A risk assessment method for process curves affecting the full-length quality of hot-rolled strip

Abstract

The present invention provides a process curve risk assessment method that affects the full-length quality of hot-rolled strip steel, and belongs to the field of metallurgical automation technology. The method first obtains the process curve that affects the quality of hot-rolled strip steel, collects strip steel and process curves with qualified quality as an excellent sample library; then performs data discrimination and processing on the head and tail step segments of the curve, and normalizes the length; and scales all curves to unit length in proportion to the longest curve; all the scaled curves are re-equidistantly taken, and a single group of risk coefficients are calculated according to a given threshold in the on-site process table; finally, the risk coefficient of the entire curve is solved and evaluated based on the single group of risk coefficients. This method can achieve rapid positioning of the cause of quality anomalies, and greatly improve the accuracy and efficiency of quality anomaly analysis.

Description

Technical Field

[0001] The invention relates to the technical field of metallurgical automation, and in particular to a process curve risk assessment method that affects the full-length quality of hot-rolled steel strip. Background Art

[0002] During the rolling process of the strip, the process personnel judge whether the quality of the strip is qualified based on several important quality indicators of the strip. The influencing factors include: thickness, width, FDT (final rolling temperature), CT (coiling temperature), convexity, wedge shape, symmetrical straightness, and asymmetrical straightness. During the rolling process of the strip, each process step will have a certain impact on the quality of the strip, and the change of the process curve can reflect the change of the rolling state of the full length of the strip. For example, the size of the incoming billet and the gap between each roller during the rolling process will directly affect the width and thickness of the finished strip; the incoming material temperature, rolling speed, cooling water volume, etc. will directly affect the FDT and CT of the strip; the direction and amount of roller shifting will affect the straightness of the finished strip. When the rolling process tends to be stable, the quality of the products produced can be guaranteed. Therefore, the parameters and curves of the rolling process of the steel coils of the same specifications and the same steel grade with excellent quality are basically stable within a certain range. When the external environment changes and affects the strip steel, for example, the incoming slab has a water beam mark, which will cause the incoming temperature to be uneven, and the incoming temperature curve will have multiple local extreme points. When the strip steel is rolled according to the set rolling force and the set roll gap, the local maximum value of the incoming temperature curve will cause the strip steel to be pressed too much, and the local minimum value will cause the pressure to be too small, resulting in the thickness of the finished strip steel exceeding the limit. At present, the process personnel of the steel plant do not have methods and tools for evaluating the process curve. They only judge the process curve through manual experience, which is inefficient and accurate. Therefore, a process curve risk assessment method that affects the full-length quality of hot-rolled strip steel is proposed to quickly locate the cause of quality abnormalities, save manpower, improve efficiency, and improve accuracy.

[0003] At present, there is no evaluation method for the rolling process curve in hot rolling quality control, but there are some studies on the evaluation of curve similarity. For example, in the prior art curve / surface quality quantitative evaluation method, the coordinate transformation of the curve / surface is performed, and then multiple wavelet smoothing is performed to obtain a reconstruction matrix, and then the curve / surface is reconstructed, and the detail information is calculated, and the curve / surface is quantitatively evaluated by the detail information. This method evaluates the quality of the curve itself, and the method of the present invention evaluates the quality of the curve by comparing the curve with the sample. The prior art industrial equipment characteristic curve similarity acquisition method and device, and the curve similarity calculation method all evaluate the similarity of the curve by calculating the weighted distance between the curves, which cannot clearly reflect the abnormal position of the curve. The method of the present invention can reflect the abnormal position of the curve by decomposing the curve into a single point evaluation.

[0004] To solve the above problems, the present invention proposes a process curve risk assessment method that affects the full-length quality of hot-rolled strip steel. The strip steel rolling process curve is collected, and a threshold is set through process thinking to conduct risk assessment on the curve. The process curve risk assessment method that affects the full-length quality of hot-rolled strip steel proposed by the present invention can quickly locate the cause of quality abnormality and improve the efficiency and accuracy of manual judgment of rolling abnormality process. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a process curve risk assessment method that affects the full-length quality of hot-rolled strip. By evaluating the process curve, the state of the strip during rolling can be more intuitively reflected, and the cause of quality abnormality can be quickly located.

[0006] The method comprises the following steps:

[0007] (1) Using existing equipment to collect process curves that affect the quality of hot-rolled strip steel, including measurable curves such as temperature, speed, rolling force, and roll gap during the rolling process, and collect strip steel with qualified quality and its process curves as an excellent sample library;

[0008] (2) performing data discrimination and processing on the head and tail step segments of all process curves obtained in step (1), eliminating abnormal data in the step segments, and normalizing the length of the curves after eliminating the abnormal data;

[0009] (3) Scale all curves to unit length in proportion to the longest curve;

[0010] (4) All the curves after scaling are re-sampled with equal intervals, and all the sample points taken each time are taken as a group. The risk coefficient is calculated using the single group risk coefficient calculation method according to the threshold given in the on-site process table;

[0011] (5) Solve the risk coefficient of the entire curve based on the single group risk coefficient, sort all the curves in descending order according to the size of the curve risk coefficient, and the top three process curves are the main influencing factors of the current curve in this quality analysis item.

[0012] Among them, the qualified strip steel in step (1) refers to the strip steel with the same steel type, width and thickness and the quality hit rate conforming to the 2σ principle, that is, the quality hit rate is greater than 95.45%.

[0013] In step (2), the data difference and treatment of the curve head and tail step segments are specifically as follows: the curve head and tail step segments are removed, and the formula for the length of the removed data is as follows:

[0014]

[0015] Among them, L CH , L CTare the length of the head removed and the length of the tail removed, respectively, h E h is the actual thickness of the strip at the entrance of finishing rolling, T It is the target thickness of strip finishing.

[0016] In step (2), the length of the curve after removing the head and tail is normalized to the interval [0,1]. The normalization calculation formula is as follows:

[0017]

[0018] Among them, L ni is the normalized length of the curve, L i is the original curve length, L max , L min are the maximum and minimum lengths of the curves in the excellent sample library, respectively, and i is the curve number.

[0019] In step (3), the curve scaling ratio is the ratio of the normalized length of the curve to the normalized length of the longest curve as the scaling factor, that is:

[0020]

[0021] Among them, L mi is the length of the scaled curve, L ni is the normalized length of the curve, L nmax is the length of the longest curve after normalization.

[0022] In step (4), the re-equidistant points are taken according to the length of the longest curve after removing the step segment, and one point is taken every 1 meter. The final total number of points taken is the number of re-points taken for all curves.

[0023] In step (4), all the points newly obtained from all curves are taken as a group, and the total number of groups is the number of points newly obtained from the curve. The risk coefficient of each group is calculated using the single group risk coefficient calculation method. The single group risk coefficient calculation method is as follows:

[0024] S41: Arrange all sample points except the point taken by the current steel coil curve from small to large, and select the sample median x Cen , when the number of samples is an odd number, the middle value of the sample is selected as the median; when the number of samples is an even number, the mean of the two middle samples is selected as the median;

[0025] S42: The point x of the current coil curve c With the median x Cen Make the difference, recorded as deviation δ:

[0026] δ=|x c -x Cen |

[0027] S43: Compare the deviation δ with the upper and lower thresholds, and define the upper threshold as T up , the lower threshold is T low , the upper threshold and the lower threshold are obtained by querying the on-site process table, then the single group risk coefficient Risk p for:

[0028]

[0029] In step (5), the average value of all the single-group risk coefficients obtained in step (4) is taken to represent the risk coefficient of the entire curve, and the curve risk coefficient is defined as Risk C :

[0030]

[0031] Among them, n is the number of risk coefficients in a single group, that is, the number of equally spaced points on each curve, and i is the serial number of each group of risk coefficients; Risk pi For a single group of risk coefficients, all curves are sorted in descending order according to the risk coefficient size. The top three process curves in risk coefficient ranking are the main influencing factors of the current curve in this quality analysis item.

[0032] The beneficial effects of the above technical solution of the present invention are as follows:

[0033] In the above scheme, the strip rolling process curve is collected, the threshold is set through process thinking, and the risk assessment is performed on the curve, which can quickly locate the cause of quality abnormalities and improve the efficiency and accuracy of manual judgment of rolling abnormalities. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flow chart of the process curve risk assessment method affecting the full-length quality of hot-rolled steel strip of the present invention;

[0035] Figure 2 Schematic diagram of the steps for calculating a single group risk factor in an embodiment of the present invention;

[0036] Figure 3 It is a roll gap curve diagram of the strip steel F3 frame in the embodiment of the present invention;

[0037] Figure 4 It is a temperature curve diagram of the steel strip FET in the embodiment of the present invention;

[0038] Figure 5 It is a curve diagram of the angle of the F4 looper of the strip steel in the embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0040] The invention provides a process curve risk assessment method affecting the full-length quality of hot-rolled strip steel.

[0041] like Figure 1 As shown, the method comprises the following steps:

[0042] (1) Obtain the process curve that affects the quality of hot-rolled strip steel, and collect strip steel with qualified quality and its process curve as an excellent sample library;

[0043] (2) performing data discrimination and processing on the head and tail step segments of all process curves obtained in step (1), eliminating abnormal data in the step segments, and normalizing the length of the curves after eliminating the abnormal data;

[0044] (3) Scale all curves to unit length in proportion to the longest curve;

[0045] (4) All the curves after scaling are re-sampled with equal intervals, and all the sample points taken each time are taken as a group. The risk coefficient is calculated using the single group risk coefficient calculation method according to the threshold given in the on-site process table;

[0046] (5) Based on the risk coefficient of each single group, the risk coefficient of the entire curve is solved and evaluated.

[0047] In step (4), each group of risk coefficients is calculated using a single group risk coefficient calculation method, and the single group risk coefficient calculation method is as follows:

[0048] S41: Arrange all sample points except the point taken by the current steel coil curve from small to large, and select the sample median x Cen , when the number of samples is an odd number, the middle value of the sample is selected as the median; when the number of samples is an even number, the mean of the two middle samples is selected as the median;

[0049] S42: The point x of the current coil curve c With the median x Cen Make the difference, recorded as deviation δ:

[0050] δ=|x c -x Cen |

[0051] S43: Compare the deviation δ with the upper and lower thresholds, and define the upper threshold as T up , the lower threshold is T low , the upper threshold and the lower threshold are obtained by querying the on-site process table, then the single group risk coefficient Risk p for:

[0052]

[0053] The single group risk factor calculation method process is as follows Figure 2 shown.

[0054] This will be described below with reference to specific embodiments.

[0055] Take the curve data of a hot-rolled 2250 production line roll, the steel grade is CP800, the finished product thickness is 4.08mm, the finished product width is 1250mm, the first roll is the current roll, the rest are excellent samples, and the quality analysis item is thickness. This embodiment specifically includes the following steps:

[0056] S1: Get the current roll gap curve of the F3 stand of the steel coil, and add the process curves of the steel coils with the same steel type, width, thickness and quality hit rate greater than 95.45% to the excellent sample library. The specific data is shown in Table 1:

[0057] Table 1 Measured roll gap curve of hot-rolled strip F3 stand

[0058]

[0059] S2: Remove invalid data at the beginning and end of the curve:

[0060]

[0061] After removing the step segment data, the curve is as follows Figure 3 The specific data are shown in Table 2:

[0062] Table 2 F3 frame roll gap removal step curve

[0063]

[0064] Normalize the curve length:

[0065]

[0066] S3: The curve scaling ratio is the ratio of the normalized length of the curve to the normalized length of the longest curve as the scaling factor, that is:

[0067]

[0068] S4: The number of points to be retaken is based on the length of the longest curve after removing the step segment, and 1 point is taken for every 1 meter. The final total number of points is used as the number of points to be retaken for all curves. All samples are taken as a group each time, and the risk coefficient of a single group is calculated according to the threshold given in the on-site process table. The re-pointing data is shown in Table 3, and the specific process table for the upper and lower thresholds is shown in Table 4:

[0069] Table 3 Re-pointing values ​​of F3 stand roll gap curve

[0070]

[0071] Table 4 Roll gap deviation threshold process table

[0072]

[0073]

[0074] S5: Calculate the risk coefficient of the whole group using the single group risk coefficient calculation method. The steps of the single group risk coefficient calculation method are as follows:

[0075] S51: Arrange all sample points except the point taken by the current steel coil curve from small to large, and select the sample median x Cen , when the number of samples is an odd number, the middle value of the sample is selected as the median; when the number of samples is an even number, the average of the two middle samples is selected as the median;

[0076] S52: The point x of the current coil curve c With the median x Cen The difference is recorded as deviation δ. The median of the first group of samples x Cen is 8.234, the current steel coil curve point x c is 9.336, then the deviation δ is:

[0077] δ=|x c -x Cen |=|9.336-8.234|=1.102

[0078] S53: The calculated deviation δ is between the upper and lower thresholds, that is, T low =0<δ=1.102<T up =1.4, then the risk factor of a single group is:

[0079]

[0080] The calculation process of the remaining single-group risk coefficients is the same as above, and the single-group risk coefficients can be obtained as shown in Table 5 below:

[0081] Table 5 Single group risk coefficient

[0082] Points 1 2 3 4 5 6 ... Risk Factor % 78.71 82.06 78.79 76.22 69.29 71.80 ...

[0083] The average value of all single-group risk coefficients represents the risk coefficient of the entire curve, and the curve risk coefficient is defined as Risk C , then the curve risk coefficient is:

[0084]

[0085] That is, the current risk factor of the roll gap curve of the F3 stand of the coil steel is 73.78%.

[0086] Similarly, the risk coefficients of other process curves are calculated and ranked as shown in Table 6 below:

[0087] Table 6 Ranking of process curve risk factors

[0088]

[0089] FET temperature curve and F4 looper angle curve are as follows: Figure 4 , 5 As shown, it can be concluded that the risk coefficients of F3 stand roll gap, FET temperature and F4 looper tension are relatively large, and they are the main influencing factors affecting the current strip thickness.

[0090] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A process curve risk assessment method affecting the full-length quality of hot-rolled strip, characterized by: The steps include: (1) Using existing equipment to collect process curves that affect the quality of hot-rolled strip steel, including temperature, speed, rolling force, and roll gap curve during the rolling process, and collect strip steel with qualified quality and its process curves as an excellent sample library; (2) performing data discrimination and processing on the head and tail step segments of all process curves obtained in step (1), eliminating abnormal data in the step segments, and normalizing the length of the curves after eliminating the abnormal data; (3) Scale all curves to unit length in proportion to the longest curve; (4) All the curves after scaling are re-sampled with equal intervals, and all the sample points taken each time are taken as a group. The risk coefficient is calculated using the single group risk coefficient calculation method according to the threshold given in the on-site process table; (5) Solve the risk coefficient of the entire curve based on the single group risk coefficient, sort all curves in descending order according to the size of the curve risk coefficient, and the top three process curves are the main influencing factors of the current curve in this quality analysis item; In step (2), the data difference and treatment of the head and tail step segments of all process curves are specifically as follows: the head and tail step segments of the curve are removed, and the formula for the length of the removed data is as follows: Among them, L CH , L CT are the length of the head removed and the length of the tail removed, respectively, h E h is the actual thickness of the strip at the entrance of finishing rolling, T is the target thickness of strip finishing; The normalized calculation formula in step (2) is as follows: Among them, L ni is the normalized length of the curve, L i is the original curve length, L max , L min are the maximum and minimum lengths of the curves in the excellent sample library, respectively, and i is the curve number; In step (3), the curve scaling ratio is the ratio of the normalized length of the curve to the normalized length of the longest curve as the scaling factor, that is: Among them, L mi is the length of the scaled curve, L ni is the normalized length of the curve, L nmax is the length of the longest curve after normalization; In step (4), the re-equidistant points are taken according to the length of the longest curve after removing the step segment, and one point is taken for every 1 meter, and the final total number of points taken is used as the number of re-points taken for all curves; In step (4), all the points newly obtained from all curves are taken as a group, and the total number of groups is the number of points newly obtained from the curve. The risk coefficient of each group is calculated using the single group risk coefficient calculation method. The single group risk coefficient calculation method is as follows: S41: Arrange all sample points except the point taken by the current steel coil curve from small to large, and select the sample median x Cen , when the number of samples is an odd number, the middle value of the sample is selected as the median; when the number of samples is an even number, the mean of the two middle samples is selected as the median; S42: The point x of the current coil curve c With the median x Cen Make the difference, recorded as deviation δ: δ=|x c -x Cen | S43: Compare the deviation δ with the upper and lower thresholds, and define the upper threshold as T up , the lower threshold is T low , the upper threshold and the lower threshold are obtained by querying the on-site process table, then the single group risk coefficient Risk p for: In step (5), the average value of all the single-group risk coefficients obtained in step (4) is taken to represent the risk coefficient of the entire curve, and the curve risk coefficient is defined as Risk C : Among them, n is the number of risk coefficients in a single group, that is, the number of equally spaced points on each curve; Risk pi is the single group risk coefficient, and i is the serial number of each group of single group risk coefficients.

2. The process curve risk assessment method affecting the full-length quality of hot-rolled strip according to claim 1 is characterized in that: The qualified steel strip in step (1) refers to a steel strip with the same steel type, width and thickness and a quality hit rate that complies with the 2σ principle, that is, a quality hit rate greater than 95.45%.

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