A tire automatic studding device and method for intelligently adjusting the studding depth

Abstract

The present application relates to the technical field of industrial data regulation, and specifically relates to a tire automatic nail inserting device and method for intelligently adjusting nail insertion depth, comprising: acquiring angle data of each driving shaft on a multi-axis nail inserting driving member, pressure data, position data and depth data of a nail head; obtaining a deviation index of the nail inserting device according to differences in change trends and time differences between the angle data of different driving shafts; obtaining an error transmission index of the nail inserting device according to deviation conditions between the angle data and preset expected angles, deviation conditions between the position data and preset expected positions; obtaining a non-coordination index of the nail inserting device according to a cooperative difference condition between a change trend of the pressure data and a change trend of the depth data; and determining an error compensation coefficient according to the deviation index, the error transmission index and the non-coordination index, and adjusting the angle data of the nail head. The present application effectively suppresses the accumulation of dynamic errors caused by multi-axis cooperative control.

Description

Technical Field

[0001] This invention relates to the field of industrial data control technology, specifically to an automatic tire nailing device and method for intelligently adjusting the nail insertion depth. Background Technology

[0002] With the rapid development of the automotive industry and intelligent manufacturing technology, tire safety performance and intelligent production levels have become important evaluation indicators for vehicle manufacturing. Tire studding technology, as a key process for improving tire grip on low-traction surfaces such as ice, snow, and mud, is gradually evolving from traditional manual or semi-automatic studding methods towards full automation and intelligence. Especially in the production of winter studded tires, precise control of the stud depth directly affects the tire's friction performance, wear resistance, and noise levels during use, making it one of the key parameters in the tire studding process.

[0003] In existing automated tire studding production lines, multi-axis linkage mechanical structures (such as robotic arms) are typically used as the main actuators. These actuators achieve automatic positioning and insertion of the studs through lateral, longitudinal, and vertical movements. Furthermore, to accommodate different tire tread curvatures and tread angles, the mechanical head of the studding device usually has a certain degree of angle adjustment freedom to ensure that the studs are inserted along the normal direction of the tire tread.

[0004] However, in the process of multi-axis collaborative control and angle adjustment, there are complex dynamic coupling relationships between mechanical structures. Small pose deviations and angle errors are gradually amplified through the mechanical transmission chain, resulting in unstable changes in the nail insertion depth. Specifically, when the robotic arm performs nail insertion path planning, its end position and posture are formed by the superposition of multi-axis motions. Any single-axis servo response delay, mechanical backlash, or angle compensation error will manifest as a slight offset in the end nail insertion direction after spatial superposition. When the nail head performs angle adjustment or pressure compensation, if the angle servo and displacement execution are not completely synchronized, the nail's incident angle will change slightly, causing local nail insertion depth deviation or nail tilting. Existing methods, which directly correct through depth sensors or pressure feedback, ignore the source of mechanical structure coupling error (i.e., the angle error of multi-axis collaborative control) and only perform depth compensation at the result level, making it difficult to eliminate the fundamental deviation caused by multi-axis collaborative errors. Summary of the Invention

[0005] To address the technical problem that existing methods only compensate for depth in case of errors, making it difficult to eliminate the actual errors caused by angular errors resulting from multi-axis collaborative control, the present invention aims to provide an intelligent tire stud-inserting automatic device and method for adjusting the stud insertion depth. The specific technical solution adopted is as follows:

[0006] In a first aspect, the present invention provides an automatic tire stud insertion method with intelligent adjustment of stud depth, comprising:

[0007] Under the current pin insertion cycle, acquire the angle data of each drive axis on the multi-axis pin insertion drive component, as well as the pressure data, position data, and depth data of the pin insertion head;

[0008] Based on the differences in the changing trends of angle data between different drive shafts and the time differences in the response time of angle data fluctuations, the deviation index of the pin-mounting device is obtained.

[0009] The error propagation index of the nailing device is obtained based on the deviation between the angle data and the preset desired angle, and the deviation between the position data and the preset desired position.

[0010] Based on the coordination difference between the changing trends of pressure data and depth data, the non-coordination index of the nail-setting device is obtained; based on the deviation index, error transmission index, and non-coordination index, the error compensation coefficient is determined, and the angle data of the nail head is adjusted.

[0011] Preferably, the step of obtaining the deviation index of the pin-setting device based on the differences in the changing trends of the angle data of different drive shafts and the time differences in the response time of the angle data fluctuations specifically includes:

[0012] The data deviation factor is obtained based on the difference distribution of the angle data change trend between each drive shaft and other drive shafts at the same time.

[0013] Obtain the inflection point data of all angle data for each drive shaft, obtain the time interval between the corresponding inflection point data of each pair of drive shafts in chronological order, and obtain the average of all time intervals as the time deviation factor.

[0014] The product of the data deviation factor and the time deviation factor is determined as the deviation index of the pinning device.

[0015] Preferably, the step of obtaining the data deviation factor based on the difference distribution of angle data change trends between each drive shaft and other drive shafts at the same time specifically includes:

[0016] For any drive axis, obtain the slope value between the angle data at each time step and the angle data at adjacent time steps;

[0017] The difference in slope values ​​between any two drive shafts at the same time is averaged to obtain the angle deviation value between any two drive shafts. The mean of the angle deviation values ​​corresponding to all drive shafts is then used as the data deviation factor.

[0018] Preferably, the step of obtaining the error propagation index of the setting device based on the deviation between the angle data and the preset desired angle, and the deviation between the position data and the preset desired position, specifically includes:

[0019] Based on the difference between the angle data of each drive shaft at each moment and the preset desired angle, the angle error data of each drive shaft at each moment is determined; based on the distance between the position data of the pin head at each moment and the preset desired position, the position error data of the pin head at each moment is determined.

[0020] The first feature factor is obtained based on the numerical difference between the angular error data of each drive shaft and the position error data of the insert head;

[0021] The second feature factor is obtained based on the difference in the changing trends between the angular error data of each drive shaft and the position error data of the insert head.

[0022] The error propagation index of the pin-mounting device is determined based on the product of the first and second characteristic factors corresponding to each drive shaft.

[0023] Preferably, obtaining the first feature factor based on the numerical difference between the angular error data of each drive shaft and the position error data of the insert head specifically includes:

[0024] The mean square error between all angular error data of each drive shaft and all position error data of the pin head is negatively correlated to obtain the first characteristic factor corresponding to each drive shaft.

[0025] Preferably, the step of obtaining the second feature factor based on the difference in the changing trends between the angular error data of each drive shaft and the position error data of the insert head specifically includes:

[0026] Obtain the slope value between the angle error data of each drive shaft at each moment and the angle error data at adjacent moments; obtain the slope value between the position error data of the pin head at each moment and the position error data at adjacent moments.

[0027] Based on the difference in slope values ​​between each drive shaft and the insert head at the same time, the second characteristic factor corresponding to each drive shaft is obtained.

[0028] Preferably, the step of obtaining the non-cooperation index of the pin-setting device based on the cooperational differences between the changing trends of pressure data and depth data specifically includes:

[0029] Obtain the first fluctuation coefficient of all pressure data and the second fluctuation coefficient of all depth data, and determine the absolute value of the difference between the first fluctuation coefficient and the second fluctuation coefficient as the first difference factor.

[0030] Obtain the first slope value of the pressure data at each time point and the second slope value of the depth data at each time point. Calculate the mean of the absolute values ​​of the differences between the first slope value and the second slope value at the same time point to obtain the second difference factor.

[0031] The product of the first difference factor and the second difference factor is used as the non-cooperation index of the studded device.

[0032] Preferably, the step of determining the error compensation coefficient based on the deviation index, error propagation index, and non-cooperation index, and adjusting the angle data of the nail head, specifically includes:

[0033] The product of the deviation index, error propagation index, and non-cooperation index is normalized to obtain the error compensation coefficient. The error compensation coefficient is then used to adjust the preset desired angle of the nail head.

[0034] Preferably, adjusting the preset desired angle of the nail head using an error compensation coefficient specifically includes:

[0035] The angle difference between the angle data of the nail head at the last moment and the expected angle is obtained. The product of the angle difference and the error compensation coefficient is determined as the angle correction amount. The sum of the angle correction amount and the expected angle is the adjusted angle data.

[0036] Secondly, the present invention provides an automatic tire stud insertion device for intelligently adjusting the stud insertion depth, comprising a processor and a memory, wherein the processor is used to process instructions stored in the memory to implement a data processing process for an automatic tire stud insertion method for intelligently adjusting the stud insertion depth.

[0037] The embodiments of the present invention have at least the following beneficial effects:

[0038] This invention first requires the simultaneous collection of multi-dimensional data during the tire automatic tire studding device's studding operation, providing a foundation for subsequent error analysis and compensation. Then, it analyzes the differences in the angular change trends of different drive shafts and the differences in the response time of different drive shafts to key actions. By combining the results of these two aspects of feature analysis, it is possible to fully understand the multi-axis deviation results caused by the synergistic effects of motion trend differences and timing differences, and determine the overall deviation situation. Furthermore, it quantifies the amplification effect and correlation strength of the drive shaft angular deviation transmitted to the studding head position through the mechanical transmission chain, measures the numerical correlation between angular deviation and positional deviation, and measures the synchronicity of their changing trends, ultimately integrating these two types of correlation features to reflect the essence of error transmission. Finally, it comprehensively evaluates the coupling relationship from two dimensions: fluctuation amplitude and rate of change. By combining the results of the three aspects of feature analysis, it accurately assesses the degree of error compensation, which can eliminate the actual error caused by angular errors due to multi-axis collaborative control, effectively suppress the accumulation of dynamic errors and the stud depth deviation caused by multi-axis coupling or force fluctuations, and improve the overall stability and production efficiency of the tire automatic tire studding device. Attached Figure Description

[0039] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of the steps of an automatic tire nailing method with intelligent adjustment of nail insertion depth provided by the present invention;

[0041] Figure 2 This is a flowchart of the steps for obtaining the deviation index of the pin-setting device provided by the present invention;

[0042] Figure 3 This is a flowchart of the steps for obtaining the error propagation index of the pin-setting device provided by the present invention. Detailed Implementation

[0043] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an automatic tire stud-inserting device and method for intelligently adjusting stud insertion depth according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0045] The following description, in conjunction with the accompanying drawings, details the specific solution of the automatic tire nailing device and method for intelligently adjusting the nail insertion depth provided by the present invention.

[0046] The specific implementation scenario of this invention is as follows: This solution is applicable to the automotive tire manufacturing industry, especially to automated stud-setting production lines for winter anti-skid tires, off-road tires, and special engineering tires. These types of tires typically require precise embedding of metal anti-skid studs at specific locations on the tread to improve their grip and braking stability on icy and muddy roads. In intelligent manufacturing workshops, production lines are generally equipped with multi-axis collaborative mechanical stud-setting systems that automatically complete the clamping, rotation, positioning, and stud-setting operations of tires through three-dimensional path planning. The stud-setting mechanism (e.g., the stud head) installed at the end of the multi-axis stud-setting drive component has the ability to move laterally and vertically and adjust its attitude angle, and can dynamically match the stud insertion direction according to the tread curvature. It should be understood that there are many automated stud-setting devices in the prior art; this embodiment describes an automated stud-setting device where the stud-setting component is composed of multiple drive shafts. The drive shaft can be understood as the part with the ability to adjust the posture angle during the nail insertion process, so as to achieve precise control of the nail insertion angle by driving multiple drive shafts together. It should also be noted that the nail insertion head can be regarded as a special drive shaft, that is, the nail insertion head is located at the end of the entire multi-axis nail insertion drive component, and is the structure that directly performs the nail insertion operation.

[0047] Please see Figure 1 The diagram illustrates a flowchart of an automatic tire stud insertion method with intelligent adjustment of stud depth according to an embodiment of the present invention. The method includes the following steps:

[0048] Step S100: In the current nail insertion cycle, acquire the angle data of each drive axis on the multi-axis nail insertion drive component, the pressure data, position data, and depth data of the nail head.

[0049] First, when the automatic tire stud insertion device performs the stud insertion operation, it is necessary to simultaneously collect multi-dimensional data using multiple types of high-precision equipment to construct a complete data chain covering multi-axis drive, end-effector pose, stud insertion interaction, and angle execution, providing a foundation for subsequent error analysis and compensation. The specific data acquisition logic, involved equipment, and parameters are as follows:

[0050] The first step is to acquire the real-time angle changes of each drive shaft during the nail insertion cycle, that is, to acquire the angle data at each moment within the nail insertion cycle. Here, a nail insertion cycle refers to a complete nail insertion action, and the process may include positioning → contacting the tire tread → nail insertion → resetting. A complete nail insertion cycle can be directly acquired by the motion controller of the automatic nail insertion device.

[0051] Specifically, the automatic nail-setting device includes a multi-axis nail-setting drive component composed of multiple drive shafts, comprising multiple drive shafts (such as robotic arm joint shafts) and a nail-setting head. The nail-setting head is located at the end of this component. Its core function is to drive the nail-setting head to move by rotating the drive shafts, so that it reaches a preset nail-setting position and angle, thereby completing the nail-setting operation. In this embodiment, the nail-setting head at the end of the component can be regarded as a special drive shaft.

[0052] The encoder of each drive shaft acquires the actual rotation angle of the drive shaft in real time. Each encoder is installed on the servo motor of each drive shaft and has high resolution (usually micrometer or arcsecond level) to capture the rotation angle and speed of the drive shaft in real time.

[0053] In other embodiments, to simplify the data calculation process, angle data can be collected only for drive shafts whose angles change during the motion, while drive shafts whose angles do not change do not need to have their corresponding angle data collected.

[0054] The second step is to obtain the real-time spatial position of the nail head during the nail insertion process, that is, to obtain the spatial position coordinates of the nail head at each moment during the nail insertion cycle as position data.

[0055] Specifically, data acquisition is achieved through a high-precision inertial measurement unit (IMU) and a 3D position sensor. More specifically, the IMU is installed at the connection between the drive shaft and the insert head, and can measure the insert head's acceleration, angular velocity, and attitude angles (such as roll, pitch, and yaw angles) in real time, reflecting the dynamic attitude changes of the insert head. The 3D position sensor works in conjunction with the IMU, using laser positioning, visual recognition, and other methods to accurately capture the 3D coordinates (X, Y, and Z axis coordinates) of the insert head in the multi-axis insert drive component coordinate system, recording the instantaneous changes in the insert head's position.

[0056] The third step involves acquiring the force feedback and stroke changes when the nail head contacts the tire and performs the nail insertion action, quantifying the force fluctuations and depth deviations during the nail insertion process. In other words, it involves acquiring the pressure and depth data of the nail head at each moment within the nail insertion cycle.

[0057] Specifically, a displacement sensor installed on the nail-pushing mechanism of the nail insert head measures the displacement data of the nail-pushing rod as the nail insert depth data. In other embodiments, the implementer can choose other suitable data acquisition methods according to the specific implementation scenario to monitor the nail insertion depth of the nail insert head. A pressure sensor embedded in the nail insert head contact end or the nail-pushing mechanism detects the normal pressure when the nail insert head contacts the tire, providing pressure data for the nail insert head. The pressure sensor embedded in the nail insert head collects the normal pressure of the nail insert head on the tire tread in real time from the moment the nail insert head contacts the tire tread until the nail insert head leaves the tire tread after insertion, obtaining pressure data at each moment of the current nail insertion cycle.

[0058] Finally, all devices (encoders, IMUs, sensors, servo mechanisms) are synchronized via the clock mechanism of the central control module to ensure that data collected at the same time corresponds to the same pin insertion state, avoiding analysis errors caused by time differences. Data acquisition uses a fixed short-term observation window of a single pin insertion operation cycle (i.e., the pin insertion cycle) to ensure that data covers the complete process of a single pin insertion. Implementers can perform data acquisition operations according to specific implementation scenarios. Invalid data (such as outliers caused by sensor failures and idle data before pin insertion) are filtered out to provide a well-organized data foundation for subsequent multi-axis collaborative deviation analysis and error propagation analysis.

[0059] Step S200: Based on the differences in the changing trends of the angle data of different drive shafts and the time differences in the response time of the angle data fluctuations, the deviation index of the nailing device is obtained.

[0060] The core of the deviation index is to quantify the consistency deviation of multi-axis coordinated motion. Its analysis process focuses on measuring the differences in motion trends between axes and the differences in response timing between axes.

[0061] Firstly, the difference in the angular change trends of different drive axes reflects the motion state of multi-axis collaborative deviation. In automated tire studding, multi-axis drive components need to move synchronously to ensure accurate positioning of the stud head. If the rate of angular change of one axis differs significantly from that of other axes, it will cause a shift in the end-effector's pose, resulting in a deviation in the stud insertion angle. Comparing the angular change trends of different axes at the same time point allows for an objective quantification of the overall difference in motion trends between axes, providing a basis for the motion consistency dimension of subsequent deviation indices.

[0062] Secondly, the difference in response time of different drive axes to key actions reflects the temporal state of multi-axis coordination deviation. The inflection points of the angle data corresponding to key actions (start, acceleration, deceleration, and stop) during the pin insertion process should ideally be reached by all axes at the same time. If the time difference between the inflection points of the axes is large, it will lead to asynchronous multi-axis actions, which, after spatial superposition, amplify the end-position offset (such as pin head tilt). Analyzing the average response delay during the key action phase provides a basis for the temporal consistency dimension of the subsequent deviation index.

[0063] Finally, by combining the results of these two characteristic analyses, we can fully understand the multi-axis deviation results caused by the combined effects of differences in motion trends and temporal differences, and determine the overall deviation situation. For example... Figure 2 As shown, the method for obtaining the deviation index of the pin fitting device can be implemented by steps S201 to S203.

[0064] Step S201: Based on the difference distribution of angle data change trends between each drive shaft and other drive shafts at the same time, obtain the data deviation factor.

[0065] Specifically, in this embodiment, we will first take any one drive shaft as an example. The first step is to obtain the slope value between the angle data at each moment and the angle data at adjacent moments for any one drive shaft.

[0066] As a concrete example, we can first perform curve fitting on the angle data at all times, and obtain the rate of change of the angle data at each time on the fitted curve. That is, we can calculate the slope value at each time based on the fitted curve. The method for calculating the slope of the data points on the curve is a well-known technique, and will not be elaborated on here.

[0067] In other embodiments, the difference between the angle data at each moment and the angle data at the adjacent previous moment can be calculated, and the ratio of this difference to the time interval between two adjacent moments can be used as the corresponding slope value. This slope value characterizes the rate at which the angle data changes over time.

[0068] The second step is to average the difference in slope values ​​between two drive shafts at the same time to obtain the angle deviation value between two drive shafts, and then take the mean of the angle deviation values ​​of all drive shafts as the data deviation factor.

[0069] Specifically, for each drive axis, there is a slope value between every two adjacent time points, or a slope value for each time point. For any two drive axes, the absolute value of the difference between the slope values ​​at the same time point is calculated. This yields multiple absolute values ​​of the difference between the slope values ​​at the same time point. The mean of all these absolute values ​​is then calculated as the angle deviation value between the two drive axes. The angle deviation value characterizes the degree of difference in the angle change trend between the two drive axes at the same time point. The data deviation factor characterizes the average difference in the angle change trend among all drive axes.

[0070] Step S202: Obtain the inflection point data of all angle data for each drive shaft, obtain the time interval between the corresponding inflection point data of each pair of drive shafts in chronological order, and obtain the average of all time intervals as the time deviation factor.

[0071] Specifically, the inflection point data of the angle data of each drive shaft at all times can be obtained using the second-order difference method. This is a well-known technique and will not be elaborated upon here. In other embodiments, curve fitting can be performed on the angle data of each drive shaft at all times, and then the inflection point data of the fitted curve corresponding to each drive shaft can be obtained using the second-order derivative method. This will not be described in detail here.

[0072] Furthermore, for each drive axis, the inflection point data are arranged in chronological order to obtain an inflection point data sequence for each drive axis. For any two drive axes, the time interval between inflection point data with the same sequence number in the two inflection point data sequences is obtained, where the sequence number refers to the positional order of the inflection point data in the corresponding inflection point data sequence. For any two drive axes, the average time interval between any two drive axes is calculated by taking the mean of all time intervals corresponding to the same sequence number. Then, the average time difference is obtained by calculating the average time interval between any two different drive axes.

[0073] The average time difference reflects the time difference of all drive axes at the same inflection point. The larger the value, the greater the difference in inflection points between drive axes, which will lead to poor synchronization of multi-axis movements. After spatial superposition, the end position offset is amplified, resulting in a larger angular deviation of the final execution structure.

[0074] Step S203: The product between the data deviation factor and the time deviation factor is determined as the deviation index of the pinning device.

[0075] The data deviation factor reflects the data deviation in the rate of change of angle data of the drive axes at the same moment, that is, it reflects the degree of inconsistency in the speed between axes. The larger the value of the data deviation factor, the more significant the difference in motion trend, and the worse the coordination of multiple axes in the local motion phase.

[0076] The time deviation factor reflects the average response delay between axes under corresponding inflection point data. The larger the value, the worse the timing synchronization and the weaker the coordination of multiple axes in the global motion cycle.

[0077] The larger the product between the data deviation factor and the time deviation factor, the stronger the superimposed effect of motion asynchrony and temporal asynchrony, the higher the overall intensity of multi-axis coordinated deviation, and the greater the risk of unstable nail insertion depth and nail tilting. The deviation index can fully characterize the inconsistency problem of multi-axis coordinated motion, providing a core basis for subsequent error analysis and compensation.

[0078] Step S300: Based on the deviation between the angle data and the preset desired angle, and the deviation between the position data and the preset desired position, the error transmission index of the pin-setting device is obtained.

[0079] The core of the error propagation index is to quantify the amplification effect and correlation strength of the drive shaft angular deviation transmitted to the nail head position through the mechanical transmission chain. It focuses on measuring the numerical correlation between angular and positional deviations, as well as the synchronicity of their changing trends, ultimately integrating these two types of correlation characteristics to reflect the essence of error propagation. This reveals how multi-axis coupling errors are gradually propagated in space and affect the stability of the nail insertion depth, thus providing a basis for subsequent dynamic compensation and adaptive correction.

[0080] On the one hand, the numerical difference between the drive shaft angle deviation and the nail head position deviation directly reflects the magnitude correlation of error transmission. In the automatic tire nailing device, the drive shaft angle deviation is the source of error (e.g., the actual value of the shaft 1 angle differs from the expected value by 2°), and the nail head position deviation is the end result of error (e.g., the actual value of the position differs from the expected value by 0.5mm due to the shaft 1 angle deviation). The numerical correlation between the two reflects the amplification or reduction effect of the mechanical transmission chain on the error. It is necessary to comprehensively analyze the correlation of the data differences in both aspects to evaluate the directness of the transmission chain's transmission of error.

[0081] On the other hand, the difference in the changing trends of the drive shaft angle deviation and the insert head position deviation reflects the timing synchronization of error transmission. Ideally, the changing trend of the angle deviation should be synchronized with the changing trend of the position deviation, that is, the two change at the same rate. If the angle deviation has started to decrease, but the position deviation is still increasing, it indicates that there is a timing lag in error transmission, and the dynamic response of the mechanical transmission chain cannot keep up with the changes in the angle deviation in real time. Therefore, by comprehensively analyzing the differences in the changing trends and evaluating the synchronization of the two changing trends, the shorter the timing delay of error transmission, the more timely the dynamic response of the transmission chain.

[0082] Finally, the error propagation index was determined by combining the results of the two characteristic analyses, reflecting the magnitude correlation and temporal synchronization. For example, Figure 3 As shown, the method for obtaining the error propagation index of the pin-setting device can be implemented by steps S301 to S305.

[0083] Step S301: Based on the difference between the angle data of each drive shaft at each moment and the preset expected angle, determine the angle error data of each drive shaft at each moment.

[0084] It should be understood that the motion controller or trajectory planning module of the automatic nailing device will generate control commands before the current nailing operation, including the desired angle and desired position of the nailing operation. The desired angle refers to the angle data that the nail head needs to meet during the nailing operation, and the desired position refers to the three-dimensional coordinate position that the nail head needs to reach during the nailing operation, which is also the position data.

[0085] Furthermore, by calculating the difference between the angle data of each drive shaft at each moment and the preset desired angle, the angle deviation of each drive shaft at each moment can be obtained. This difference is used as the angle error data of each drive shaft at each moment. The angle error data reflects the source deviation of each drive shaft.

[0086] Step S302: Based on the distance between the position data of the nail head at each moment and the preset desired position, determine the position error data of the nail head at each moment.

[0087] Based on a similar idea to angular error data, the Euclidean distance between the position data of the pin head at each moment and the desired position is calculated as the position error data at each moment. The position error data reflects the deviation of the end result of the pin-setting device.

[0088] Step S303: Obtain the first feature factor based on the numerical difference between the angle error data of each drive shaft and the position error data of the insert head.

[0089] The transmission effect cannot be determined by a single deviation (angle or position). The mean square error (MSE) of the angle error data and the position error data should be calculated as the first characteristic factor. The smaller the MSE, the closer the magnitude of the two values ​​are. The larger the value of the first characteristic factor, the higher the magnitude correlation of the error transmission, and the more direct the transmission chain is to the error.

[0090] Specifically, the mean square error between all angular error data of each drive shaft and all position error data of the insert head is negatively correlated to obtain the first feature factor corresponding to each drive shaft.

[0091] As a concrete example, for any drive shaft, the negative correlation coefficient between the mean square error of all angular error data of the drive shaft and all position error data of the insert head is... As the first characteristic factor corresponding to any one of the drive shafts. This represents an exponential function with base e. This represents the mean square error between all angular error data of the drive shaft and all positional error data of the insert head.

[0092] The first characteristic factor is the core indicator for quantifying the correlation between the amplitude of the drive shaft angular deviation and the pin head position deviation. It characterizes the directness and amplitude matching degree of the transformation from angular error to position error in the mechanical transmission chain.

[0093] The smaller the mean square error (MSE), the closer the amplitude of the angular deviation and the positional deviation are. This means that the process of converting the angular deviation into the positional deviation through the transmission chain is more direct, without significant error amplification or attenuation. The larger the value of the first characteristic factor, the higher the amplitude correlation between the angular error and the positional error. The transmission chain transmits the error without distortion. At this time, the small deviation of the drive shaft angle will be directly and synchronously reflected in the position of the insert head. It is necessary to focus on monitoring the angular error to avoid the positional deviation from exceeding the standard.

[0094] The larger the mean square error (MSE), the worse the numerical matching between the two, reflecting problems such as insufficient clearance and stiffness in the transmission chain, which hinders the direct transmission of angular error to position error. The smaller the value of the corresponding first characteristic factor, the lower the amplitude correlation between angular error and position error, indicating that there is an error buffer in the transmission chain. Although it may mask the impact of angular error in the short term, it is prone to sudden changes in correlation due to wear of transmission components in the long term, causing a sudden increase in position error. The stability of the mechanical state of the transmission chain needs to be monitored.

[0095] Step S304: Obtain the second feature factor based on the difference in the changing trends between the angle error data of each drive shaft and the position error data of the insert head.

[0096] The lag cannot be captured by numerical differences alone. It is necessary to compare the slope of the angle error data with the slope of the position error data, and determine the second characteristic factor based on the mean difference between the two. The smaller the mean difference, the more synchronized the changing trends of the two are, the shorter the time delay of error transmission, and the more timely the dynamic response of the transmission chain.

[0097] Specifically, the first step is to obtain the slope value between the angle error data of each drive shaft at each moment and the angle error data at adjacent moments, and to obtain the slope value between the position error data of the nail head at each moment and the position error data at adjacent moments.

[0098] Following the same method as for the slope value of the angle data in step S201, the slope values ​​of the angle error data and the position error data are obtained. Specifically, for any drive shaft, curve fitting can be performed on the angle error data at all times. The rate of change of the angle error data at each time moment is obtained from the fitted curve, that is, the slope value of the angle error data at each time moment is calculated based on the fitted curve. The method for calculating the slope of the data points on the curve is a well-known technique and will not be elaborated further here.

[0099] In other embodiments, for any drive shaft, the difference between the angle error data at each moment and the angle error data at the adjacent previous moment can also be calculated, and the ratio of this difference to the time interval between two adjacent moments can be used as the corresponding slope value, which characterizes the rate at which the angle error data changes over time.

[0100] It should be noted that the slope value of the position error data is obtained in the same way. This slope value represents the rate at which the position error data changes over time, and will not be elaborated on further here.

[0101] The second step is to obtain the second characteristic factor for each drive shaft based on the difference in slope values ​​between each drive shaft and the insert head at the same time.

[0102] Specifically, for each drive shaft, there is either a slope value for angular error data between every two adjacent moments, or a slope value for angular error data at each moment. For the insert head, there is either a slope value for position error data between every two adjacent moments, or a slope value for position error data at each moment.

[0103] For any drive shaft and insert head, calculate the difference between the slope values ​​at the same time, and then calculate the mean of the differences at all the same time as the second characteristic factor corresponding to the arbitrary drive shaft and insert head.

[0104] As a concrete example, taking any drive shaft as an example, the method for obtaining the second feature factor corresponding to the i-th drive shaft and the pin head can be expressed as:

[0105]

[0106] in, This represents the second characteristic factor corresponding to the i-th drive shaft and the pin head. Indicates the number of slope values. This represents the x-th slope value of the angle error data for the i-th drive shaft. This represents the x-th slope value of the pinhead position data. It is a very small positive number. To avoid the denominator being 0, it is set to 0.01 in this embodiment.

[0107] slope ratio The closer the value is to 1, the higher the synchronicity between the changing trends of the angular error and the changing trends of the position error; the slope ratio The greater the difference from 1, the more significant the deviation between the trend of angular error and the trend of position error. The second characteristic factor is a quantitative index of the transient amplification effect of single-axis angular error on end position error and synchronization deviation. It is used to evaluate the degree of deviation of the rate of change of the angular error of a single drive shaft on the end position error after mechanical transmission.

[0108] This represents the average amplification effect when the angular error of a single axis is transmitted to the end-position error. A smaller value indicates that the rates of change of the angular error and the position error are basically the same, suggesting that the angular error of the drive shaft is not significantly amplified after transmission, with only a slight synchronization deviation. A larger value indicates a significant difference in the rates of change between the angular error and the position error, suggesting that the angular error of the drive shaft is amplified after mechanical transmission, and the rate of change of the end-position error is much higher than that of the angular error.

[0109] Step S305: Determine the error propagation index of the stud device based on the product of the first characteristic factor and the second characteristic factor corresponding to each drive shaft.

[0110] A larger value for the first characteristic factor indicates that the amplitude changes of the angular error and the positional error are more similar, meaning that the time synchronization of data changes is higher, and changes in the angular error can be transmitted to the end in real time. A larger value for the second characteristic factor indicates a stronger average amplification effect of the drive shaft, and the angular error is significantly amplified after mechanical transmission. Finally, a larger value for the error propagation index results in a greater error propagation capability of the entire system, leading to an increased risk of pin insertion depth deviation.

[0111] Specifically, the product between the first characteristic factor and the second characteristic factor of each drive shaft is calculated, and then the average of the product of all drive shafts is taken to obtain the error propagation index of the nailing device, which provides a key basis for subsequent analysis of the cumulative effect of nailing depth error.

[0112] The larger the value of the error propagation index, the stronger the propagation intensity of the multi-axis angle error to the nail head position error. The nail insertion process is extremely sensitive to angle errors, and it is necessary to strengthen the real-time calibration of the multi-axis angle to prevent the accumulation of position deviations from causing abnormal nail insertion depth.

[0113] The smaller the value of the error propagation index, the lower the propagation intensity of the multi-axis angle error to the pin head position error, the poor synchronization between drive shafts (there is a delay between the change of angle error and the change of end error, and the error propagation is weakened by the time difference), the weak average amplification effect (there is no significant amplification after the angle error is propagated to the end), the weak overall error propagation capability, and the more stable pin insertion depth.

[0114] Step S400: Based on the coordination difference between the changing trends of pressure data and depth data, obtain the non-coordination index of the nail-setting device; based on the deviation index, error transmission index, and non-coordination index, determine the error compensation coefficient, and adjust the angle data of the nail-setting head.

[0115] The main purpose of this step is to first quantify the dynamic synergy between the force on the nail head and the nail insertion depth, and then integrate multi-dimensional error features to generate an appropriate angle adjustment strategy.

[0116] Firstly, the discrepancy between the pressure and depth data of the nail insertion head directly reflects the dynamic stability of the nail insertion process. In tire nailing, pressure and depth are coupled. Ideally, pressure increases with depth, and the amplitude and rate of change of both should be highly consistent. If pressure fluctuates drastically but depth remains constant, or depth changes rapidly but pressure shows no response, it indicates an abnormality in the nail insertion process (such as uneven tread hardness or nail jamming), which can easily lead to deviations in insertion depth. Therefore, it is necessary to comprehensively evaluate the coupling relationship from both the amplitude and rate of change dimensions.

[0117] The first step is to obtain the non-cooperation index of the nailing device based on the coordination difference between the changing trends of pressure data and depth data.

[0118] Specifically, the first fluctuation coefficient of all pressure data and the second fluctuation coefficient of all depth data are obtained, and the absolute value of the difference between the first fluctuation coefficient and the second fluctuation coefficient is determined as the first difference factor; the first slope value of the pressure data at each time moment and the second slope value of the depth data at each time moment are obtained, and the average value of the absolute value of the difference between the first slope value and the second slope value at the same time moment is calculated to obtain the second difference factor; the product between the first difference factor and the second difference factor is used as the non-cooperation index of the stud device.

[0119] As a concrete example, the method for obtaining the non-cooperation index of a studded device can be expressed by the formula:

[0120]

[0121] in, Indicates the non-cooperation index of the stud device. This represents the standard deviation of all pressure data. This represents the standard deviation of all depth data. This represents the slope value of the pressure data at each moment. This represents the slope value of the depth data at each time step. express This function is used to normalize the calculation results.

[0122] As the first difference factor, it reflects the matching degree of the overall volatility amplitude. The smaller the difference in volatility coefficient, the closer the long-term volatility levels of the two are and the better the basic synergy. The second difference factor represents the mean of the absolute values ​​of the difference between the slope values ​​corresponding to pressure and depth. It reflects the synchronicity of the transient change rate. The smaller the mean slope difference, the more consistent the real-time change trend of the two is, and the better the dynamic coordination. The smaller the non-coordination index value is.

[0123] The smaller the non-coordination index, the smaller the difference in coordination between pressure and depth, and the stronger the dynamic stability of the pinning process; the larger the non-coordination index, the more significant the difference in coordination, and the higher the risk of pinning depth being affected by force fluctuations, providing a basis for the dynamic risk characteristics dimension of the execution layer for subsequent error compensation.

[0124] It should be noted that in this embodiment, the standard deviation is used to represent the fluctuation coefficient. The first fluctuation coefficient refers to the fluctuation coefficient corresponding to the pressure data, and the second fluctuation coefficient refers to the fluctuation coefficient corresponding to the depth data. For the same reason, the first slope value refers to the slope value corresponding to the pressure data, and the second slope value is the slope value corresponding to the depth data. It should be further noted that the methods for obtaining the first and second slope values ​​are the same as the slope value acquisition method described in step S201. The implementer can choose other suitable conventional coefficients to characterize the degree of data fluctuation, such as variance, according to the specific implementation scenario. At the same time, the implementer can choose other suitable methods for obtaining the data slope according to the specific implementation scenario, without limitation.

[0125] Secondly, the error compensation coefficient needs to integrate multi-axis coordinated deviation, error transmission intensity, and force-depth coordinated risk to achieve precise adjustment of the nail head angle. A single-dimensional error characteristic cannot fully cover the causes of nail insertion deviation; the deviation index only reflects the consistency problem of multi-axis motion, the error transmission index characterizes the transmission effect of angular error to positional error, and the non-coordinated index only measures the dynamic coupling of pressure and depth. Based on this, the degree of error compensation is accurately assessed by comprehensively analyzing the characteristics of these three aspects.

[0126] The second step is to determine the error compensation coefficient based on the deviation index, error transmission index, and non-cooperation index, and then adjust the angle data of the nail head.

[0127] Specifically, the error compensation coefficient is obtained by normalizing the product of the deviation index, the error propagation index, and the non-cooperation index. As a concrete example, the error compensation coefficient... The calculation process can be represented as: ,in The deviation index The error propagation index, It is a non-coordinated index. express This function is used to normalize the calculation results. The larger the product result, the larger the corresponding error compensation coefficient, indicating that the error has a significant impact in the current nail insertion cycle, and the angle adjustment needs to be strengthened through the error compensation coefficient; conversely, the adjustment is weakened.

[0128] Finally, the preset desired angle of the nail head is adjusted using the error compensation coefficient.

[0129] Specifically, the angle difference between the desired angle and the angle data at the last moment of the nail head is obtained, and the product of the angle difference and the error compensation coefficient is determined as the angle correction amount. The sum of the angle correction amount and the desired angle is the adjusted angle data.

[0130] As a concrete example, angle correction amount The calculation formula can be expressed as: ;in, This is the error compensation coefficient. For the preset desired angle, This refers to the actual angle data, which is the angle data ultimately reached in the current pinning cycle.

[0131] when When the angle correction is negative, the adjusted angle data = current desired angle + negative angle correction. This means the adjusted angle data is used as the new target angle. The new desired angle decreases, and after error accumulation, the actual angle data decreases, better matching the desired angle value, thus reducing the error. At this time, the angle correction amount is positive. The adjusted angle data = the current expected angle + the angle correction amount, which means that the adjusted angle data is used as the new target angle. At this time, the new expected angle increases, which increases the actual angle data and reduces the error.

[0132] In other embodiments, the drive shaft can be manually inspected periodically to prevent sudden changes in correlation caused by long-term wear of transmission components, which could lead to a sudden increase in position error. The stability of the mechanical state of the transmission chain needs to be monitored. An alert can also be set to trigger when the deviation between the angular data and the desired angle reaches the maximum difference set by the system, reminding relevant personnel to perform shutdown maintenance and other related operations.

[0133] It should be noted that the system sends the adjusted angle data to the nail head servo controller or the robotic arm end effector to perform the angle adjustment; the nail head performs the nail insertion operation according to the adjusted angle data, while simultaneously collecting new angle and depth data in real time, continuously monitoring the error of various dimensions such as angle, and realizing real-time closed-loop control to ensure that the nail head maintains the optimal incident angle in the continuous nail insertion cycle, and suppressing the nail insertion depth deviation caused by multi-axis coupling, force fluctuation or error accumulation.

[0134] This invention also provides an automatic tire stud insertion device for intelligently adjusting stud depth, including a processor and a memory. The processor is used to process instructions stored in the memory to implement a data processing process for an automatic tire stud insertion method for intelligently adjusting stud depth.

[0135] It should be noted that this device is essentially a processor device, implemented by an internal data processing process. Since the data processing process has been described in detail in the above embodiment of an automatic tire nailing method for intelligent adjustment of nail insertion depth, it will not be repeated here.

[0136] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for automatically inserting nails into tires with intelligent adjustment of nail insertion depth, characterized in that, The method includes the following steps: Under the current pin insertion cycle, acquire the angle data of each drive axis on the multi-axis pin insertion drive component, as well as the pressure data, position data, and depth data of the pin insertion head; Based on the differences in the changing trends of angle data between different drive shafts and the time differences in the response time of angle data fluctuations, the deviation index of the pin-mounting device is obtained, specifically including: Based on the difference distribution of the angle data change trend between each drive axis and other drive axes at the same time, the data deviation factor is obtained. Specifically, it includes: for any drive axis, obtaining the slope value between the angle data at each time and the angle data at adjacent times; averaging the difference of the slope values ​​between two drive axes at the same time to obtain the angle deviation value between two drive axes; and obtaining the mean of the angle deviation values ​​corresponding to all drive axes as the data deviation factor. Obtain the inflection point data of all angle data for each drive shaft, obtain the time interval between the corresponding moments of the corresponding inflection point data between every two drive shafts in chronological order, and obtain the average of all time intervals as the time deviation factor. The product of the data deviation factor and the time deviation factor is determined as the deviation index of the pinning device; Based on the deviation between the angle data and the preset desired angle, and the deviation between the position data and the preset desired position, the error propagation index of the pin-setting device is obtained, specifically including: Based on the difference between the angle data of each drive shaft at each moment and the preset desired angle, the angle error data of each drive shaft at each moment is determined; based on the distance between the position data of the nail head at each moment and the preset desired position, the position error data of the nail head at each moment is determined; a first feature factor is obtained based on the numerical difference between the angle error data of each drive shaft and the position error data of the nail head; a second feature factor is obtained based on the difference in the changing trend between the angle error data of each drive shaft and the position error data of the nail head; based on the product of the first feature factor and the second feature factor corresponding to each drive shaft, the error transmission index of the nail insertion device is determined. Based on the coordination difference between the changing trends of pressure data and depth data, the non-coordination index of the nail-setting device is obtained; based on the deviation index, error transmission index, and non-coordination index, the error compensation coefficient is determined, and the angle data of the nail head is adjusted.

2. The method for automatically inserting nails into a tire with intelligent adjustment of nail insertion depth according to claim 1, characterized in that, The first feature factor is obtained based on the numerical difference between the angular error data of each drive shaft and the position error data of the insert head, specifically including: The mean square error between all angular error data of each drive shaft and all position error data of the pin head is negatively correlated to obtain the first characteristic factor corresponding to each drive shaft.

3. The method for automatically inserting nails into a tire with intelligent adjustment of nail insertion depth according to claim 1, characterized in that, The second feature factor is obtained based on the difference in the changing trends between the angular error data of each drive shaft and the position error data of the insert head, specifically including: Obtain the slope value between the angle error data of each drive shaft at each moment and the angle error data at adjacent moments; obtain the slope value between the position error data of the pin head at each moment and the position error data at adjacent moments. Based on the difference in slope values ​​between each drive shaft and the insert head at the same time, the second characteristic factor corresponding to each drive shaft is obtained.

4. The method for automatically inserting nails into a tire with intelligent adjustment of nail insertion depth according to claim 1, characterized in that, The non-cooperation index of the pin-mounting device is obtained based on the coordination difference between the changing trends of pressure data and depth data, specifically including: Obtain the first fluctuation coefficient of all pressure data and the second fluctuation coefficient of all depth data, and determine the absolute value of the difference between the first fluctuation coefficient and the second fluctuation coefficient as the first difference factor. Obtain the first slope value of the pressure data at each time point and the second slope value of the depth data at each time point. Calculate the mean of the absolute values ​​of the differences between the first slope value and the second slope value at the same time point to obtain the second difference factor. The product of the first difference factor and the second difference factor is used as the non-cooperation index of the studded device.

5. The method for automatically inserting nails into a tire with intelligent adjustment of nail insertion depth according to claim 1, characterized in that, The step of determining the error compensation coefficient based on the deviation index, error propagation index, and non-cooperation index, and adjusting the angle data of the nail head, specifically includes: The product of the deviation index, error propagation index, and non-cooperation index is normalized to obtain the error compensation coefficient. The error compensation coefficient is then used to adjust the preset desired angle of the nail head.

6. The method for automatically inserting nails into a tire with intelligent adjustment of nail insertion depth according to claim 5, characterized in that, The method of adjusting the preset desired angle of the nail head using an error compensation coefficient specifically includes: The angle difference between the angle data of the nail head at the last moment and the expected angle is obtained. The product of the angle difference and the error compensation coefficient is determined as the angle correction amount. The sum of the angle correction amount and the expected angle is the adjusted angle data.

7. An intelligent tire stud insertion device with adjustable stud depth, characterized in that, It includes a processor and a memory, the processor being used to process instructions stored in the memory to implement the data processing process of an automatic tire studding method for intelligently adjusting the stud depth as described in any one of claims 1-6.

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