CN112692418A - Spot welding quality monitoring device and method based on dynamic machine vision - Google Patents
Spot welding quality monitoring device and method based on dynamic machine vision Download PDFInfo
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- 238000003466 welding Methods 0.000 title claims abstract description 186
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- 238000012806 monitoring device Methods 0.000 title claims abstract description 15
- 238000012544 monitoring process Methods 0.000 claims abstract description 26
- 238000012545 processing Methods 0.000 claims abstract description 14
- 239000011521 glass Substances 0.000 claims description 4
- 230000001681 protective effect Effects 0.000 claims description 2
- 238000001514 detection method Methods 0.000 claims 5
- 238000010586 diagram Methods 0.000 description 5
- 238000007689 inspection Methods 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 5
- 238000005299 abrasion Methods 0.000 description 2
- 238000009776 industrial production Methods 0.000 description 2
- 239000002184 metal Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
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- 239000000463 material Substances 0.000 description 1
- 230000013011 mating Effects 0.000 description 1
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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
- B23K11/00—Resistance welding; Severing by resistance heating
- B23K11/24—Electric supply or control circuits therefor
- B23K11/25—Monitoring devices
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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
- B23K11/00—Resistance welding; Severing by resistance heating
- B23K11/10—Spot welding; Stitch welding
- B23K11/11—Spot welding
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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
- B23K11/00—Resistance welding; Severing by resistance heating
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Abstract
本发明涉及电阻点焊应用技术领域，尤其涉及一种基于动态机器视觉的点焊质量监测装置及方法，该装置包括被焊工件、点焊电极、电极杆、工业方形电源和动态机器视觉监测系统，所述点焊电极形成于所述电极杆的端部，所述被焊工件设置于两个所述点焊电极之间；所述工业方形电源设置于所述被焊工件的一端外侧，所述动态机器视觉监测系统设置于所述被焊工件的另一端外侧；该方法包括获取与电阻点焊过程相关联的数据，从获取的数据中检测与飞溅相关联的参数，分析检测的参数，并用高速工业相机拍摄点焊期间的飞溅情况，采用图像处理确定产生的飞溅数量、飞溅形貌及飞溅方向，建立数据库，以监测电阻点焊过程的飞溅产生事件。
The invention relates to the technical field of resistance spot welding applications, in particular to a spot welding quality monitoring device and method based on dynamic machine vision. The device includes a workpiece to be welded, a spot welding electrode, an electrode rod, an industrial square power supply and a dynamic machine vision monitoring system , the spot welding electrode is formed at the end of the electrode rod, the workpiece to be welded is arranged between the two spot welding electrodes; the industrial square power supply is arranged outside one end of the workpiece to be welded, so The dynamic machine vision monitoring system is arranged outside the other end of the workpiece to be welded; the method includes acquiring data associated with the resistance spot welding process, detecting parameters associated with spatter from the acquired data, analyzing the detected parameters, A high-speed industrial camera was used to photograph the spatter during spot welding, and image processing was used to determine the amount of spatter, the shape of the spatter and the direction of the spatter, and a database was established to monitor the spatter generation events in the resistance spot welding process.
Description
Technical Field
The invention relates to the technical field of resistance spot welding application, in particular to a spot welding quality monitoring device and method based on dynamic machine vision.
Background
Resistance spot welding is a method of pressing a workpiece to be welded between two electrodes, applying electric current, and heating the workpiece to a molten state by resistance heat generated by the electric current flowing through a contact surface and an adjacent area of the workpiece, so as to form metal bonding. Resistance spot welding has been widely used in the fields of automobiles, rail transit, aerospace, low-voltage appliances, home appliances, batteries, and the like, because of its advantages of low cost, high production efficiency, and easy automation. In the automotive industry in particular, over 90% of the body assembly work is done by resistance spot welding.
However, during resistance spot welding, spatter is inevitably generated. The root cause of the spattering event is the loss of containment of the bath metal due to incomplete or broken plastic ring formation. In various industries, workpieces welded with spatter events may be considered poor quality features. In the process of mass and fast-beat installation and production of vehicle bodies, interference conditions such as electrode abrasion, assembly gaps, workpiece surface pollution and the like are usually accompanied, and the uncertain factors increase the instability of the welding process, so that the probability of incomplete formation or breakage of a plastic ring is increased, welding quality problems such as splashing and the like are caused, and the reliability of welding spots is reduced. In order to ensure the quality of welding spots, the strategies of 'post-welding manual sampling inspection and offline industrial adjustment' are commonly adopted by domestic and foreign enterprises, the quality of the welding spots is evaluated by means of human working medium inspection such as ultrasonic inspection or anatomical experiments, and then the offline feedback adjustment of welding process parameters is carried out according to the statistical qualified rate. The quality inspection method has low efficiency and poor timeliness of quality feedback, can not meet the production requirement of fast beat, can not ensure the inspection of 100 percent of welding spots, and can ensure the quality only by increasing the number of the welding spots by enterprises, which inevitably causes the cost increase and the efficiency reduction. Therefore, the invention provides a method and a device for monitoring the spot welding quality based on dynamic machine vision. The welding spot quality is judged and the technological parameters are adjusted by monitoring the situation of the spattering event in the spot welding process and analyzing and comparing the situation with the data in the database to judge the reason of the spattering event.
Based on the reasons, the invention provides a device and a method for monitoring the spot welding quality based on dynamic machine vision.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a spot welding quality monitoring device and method based on dynamic machine vision, which can judge the quality of a resistance spot welding spot by monitoring the welding spatter condition.
In order to realize the purpose of the invention, the invention adopts the technical scheme that:
the invention discloses a spot welding quality monitoring device based on dynamic machine vision, which comprises a welded workpiece, spot welding electrodes, an electrode rod, an industrial square power supply and a dynamic machine vision monitoring system, wherein the spot welding electrodes are formed at the end parts of the electrode rod, the welded workpiece is arranged between the two spot welding electrodes, and the included angle between the electrode rod and the outer wall of the welded workpiece is 90 degrees; the industrial square power supply is arranged on the outer side of one end of the workpiece to be welded, and the dynamic machine vision monitoring system is arranged on the outer side of the other end of the workpiece to be welded.
The industrial square power supply is connected with the light source trigger simulator through the light source fixing frame, the light source fixing frame is of an inverted L-shaped structure, the light source trigger simulator is electrically connected with the industrial square power supply, and a controller switch and a brightness adjusting button are arranged on the light source trigger simulator.
And protective glass is arranged between the dynamic machine vision monitoring system and the end face of the workpiece to be welded.
The invention discloses a spot welding quality monitoring method based on dynamic machine vision, which comprises the following steps,
In step 920, detecting parameters associated with spatter includes detecting welding current, detecting electrode pressure, detecting welding time, detecting welded plate state, detecting electrode wear, detecting perpendicularity of electrode rods, and detecting positions of welding spots.
In step 930, analyzing the detected parameters includes determining that the welding current is greater than or equal to a certain value and spatters are generated, determining that the electrode pressure is less than or equal to a certain value and spatters are generated, determining when the welding time is during a certain period of time and spatters are generated, determining the relationship between the wear degree of the spot welding electrode and spatters, determining the relationship between the angle between the electrode rod and the workpiece to be welded and the welding spatters, and determining the relationship between the welding spot welding edge welding degree and the welding spatters.
In the step 940, the shooting of the spatter during the spot welding by the high-speed industrial camera includes setting the parameters related to the spatter in the step 920 to the picture and the video of the spatter generated during the spot welding process, respectively.
The shooting of the spattering condition during spot welding comprises the steps of setting the welding current larger than a threshold value, the electrode pressure smaller than the threshold value, the welding time longer than the threshold value, the surface state and the matching state of a plate, the electrode abrasion degree, the verticality between an electrode rod and a workpiece and the position of a welding point in the resistance spot welding process as single variables respectively, and shooting and storing pictures and videos of the spot welding spattering process caused by the variables.
In step 950, determining the amount, shape and direction of the splashes generated by image processing includes calculating the amount of the splashes generated by the splash event using a differential evolution algorithm, and analyzing the shape and direction of the splashes generated by the splash event using image processing.
In step 957, the step of creating a database includes associating the splash images and videos processed by the image processing in step 930 with the splash event-generating variables thereof, storing the images and videos in a unified manner, and creating a database that can be called.
The invention has the beneficial effects that:
1. the spot welding quality monitoring device based on the dynamic machine vision can judge the quality of the resistance spot welding spot by monitoring the welding spatter condition.
2. The spot welding quality monitoring device based on the dynamic machine vision can monitor the resistance spot welding process in real time, can know the reason of the splash event through image processing and data comparison in a database, and improves the welding process by adjusting welding parameters, thereby having high efficiency.
3. The spot welding quality monitoring device based on the dynamic machine vision is very simple to build, and the required hardware cost is low.
4. The database in the spot welding quality monitoring device based on the dynamic machine vision consists of 8 sub-databases, each sub-database corresponds to a reason for occurrence of the resistance spot welding spattering event, and the 8 sub-databases contain all the reasons which can possibly generate the resistance spot welding spattering event, so that the established database is rich in content, wide in coverage and strong in persuasion.
Drawings
FIG. 1 is a schematic structural diagram of a dynamic machine vision based spot welding quality monitoring device of the present invention;
FIG. 2 is a flow chart illustrating a process for capturing spatter during spot welding with a high speed industrial camera in accordance with aspects of the present invention.
FIG. 3 is a flow chart illustrating a method for determining a distance between a generated spatter and a weld spot, a spatter topography, and a spatter direction using image processing and building a database, in accordance with aspects of the present invention.
FIG. 4 is a block diagram of a sub-database built with "welding current" as a variable
FIG. 5 is a schematic diagram of an embodiment for use in applying a sheet material mating condition as a variable to a spot welding process in accordance with aspects of the present invention.
FIG. 6 is a diagrammatic view of an embodiment used in applying the degree of wear of the electrode cap as a variable to a spot welding process in accordance with aspects of the present invention.
Fig. 7 is a diagrammatic view of an embodiment used in applying electrode rod perpendicularity as a variable to a spot welding process in accordance with aspects of the present invention.
FIG. 8 is a diagrammatic view of an embodiment used in applying spot weld location as a variable to a spot welding process in accordance with aspects of the present invention.
FIG. 9 is a flow diagram of an embodiment of a method for dynamic machine vision based spot weld quality monitoring in accordance with aspects of the present invention.
Fig. 10 is a diagram of the quality of spot welds for use in actual industrial production based on dynamic machine vision in accordance with the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples:
see fig. 1-10.
The invention discloses a spot welding quality monitoring device based on dynamic machine vision, as shown in figure 1, a workpiece 100 to be welded is placed between two identical spot welding electrodes 200, the perpendicularity between an electrode rod 300 and the workpiece 100 to be welded is 90 degrees, an optimal spot welding angle is ensured, and a nugget position 400 is positioned at the positive center of the workpiece, so that the pressure applied to the workpiece 100 to be welded in the spot welding process is ensured to be uniform. The industrial square light source 500 is parallel to the right side of the welded workpiece 100, and the industrial square light source 500 can provide high-brightness high-uniformity illuminating light and is fixed by the light source fixing frame 600 to be stable. The light source triggering simulator 700 is connected with the industrial square light source 500, the light source triggering simulator 700 can control the industrial square light source 500 to be turned on and off through the controller switch 701, the brightness adjusting button 702 can adjust the brightness of the industrial square light source 500 through rotation according to actual needs, the clockwise rotation is to increase the brightness, and the counterclockwise rotation is to decrease the brightness. The protection glass 800 and the dynamic machine vision monitoring system 900 are located at the opposite time of the industrial square light source 500, and the protection glass 800 is placed between the dynamic machine vision monitoring system 900 and the workpiece 100 to be welded, which can protect the dynamic machine vision monitoring system 900 from being damaged by splashing in the spot welding process.
Generally, factors that can cause spattering during spot welding include excessive welding current, low electrode pressure, long welding time, wear of the electrode, the surface cleaning state of the workpiece, i.e., the plate, and the matching degree of the plate, the position of the welding point, and the perpendicularity between the electrode rod and the workpiece. Fig. 2 shows that the above-mentioned factors generating the spattering event during the spot welding process are respectively applied to the process of shooting the spattering event as a single variable, that is, three sets of currents greater than the threshold current are set, the optimal values of the other variables are selected and applied to the spot welding process, and spattering pictures and videos 941 during the spot welding process are collected at the three sets of currents greater than the threshold current; setting three groups of electrode pressures smaller than the threshold voltage, selecting the optimal value for the other variables to be applied to the spot welding process, and collecting the spattering pictures and videos 942 of the spot welding process under the three groups of electrode pressures smaller than the threshold voltage; setting three groups of welding time longer than the threshold time, selecting the optimal value for the other variables to be applied to the spot welding process, and collecting the spatter pictures and videos 943 of the spot welding process under the three groups of welding time longer than the threshold time; setting other variables of a standard electrode 200, a slightly worn electrode 201 and a severely worn electrode 202 to select optimal values to be applied to the spot welding process respectively, and selecting the optimal values for the other variables to acquire a splashing picture and a video 944 in the spot welding process under the three electrodes; setting three plate matching modes of 100 plate complete matching, 101 plate clearance degree smaller and 102 plate clearance degree larger to be applied to the spot welding process, selecting the optimal value for the other variables, and collecting the splashing picture and video 945 in the spot welding process under the three plate matching modes; setting a central welding point position 400, a welding point position 401 for forming about two thirds of nuggets, a welding point position 402 for forming about one half of nuggets and a welding point position 403 for forming about one third of nuggets to be applied to the spot welding process respectively, and selecting optimal values for other variables to acquire splashing pictures and videos 946 in the spot welding process at different welding point positions; setting the verticality between the electrode rod and the workpiece to be 90 degrees 300, the verticality between the electrode rod and the workpiece to be larger but smaller than 90 degrees 301, and the verticality between the electrode rod and the workpiece to be smaller 302, respectively applying the verticality to the spot welding process, and selecting the optimal value for other variables to acquire the splashing pictures and videos of the spot welding process under different verticality between the electrode rod and the workpiece 947; the method comprises the steps of setting good surface state (oil stain and oxide are 0), general surface state (oil stain and oxide are less) and poor surface state (oil stain and oxide are more) of the plate to be applied to a spot welding process respectively, selecting optimal values for other variables to collect splashing pictures and videos 948 in the spot welding process under different surface states of the plate, screening all the pictures and videos, namely comparing the pictures and videos which generate splashing events under the same variable, determining the pictures which are most serious in splashing conditions under the variable to be stored, filing the pictures and the videos together with the corresponding variable 949, for example, assuming that the threshold current is 7KA, setting the splashing currents of 8KA, 9KA and 10KA in the spot welding process respectively, collecting the splashing pictures and videos, and slowly playing the videos under the current of 8KA to intercept the two pictures which are most serious in splashing conditions, and comparing the picture captured from the video with the picture directly acquired by the high-speed industrial camera, accurately finding out the picture with the most serious splashing condition under the current of 8KA, archiving the picture and the 8KA together, and naming the picture as an event A1, and naming the 9KA and the splashing event picture generated by the 9KA as an event A2, and so on.
The individual pictures obtained in 949 are processed to calculate the distance between the spatter and the weld in the picture. Blurring the background color of the picture, (here we assume white as foreground color, and black as background color), converting the value of the pixel in the foreground to the distance 952 that point reaches the nearest background point,
| X | is a point (X)2+y2) Euclidean distance to the origin. Namely, a two-dimensional rectangular coordinate system is established by taking a welding spot as an origin, and the position (x) of each splash point is calibratedi+yi) (i 1.2.3.. said.), the distances between the farthest spattering point and the nearest spattering point and the welding point are calculated, and the distances between spatters and the welding point of spattering events caused by the same variables are sorted and filed 953, for example, the distance between spatters and the welding point is "1" when the welding current is 8KA, and the distance between spatters and the welding point is "1" when the welding current is 9KAThe distance between the points is '2', the distance between the flying spatter and the welding point when the welding current is 10KA is '3', the distance between the farthest spatter point and the welding point when the welding current is 8KA is smaller than the distance between the farthest spatter point and the welding point when the welding current is 9KA is smaller than the distance between the farthest spatter point and the welding point when the welding current is 10KA, the distance between the closest spatter point and the welding point when the welding current is 8KA is smaller than the distance between the closest spatter point and the welding point when the welding current is 9KA is smaller than the distance between the closest spatter point and the welding point when the welding current is 10KA, and the distances between the spatter caused by the welding current and the welding point can be sorted into<9KA<10KA "and archiving the distance" 1 "with the welding current 8KA, named" event a1.1 ", the distance" 2 "with the welding current 9KA, named" event a2.1 ", the distance" 3 "with the welding current 10KA, named" event a3.1 ", and so on. The images obtained in 949 are processed, the images are digitized by using image processing software to obtain the appearance and the outline of the spatter, the spatter direction can be accurately obtained after the images are processed, the data obtained after the processing and the images and the corresponding parameters are jointly filed 954, for example, the image processing software is used for processing the spatter event image when the welding current is 8KA, the obtained spatter appearance is named as "T1", the obtained spatter direction is "D1", and the "T1" and the "D1" are jointly filed to be named as "event A1.2". The processed data are sorted, the splash events caused by the same kind of variables are used as a sub-database 956, and all the sub-databases are combined to obtain a total database 957. For example, the spatter event caused by the welding current is a sub-database and is designated "I", and "I" includes "event A1" generated at the welding current of 8KA and "event A1" includes "event a 1.1" and "event a 1.2"; also included in "I" is "event A2" generated when the welding current is 9KA and "event A2" includes "event a 2.1" and "event a 2.2".
After the total database is finished, practical production can be carried out. Fig. 10 is a flowchart of an embodiment of a spot welding quality monitoring method based on dynamic machine vision in actual industrial production. With the start 1 of the resistance spot welding process, the shooting spot welding process 2 is started to judge whether the spatter production 3 exists, if the process has no spatter event, the quality of the welding spot is considered to be good 3.1, if the spatter event occurs, the collected picture and video are required to be subjected to image processing to obtain the distance between the farthest spatter point, the nearest spatter point and the welding spot, the spatter appearance and the spatter direction 3.2 of the spatter event, the subdata in the database is obtained, the data of the spot welding process is compared with the subdata in a reference manner, if the matching degree of the comparison result is high, namely the spatter profile is the same, the spatter direction is the same, and the distance between the spatter and the nugget is the same, the subdatabase 4 to which the spatter event belongs can be obtained, the variable 5 corresponding to the subdatabase can be found, the spot welding process 6 can be improved by adjusting the variable, for example, if the spatter event data obtained, namely, the distance between the farthest spattering point and the nearest spattering point and the welding point is between an event A1.1 and an event A2.1, the spattering appearance is the same as the event A, and the spattering direction is the same as the event A, so that the spattering event can be confirmed to be caused by overlarge welding current, and the welding point with good performance can be obtained by adjusting the process parameters, namely reducing the welding current.
The described embodiments are only some embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Claims (10)
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| CN113732557A (en) * | 2021-07-30 | 2021-12-03 | 武汉理工大学 | Welding monitoring method based on digital twinning |
| CN113770497A (en) * | 2021-06-12 | 2021-12-10 | 中国第一汽车股份有限公司 | Device and method for detecting actual pressurizing position in robot spot welding process |
| DE102021114630A1 (en) | 2021-06-08 | 2022-12-08 | Audi Aktiengesellschaft | Method and analysis system for locating a weld spatter on a workpiece |
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