CN115128130B - Online resistance spot welding quality assessment system and method based on dynamic resistance signals - Google Patents
Online resistance spot welding quality assessment system and method based on dynamic resistance signals Download PDFInfo
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Abstract
The invention discloses a resistance spot welding quality on-line evaluation system and method based on dynamic resistance signals, which synchronously acquire welding current and electrode voltage. On the basis of an equivalent circuit of the spot welder, the Hilbert transformation is utilized to calculate the dynamic resistance of the alternating current spot welding process. The reliability, feasibility and effectiveness of the newly proposed dynamic resistance measurement method are verified, 8 characteristic points are extracted from the time domain of the dynamic resistance, the correlation between the extracted characteristic and the strength of the resistance spot welding joint is discussed, and finally, a relation model between the characteristic points and the strength of the resistance spot welding joint is established by utilizing an artificial neural network so as to be used for detecting the quality of the resistance spot welding joint. The invention can rapidly realize nondestructive detection and evaluation of the resistance spot welding spot and the maximum shearing force, and is particularly suitable for online quality detection of welding production sites.
Description
Technical Field
The invention relates to the technical field of nondestructive testing of quality of resistance spot welding spots, in particular to an on-line resistance spot welding quality evaluation system and method based on dynamic resistance signals.
Background
Resistance spot welding is widely applied in the automobile industry, and a white automobile body is provided with 4000-7000 welding spots according to statistics, and the quality of the welding spots directly determines the safety and the service life of the whole automobile. Therefore, the detection of the quality of the resistance spot welding spot is very important, and the high-efficiency sensing, detection and evaluation of the welding quality in the welding process are of great significance for improving the production efficiency and the welding quality and saving the production cost. However, in the production enterprises, destructive experiments are usually carried out after welding to check the welding quality. Such a detection method is not only inefficient but also costly. Because the sealing of the resistance spot welding process is invisible, the spot welding nugget is positioned on the joint surface of the laminated workpieces, and the growth process of the nugget cannot be directly observed, so that the formation and growth process of the nugget can be indirectly inferred only by detecting the physical phenomenon accompanied by the spot welding process. Various dynamic signals during spot welding have been measured over the last decades, such as welding current, welding voltage, dynamic resistance, electrode force, electrode displacement, acoustic emission and temperature, etc. Electrode displacement is one of the most widely used signals in spot welding quality monitoring, but its measurement is inevitably disturbed by displacement sensors of fixed parts around the welding gun. Dynamic resistance signals are another widely used signal that can provide rich information about the formation and growth of weld nuggets, which is closely related to the quality of the weld.
The most widely used dynamic resistance calculation method so far is to divide the instantaneous voltage by the current at the peak current point. This method is believed to eliminate the induced noise caused by the transformer. By concatenating the resulting data points, a dynamic resistance curve can be obtained. However, the dynamic resistance curve of this method is highly discrete, as the resistance calculation is performed only once per half welding cycle (i.e., only one resistance data is obtained per half welding cycle).
The prior art relates to a micro resistance spot welding quality on-line monitoring method, and belongs to the technical field of micro resistance spot welding quality monitoring. Firstly, a single photon detector is utilized to monitor the photon number change condition of the contact edge of the electrode and the workpiece in the micro resistance spot welding production process in real time, and a photon change curve is drawn. Photon characteristic quantities of the quality of the micro resistance spot welding joints with different quality grades are extracted by utilizing a data mining means, a micro resistance spot welding quality classification model is generated, a database for monitoring the quality of the micro resistance spot welding based on photon signals is established, and automatic online monitoring of the quality of the micro resistance spot welding joints under different process conditions is realized.
Disclosure of Invention
It is a primary object of the present invention to provide an on-line assessment system for resistance spot welding quality based on dynamic resistance signals, providing more details of dynamic resistance changes over time.
It is a further object of the present invention to provide an on-line assessment method for resistance spot welding quality based on dynamic resistance signals.
In order to solve the technical problems, the technical scheme of the invention is as follows:
the resistance spot welding quality on-line evaluation system based on the dynamic resistance signal is characterized by comprising a data acquisition and signal processing system and a computer, wherein:
The data acquisition and signal processing system measures welding voltage between two electrodes of the resistance spot welder through a twisted pair shielding wire, the data acquisition and signal processing system measures welding current of the resistance spot welder through a Hall current sensor, and the data acquisition and signal processing system preprocesses the welding voltage and the welding current which are synchronously obtained and then sends the welding voltage and the welding current to the computer;
the computer obtains a dynamic resistance curve according to the welding voltage and the welding current, extracts characteristic points in the dynamic resistance curve, inputs the characteristic points into an artificial neural network for detection, calculates and outputs the joint strength of a resistance spot welding point, and if the joint strength is smaller than a set threshold value, the welding point is judged to be unqualified; if the welding point is not smaller than the set threshold value, the welding point is judged to be qualified, and the next welding is carried out.
Preferably, the ADC resolution of the data acquisition and signal processing device is 16 bits.
Preferably, the sampling rate of the data acquisition and signal processing device is 100KHz.
Preferably, the data acquisition and signal processing device performs preprocessing on the welding voltage and the welding current obtained synchronously, specifically:
and filtering the obtained welding voltage and induction noise in the welding current by adopting a wavelet denoising technology.
Preferably, the computer obtains a dynamic resistance curve according to the welding voltage and the welding current, specifically:
Converting the welding current I (t) and welding voltage V (t) signals into analytic signals:
Where h [ ] represents the Hilbert transform, Analysis signals respectively representing the welding current I (t) and the welding voltage V (t);
let I (t) =i m sin ωt, then Is the phase difference between the welding voltage and the current, I m、Vm represents the amplitude of the welding current I (t) and the welding voltage V (t), respectively, then/>Written separately as:
The complex electrical impedance Z is:
wherein the real part of the complex resistance Z corresponds to the dynamic resistance and the imaginary part corresponds to the dynamic reactance.
Preferably, the computer also removes subharmonic components from the solved dynamic resistance curve using FRR zero-phase filtering techniques.
Preferably, the FRR zero-phase filtering technique is specifically:
Firstly, a causal real coefficient filter is carried out on signal data, the causal real coefficient filter is called first filtering, then time back wave is carried out on first filtering output, filtering is carried out by the same filter, the causal real coefficient filter is called second filtering, and finally time back wave is carried out on second filtering output;
for a length n+1 input sample sequence x (N), the first filtered output is written as:
y1(n)＝x(n)*h(n)
Where h (n) is the impulse response sequence of the digital filter used;
the first time-reversed sequence y 2 (n) is denoted as:
y2(n)＝y1(N-n)
when y 2 (n) is processed through the same digital filter, the filter output y 3 (n) is:
y3(n)＝y2(n)*h(n)
thus, the second time-reversed sequence y (n) is:
y(n)＝y3(N-n)。
preferably, the extracting the feature points in the dynamic resistance curve specifically includes:
The number of sampling data D1 from the beginning of the welding process to the peak point, the resistance difference D2 between the trough point and the peak point, the number of sampling data D3 from the trough point to the peak point, the resistance value D4 from the trough point, the sampling time D5 from the peak point to the end point of the resistance change amount ratio peak point to the end point, the resistance value D6 from the end point, the average value D7 of the resistance of the whole process and the root mean square value D8 of the resistance of the whole process.
Preferably, feature points in the dynamic resistance curve are extracted each time and sent to a database for storage, and the feature points are used for training the artificial neural network.
The method for evaluating the quality of the resistance spot welding on line based on the dynamic resistance signal is characterized by being applied to the system for evaluating the quality of the resistance spot welding on line based on the dynamic resistance signal, and comprises the following steps of:
s1: starting welding by a resistance spot welder;
S2: acquiring welding current and welding voltage of a resistance electric welding machine;
S3: converting welding current and welding voltage into analysis signals through Hilbert transformation, and taking the quotient of the analysis voltage and the current to obtain dynamic resistance;
s4: the FRR zero-phase filtering technology is adopted to process the dynamic resistor, and subharmonic components are removed;
S5: extracting characteristic points of the dynamic resistance signals, inputting the characteristic points into an artificial neural network for identification, and finally outputting an evaluation result;
S6: if the evaluation result is smaller than the set threshold, the welding spot is judged to be unqualified, and an alarm warning is sent out, and if the evaluation result is not smaller than the set threshold, the welding spot is judged to be normal, and the next welding is carried out.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
The Hilbert transformation is used for calculating the dynamic resistance in the alternating current spot welding process, the characteristic that the current analysis signal is constantly larger than zero is utilized to widen the signal calculation time domain, the sampling rate is further improved, and the resistance curve provided by the novel method can provide more details of the dynamic resistance changing along with time. And by means of characteristic quantities extracted from experimental data, a reliable mathematical model is established by utilizing an artificial neural network, a corresponding mathematical model is called in a database, the detected dynamic resistance is input, and a computer system calculates and outputs the joint strength of a resistance spot welding joint, if the joint strength is smaller than a corresponding set threshold value, the joint is judged to be unqualified. The invention can rapidly realize nondestructive detection and evaluation of the diameter and the maximum shearing force of the resistance spot welding spot, and is particularly suitable for online quality detection of welding production sites.
Drawings
FIG. 1 is a schematic diagram of a system module according to the present invention.
Fig. 2 is a schematic circuit diagram of a single-phase spot welder according to an embodiment.
FIG. 3 is a schematic flow chart of the method of the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
For the purpose of better illustrating the embodiments, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the actual product dimensions;
It will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
Example 1
The embodiment provides a resistance spot welding quality online evaluation system based on a dynamic resistance signal, as shown in fig. 1, which comprises a data acquisition and signal processing system and a computer, wherein:
The data acquisition and signal processing system measures welding voltage between two electrodes of the resistance spot welder through a twisted pair shielding wire, the data acquisition and signal processing system measures welding current of the resistance spot welder through a Hall current sensor, and the data acquisition and signal processing system preprocesses the welding voltage and the welding current which are synchronously obtained and then sends the welding voltage and the welding current to the computer;
the computer obtains a dynamic resistance curve according to the welding voltage and the welding current, extracts characteristic points in the dynamic resistance curve, inputs the characteristic points into an artificial neural network for detection, calculates and outputs the joint strength of a resistance spot welding point, and if the joint strength is smaller than a set threshold value, the welding point is judged to be unqualified; if the welding point is not smaller than the set threshold value, the welding point is judged to be qualified, and the next welding is carried out.
Example 2
The present embodiment continues to disclose the following on the basis of embodiment 1:
the ADC resolution of the data acquisition and signal processing device is 16 bits.
The sampling rate of the data acquisition and signal processing device is 100KHz.
The data acquisition and signal processing device preprocesses the welding voltage and the welding current which are synchronously obtained, and specifically comprises the following steps:
and filtering the obtained welding voltage and induction noise in the welding current by adopting a wavelet denoising technology.
The computer obtains a dynamic resistance curve according to the welding voltage and the welding current, and specifically comprises the following steps:
The resistance spot welder in this embodiment is a single-phase spot welder, the circuit of which is typically composed of two parallel silicon controlled rectifiers and a transformer, as shown in fig. 2. Wherein R p and L p are the equivalent resistance and the inductive reactance in the primary winding of the transformer, respectively. R s and L s represent the same parameters in the secondary coil. The single loop formed by the welder throat also presents an inductance L in the secondary circuit. R represents the dynamic resistance between the upper and lower electrodes during spot welding.
Based on the equivalent circuit, the electrical impedance Z of the secondary circuit can be expressed as:
Z＝(R+Rs)+jω(L+Ls)
wherein ω is angular frequency;
The above can also be written as:
wherein,
Is the phase difference between the welding voltage and current, therefore,/>Is a dynamic resistance that converts electricity into joule heat. /(I)Corresponding to a dynamic reactance related to the electric power conserved among the reactances.
To calculate the impedance Z, the welding current I (t) and welding voltage V (t) signals are converted into analytic signals:
Where h [ ] represents the Hilbert transform, Analysis signals respectively representing the welding current I (t) and the welding voltage V (t);
The hilbert transform can be expressed as:
let I (t) =i m sin ωt, then Is the phase difference between the welding voltage and the current, I m、Vm represents the amplitude of the welding current I (t) and the welding voltage V (t), respectively, then/>Written separately as:
The complex electrical impedance Z is:
wherein the real part of the complex resistance Z corresponds to the dynamic resistance and the imaginary part corresponds to the dynamic reactance.
The computer also adopts FRR zero-phase filtering technology to remove subharmonic components for the solved dynamic resistance curve.
The FRR zero-phase filtering technology specifically comprises the following steps:
Firstly, a causal real coefficient filter is carried out on signal data, the causal real coefficient filter is called first filtering, then time back wave is carried out on first filtering output, filtering is carried out by the same filter, the causal real coefficient filter is called second filtering, and finally time back wave is carried out on second filtering output;
for a length n+1 input sample sequence x (N), the first filtered output is written as:
y1(n)＝x(n)*h(n)
Where h (n) is the impulse response sequence of the digital filter used;
the first time-reversed sequence y 2 (n) is denoted as:
y2(n)＝y1(N-n)
when y 2 (n) is processed through the same digital filter, the filter output y 3 (n) is:
y3(n)＝y2(n)*h(n)
thus, the second time-reversed sequence y (n) is:
y(n)＝y3(N-n)。
The FRR zero-phase filter has no significant phase shift between the two curves compared to the curves obtained by the conventional method (using the instantaneous voltage and current at the peak current point per half cycle), and the dynamic resistance curve in the new method can provide more detailed information about the change in resistance over time.
The extracting the characteristic points in the dynamic resistance curve specifically comprises the following steps:
The number of sampling data D1 from the beginning of the welding process to the peak point, the resistance difference D2 between the trough point and the peak point, the number of sampling data D3 from the trough point to the peak point, the resistance value D4 from the trough point, the sampling time D5 from the peak point to the end point of the resistance change amount ratio peak point to the end point, the resistance value D6 from the end point, the average value D7 of the resistance of the whole process and the root mean square value D8 of the resistance of the whole process.
And evaluating the correlation between the extracted characteristic and the tensile and shearing strength of the resistance spot welding point through the extracted characteristic points, wherein the characteristic is closely related to the welding strength, and can be used for welding quality monitoring.
And feature points in the dynamic resistance curve are extracted each time and sent to a database for storage, and the feature points are used for training the artificial neural network.
Example 3
The present embodiment provides a method for evaluating the quality of a resistance spot welding based on a dynamic resistance signal on line, as shown in fig. 3, and the evaluating method is applied to the system for evaluating the quality of a resistance spot welding based on a dynamic resistance signal on line described in embodiment 1 and embodiment 2, and the evaluating method includes the following steps:
s1: starting welding by a resistance spot welder;
S2: acquiring welding current and welding voltage of a resistance electric welding machine;
S3: converting welding current and welding voltage into analysis signals through Hilbert transformation, and taking the quotient of the analysis voltage and the current to obtain dynamic resistance;
s4: the FRR zero-phase filtering technology is adopted to process the dynamic resistor, and subharmonic components are removed;
S5: extracting characteristic points of the dynamic resistance signals, inputting the characteristic points into an artificial neural network for identification, and finally outputting an evaluation result;
S6: if the evaluation result is smaller than the set threshold, the welding spot is judged to be unqualified, and an alarm warning is sent out, and if the evaluation result is not smaller than the set threshold, the welding spot is judged to be normal, and the next welding is carried out.
In step S3, the welding current and the welding voltage are converted into analysis signals through hilbert transformation, and the quotient of the analysis voltage and the current is taken to obtain the dynamic resistance, which specifically comprises the following steps:
Converting the welding current I (t) and welding voltage V (t) signals into analytic signals:
Where h [ ] represents the Hilbert transform, Analysis signals respectively representing the welding current I (t) and the welding voltage V (t);
let I (t) =i m sin ωt, then Is the phase difference between the welding voltage and the current, I m、Vm represents the amplitude of the welding current I (t) and the welding voltage V (t), respectively, then/>Written separately as:
The complex electrical impedance Z is:
wherein the real part of the complex resistance Z corresponds to the dynamic resistance and the imaginary part corresponds to the dynamic reactance.
In the step S4, the FRR zero-phase filtering technology is adopted to process the dynamic resistor, specifically:
Firstly, a causal real coefficient filter is carried out on signal data, the causal real coefficient filter is called first filtering, then time back wave is carried out on first filtering output, filtering is carried out by the same filter, the causal real coefficient filter is called second filtering, and finally time back wave is carried out on second filtering output;
for a length n+1 input sample sequence x (N), the first filtered output is written as:
y1(n)＝x(n)*h(n)
Where h (n) is the impulse response sequence of the digital filter used;
the first time-reversed sequence y 2 (n) is denoted as:
y2(n)＝y1(N-n)
when y 2 (n) is processed through the same digital filter, the filter output y 3 (n) is:
y3(n)＝y2(n)*h(n)
thus, the second time-reversed sequence y (n) is:
y(n)＝y3(N-n)。
the same or similar reference numerals correspond to the same or similar components;
The terms describing the positional relationship in the drawings are merely illustrative, and are not to be construed as limiting the present patent;
it is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.
Claims (8)
1. The resistance spot welding quality on-line evaluation system based on the dynamic resistance signal is characterized by comprising a data acquisition and signal processing system and a computer, wherein:
The data acquisition and signal processing system measures welding voltage between two electrodes of the resistance spot welder through a twisted pair shielding wire, the data acquisition and signal processing system measures welding current of the resistance spot welder through a Hall current sensor, and the data acquisition and signal processing system preprocesses the welding voltage and the welding current which are synchronously obtained and then sends the welding voltage and the welding current to the computer;
The computer obtains a dynamic resistance curve according to the welding voltage and the welding current, extracts characteristic points in the dynamic resistance curve, inputs the characteristic points into an artificial neural network for detection, calculates and outputs the joint strength of a resistance spot welding point, and if the joint strength is smaller than a set threshold value, the welding point is judged to be unqualified; if the welding point is not smaller than the set threshold value, judging that the welding point is qualified, and entering the next welding;
The computer obtains a dynamic resistance curve according to the welding voltage and the welding current, and specifically comprises the following steps:
Converting the welding current I (t) and welding voltage V (t) signals into analytic signals:
Where h [ ] represents the Hilbert transform, Analysis signals respectively representing the welding current I (t) and the welding voltage V (t);
let I (t) =i m sin ωt, then Is the phase difference between the welding voltage and the current, I m、Vm represents the amplitude of the welding current I (t) and the welding voltage V (t), respectively, then/>Written separately as:
The complex electrical impedance Z is:
Wherein the real part of the complex resistance Z corresponds to the dynamic resistance, and the imaginary part corresponds to the dynamic reactance;
The extracting the characteristic points in the dynamic resistance curve specifically comprises the following steps:
The number of sampling data D1 from the beginning of the welding process to the peak point, the resistance difference D2 between the trough point and the peak point, the number of sampling data D3 from the trough point to the peak point, the resistance value D4 from the trough point, the sampling time D5 from the peak point to the end point of the resistance change amount ratio peak point to the end point, the resistance value D6 from the end point, the average value D7 of the resistance of the whole process and the root mean square value D8 of the resistance of the whole process.
2. The dynamic resistance signal based resistance spot welding quality online evaluation system of claim 1, wherein the ADC resolution of the data acquisition and signal processing device is 16 bits.
3. The dynamic resistance signal based resistance spot welding quality online evaluation system of claim 1, wherein the sampling rate of the data acquisition and signal processing device is 100KHz.
4. The dynamic resistance signal-based resistance spot welding quality online evaluation system according to claim 1, wherein the data acquisition and signal processing device preprocesses welding voltage and welding current obtained synchronously, specifically:
and filtering the obtained welding voltage and induction noise in the welding current by adopting a wavelet denoising technology.
5. The dynamic resistance signal based resistance spot welding quality online evaluation system according to claim 1, wherein the computer further removes subharmonic components on the solved dynamic resistance curve using FRR zero-phase filtering technique.
6. The dynamic resistance signal based resistance spot welding quality online evaluation system of claim 5, wherein the FRR zero-phase filtering technique is specifically:
Firstly, a causal real coefficient filter is carried out on signal data, the causal real coefficient filter is called first filtering, then time back wave is carried out on first filtering output, filtering is carried out by the same filter, the causal real coefficient filter is called second filtering, and finally time back wave is carried out on second filtering output;
for a length n+1 input sample sequence x (N), the first filtered output is written as:
y1(n)＝x(n)*h(n)
Where h (n) is the impulse response sequence of the digital filter used;
the first time-reversed sequence y 2 (n) is denoted as:
y2(n)＝y1(N-n)
when y 2 (n) is processed through the same digital filter, the filter output y 3 (n) is:
y3(n)＝y2(n)*h(n)
thus, the second time-reversed sequence y (n) is:
y(n)＝y3(N-n)。
7. The dynamic resistance signal-based resistance spot welding quality online evaluation system according to claim 1, wherein feature points in each extracted dynamic resistance curve are sent to a database for storage for training the artificial neural network.
8. An on-line resistance spot welding quality assessment method based on a dynamic resistance signal, characterized in that the assessment method is applied to the on-line resistance spot welding quality assessment system based on a dynamic resistance signal as claimed in any one of claims 1 to 7, and the assessment method comprises the following steps:
s1: starting welding by a resistance spot welder;
S2: acquiring welding current and welding voltage of a resistance electric welding machine;
S3: converting welding current and welding voltage into analysis signals through Hilbert transformation, and taking the quotient of the analysis voltage and the current to obtain dynamic resistance;
s4: the FRR zero-phase filtering technology is adopted to process the dynamic resistor, and subharmonic components are removed;
S5: extracting characteristic points of the dynamic resistance signals, inputting the characteristic points into an artificial neural network for identification, and finally outputting an evaluation result;
S6: if the evaluation result is smaller than the set threshold, the welding spot is judged to be unqualified, and an alarm warning is sent out, and if the evaluation result is not smaller than the set threshold, the welding spot is judged to be normal, and the next welding is carried out.
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