several subsequent standard deviations are below the predefined first threshold 62, the first abnormality output 64 is set to zero.

[0067] FIG. 4 shows a derivative 70 of the signal 50 presented in FIG. 3. The time period in FIG. 4 is the same as in FIG. 3. The presented data, here in the form of the derivative of the standard deviation of the welding current, are computed. In this example, when several subsequent derivatives of the standard deviation are above a predefined second threshold 72, a second abnormality output 74 is set to one. When several subsequent derivatives of the standard deviation are below the predefined threshold, the second abnormality output 74 is set to zero.

Items

[0068] 1. A method of controlling a welding operation by automatic detection of welding abnormalities executing a welding operation by operating a welding machine by a motion mechanism, said method comprising the steps of:

[0069] acquiring welding data during the welding operation, and supplying said welding data to a detection unit, which comprises a neural network-based abnormality detection system;

[0070] computing the data in a neural network, such as a Long Short-Term Memory (LSTM) network, and producing a neural network output, which is forwarded to a post-processor;

[0071] detecting if an abnormality is detected in said post-processor by preparing and buffering incoming neural network output signals and then processing a plurality of buffered signals to produce an abnormality detection decision output; and

[0072] transferring this abnormality detection decision output to a robot controller, which is controlling the welding machine and the automatic motion generating mechanism.

2. A method according to item 1, whereby the welding operation is an automatic arc-welding operation.
3. A method according to any one of items 1 or 2, whereby the motion mechanism is an automatic motion mechanism, such as a robot.
4. A method according to any one of the preceding items, whereby the welding data includes the welding current, the welding voltage, the energy used for the welding and/or arc-sensor signals, such as signals relating to Through Arc-sensor Seam Tracking (TAST).
5. A method according to any one of the preceding items, whereby the detection unit comprises a pre-processor for preparing the collected data for the neural network.
6. A method according to any one of the preceding items, whereby the detection unit comprises a Long Short-Term memory (LSTM) network.
7. A method according to any one of the preceding items, whereby the step of the neural network output is squared and then recorded in a short memory queue, whereafter the average of the buffer is calculated and compared with welding parameters to produce a decision signal, which is added to a buffer, said detection signal is a binary signal representing either abnormality detected or abnormality not detected.
8. A method according to any one of the preceding items, whereby the abnormality detection decision output is produced based on a predetermined number of decision signals in the buffer, such as 10 decision signals, preferably where a positive detection decision is the outcome of a majority of the detection signals in the buffer.
9. A system for controlling a welding operation by automatic detection of welding abnormality, said system comprising:

[0073] a welding machine for performing a welding process;

[0074] an automatic motion generating mechanism for moving the welding gun of the welding machine along a welding path; and

[0075] a robot controller, which is monitoring and controlling the welding process performed on the welding machine and the movements of the automatic motion generating mechanism; wherein

the robotic controller is provided with a detection unit which is receiving welding data during the welding operation; said detection unit comprising a neural network-based abnormality detection system, such as a Long Short-Term Memory (LSTM) network, for computing the welding data to produce a neural network output, which is forwarded to a post-processor, wherein it is detected if an abnormality in the welding operation by preparing and buffering incoming neural network output signals and then processing a plurality of buffered signals to produce an abnormality detection decision output; and transferring this abnormality detection decision output to a robot controller, which is controlling the welding machine and the automatic motion generating mechanism.
10. A system according to item 9, wherein the welding operation is an automatic arc-welding operation.
11. A system according to any items 9 or 10, wherein the welding data includes the welding current, the welding voltage, the energy used for the welding and/or arc-sensor signals, such as signals relating to Through Arc-sensor Seam Tracking (TAST).
12. A system according to any one of items 9 to 11, wherein the detection unit comprises a pre-processor for preparing the collected data for the neural network.
13. A system according to any one of items 9 to 12, wherein the Long Short-Term memory (LSTM) network comprises at least 600 neurons or cells.
14. A system according to any one of items 9 to 13, wherein the output or the neural network output is squared in the post-processor and then recorded in a short memory queue, whereafter the average of the buffer is calculated and compared with welding parameters to produce a decision signal, which is added to a buffer, said detection signal is a binary signal representing either abnormality detected or abnormality not detected.
15. A system according to any one of items 9 to 14, wherein the abnormality detection decision output is produced based on a predetermined number of decision signals in the buffer, such as 10 decision signals, preferably where a positive detection decision is the outcome of a majority of the detection signals in the buffer.