Strip steel cross-section defect identification method and system

Abstract

The application provides a strip steel cross-section defect identification method and system, which comprises the following steps: taking multiple cross-sections in the length direction of the strip steel as identification targets, obtaining thickness data of multiple measuring points in the width direction of each identification target to construct a one-dimensional thickness data sequence, wherein the first element value and the last element value in the one-dimensional thickness data sequence are the thickness values of the corresponding side edges of the strip steel; taking a preset check interval as a sliding window to slide and group the one-dimensional thickness data sequence, determining that the points with a deviation absolute value greater than or equal to a thickness threshold value in each group are local high points to obtain all local high points in the one-dimensional thickness data sequence; and if the number of identification targets with local high points in the length direction of the strip steel exceeds a preset defect determination threshold value, it is determined that the strip steel has a cross-section local high point defect. The application eliminates the subjectivity of manual determination and realizes online, real-time, high-precision and automatic identification of the cross-section local high point defect of the strip steel.

Description

Technical Field

[0001] This invention relates to the field of process control technology for hot strip rolling in metallurgy, and particularly to a method and system for identifying defects in the cross-section of strip steel. Background Technology

[0002] In the hot-rolled strip steel production process, due to the influence of various complex factors such as rolling technology, equipment condition, and temperature distribution, localized areas of excessive thickness may appear on the cross-section of the strip at the finishing mill exit, commonly referred to in the industry as "local high points." When the number or height of local high points reaches a certain level, the strip can be identified as having a local high point defect. Local high point defects can seriously affect the processing quality of subsequent cold rolling processes and the final product performance. To avoid batch quality defects, the relevant methods for identifying local high point defects mainly rely on manual judgment by on-site quality inspectors. Specifically, quality inspectors need to manually observe and judge based on the cross-sectional thickness profile curve detected and displayed by a multi-function instrument. Once a local high point is determined to be greater than 20μm, the finishing mill work rolls must be replaced immediately to prevent batches of local high point defects from occurring in the cold-rolled base material. However, this manual judgment method lacks objective standards, has the potential for missed detections and misjudgments, and is labor-intensive, inefficient, and lacks data utilization, failing to achieve automation. Summary of the Invention

[0003] This invention provides a method and system for identifying defects in strip steel cross-sections, in order to improve the accuracy and real-time performance of strip steel cross-section defect identification, reduce the subjectivity and labor intensity of manual judgment, and realize online, real-time, and high-precision automatic identification and early warning of local high-point defects in strip steel cross-sections.

[0004] This invention provides a method for identifying defects in the cross-section of steel strip, comprising the following steps: Multiple cross sections along the length of the strip are used as identification targets. Thickness data of multiple measurement points along the width of each identification target are obtained to construct a one-dimensional thickness data sequence. The first and last element values ​​in the one-dimensional thickness data sequence are the thickness values ​​of the corresponding side of the strip, respectively. Using a preset verification interval as a sliding window, the one-dimensional thickness data sequence is grouped in a sliding manner. The points in each group whose absolute deviation value is greater than or equal to the thickness threshold are determined as local high points, thereby obtaining all local high points in the one-dimensional thickness data sequence. The absolute value of the thickness deviation is determined based on the absolute value of the deviation between the average thickness of the elements located at the endpoints in the corresponding group and the maximum thickness value within the sliding window. If the number of targets with local high points along the length of the strip exceeds the preset defect judgment threshold, then the strip is determined to have a local high point defect in the cross section.

[0005] In one embodiment of the present invention, the step of determining local high points in each group by sliding grouping the one-dimensional thickness data sequence with a preset verification interval as a sliding window and determining the absolute value of the deviation in each group as greater than or equal to the thickness threshold includes: From the first element to the last element of the one-dimensional thickness data sequence, each element is selected as the left endpoint in turn; Based on the distance between each point and the side of the strip, search backwards for elements within the sliding window to retrieve the maximum thickness value, and calculate the absolute value of the deviation to determine whether there is a local high point within the sliding window; if so, the measurement point corresponding to the maximum thickness value is taken as the local high point, and the absolute value of the deviation is taken as the height value of the local high point.

[0006] In one embodiment of the present invention, after obtaining all local high points in the one-dimensional thickness data sequence, the method further includes recording the height value of each local high point and its coordinates in the width direction.

[0007] In one embodiment of the present invention, an alarm signal is triggered if it is determined that there is a local high point defect in the cross section of the strip.

[0008] In one embodiment of the present invention, the preset defect determination threshold is determined by the product of the total number of cross-sectional data groups in the length direction and the defect determination ratio, wherein the defect determination ratio is between 0 and 1.

[0009] In one embodiment of the present invention, the measurement points are set at equal intervals, and the sliding step size of the sliding window is equal to the distance between adjacent measurement points.

[0010] The present invention also provides an automatic identification system for defects in the cross-section of strip steel, characterized in that it includes: The data acquisition module is used to take multiple cross sections along the length of the strip as identification targets, and acquire the thickness data of multiple measurement points along the width of each identification target to construct a one-dimensional thickness data sequence, wherein the first element value and the last element value in the one-dimensional thickness data sequence are the thickness values ​​of the corresponding side of the strip. The local high point determination module is used to slide and group the one-dimensional thickness data sequence with a preset verification interval as a sliding window, and determine the local high point in each group where the absolute value of the deviation is greater than or equal to the thickness threshold, thereby obtaining all local high points in the one-dimensional thickness data sequence. The absolute value of the thickness deviation is determined based on the absolute value of the deviation between the average thickness of the elements located at the endpoints in the corresponding group and the maximum thickness value within the sliding window. The defect identification module is used to determine that there is a local high point defect in the strip if the number of identification targets with local high points in the length direction of the strip exceeds the preset defect judgment threshold.

[0011] The beneficial effects of this invention are as follows: The automatic identification method and system for strip steel cross-section defects proposed in this invention can achieve digital characterization of the thickness distribution of strip steel cross-section by arraying the thickness data in the width direction of the strip steel cross-section into a one-dimensional array and establishing a coordinate system in the width direction. Based on this, a sliding window method is used to geometrically determine local high points on a single cross-section. By calculating the difference between the maximum thickness value within an interval and the average thickness value at both ends of the interval, and comparing it with an inspection threshold, the height and location coordinates of local high points on a single cross-section can be automatically and accurately identified and recorded, effectively avoiding the subjectivity and uncertainty of manual visual observation. Attached Figure Description

[0012] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0013] In the attached diagram: Figure 1 This is a flowchart illustrating a method for identifying defects in the cross-section of a strip steel according to an embodiment of the present invention. Figure 2 This is a block diagram of a strip steel section defect identification system provided in one embodiment of the present invention. Detailed Implementation

[0014] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0015] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. The drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0016] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0017] The inventor discovered through research that: The current method of manually identifying local high-point defects in quality inspection carries significant risks of human error, omission, and failure to detect them. It fails to achieve automatic identification of local high-point defects in strip steel, posing a risk of batch quality accidents. The main problems with the existing control technology are as follows: 1. The existing defects in the local high points of the strip cross-section thickness rely entirely on manual judgment, lacking defect judgment criteria based on statistical methods. Furthermore, the manual judgment standards are inconsistent, posing a significant risk of misjudgment, omission, and missed detection, and creating a potential risk of batch quality accidents. 2. Existing technology relies entirely on manual inspection of cross-sectional thickness contour curves for judgment. Manual judgment is labor-intensive, labor-intensive for quality inspectors, and inefficient, making it difficult to meet the requirements for rapid online defect judgment. 3. Existing thickness profile curves for precision rolling mill exit sections are only used for curve display and statistics, lacking a fully automated and standardized method for identifying local high-point defects.

[0018] To address the problems in related technologies, this invention proposes an automatic identification method and system for defects in strip steel cross-sections. The technical solution of this invention will be described in detail below with reference to specific embodiments.

[0019] Please see Figure 1 This invention provides an automatic identification method for defects in the cross-section of strip steel, the method comprising the following steps: Step S100: Multiple cross sections along the length of the strip are used as identification targets. Thickness data of multiple measurement points along the width of each identification target are obtained to construct a one-dimensional thickness data sequence. The first and last element values ​​in the one-dimensional thickness data sequence are the thickness values ​​of the corresponding side of the strip.

[0020] In one embodiment, during the hot strip rolling process, a multi-function instrument at the finishing mill exit collects cross-sectional thickness data at equal intervals along the strip's length. Depending on the product thickness, the strip length is typically 150m. Within a range of 1500m, dozens to hundreds of sets of cross-sectional thickness data can be detected. Thickness data from multiple measurement points evenly spaced along the width of the strip cross-section are acquired, and a one-dimensional thickness data sequence is constructed, where the equal spacing between adjacent measurement points is denoted as dw. The first element value h1 in the one-dimensional thickness data sequence is considered the thickness value of one side of the strip, and the last element value hm is considered the thickness value of the other side of the strip. Based on the equal spacing dw of the measurement points, the distance between the measurement point corresponding to each element in the one-dimensional thickness data sequence and the starting edge of the strip is calculated to establish a coordinate system along the width direction of the strip.

[0021] Step S110: Using a preset verification interval as a sliding window, the one-dimensional thickness data sequence is grouped into sliding groups. The points in each group whose absolute deviation value is greater than or equal to the thickness threshold are determined as local high points, thereby obtaining all local high points in the one-dimensional thickness data sequence. The absolute value of the thickness deviation is determined based on the absolute value of the deviation between the average thickness of the elements located at the endpoints in the corresponding group and the maximum thickness value within the sliding window.

[0022] In one embodiment, the step of determining the thickness by sliding groups in the one-dimensional thickness data sequence with a preset verification interval as a sliding window, and identifying points in each group whose absolute deviation value is greater than or equal to the thickness threshold as local high points, includes: sequentially selecting each element as the left endpoint from the first element to the last element of the one-dimensional thickness data sequence; searching for elements within the sliding window based on the distance between each point and the side edge of the strip, retrieving the maximum thickness value, and calculating the absolute deviation value to determine whether there is a local high point within the sliding window; if there is, then taking the measurement point corresponding to the maximum thickness value as the local high point, and taking the absolute deviation value as the height value of the local high point.

[0023] Specifically, the width xSpan of the inspection interval and the inspection threshold hLim (i.e., the thickness threshold) are set. Two points are determined in the coordinate system, with their coordinates in the width direction being the left endpoint xL and the right endpoint xR, respectively. The distance between the left endpoint xL and the right endpoint xR corresponds to the width xSpan of the inspection interval, i.e., it satisfies |xR|. xL | ≈ xSpan, let the measured strip thickness at the left endpoint coordinate xL and the right endpoint coordinate xR be the left endpoint thickness hL and the right endpoint thickness hR, respectively. Within the interval between the left endpoint coordinate xL and the right endpoint coordinate xR, the maximum strip thickness, i.e., the maximum thickness value hMax, is retrieved, and the coordinates of the measurement point corresponding to the maximum thickness value hMax in the width direction are set as the maximum thickness point coordinate xMax. The local high point height value dh is calculated according to the formula: dh=hMax (hL+hR) / 2 (1) Wherein, dh is the local high point height value; hMax is the maximum thickness value within the interval; hL is the thickness value at the left endpoint; and hR is the thickness value at the right endpoint. If the calculated local high point height value dh satisfies the inequality dh≥hLim, then it is determined that there exists a local high point on the strip cross-section at the maximum thickness point coordinate xMax that satisfies the inspection interval width xSpan and the inspection threshold hLim, and the local high point height value dh is the height value corresponding to the local high point. In step S107, a sliding window search is performed on the entire width range of the current cross-section to detect all local high points on the cross-section, and the height value dh of each local high point and the coordinates of each high point in the width direction, i.e., the maximum thickness point coordinate xMax, are automatically recorded. In step S108, for each set of cross-section data in the length direction of the entire strip, the above sliding window search is used for automatic inspection, and the inspection results of each set of cross-section data are recorded sequentially.

[0024] In another embodiment, the specific steps for performing a sliding window search across the entire width range of the current cross-section are as follows: Initialize the pointer; sequentially select each element from the first element to the last element of the one-dimensional thickness data sequence as the left endpoint, with its corresponding physical position being the left endpoint coordinate xL; based on the distance between each point and the edge of the strip, find the element closest to the value of xL + xSpan as the right endpoint, with its corresponding physical position being the right endpoint coordinate xR; within the closed interval between the left endpoint coordinate xL and the right endpoint coordinate xR, retrieve the maximum thickness value, i.e., the maximum thickness value hMax, and record its corresponding maximum thickness point coordinate xMax; calculate the local high point height value dh according to the formula; determine whether the local high point height value dh satisfies dh ≥ hLim; if it satisfies, record the height value dh of the local high point. And the coordinates of the point in the width direction, i.e., the coordinates of the maximum thickness point xMax; if not satisfied, skip the current interval; move the pointer one element to the right, repeat the above process, and determine whether the end of the array has been searched. If not, return to continue the search; if so, end the search for the local high point of the current section.

[0025] In contrast, if the traditional global extreme point search algorithm is adopted, due to the existence of a large number of tiny bumps and noise interference on the surface of hot-rolled strip steel, a large number of local extremes are extremely likely to be generated in the non-defect area, resulting in a geometric series increase in the computational complexity and being unable to meet the online real-time calculation requirements of the high-speed hot continuous rolling production line (the rolling speed is usually greater than 10 m / s). By setting a fixed inspection interval width xSpan for one-way traversal in a sliding window manner, this method simplifies the complex two-dimensional contour search into a local sliding retrieval of a one-dimensional array, greatly reducing the memory overhead of the computer and the computational load of the processor, and ensuring extremely high real-time performance and accuracy of defect recognition.

[0026] Step S120: If the number of recognition targets of local high points in the strip steel length direction exceeds the preset defect determination threshold, it is determined that the strip steel has a cross-section local high point defect.

[0027] In an embodiment, the total number of cross-section data groups collected in the strip steel length direction is set as N0. In step S109, a defect determination ratio p is set, where 0 < p ≤ 1. Finally, in step S110, a statistical inspection is performed on the inspection results of the entire coil of strip steel: when the cumulative number of cross-section groups with the local high points in a parameter-adjustable interval near a certain point in the strip steel width direction is N1, it is judged whether the statistical determination condition is satisfied: N1 > N0 × p Formula (2) where, N1 is the cumulative number of cross-section groups with local high points in a parameter-adjustable interval near a certain point in the strip steel width direction; N0 is the total number of cross-section data groups collected in the strip steel length direction; p is the defect determination ratio. If the statistical determination condition is satisfied, it is determined that the entire coil of strip steel has a cross-section local high point defect near the width position.

[0028] In contrast, if the traditional method of observing the contour curve by the naked eye of workers is adopted, due to the inconsistent experience and subjective standards of different quality inspectors, it is extremely likely to cause missed inspections and misjudgments, and it is unable to adapt to the real-time processing of a large amount of data under high-speed rolling. Through the synergistic effect of sliding window geometric determination and longitudinal statistical cumulative determination, this method eliminates the subjectivity of manual determination, realizes the online, real-time, and high-precision automatic recognition of the cross-section local high point defect of the strip steel, and effectively avoids serious processing quality defects such as batch local waviness and ribbing during the subsequent cold rolling process of cold-rolled base materials.

[0029] In one embodiment, after determining whether a local high-point defect exists in the cross-section of the entire strip coil, an automatic alarm and closed-loop control prompt are triggered. Specifically, if a local high-point defect is determined to exist in the cross-section of the entire strip coil, the system automatically triggers an alarm signal to prompt on-site operators to take corrective measures. In contrast, in related technologies, the strip cross-sectional thickness profile curve detected by the multi-function instrument is only used for curve display on the operator's screen and simple data statistics afterward, lacking real-time linkage with the production control system. When a serious local high-point defect occurs, it relies entirely on on-site quality inspectors to manually observe the curve on the screen and make subjective judgments. Once the quality inspectors are fatigued or distracted, they are very likely to miss the over-limit signal, resulting in a batch of local high-point defects in dozens of subsequent steel coils, causing significant batch downgrading or return losses. This method, by automatically and immediately triggering an alarm signal after the algorithm determines the existence of a defect, achieves a fundamental shift from "passive manual monitoring" to "active system early warning." The alarm signal can be directly connected to the plant's automated control network, providing on-site operators with immediate and mandatory closed-loop control prompts, thereby cutting off the path to batch defects at the first moment and significantly improving the level of intelligent and automated control of the hot rolling production line.

[0030] Please see Figure 2 Based on the same inventive concept, the present invention also provides an automatic strip steel section defect identification system, which includes: a data acquisition module 20, used to take multiple cross sections along the length of the strip steel as identification targets, acquire thickness data of multiple measurement points along the width of each identification target to construct a one-dimensional thickness data sequence, wherein the first element value and the last element value in the one-dimensional thickness data sequence are the thickness values ​​of the corresponding side of the strip steel; a local high point determination module 21, used to slide and group the one-dimensional thickness data sequence with a preset verification interval as a sliding window, determine the points in each group whose absolute deviation value is greater than or equal to the thickness threshold as local high points, and obtain all local high points in the one-dimensional thickness data sequence, wherein the absolute value of the thickness deviation is determined according to the absolute value of the deviation between the average thickness value of the element located at the end of the corresponding group and the maximum thickness value within the sliding window; and a defect identification module 22, used to determine that the strip steel has a section local high point defect if the number of identification targets with local high points along the length of the strip steel exceeds a preset defect determination threshold.

[0031] The interaction process of each module in the specific system has been described in detail in the aforementioned method embodiments, and will not be repeated here.

[0032] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A strip steel cross-section defect recognition method characterized by, Includes the following steps: Multiple cross sections along the length of the strip are used as identification targets. Thickness data of multiple measurement points along the width of each identification target are obtained to construct a one-dimensional thickness data sequence. The first and last element values ​​in the one-dimensional thickness data sequence are the thickness values ​​of the corresponding side of the strip, respectively. Using a preset verification interval as a sliding window, the one-dimensional thickness data sequence is grouped in a sliding manner. The points in each group whose absolute deviation value is greater than or equal to the thickness threshold are determined as local high points, thereby obtaining all local high points in the one-dimensional thickness data sequence. The absolute value of the thickness deviation is determined based on the absolute value of the deviation between the average thickness of the elements located at the endpoints in the corresponding group and the maximum thickness value within the sliding window. If the number of targets with local high points along the length of the strip exceeds the preset defect judgment threshold, then the strip is determined to have a local high point defect in the cross section.

2. The method for identifying defects in the cross-section of strip steel according to claim 1, characterized in that, The process of determining local high points in each group by sliding grouping the one-dimensional thickness data sequence with a preset verification interval as the sliding window, and defining the points in each group whose absolute deviation is greater than or equal to the thickness threshold, includes: From the first element to the last element of the one-dimensional thickness data sequence, each element is selected as the left endpoint in turn; Based on the distance between each point and the side of the strip, search backwards for elements within the sliding window to retrieve the maximum thickness value, and calculate the absolute value of the deviation to determine whether there is a local high point within the sliding window; if so, the measurement point corresponding to the maximum thickness value is taken as the local high point, and the absolute value of the deviation is taken as the height value of the local high point.

3. The method for identifying defects in the cross-section of strip steel according to claim 2, characterized in that, After obtaining all local high points in the one-dimensional thickness data sequence, the method further includes recording the height value of each local high point and its coordinates in the width direction.

4. The method for identifying defects in the cross-section of strip steel according to claim 1, characterized in that, It also includes triggering an alarm signal if it is determined that there is a local high point defect in the strip section.

5. The method for identifying defects in the cross-section of strip steel according to claim 1, characterized in that, The preset defect determination threshold is determined by the product of the total number of cross-sectional data groups in the length direction and the defect determination ratio, wherein the defect determination ratio is between 0 and 1.

6. The method for identifying defects in the cross-section of strip steel according to claim 1, characterized in that, The measurement points are set at equal intervals, and the sliding step of the sliding window is equal to the distance between adjacent measurement points.

7. An automatic identification system for defects in the cross-section of steel strip, characterized in that, include: The data acquisition module is used to take multiple cross sections along the length of the strip as identification targets, and acquire the thickness data of multiple measurement points along the width of each identification target to construct a one-dimensional thickness data sequence, wherein the first element value and the last element value in the one-dimensional thickness data sequence are the thickness values ​​of the corresponding side of the strip. The local high point determination module is used to slide and group the one-dimensional thickness data sequence with a preset verification interval as a sliding window, and determine the local high point in each group where the absolute value of the deviation is greater than or equal to the thickness threshold, thereby obtaining all local high points in the one-dimensional thickness data sequence. The absolute value of the thickness deviation is determined based on the absolute value of the deviation between the average thickness of the elements located at the endpoints in the corresponding group and the maximum thickness value within the sliding window. The defect identification module is used to determine that there is a local high point defect in the strip if the number of identification targets with local high points in the length direction of the strip exceeds the preset defect judgment threshold.

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