CN113984837A - Resistance spot welding spot quality detection method based on welding spot characteristic information fusion - Google Patents
Resistance spot welding spot quality detection method based on welding spot characteristic information fusion Download PDFInfo
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- 238000007373 indentation Methods 0.000 claims abstract description 33
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- 238000013528 artificial neural network Methods 0.000 claims abstract description 15
- 238000005516 engineering process Methods 0.000 claims abstract description 13
- 210000002569 neuron Anatomy 0.000 claims abstract description 5
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- 229910001335 Galvanized steel Inorganic materials 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
- G01N27/04—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
- G01N27/041—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance of a solid body
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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
- 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
- B23K31/00—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by only one of the preceding main groups
- B23K31/12—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by only one of the preceding main groups relating to investigating the properties, e.g. the weldability, of materials
- B23K31/125—Weld quality monitoring
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- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/28—Measuring arrangements characterised by the use of optical techniques for measuring areas
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
- G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
- G01R27/08—Measuring resistance by measuring both voltage and current
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Abstract
The invention discloses a resistance spot welding spot quality detection method based on spot welding characteristic information fusion, which comprises the following steps: s1, setting the secondary current pulse lag behind the main current pulse of resistance spot welding, and the secondary current pulse period is in the electrode pressure holding stage; s2, clamping and fixing the to-be-welded piece, and pressurizing the electrode to start welding; s3, in the welding process, collecting electrode voltage signals and current pulse signals generated by secondary current pulses, and performing division operation on the collected electrode voltage signals and current pulse signals generated by the secondary current pulses to obtain characteristic values of the resistance of the welding spot; s4, after welding, collecting image information of the indentation on the surface of the welding spot, and obtaining the characteristic value of the indentation image of the welding spot based on the machine vision technology; and S5, inputting the characteristic values of the resistance of the welding spot and the characteristic values of the image of the indentation of the welding spot as input layer neurons into the artificial neural network model to calculate and obtain the detection value of the tensile strength of the welding spot. The detection value of the tensile strength of the welding spot can be obtained without damaging the welding spot, and the accuracy is high.
Description
Technical Field
The invention relates to an intelligent detection technology for welding manufacture, in particular to a method for detecting the quality of a resistance spot welding spot based on the fusion of characteristic information of the spot welding spot.
Background
Resistance spot welding is a welding method widely applied to automobile manufacturing, and is widely applied to welding of automobile body metal structures of modern cars, for example, about 3000-6000 resistance spot welding spots are formed on one modern car. Therefore, the quality of resistance spot welds is very important to the structural reliability and operational safety of automotive bodies.
Under the environment of new technology, new industry, new state and new mode briskly development in China, the manufacturing industry is increasingly combined with information technology and intelligent technology. As a resistance spot welding manufacturing technology of the traditional manufacturing technology, the method has important significance for improving the production efficiency and the welding quality, saving the production cost and promoting the upgrading of the production technology by efficiently carrying out information sensing and intelligent detection and evaluation on the welding spot quality. However, during resistance spot welding, the formation of the welding spot is hidden in the workpiece and cannot be directly observed, which brings difficulties for real-time sensing of welding quality and online evaluation of the welding spot quality. Therefore, in a production enterprise, generally, after welding, according to quality inspection, a certain proportion of produced welded structure products need to be extracted, destructive experiment detection is carried out, and detection mainly aims at welding spot strength mechanical property indexes. The detection method has low efficiency, causes product damage, increases production cost and can not ensure the quality reliability of the undetected product. Therefore, the sensing of the quality information of the welding spot in the resistance spot welding process and the application of the sensing to online detection have important significance for realizing high-efficiency detection of the quality of the welding spot of the resistance spot welding by using a nondestructive detection method.
Disclosure of Invention
The invention aims to provide a method for detecting the quality of a resistance spot welding spot based on the fusion of characteristic information of the spot welding spot, which does not need to destroy an interface of the spot welding spot to obtain a tensile strength detection value of the spot welding spot and has high accuracy.
The invention relates to a method for detecting the quality of a resistance spot welding spot based on the fusion of characteristic information of the spot welding spot, which calculates the resistance characteristic value of the resistance spot welding spot and the characteristic value of an indentation image of the spot welding spot to obtain the tensile strength detection value of the spot welding spot and realizes the online detection of the quality of the resistance spot welding spot, and the detection method comprises the following steps:
s1, setting the secondary current pulse lag behind the main current pulse of resistance spot welding, and the secondary current pulse period is in the electrode pressure holding stage;
s2, clamping and fixing the to-be-welded piece, and pressurizing the electrode to start welding;
s3, in the welding process, collecting electrode voltage signals and current pulse signals generated by secondary current pulses, and performing division operation on the collected electrode voltage signals and current pulse signals generated by the secondary current pulses to obtain characteristic values of the resistance of the welding spot;
s4, after welding, collecting image information of the indentation on the surface of the welding spot, and obtaining the characteristic value of the indentation image of the welding spot based on the machine vision technology;
and S5, inputting the characteristic value of the resistance of the welding spot obtained by the calculation of the S3 and the characteristic value of the image of the indentation of the welding spot obtained by the S4 as the neuron of the input layer into an artificial neural network model to calculate and obtain the detection value of the tensile strength of the welding spot.
Further, the establishment of the artificial neural network model is as follows: establishing a big data sample pair among the characteristic value of the resistance of the welding spot, the characteristic value of the indentation image of the welding spot and the tensile strength of the welding spot through the test of a plurality of samples to form a training set; and establishing an artificial neural network model with the characteristic value of the resistance of the welding spot, the characteristic value of the indentation image of the welding spot as an input layer and the tensile strength of the welding spot as an output layer, and training the obtained sample by using the artificial neural network model.
Further, the artificial neural network model is at least one of … ….
Further, the characteristic values of the weld spot indentation image comprise at least two of the area of an inner circle of the weld spot indentation, the diameter of the inner circle of the weld spot indentation and the length of the inner circumference of the weld spot indentation.
Further, a pulse power supply connected in parallel with the electrodes outputs secondary current pulses and acts on the welding spots through the upper electrodes and the lower electrodes.
Furthermore, the current amplitude of the secondary current pulse is reasonably set according to the material and the thickness of the piece to be welded, so that the welding spot is ensured not to generate obvious resistance heat.
Further, the current amplitude of the secondary current pulse is 5-50A.
Further, the secondary current pulse lags the main current pulse by 0.4s and more.
Furthermore, the duration of the secondary current pulse is 0.02-0.2 s.
Further, the resistance characteristic value is at least one of an arithmetic mean value, a root mean square value and a peak value.
Compared with the prior art, the invention has the following beneficial effects.
1. The invention sets that the secondary current pulse lags behind the main current pulse of the resistance spot welding, the period of the secondary current pulse is positioned at the electrode pressure holding stage, the electrode voltage signal and the current pulse signal generated by the secondary current pulse are used for division operation to obtain the resistance characteristic value of a welding spot, then the welding spot image information after welding is collected, the characteristic value of a welding spot indentation image is obtained based on the machine vision technology, the characteristic value of the welding spot indentation image and the calculated resistance characteristic value are used as input layer neurons and input into an artificial neural network model to calculate to obtain the tensile strength detection value of the welding spot, the detection accuracy is high, the error can be controlled within 2 percent, and the invention is particularly suitable for the online detection application of a resistance spot welding production line.
2. The detection method can obtain the tensile strength detection value of the welding spot without damaging the welding spot, realizes full coverage of the quality detection of the welding spot, and avoids the influence on the product structure caused by damage detection.
3. The method of the invention is easy to realize the automation, informatization and intellectualization of the quality detection of the welding spot, has high use flexibility, can meet the application requirements of various production occasions, and is beneficial to the realization of modern digital factories.
Drawings
FIG. 1 is a schematic structural diagram of a resistance spot welding spot quality detection system based on spot welding characteristic information fusion;
FIG. 2 is a timing diagram of the output of secondary current pulses from the pulsed power supply in accordance with the present invention.
In the figure, 1-a pulse power supply, 2-a preposition signal processing and signal collecting device, 3-a computer, 4-a Rogowski coil, 5-a current pulse signal collecting information flow, 6-an electrode voltage signal collecting information flow, 7-a secondary current pulse, 8-an upper electrode, 9-a lower electrode, 10-a workpiece to be welded, 11-a welding spot, 12-a welding spot indentation, 13-an industrial camera, 14-an image collecting card,
15-main current pulse, 16-electrode pressure curve, 17-electrode pre-pressing stage, and 18-electrode pressure maintaining stage.
Detailed Description
The invention is described in detail below with reference to the figures and the specific embodiments.
A method for detecting the quality of a resistance spot welding spot based on the fusion of characteristic information of the spot welding spot is characterized in that the tensile strength detection value of the spot welding spot is obtained by calculating the resistance characteristic value of the resistance spot welding spot and the characteristic value of an indentation image of the spot welding spot, so that the quality of the spot welding spot of the resistance spot welding spot is detected on line. Referring to fig. 1, a to-be-welded part 10 is a galvanized steel plate with the thickness of 2.0mm, a resistance spot welding process is set to be a double-pulse welding process, wherein a main current pulse 12 is a welding current pulse, and an auxiliary current pulse is a secondary current pulse 7.
S1, setting the resistance spot welding process to a double pulse welding process, see fig. 2, with the main current pulse 15 parameters set to: welding pulse current 18000A, main current pulse 15 duration 0.2 s; the parameters of the secondary current pulse 7 output by the pulse power supply are set as follows: secondary pulse current 10A, secondary pulse current duration 0.1 s. The electrode pressure parameters between the upper electrode 8 and the lower electrode 9 are set as: the electrode pressure is 2500N, the duration of the electrode pre-pressing stage 17 is 0.4s, and the duration of the electrode pressure maintaining stage 18 is 0.8 s. The secondary current pulse 7 is set to lag the primary current pulse 15 in time sequence and in the electrode pressure hold phase 15 of the electrode pressure curve 16.
And S2, clamping and fixing the to-be-welded part 10, starting a trigger switch, and starting welding by matching and pressurizing the upper electrode 8 and the lower electrode 9 until the welding is finished.
S3, in the welding process, the Rogowski coil 4 is used as a sensor to collect a current pulse signal collecting information flow 5, including a current pulse signal generated by a secondary current pulse 7 output by a pulse power supply 1; the information flow 6 is acquired by acquiring electrode voltage signals through parallel wires at two ends of the electrodes. All welding process data information is preprocessed by the preposed signal processing and signal collecting device 2, is subjected to analog-to-digital conversion, and then is transmitted to an analysis system of the computer 3. And (3) dividing the electrode voltage signal and the current pulse signal generated by the acquired secondary current pulse 7 to obtain a dynamic resistance change curve in the process of forming the welding spot, and calculating the dynamic resistance change curve by adopting a root mean square to obtain a resistance characteristic value of the welding spot.
And S4, after welding, the industrial camera 13 system collects the indentation image information of the welding spot 11 on line, and the information is transmitted to the analysis system of the computer 3 after being processed by the image acquisition card 14. The analysis system of the computer 3 calculates and identifies the range of the inner circle of the welding spot indentation 12 by using a machine vision technology, and in the embodiment, the characteristic values of the image of the inner circle of the welding spot indentation 12, including the pixel values of the inner circle area and the inner circle length, are calculated and obtained based on a digital image processing technology.
And S5, inputting the characteristic value of the resistance of the welding spot obtained by the calculation of the S3 and the characteristic value of the image of the indentation of the welding spot obtained by the S4 as the neuron of the input layer into an artificial neural network model to calculate and obtain the detection value of the tensile strength of the welding spot.
The establishment of the artificial neural network model comprises the following steps: establishing a big data sample pair among the characteristic value of the resistance of the welding spot, the characteristic value of the indentation image of the welding spot and the tensile strength of the welding spot through the test of a plurality of samples to form a training set; and establishing a Back Propagation artificial neural network model with the characteristic value of the resistance of the welding spot, the characteristic value of the indentation image of the welding spot as an input layer and the tensile strength of the welding spot as an output layer, and training the obtained sample by using the artificial neural network model.
The above-mentioned embodiments are descriptions of typical preferred embodiments of the present invention, but the technical solution of the present invention is not limited thereto, and any changes and modifications made by those skilled in the art based on the main technical concept of the present invention will fall within the technical scope of the present invention.
The method for detecting the quality of the resistance spot welding spot based on the fusion of the characteristic information of the welding spot not only can realize the nondestructive detection of the resistance spot welding spot, but also can realize the detection error of not more than 2 percent, is convenient and quick, is suitable for the automatic, information and intelligent online detection application, and has wide popularization and application prospects.
Claims (9)
1. A method for detecting the quality of a resistance spot welding spot based on the fusion of characteristic information of the spot welding spot is characterized in that a detection value of the tensile strength of the spot welding spot is obtained by calculating the resistance characteristic value of the resistance spot welding spot and the characteristic value of an indentation image of the spot welding spot, so that the quality of the resistance spot welding spot is detected on line, and the detection method comprises the following steps:
s1, setting the secondary current pulse lag behind the main current pulse of resistance spot welding, and the secondary current pulse period is in the electrode pressure holding stage;
s2, clamping and fixing the to-be-welded piece, and pressurizing the electrode to start welding;
s3, in the welding process, collecting electrode voltage signals and current pulse signals generated by secondary current pulses, and performing division operation on the collected electrode voltage signals and current pulse signals generated by the secondary current pulses to obtain characteristic values of the resistance of the welding spot;
s4, after welding, collecting image information of the indentation on the surface of the welding spot, and obtaining the characteristic value of the indentation image of the welding spot based on the machine vision technology;
and S5, inputting the characteristic value of the resistance of the welding spot obtained by the calculation of the S3 and the characteristic value of the image of the indentation of the welding spot obtained by the S4 as the neuron of the input layer into an artificial neural network model to calculate and obtain the detection value of the tensile strength of the welding spot.
2. The secondary current pulse-based resistance spot welding spot quality detection method according to claim 1, characterized in that: the establishment of the artificial neural network model comprises the following steps: establishing a big data sample pair among the characteristic value of the resistance of the welding spot, the characteristic value of the indentation image of the welding spot and the tensile strength of the welding spot through the test of a plurality of samples to form a training set; and establishing an artificial neural network model with the characteristic value of the resistance of the welding spot, the characteristic value of the indentation image of the welding spot as an input layer and the tensile strength of the welding spot as an output layer, and training the obtained sample by using the artificial neural network model.
3. The secondary current pulse-based resistance spot welding spot quality detection method according to claim 1 or 2, characterized in that: the characteristic values of the welding spot indentation image comprise at least two of the area of an inner circle of the welding spot indentation, the diameter of the inner circle of the welding spot indentation and the length of an inner circumference of the welding spot indentation.
4. The secondary current pulse-based resistance spot welding spot quality detection method according to claim 1 or 2, characterized in that: the pulse power supply connected in parallel with the electrodes outputs secondary current pulses and acts on welding spots through the upper electrode and the lower electrode.
5. The secondary current pulse-based resistance spot welding spot quality detection method according to claim 1 or 2, characterized in that: the current amplitude of the secondary current pulse is reasonably set according to the material and the thickness of the piece to be welded, so that the welding spot is ensured not to generate obvious resistance heat.
6. The secondary current pulse-based resistance spot welding spot quality detection method according to claim 5, characterized in that: the current amplitude of the secondary current pulse is 5-50A.
7. The secondary current pulse-based resistance spot welding spot quality detection method according to claim 1 or 2, characterized in that: the secondary current pulse lags the main current pulse by 0.4s and more.
8. The secondary current pulse-based resistance spot welding spot quality detection method according to claim 1 or 2, characterized in that: the secondary current pulse duration is 0.02-0.2 s.
9. The secondary current pulse-based resistance spot welding spot quality detection method according to claim 1 or 2, characterized in that: the resistance characteristic value is at least one of an arithmetic mean value, a root mean square value and a peak value.
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|---|---|---|---|---|
| CN116237664A (en) * | 2022-12-15 | 2023-06-09 | 广东工业大学 | Characteristic parameter radar map mapping method, resistance spot welding quality online detection method and system |
| CN116237664B (en) * | 2022-12-15 | 2024-06-21 | 广东工业大学 | Characteristic parameter radar map mapping method, resistance spot welding quality online detection method and system |
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