Cold-rolled strip steel lap welding result evaluation method based on big data

Abstract

The invention discloses a big data-based cold-rolled strip steel lap welding result evaluation method, which comprises the following steps of: transmitting actual performance data (welding current, welding speed, lap amount, lap compensation amount, welding wheel pressure and temperature) of a current welding seam recorded by a digital steel coil to a software system through a data interface by taking welding historical big data as a sample set; welding actual performance data (welding current, welding wheel pressure, welding speed, lap joint amount, lap joint compensation amount and temperature) are collected through an inherent collection function of a welding machine PLC, an external laser range finder is connected to collect welding seam height information, then hardware systems such as the welding machine PLC and the laser range finder are all communicated with an FDAA collection system, the FDAA collection system interacts with a digital steel coil system, and the digital steel coil system is connected with the digital steel coil system. The digital steel coil system interacts with the OLW, judgment is carried out through integrated welding result judgment logic (embedded in the OLW), quantitative evaluation of the welding quality is achieved, a judgment result (releasing or rewelding) is transmitted to a welding machine through a software system, and therefore the welding quality evaluation is achieved, and a quantitative result is given.

Description

Technical Field

[0001] The invention relates to a thin strip steel welding control technology, and in particular to a cold-rolled strip steel lap welding result evaluation method based on big data. Background Art

[0002] There are many factors that affect the quality of lap welding, which can be summarized into three categories: First, the rationality of welding process parameters, mainly including the set values ​​of welding process parameters, often called L2 issued values, including current setting, rolling wheel pressure setting, welding speed, overlap amount and compensation amount setting; Second, the influence of the workpiece material itself, mainly including the steel type (material code), width, thickness and surface condition of the strip; Third, the equipment status parameters of the welding machine itself, such as clamping force and its uniformity, strip positioning accuracy, welding wheel status, pressure stability of the flattening wheel after welding, etc.

[0003] At present, the quality evaluation of the weld seam of the lap welding machine of cold-rolled strip mainly relies on manual work. After welding, the on-site operator first confirms the output results of the QCDS system, and supplements it with manual on-site confirmation methods at the crescent shear, such as the early drop hammer, the cupping test and bending test commonly used now, and then the operator makes a "decision" on whether to release it. However, for this operation mode, on the one hand, there is a lack of a data platform for effective analysis of welding history data; on the other hand, the welding quality assessment process relies to a large extent on subjective judgment by people and auxiliary manual tests, which not only makes the knowledge acquisition and inheritance relatively one-sided, but also fails to provide effective guidance for quantitative evaluation of welding quality and maintenance of welding machines. Summary of the invention

[0004] In order to solve the above problems in the prior art, the present invention provides a cold-rolled strip lap welding result evaluation method based on big data. The cold-rolled strip lap welding historical big data is used as a sample set, the welding history big data is sorted out and analyzed to summarize the welding rules, and a welding quality logical judgment process is established. Through logical judgment, welding quality evaluation is achieved and quantitative results are given.

[0005] A cold-rolled strip lap welding result evaluation method based on big data comprises the following steps:

[0006] S1. According to the historical big data of lap welding of cold-rolled strip steel, the corresponding welding parameters associated with high welding temperature are screened out, and the threshold value of each parameter is preset;

[0007] S2. Collect the above actual welding parameters through the welding machine PLC. If all the welding parameters meet the set threshold, it is judged as qualified and scored, and then proceed to the next step; if any parameter does not meet the requirements, the weld is judged as unqualified;

[0008] S3. The upper and lower limits of the measured height of the weld are determined. If they meet the requirements, the weld is determined to be qualified and scored, and the next step is entered; if they do not meet the requirements, the weld is determined to be unqualified;

[0009] S4. Obtain the effective zone data of the weld temperature, and determine and score the trend, volatility and temperature distribution of the weld temperature in turn;

[0010] S5. If the total score of steps S2-S4 is higher than the set value, the weld determination result and score are sent to the welding machine.

[0011] In step S1, the welding parameters include welding current, welding speed, overlap amount / overlap compensation amount, and welding wheel pressure.

[0012] In step S1, the set threshold is a deviation value of ±4% between the corresponding set parameter and the actual parameter.

[0013] In step S2, the score for passing the judgment is 10 points / each parameter.

[0014] In step S3, the upper and lower limit judgments include whether it is a three-piece weld and whether the height is inconsistent in the weld length; the upper limit is 1.5 times the maximum thickness of the preceding and succeeding coils, and the lower limit is the minimum thickness of the preceding and succeeding coils; the score for a qualified judgment is 10 points.

[0015] In step S4, the trends and fluctuations of the temperatures of multiple welds are statistically analyzed, and the trend and fluctuation determination and scoring criteria are formulated according to the density function of the trend and fluctuation distribution of the weld temperatures as follows:

[0016] If the temperature trend is between [-1, 0.2], 20 points will be awarded; if the temperature trend is between [-1.2, -1), (0.2, 0.5], 10 points will be awarded; if the temperature trend is between [-2, -1.2), (0.5, 2], 5 points will be awarded; if the absolute value of the temperature trend exceeds 2, it is NG;

[0017] If the temperature volatility is between [0, 25], 20 points will be awarded; if the temperature volatility is between (25, 40], 10 points will be awarded; if the temperature volatility is between (40, 60], 5 points will be awarded; if the temperature volatility exceeds 60, it will be NG.

[0018] In step S4, the temperature distribution of each point in the effective area is evaluated using the temperature mean ±3SD (standard deviation): temperature points falling within the mean ±3SD range are judged to be qualified; those exceeding the range are considered unqualified; a deduction system is used, with a total of 10 points, and one point is deducted for each point judged to be unqualified until it is reduced to 0 points.

[0019] In step S5, the set value is 60 minutes.

[0020] The cold-rolled strip lap welding result evaluation method based on big data of the present invention can analyze the welding history big data and establish the welding process logic judgment, and realize the quantitative scoring of the welds with known input parameters (welding current, welding wheel pressure, overlap amount, overlap compensation amount, welding speed, temperature). The method determines the weld quality immediately after the welding is completed based on the welding history big data, and realizes the quantification of the weld quality, thereby changing the process of welding quality evaluation, which relies largely on subjective judgment and auxiliary manual experiments by people, not only the knowledge acquisition and inheritance are relatively one-sided, but also the problem of being unable to form a quantitative evaluation of welding quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a principle block diagram of the evaluation method of the present invention;

[0022] Figure 2 is a thermal diagram of correlation coefficients between welding process parameters and welding temperature of the present invention;

[0023] Figure 2 In the figure, H1: thickness of the preceding coil; H2: thickness of the succeeding coil; V: welding speed; P1: welding pressure; A: welding current; M1: overlap amount; P2: rolling wheel pressure; M2: overlap compensation amount. DETAILED DESCRIPTION

[0024] The following is a further description of a cold-rolled strip lap welding result evaluation method based on big data of the present invention. The principle of the method is as follows: Figure 1 As shown:

[0025] Taking welding history big data as the sample set, the actual performance data of the current weld (welding current, welding speed, overlap, overlap compensation, welding wheel pressure, and temperature) recorded by the digital steel coil is transmitted to the software system through the data interface (through the inherent acquisition function of the welder PLC, the welding performance data (welding current, welding wheel pressure, welding speed, overlap, overlap compensation, and temperature) are collected, and the external laser rangefinder collects the weld height information. Then the hardware systems such as the welder PLC and the laser rangefinder communicate with the FDAA acquisition system, the FDAA acquisition system interacts with the digital steel coil system, and the digital steel coil system interacts with OLW). The integrated welding result judgment logic (embedded in OLW) makes a judgment to realize the quantitative evaluation of welding quality, and the judgment result (release or re-welding) is transmitted to the welder through the software system.

[0026] Decision logic

[0027] After welding, OLW's judgment of the weld follows the following logic:

[0028] Table 1 Decision logic table

[0029] step Judgment items Judgment basis Judgment result A Welding machine status Predefined parameters Pass(score) A-1 Welding speed ±4% of setting value Pass (10 points) A-2 Welding wheel pressure ±4% of setting value Pass (10 points) A-3 Overlap / compensation Setting value ±4% Pass (10 points) A-4 Welding current ±4% of setting value Pass (10 points) B Weld height [Hmin, 1.5Hmax] Pass (10 points) C Soldering temperature Predefined upper and lower temperature limits Score, OK / NK C-1 Temperature Trend 3000 weld trend statistics Pass (20 points) C-2 Temperature fluctuations 3000 weld fluctuation statistics Pass (20 points) C-3 Temperature Level 30° above the lower limit Pass (10 points)

[0030] Note: H is the maximum thickness of the cold-rolled steel strip to be welded.

[0031] The details are as follows:

[0032] S1. According to the historical big data of lap welding of cold-rolled strip steel, the corresponding welding parameters associated with high welding temperature are screened out, and the threshold value of each parameter is preset;

[0033] S2. Collect the above actual welding parameters through the welding machine PLC. If all the welding parameters meet the set threshold, it is judged as qualified and scored, and then proceed to the next step; if any parameter does not meet the requirements, the weld is judged as unqualified;

[0034] First, determine whether the welding parameters are stable and reasonable, which is the premise of welding. So first perform soft monitoring of welding data to determine whether the welding parameters used are reasonable? The score for the qualified judgment is 10 points / each parameter. All qualified: OK (40 points), if one item is unqualified, the weld is unqualified.

[0035] The specific ideas are:

[0036] Further research on the relationship between welding process parameters and welding temperature will help determine the welding results (temperature). Figure 2 As shown, according to the correlation analysis, each parameter is almost highly correlated with temperature. Figure 2 This is a thermal diagram of the correlation coefficient between welding parameters and temperature of the welding performance data of a certain unit in the past six months. Among them, the welding current, welding wheel pressure, rolling pressure, etc. are important parameters of the welding machine, and are related to the welding quality (closely related to the temperature generated by welding). After analyzing the historical data of welds that were manually judged as unqualified on site, the threshold value (set value and actual welding value) deviation is set in advance to (±4%). If all the welding performance parameters are qualified, the next step of judgment will be carried out. If one item is unqualified, the weld will be judged as unqualified.

[0037] S3. The upper and lower limits of the measured height of the weld are determined. If they meet the requirements, the weld is determined to be qualified and scored, and the next step is entered; if they do not meet the requirements, the weld is determined to be unqualified;

[0038] Weld height (welding result) is determined by the weld height test data. Is it a three-piece weld (normally, the front coil and the rear coil are welded together. Three-piece weld means three steel plates are welded together, and the weld height will increase)? Is the height inconsistent along the weld length? No: OK (10 points), Yes: NG.

[0039] Method for determining weld height: Install a laser rangefinder (one on the top and one on the bottom) on one side of the welding machine grinding wheel to monitor the weld height in real time. Through the modbus TCP to OPC DA gateway, the laser rangefinder acquisition signal is converted into the OPC DA interface, and then the electrical signal of the laser rangefinder is transmitted to FDAA through the network cable, and then FDAA transmits it to OLW through the digital steel coil data terminal. OLW has built-in software for converting electrical signals into height signals, so that OLW receives weld height information. The specific logic is to judge the upper and lower limits of the weld height, the upper limit is 1.5 times the maximum thickness of the previous and next coils, and the lower limit is the minimum thickness of the previous and next coils (the threshold given by field work experience).

[0040] S4. Obtain the effective zone data of weld temperature, and judge and score the weld trend, volatility and temperature distribution in turn;

[0041] The weld temperature (welding result) is judged as OK if passed, otherwise NG, and the weld quality score is given at the same time.

[0042] Temperature is the result of welding and the most important criterion for weld quality. First, determine the effective area data of the weld, remove the temperature data that does not conform to the convention, such as obvious sharp points, and then determine the trend and volatility of the weld temperature (the optimal weld temperature curve should be a stable horizontal line, that is, there is no gradual increase or decrease trend. In other words, the trend is the slope of the temperature linear fitting. Here, the least squares method is used for linear fitting to obtain the slope), and then determine the volatility of the weld temperature (measured by standard deviation).

[0043] On the basis of the above, the average value of the weld temperature is calculated. If the average value is within the upper and lower limits of the weld temperature, the temperature condition is met. According to the scoring rules, the final temperature of the weld is obtained.

[0044] Early data processing stage:

[0045] The temperature spikes (abnormal data points) are screened (after the welding machine starts welding, the data of the first and last points collected by the welding machine PLC are deleted because they are abnormal from the normal values. This stage is also called the weld effective area data screening). Then the data of the weld effective area is used to determine whether it exceeds the upper and lower temperature limits. If it exceeds, it is NG (site requirement). If it is qualified, the valid welding data is retained for the following judgment.

[0046] Determination stage:

[0047] The trends and volatility of 3,000 welds were statistically analyzed, and then based on the density function of the distribution of the trends and volatility of the 3,000 welds (the more distribution intervals, the higher the score, and the lower distribution intervals, the lower the score), after consultation with the on-site staff, the scoring criteria were formulated as follows:

[0048] 1. Trend determination

[0049] 1. The trend is between [-1, 0.2] and gets 20 points;

[0050] 2. If the trend is between [-1.2, -1), (0.2, 0.5], 10 points will be awarded;

[0051] 3. If the trend is between [-2, -1.2), (0.5, 2], 5 points will be awarded;

[0052] 4. If the absolute value of linearity exceeds 2, it is NG.

[0053] 2. Fluctuation determination

[0054] 1. Volatility between [0,25] is 20 points;

[0055] 2. Volatility between (25, 40] is 10 points;

[0056] 3. Volatility between (40, 60] is 5 points;

[0057] 4. If volatility exceeds 60, it is NG.

[0058] 3. Temperature distribution judgment

[0059] The temperature distribution of each point in the effective area is evaluated using the mean ± 3SD (standard deviation). Temperature points that fall within the mean ± 3SD range are qualified (after discussion and agreement with the site), and those that exceed the range are unqualified, so a deduction system is used. There are 10 points in total, and one point is deducted for each additional point (outside the mean ± 3SD range) until it is reduced to 0 points.

[0060] S5. If the total score of steps S2-S4 is higher than the set value, the weld determination result and score are sent to the welding machine.

[0061] Finally, the OLW software communicates with the welding machine and sends the weld determination results and scores:

[0062] OLW communicates directly with the welding machine and sends the judgment result and score (if the score is less than 60 points, the result is NG, and if it is greater than or equal to 60 points, the result is OK) to the welding machine. After the welding machine PLC obtains the result, it is displayed on the welding machine operation terminal for the operator to release and re-weld.

[0063] Example 1

[0064] 1) Before welding, welding parameters are displayed on the computer operation terminal;

[0065] In this embodiment, the leading strip has a coil number of 521697100, steel type 2, strip width of 1409 mm, and strip thickness of 0.7 mm; the succeeding strip has a coil number of 521698100, steel type 2, strip width of 1276 mm, and strip thickness of 0.8 mm.

[0066] In this embodiment, the preset welding parameters of the welding machine are: welding current 16KA, welding speed 10cm / s, welding wheel pressure 9kN, overlap 1mm, overlap compensation 0.6mm, the upper temperature limit is 1100℃, and the lower temperature limit is 820℃.

[0067] 2) At the same time, the digital coil data platform sends the coil number, thickness and other information of the previous and next coils to the OLW software.

[0068] 3) When welding starts, the infrared detection device synchronously sends the real-time weld height to the OLW software; the welding machine PLC synchronously sends the real-time welding setting data to the digital steel coil data platform, and then the data platform synchronously forwards the setting data to the OLW software;

[0069] In step 3), welding performance data: welding current, welding wheel pressure, overlap amount, overlap compensation amount, welding speed, welding temperature;

[0070] Table 2 Welding performance data

[0071]

[0072]

[0073]

[0074]

[0075] 5) After the above steps are completed, the OLW software makes a judgment.

[0076] First: judge the status of the welding machine. If all are qualified: OK. If one item is unqualified, the weld is unqualified.

[0077] According to the pre-set threshold (set value and actual welding value) deviation (±4%), the welding parameters of the above welding data are all qualified, and the next step of judgment is carried out.

[0078] Then: determine the weld height.

[0079] According to the height of the entire weld, weld defects such as three-piece welding are judged. The specific logic is to judge the upper and lower limits of the weld height. The upper limit is 1.5 times the maximum thickness of the previous and next coils, and the lower limit is the minimum thickness of the previous and next coils (the threshold given by field work experience). If three-piece welding occurs, the weld height increases. If the overlap is insufficient, the weld height is too small. The average weld height is 0.9mm, which is within the preset range. It is judged to be qualified and the next step is carried out.

[0080] Finally: judge the weld temperature.

[0081] If the temperature is within the preset upper and lower limits (upper limit is 1100℃, lower limit is 870℃), it is OK, otherwise it is NG, and the weld quality score is given at the same time. Temperature is the welding result and also the most important criterion for weld quality.

[0082] The specific logic is: first determine the effective temperature zone data of the weld, eliminate the temperature data that does not conform to the norm, such as obvious sharp points in the effective temperature zone data, then determine the temperature trend of the weld, then determine the volatility of the weld, and finally score each temperature distribution point.

[0083] That is, the welding process parameters are qualified, 40 points; the weld height is qualified, 10 points; the trend is -0.76, 20 points; the volatility is 23.73℃, 20 points; the mean ± SD interval is [859, 1001], and there are two temperatures outside this interval, 8 points. The overall score of the weld is: 40+10+20+20+8=98 points, qualified, then OK.

[0084] According to the scoring and judgment of OLW software, the weld score is 98 points and is judged to be qualified (OK).

[0085] 6) In step 5), the OLW determination result is transmitted back to the welding machine PLC and displayed on the computer terminal;

[0086] 7) If the welding machine PLC judgment is inconsistent with the result displayed in step 6), the operator will conduct a re-inspection for further judgment.

[0087] In summary, lap welding machines are usually equipped with a relatively simple QCDS system. The current common practice for evaluating the quality of lap welding seams is that after welding, the on-site operator first confirms the output results of the QCDS (based on the welding machine process parameters) system, and assists with manual on-site confirmation methods at the crescent shear, such as the early drop hammer, the cupping test and bending test commonly used now, and then the operator makes a "decision" on whether to release it. This method relies to a large extent on people's subjective judgment and auxiliary manual tests. Not only is the knowledge acquisition and inheritance relatively one-sided, but it is also impossible to form a quantitative evaluation of welding quality and provide effective guidance for welding machine maintenance. And through the welding result evaluation method of the present invention, it is possible to evaluate the welding quality online and achieve quantitative scoring. Within one month of on-site trial operation, the number of re-welding caused by software misjudgment is less than 1 / 3 day, and the number of generation breaks caused by software misjudgment is less than 0.

[0088] However, those skilled in the art should recognize that the above embodiments are only used to illustrate the present invention, and are not intended to limit the present invention. As long as they are within the spirit of the present invention, any changes or modifications to the above embodiments will fall within the scope of the claims of the present invention.

Claims

1. A cold-rolled strip lap welding result evaluation method based on big data, characterized in that , including the following steps: S1. According to the historical big data of lap welding of cold-rolled strip steel, the corresponding welding parameters associated with high welding temperature are screened out, and the threshold value of each parameter is preset; S2. Collect the above actual welding parameters through the welding machine PLC. If all the welding parameters meet the set threshold, it is judged as qualified and scored, and then proceed to the next step; if any parameter does not meet the requirements, the weld is judged as unqualified; S3. The upper and lower limits of the measured height of the weld are determined. If they meet the requirements, the weld is determined to be qualified and scored, and the next step is entered; if they do not meet the requirements, the weld is determined to be unqualified; S4. Obtain the effective zone data of the weld temperature, and determine and score the trend, volatility and temperature distribution of the weld temperature in turn; S5. If the total score of steps S2-S4 is higher than the set value, the weld is judged to be qualified, and the judgment result and specific score are sent to the welding machine.

2. The cold-rolled strip lap welding result evaluation method based on big data according to claim 1, characterized in that: In step S1, the welding parameters include welding current, welding speed, overlap amount / overlap compensation amount, and welding wheel pressure.

3. A cold-rolled strip lap welding result evaluation method based on big data as claimed in claim 1 or 2, characterized in that: In step S1, the set threshold is a deviation value between the corresponding set parameter and the actual parameter of ±4%.

4. The cold-rolled strip lap welding result evaluation method based on big data according to claim 1, characterized in that: In step S2, the score for passing the judgment is 10 points / each parameter.

5. The cold-rolled strip lap welding result evaluation method based on big data according to claim 1, characterized in that: In step S3, the upper and lower limit judgments include whether it is a three-piece weld and whether the height is inconsistent in the weld length; the upper limit is 1.5 times the maximum thickness of the preceding and succeeding coils, and the lower limit is the minimum thickness of the preceding and succeeding coils; the score for a qualified judgment is 10 points.

6. The cold-rolled strip lap welding result evaluation method based on big data according to claim 1, characterized in that: In step S4, the trends and fluctuations of the temperatures of multiple welds are statistically analyzed, and the trend and fluctuation determination and scoring criteria are formulated according to the density function of the trend and fluctuation distribution of the weld temperatures as follows: If the temperature trend is between [-1, 0.2], 20 points will be awarded; if the temperature trend is between [-1.2, -1), (0.2, 0.5], 10 points will be awarded; if the temperature trend is between [-2, -1.2), (0.5, 2], 5 points will be awarded; if the absolute value of the temperature trend exceeds 2, it is NG; If the temperature volatility is between [0, 25], 20 points will be awarded; if the temperature volatility is between (25, 40], 10 points will be awarded; if the temperature volatility is between (40, 60], 5 points will be awarded; if the temperature volatility exceeds 60, it will be NG.

7. A cold-rolled strip lap welding result evaluation method based on big data as claimed in claim 1 or 6, characterized in that: In step S4, the temperature distribution of each point in the effective area is evaluated using the temperature mean ±3SD: temperature points falling within the mean ±3SD range are judged to be qualified; those exceeding the range are considered unqualified; a deduction system is used, with a total of 10 points, and one point is deducted for each point judged to be unqualified until it is reduced to 0 points.

8. The cold-rolled strip lap welding result evaluation method based on big data according to claim 1, characterized in that: In step S5, the set value is 60 minutes.

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