On-line detection method, system and device for cutting angle of oblique cutting machine

Abstract

The invention relates to the technical field of tire production, in particular to an on-line detection method, system and device for the cutting angle of an oblique cutting machine, and the method comprises the steps: obtaining the state information of a belted layer, and determining the deformation influence of the action of a roller on the belted layer; obtaining head image data of the belted layer; matching the scanned image with an actual belted layer; performing straight line fitting, and calculating an included angle between two straight lines to obtain an initial beveling angle value; a reverse correction coefficient is superposed on the initial beveling angle value, and the initial beveling angle value is corrected; and displaying and recording. By combining an indentation prediction model established based on roller distribution and belted layer physical characteristics, dynamic prediction and comparison are performed on deformation in a roller transmission process, a reverse correction coefficient is generated to correct an initial angle, and dynamic correction is realized by combining indentation prediction and real-time deformation comparison. The angle error caused by local deformation of the material due to roller transmission is made up, so that the accuracy of the detection result is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tire production, and particularly relates to a slitting machine cutting angle online detection method, system and device. BACKGROUND

[0002] Modern radial tires are generally composed of multiple layers of composite structures such as a tread, a carcass, a belt layer, an inner liner and a sidewall, wherein the belt layer is located between the carcass and the tread, and is usually composed of multiple layers of high-strength steel cord or fiber cord laminated together, and is an indispensable core part of the radial tire structure. The belt layer plays a role in enhancing the rigidity of the tread, controlling the deformation of the tread, suppressing the outward expansion of the carcass, improving the wear resistance and driving stability during the driving process of the tire, and has a decisive influence on the high-speed performance, load-carrying performance and service life of the tire.

[0003] On the existing tire production line, the belt layer usually needs to be slanted cut according to the set angle to ensure that the subsequent splicing and molding processes meet the design requirements of the mechanical properties of the tire. The existing slant cutting angle is usually confirmed by manual measurement or periodic sampling inspection. If the belt layer is deformed or the angle deviates due to mechanical reasons such as roller transmission and manual detection release during the production process, it is often difficult to find out in time, which can easily cause angle errors, resulting in difficulties in subsequent splicing, product defects and even batch rejection.

[0004] Therefore, a method for online, non-contact and real-time detection of the cutting angle of the slitting machine is needed, and the detection results are dynamically corrected in combination with the material properties and transmission state. Therefore, the existing technology has defects and needs to be improved. SUMMARY

[0005] The present application aims to provide a slitting machine cutting angle online detection method, system and device to solve the problem that the existing technology is often difficult to find out in time due to mechanical reasons such as roller transmission and manual detection release during the production process, which can easily cause angle errors, resulting in difficulties in subsequent splicing, product defects and even batch rejection.

[0006] The present application provides a slitting machine cutting angle online detection method, comprising: Obtaining the state information of the belt layer after cutting and passing through the roller, and determining the deformation influence of the roller on the belt layer according to the roller distribution and the hardness of the belt layer; Non-contact scanning of the acute angle of the head of the belt layer to obtain the image data of the head of the belt layer; According to the pulse information output by the encoder, the scanned image is matched with the actual belt layer 1:1; Linear fitting is performed on the head image data of the belt layer to fit the perpendicular side and the oblique side of the acute angle of the head of the belt layer, and the included angle of the two straight lines is calculated to obtain the initial slant cutting angle value; According to the deformation influence result, a reverse correction coefficient is superimposed on the initial beveling angle value, the initial beveling angle value is corrected, and a corrected belt layer beveling angle value is obtained. The corrected beveling angle value is sent to the upper computer in real time for display and recording.

[0007] As a preferred technical scheme of the slitting angle online detection method of the slitting machine, the state information of the belt layer after cutting and passing through the roller includes: The arrangement position, number and distance of the roller are detected by the position sensor installed on the production line, and the roller distribution information is sent to the upper computer as an input parameter for modeling the deformation influence.

[0008] As a preferred technical scheme of the slitting angle online detection method of the slitting machine, the deformation influence result is generated by a indentation prediction model, the indentation prediction model takes the roller distribution information, the belt layer material hardness parameter and the transmission speed as input, and outputs the theoretical deformation variable of the belt layer through a pre-trained deformation prediction algorithm.

[0009] As a preferred technical scheme of the slitting angle online detection method of the slitting machine, the reverse correction coefficient is generated according to the theoretical deformation variable output by the indentation prediction model, and the correction coefficient corrects the initial beveling angle value.

[0010] As a preferred technical scheme of the slitting angle online detection method of the slitting machine, the real-time detection of the belt layer deformation variable includes: obtaining the belt layer surface profile data through a 3D sensor, and combining the known deformation variable threshold to extract the deformation variable deviation information.

[0011] As a preferred technical scheme of the slitting angle online detection method of the slitting machine, the encoder is used to output the pulse displacement signal of the belt layer transmission, and is matched with the scanning pulse of the 3D sensor in synchronization, so that the scanning image is consistent with the physical length of the belt layer.

[0012] As a preferred technical scheme of the slitting angle online detection method of the slitting machine, the straight line fitting step adopts a least square fitting algorithm to fit the edge points of the belt layer head image, and filters the fitting error.

[0013] As a preferred technical scheme of the slitting angle online detection method of the slitting machine, it also includes: when the corrected beveling angle deviates from the set threshold, a feedback signal is sent to the slitting machine cutter control unit to automatically adjust the cutting angle.

[0014] The application also provides a slitting angle online detection system of a slitting machine, which comprises: A state analysis module is configured to obtain state information of the belt layer after cutting and passing through the roller, and determine the deformation influence of the roller on the belt layer according to the roller distribution and the hardness of the belt layer. a data acquisition module configured to non-contact scan the acute angle of the belt head portion and acquire image data of the belt head portion; a data matching module configured to ensure that the scanned image matches the actual belt in a 1:1 manner according to pulse information output by the encoder; a data analysis module configured to perform linear fitting on the belt image obtained by scanning, fit the perpendicular side and the oblique side of the acute angle of the belt head portion, and calculate the included angle of the two straight lines to obtain an initial bevel angle value; a correction module configured to superimpose a reverse correction coefficient on the initial bevel angle value according to the deformation influence result, correct the initial bevel angle value, and obtain a corrected belt bevel angle value; an output module configured to send the corrected bevel angle value to a host computer in real time for display and recording.

[0015] An online detection device for a bevel cutting machine cutting angle, comprising: a state analysis unit configured to acquire state information of the belt after being cut and transmitted by the roller, and determine the deformation influence of the roller on the belt according to the roller distribution and the hardness of the belt; a 3D scanning unit configured to non-contact scan the acute angle of the belt head portion and acquire image data of the belt head portion; a positioning unit configured to ensure that the scanned image matches the actual belt in a 1:1 manner according to pulse information output by the encoder; a data analysis unit configured to perform linear fitting on the belt image obtained by scanning, fit the perpendicular side and the oblique side of the acute angle of the belt head portion, and calculate the included angle of the two straight lines to obtain an initial bevel angle value; a data correction unit configured to superimpose a reverse correction coefficient on the initial bevel angle value according to the deformation influence result, correct the initial bevel angle value, and obtain a corrected belt bevel angle value; a display unit configured to send the corrected bevel angle value to a host computer in real time for display and recording.

[0016] Compared with the prior art, the beneficial effects of the present application are that the present application combines the belt roller transmission state, roller distribution information and belt layer material hardness parameters on the production line, realizes non-contact high-precision scanning of the sharp angle of the belt layer head by using a line laser 3D sensor and an encoder, obtains an initial beveling angle value through fitting calculation, dynamically predicts and compares the deformation amount in the roller transmission process by combining the indentation prediction model established based on the roller distribution and the physical properties of the belt layer, generates a reverse correction coefficient to correct the initial angle, thereby realizing online, real-time and non-contact detection of the beveling angle of the belt layer, and realizing dynamic correction by combining indentation prediction and real-time deformation amount comparison, which makes up for the angle error caused by local deformation of the material due to roller transmission, thereby ensuring the accuracy of the detection result.

[0017] Further, according to the comparison result of the corrected angle value and the set threshold value, the present application automatically generates a feedback signal and adjusts the angle of the cutting knife of the beveling machine, forms a closed-loop adaptive adjustment, significantly improves the angle stability and product consistency in the production process, and thereby reduces the rework rate and scrap rate in the subsequent splicing and forming process. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The step flow chart of the beveling machine cutting angle online detection method of the embodiment of the present application; Figure 2 The structure block diagram of the beveling machine cutting angle online detection system of the embodiment of the present application. DETAILED DESCRIPTION

[0019] The features and exemplary embodiments of various aspects of the present application will be described in detail below, in order to make the purposes, technical solutions and advantages of the present application more clear and apparent, the present application will be further described in detail below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, but not to limit the present application. The present application can be implemented without some of these specific details by those skilled in the art. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0020] It is to be noted that the relative terms such as first and second and the like are used herein only to differentiate one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between such entities or operations. Moreover, the terms "comprising", "including", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the elements defined by the statement "comprise" do not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the stated elements.

[0021] Referring to Figure 1 The step flow chart of the cutting angle online detection method of the bias cutting machine is shown in the figure, which includes the following steps: Step S1. Obtain the state information of the belt layer after cutting and transmission through the roller, and determine the deformation influence of the roller on the belt layer according to the roller distribution and the hardness of the belt layer; Step S2. Non-contact scanning of the acute angle of the belt layer head, obtaining the image data of the belt layer head; Step S3. According to the pulse information output by the encoder, ensure that the scanned image matches the actual belt layer 1:1; Step S4. Linear fitting of the belt layer head image data, fitting the perpendicular side and the oblique side of the acute angle of the belt layer head, and calculating the included angle of the two straight lines to obtain the initial bias cutting angle value; Step S5. According to the deformation influence result, superimpose a reverse correction coefficient on the initial bias cutting angle value, correct the initial bias cutting angle value, and obtain the corrected belt layer bias cutting angle value; Step S6. Real-time transmission of the corrected bias cutting angle value to the host computer for display and recording.

[0022] In the implementation, the state information includes the transmission posture of the belt layer on the roller (such as whether it is centered, whether there is an offset), the real-time rotation state of the roller (detected by an angle sensor), the hardness parameter of the belt layer (retrieved from the MES system, such as Shore hardness 80±5HA), and the belt layer is conveyed by multiple groups of rollers after cutting in the tire production line. The line laser 3D sensor is installed at the end of the conveying belt to non-contact scan the head of the belt layer, and the encoder is used to collect the conveying pulse information of the roller to ensure that the scanning is consistent with the actual length. After the host computer receives the sensor data, it first performs linear fitting to calculate the initial angle, then generates the deformation influence combined with the roller distribution and the hardness parameter, and automatically superimposes a reverse correction coefficient to output the corrected angle.

[0023] In detail, the technical scheme above obtains the roller distribution and the belt layer hardness information, combines the line laser 3D sensor and the encoder to perform non-contact online detection, and combines the indentation prediction and the real-time deformation variable comparison to realize dynamic correction, so as to compensate the angle error caused by the local deformation of the material due to the roller transmission, thereby ensuring the accuracy of the detection result.

[0024] The state information of the belt layer after cutting and passing through the roller includes: The arrangement position, number and interval of the roller are detected by the position sensor installed on the production line, and the roller distribution information is sent to the upper computer as an input parameter for deformation influence modeling.

[0025] In implementation, the laser ranging sensor (detection accuracy ±0.1mm) is installed on the transmission line at both sides along the length direction at an interval of 0.5m, the sensor emits laser to the roller shaft position, and the three-dimensional coordinates (X axis is the transmission direction, Y axis is the width direction, and Z axis is the height direction) of each roller are obtained through the triangulation principle; at the same time, the number of rollers is counted by the photoelectric counter, and the center distance (error ≤0.5mm) of adjacent rollers is calculated. The sensor packs the coordinates, number and interval data into JSON format, and sends them to the training database of the indentation prediction model through the RS485 bus as the basic input parameter for deformation influence modeling.

[0026] Further, the deformation influence result is generated by the indentation prediction model, the indentation prediction model takes the roller distribution information, the belt layer material hardness parameter and the transmission speed as input, and outputs the theoretical deformation variable of the belt layer through the pre-trained deformation prediction algorithm.

[0027] Specifically, the indentation prediction model can adopt finite element simulation or machine learning algorithm based on historical production data, the embodiment of the present application trains the indentation prediction model by using historical data (containing 10000 groups of belt layer deformation measured values under different roller distribution, hardness and speed), generates corresponding theoretical deformation variable data by inputting the roller distribution information, the belt layer hardness and the conveying line speed. The model can be deployed in an edge computing device or an upper computer, and supports regular training update to improve the accuracy of deformation prediction. Training of the data model according to the data belongs to the prior art, and will not be described here.

[0028] Further, the present application introduces the indentation prediction model, combines multiple parameter inputs such as roller distribution, belt layer hardness and transmission speed, and can predict the theoretical deformation variable of the belt layer in transmission, thereby providing an accurate theoretical basis for subsequent angle correction, improving the correction accuracy of the detection result, and thereby ensuring the accuracy of the detection result.

[0029] Further, the reverse correction coefficient is generated according to the theoretical deformation variable output by the indentation prediction model, and the initial beveling angle value is corrected according to the correction coefficient.

[0030] In the implementation, after the bending, the angle will change to a certain extent, that is, the two-dimensional angle is bent in a three-dimensional space, and the angle will change to a certain extent, the existing state in the three-dimensional space (that is, the initial bevel angle value in the application) and the angle are known, the angle formed in the three-dimensional space due to bending and turning is restored to the angle in the two-dimensional plane through reverse deduction, which is essentially a "planar reduction" operation of the three-dimensional bending structure, similar to unfolding a curved surface into a plane (such as unfolding a cylindrical surface into a rectangle, and unfolding a conical surface into a sector), and finally obtaining the original angle in the two-dimensional space, the calculation process is the prior art, which will not be described here.

[0031] In detail, the application generates corresponding reverse correction coefficients by comparing the theoretical deformation variable with the actual detected deformation variable and uses the correction coefficients for angle correction, realizes dynamic closed-loop correction of detection data, makes up for the angle error caused by the local deformation of the material due to the roller transmission, and further ensures the accuracy of the detection result.

[0032] Further, the real-time detection of the belt deformation variable includes: obtaining the belt surface profile data through the 3D sensor, and extracting the deformation variable deviation information in combination with the known deformation variable threshold.

[0033] In detail, the 3D sensor is used to obtain the belt surface profile data in real time, and the deviation is extracted in combination with the deformation variable threshold, so that the deformation variable detection has real-time and high sensitivity, and the consistency of the indentation prediction result and the actual state is ensured.

[0034] In detail, the encoder is used to output the pulse displacement signal of the belt transmission, and is matched with the scanning pulse of the 3D sensor in synchronization, so that the scanning image is consistent with the physical length of the belt.

[0035] In the implementation, the encoder and the 3D sensor are time-synchronized through the same controller, and the pulse displacement signal output by the encoder is transmitted to the main control unit in real time, which is used to calibrate the scanning speed and scanning row number of the 3D sensor, so as to ensure that the imaging and the physical size are one-to-one corresponding, and the length mismatch is avoided.

[0036] Specifically, the application synchronously matches the pulse displacement signal of the encoder with the scanning pulse of the 3D sensor, ensures that the scanning image is consistent with the physical length of the belt, effectively avoids the angle calculation deviation caused by the displacement error, and ensures the detection accuracy.

[0037] In detail, the straight line fitting step uses the least square fitting algorithm to fit the edge points of the belt head image, and filters the fitting error.

[0038] In implementation, the image data is preprocessed (grayscale, threshold segmentation, edge extraction), two edge profile points (not less than 50 feature points) of the head sharp angle are extracted, the least square straight line fitting algorithm is used to fit the right angle side (along the transmission direction) and the hypotenuse (cutting edge), and the fitting result is error corrected by combining a filtering algorithm (such as Kalman filtering or median filtering), and local abnormal points are filtered out, and the included angle of the two straight lines is calculated, that is, the initial bevel angle value.

[0039] Further, the present application adopts the least square fitting algorithm to fit the edge points of the belt layer head, and filters the fitting error, reduces the measurement fluctuation caused by image noise or defects, and improves the accuracy of the fitting angle, thereby further ensuring the accuracy of the detection result.

[0040] Further, the slitting machine cutting angle online detection method further comprises: when the corrected bevel angle deviates from the set threshold, a feedback signal is sent to the slitting machine cutter control unit to automatically adjust the cutting angle.

[0041] Referring to Figure 2 As shown in the figure, it is a structural block diagram of a slitting machine cutting angle online detection system according to an embodiment of the present application, comprising: The state analysis module is configured to obtain state information of the belt layer after cutting and transmission through the roller, and determine the deformation influence of the roller on the belt layer according to the roller distribution and the hardness of the belt layer; The data acquisition module is configured to perform non-contact scanning on the sharp angle of the belt layer head, and obtain image data of the belt layer head; The data matching module is configured to ensure 1:1 matching of the scanning image and the actual belt layer according to the pulse information output by the encoder; The data analysis module is configured to perform straight line fitting on the belt layer image obtained by scanning, fit the right angle side and the hypotenuse of the sharp angle of the belt layer head, and calculate the included angle of the two straight lines to obtain the initial bevel angle value; The correction module is configured to add a reverse correction coefficient to the initial bevel angle value according to the deformation influence result, correct the initial bevel angle value, and obtain the corrected belt layer bevel angle value; The output module is configured to send the corrected bevel angle value to the upper computer in real time for display and recording.

[0042] Further, the present application further provides a slitting machine cutting angle online detection device, comprising: The state analysis unit is configured to obtain state information of the belt layer after cutting and transmission through the roller, and determine the deformation influence of the roller on the belt layer according to the roller distribution and the hardness of the belt layer; The 3D scanning unit is configured to perform non-contact scanning on the sharp angle of the belt layer head, and obtain image data of the belt layer head; A positioning unit is configured to ensure that the scanned image matches the actual belt 1:1 according to the pulse information output by the encoder; A data analysis unit is configured to perform linear fitting on the scanned belt image to fit the right angle side and the oblique side of the acute angle of the belt head, and to calculate the included angle of the two lines to obtain an initial bevel angle value; A data correction unit is configured to superimpose a reverse correction coefficient on the initial bevel angle value according to the deformation influence result, and to correct the initial bevel angle value to obtain a corrected belt bevel angle value; A display unit is configured to send the corrected bevel angle value to a host computer in real time for display and recording.

[0043] Obviously, the above embodiments of the present application are only examples for clearly illustrating the present application, and are not intended to limit the implementation modes of the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the claims of the present application.

Claims

1. A method for online detection of the cutting angle of a bias cutting machine, characterized in that, include: Obtain the state information of the belt layer after cutting and transmission through rollers, and determine the influence of roller action on the deformation of the belt layer based on roller distribution and belt layer hardness; Non-contact scanning is performed on the acute angle of the banded layer head to acquire image data of the banded layer head; Based on the pulse information output by the encoder, ensure that the scanned image matches the actual belt layer 1:1; Linear fitting is performed on the head image data of the belt layer to fit the right-angle side and hypotenuse of the acute angle of the belt layer head, and the angle between the two lines is calculated to obtain the initial oblique angle value. Based on the deformation effect results, a reverse correction coefficient is superimposed on the initial oblique angle value to correct the initial oblique angle value, thereby obtaining the corrected oblique angle value of the belt layer. The corrected oblique angle value is sent to the host computer in real time for display and recording.

2. The online detection method for the cutting angle of a bias cutting machine according to claim 1, characterized in that, The acquisition of status information transmitted via rollers after the belt layer is cut includes: Position sensors installed on the production line detect the arrangement, number, and spacing of the rollers, and send the roller distribution information to the host computer as input parameters for deformation influence modeling.

3. The online detection method for the cutting angle of a bias cutting machine according to claim 2, characterized in that, The deformation effect result is generated by the indentation prediction model, which takes the roller distribution information, belt layer material hardness parameters and transmission speed as inputs, and outputs the theoretical deformation of the belt layer through a pre-trained deformation prediction algorithm.

4. The online detection method for the cutting angle of a bias cutting machine according to claim 3, characterized in that, The reverse correction coefficient is generated based on the theoretical deformation output by the indentation prediction model, and the initial oblique angle value is corrected based on the correction coefficient.

5. The online detection method for the cutting angle of a bias cutting machine according to claim 4, characterized in that, The real-time detection of the belt layer deformation includes: acquiring the surface contour data of the belt layer through a 3D sensor, and extracting deformation deviation information by combining it with a known deformation threshold.

6. The online detection method for the cutting angle of a bias cutting machine according to claim 1, characterized in that, The encoder is used to output the pulse displacement signal transmitted by the belt layer and synchronize it with the scanning pulse of the 3D sensor so that the scanned image is consistent with the physical length of the belt layer.

7. The online detection method for the cutting angle of a bias cutting machine according to claim 1, characterized in that, The linear fitting step uses the least squares fitting algorithm to fit the edge points of the banded layer head image and filters the fitting error.

8. The online detection method for the cutting angle of a bias cutting machine according to claim 1, characterized in that, Also includes: When the corrected beveling angle deviates from the set threshold, a feedback signal is sent to the beveling cutter control unit to automatically adjust the cutting angle.

9. An online detection system for the cutting angle of a bias cutting machine, used to implement the online detection method for the cutting angle of a bias cutting machine as described in any one of claims 1 to 8, characterized in that, include: The state analysis module is used to obtain the state information of the belt layer after cutting and transmission through the rollers, and to determine the influence of the roller action on the deformation of the belt layer based on the roller distribution and the hardness of the belt layer. The data acquisition module is used to perform non-contact scanning of the acute angle of the bandgap head to acquire image data of the bandgap head. The data matching module is used to ensure that the scanned image matches the actual belt layer 1:1 based on the pulse information output by the encoder. The data analysis module is used to perform line fitting on the scanned banded layer image, fit the right-angled side and hypotenuse of the acute angle at the head of the banded layer, and calculate the angle between the two lines to obtain the initial oblique angle value. The correction module is used to correct the initial oblique angle value by superimposing a reverse correction coefficient on the initial oblique angle value according to the deformation influence result, so as to obtain the corrected oblique angle value of the belt layer. The output module is used to send the corrected oblique angle value to the host computer in real time for display and recording.

10. An online detection device for the cutting angle of a bias cutting machine, characterized in that, include: The state analysis unit is used to obtain the state information of the belt layer after cutting and transmission through the rollers, and to determine the influence of the roller action on the deformation of the belt layer based on the roller distribution and belt layer hardness. The 3D scanning unit is used to perform non-contact scanning of the acute angle of the bandgap head to acquire image data of the bandgap head. The positioning unit is used to ensure that the scanned image matches the actual belt layer 1:1 based on the pulse information output by the encoder. The data analysis unit is used to perform line fitting on the scanned banded layer image, fit the right-angled side and hypotenuse of the acute angle of the banded layer head, and calculate the angle between the two lines to obtain the initial oblique angle value. The data correction unit is used to correct the initial oblique angle value by superimposing a reverse correction coefficient on the initial oblique angle value according to the deformation influence result, so as to obtain the corrected oblique angle value of the belt layer. The display unit is used to send the corrected oblique angle value to the host computer in real time for display and recording.

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