A lap welding process parameter setting method based on the combination of cold-rolled strip steel tapping mark and thickness

Abstract

The application discloses a lap welding process parameter setting method based on a combination of a cold-rolled strip steel tapping mark and thickness, realizes output of applicable welding machine process parameters for known input parameters based on historical welding performance data analysis, realizes weldability grading early warning before welding by using a C equivalent difference value, and completes lap welding parameter recommendation by obtaining a tapping mark, thickness and chemical composition of a running strip steel before welding according to a generated static table in combination with a tapping mark six-digit code and thickness.

Description

Technical Field

[0001] This invention relates to welding control technology for thin strip steel, and more specifically, to a method for setting welding process parameters for lap welding based on the combination of cold-rolled strip steel tap marks and thickness. Background Technology

[0002] Currently, lap welding machines for cold-rolled strip steel mainly rely on welding codes for welding. A single production line typically has only 5-6 welding codes, and some of these codes cannot be welded together. This is because welding codes are related to the strength of the strip steel; significant differences in strength prevent welding. Therefore, static tables of welding parameters based on welding codes are often sparse. Generally, a cold-rolled strip steel production line often produces over a hundred types of cold-rolled steel strips, with only 5-6 welding codes. Therefore, using welding codes to recommend welding parameters results in a wide range of steel grades, leading to significant deviations in the provided welding process parameters and potentially poor weld quality.

[0003] When a new steel grade is introduced, it is necessary to conduct a live simulation experiment on the welding process parameters of the cold-rolled strip steel to be welded, either during unit shutdown or direct shutdown, followed by manual inspection. In this situation, the welding code provides very little guidance for optimizing the welding process parameters.

[0004] Meanwhile, during the welding process, the carbon equivalent of the materials to be welded has a direct impact on the welding quality. However, in current lap welding of cold-rolled strip steel, the influence of the carbon equivalent is rarely considered. Therefore, in current lap welding of cold-rolled strip steel, there is a lack of consideration for the carbon equivalent of the materials to be welded before welding. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method for setting welding process parameters for lap welding based on the combination of cold-rolled strip steel tap mark and thickness. Optimized welding process parameters are obtained based on the six-digit tap mark, thickness, and chemical composition of the strip steel to be welded.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for setting welding process parameters for lap welding based on the combination of cold-rolled strip tapping marks and thickness:

[0008] Based on the analysis of historical welding performance data, the system can output applicable welding machine process parameters for known input parameters. At the same time, before welding, the system uses the C equivalent difference to realize the weldability classification and early warning. Based on the generated static table according to the six-digit code of the steel tapping mark and the thickness combination, the system obtains the steel tapping mark, thickness and chemical composition of the running strip before welding to complete the recommendation of lap welding parameters.

[0009] Preferably, the method for setting the welding process parameters for lap welding specifically includes the following steps:

[0010] S1, collect historical welding performance data, including the steel exit marks, thickness, chemical composition of the material, and the corresponding welding process parameters and welding results as target parameters for the forward and backward coils;

[0011] S2, based on historical welding performance data, extract the first six digits of the steel output marks of the preceding and following coils, and convert the six digits into a string;

[0012] S3, classify according to the six-digit code combination and thickness combination, and filter out the six-digit code and thickness combination of the same steel mark, chemical composition, welding process parameters and welding results;

[0013] S4. For each combination, the welding process parameters and welding results are selected based on the mean, volatility, and quartiles to identify the most effective welding process parameters, ultimately forming a static table of one code, one thickness, and one welding parameter.

[0014] S5, based on the C-equivalent calculation formula, calculates the C-equivalent using chemical composition as input;

[0015] S6. Based on the absolute value calculation results of the C-equivalent interpolation of the forward and backward coiled steel strips, the range of safe welding C-equivalent difference and the range of risky welding C-equivalent difference are determined according to the density distribution function.

[0016] S7 determines whether there is a welding risk based on the C equivalent difference before welding, and issues a graded warning. At the same time, it sends the optimized welding process parameters to the welding machine and sets them accordingly.

[0017] Preferably, in step S1, the welding process parameters include welding current, welding speed, welding wheel pressure, overlap amount, and overlap compensation amount.

[0018] Preferably, in step S4, the static table of one code, one thickness, and one welding parameter specifically includes:

[0019] The mean, volatility, and quartile mathematical characteristics together identify the optimal value or optimal range of welding process parameters for each combination;

[0020] Each welding process parameter is combined to form the welding process parameters under that combination.

[0021] Each combination is combined to form a static table of the smallest particles under the six-digit code of the steel mark and the thickness combination.

[0022] Preferably, in step S5, the chemical composition of the material includes C, Mn, Cr, Cu, Mo, V, and Ni.

[0023] Preferably, in step S5, the formula for calculating the C equivalent is:

[0024] CE=c+Mn / 6+(Cr+Mo+V) / 5+(Ni+Cu) / 15(%).

[0025] Preferably, in step S6, the density distribution function is a KS density distribution, and its formula is:

[0026] [f, xi] = ksdensity(x)

[0027] This formula is used to calculate the probability density estimate of a sample vector x, returning the probability density f at point xi.

[0028] The present invention provides a method for setting welding process parameters for lap welding based on the combination of cold-rolled strip tapping marks and thickness, which has the following advantages:

[0029] 1) By analyzing historical welding big data, a static table is created that assigns one code, one thickness, and one welding parameter, enabling the recommendation and optimization of welding process parameters for strip steel operating online. Compared to welding codes, the six-digit steel output mark and the smallest granular static table under thickness combinations have a one-to-one correspondence and are more detailed;

[0030] 2) Considering the impact of C equivalent on welding results, weldability is graded and warned before welding to safeguard the welding results;

[0031] 3) It will play a very positive role in improving production efficiency, product quality and product competitiveness. Attached Figure Description

[0032] Figure 1 This is a schematic diagram illustrating the principle of the lap welding process parameter setting method of the present invention;

[0033] Figure 2 This is a schematic diagram of the distribution of weld C equivalent difference in the lap welding process parameter setting method of the present invention;

[0034] Figure 3 This is a schematic diagram of the probability density estimation of the C equivalent difference in the lap welding process parameter setting method of the present invention. Detailed Implementation

[0035] To better understand the above-mentioned technical solutions of the present invention, the technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0036] This invention provides a method for setting welding process parameters for lap welding based on the combination of cold-rolled strip steel tapping marks and thickness:

[0037] Based on the analysis of historical welding performance data, the system can output applicable welding machine process parameters for known input parameters. At the same time, before welding, the C equivalent difference is used for weldability classification and early warning. Based on the generated static table of six-digit code of steel tapping mark and thickness combination, the system obtains the steel tapping mark, thickness and chemical composition of the running strip before welding to complete the recommendation of lap welding parameters.

[0038] The method for setting welding process parameters for lap welding of the present invention specifically includes the following steps:

[0039] S1, collect historical welding performance data, including the steel exit marks, thickness, chemical composition of the material, and the corresponding welding process parameters and welding results (outcomes) as target parameters for the forward and backward coils; welding process parameters include welding current, welding speed, welding wheel pressure, lap amount, and lap compensation amount.

[0040] S2, based on historical welding performance data, extract the first six digits of the steel output mark of the previous and subsequent coils (a total of eight digits, of which the first six digits are related to the welding process), and convert the six-digit code into a string;

[0041] S3, classify according to the six-digit code combination and thickness combination, and filter out the six-digit code and thickness combination of the same steel mark, chemical composition, welding process parameters and welding results;

[0042] S4. For each combination, the welding process parameters and welding results are selected based on the mean, variability (standard deviation), and quartiles to identify the most effective welding process parameters, ultimately forming a static table of one code, one thickness, and one welding parameter.

[0043] S5, based on the C-equivalent calculation formula, calculates the C-equivalent using chemical composition as input;

[0044] S6. Based on the absolute value calculation results of the C-equivalent interpolation of the forward and backward coiled steel strips, the range of safe welding C-equivalent difference and the range of risky welding C-equivalent difference are determined according to the density distribution function.

[0045] S7 determines whether there is a welding risk based on the C equivalent difference before welding, and issues a graded warning. At the same time, it sends the optimized welding process parameters to the welding machine and sets them accordingly.

[0046] In step S4 above, the static table of one code, one thickness, and one welding parameter specifically includes:

[0047] Mathematical characteristics such as mean, volatility (standard deviation), and quartiles are used to identify the optimal value or optimal range of welding process parameters for each combination.

[0048] Each welding process parameter is combined to form the welding process parameters under that combination.

[0049] Each combination is combined to form a static table of the smallest particles under the six-digit code of the steel mark and the thickness combination.

[0050] In step S5 above, the chemical composition of the material includes C, Mn, Cr, Cu, Mo, V, and Ni.

[0051] In step S5 above, the formula for calculating the C equivalent is:

[0052] CE=C+Mn / 6+(Cr+Mo+V) / 5+(Ni+Cu) / 15(%).

[0053] In step S6 above, the density distribution function is the KS density distribution, and its formula is:

[0054] [f, xi] = ksdensity(x)

[0055] This formula is used to calculate the probability density estimate of a sample vector x, and returns the probability density f at point xi (which is related to the set data width; the wider the data width, the greater the probability).

[0056] The system used in this invention for setting lap welding process parameters based on the combination of cold-rolled strip tapping marks and thickness is as follows: Figure 1 As shown, it includes a welding machine system, a supporting software system (OLW), and corresponding data services and support (such as a digital steel coil data platform).

[0057] Its working principle is as follows:

[0058] Using the on-site production data collected by the digital steel coil as a carrier, the system records the tapping marks, thickness, and chemical composition (C, Mn, Cr, Cu, Ni, Mo, V, etc.) of the strip at the uncoiler (rear-coiled strip) and the strip in operation (forward-coiled strip). Then, based on the C equivalent, the weldability is graded and warned. After the weldability assessment is passed, the data is transmitted to the OLW (Operational Welding Machine) which embeds the combination of tapping marks and thickness through the data interface, and the recommended welding parameters are output. The OLW then transmits the data to the welding machine for welding.

[0059] In the existing technical framework, the method of determining welding parameters by regression analysis based on steel grade and thickness is essentially a coarse classification based on steel grade and thickness, and does not take into account the influence of material C equivalent (C, Mn, Cu, Si, etc., chemical composition is closely related to welding quality).

[0060] The static table of steel tapping marks and thickness combinations is as follows:

[0061] Collect historical welding performance data, including the steel exit marks of the front and rear coils, thickness, chemical composition of the material, as well as the corresponding welding process parameters and welding results (temperature) as target parameters. Welding process parameters include welding current, welding speed, welding wheel pressure, lap amount, and lap compensation amount.

[0062] Based on historical welding data, the first six digits of the steel output marks of the preceding and following coils (a total of eight digits, of which the first six digits are related to the welding process) are taken and converted into strings. The six-digit codes are then classified according to the combination of the six-digit codes and the thickness combination, and the chemical composition, welding process parameters and welding temperature under the same steel output mark six-digit code and thickness combination are selected.

[0063] For each combination, the welding process parameters and welding results are selected based on the mean, fluctuation (standard deviation), quartiles, etc., to find the most effective welding process parameters; each welding process parameter is combined to form the welding process parameters for that combination; each combination is then combined to form the smallest particle static table under the six-digit steel mark code and thickness combination.

[0064] Obtain the chemical composition of the steel coils before and after processing. Based on the C-equivalent calculation formula, use the chemical composition as input to calculate the C-equivalent. The chemical composition of the steel coils before and after processing includes C, Mn, Cr, Cu, Mo, V, and Ni; the formula for calculating the C-equivalent is:

[0065] CE=C+Mn / 6+(Cr+Mo+V) / 5+(Ni+Cu) / 15(%)

[0066] Based on the calculation results of the absolute value of the C equivalent difference between the front and rear coiled strips, the range of safe welding C equivalent difference and the range of risky welding C equivalent difference are determined according to the density function. Before welding, the C equivalent difference is used to determine whether there is a welding risk and to issue a graded warning. At the same time, the optimized welding process parameters are sent to the welding machine and set accordingly.

[0067] The density distribution function is the KS density distribution (probability density estimate, ksdensity), and its formula is:

[0068] [f, xi] = ksdensity(x)

[0069] This formula calculates the probability density estimate of a sample vector x and returns the probability density f at point xi (which depends on the set data width; the wider the data width, the higher the probability).

[0070] The OLW software communicates with the welding machine and sends optimized process parameters: Before welding begins, the OLW software provides parameters from a static table of steel mark and thickness combinations based on the six-digit code of the steel tapping mark and the thickness of the cold-rolled strip to be welded. The OLW software then communicates directly with the welding machine, sending the optimized welding process parameters. The welding machine's PLC receives these parameters, displays them on the welding machine's operating terminal, and performs the corresponding welding.

[0071] Example

[0072] This embodiment obtains the chemical composition of the front and rear steel coils, and calculates the C-equivalent based on the C-equivalent calculation formula, using the chemical composition as input. The chemical composition of the front and rear steel coils includes C, Mn, Cr, Cu, Mo, V, and Ni; the C-equivalent calculation formula is as follows:

[0073] CE=C+Mn / 6+(Cr+Mo+V) / 5+(Ni+Cu) / 15(%)

[0074] Based on the calculation results of the absolute value of the C equivalent difference between the front and rear coiled strips, the range of safe welding C equivalent difference and the range of risky welding C equivalent difference are determined according to the density function. Before welding, the C equivalent difference is used to determine whether there is a welding risk and to issue a graded warning. At the same time, the optimized welding process parameters are sent to the welding machine and set accordingly.

[0075] The density distribution function is the KS density distribution (probability density estimation, ksdensity), and its formula is:

[0076] [f, xi] = ksdensity(x)

[0077] This formula calculates the probability density estimate of a sample vector x and returns the probability density f at point xi (which depends on the set data width; the wider the data width, the higher the probability).

[0078] A statistical analysis of the weld seams of a certain unit over the past six months shows the distribution of weld C-equivalent differences as follows: Figure 2 As shown, the probability density function of the C equivalent difference is as follows: Figure 3 As shown. According to Figure 2 The distribution of C-equivalent difference values ​​shown indicates that the absolute values ​​of C-equivalent difference in welds are mainly concentrated within 0.2. The probability density estimate of the absolute values ​​of C-equivalent difference values ​​less than 0.05 is approximately xi, which is about 0.3. Based on field welding experience, welds with an absolute value of C-equivalent difference less than or equal to 0.3 are considered safe. There is a certain risk when the absolute value of C-equivalent difference is within (0.3, 0.4), and welding is not allowed when the absolute value of C-equivalent difference exceeds 0.4.

[0079] Based on this conclusion, after obtaining the six-digit code of the steel tapping mark and chemical composition of the front and rear strips before welding, the C equivalent of each strip and its difference can be calculated, and a graded early warning can be issued. The C equivalent calculation results are shown in Table 1.

[0080] Table 1 shows the calculation results of C-equivalent for some steel tapping marks.

[0081] AN0543 120543 0.072272919 AN0691 120691 0.081770971 AN0891 120891 0.114100772 AN1053 121053 0.101605655 AN1064 121064 0.107455852 AN1111 121111 0.078671 776 AN1122 121122 0.078976809 AN1540 121540 0.096343528 AN1950 121950 0.140293371 AN2222 122222 0.054047103 AN2233 122233 0.070794031 AN4444 124444 0.106337912 AN6666 126666 0.028612841 AN8888 128888 0.056978571 AP0540 130540 0.070372042 AP0941 130941 0.083058716

[0082] As shown in Table 2, historical welding performance data were collected, including the steel exit marks of the front and rear coils, the thickness, and the corresponding welding process parameters as target parameters. The welding process parameters include welding current, welding speed, welding wheel pressure, overlap amount, and overlap compensation amount.

[0083] Based on historical welding data, the first six digits of the steel output marks of the preceding and following coils (a total of eight digits, of which the first six digits are related to the welding process) are taken and converted into strings. The six-digit codes are then classified according to the combination of the six-digit codes and the thickness combination, and the chemical composition, welding process parameters and welding temperature under the same steel output mark six-digit code and thickness combination are selected.

[0084] For each combination, the welding process parameters and welding results were analyzed, and the most effective welding process parameters were selected based on the mean, variability (standard deviation), quartiles, etc. The selection process is as follows:

[0085] First, based on historical data from the past six months, target welds are screened according to the same steel marking and thickness range. Then, the mean and standard deviation are calculated to initially screen data within the range of mean ± 3 standard deviations. Next, data within the [25%, 75%] range are further screened based on quartiles. Finally, the mode is calculated. After multiple rounds of data screening to eliminate unstable and random data, the final mode is our optimal parameter.

[0086] Then, each welding process parameter is combined to form the welding process parameters under that combination; each combination is combined to form the minimum particle static table under the six-digit code of the steel output mark and the thickness combination (as shown in Table 3).

[0087] Table 2 Welding Performance Data

[0088]

[0089] Table 3. Static table of six-digit steel tapping mark and corresponding thickness.

[0090]

[0091] Those skilled in the art should recognize that the above embodiments are merely illustrative of the present invention and are not intended to limit the present invention. Any variations or modifications to the above embodiments that are within the spirit and essence of the present invention will fall within the scope of the claims of the present invention.

Claims

1. A method for setting welding process parameters for lap welding based on the combination of cold-rolled strip tapping marks and thickness, characterized in that: Based on historical welding performance data analysis, the system outputs applicable welding machine process parameters for known input parameters. Simultaneously, before welding, it utilizes the C-equivalent difference to provide a pre-welding weldability classification and early warning. Furthermore, based on a generated static table combining the six-digit code of the steel strip's exit mark and thickness, it obtains the exit mark, thickness, and chemical composition of the running strip before welding to recommend lap weld parameters. The method for setting the welding process parameters for lap welding specifically includes the following steps: S1, collect historical welding performance data, including the steel exit marks, thickness, material chemical composition, and corresponding welding process parameters and welding results of the front and rear coils as target parameters; S2, based on historical welding performance data, extract the first six digits of the steel output marks of the preceding and following coils, and convert the six digits into a string; S3, classify according to the six-digit code combination and thickness combination, and filter out the six-digit code and thickness combination of the same steel mark, chemical composition, welding process parameters and welding results; S4, for each combination, the welding process parameters and welding results are analyzed based on the mean, volatility, and quartiles to select the most effective welding process parameters, ultimately forming a static table of one code, one thickness, and one welding parameter. The static table of one code, one thickness, and one welding parameter specifically includes: The mean, volatility, and quartile mathematical characteristics together identify the optimal value or optimal range of welding process parameters for each combination; Each welding process parameter is combined to form the welding process parameters under that combination. Combine each combination to form the smallest static table of steel production mark six-digit code and thickness combination; S5, based on the C-equivalent calculation formula, calculates the C-equivalent using chemical composition as input; S6. Based on the absolute value calculation results of the C-equivalent interpolation of the forward and backward coiled steel strips, the range of safe welding C-equivalent difference and the range of risky welding C-equivalent difference are determined according to the density distribution function. S7 determines whether there is a welding risk based on the C equivalent difference before welding, and issues a graded warning. At the same time, it sends the optimized welding process parameters to the welding machine and sets them accordingly.

2. The method for setting welding process parameters for lap welding based on the combination of cold-rolled strip steel tapping marks and thickness as described in claim 1, characterized in that: In step S1, the welding process parameters include welding current, welding speed, welding wheel pressure, overlap amount, and overlap compensation amount.

3. The method for setting welding process parameters for lap welding based on the combination of cold-rolled strip steel tapping marks and thickness, as described in claim 1, is characterized in that: In step S5, the chemical composition of the material includes C, Mn, Cr, Cu, Mo, V, and Ni.

4. The method for setting lap welding process parameters based on the combination of cold-rolled strip steel tapping marks and thickness, as described in claim 3, is characterized in that... In step S5, the formula for calculating the C equivalent is: 。 5. The method for setting lap welding process parameters based on the combination of cold-rolled strip steel tapping marks and thickness, as described in claim 1, is characterized in that... In step S6, the density distribution function is the KS density distribution, and its formula is: ； This formula is for calculating sample vectors. The probability density estimate is returned in Point probability density .

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