A method and system for detecting fine cracks on the tire surface based on tactile sensing and predicting the crack propagation direction

Abstract

The present invention relates to the technical field of tire detection, and in particular to a method and system for detecting fine cracks on the tire surface based on tactile sensing and predicting the crack propagation direction. This method combines the use of a three-dimensional displacement stage and a three-axis force sensor to precisely control the sensor to scan the tire surface and collect the force data in the X, Y, and Z directions. Through image processing techniques, including Canny edge detection and morphological operations, the crack area is extracted; further, the force gradient is calculated, and the Sobel operator is used to obtain the force change direction in the crack area. Combining with the Hough transform technology to extract the main crack direction, thereby predicting the crack propagation direction. By judging whether the crack propagation enters a preset dangerous area, the system automatically issues a warning signal to remind the operator to take necessary maintenance measures. This technology can detect fine cracks with high precision and predict their propagation trend, effectively improving the tire safety management level and reducing the safety hazards and losses caused by cracks.

Description

Technical Field

[0001] The present invention relates to the technical field of tire detection, and particularly to a method and system for detecting fine cracks on the tire surface based on tactile sensing and predicting the crack propagation direction. Background Art

[0002] With the continuous improvement of the requirements for material performance and reliability in the industrial field, especially in the production and use of automobile tires, the detection and propagation prediction of cracks on the tire surface have become important technical issues. As a key component of an automobile, the surface cracks of a tire not only affect the safety and service life of the tire, but may also lead to serious traffic accidents. Therefore, early detection and accurate prediction of the propagation direction of fine cracks on the tire surface are of great significance for ensuring traffic safety, improving the performance of tires, and reducing maintenance costs.

[0003] Most traditional tire crack detection methods rely on manual visual inspection, ultrasonic detection, or optical image processing technology. Although manual inspection can detect large cracks, there are certain limitations in detecting small cracks or internal layer cracks, and the operation efficiency is low, with high false detection and missed detection rates. Existing automated detection methods based on optical imaging, although able to detect cracks more precisely, also face problems such as changes in illumination, unstable image clarity, and complex crack textures. These methods are difficult to form effective image features for fine cracks or areas with similar textures, resulting in limited detection accuracy.

[0004] As an emerging non-destructive testing technology, tactile sensing technology has the advantages of high sensitivity, real-time performance, and convenience. The Chinese invention patent application (Publication No.: CN116818172A, Publication Date: September 29, 2023) discloses a tactile sensing-based flexible interface detection method and device. The method includes the following steps: controlling the three-dimensional displacement stage to move downward along the Z-axis direction, and obtaining the Z-direction force value of the triaxial force sensor in real time. When the force value reaches a preset threshold, stop moving and establish a reference plane for the sensor to scan the flexible interface; on the reference plane, realize the synchronous control of the data acquisition of the triaxial force sensor and the displacement stage based on a hard trigger method; perform a row-by-row scan of the flexible interface based on the data acquisition method, and collect the force change data in the X, Y, and Z directions when the sensor contacts the flexible interface as three-dimensional tactile information; perform fusion processing and calculation on the three-dimensional force data to achieve high-precision, high-resolution, multi-dimensional tactile scanning imaging of the flexible interface, and finally realize the detection of the flexible interface based on tactile sensing.

[0005] However, the existing tactile perception technology is not yet mature when applied to the detection of small cracks in tires. In particular, the stress gradient information and image processing technology have not been effectively combined to predict the crack propagation direction. The propagation of cracks is usually affected by stress concentration and material structure. Accurately predicting the crack propagation direction is the key to achieving intelligent early warning and optimizing the maintenance plan. Although some traditional methods attempt to use mechanical models or simplified algorithms to predict crack propagation, due to the complexity and application limitations of mechanical models, the accuracy and practicality of these methods still face great challenges.

[0006] Therefore, there is an urgent need for a new detection and prediction method that can combine tactile perception technology, stress gradient information, and image processing algorithms to accurately detect small cracks on the tire surface and predict the crack propagation direction, so as to improve the detection accuracy and prediction accuracy, and provide a scientific basis for tire safety monitoring and maintenance decisions. Summary of the Invention

[0007] To achieve the above objectives, the object of the present invention is to provide a method for detecting small cracks on the tire surface based on touch and predicting the crack propagation direction. This method can effectively detect small cracks on the tire surface that are difficult to discover with the naked eye, and predict the crack propagation direction by analyzing the stress gradient in the crack area and applying the Hough transform technology, thereby providing an accurate judgment basis for tire safety monitoring and maintenance.

[0008] To achieve the above objectives, the present invention adopts the following technical solutions:

[0009] A method for detecting small cracks on the tire surface based on touch and predicting the crack propagation direction, the method comprising the following steps:

[0010] 1) Use a three-dimensional displacement stage to control the three-axis force sensor to slowly move down along the Z-axis direction and collect the force value in the Z-direction in real time. When the force value in the Z-direction reaches a preset threshold, stop the displacement and establish a reference plane on the tire surface;

[0011] 2) On the reference plane, control the three-dimensional displacement stage to scan row by row in the X and Y directions, and collect the friction force in the X and Y directions and the normal pressure data in the Z-direction of the three-axis force sensor in real time to obtain the force data on the tire surface;

[0012] 3) Perform image processing on the force data, and use edge detection and morphological processing to extract the crack area on the tire surface;

[0013] 4) Calculate the gradient information of the force data in the X and Y directions of the crack area to obtain the stress gradient direction of the crack area;

[0014] 5) Apply the Hough transform to the crack region to extract the main direction of the crack, and compare it with the stress gradient direction to predict the crack propagation direction;

[0015] 6) Based on the predicted crack propagation direction, judge the future propagation trend of the crack and issue a warning.

[0016] Preferably, the reference plane is determined by precisely moving the three-dimensional displacement stage along the Z-axis direction and real-time collecting the stress data in the Z direction, and when the stress data reaches the set threshold.

[0017] Preferably, the edge detection uses the Canny edge detection algorithm, and the morphological processing includes dilation and erosion operations to eliminate noise and enhance the coherence of the crack region.

[0018] Preferably, the gradient calculation of the stress data uses the Sobel operator, and by calculating the stress changes in the X and Y directions, a stress gradient map is obtained.

[0019] Preferably, the Hough transform is used to extract the main direction line from the edge image of the crack region, and the angle of the line is combined with the stress gradient direction to predict the crack propagation direction.

[0020] Preferably, the prediction of the crack propagation direction is calculated according to the angle between the gradient direction and the line direction extracted by the Hough transform. When the angle is less than the predetermined threshold, it is considered that the crack will propagate along this direction.

[0021] Furthermore, the present invention also discloses a system for detecting fine cracks on the tire surface based on tactile sensing and predicting the crack propagation direction. The system implements the above method and includes:

[0022] A three-dimensional displacement stage for precisely moving the three-axis force sensor in the X, Y, and Z directions;

[0023] A three-axis force sensor capable of simultaneously detecting the frictional forces in the X and Y directions and the normal pressure in the Z direction, and outputting stress signals; An integrated acquisition and control platform for controlling the movement of the three-dimensional displacement stage, collecting the stress data of the three-axis force sensor, and synchronously processing the data;

[0024] A data processing unit for receiving the stress data of the three-axis force sensor, performing image processing, gradient calculation, and Hough transform processing, so as to extract the crack region and predict the crack propagation direction;

[0025] A display device for real-time displaying the crack region on the tire surface, the predicted crack propagation direction, and the detection results.

[0026] Preferably, the three-dimensional displacement stage includes a stepper motor, a precision guide rail, and a transmission lead screw, and can provide precise displacement control.

[0027] Preferably, the data processing unit includes a central processing unit and a programmable logic device (FPGA). The central processing unit is responsible for system control and data storage, and the programmable logic device is responsible for efficient data acquisition and processing.

[0028] Preferably, the display device includes a display, an image processing unit, and an alarm module, and is used to display the crack detection results and issue a warning signal according to the crack propagation prediction results.

[0029] Furthermore, the present invention also discloses a computer-readable storage medium, on which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the steps 3)-6) of the method are implemented.

[0030] Furthermore, the present invention also discloses a computer program product, including a computer program or instruction. When the computer program or instruction is executed by a processor, the steps 3)-6) of the method are implemented.

[0031] Due to the adoption of the above technical solution, the present invention can efficiently and accurately detect the fine cracks on the tire surface and effectively predict their propagation directions by combining a high-precision three-dimensional displacement stage, a three-axis force sensor, and advanced image processing technology. The implementation of this technical solution brings the following remarkable technical effects:

[0032] 1. High-precision fine crack detection: The present invention combines a three-dimensional displacement stage with a three-axis force sensor, and precisely controls the sensor to scan along the X, Y, and Z directions with micron-level precision, effectively capturing the force changes of the fine cracks on the tire surface. Traditional manual visual inspection or detection methods based on optical imaging cannot accurately capture fine cracks, while the present invention can achieve high-precision detection of these micro-cracks, greatly improving the sensitivity and accuracy of crack detection and significantly reducing the risk of missed detection.

[0033] 2. Strong environmental adaptability and overcoming light interference: Different from traditional crack detection methods that rely on optical images, the detection method of the present invention is based on tactile perception technology and is completely independent of changes in external light sources. Therefore, it can work stably in low light, high light, shadow, or other complex environments. This feature enables the present invention to maintain consistent performance and accuracy in different detection environments, greatly improving the environmental adaptability of the tire detection system.

[0034] 3. Real-time prediction of crack propagation direction: By calculating the gradient information of the force data in the X and Y directions in the crack area and combining the Hough transform to extract the main trend of the crack, the present invention can accurately predict the crack propagation direction. This technical effect significantly improves the intelligent level of tire crack management. By identifying the crack propagation trend in advance, operators can take appropriate measures, such as repairing or replacing the tire, before the crack further expands, avoiding potential safety hazards and reducing unnecessary losses.

[0035] 4. Efficient and automated detection, reducing human errors: The automated detection system of the present invention does not need to rely on manual inspections one by one, and can efficiently and stably perform fully automatic tire surface crack detection and crack propagation prediction. Compared with the traditional manual detection method, the present invention can significantly improve the detection efficiency, reduce the influence of human factors on the detection results, and at the same time improve the accuracy of data processing, ensuring the reliability of the detection results.

[0036] 5. Data storage and historical traceability: The system of the present invention has a data storage function, can store the crack data and crack propagation prediction results of each detection in real time, and provides a historical data traceability function. This not only helps with the long-term maintenance and management of tires, but also provides important data support for future technical optimization. The traceability and analysis of historical data can help enterprises identify potential quality problems and optimize tire design and production processes.

[0037] Through the above technical effects, the present invention can significantly improve the accuracy, efficiency, and intelligent level of tire crack detection, provide a reliable technical guarantee for tire safety management and maintenance, and at the same time provide strong support for the innovative development in the fields of intelligent manufacturing and industrial inspection. The present invention effectively avoids the further development of cracks during use through early crack detection and propagation prediction, reduces the risk of tire failures and accidents caused by cracks, thereby improving the safety of tires. In addition, timely handling and repair of cracks can extend the service life of tires and reduce operating costs. The application of the present invention not only has a wide application prospect in tire production and detection, but also can promote the automation and intelligent development in the field of industrial inspection. The high efficiency and intelligent analysis ability of the system can provide technical references for the detection of other similar industrial products, promote the adoption of advanced tactile sensing technologies in more fields, and improve the overall industrial production and quality inspection level. Detailed implementation manners

[0038] Next, in combination with the embodiments of the present invention, the technical solutions in the embodiments will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0039] A system for detecting small cracks on the tire surface based on tactile sensation and predicting the direction of crack propagation. The hardware of this system adopts the flexible interface detection method of the Chinese invention patent application (publication number: CN116818172A, publication date: 2023-09-29). By combining a high-precision three-dimensional displacement stage, a three-axis force sensor and advanced image processing technology, it can efficiently and accurately detect small cracks on the tire surface and effectively predict their propagation direction. The following are the specific implementation steps and their implementation methods.

[0040] 1. System hardware composition

[0041] 1.1 Three-dimensional translation stage

[0042] The 3D translation stage is one of the core components of the system. Its main function is to control the precise movement of the sensor in the X, Y and Z directions to ensure full coverage scanning of the tire surface. The translation stage is composed of a stepper motor, precision guide rails and a transmission screw. It has micron-level accuracy and can ensure high-precision positioning of the sensor. By precisely controlling the movement of the translation stage, line-by-line scanning of the tire surface can be achieved.

[0043] 1.2 Three-axis force sensor

[0044] The three-axis force sensor can simultaneously measure the friction force in the X and Y directions and the normal pressure in the Z direction, and output the force data in real time. The sensor has high resolution and can capture the slight force changes caused by small cracks. The three-axis force sensor works with the three-dimensional translation stage to complete the tactile scanning of the tire surface and generate force data.

[0045] 1.3 Integrated acquisition and control platform

[0046] The platform uses Xilinx Zynq SoC (system-on-chip), which integrates a central processing unit (ARM) and a programmable logic (FPGA) module. The central processing unit is used to coordinate system control, receive instructions from the host computer, store data, and interact with display devices; the FPGA module handles high-speed data acquisition, signal synchronization, and hardware acceleration algorithms to ensure the real-time and high efficiency of the system.

[0047] 1.4 Data Processing Unit

[0048] The data processing unit is responsible for receiving force data from the three-axis force sensor and processing the data. First, the crack area is extracted through Canny edge detection and morphological processing. Then, the force gradient information in the X and Y directions of the crack area is calculated, and the main direction of the crack is extracted from the crack edge image using Hough transform. Finally, the crack extension direction is predicted by analyzing the main direction of the crack and the force gradient.

[0049] 1.5 Display device

[0050] The display device is used to display the crack detection results on the tire surface and the predicted crack propagation direction in real time. The operator can view the location information of the cracks through the display interface and decide whether further maintenance or tire replacement is needed based on the prediction results.

[0051] II. Method steps based on tactile detection

[0052] 2.1 Establishment of the reference plane

[0053] Before starting the detection, first move the three-dimensional displacement stage slowly downward along the Z-axis direction and collect the force data in the Z direction in real time. When the detected force value in the Z direction reaches the preset threshold (5 N), stop the displacement and establish the reference plane on the tire surface. The establishment of the reference plane ensures the height reference value for subsequent scans and guarantees the accuracy of each scan.

[0054] 2.2 Scanning the force data row by row

[0055] After establishing the reference plane, the three-dimensional displacement stage starts to scan the tire surface row by row in the X and Y directions, collecting the friction force data in the X and Y directions and the normal pressure data in the Z direction of the sensor. By scanning row by row, the full-coverage force data on the tire surface can be obtained, and small crack areas are ensured not to be missed.

[0056] 2.3 Image processing and crack area extraction

[0057] For the collected force data, the data processing unit first performs image processing, including edge detection and morphological operations. Canny edge detection is used to identify the edges in the image, and morphological processing (dilation and erosion operations) helps to remove noise and enhance the coherence of the crack area. Finally, the crack area can be extracted from the entire force data image.

[0058] 2.3.1 Edge detection

[0059] Step 1.1: Canny edge detection

[0060] Canny edge detection is a commonly used edge detection method that determines the edge position by detecting the gradient change of pixel values in the image. For the force data image, Canny edge detection can help identify the boundaries of the cracks, which is very important for subsequent crack area extraction.

[0061] Steps of Canny edge detection:

[0062] 1) Smooth the image: First, apply a Gaussian filter to the input image to remove noise. The role of the Gaussian filter is to smooth the image by weighted averaging of adjacent pixel values, reducing the impact of noise on the edge detection result. The Gaussian filter formula is as follows:

[0063]

[0064] where G(x, y) represents the Gaussian kernel function, and σ is the standard deviation, which determines the width of the filter.

[0065] 2) Calculate the gradient: Use the Sobel operator to calculate the horizontal gradient G x and the vertical gradient G y . The Sobel operator is used to calculate the direction with the largest gray-level change in the image. The formula for calculating the gradient is:

[0066]

[0067] where I(x, y) is the pixel value in the image, and Gx and Gy are the gradients in the horizontal and vertical directions respectively.

[0068] 3) Non-maximum suppression: To improve the accuracy of the edges, the Canny algorithm performs non-maximum suppression on the gradient magnitude in the image. The purpose of non-maximum suppression is to eliminate pixels that do not belong to the edges and only retain the pixel points with the largest gradient magnitude.

[0069] 4) Double-threshold segmentation: The Canny algorithm determines the edges in the image through double-threshold segmentation. The specific method is: divide the gradient magnitude in the image into strong edges, weak edges, and non-edges. Strong edges are directly marked as edges, weak edges are considered edges only when connected to strong edges, and non-edges are excluded. The edge detection result can be determined by the following formula:

[0070]

[0071] where T is the gradient magnitude, T high and T low are the high threshold and the low threshold. If T low ≤T<T high then Edge is a weak edge.

[0072] 2.3.2 Morphological processing

[0073] Step 1): Dilation operation

[0074] The dilation operation is a morphological processing method, usually used to enhance the connectivity of objects in the image. Through dilation, the edges in the image will be expanded, thereby filling the gaps in the crack area and ensuring the integrity of the crack is retained. The mathematical formula for the dilation operation is as follows:

[0075] D(A)=A⊕B={z∣(B z ∩A)≠ }

[0076] where A is the original image, B is the structuring element, and B z is the translation of the structuring element on the image A, and ⊕ represents the dilation operation. The dilation operation will expand the region with a gray value of 1 in the image.

[0077] Step 2): Erosion operation

[0078] The erosion operation is usually used together with the dilation operation, and its main function is to remove small noises in the image and maintain the coherence of the crack region. The mathematical formula of the erosion operation is as follows:

[0079] E(A)=A B={z∣(B z A)

[0080] where represents the erosion operation, A is the original image, B is the structuring element, and B z is the translation of the structuring element on the image A. The erosion operation will remove the noises at the edges of the image and refine the crack region.

[0081] Through the combination of the dilation and erosion operations, morphological processing can enhance the coherence of the crack region and remove small noises. Finally, the crack region can be extracted from the entire stress data image. Through these morphological operations, the crack edges are clearer, facilitating subsequent crack analysis and propagation prediction.

[0082] Through the above edge detection and morphological processing steps, the data processing unit can extract the regions on the tire surface where cracks exist. The extraction of these crack regions is crucial for subsequent prediction of the crack propagation direction. On this basis, by calculating the stress gradient direction of the crack region and applying the Hough transform to extract the main trend line, the crack propagation trend can be further predicted. Finally, the extraction results of the crack regions and the prediction results of the propagation direction will be displayed on the display device, providing a timely basis for maintenance decisions for the operator.

[0083] 2.4 Stress gradient calculation

[0084] After extracting the crack region, the data processing unit uses the Sobel operator to calculate the stress gradients in the X and Y directions of the crack region. This process can obtain the stress change direction within the crack region, thereby helping to judge the crack propagation direction. The stress gradient direction reflects the degree of stress concentration on the material surface and is a key factor in crack propagation.

[0085] 2.4.1 Calculate the force gradients in the X and Y directions

[0086] Step 1): Sobel operator

[0087] The Sobel operator is used to calculate the gradients in various directions in an image. By performing a convolution operation on the image, the Sobel operator can calculate the gradient values of each pixel point in the horizontal direction (X direction) and the vertical direction (Y direction).

[0088] The Sobel operator calculates the gradient through the following convolution kernels:

[0089] Sobel convolution kernel in the X direction (detecting the gradient in the horizontal direction):

[0090]

[0091] Sobel convolution kernel in the Y direction (detecting the gradient in the vertical direction):

[0092]

[0093] where G x and G y represent the gradient calculation kernels in the horizontal and vertical directions respectively.

[0094] Step 2): Calculate the gradient value of each pixel point

[0095] By convolving the force image with the Sobel convolution kernel, the force gradients of each pixel point in the horizontal direction (X direction) and the vertical direction (Y direction) in the image are obtained. For each pixel point (x, y), its force gradients in the X and Y directions are calculated as follows:

[0096] Force gradient in the X direction:

[0097]

[0098] where I(x+i,y+j) represents the pixel value at the position ( x+i,y+j ) in the image, and K x (i, j) is an element of the Sobel X - direction convolution kernel.

[0099] Force gradient in the Y direction:

[0100]

[0101] where I( x+i,y+j ) represents the pixel value at the position ( x+i,y+j ) in the image, and K y(i, j) is an element of the Sobel Y-direction convolution kernel.

[0102] Step 3): Calculate the gradient magnitude and direction

[0103] After calculating the gradients in the X and Y directions, the gradient magnitude and direction of each pixel can be calculated next.

[0104] The gradient magnitude G(x, y) reflects the intensity of the force change, and the calculation formula is as follows:

[0105]

[0106] The gradient direction θ(x, y) reflects the direction of the force change, and the calculation formula is as follows:

[0107]

[0108] Among them, atan2(G y (x, y) is the arctangent function for calculating the gradient direction, and the returned angle is usually in radians, representing the direction of the force change.

[0109] 2.4.2 Crack propagation direction prediction using the force gradient

[0110] Step 1): Relationship between the gradient direction and the crack propagation direction

[0111] Cracks usually propagate along the direction where the force change on the material surface is the most intense. Therefore, by analyzing the force gradient direction, the crack propagation trend can be predicted. The force gradient direction (i.e., the calculated gradient direction) provides the main direction in which the crack may propagate.

[0112] Step 2): Combine the Hough transform to predict the crack propagation direction

[0113] The Hough transform is used to extract the main straight line of the crack from the edge image of the crack area and compare it with the force gradient direction. When the force gradient direction of the crack is close to the angle of the main crack direction obtained by the Hough transform, it can be predicted that the crack will propagate along this direction.

[0114] Step 3): Calculate the included angle and judge the propagation direction

[0115] To more accurately predict the crack propagation direction, the included angle between the gradient direction and the main crack direction extracted by the Hough transform can be calculated. If the included angle is less than a predetermined threshold (e.g., 10°), it is considered that the crack will propagate along this direction. If the included angle is large, it means that the crack propagation direction deviates significantly from the main direction, and the crack propagation trend may need to be re-analyzed.

[0116] The calculation formula for the included angle is as follows:

[0117] angle(x,y)=∣θ(x,y - θ line ∣

[0118] where θline is the angle of the main crack direction extracted by the Hough transform, and θ(x,y) is the angle of the stress gradient direction.

[0119] 2.5 Hough Transform and Prediction of Extended Direction

[0120] Apply the Hough transform to the crack area to extract the straight line of the main crack direction. By comparing the stress gradient direction in the crack area with the straight line direction extracted by the Hough transform, predict the possible extended direction of the crack. If the angle between the gradient direction and the main direction is close, it is speculated that the crack will extend along this direction.

[0121] 2.6 Prediction and Warning of Crack Propagation

[0122] According to the prediction result of the crack propagation direction, the system will make a further judgment. If it is predicted that the crack extends to the dangerous area, a warning signal will be issued. The warning information will be notified to the operator through the display device to take necessary measures to prevent further crack propagation or fracture.

[0123] 2.6.1 Judge Whether the Crack Extends to the Dangerous Area

[0124] Step 1): Define the dangerous area

[0125] First of all, it is necessary to define the range of the "dangerous area". The dangerous area usually refers to the area where crack propagation may cause tire failure or rupture. This area can be determined according to the tire design and usage conditions, such as the tire load-bearing pressure area, heat stress concentration area, etc.

[0126] Assume that the dangerous area is a circular area with a radius of R on the tire surface danger and the center position is the central axis of the tire. The definition of the dangerous area can be expressed by the following formula:

[0127] R danger = the radius of the dangerous area (measured by distance, with the tire center as the reference point).

[0128] Step 2): Prediction of Crack Propagation

[0129] In the process of predicting crack propagation, through the main crack direction extracted by the stress gradient and the Hough transform, the system predicts the extended direction and possible extended distance of the crack. The extended direction and extended distance are obtained by combining the predicted crack direction with the stress gradient.

[0130] Assume that the predicted crack propagation direction is θ expand, and the possible crack propagation distance is d expand (this value can be obtained through simulation calculations or empirical data), then the coordinates (x end , y end ) of the crack tip can be calculated according to the following formula:

[0131]

[0132] where, (x start , y start ) is the current starting position of the crack, d expand is the crack propagation distance, and θ expand is the crack propagation direction.

[0133] Step 1.3: Determine whether the crack enters the dangerous area

[0134] To determine whether the crack will enter the dangerous area, it is necessary to calculate whether the predicted position (x end , y end ) of the crack tip is within the dangerous area. If (x end , y end ) is within the dangerous area, that is , it is considered that the crack will extend to the dangerous area. This condition can be judged by the following formula:

[0135] If ,

[0136] then an alarm will be issued to notify the operator.

[0137] 2.6.2 Issuance of early warning signals

[0138] Step 1): Early warning mechanism

[0139] When the predicted position of the crack enters the dangerous area, the system will trigger the early warning mechanism. The early warning mechanism will notify the operator through the display device to remind them to take corresponding measures, such as stopping using the tire, performing maintenance, or replacing the tire.

[0140] Step 2): Output of early warning signals

[0141] The early warning signal can be output in the following ways:

[0142] Acoustic and optical alarm: The system emits a sound alarm and a flashing light signal;

[0143] Display device warning: An early warning notice pops up on the interface of the display device, and the position of the crack and the predicted propagation direction are highlighted;

[0144] Automatic Report: The system automatically generates a report and sends it to relevant staff via network or wireless communication, or conducts remote monitoring through a cloud platform.

[0145] Specific warning information may include:

[0146] The current location and propagation direction of the crack;

[0147] The final location where the crack may propagate;

[0148] Whether it has entered a dangerous area and give handling suggestions (such as whether to stop using, repair or replace the tire).

[0149] Step 3): Automatic Response (Optional)

[0150] To improve the intelligence level of the system, an automatic response function can be set. For example, when the crack enters a dangerous area, the system not only issues a warning signal but also can automatically trigger certain operations, such as shutting down the machine, locking the system or starting an emergency handling procedure.

[0151] 2.6.3 Comprehensive Evaluation and Follow-up Processing

[0152] Step 1): Evaluation of Crack Propagation Trend

[0153] In addition to simply judging whether the crack has entered a dangerous area, the system can also comprehensively evaluate the crack propagation trend through historical data and real-time data. For example, combining factors such as the crack propagation speed, stress concentration degree and material fatigue, evaluate the possibility of crack propagation in the next few days or weeks. For situations where the crack propagation speed is relatively fast or may cause major safety hazards, the system can notify the operator in advance to take preventive measures.

[0154] Step 2): Regular Monitoring and Feedback

[0155] The system can also regularly conduct crack detection and update the crack propagation prediction based on new detection data to form a closed-loop monitoring. After each detection, the system will automatically evaluate the current state of the crack and decide whether to issue a new warning signal.

[0156] Furthermore, the present invention also provides a computer-readable storage medium, including one or more programs for execution by one or more processors of an electronic device, and the one or more programs include instructions for executing the method described in this specific embodiment.

[0157] Note that computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for information storage. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0158] The present invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

[0159] The above is a description of the embodiments of the present invention. Through the above description of the disclosed embodiments, those skilled in the art can implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather should be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for detecting fine cracks on a tire surface and predicting the direction of crack propagation based on tactile sensation, characterized in that: The method comprises the following steps: 1) Using a three-dimensional translation stage to control the three-axis force sensor to slowly move downward along the Z-axis direction and collect the force value in the Z-direction in real time. When the force value in the Z-direction reaches a preset threshold, the displacement is stopped to establish a tire surface reference plane; 2) On the reference plane, control the three-dimensional translation stage to scan line by line along the X and Y directions, collect the friction force data of the three-axis force sensor in the X and Y directions and the normal pressure data in the Z direction in real time, and obtain the force data of the tire surface; 3) performing image processing on the force data, and extracting the crack area on the tire surface by edge detection and morphological processing; 4) Calculating the gradient information of the force data in the X and Y directions of the crack region to obtain the force gradient direction of the crack region; 5) Apply Hough transform to the crack area to extract the main direction of the crack, and compare it with the force gradient direction to predict the crack extension direction θ expand ; By calculating the angle between the gradient direction and the main direction of the crack extracted by Hough transform; if the angle is less than the predetermined threshold, it is considered that the crack will expand in this direction; if the angle is larger, it means that the direction of crack expansion has a large deviation from the main direction, and the crack expansion trend is re-analyzed; the angle calculation formula is as follows: angle(x,y)=∣θ(x,y)−θ line ∣, Among them, θ line is the angle of the main crack direction extracted by Hough transform, and θ(x,y) is the angle of the force gradient direction; 6) According to the predicted crack propagation direction, judge the future crack expansion trend and issue early warning; Assume that the predicted crack growth direction is θ expand , and the possible extension distance of the crack is d expand , d expand The coordinates of the crack end (x end ,y end ) is calculated according to the following formula: ， Among them, (x start ,y start ) is the current starting position of the crack, d expand is the distance the crack extends, θ expand is the direction of crack propagation; Assume that the danger zone is the tire surface with a radius of R danger The circular area with the center position as the tire centerline; calculate the predicted position of the crack end (x end ,y end ) is in the danger zone; if (x end ,y end ) in the danger zone, i.e. , it is considered that the crack will extend to the dangerous area.

2. The method according to claim 1, characterized in that The reference plane is determined by controlling the precise movement of the three-dimensional translation stage along the Z-axis direction and collecting the force data in the Z-direction in real time, and when the force data reaches a set threshold.

3. The method according to claim 1, characterized in that The edge detection adopts the Canny edge detection algorithm, and the morphological processing includes dilation and erosion operations to eliminate noise and enhance the coherence of the crack area.

4. The method according to claim 1, characterized in that: The Sobel operator is used to calculate the gradient of the force data, and the force gradient diagram is obtained by calculating the force changes in the X direction and the Y direction.

5. A system for detecting small cracks on the tire surface and predicting the direction of crack propagation based on tactile sensation, characterized in that: The system implements the method according to any one of claims 1 to 4, including: Three-dimensional translation stage, used to control the precise movement of the three-axis force sensor along the X, Y, and Z directions; The three-axis force sensor can simultaneously detect the friction force in the X and Y directions and the normal pressure in the Z direction, and output the force signal; the integrated acquisition control platform is used to control the movement of the three-dimensional translation stage, collect the force data of the three-axis force sensor, and process the data synchronously; A data processing unit, used to receive the force data of the three-axis force sensor, perform image processing, gradient calculation and Hough transform processing, so as to extract the crack area and predict the crack extension direction; The display device is used to display the crack area on the tire surface, the predicted crack propagation direction and the detection result in real time.

6. The system according to claim 5, characterized in that The three-dimensional displacement stage comprises a stepping motor, a precision guide rail and a transmission lead screw, and can provide precise displacement control.

7. The system according to claim 5, characterized in that The data processing unit includes a central processing unit and a programmable logic device, wherein the central processing unit is responsible for system control and data storage, and the programmable logic device is responsible for efficient data collection and processing.

8. The system according to claim 5, characterized in that The display device comprises a display, an image processing unit and an alarm module, and is used for displaying crack detection results and issuing an early warning signal according to crack extension prediction results.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, step 3) to step 6) of the method according to any one of claims 1 to 4 are implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, step 3) to step 6) of the method according to any one of claims 1 to 4 are implemented.

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