CN111590244A - Workshop machine weld seam deviation real-time detection method and device based on cloud management and control - Google Patents
Workshop machine weld seam deviation real-time detection method and device based on cloud management and control Download PDFInfo
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Abstract
The invention discloses a real-time detection method and a real-time detection device for welding deviation of machine welding seams of a workshop based on cloud management and control. The welding deviation cloud management module determines the type of a welding seam to be detected according to the service and sends the newly added welding seam type to the machine welding point acquisition module; the machine welding point acquisition module acquires standard welding point data according to the welding seam type defined by the welding deviation cloud management module and sends the acquired welding point data to the standard welding seam generation module; the standard welding seam generation module carries out standard welding seam modeling on the received welding seam points through a multivariate nonlinear regression algorithm, and stores the modeled model to a welding deviation cloud management center for the welding deviation detection module to use; and finally, deploying the welding deviation detection module into a welding production line, and transmitting the welding point into a cloud management center in real time to check whether the welding result is subjected to welding deviation or not.
Description
Technical Field
The invention relates to the technical field of workshop machine weld seam welding detection, in particular to a workshop machine weld seam welding deviation real-time detection method and device based on cloud management and control, which can be widely applied to welding deviation detection in a production workshop, and can realize real-time supervision, early warning and timely intervention.
Background
With the rapid development of information technology and intelligent manufacturing, automation of the welding process becomes a necessary technology, and automation and intellectualization are important technical means for improving labor conditions, improving production efficiency, reducing production cost and ensuring the quality of welding products, and are also important development directions of welding technologies. With the gradual disappearance of the advantages of Chinese population dividends and the continuous deepening of transformation and upgrading of industrial technologies, welding robots and automatic equipment have already come to the strategic development stage. The weld seam tracking and detection are one of the key problems to be solved for realizing welding automation, wherein the deviation information detection is the core technology of automatic tracking. The existing weld tracking method is mainly based on contact sensing, arc sensing, ultrasonic sensing, electromagnetic sensing, infrared sensing, visual sensing and the like, wherein the weld tracking technology based on visual sensing is a research hotspot. In the visual tracking of the welding seam, a laser point or a structural light source is used for irradiating the surface of an object to be detected to obtain the characteristic information of the welding seam image by an active visual advanced detection method, and the research result of realizing the tracking control of the welding seam is more, and the existing industrial application products are provided. Meanwhile, the welding molten pool contains rich welding process information, and in the actual welding process, a welder mainly observes the molten pool and a welding torch through eyes to judge whether welding is centered. And based on weld seam tracking of the weld pool image, simulating the operation process of a welder, detecting the visual image of the weld pool and extracting the deviation information of the weld seam center line and the controlled welding torch.
And machine welding often appears the problem of welding partially in the department that starts an arc or the welding seam bending in the factory workshop, if the condition of welding partially can't in time feed back and give timely intervention, the quality of welding seam will can't obtain guaranteeing. In many precise machining occasions, the requirement on the quality of the welding seam is very high, and if the phenomenon of welding deviation occurs, great loss can be brought. At present, the conventional method for solving the secondary problems is manual supervision, but a large amount of time and manpower are consumed, and machine vision path prediction, magneto-optical path prediction and the like need high cost, cannot effectively deal with complex welding deviation paths, cannot meet the use requirement of precise machining occasions, and further increases the maintenance cost.
In order to overcome the welding defects such as unstable welding process and the like caused by assembly errors, unmatched parameters and the like in the laser welding process, the quality monitoring system for the welding process based on the coaxial image sensing technology is provided, and a set of online quality monitoring system for the laser welding process is established by collecting and analyzing molten pool images in the welding process. However, the method is only limited to laser welding, and due to the influence of the external environment, the acquired weld pool image does not contain or contains fewer analyzable features, which results in a reduction in detection effect. A sandwich plate laser welding offset state detection method based on the image characteristics of the tail of the molten pool is also provided, the shot image of the tail of the molten pool can be carried out under the condition of no external light source, the edge image of the tail of the molten pool is extracted through an improved Snake algorithm, and then the length and the area of the tail of the molten pool and the area difference of the molten pool on two sides of incident laser are calculated according to the characteristic parameters of 3 molten pool images. Although the characteristic parameters of the method are easy to extract and calculate and obviously change along with the change of the offset of the incident laser, the calculation complexity of the method is too high, and the requirement of real-time property cannot be met.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a workshop machine weld seam deviation real-time detection method and device based on cloud management and control, so that the manpower and material resources are reduced, the cost is saved, and the welding quality is effectively improved.
The invention is realized by the following technical scheme: a workshop machine weld seam welding deviation real-time detection method based on cloud management and control is characterized in that: the welding deviation detection system comprises a machine welding point acquisition module, a standard welding point generation module, a welding deviation detection module and a welding deviation cloud management module;
firstly, a welding deviation cloud management module determines the type of a welding seam to be detected according to a service, and sends the newly added welding seam type to a machine welding point acquisition module;
secondly, the machine welding point acquisition module acquires standard welding point data according to the welding seam type defined by the welding deviation cloud management module and sends the acquired welding point data to the standard welding seam generation module;
thirdly, the standard welding seam generation module carries out standard welding seam modeling on the received welding seam points through a multivariate nonlinear regression algorithm, and stores the modeled model to a welding deviation cloud management center for the welding deviation detection module to use;
and finally, deploying the welding deviation detection module into a welding production line, and transmitting the welding point into a cloud management center in real time to check whether the welding result is subjected to welding deviation or not.
Preferably, the welding point data acquired by the welding machine to be detected specifically includes the current machine equipment model, the current welded product number, and coordinate point information of the welding point, where the coordinate point information is multidimensional information.
Preferably, the standard weld generating module generates a standard weld, specifically as follows:
(1) the welding deviation cloud management module determines the type of a welding seam to be detected according to the service and sends the newly added welding seam type to the machine welding point acquisition module;
(2) the machine welding point acquisition module acquires standard welding point data according to the welding seam type defined by the welding deviation cloud management module and sends the acquired welding point data to the standard welding seam generation module;
(3) the standard welding seam generation module carries out standard welding seam modeling on the received welding seam points through a multivariate nonlinear regression algorithm;
(4) and the standard welding line generation module stores the modeled model to a welding deviation cloud management center for the welding deviation detection module to use, and records the machine equipment model corresponding to the welding line and the product number of current welding.
Preferably, the welding seam point is modeled by a multiple nonlinear regression algorithm, and the specific modeling working method comprises the following steps:
given input set X ═ X1,x2,……,xnAnd (5) establishing a nonlinear regression model, wherein n is the number of samples of the input set:
where f (X, θ) is a non-linear function, and is an error with a mean value of 0, the model optimizes the parameter θ using a least squares method with the input set X known, and the optimized loss function is:
wherein,
the method for updating the value of the parameter theta is gradient descent, and the formula is as follows:
where γ is the learning rate.
Preferably, the learning rate γ is 1 e-3.
Preferably, the weld deviation detection module detects the weld in real time, specifically as follows:
(1) firstly, deploying a welding deviation detection module into a corresponding machine of a welding production line, and acquiring a welding point of the welding machine, a corresponding machine equipment model and a currently welded product number according to the protocol requirement of the machine;
(2) the welding deviation detection module transmits welding points, corresponding machine equipment models and currently welded product numbers collected in the production process to a welding deviation cloud management center in real time;
(3) and comparing the obtained welding deviation points with the stored marked welding lines according to the equipment model and the product number by the welding deviation cloud management center, calculating the welding deviation distance, if the welding deviation distance exceeds a predefined threshold value, sending a welding deviation warning to a manager by the welding deviation cloud management center, and if the welding deviation distance does not exceed the predefined threshold value, sending no response by the system.
Preferably, the formula for calculating the welding offset distance is as follows:
D(xi,xj)＝||xi-xj||,
wherein x isiA sample point x transmitted to the welding deviation cloud management center for the welding deviation detection modulejCorresponding weld model points stored for a cloud management center
Preferably, the communication between the welding deviation cloud management module and each module is carried out by adopting a socket mode, a communication data protocol is negotiated before each module, and the data format is JSON.
Workshop machine weld seam welding deviation real-time detection device based on high in clouds management and control, include
The welding deviation cloud management module is used for determining the type of a welding seam to be detected according to the service, determining the function of a welding deviation distance threshold value and providing the function of checking, deleting and modifying the stored related information of the standard welding seam type for an administrator; and sending the newly added welding seam type to a machine welding point acquisition module
The machine welding point acquisition module is used for acquiring standard welding point data according to the welding seam type defined by the welding deviation cloud management module and sending the acquired welding point data to the standard welding seam generation module;
the standard welding seam generation module is used for carrying out standard welding seam modeling on the received welding seam points through a multivariate nonlinear regression algorithm, and storing the modeled model to a welding deviation cloud management center for the welding deviation detection module to use;
and the welding deviation detection module is deployed in a welding production line, and transmits the welding point to a cloud management center in real time to check whether the welding result is subjected to welding deviation or not.
Preferably, the welding machine to be tested has the function of acquiring the welding point.
Compared with the prior art, the invention has the beneficial effects that:
(1) on the basis of the advantages of the prior art, each part respectively plays its own role, clearly divides labor and is convenient for realizing and maintaining codes;
(2) the automatic real-time detection of machine seam welding deviation is realized, the manpower and material resources are reduced, the cost is saved, and the welding quality is effectively improved;
(3) the method can be widely applied to welding deviation detection in a production workshop, and can realize real-time supervision, early warning and timely intervention.
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The invention will be further explained with reference to the drawings.
FIG. 1 is a schematic view of the overall structure of the present invention;
FIG. 2 is a flowchart of the standard weld generation process of the present invention;
FIG. 3 is an example diagram of a standard weld generated by a multivariate nonlinear algorithm in the present invention;
FIG. 4 is a flowchart of the real-time weld seam detection process of the present invention;
fig. 5 is a schematic diagram of a main packet format of communication between a welding deviation cloud management center and a machine welding point acquisition module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the specific embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
As shown in fig. 1, a method for implementing real-time detection of welding deviation of machine welding seams of a workshop based on cloud management and control comprises a welding deviation cloud management module, a machine welding point acquisition module, a standard welding seam generation module and a welding deviation detection module;
firstly, a welding deviation cloud management module determines the type of a welding seam to be detected according to a service, and sends the newly added welding seam type to a machine welding point acquisition module; secondly, the machine welding point acquisition module acquires standard welding point data according to the welding seam type defined by the welding deviation cloud management module and sends the acquired welding point data to the standard welding seam generation module; thirdly, the standard welding seam generation module carries out standard welding seam modeling on the received welding seam points through a multivariate nonlinear regression algorithm, and stores the modeled model to a welding deviation cloud management center for the welding deviation detection module to use; and finally, deploying the welding deviation detection module into a welding production line, and transmitting the welding point into a cloud management center in real time to check whether the welding result is subjected to welding deviation or not.
As shown in fig. 2, the standard weld generation module specifically generates the standard weld according to the following working procedure:
(1) the welding deviation cloud management module determines the type of a welding seam to be detected according to the service and sends the newly added welding seam type to the machine welding point acquisition module;
(2) the machine welding point acquisition module acquires standard welding point data according to the welding seam type defined by the welding deviation cloud management module and sends the acquired welding point data to the standard welding seam generation module;
(3) the standard welding seam generation module carries out standard welding seam modeling on the received welding seam points through an algorithm;
(4) and the standard welding line generation module stores the modeled model to a welding deviation cloud management center for the welding deviation detection module to use, and records the machine equipment model corresponding to the welding line and the product number of current welding.
The communication between the welding bias cloud management module and each module is carried out in a socket mode, a communication data protocol is negotiated before each module, and the data format is JSON.
The algorithm used by the standard weld generation module is a multiple nonlinear regression algorithm, as shown in fig. 3.1-3.3, which is an example of the method after modeling the weld points by the multiple nonlinear regression algorithm. The curve is the result of regression of standard weld points, and it can be seen that the weld trajectory can be normally and accurately regressed by the multivariate nonlinear regression algorithm. Specifically, the working method of modeling using the multivariate nonlinear algorithm is as follows:
given input set X ═ X1,x2,……,xnTherein ofAnd n is the number of samples of the input set, and a nonlinear regression model is established:
where f (X, θ) is a non-linear function, and is an error with a mean value of 0, the model optimizes the parameter θ using a least squares method with the input set X known, and the optimized loss function is:
wherein,
the method for updating the value of the parameter theta is gradient descent, and the formula is as follows:
wherein gamma is a learning rate, in the specific implementation process, adjustment is needed according to the sample, and the default of the method is set to be 1 e-3.
As shown in fig. 4, the working process of detecting the weld in real time in the present invention is specifically as follows:
(1) firstly, deploying a welding deviation detection module into a corresponding machine of a welding production line, and acquiring a welding point of the welding machine, a corresponding machine equipment model and a currently welded product number according to the protocol requirement of the machine;
(2) the welding deviation detection module transmits welding points, corresponding machine equipment models and currently welded product numbers collected in the production process to a welding deviation cloud management center in real time;
(3) and comparing the obtained welding deviation points with the stored marked welding lines according to the equipment model and the product number by the welding deviation cloud management center, calculating the welding deviation distance, if the welding deviation distance exceeds a predefined threshold value, sending a welding deviation warning to a manager by the welding deviation cloud management center, and if the welding deviation distance does not exceed the predefined threshold value, sending no response by the system.
Specifically, the formula for calculating the weld offset distance is as follows:
D(xi,xj)＝||xi-xj||,
wherein x isiA sample point x transmitted to the welding deviation cloud management center for the welding deviation detection modulejCorresponding weld model points stored for a cloud management center
As shown in fig. 5, the main packet formats of the client to be verified and the registration code generation center in the present invention include: the method comprises the steps of equipment number, welding seam point x coordinate, welding seam point y coordinate, welding seam point z coordinate, creation time and welding seam point state, wherein the state comprises a welding starting point, a middle point and an ending point.
According to the technical scheme, the method integrates the advantages of the prior technical scheme, realizes automatic real-time detection of machine weld deviation, reduces manpower and material resources, saves cost, effectively improves welding quality, can be widely applied to weld deviation detection in a production workshop, and achieves real-time supervision, early warning and timely intervention.
The foregoing is a preferred embodiment of the present patent, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present patent, and these modifications and decorations are also regarded as the protection scope of the present patent.
Claims (10)
1. A workshop machine weld seam welding deviation real-time detection method based on cloud management and control is characterized in that: the welding deviation detection system comprises a machine welding point acquisition module, a standard welding point generation module, a welding deviation detection module and a welding deviation cloud management module;
firstly, a welding deviation cloud management module determines the type of a welding seam to be detected according to a service, and sends the newly added welding seam type to a machine welding point acquisition module;
secondly, the machine welding point acquisition module acquires standard welding point data according to the welding seam type defined by the welding deviation cloud management module and sends the acquired welding point data to the standard welding seam generation module;
thirdly, the standard welding seam generation module carries out standard welding seam modeling on the received welding seam points through a multivariate nonlinear regression algorithm, and stores the modeled model to a welding deviation cloud management center for the welding deviation detection module to use;
and finally, deploying the welding deviation detection module into a welding production line, and transmitting the welding point into a cloud management center in real time to check whether the welding result is subjected to welding deviation or not.
2. The cloud management and control-based workshop machine weld seam weld deviation real-time detection method according to claim 1, characterized in that: the welding point data acquired by the welding machine to be detected specifically comprises the current machine equipment model, the current welded product number and coordinate point information of the welding point, wherein the coordinate point information is multidimensional information.
3. The method for realizing real-time detection of welding deviation of machine welding seams of the cloud management and control-based workshop according to claim 1, is characterized in that: the standard welding seam generation module generates a standard welding seam, which specifically comprises the following steps:
(1) the welding deviation cloud management module determines the type of a welding seam to be detected according to the service and sends the newly added welding seam type to the machine welding point acquisition module;
(2) the machine welding point acquisition module acquires standard welding point data according to the welding seam type defined by the welding deviation cloud management module and sends the acquired welding point data to the standard welding seam generation module;
(3) the standard welding seam generation module carries out standard welding seam modeling on the received welding seam points through a multivariate nonlinear regression algorithm;
(4) and the standard welding line generation module stores the modeled model to a welding deviation cloud management center for the welding deviation detection module to use, and records the machine equipment model corresponding to the welding line and the product number of current welding.
4. The cloud management and control-based workshop machine weld seam weld deviation real-time detection method according to claim 3, characterized in that: modeling is carried out on the welding seam point through a multivariate nonlinear regression algorithm, and the specific working method of modeling is as follows:
given input set X ═ X1,x2,……,xnAnd (5) establishing a nonlinear regression model, wherein n is the number of samples of the input set:
where f (X, θ) is a non-linear function, and is an error with a mean value of 0, the model optimizes the parameter θ using a least squares method with the input set X known, and the optimized loss function is:
wherein,
the method for updating the value of the parameter theta is gradient descent, and the formula is as follows:
where γ is the learning rate.
5. The cloud management and control-based workshop machine weld seam weld deviation real-time detection method according to claim 4, characterized in that: the learning rate γ is 1 e-3.
6. The cloud management and control-based workshop machine weld seam weld deviation real-time detection method according to claim 1, characterized in that: the welding deviation detection module detects the welding seam in real time, and comprises the following specific steps:
(1) firstly, deploying a welding deviation detection module into a corresponding machine of a welding production line, and acquiring a welding point of the welding machine, a corresponding machine equipment model and a currently welded product number according to the protocol requirement of the machine;
(2) the welding deviation detection module transmits welding points, corresponding machine equipment models and currently welded product numbers collected in the production process to a welding deviation cloud management center in real time;
(3) and comparing the obtained welding deviation points with the stored marked welding lines according to the equipment model and the product number by the welding deviation cloud management center, calculating the welding deviation distance, if the welding deviation distance exceeds a predefined threshold value, sending a welding deviation warning to a manager by the welding deviation cloud management center, and if the welding deviation distance does not exceed the predefined threshold value, sending no response by the system.
7. The cloud management and control-based workshop machine weld seam weld deviation real-time detection method according to claim 6, characterized in that: the formula for calculating the weld offset distance is as follows:
D(xi,xj)＝||xi-xj||,
wherein x isiA sample point x transmitted to the welding deviation cloud management center for the welding deviation detection modulejAnd corresponding welding seam model points stored for the cloud management center.
8. The cloud management and control-based workshop machine weld seam weld deviation real-time detection method according to claim 1, characterized in that: the communication between the welding bias cloud management module and each module is carried out by adopting a socket mode, a communication data protocol is negotiated before each module, and the data format is JSON.
9. The utility model provides a workshop machine weld seam welding real-time detection device that deviates from a straight line based on high in clouds management and control which characterized in that: comprises that
The welding deviation cloud management module is used for determining the type of a welding seam to be detected according to the service, determining the function of a welding deviation distance threshold value and providing the function of checking, deleting and modifying the stored related information of the standard welding seam type for an administrator; and sending the newly added welding seam type to a machine welding point acquisition module
The machine welding point acquisition module is used for acquiring standard welding point data according to the welding seam type defined by the welding deviation cloud management module and sending the acquired welding point data to the standard welding seam generation module;
the standard welding seam generation module is used for carrying out standard welding seam modeling on the received welding seam points through a multivariate nonlinear regression algorithm, and storing the modeled model to a welding deviation cloud management center for the welding deviation detection module to use;
and the welding deviation detection module is deployed in a welding production line, and transmits the welding point to a cloud management center in real time to check whether the welding result is subjected to welding deviation or not.
10. The cloud management and control-based workshop machine weld seam welding deviation real-time detection device according to claim 9, characterized in that: the welding machine to be detected has the function of acquiring the welding point.
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| CN115106615A (en) * | 2022-08-30 | 2022-09-27 | 苏芯物联技术(南京)有限公司 | Welding deviation real-time detection method and system based on intelligent working condition identification |
| CN117745718A (en) * | 2024-02-19 | 2024-03-22 | 工业云制造(四川)创新中心有限公司 | Information interaction method based on cloud manufacturing |
| CN117745718B (en) * | 2024-02-19 | 2024-07-02 | 工业云制造(四川)创新中心有限公司 | Information interaction method based on cloud manufacturing |
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