CN111069736A - Storage medium, welding equipment, welding abnormity detection method and device - Google Patents
Storage medium, welding equipment, welding abnormity detection method and device Download PDFInfo
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- CN111069736A CN111069736A CN201911379262.2A CN201911379262A CN111069736A CN 111069736 A CN111069736 A CN 111069736A CN 201911379262 A CN201911379262 A CN 201911379262A CN 111069736 A CN111069736 A CN 111069736A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/095—Monitoring or automatic control of welding parameters
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/12—Automatic feeding or moving of electrodes or work for spot or seam welding or cutting
- B23K9/133—Means for feeding electrodes, e.g. drums, rolls, motors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/32—Accessories
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Abstract
The disclosure relates to the technical field of welding, and provides a storage medium, welding equipment, a welding abnormity detection method and a welding abnormity detection device. The welding abnormality detection method may include: acquiring training data, wherein the training data comprises historical welding parameters including label information, and the label information is used for identifying the historical welding parameters as abnormal welding parameters or normal welding parameters; training an initial anomaly detection model according to the training data to obtain a target anomaly detection model; acquiring real-time welding parameters in the welding process; and detecting whether welding abnormity occurs in the welding process according to the real-time welding parameters and the target abnormity detection model. The present disclosure can reduce the workload of workers.
Description
Technical Field
The present disclosure relates to the field of welding technologies, and in particular, to a storage medium, a welding device, a welding abnormality detection method, and a welding abnormality detection apparatus.
Background
Welding is one of the most important processes in the field of mechanical manufacturing, and can be divided into manual welding, semi-automatic welding, automatic welding and the like according to different welding properties, modes, application occasions and the like.
Due to the occurrence of welding abnormity in the welding process, phenomena of welding missing, welding penetration, uneven welding seam forming and the like are often caused. At present, welding abnormity in the welding process mostly depends on manual detection, the welding abnormity depends on the technology and experience of detection workers to a great extent, a plurality of unstable factors can be generated, the workload of the workers is increased, and the working efficiency of the workers is reduced.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the present disclosure is to provide a storage medium, a welding apparatus, a welding abnormality detection method, and a welding abnormality detection device, which can reduce the workload of workers.
According to an aspect of the present disclosure, there is provided a welding abnormality detection method including:
acquiring training data, wherein the training data comprises historical welding parameters including label information, and the label information is used for identifying the historical welding parameters as abnormal welding parameters or normal welding parameters;
training an initial anomaly detection model according to the training data to obtain a target anomaly detection model;
acquiring real-time welding parameters in the welding process;
and detecting whether welding abnormity occurs in the welding process according to the real-time welding parameters and the target abnormity detection model.
In an exemplary embodiment of the present disclosure, the welding parameter includes arc sound information.
In an exemplary embodiment of the present disclosure, the anomaly detection model employs a convolutional neural network or a self-coding neural network.
In an exemplary embodiment of the disclosure, the anomaly detection model employs the self-encoder neural network, and detecting whether a welding anomaly occurs in a welding process according to the real-time welding parameters and the target anomaly detection model includes:
the real-time welding parameters are used as input quantity and input into the neural network of the self-encoder to obtain corresponding output quantity;
and judging whether welding abnormity occurs in the welding process according to the difference value between the output quantity and the input quantity.
In an exemplary embodiment of the present disclosure, training an initial anomaly detection model according to the training data includes:
randomly dividing the training data into a training set and a verification set;
training an initial anomaly detection model based on the training set to obtain a reference detection model;
and adjusting the reference detection model based on the verification set to obtain a target abnormity detection model.
According to an aspect of the present disclosure, there is provided a welding abnormality detection apparatus including:
the device comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring training data, the training data comprises welding parameters including label information, and the label information is used for identifying the welding parameters as welding abnormal parameters or welding normal parameters;
the training module is used for training the initial anomaly detection model according to the training data to obtain a target anomaly detection model;
the second acquisition module is used for acquiring real-time welding parameters in the welding process;
and the detection module is used for detecting whether welding abnormity occurs in the welding process according to the real-time welding parameters and the target abnormity detection model.
According to an aspect of the present disclosure, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements a welding anomaly detection method according to any one of the above.
According to an aspect of the present disclosure, there is provided a welding apparatus including:
a processor; and
a memory for storing one or more computer programs which, when executed by the processor, implement the welding anomaly detection method as described in any one of the above.
In an exemplary embodiment of the present disclosure, the welding apparatus further includes:
and the acquisition device is connected with the processor and used for acquiring the real-time welding parameters in the welding process and sending the real-time welding parameters to the processor.
In an exemplary embodiment of the present disclosure, the welding apparatus further includes:
the welding machine is connected to the processor, the processor is used for sending an alarm signal to the welding machine when welding abnormity occurs in the welding process, and the welding machine is used for judging whether to stop welding according to the alarm signal.
According to the storage medium, the welding equipment, the welding abnormity detection method and the welding abnormity detection device, the initial abnormity detection model is trained through the training data comprising the historical welding parameters, the target abnormity detection model is obtained, whether welding abnormity occurs in the welding process is detected through the target abnormity detection model and the real-time welding parameters, manual detection is avoided, and the workload of workers is reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The above and other features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
Fig. 1 is a flow chart of a welding anomaly detection method according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a self-encoding neural network of an embodiment of the present disclosure;
fig. 3 is a block diagram of a welding anomaly detection device according to an embodiment of the present disclosure;
fig. 4 is a block diagram of a welding apparatus of an embodiment of the present disclosure.
In the figure: 1. an input layer; 2. a hidden layer; 3. an output layer; 4. a welding abnormality detection device; 401. a first acquisition module; 402. a training module; 403. a detection module; 404. a second acquisition module; 5. a processor; 6. a memory; 7. a collection device; 8. and (5) welding the workpiece.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, materials, devices, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure. The same reference numerals in the drawings denote the same or similar structures, and thus their detailed description will be omitted.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. The terms "a" and "the" are used to indicate the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. other than the listed elements/components/etc.
The embodiment of the disclosure provides a welding abnormity detection method for welding equipment. As shown in fig. 1, the welding abnormality detection method may include steps S100 to S130, in which:
step S100, training data are obtained, wherein the training data comprise historical welding parameters including label information, and the label information is used for identifying the historical welding parameters as abnormal welding parameters or normal welding parameters.
And S110, training the initial anomaly detection model according to the training data to obtain a target anomaly detection model.
And S120, acquiring real-time welding parameters in the welding process.
And S130, detecting whether welding abnormity occurs in the welding process according to the real-time welding parameters and the target abnormity detection model.
According to the welding abnormity detection method, the initial abnormity detection model is trained through the training data comprising the historical welding parameters, the target abnormity detection model is obtained, whether welding abnormity occurs in the welding process is detected through the target abnormity detection model and the real-time welding parameters, manual detection is avoided, and the workload of workers is reduced.
The following describes in detail the steps of the welding assistance method according to the embodiment of the present disclosure:
in step S100, training data is obtained, where the training data includes historical welding parameters including label information, and the label information is used to identify the historical welding parameters as welding abnormal parameters or welding normal parameters.
The welding parameters may include arc sound information. The welding device may comprise a welder. The welding parameters may also include, but are not limited to, a welding current of the welder, and may also include a welding voltage. The welding apparatus may also include a wire feeder. The wire feeder may include a motor. The welding parameters may also include amplitude information of the motor, but the disclosed embodiments are not limited in this respect. The historical welding parameters are historical numerical values of the welding parameters. In addition, the welding parameters may also include image information during the welding process, such as weld images and the like. The welding parameters include tag information. The label information can be that welding is abnormal, namely the historical welding parameters are abnormal welding parameters; the label information can also be that welding is not abnormal, namely the historical welding parameters are normal welding parameters.
In step S110, the initial anomaly detection model is trained according to the training data to obtain a target anomaly detection model.
Training the initial anomaly detection model according to the training data comprises: randomly dividing training data into a training set and a verification set; training the initial anomaly detection model based on a training set to obtain a reference detection model; and adjusting the reference detection model based on the verification set to obtain a target abnormity detection model. The anomaly detection model may employ a convolutional neural network or a self-coding neural network. And when the quantity of the training data is more than 200, training the initial anomaly detection model according to the training data. Furthermore, the model training may be implemented under the TensorFlow framework. After the model training is completed, model conversion may be performed to apply the model to a different framework.
In step S120, real-time welding parameters in the welding process are acquired.
The real-time welding parameter may be arc sound information, welding current, welding voltage, amplitude information of a motor, or a weld image during a real-time welding process.
In step S130, it is detected whether a welding abnormality occurs during the welding process based on the real-time welding parameters and the target abnormality detection model.
As shown in fig. 2, taking an example that the anomaly detection model adopts a self-coding neural network, detecting whether a welding anomaly occurs in a welding process according to the real-time welding parameters and the target anomaly detection model includes: using real-time welding parameters as input quantity L1And inputting the output signal to a neural network of a self-encoder to obtain a corresponding output L2(ii) a According to the output L2And the input quantity L1And judging whether welding abnormity occurs in the welding process or not by the difference value. Wherein the output quantity L obtained by the present disclosure2And the input quantity L1May be plural, and each difference value isAnd comparing with a set value. If the percentage of the number of the difference values larger than the set value in the plurality of difference values to the total number of the difference values is larger than the preset percentage, judging that welding abnormity occurs in the welding process; and if the percentage of the number of the difference values larger than the set value in the plurality of difference values to the total number of the difference values is smaller than the preset percentage, judging that no welding abnormity occurs in the welding process. The preset percentage may be set according to actual conditions, and may be, for example, 80%, but the disclosure is not limited thereto. The self-coding neural network may include an input layer 1, a hidden layer 2, and an output layer 3, among others. Furthermore, as shown in FIG. 2, the output L is used2And the input quantity L1For example, the arc sound information may be a sound spectrogram. The abscissa of the sound spectrogram is frequency in Hz; the ordinate is the functional spectral density in dB/Hz.
The embodiment of the disclosure also provides a welding abnormity detection device. As shown in fig. 3, the welding anomaly detection device 4 may include a first acquisition module 401, a training module 402, a second acquisition module 404, and a detection module 403, wherein:
the first obtaining module 401 is configured to obtain training data, where the training data includes a welding parameter including label information, and the label information is used to identify the welding parameter as a welding abnormal parameter or a welding normal parameter; the training module 402 is configured to train the initial anomaly detection model according to the training data to obtain a target anomaly detection model. The second obtaining module 404 is configured to obtain real-time welding parameters during the welding process. The detection module 403 is configured to detect whether a welding anomaly occurs in the welding process according to the real-time welding parameters and the target anomaly detection model.
In the welding anomaly detection device 4 according to the embodiment of the present disclosure, the training module 402 trains the initial anomaly detection model by using training data including historical welding parameters, so as to obtain a target anomaly detection model; the detection module 403 detects whether welding abnormality occurs in the welding process through the target abnormality detection model and the real-time welding parameters, so that manual detection is avoided, and the workload of workers is reduced.
The welding parameters may include arc sound information. The welding device may comprise a welder. The welding parameters may also include, but are not limited to, a welding current of the welder, and may also include a welding voltage. The welding apparatus may also include a wire feeder. The wire feeder may include a motor. The welding parameters may also include amplitude information of the motor, but the disclosed embodiments are not limited in this respect. The historical welding parameters are historical numerical values of the welding parameters. In addition, the welding parameters may also include image information during the welding process, such as weld images and the like. The welding parameters include tag information. The label information can be that welding is abnormal, namely the historical welding parameters are abnormal welding parameters; the label information can also be that welding is not abnormal, namely the historical welding parameters are normal welding parameters.
The training module 402 may include a partitioning module, a first training sub-module, and a second training sub-module. The partitioning module is used for randomly partitioning the training data into a training set and a validation set. The first training submodule is used for training the initial anomaly detection model based on a training set to obtain a reference detection model. The second training submodule is used for adjusting the reference detection model based on the verification set to obtain a target abnormity detection model. The anomaly detection model may employ a convolutional neural network or a self-coding neural network.
The real-time welding parameter may be arc sound information, welding current, welding voltage, amplitude information of a motor, or a weld image during a real-time welding process.
Taking the self-encoding neural network as an example of the anomaly detection model, the detection module 403 can take the real-time welding parameters as input quantity and input the input quantity into the self-encoder neural network to obtain corresponding output quantity; the detection module 403 can also determine whether welding abnormality occurs in the welding process according to the difference between the output quantity and the input quantity.
The disclosed embodiments also provide a storage medium having a computer program stored thereon. The computer program, when executed by a processor, implements the welding anomaly detection method of any of the above embodiments.
The embodiment of the disclosure also provides welding equipment. As shown in fig. 4, the welding device may include a processor 5 and a memory 6. The memory 6 is used for storing one or more computer programs, and the one or more computer programs, when executed by the processor 5, implement the welding anomaly detection method according to any one of the above embodiments. Since the welding equipment can realize the welding abnormality detection method according to any one of the above embodiments, the welding equipment has the same beneficial effects as the embodiment of the welding abnormality detection method, and details are not repeated here.
The welding device may further comprise a pick-up device 7. The acquisition means 7 may be connected to the processor 5. The acquisition device 7 is used for acquiring real-time welding parameters in the welding process and sending the real-time welding parameters to the processor 5. The acquisition device 7 may be a sensor, but the disclosure is not limited thereto. The welding apparatus may further comprise a welder 8. The welder 8 may be connected to the processor 5. Wherein the welding may be connected to the processor 5 through an input/output interface, but the disclosed embodiments are not limited thereto. The processor 5 is used for sending an alarm signal to the welding machine 8 when welding abnormity occurs in the welding process. The welder 8 is used for judging whether to stop welding according to the alarm signal. Further, after the welder 8 is started, the welder 8 may send a start signal to the processor 5. The processor 5 may respond to the start signal and perform the welding anomaly detection method according to any of the above embodiments. Wherein the processor 5 may be provided with an ethernet interface. The user may set the above-mentioned abnormality detection threshold through the ethernet interface, but the present invention is not limited thereto, and may also set relevant configuration data such as a target abnormality detection model ID, a detection start/stop signal, and an abnormality signal transmission parameter.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A welding anomaly detection method, comprising:
acquiring training data, wherein the training data comprises historical welding parameters including label information, and the label information is used for identifying the historical welding parameters as abnormal welding parameters or normal welding parameters;
training an initial anomaly detection model according to the training data to obtain a target anomaly detection model;
acquiring real-time welding parameters in the welding process;
and detecting whether welding abnormity occurs in the welding process according to the real-time welding parameters and the target abnormity detection model.
2. The welding anomaly detection method according to claim 1, characterized in that said welding parameters comprise arc sound information.
3. The welding anomaly detection method according to claim 1, wherein said anomaly detection model employs a convolutional neural network or a self-coding neural network.
4. The welding anomaly detection method according to claim 3, wherein the anomaly detection model employs the self-encoder neural network, and detecting whether a welding anomaly occurs in a welding process according to the real-time welding parameters and the target anomaly detection model comprises:
the real-time welding parameters are used as input quantity and input into the neural network of the self-encoder to obtain corresponding output quantity;
and judging whether welding abnormity occurs in the welding process according to the difference value between the output quantity and the input quantity.
5. The welding anomaly detection method according to claim 1, wherein training an initial anomaly detection model based on the training data comprises:
randomly dividing the training data into a training set and a verification set;
training an initial anomaly detection model based on the training set to obtain a reference detection model;
and adjusting the reference detection model based on the verification set to obtain a target abnormity detection model.
6. A welding abnormality detection device characterized by comprising:
the device comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring training data, the training data comprises welding parameters including label information, and the label information is used for identifying the welding parameters as welding abnormal parameters or welding normal parameters;
the training module is used for training the initial anomaly detection model according to the training data to obtain a target anomaly detection model;
the second acquisition module is used for acquiring real-time welding parameters in the welding process;
and the detection module is used for detecting whether welding abnormity occurs in the welding process according to the real-time welding parameters and the target abnormity detection model.
7. A storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the welding anomaly detection method according to any one of claims 1 to 5.
8. A welding apparatus, comprising:
a processor; and
a memory for storing one or more computer programs that, when executed by the processor, implement the welding anomaly detection method of any one of claims 1-5.
9. The welding apparatus of claim 8, further comprising:
and the acquisition device is connected with the processor and used for acquiring the real-time welding parameters in the welding process and sending the real-time welding parameters to the processor.
10. The welding apparatus of claim 8, further comprising:
the welding machine is connected to the processor, the processor is used for sending an alarm signal to the welding machine when welding abnormity occurs in the welding process, and the welding machine is used for judging whether to stop welding according to the alarm signal.
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