Multi-axis linkage winding quality monitoring equipment based on numerical control fiber winding

Abstract

The invention relates to the technical field of thickness quality monitoring of a winding process, in particular to multi-axis linkage winding quality monitoring equipment based on numerical control fiber winding, which comprises a memory and a processor, the method comprises the following steps: acquiring winding thickness data of a to-be-analyzed material at different positions in the axial direction at each moment; local position segments are obtained according to the difference between the winding thickness data of each position and the winding thickness data of the adjacent position at each moment; a winding abnormal value is obtained according to the difference between the winding thickness data corresponding to each local position segment and the winding thickness data corresponding to other local position segments at each moment and the difference between the winding thickness data and the winding thickness data in the normal state; and monitoring the fiber winding forming process based on the winding abnormal value of each local position segment at each moment to obtain a winding quality monitoring result. According to the invention, the precision of the thickness anomaly detection result in the monitoring process is improved, so that the analysis result of thickness quality monitoring is better.

Description

Technical Field

[0001] The present invention relates to the technical field of thickness quality monitoring of a winding process, and in particular to a multi-axis linkage winding quality monitoring device based on numerically controlled fiber winding. Background Art

[0002] Filament winding is an efficient manufacturing process commonly used to prepare composite poles and towers (such as tapered poles, communication towers, etc.). This process usually uses CNC winding equipment to control the axial movement of the winding components and the rotation of the main shaft to achieve precise superposition of fibers on the mold surface at set angles (such as ±θ, 0°, 90°, etc.). With the development of equipment performance and material systems, the manufacturing of composite poles and towers has gradually introduced a layered winding strategy for structural layers and weather-resistant layers, combined with steam internal heating and cooling demolding processes, which has significantly improved the strength, weather resistance and manufacturing efficiency of the products. In actual production, in order to ensure the quality of winding, CNC programs are usually relied upon to control process parameters such as winding angle, speed, tension, etc., and tension sensors and other devices are combined to maintain winding stability.

[0003] During the layered winding process of composite pole towers, the structural layer and weathering layer are typically wound sequentially, each assuming load-bearing and protective functions, respectively. Small fluctuations in the winding quality of the structural layer are often amplified during the subsequent winding of the weathering layer, affecting the appearance consistency and service performance of the entire pole tower. Traditional CNC winding equipment primarily relies on program settings and limited tension control methods to monitor overall material quality anomalies. This equipment lacks the ability to perceive actual thickness changes during the winding process in real time, making it difficult to promptly detect local thickness deviations within the structural layer and to pinpoint the location of winding quality issues. Summary of the Invention

[0004] In order to solve the technical problem that existing quality monitoring methods rely on degree setting means to monitor abnormalities of the entire winding material, resulting in low accuracy of thickness abnormality monitoring results, the purpose of the present invention is to provide a multi-axis linkage winding quality monitoring device based on CNC filament winding. The technical solution adopted is as follows:

[0005] A multi-axis linkage winding quality monitoring device based on CNC filament winding includes a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the following steps:

[0006] During the filament winding process, the winding thickness data of the material to be analyzed at different axial positions at each moment is obtained;

[0007] According to the difference between the winding thickness data of each position at each moment and the winding thickness data of the adjacent positions in the neighborhood of the position distribution, all axial positions of the material to be analyzed at each moment are segmented to obtain each local position segment at each moment;

[0008] According to the difference between the winding thickness data corresponding to each local position segment and other local position segments at each moment, and the difference between the winding thickness data of each local position segment at each moment and the normal state, the winding abnormal value of each local position segment at each moment is obtained;

[0009] Based on the winding abnormality value of each local position segment at each moment, the fiber winding molding process is monitored to obtain the winding quality monitoring results.

[0010] Preferably, the segmenting of all axial positions of the material to be analyzed at each moment according to the difference between the winding thickness data of each position at each moment and the winding thickness data of adjacent positions within the neighborhood of the position distribution to obtain each local position segment at each moment specifically includes:

[0011] According to the difference between the winding thickness data on one side of the axial direction and the winding thickness data on the other side of the axial direction at each position at each moment, the mutation degree of each position at each moment is obtained;

[0012] The position corresponding to the maximum point of the mutation degree at all positions at each moment is used as the segmentation point, and all positions under the axis of the material to be analyzed are segmented to obtain the segmentation of each local position at each moment.

[0013] Preferably, obtaining the mutation degree of each position at each moment according to the difference between the winding thickness data on one side and the winding thickness data on the other side of each position in the axial direction at each moment specifically includes:

[0014] At any moment, any position is recorded as a target position, and winding thickness data of the target position and a preset number of positions adjacent to the left of the target position are obtained to form a first side data sequence of the target position;

[0015] Acquire winding thickness data of a target position and a preset number of positions adjacent to the right of the target position to form a second side data sequence of the target position;

[0016] The degree of mutation of the target position at any one moment is determined based on the difference between the mean of all data in the first-side data sequence and the mean of all data in the second-side data sequence.

[0017] Preferably, obtaining the winding abnormality value of each local position segment at each moment based on the difference between the winding thickness data corresponding to each local position segment and other local position segments at each moment, and the difference between the winding thickness data of each local position segment at each moment and the normal state, specifically includes:

[0018] According to the difference distance between the winding thickness data corresponding to each local position segment and other local position segments at each moment, all local position segments at each moment are clustered to obtain the characteristic clusters and normal clusters at each moment;

[0019] According to the difference in winding thickness data between each feature cluster and the normal cluster, the winding anomaly value of each local position segment in each feature cluster at each moment is obtained.

[0020] Preferably, clustering all local position segments at each moment according to the difference distance between the winding thickness data corresponding to each local position segment and other local position segments at each moment to obtain the characteristic clusters and normal clusters at each moment specifically includes:

[0021] At any moment, the winding thickness data of all positions in each local position segment constitute a thickness data sequence of each local position segment;

[0022] The DTW distance of the thickness data series between each two different local position segments is used as the metric distance between each two local position segments, and a clustering algorithm is used to cluster all local position segments to obtain multiple clusters;

[0023] The difference between the mean of the metric distance between each two local position segments in each cluster and the mean of the metric distance between each two local position segments in each other cluster is determined as the degree of data difference between each cluster and each other cluster;

[0024] The cluster corresponding to the minimum value of the cumulative sum of the data differences between each cluster and all other clusters is obtained as the normal cluster at any moment, and all clusters other than the normal cluster are taken as the characteristic clusters at any moment.

[0025] Preferably, the method of obtaining the winding anomaly value of each local position segment within each feature cluster at each moment according to the difference between the winding thickness data contained in each feature cluster and the normal cluster specifically includes:

[0026] At any moment, the normalized result of the data difference between each feature cluster and the normal cluster is used as the winding outlier value of each local position segment within each feature cluster.

[0027] Preferably, the monitoring of the filament winding process based on the winding abnormality value of each local position segment at each moment specifically includes:

[0028] When the winding anomaly value of each local position segment at each moment is greater than the preset anomaly threshold, the position included in the local position segment has a winding anomaly condition;

[0029] When the winding anomaly value of each local position segment at each moment is less than or equal to a preset anomaly threshold, there is no winding anomaly condition at the position included in the local position segment.

[0030] Preferably, after obtaining the winding quality monitoring result, the method further includes:

[0031] Obtain the actual tension data of different axial positions of the material to be analyzed at each moment;

[0032] When a winding abnormality exists in a local position segment, the actual tension data is corrected according to the difference between the winding thickness data and the normal state data of each position in the local position segment and the winding abnormality value to obtain the adjusted tension data of each position at each moment;

[0033] The next round of fiber winding operation is performed using the adjusted tension data, and the winding abnormality value of each local position segment at each moment in the next round of operation is obtained, and the fiber winding molding process is monitored.

[0034] Preferably, the actual tension data is corrected based on the difference between the winding thickness data and the normal state data at each position in the local position segment and in combination with the winding abnormal value to obtain the adjusted tension data at each position at each moment, specifically including:

[0035] When a winding abnormality exists in a local position segment, the correction coefficient corresponding to the local position segment is obtained based on the difference between the winding thickness data contained in the characteristic cluster class where the local position segment is located and the normal cluster class at the corresponding moment, combined with the winding abnormality value of the characteristic cluster class where the local position segment is located; the product of the correction coefficient and the actual tension data of each position in the local position segment is used as the corrected tension data.

[0036] Preferably, the correction coefficient corresponding to the local position segment is obtained based on the difference between the winding thickness data contained in the characteristic cluster class where the local position segment is located and the normal cluster class at the corresponding moment, combined with the winding abnormal value of the characteristic cluster class where the local position segment is located, specifically including:

[0037] When judging whether the mean value of the winding thickness data of all positions in the characteristic cluster class where the local position segment is located is less than the mean value of the winding thickness data of all positions in the normal cluster class;

[0038] If so, the difference between the value 1 and the winding anomaly value of the characteristic cluster where the local position segment is located is used as the correction coefficient corresponding to the local position segment;

[0039] If not, the sum of the value 1 and the winding anomaly value of the characteristic cluster where the local position segment is located is used as the correction coefficient corresponding to the local position segment.

[0040] The embodiments of the present invention have at least the following beneficial effects:

[0041] The present invention first monitors the axial winding thickness data of the material in the winding molding process in real time. Then, by analyzing the difference between the winding thickness data of each position and the adjacent position, the data mutation situation of each position at each moment is analyzed, and then all positions are segmented to obtain the segmentation of each local position at each moment. Positions with similar data distribution can be divided into a segment, and then the material to be analyzed is divided into different local areas for feature detection. Further, the first aspect analyzes the data distribution difference between different local position segments at the same moment, and the second aspect analyzes the data difference between each local position segment and the normal state at each moment. Combining the difference analysis results of the two aspects, the winding anomaly value of each local position segment at each moment may be evaluated, which is manifested by the abnormal size of the thickness. By monitoring the winding thickness data of each position in real time, the present invention can be specific to the phenomenon of abnormal winding thickness at each local position, and can obtain the local position where the material to be analyzed has abnormal thickness during the winding process, thereby improving the accuracy of the thickness anomaly detection results during the monitoring process, and making the analysis results of the quality monitoring of the material to be analyzed better. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is a flowchart of the steps of a multi-axis linkage winding quality monitoring method based on CNC fiber winding provided by the present invention;

[0044] Figure 2 is a flowchart of the steps of the method for obtaining each local position segment at each moment provided by the present invention;

[0045] Figure 3 is a flowchart of the steps of the method for obtaining the winding anomaly value of each local position segment provided by the present invention;

[0046] Figure 4 It is a flowchart of the steps of the method for obtaining characteristic clusters and normal clusters provided by the present invention. DETAILED DESCRIPTION

[0047] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following, in combination with the accompanying drawings and preferred embodiments, describes in detail the specific implementation method, structure, characteristics and effects of the multi-axis linkage winding quality monitoring equipment based on CNC fiber winding proposed by the present invention.

[0048] Before introducing the specific solutions provided in the embodiments of the present application, some of the terms in the present application are explained to facilitate understanding by those skilled in the art, and are not intended to limit the use in the present application.

[0049] CNC winding machine position data: Pulse feedback from a servo motor or stepper motor controls the axial movement of the CNC winding machine and records the position of the winding machine in real time. Spindle rotation speed: The rotation speed of the conical mold core during the winding process. Winding speed: Determined by the discharge speed of the CNC winding machine; Winding angle: Calculated from the spindle speed and the carriage feed speed. No actual detection is performed, relying on program accuracy; Winding tension: The tension is set and adjusted using the tension motor, and the tension is monitored in real time using the tension sensor. Winding surface profile data: A 3D line laser displacement sensor obtains the relative distance of each winding position in real time, reflecting the thickness of the winding at each position. To obtain real-time winding data, a line laser displacement sensor is installed on a guide rail along the axial direction of the conical mold during the winding process. As the winding process progresses, it can detect changes in the surface thickness of the pole tower in real time.

[0050] In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0051] Unless defined otherwise, 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 belongs.

[0052] The following describes in detail, with reference to the accompanying drawings, a specific embodiment of a multi-axis linkage winding quality monitoring device for CNC filament winding provided by the present invention. The multi-axis linkage winding quality monitoring device for CNC filament winding includes a memory and a processor. The processor executes a computer program stored in the memory to implement the steps of a multi-axis linkage winding quality monitoring method for CNC filament winding.

[0053] More specifically, see Figure 1, which shows a flowchart of a multi-axis linkage winding quality monitoring method based on CNC filament winding provided by one embodiment of the present invention, the method comprising the following steps:

[0054] Step S100 : During the fiber winding process, the winding thickness data of the material to be analyzed at different axial positions at each moment are obtained.

[0055] As a specific example, the material to be analyzed is a pole tower. The winding process using a conical mold is briefly described. During the winding process, the inner layer of the pole tower is the structural material. After winding, the inner conical mold is heated to a gel state. A weather-resistant material is then wrapped around the outer layer, and the winding is completed after heating and curing.

[0056] During the winding process, the winding line and the tension of the wound composite material are set by the CNC machine tool. During the winding process, the internal heating (steam heating) and external cooling process will cause uneven temperature fields in the mold core, resulting in "bulging" or "depression" after winding. Based on this problem, we consider the relative distance data of the winding surface in real time to achieve the purpose of thickness monitoring during the winding process.

[0057] Specifically, a guide rail is positioned directly above the mold's central axis, parallel to the mold's center axis. Laser displacement sensors are installed at different locations on the rail. Implementers can choose the sensor locations based on the specific implementation scenario, ensuring equal spacing between adjacent locations to maximize coverage of the pole. This allows for real-time monitoring of winding anomalies by analyzing the winding thickness distribution at multiple locations. During the pole winding process, as the winding rotates, the sensor can be used to collect the relative distance between the sensor location and the pole.

[0058] To measure the thickness of the winding process, sensors are first used to measure the relative distance between each sensor location and the initial mold surface before winding begins, establishing an initial reference distance for measuring thickness at each location. The winding process is then monitored in real time. At each instant, sensors are used to measure the relative distance between each location and the winding pole, representing the actual relative distance. Finally, the difference between the actual relative distance and the initial reference distance at each location at each instant is used to determine the winding thickness data for each location at that instant. This data is then used to monitor the thickness distribution of the winding material at different axial locations during the winding process in real time.

[0059] Step S200 , segmenting all axial positions of the material to be analyzed at each moment according to the difference between the winding thickness data of each position at each moment and the winding thickness data of adjacent positions in the neighborhood of the position distribution, to obtain each local position segment at each moment.

[0060] Taking into account that the winding angles corresponding to different winding processes are not fixed during the winding process, when the winding angle is small, the winding direction is more perpendicular to the mold axis. During a round of winding from one end of the mold to the other end, the thickness of the mold surface changes relatively uniformly; on the contrary, when the winding angle is large, after one round of winding, since the winding material does not cover the entire mold, its thickness increase will show periodic changes with the size of the winding angle. At a certain moment, the change of the winding thickness data shows a certain periodicity with different positions. In order to merge and analyze positions with relatively similar thickness distributions, the positions with the same or similar winding thickness data at the same moment can be first segmented.

[0061] In some embodiments, as Figure 2 As shown, the method for obtaining each local position segment at each moment can be implemented by step S201 and step S202.

[0062] Step S201 : obtaining the mutation degree of each position at each moment according to the difference between the winding thickness data on one side and the winding thickness data on the other side of each position in the axial direction at each moment.

[0063] It should be understood that this embodiment monitors the thickness of the tower during the winding process, so this method is implemented at each time a quality assessment is required. It should be noted that the likelihood of abnormalities occurring is generally low during the initial stages of the winding process. In some embodiments, the implementer can also set the winding process to perform abnormality monitoring after a fixed period of winding. Based on this, this embodiment uses an arbitrary moment as an example to monitor the thickness distribution at different locations at that moment in time, identifying local locations where thickness anomalies may exist in real time. For ease of understanding and description, the current moment will be used as the arbitrary moment in the subsequent steps.

[0064] Specifically, starting from the left end point of the tower, the changes in the winding thickness data are obtained from each position from left to right. The corresponding position number of each position is used as the horizontal coordinate, and the winding thickness data of each position at the same time is used as the vertical coordinate. Curve fitting is performed to obtain a thickness fitting curve. This curve can reflect the winding change trend of different positions at the same time. When the winding thickness data before and after a certain position differs greatly, this position appears as a mutation point on the thickness fitting curve. It can be understood that the difference in the winding thickness data corresponding to the adjacent positions on the left and right of this mutation point is maximized. Based on this feature, the possibility of a mutation in the data at each position at the current moment is analyzed based on the difference between the winding thickness data at each position at the current moment and the winding thickness data on the adjacent two sides.

[0065] In the first step, at any moment, any position is recorded as a target position, and winding thickness data of the target position and a preset number of positions adjacent to the left of the target position are obtained to form a first side data sequence of the target position.

[0066] In the second step, the winding thickness data of the target position and a preset number of positions adjacent to the right of the target position are obtained to form a second side data sequence of the target position.

[0067] As a specific example, the preset number is 5, which can be determined by the implementer according to the length of the tower and the number of locations during the winding process. At the current moment, both the first side data sequence and the second side data sequence contain 6 winding thickness data.

[0068] The third step is to determine the mutation degree of the target position at any moment based on the difference between the mean of all data in the first-side data sequence and the mean of all data in the second-side data sequence.

[0069] As a specific example, at the current moment, the absolute difference between the mean of all winding thickness data in the first-side data sequence and the mean of all winding thickness data in the second-side data sequence at the target location is used as the mutation degree of the target location at the current moment. The mutation degree reflects the difference between the data on both sides of the target location, representing the magnitude of the mutation in the data at the target location.

[0070] Step S202: Using the position corresponding to the maximum point of the mutation degree at all positions at each moment as a segmentation point, segmenting all positions along the axis of the material to be analyzed, and obtaining each local position segment at each moment.

[0071] For example, if the positions x1 and x2 are the maximum points of the mutation degree, the corresponding positions of x1 and x2 are used as segmentation points. Thus, the position from the starting position to the position with the sequence number x1 belongs to the same local position segment, the position from x1+1 to x2 belongs to the same local position segment, and the position from x2 to the end position belongs to the same local position segment. It should be noted that the method of obtaining the maximum point is well known in the art and will not be further explained here. The segmentation point is the location where the winding thickness data has a mutation.

[0072] Step S300, based on the difference between the winding thickness data corresponding to each local position segment and other local position segments at each moment, and the difference between the winding thickness data of each local position segment at each moment and the normal state, obtain the winding abnormality value of each local position segment at each moment.

[0073] Since the thickness of different locations may be abnormal due to various factors during the winding process, it is necessary to detect the abnormality of each local position segment. The winding thickness of different locations on the tower surface needs to be distinguished from normal process fluctuations and real abnormalities. The abnormality is not only reflected in the deviation of the thickness of a single point, but also in the coordinated deviation relationship between the local area and the overall state. Based on this consideration, the difference distance of the winding thickness data between each local position segment is firstly clustered to divide the position set of normal state and the position set of possible abnormal state. Then, the deviation between the local and the overall can be measured to quantify the winding abnormality performance of each local position segment.

[0074] As a specific example, Figure 3 As shown, the method for obtaining the winding anomaly value of each local position segment can be implemented by step S301 and step S302.

[0075] Step S301 : clustering all local position segments at each moment according to the difference distance between the winding thickness data corresponding to each local position segment and other local position segments at each moment, to obtain feature clusters and normal clusters at each moment.

[0076] In order to identify the spatial anomaly pattern of winding thickness, it is necessary to establish the state association of each local position segment at the current moment. Since the winding angle changes dynamically, that is, the thickness is uniformly distributed at small angles and fluctuates periodically at large angles, directly comparing the global thickness data will mask local anomalies. Therefore, the data distribution differences of different local position segments are compared to achieve the division operation of similar situations.

[0077] More specifically, if Figure 4 As shown, the method for obtaining characteristic clusters and normal clusters can be implemented by steps S3011 to S3014.

[0078] Step S3011: at any moment, the winding thickness data of all positions in each local position segment are used to form a thickness data sequence for each local position segment.

[0079] Step S3012: using the DTW distance of the thickness data sequence between every two different local position segments as the metric distance between every two local position segments, and clustering all the local position segments using a clustering algorithm to obtain multiple clusters.

[0080] Specifically, a K-means clustering algorithm can be used for processing, and the number of clusters can be calculated using the elbow method or the silhouette coefficient, both of which are well-known techniques and will not be described in detail here. At the current moment, all local position segments contained in each cluster show relatively similar trends in the change of winding thickness data.

[0081] Step S3013 : Determine the difference between the mean of the metric distance between each two local position segments in each cluster and the mean of the metric distance between each two local position segments in each other cluster as the degree of data difference between each cluster and each other cluster.

[0082] At the current moment, any cluster is taken as the target cluster, and all clusters except the target cluster are reference clusters. A metric distance can be calculated between each two different local position segments. The absolute value of the difference between the mean of all metric distances in the target cluster and the mean of all metric distances in each reference cluster is calculated to obtain the degree of data difference between the target cluster and each reference cluster.

[0083] The degree of data difference reflects the difference in winding thickness difference data between different clusters. When there is a large data difference between the target cluster and each reference cluster, it indicates that the possibility of abnormality in the target cluster is greater.

[0084] Step S3014: obtaining the cluster corresponding to the minimum value of the cumulative sum of the data differences between each cluster and all other clusters as the normal cluster at any moment, and taking all clusters other than the normal cluster as the characteristic cluster at any moment.

[0085] Considering that when "bulges" or "dents" occur, some clusters may differ significantly from the rest. The cluster with the smallest sum of differences from the rest is selected to represent the normal thickness range. The normal cluster represents the normal distribution range of the winding thickness data at the current moment. The characteristic cluster represents the data distribution set that may contain thickness anomalies.

[0086] Step S302 : obtaining the winding abnormality value of each local position segment in each feature cluster at each moment according to the difference between the winding thickness data contained in each feature cluster and the normal cluster.

[0087] Specifically, at any moment, the normalized result of the data difference between each feature cluster and the normal cluster is used as the winding anomaly value for each local position segment within each feature cluster. The normalization method can be processed using the maximum and minimum methods, which will not be further explained here.

[0088] More specifically, according to the same method as step S3013, the degree of data difference between each feature cluster and the normal cluster can be obtained at the current moment, which characterizes the difference between each feature cluster and the normal thickness distribution range. The larger the difference, the greater the difference between each feature cluster and the normal state, and thus the greater the possibility of abnormality in each feature cluster, and the larger the corresponding winding anomaly value.

[0089] Step S400 : monitoring the fiber winding process based on the winding abnormality value of each local position segment at each moment to obtain a winding quality monitoring result.

[0090] The larger the winding anomaly value of the feature cluster class where each local position segment is located at the current moment, the greater the possibility that the corresponding feature cluster class has a thickness anomaly. The smaller the winding anomaly value of the feature cluster class where each local position segment is located at the current moment, the smaller the possibility that the corresponding feature cluster class has a thickness anomaly.

[0091] Furthermore, when the winding anomaly value of the feature cluster class of each local position segment at each moment is greater than the preset anomaly threshold, there is a winding anomaly at the position contained in the local position segment, and the winding quality is poor at this time. For the current moment, this means that during the winding process, the feature cluster class greater than the anomaly threshold contains a serious bulge or depression in the local position segment, and the thickness difference during the winding process is too large, and the winding needs to be stopped for further screening.

[0092] When the winding anomaly value for each feature cluster of each local position segment at each moment is less than or equal to the preset anomaly threshold, the location within that local position segment is free of winding anomalies and the winding quality is considered good. For the current moment, this indicates that the thickness variation among the local position segments meeting the threshold during the winding process is small, making the likelihood of thickness anomalies low and thus eliminating winding anomalies. As a specific example, the anomaly threshold is set to 0.7; implementers can adjust this threshold based on their specific implementation scenarios.

[0093] So far, the main purpose of this embodiment is to perform thickness anomaly detection on the entire material being wound. In other embodiments, the implementer can perform further quality monitoring operations on materials with local anomalies based on the specific implementation scenario. For example, the implementer can also remind relevant professional staff to conduct further inspections on each location where anomalies exist.

[0094] In summary, this embodiment, through real-time monitoring of winding thickness data at each location, can pinpoint any local locations where abnormal winding thickness exists. This allows the identification of locations where abnormal thickness exists during the winding process of the material being analyzed. This improves the accuracy of thickness anomaly detection during monitoring, resulting in better analysis results for material quality monitoring. Winding can be stopped at locations with abnormal thickness, allowing personnel to investigate the abnormality and take timely measures to prevent it from impacting the subsequent winding process.

[0095] In some embodiments, taking into account that wrapping the outer layer before the structural layer enters the proper gel state will result in poor interlayer bonding, especially when using a steam internal heating structure, the temperature distribution is complex and can only be completed according to the preset path and preset tension according to the program. Excessive or insufficient tension may lead to stress concentration or wrinkling, delamination, and bulging or depression. After completing the local thickness anomaly detection in step S400, the tension at the local location with the thickness anomaly can be adaptively adjusted dynamically. For example, when the winding anomaly value of a certain local location segment is large, it indicates that the possibility of an anomaly is greater. At this time, when the thickness is greater than the normal range, the tension is increased on the original basis to suppress the bulge or a small amount of protrusion. When the thickness is less than the normal range, the tension of the depressed part is reduced to prevent the depression from worsening.

[0096] Based on this, the following steps are also included after step S400:

[0097] Step S500 obtains actual tension data of different axial positions of the material to be analyzed at each moment; when a winding abnormality exists in a local position segment, the actual tension data is corrected according to the difference between the winding thickness data and the normal state data of each position in the local position segment and the winding abnormality value to obtain the adjusted tension data of each position at each moment; the next round of fiber winding operation is performed with the adjusted tension data, and the winding abnormality value of each local position segment at each moment in the next round of operation is obtained, and the fiber winding molding process is monitored.

[0098] First, a tension microsensor collects tension data at each position at each moment. In actual operation, the tension data may be a fixed value. This embodiment aims to obtain tension data at the moment when a thickness anomaly is detected, which serves as the data basis for subsequent tension fine-tuning. This allows the user to attempt to correct the anomaly by adjusting the tension even when the anomaly is relatively minor. For example, fixed tension data at the corresponding moment can be directly obtained from a CNC winding machine, or microsensors can be installed at winding devices such as the guide wire head or winding head during the winding process to collect tension data at different positions.

[0099] Then, the method for obtaining the adjusted tension data of each position at each moment is specifically as follows: when there is a winding abnormality in the local position segment, according to the difference between the winding thickness data contained in the feature cluster class where the local position segment is located at the corresponding moment and the normal cluster class, combined with the winding abnormality value of the feature cluster class where the local position segment is located, the correction coefficient corresponding to the local position segment is obtained; the product of the correction coefficient and the actual tension data of each position in the local position segment is used as the corrected tension data.

[0100] Among them, the method for obtaining the correction coefficient is specifically as follows: determine whether the mean value of the winding thickness data of all positions in the characteristic cluster class where the local position segment is located is less than the mean value of the winding thickness data of all positions in the normal cluster class; if so, use the difference between the value 1 and the winding anomaly value of the characteristic cluster class where the local position segment is located as the correction coefficient corresponding to the local position segment; if not, use the sum of the value 1 and the winding anomaly value of the characteristic cluster class where the local position segment is located as the correction coefficient corresponding to the local position segment.

[0101] For the convenience of description, the current moment is used as an example. Assuming that the local position segment corresponding to the winding abnormality at the current moment is the i-th local position segment, the adjustment tension data corresponding to the i-th local position segment at the current moment can be expressed by the formula:

[0102]

[0103] Among them, N i ′ Indicates the adjustment tension data corresponding to the i-th local position segment at the current moment, Represents the mean value of all winding thickness data in the feature cluster of the i-th local position segment at the current moment, Represents the mean value of the winding thickness data in the normal cluster at the current moment, W i Indicates the winding anomaly value of the feature cluster where the i-th local position segment is located at the current moment, N i Indicates the tension data corresponding to the i-th local position segment at the current moment, is the correction factor.

[0104] For the i-th local position segment at the current moment, when When , it means that the overall distribution of the winding thickness of the abnormal local position segment is larger than the normal thickness range, which further indicates that there is a convex thickness abnormality in the current i-th local position segment, so it is necessary to relatively increase the tension at this position. The value is 1, and the correction coefficient is expressed as 1+W i .

[0105] when When , it means that the overall distribution of the winding thickness of the abnormal local position segment is smaller than the normal thickness range, which further indicates that the thickness of the concave segment exists in the current i-th local position segment. Therefore, the tension of this position needs to be relatively reduced. The value is -1, and the correction coefficient is expressed as 1-W i .

[0106] It should be noted that in this embodiment, the winding operation is performed using the same tension at local locations corresponding to a continuously distributed length. That is, the tension data at all locations within the same local location segment remains unchanged. Following the same method, after obtaining the adjusted tension data for each local location segment experiencing an abnormal condition at the current moment, the tension during the winding process can be controlled using a PID control method.

[0107] Furthermore, considering that a large tension adjustment range may affect the normal winding process and may even cause winding anomalies, a normal tension data value range can be set. When the adjustment tension data is not within the normal value range, the adjustment operation is not performed based on the adjustment tension data. More specifically, the minimum tension data during the winding process is used as the lower limit of the normal value range, and the maximum tension data allowed during the winding process is used as the upper limit of the normal value range. When the adjustment tension data is greater than the upper limit of the normal value range, the tension is not adjusted based on the adjustment tension data, and the tension can be adjusted based on the upper limit of the normal value range. When the adjustment tension data is less than the lower limit of the normal value range, the tension can be adjusted based on the lower limit of the normal value range.

[0108] Furthermore, in actual situations, bulges or depressions accumulate gradually. After the above-mentioned tension adjustment is completed, if the bulges or depressions are caused by the gel in the inner structural layer, the bulges or depressions will be improved to a certain extent. However, factors such as wear of the guide wheel or tension arm will cause the yarn guide track to deviate, and overlap or missing layers will occur in local areas. At this time, the abnormalities will be further superimposed, causing the thickness abnormalities in some areas to further increase during the winding process.

[0109] After performing a round of winding operation with the adjusted tension data, the method of steps S100 to S400 is used to determine whether there is still local thickness abnormality on the surface of the wound material after tension adjustment. If so, it means that the thickness abnormality cannot be solved by fine-tuning the tension. At this time, the winding needs to be stopped and an early warning is issued so that relevant professionals can investigate and repair the cause of the abnormality.

[0110] At this point, the main purpose of step S500 is to determine whether the thickness abnormality can be resolved by controlling the tension at the location where the thickness abnormality exists. This method can promptly detect the thickness abnormality at the local location in the early stage of the thickness abnormality. If a large abnormality still exists after the dynamic control parameters have been running for a period of time, relevant professionals are required to conduct further troubleshooting work at each local location.

[0111] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. Multi-axis linkage winding quality monitoring equipment based on CNC fiber winding, characterized by: The device includes a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the following steps: During the filament winding process, the winding thickness data of the material to be analyzed at different axial positions at each moment is obtained; According to the difference between the winding thickness data of each position at each moment and the winding thickness data of the adjacent positions in the neighborhood of the position distribution, all axial positions of the material to be analyzed at each moment are segmented to obtain each local position segment at each moment; According to the difference between the winding thickness data corresponding to each local position segment and other local position segments at each moment, and the difference between the winding thickness data of each local position segment at each moment and the normal state, the winding abnormal value of each local position segment at each moment is obtained; Based on the winding abnormality value of each local position segment at each moment, the fiber winding molding process is monitored to obtain the winding quality monitoring results.

2. The multi-axis linkage winding quality monitoring equipment based on CNC filament winding according to claim 1 is characterized in that: The method of segmenting all axial positions of the material to be analyzed at each moment according to the difference between the winding thickness data of each position at each moment and the winding thickness data of adjacent positions in the neighborhood of the position distribution to obtain each local position segment at each moment specifically includes: According to the difference between the winding thickness data on one side of the axial direction and the winding thickness data on the other side of the axial direction at each position at each moment, the mutation degree of each position at each moment is obtained; The position corresponding to the maximum point of the mutation degree at all positions at each moment is used as the segmentation point, and all positions under the axis of the material to be analyzed are segmented to obtain the segmentation of each local position at each moment.

3. The multi-axis linkage winding quality monitoring equipment based on CNC filament winding according to claim 2 is characterized in that: The step of obtaining the mutation degree of each position at each moment based on the difference between the winding thickness data on one side of the axial direction and the winding thickness data on the other side at each position at each moment specifically includes: At any moment, any position is recorded as a target position, and winding thickness data of the target position and a preset number of positions adjacent to the left of the target position are obtained to form a first side data sequence of the target position; Acquire winding thickness data of a target position and a preset number of positions adjacent to the right of the target position to form a second side data sequence of the target position; The degree of mutation of the target position at any one moment is determined based on the difference between the mean of all data in the first-side data sequence and the mean of all data in the second-side data sequence.

4. The multi-axis linkage winding quality monitoring equipment based on CNC filament winding according to claim 1 is characterized in that: The winding abnormality value of each local position segment at each moment is obtained according to the difference between the winding thickness data corresponding to each local position segment and other local position segments at each moment, and the difference between the winding thickness data of each local position segment at each moment and the normal state, specifically including: According to the difference distance between the winding thickness data corresponding to each local position segment and other local position segments at each moment, all local position segments at each moment are clustered to obtain the characteristic clusters and normal clusters at each moment; According to the difference in winding thickness data between each feature cluster and the normal cluster, the winding anomaly value of each local position segment in each feature cluster at each moment is obtained.

5. The multi-axis linkage winding quality monitoring equipment based on CNC filament winding according to claim 4 is characterized in that: The method of clustering all local position segments at each moment according to the difference distance between the winding thickness data corresponding to each local position segment and other local position segments at each moment to obtain the characteristic clusters and normal clusters at each moment specifically includes: At any moment, the winding thickness data of all positions in each local position segment constitute a thickness data sequence of each local position segment; The DTW distance of the thickness data series between each two different local position segments is used as the metric distance between each two local position segments, and a clustering algorithm is used to cluster all local position segments to obtain multiple clusters; The difference between the mean of the metric distance between each two local position segments in each cluster and the mean of the metric distance between each two local position segments in each other cluster is determined as the degree of data difference between each cluster and each other cluster; The cluster corresponding to the minimum value of the cumulative sum of the data differences between each cluster and all other clusters is obtained as the normal cluster at any moment, and all clusters other than the normal cluster are taken as the characteristic clusters at any moment.

6. The multi-axis linkage winding quality monitoring equipment based on CNC filament winding according to claim 5 is characterized in that: The winding anomaly value of each local position segment within each feature cluster at each moment is obtained based on the difference in winding thickness data between each feature cluster and the normal cluster, specifically including: At any moment, the normalized result of the data difference between each feature cluster and the normal cluster is used as the winding outlier value of each local position segment within each feature cluster.

7. The multi-axis linkage winding quality monitoring equipment based on CNC filament winding according to claim 4 is characterized in that: The monitoring of the filament winding process based on the winding abnormality value of each local position segment at each moment specifically includes: When the winding anomaly value of each local position segment at each moment is greater than the preset anomaly threshold, the position included in the local position segment has a winding anomaly condition; When the winding anomaly value of each local position segment at each moment is less than or equal to a preset anomaly threshold, there is no winding anomaly condition at the position included in the local position segment.

8. The multi-axis linkage winding quality monitoring equipment based on CNC filament winding according to claim 4 is characterized in that: After obtaining the winding quality monitoring results, it also includes: Obtain the actual tension data of different axial positions of the material to be analyzed at each moment; When a winding abnormality exists in a local position segment, the actual tension data is corrected according to the difference between the winding thickness data and the normal state data of each position in the local position segment and the winding abnormality value to obtain the adjusted tension data of each position at each moment; The next round of fiber winding operation is performed using the adjusted tension data, and the winding abnormality value of each local position segment at each moment in the next round of operation is obtained, and the fiber winding molding process is monitored.

9. The multi-axis linkage winding quality monitoring equipment based on CNC filament winding according to claim 8 is characterized in that: The actual tension data is corrected based on the difference between the winding thickness data and the normal state data at each position in the local position segment and in combination with the winding abnormal value to obtain the adjusted tension data at each position at each moment, specifically including: When a winding abnormality exists in a local position segment, the correction coefficient corresponding to the local position segment is obtained based on the difference between the winding thickness data contained in the characteristic cluster class where the local position segment is located and the normal cluster class at the corresponding moment, combined with the winding abnormality value of the characteristic cluster class where the local position segment is located; the product of the correction coefficient and the actual tension data of each position in the local position segment is used as the corrected tension data.

10. The multi-axis linkage winding quality monitoring equipment based on CNC filament winding according to claim 9, characterized in that: The correction coefficient corresponding to the local position segment is obtained based on the difference between the winding thickness data contained in the characteristic cluster class where the local position segment is located and the normal cluster class at the corresponding moment, combined with the winding abnormal value of the characteristic cluster class where the local position segment is located, specifically including: When judging whether the mean value of the winding thickness data of all positions in the characteristic cluster class where the local position segment is located is less than the mean value of the winding thickness data of all positions in the normal cluster class; If so, the difference between the value 1 and the winding anomaly value of the characteristic cluster where the local position segment is located is used as the correction coefficient corresponding to the local position segment; If not, the sum of the value 1 and the winding anomaly value of the characteristic cluster where the local position segment is located is used as the correction coefficient corresponding to the local position segment.

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