Intelligent detection system and method for composite spring integrally formed through punching

Abstract

The invention relates to the technical field of spring detection, in particular to an intelligent detection system and method for an integrated punch-formed composite spring. The primary detection module is used for carrying out primary detection on the composite spring to obtain detection data; the processing and analyzing module is used for determining the state of the composite spring according to the detection anomaly degree and the anomaly association degree and determining a processing mode according to the state of the composite spring; the secondary detection module is used for determining a simulation mode according to the working condition complexity and the multidirectional uniformity and determining an application setting mode according to a use influence threshold value in secondary detection analysis; the nondestructive testing module is used for determining a nondestructive testing mode according to the deformation coefficient and the relaxation dislocation coefficient and determining an adjusting mode according to the correlation degree of the detection points and an instability threshold value; the detection precision of the composite spring can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of spring detection, and particularly to an intelligent detection system and method for a composite spring formed by integral stamping. Background Art

[0002] In the field of modern industrial manufacturing, the composite spring formed by integral stamping has become an indispensable key component in many industries due to its unique advantages. However, during the manufacturing process of the composite spring, due to fluctuations in material properties, difficulty in precisely controlling stamping process parameters, and differences in heat treatment processes, the mechanical properties of the spring may deviate significantly. Therefore, how to detect the composite spring to improve the detection accuracy is a technical problem that needs to be solved urgently by those skilled in the art.

[0003] Chinese Patent Publication No. CN118549068A discloses a tool for detecting composite parameters of a spring, including: a support plate; a spring support rod; a guide rail; a telescopic mechanism; a fixing mechanism; the right surface of the support plate is fixedly connected with a spring support rod, the right surface of the support plate is fixedly connected with a guide rail, a telescopic mechanism is arranged on the spring support rod of the support plate, and a fixing mechanism is arranged on the telescopic mechanism. The telescopic mechanism includes a movable rail, the movable rail penetrates through the guide rail and is slidably connected with the guide rail, and the right surface of the movable rail is fixedly connected with the left surface of a moving plate. This tool for detecting composite parameters of a spring realizes the composite detection of the number of turns, outer diameter, pitch, and free length at the same station by using the settings of the support plate, spring support rod, guide rail, and pitch measurement block, reduces the time for switching detection tools, and can simultaneously detect the profiling of the spring lift to quickly determine whether the spring is qualified. It can be seen that the above technical solution has the following problems: it is unable to simulate the actual use scenario of the composite spring, resulting in poor detection accuracy of the composite spring. Summary of the Invention

[0004] Therefore, the present invention provides an intelligent detection system and method for a composite spring formed by integral stamping to overcome the problems in the prior art.

[0005] To achieve the above object, the present invention provides an intelligent detection system for a composite spring formed by integral stamping, including: A data acquisition module for obtaining operation monitoring data; A primary detection module for performing a primary detection on the composite spring to obtain detection data; A processing and analysis module connected to the primary detection module for determining the state of the composite spring according to the detection abnormality degree and the abnormality correlation degree, and determining the processing method as secondary detection and analysis or adjusting the fiber winding angle according to the state of the composite spring; The secondary detection module is respectively connected to the data acquisition module and the processing and analysis module, and is used to determine the simulation method according to the working condition complexity and multi-directional uniformity in the secondary detection analysis, and determine the application setting method according to the use influence threshold; The simulation method is to determine the simulation application point according to the distance coefficient and action similarity or determine the application simulation method according to the feature point category. The application simulation method is intermittent application or continuous application. The application setting method is span adjustment detection or constant threshold detection; The non-destructive detection module is connected to the secondary detection module and is used to determine the non-destructive detection method as associated setting of non-destructive detection points or uniform setting of non-destructive detection points according to the deformation coefficient and relaxation dislocation coefficient under preset detection conditions, and determine the adjustment method as adjustment for the gradient reference value or local strengthening according to the defect coefficient according to the detection point correlation degree and instability threshold.

[0006] Further, when the state of the composite spring is that the detection abnormality degree is greater than or equal to the preset detection abnormality degree and the abnormality correlation degree is greater than or equal to the preset abnormality correlation degree, the processing and analysis module determines that the processing method is to perform a reduction adjustment on the fiber winding angle; The reduction value of the fiber winding angle has a positive correlation with the abnormality coefficient.

[0007] Further, when the state of the composite spring is that the detection abnormality degree is less than the preset detection abnormality degree or the abnormality correlation degree is less than the preset abnormality correlation degree, the processing and analysis module determines that the processing method is secondary detection analysis.

[0008] Further, when the working condition complexity is less than the preset working condition complexity and the multi-directional uniformity is greater than or equal to the preset multi-directional uniformity, the secondary detection module determines that the simulation method is to determine the simulation application point according to the distance coefficient and action similarity.

[0009] Further, when the working condition complexity is greater than or equal to the preset working condition complexity or the multi-directional uniformity is less than the preset multi-directional uniformity, the secondary detection module determines that the simulation method is to determine the application simulation method according to the feature point category; If the feature point category is a type of feature point with an action duration coefficient less than the preset action duration coefficient or a change response value greater than or equal to the preset change response value, the application simulation method is intermittent application; If the feature point category is a type of feature point with an action duration coefficient greater than or equal to the preset action duration coefficient and a change response value less than the preset change response value, the application simulation method is continuous application.

[0010] Further, the secondary detection module determines the application setting method according to the use influence threshold, including: If the usage impact threshold is greater than or equal to the preset usage impact threshold, the application setting method is span adjustment detection; If the usage impact threshold is less than the preset usage impact threshold, the application setting method is constant threshold detection.

[0011] Further, the non-destructive testing module determines the non-destructive testing method according to the deformation coefficient and the relaxation dislocation coefficient, including: If the deformation coefficient is greater than or equal to the preset deformation coefficient or the relaxation dislocation coefficient is greater than or equal to the preset relaxation dislocation coefficient, the non-destructive testing method is to set non-destructive testing points in association; If the deformation coefficient is less than the preset deformation coefficient and the relaxation dislocation coefficient is less than the preset relaxation dislocation coefficient, the non-destructive testing method is to set non-destructive testing points uniformly.

[0012] Further, when the detection point correlation degree is greater than or equal to the preset detection point correlation degree and the instability threshold is greater than or equal to the preset instability threshold, the non-destructive testing module determines that the adjustment method is to reduce the gradient reference value; The reduction value of the gradient reference value has a positive correlation with the comprehensive evaluation coefficient.

[0013] Further, when the detection point correlation degree is less than the preset detection point correlation degree or the instability threshold is less than the preset instability threshold, the non-destructive testing module determines that the adjustment method is to perform local strengthening according to the defect coefficient.

[0014] The present invention also provides an intelligent detection method for an integrally stamped composite spring, including: Obtain operation monitoring data and perform a first detection on the composite spring to obtain detection data; Determine the state of the composite spring according to the detection abnormality degree and the abnormality correlation degree, and determine the processing method as secondary detection analysis or adjustment of the fiber winding angle according to the state of the composite spring; In the secondary detection analysis, determine the simulation method according to the working condition complexity and the multi-directional uniformity degree, and determine the application setting method according to the usage impact threshold; The simulation method is to determine the simulation application point according to the distance coefficient and the action similarity degree or to determine the application simulation method according to the feature point category. The application simulation method is intermittent application or continuous application, and the application setting method is span adjustment detection or constant threshold detection; Under the preset detection conditions, determine the non-destructive testing method as setting non-destructive testing points in association or setting non-destructive testing points uniformly according to the deformation coefficient and the relaxation dislocation coefficient, and determine the adjustment method as adjusting the gradient reference value or performing local strengthening according to the defect coefficient according to the detection point correlation degree and the instability threshold.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows. In the technical solution of the present invention, the basic data of the composite spring is obtained through one-time detection, and the state of the composite spring is determined according to the detection abnormality degree and the abnormality correlation degree. The detection abnormality degree and the abnormality correlation degree effectively reflect the initial performance and potential problems of the composite spring. Furthermore, different processing methods are adaptively selected according to the state of the composite spring, making the selection of the processing method more in line with the actual application scenario. Adjusting the material ratio can provide spring performance. When performing secondary detection and analysis, further in-depth detection can be carried out to accurately locate the fault point or performance deficiency, providing a basis for subsequent precise repair or improvement.

[0016] Furthermore, in the present invention, the complexity of the application working conditions of the composite spring and the uniformity of the composite spring are effectively combined through the working condition complexity and the multi-directional uniformity. Then, different simulation methods are adaptively selected according to the working condition complexity and the multi-directional uniformity, making the selection of the simulation method more in line with the actual application scenario. It can improve the simulation efficiency on the premise of ensuring the simulation accuracy, contribute to improving the detection accuracy of the composite spring, and further improve the quality control accuracy.

[0017] Furthermore, in the present invention, the force application characteristics of the simulation application points are effectively reflected by using the influence threshold. Then, different application setting methods are adaptively selected according to the influence threshold. By using span adjustment detection, the force change process of the composite spring during actual operation can be simulated to more comprehensively evaluate its performance. By using constant threshold detection, the load-bearing capacity of the composite spring can be quickly and directly determined, which is beneficial to improving the detection efficiency and detection accuracy.

[0018] Furthermore, in the present invention, the defect state is effectively reflected by the deformation coefficient and the detection abnormality degree. Then, different non-destructive testing methods are adaptively selected according to the deformation coefficient and the relaxation dislocation coefficient, making the selection of the non-destructive testing method more in line with the actual application scenario. By using the method of associatively setting non-destructive testing points, key parts can be targeted for key detection according to the characteristics and distribution laws of the defects. By using the method of uniformly setting non-destructive testing points, it is possible to avoid overly intensive and time-consuming detection of the entire spring while ensuring the comprehensiveness of the detection.

[0019] Furthermore, in the present invention, the correlation degree of the detection results of each detection point and the internal defect degree are effectively reflected by the detection point correlation degree and the instability threshold. Then, different adjustment methods are adaptively selected according to the detection point correlation degree and the instability threshold, making the selection of the adjustment method more in line with the actual application scenario, and further improving the production quality of the composite spring. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a module connection diagram of the intelligent detection system for the integrally stamped composite spring of the present invention; Figure 2 This is a flowchart for the present invention to determine the processing method according to the state of the composite spring; Figure 3 This is a flowchart for the present invention to determine the simulation method according to the working condition complexity and multi-directional uniformity; Figure 4 This is a schematic diagram of the intelligent detection method for the integrally stamping formed composite spring of the present invention. Detailed implementation manners

[0021] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] The preferred implementation manners of the present invention will be described below with reference to the drawings. Those skilled in the art should understand that these implementation manners are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0023] It should be noted that in the description of the present invention, the terms indicating the direction or positional relationship such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention.

[0024] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0025] Please refer to Figures 1 to 3 As shown, the present invention provides an intelligent detection system for an integrally stamping formed composite spring, including: A data acquisition module for acquiring operation monitoring data; A primary detection module for performing a primary detection on the composite spring to obtain detection data; A processing and analysis module connected to the primary detection module for determining the state of the composite spring according to the detection abnormality degree and the abnormality correlation degree, and determining the processing method as secondary detection analysis or adjusting the fiber winding angle according to the state of the composite spring; The secondary detection module is respectively connected to the data acquisition module and the processing and analysis module, and is used to determine the simulation method according to the working condition complexity and the multi-directional uniformity during secondary detection and analysis, and determine the application setting method according to the use influence threshold; The simulation method is to determine the simulation application point according to the distance coefficient and the action similarity or determine the application simulation method according to the feature point category. The application simulation method is intermittent application or continuous application, and the application setting method is span adjustment detection or constant threshold detection; The non-destructive detection module is connected to the secondary detection module, and is used to determine the non-destructive detection method as associatively setting non-destructive detection points or uniformly setting non-destructive detection points according to the deformation coefficient and the relaxation dislocation coefficient under preset detection conditions, and determine the adjustment method as adjusting according to the gradient reference value or locally strengthening according to the defect coefficient according to the detection point correlation degree and the instability threshold.

[0026] The application scenario of the present invention is the quality detection of composite springs. In the present invention, a number of historical records are correspondingly set. Any historical record records at least one detection abnormality degree, abnormality correlation degree, working condition complexity, multi-directional uniformity, distance coefficient, action similarity, etc. in the historical process of the quality detection of the composite spring, and each historical record corresponds to a qualified mark. The qualified mark records whether the quality detection process of the composite spring meets the user's requirements. The qualified mark can be manually recorded. It can be understood that the user can determine whether the quality detection process of the composite spring meets the requirements according to the self-set index. The self-set index can be, but is not limited to, the detection accuracy rate, which will not be elaborated here. Among them, the detection accuracy rate = the number of composite springs with accurate detection results / the total number of composite springs subjected to detection; The production process of the composite spring in the present invention includes: Winding and forming: Traction of glass fiber to the glue pool through a guide plate, scraping off the excess glue slurry and then twisting to form the inner core and the intermediate layer. The glass fiber is tractioned to the glue pool through a high-precision guide plate with a tension of 5N. The glue pool is filled with epoxy resin glue slurry with a viscosity of 500 cPs, and the temperature is controlled at 25±2°C to maintain the stability of the glue liquid. The fiber impregnation time is 2 seconds. A double-roller glue scraping system is used to scrape off the excess glue slurry. The fiber passes through a twister with a rotation speed of 500 - 2000 rpm to form an inner core with 3 layers of fiber winding. The fiber traction speed and the mold rotation speed are linked to control the fiber angle at 45°; Coating the fixed layer: Using PE film, PET film, etc. as the inner fixed layer to coat on the inner core to form a uniform winding layer. Using fluororubber tube, PO hose, etc. as the outer support layer and embedding them into the already coated spring wire.

[0027] Heat curing: Place the mold in an incubator. Starting from an initial heating temperature of 25°C, increase the temperature uniformly to the final heating temperature. The heating-up time is 50 min, the final heating temperature is 150°C, and heat curing is carried out by heating at the final heating temperature for 25 min.

[0028] In the present invention, a target coefficient and related thresholds are set. The corresponding relationship between the target coefficient and the related thresholds is represented by a weight formula: Target coefficient = Weight coefficient × Related threshold. Specifically, in the present invention, the decrease value of the fiber winding angle, the duration of the application period corresponding to a type of characteristic point, the value of w, the selection interval, the decrease value of the gradient reference value, and the increase value of the number of fiber winding layers in the strengthening section are recorded as the target coefficient, and the abnormal coefficient, the characteristic evaluation threshold corresponding to a type of characteristic point, the length of the helix, the total number of points to be selected, the comprehensive evaluation coefficient, and the strengthening threshold are recorded as the related threshold. It can be understood that there is a related threshold corresponding to each target coefficient. For example, there is a positive correlation between the decrease value of the fiber winding angle and the abnormal coefficient, and the positive correlation between the decrease value of the fiber winding angle and the abnormal coefficient is represented by the weight formula. The value of the weight coefficient can be determined according to the historical experience of the user based on the influence degree of the abnormal coefficient on the decrease value of the fiber winding angle, and the value of the weight coefficient can be optimized by combining the historical records of the quality inspection process of multiple composite springs with a multi-layer perceptron. Optimizing the value of the weight coefficient using a multi-layer perceptron is easily understood by those skilled in the art and will not be elaborated here. The principle of obtaining the value of the weight coefficient corresponding to other target coefficients and related thresholds is the same and will not be elaborated here.

[0029] Conduct a single inspection on the composite spring, including: For a single composite spring, place the composite spring in a target container with its central axis perpendicular to the ground. Install a pressure sensor at the center position of the bottom of the target container, and install a displacement sensor at any position on the top of the composite spring. Control a pressure device indenter to apply a constant force to compress the composite spring at the center position on the top of the composite spring, and continuously record the pressure value detected by the pressure sensor and the displacement amount detected by the displacement sensor. Stop compressing when the height of the composite spring is one-third of the standard height, and obtain the inspection data; the inspection data is the pressure value continuously detected by the pressure sensor and the displacement amount detected by the displacement sensor. The standard height is the length of the composite spring when the composite spring is placed in the target container and is not subject to external forces.

[0030] The target container is in the shape of a cylinder with openings at both the top and bottom, and one of the openings is placed closely against the ground. The diameter and height of the target container are set according to the actual needs of the user. It is necessary to ensure that the target container can accommodate the composite spring, and when the composite spring is placed in the target container, the central position of the bottom of the composite spring coincides with the central position of the target container; the central position of the bottom of the target container is the center of the circle at the bottom of the target container, and the central position of the top of the composite spring is the center of the circle at the top of the composite spring when the composite spring is placed in the target container and the central axis of the composite spring is perpendicular to the ground.

[0031] The constant force can be set according to the quality requirements of the composite spring during the production process. In the present invention, the direction of the constant force is vertically downward, and a value of the constant force is provided. The magnitude of the constant force is 50 Newtons. The characteristic curve takes the displacement measured by the displacement sensor as the x-axis and the pressure value measured by the pressure sensor as the y-axis. The specific characteristics of the characteristic curve are as follows: when x = 0, that is, when the composite spring is not compressed, the intersection point of the curve and the y-axis is the pressure value of the pressure sensor when the composite spring is not compressed; as the displacement x continuously increases, the pressure value y also gradually increases until the maximum pressure value is reached. The preset detection condition is that the secondary anomaly coefficient is greater than the preset secondary anomaly coefficient.

[0032] Specifically, when the state of the composite spring is that the detection anomaly degree is greater than or equal to the preset detection anomaly degree and the anomaly correlation degree is greater than or equal to the preset anomaly correlation degree, the processing analysis module determines that the processing method is to reduce and adjust the fiber winding angle. The reduction value of the fiber winding angle has a positive correlation with the anomaly coefficient.

[0033] Among them, the composite springs with qualified detection results in the historical records are recorded as qualified springs. The abscissa corresponding to the curve in the characteristic curve is equally divided into n parts. The greater the user's demand for detection accuracy, the larger the value of n. A value of n is provided, and n is 15. The composite spring being detected is recorded as the target composite spring. The detection anomaly degree is the average value of the sub-anomaly degrees corresponding to each equal division point in the characteristic curve of the target composite spring. The sub-anomaly degree corresponding to a single equal division point = the average value of the pressure values corresponding to this equal division point in the characteristic curves of the qualified springs - the pressure value corresponding to this equal division point in the characteristic curve of the target composite spring. The anomaly correlation degree = 1 / (the maximum value of the correlation coefficients corresponding to the characteristic curves of the target composite spring and the characteristic curves of the qualified springs + 1). The calculation formula for the correlation coefficient r corresponding to any two characteristic curves is:

[0034] Wherein, n is the number of equally divided points in a single characteristic curve; and are respectively the pressure values corresponding to the i-th equally divided point in two characteristic curves, is the average value of the pressure values corresponding to each equally divided point in the corresponding characteristic curve, is the average value of the pressure values corresponding to each equally divided point in the corresponding characteristic curve, i = 1, 2, 3, ……, n; The values of the preset detection abnormality degree and the preset abnormality correlation degree can be determined by the user according to the actual application scenario. The greater the user's demand for improving the processing quality of the composite spring, the smaller the values of the preset detection abnormality degree and the preset abnormality correlation degree. A method for obtaining the values of the preset detection abnormality degree and the preset abnormality correlation degree is provided. Detect the historical records of the user's reduction adjustment of the material ratio, and respectively record the average value of the detection abnormality degree and the average value of the abnormality correlation degree corresponding to the historical records that can meet the user's needs as the preset detection abnormality degree and the preset abnormality correlation degree; The fiber winding angle θ is the angle between the direction of the glass fiber and the axis of the spring, θ = arctan(π×D×Ω / v), where D is the diameter of the composite spring, Ω is the die rotation speed, and v is the fiber traction speed.

[0035] It should be noted that the diameter of the composite spring is 0.02m. When adjusting the fiber winding angle to be reduced, D remains unchanged, and only Ω or v is adjusted. The specific adjustment parameters are not limited as long as they can meet the user's needs. The initial die rotation speed is 10 rad / s, and the initial fiber traction speed is 1 m / s.

[0036] Abnormality coefficient = detection abnormality degree / preset detection abnormality degree + abnormality correlation degree / preset abnormality correlation degree.

[0037] Specifically, when the state of the composite spring is that the detection abnormality degree is less than the preset detection abnormality degree or the abnormality correlation degree is less than the preset abnormality correlation degree, the processing analysis module determines that the processing method is secondary detection and analysis.

[0038] Specifically, when the working condition complexity is less than the preset working condition complexity and the multi-directional uniformity is greater than or equal to the preset multi-directional uniformity, the secondary detection module determines that the simulation method is to determine the simulation application point according to the distance coefficient and the action similarity.

[0039] Among them, the present invention includes a number of operation monitoring data. A single operation monitoring data is sub-monitoring data corresponding to each characteristic point of a single composite spring. The sub-monitoring data is the force value of a single characteristic point monitored in real time during the actual use of the composite spring over the service time. The number and positions of the characteristic points corresponding to each composite spring are the same. The characteristic point is the position where a force sensor is installed in the composite spring. The force value of the characteristic point is monitored by a force measuring sensor. The user can install force measuring sensors at the fixed end and the loading end of the composite spring according to actual needs. The fixed end is the part of the composite spring that is fixed during installation and use, and the loading end is the part of the composite spring that bears external forces; The working condition complexity = the reference value of the characteristic point × the characteristic point distribution coefficient. The reference value of the characteristic point is the number of characteristic points in a single composite spring. The characteristic point distribution coefficient is the average value of the distance reference values corresponding to each characteristic point. The distance reference value corresponding to a single characteristic point is the average value of the shortest distances from this characteristic point to other characteristic points outside this characteristic point; The multi-directional uniformity is the average value of the sub-uniformities corresponding to each operation monitoring data. The sub-uniformity corresponding to a single operation monitoring data = 1 / (the standard deviation of the multi-point differences corresponding to each time point + 1). The multi-point difference corresponding to a single time point in a single operation monitoring data is the standard deviation of the force values of each characteristic point at this time point in this operation monitoring data; The time points are set by the user himself. A method for setting time points is provided. Taking the starting moment of a single operation monitoring data as the starting point, an interval point is set every 5 s in chronological order, and the starting point and each interval point are all recorded as time points; The values of the preset working condition complexity and the preset multi-directional uniformity can be determined by the user according to the actual application scenario. The greater the user's demand for improving the simulation accuracy, the smaller the values of the preset working condition complexity and the preset multi-directional uniformity. A method for determining the values of the preset working condition complexity and the preset multi-directional uniformity is provided. Detect the historical records of the user's determination of the simulation application points according to the distance coefficient and the action similarity, and respectively record the average value of the working condition complexity and the average value of the multi-directional uniformity corresponding to the historical records that can meet the user's needs as the preset working condition complexity and the preset multi-directional uniformity; Determining the simulation application points according to the distance coefficient and the action similarity includes: performing correlation analysis for each characteristic point. When performing correlation analysis for a single characteristic point, this characteristic point is recorded as the target characteristic point, and the other characteristic points that have not been recorded in the correlation combination except this characteristic point are recorded as reference characteristic points. The reference characteristic points and the target characteristic point whose distance coefficient from the target characteristic point is less than the preset distance coefficient and the action similarity is greater than the preset action similarity are recorded as a correlation combination, and continue to perform correlation analysis for the characteristic points that have not been recorded in the correlation combination until all characteristic points are recorded in the correlation combination, then stop the correlation analysis; Any one of the characteristic points in each correlation combination is used as the simulation application point, and each correlation combination corresponds to a simulation application point; The distance coefficient corresponding to any two feature points is the shortest distance between the two feature points. The action similarity corresponding to any two feature points = the number of time points with the same force value in the sub-monitoring data corresponding to the two feature points / the number of time points in a single sub-monitoring data. For the values of the preset distance coefficient and the preset action similarity, the user can determine them according to the actual application scenario. The greater the user's demand for improving the simulation accuracy, the smaller the value of the preset distance coefficient and the greater the value of the preset action similarity. Provide a set of values for the preset distance coefficient and the preset action similarity, extract the average value of the reference distance coefficients and the average value of the reference action similarities corresponding to each historical record that can meet the user's needs, and record them as the preset distance coefficient and the preset action similarity respectively. The reference distance coefficient is the distance coefficient corresponding to any two feature points in a single associated combination of a single historical record, and the reference action similarity is the action similarity corresponding to any two feature points in a single associated combination of a single historical record.

[0040] Specifically, when the working condition complexity is greater than or equal to the preset working condition complexity or the multi-directional uniformity is less than the preset multi-directional uniformity, the secondary detection module determines that the simulation method is to determine the applied simulation method according to the feature point category. If the feature point category is a type of feature point with an action duration coefficient less than the preset action duration coefficient or a change response value greater than or equal to the preset change response value, the applied simulation method is intermittent application. If the feature point category is a type of feature point with an action duration coefficient greater than or equal to the preset action duration coefficient and a change response value less than the preset change response value, the applied simulation method is continuous application.

[0041] Among them, the action duration coefficient corresponding to a single feature point is the average value of the sub-duration coefficients corresponding to each operation monitoring data, and the sub-duration coefficient corresponding to a single operation monitoring data = the number of time points with a non-zero force value corresponding to the feature point in the operation monitoring data / the total number of time points in the operation monitoring data. The change response value is the standard deviation of the force values corresponding to each time point of the feature point in the operation monitoring data. During continuous application, each type-two feature point is used as a simulation application point within the detection time. During intermittent application, the type-one feature points are used as simulation application points within the feature time corresponding to the type-one feature points. The characteristic time corresponding to a single type-I characteristic point is the time range that coincides with the characteristic time in the (2n + 1)-th application period, where n is 0, 1, 2, ……, n0, and n0 is the smallest integer greater than or equal to n1, and n1 = detection time / duration of a single application period corresponding to this type-I characteristic point; the user can determine the duration of the detection time according to actual detection requirements. A value of the detection time is provided, and the detection time is 1 h. In the present invention, each type-I characteristic point is provided with a continuously circulating application period within the detection time. The duration of the application period corresponding to a single type-I characteristic point and the characteristic evaluation threshold corresponding to this type-I characteristic point have a negative correlation; the characteristic evaluation threshold corresponding to a single type-I characteristic point = change response value / preset change response value - action duration coefficient / preset action duration coefficient. For the values of the preset action duration coefficient and the preset change response value, the user can determine them according to the actual application scenario. The greater the user's demand for improving the simulation accuracy, the smaller the value of the preset action duration coefficient and the greater the value of the preset change response value. A method for obtaining the values of the preset action duration coefficient and the preset change response value is provided. Detect the historical records of intermittent application, and respectively record the average value of the action duration coefficient and the average value of the change response value corresponding to the historical records that can meet the user's needs as the preset action duration coefficient and the preset change response value.

[0042] Specifically, the secondary detection module determines the application setting method according to the usage impact threshold, including: If the usage impact threshold is greater than or equal to the preset usage impact threshold, the application setting method is span adjustment detection; If the usage impact threshold is less than the preset usage impact threshold, the application setting method is constant threshold detection.

[0043] Among them, the usage impact threshold corresponding to a single simulation application point is the average value of the sub-impact thresholds corresponding to this simulation application point in each operation monitoring data; The sub-impact threshold corresponding to a single simulation application point in a single operation monitoring data = maximum duration ratio / preset maximum duration ratio + maximum force value / preset maximum force value; The maximum duration ratio = the number of time points with the maximum force value in the sub-monitoring data corresponding to a single simulation application point / the total number of time points in the sub-monitoring data corresponding to a single simulation application point; The maximum force value is the maximum value among the force values corresponding to each time point in the sub-monitoring data corresponding to a single simulation application point; The value of the preset usage impact threshold can be determined by the user according to the actual application scenario. The larger the value of the preset usage impact threshold, the greater the user's need for constant threshold detection. Provide a method for setting the value of the preset usage impact threshold, detect the historical records of the user's constant threshold detection, and record the average value of the usage impact threshold corresponding to the historical records that can meet the user's needs as the preset usage impact threshold; The values of the preset maximum duration ratio and the preset maximum force value can be determined by the user according to the actual application scenario. The larger the values of the preset maximum duration ratio and the preset maximum force value, the greater the user's need for constant threshold detection. Through a method for setting the values of the preset maximum duration ratio and the preset maximum force value, calculate the average value of the maximum duration ratio and the average value of the maximum force value corresponding to the historical records that can meet the user's needs, and record them as the values of the preset maximum duration ratio and the preset maximum force value respectively; In the span adjustment detection, for a single simulated application point, increase the applied force of this simulated application point from the initial force value at a standard increasing speed to the maximum force value, and keep the force value unchanged when it reaches the maximum force value; It should be noted that in the intermittent application, record the application period of the applied force as the analysis period. For a single analysis period, record this analysis period as the target period, and record the analysis period adjacent to the target period and after the target period as the adjacent period. The force value of the applied force at the start time of the adjacent period is the same as the force value of the applied force at the end time of the target period; In the constant threshold detection, for a single simulated application point, set the applied force of this simulated application point to the maximum force value corresponding to this simulated application point and keep the force value unchanged.

[0044] It can be understood that due to the different force characteristics of different simulated application points, the corresponding detection requirements will also vary. For some application points with larger forces or longer durations, a more detailed detection process may be required to accurately evaluate their performance and impact, and the span adjustment detection can provide more refined detection results and is suitable for these situations; while for application points with relatively smaller forces or shorter durations, the constant threshold detection can quickly and efficiently complete the detection task and meet the corresponding requirements.

[0045] The directions of the applied forces corresponding to each simulated application point are parallel to the central axis of the target composite spring and the directions of the applied forces of each simulated application point are the same; Provide a method for setting the initial force value and the standard increasing speed. The initial force value is 0N and the standard increasing speed is 10N / min; when applying forces at each simulated application point of the composite spring, use an electric push rod for application. This is easily understood by those skilled in the art and will not be elaborated here; Secondary anomaly coefficient = Deformation coefficient / Preset deformation coefficient + Relaxation dislocation coefficient / Preset relaxation dislocation coefficient; For the value of the preset secondary anomaly coefficient, the user can determine it according to the actual application scenario. The greater the user's demand for improving the detection accuracy, the smaller the value of the preset secondary anomaly coefficient. Provide a value of the preset secondary anomaly coefficient. Check the historical records of determining the composite spring as qualified. Denote the maximum value of the secondary anomaly coefficient corresponding to the historical records that can meet the user's needs as the preset secondary anomaly coefficient; It should be noted that if the secondary anomaly coefficient is less than or equal to the preset anomaly influence coefficient, it is determined that the detection result of the target composite spring is qualified.

[0046] Specifically, the non-destructive testing module determines the non-destructive testing method according to the deformation coefficient and the relaxation dislocation coefficient, including: If the deformation coefficient is greater than or equal to the preset deformation coefficient or the relaxation dislocation coefficient is greater than or equal to the preset relaxation dislocation coefficient, the non-destructive testing method is to set non-destructive testing points in an associated manner; If the deformation coefficient is less than the preset deformation coefficient and the relaxation dislocation coefficient is less than the preset relaxation dislocation coefficient, the non-destructive testing method is to set non-destructive testing points evenly.

[0047] Among them, the helix of the composite spring is equally divided into w parts. The value of w is positively correlated with the length of the helix. A displacement sensor is set at each equal division point. The helix of the composite spring forms a continuous spiral-shaped line by winding the spring wire around the central axis of the composite spring; The deformation coefficient is the maximum value among the maximum displacement amounts corresponding to each equal division point. The maximum displacement amount corresponding to a single equal division point is the maximum value among the displacement amounts corresponding to each reference time point during the monitoring time; The relaxation dislocation coefficient is the average value of the relaxation reference values corresponding to each reference time point. The relaxation reference value corresponding to a single reference time point is the standard deviation of the displacement amounts corresponding to each equal division point at this reference time point; The reference time point is set by the user himself. Provide a method for setting the reference time point. Take the starting moment of the detection time as the first starting point, and set a first interval point every 10 s in chronological order. Denote the first starting point and each first interval point as reference time points; For the values of the preset deformation coefficient and the preset relaxation dislocation coefficient, the user can determine them according to the actual application scenario. The smaller the values of the preset deformation coefficient and the preset relaxation dislocation coefficient, the greater the user's demand for setting non-destructive testing points in an associated manner. Provide the values of the preset deformation coefficient and the preset relaxation dislocation coefficient. Check the historical records of the user setting non-destructive testing points in an associated manner. Denote the average value of the deformation coefficient and the average value of the relaxation dislocation coefficient corresponding to the historical records that can meet the user's needs, and respectively denote them as the preset deformation coefficient and the preset relaxation dislocation coefficient; Mark each equal division point obtained by equally dividing the helical coils of the composite spring into w equal parts as a point to be selected; Associate and set non-destructive testing points, including: performing associated detection for each point to be selected. When performing associated detection for a single point to be selected, mark this point to be selected as the target point to be selected, mark the points to be selected that are not included in the combination of points to be selected other than the target point to be selected as reference points to be selected, include the reference points to be selected whose matching reference value with the target point to be selected is greater than the preset matching reference value and the target point to be selected into a combination of points to be selected, and continue to perform associated detection for the points to be selected that are not included in the combination of points to be selected until all points to be selected are included in the combination of points to be selected; and select any one point to be selected from each combination of points to be selected as a non-destructive testing point, and there is a non-destructive testing point in each combination of points to be selected; Uniformly set non-destructive testing points, including: starting from the first point to be selected on any one side of the helical coil, perform interval selection for each point to be selected, mark the selected points to be selected as non-destructive testing points, the selection interval is the number of points to be selected between two adjacent non-destructive testing points, and the selection interval has a positive correlation with the total number of points to be selected; The matching reference value corresponding to any two points to be selected = 1 - (the absolute value of the difference between the evaluation coefficients corresponding to the two points to be selected / the larger value of the evaluation coefficients corresponding to the two points to be selected), the evaluation coefficient of a single point to be selected = the average value of the displacement amounts corresponding to the single point to be selected at each reference time point / the standard deviation of the displacement amounts corresponding to the single point to be selected at each reference time point; the preset matching reference value; the value of the preset matching reference value can be determined by the user according to the actual application scenario. The greater the user's demand for improving the detection accuracy, the greater the value of the preset matching reference value. Provide a value of the preset matching reference value, and the preset matching reference value is 80%; Perform non-destructive testing for each non-destructive testing point, including: emitting 5MHz high-frequency ultrasonic waves into the target composite spring in a direction perpendicular to the central axis of the spring through a probe at the non-destructive monitoring point.

[0048] Specifically, when the detection point correlation degree is greater than or equal to the preset detection point correlation degree and the instability threshold is greater than or equal to the preset instability threshold, it is determined that the adjustment method is to perform a decreasing adjustment for the gradient reference value; The decreasing value of the gradient reference value has a positive correlation with the comprehensive evaluation coefficient.

[0049] Among them, the detection point correlation degree = 1 / (the standard deviation of the defect coefficients corresponding to each non-destructive testing point + 1), and the defect coefficient corresponding to a single non-destructive testing point = the echo amplitude corresponding to this non-destructive testing point / the average value of the echo amplitudes corresponding to each non-destructive testing point + the echo time corresponding to this non-destructive testing point / the average value of the echo times corresponding to each non-destructive testing point; The echo amplitude corresponding to a single non-destructive testing point is the signal intensity captured by the receiving probe after the ultrasonic wave is reflected at the defect after being emitted by the ultrasonic transmitting probe at this non-destructive testing point, with the unit of dB. The echo time corresponding to a single non-destructive testing point is the total time required for the ultrasonic wave to travel from the transmitting probe to the defect and then back to the receiving probe after the ultrasonic transmitting probe emits the ultrasonic wave at this non-destructive testing point; The instability threshold is the average value of the echo amplitudes corresponding to each non-destructive testing point; For the values of the preset detection point correlation degree and the preset instability threshold, the user can determine them according to the actual application scenario. The smaller the values of the preset detection point correlation degree and the preset instability threshold, the greater the user's need to increase the adjustment of the gradient reference value. Provide a set of values for the preset detection point correlation degree and the preset instability threshold, detect the historical records of the user's increase adjustment of the gradient reference value, and record the average value of the detection point correlation degrees corresponding to the historical records that can meet the user's needs as the preset detection point correlation degree, and record the average value of the instability thresholds corresponding to the historical records that can meet the user's needs as the preset instability threshold; Comprehensive evaluation coefficient = detection point correlation degree / preset detection point correlation degree + instability threshold / preset instability threshold; Gradient reference value = (final heating temperature - initial heating temperature) / heating-up time. It should be noted that when adjusting the gradient reference value downward, the final heating temperature and the initial heating temperature remain unchanged, and only the heating-up time is adjusted.

[0050] Specifically, when the detection point correlation degree is less than the preset detection point correlation degree or the instability threshold is less than the preset instability threshold, the non-destructive testing module determines that the adjustment method is local strengthening according to the defect coefficient.

[0051] Among them, local strengthening according to the defect coefficient includes: marking the non-destructive testing points with defect coefficients greater than the preset defect coefficient as instability points, marking each instability point and each point to be selected in the combination of points to be selected corresponding to each instability point as points to be strengthened, marking the smallest spiral line segment that can contain each point to be strengthened as the strengthening segment, increasing the number of fiber winding layers of the strengthening segment, and the increase value of the number of fiber winding layers of the strengthening segment has a positive correlation with the strengthening threshold; the strengthening threshold is the maximum value of the defect coefficients corresponding to each instability point; For the value of the preset defect coefficient, the user can determine it according to the actual application scenario. The greater the user's need to improve the preparation accuracy, the smaller the value of the preset defect coefficient. Detect the historical records of the user's local strengthening according to the defect coefficient, and record the average value of the defect coefficients corresponding to each instability point in the historical records that can meet the user's needs as the preset defect coefficient.

[0052] Please refer to Figure 4As shown, it is a schematic diagram of the intelligent detection method for the integrally stamping composite spring of the present invention. The present invention also provides an intelligent detection method for the integrally stamping composite spring, including: Obtain operation monitoring data and conduct a primary detection on the composite spring to obtain detection data; Determine the state of the composite spring according to the detection abnormality degree and the abnormality correlation degree, and determine the processing method as secondary detection analysis or adjustment of the fiber winding angle according to the state of the composite spring; In the secondary detection analysis, determine the simulation method according to the working condition complexity and the multi-directional uniformity degree, and determine the application setting method according to the use influence threshold; The simulation method is to determine the simulation application point according to the distance coefficient and the action similarity or determine the application simulation method according to the feature point category. The application simulation method is intermittent application or continuous application, and the application setting method is span adjustment detection or constant threshold detection; Under the preset detection conditions, determine the non-destructive detection method as associated setting of non-destructive detection points or uniform setting of non-destructive detection points according to the deformation coefficient and the relaxation dislocation coefficient, and determine the adjustment method as adjustment of the gradient reference value or local strengthening according to the defect coefficient according to the detection point correlation degree and the instability threshold.

[0053] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0054] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent detection system for an integrally stamped composite spring, characterized in that, Comprising: A data acquisition module for acquiring operation monitoring data; A primary detection module for performing a primary detection on the composite spring to obtain detection data; A processing and analysis module connected to the primary detection module for determining the state of the composite spring according to the detection abnormality degree and the abnormality correlation degree, and determining the processing method as secondary detection analysis or adjusting the fiber winding angle according to the state of the composite spring; A secondary detection module connected to the data acquisition module and the processing and analysis module respectively for determining the simulation method according to the working condition complexity and the multi-directional uniformity degree in the secondary detection analysis, and determining the application setting method according to the use influence threshold; The simulation method is to determine the simulation application point according to the distance coefficient and the action similarity or to determine the application simulation method according to the feature point category. The application simulation method is intermittent application or continuous application. The application setting method is span adjustment detection or constant threshold detection; A non-destructive detection module connected to the secondary detection module for determining the non-destructive detection method as associatively setting non-destructive detection points or uniformly setting non-destructive detection points according to the deformation coefficient and the relaxation dislocation coefficient under preset detection conditions, and determining the adjustment method as adjusting the gradient reference value or locally strengthening according to the defect coefficient according to the detection point correlation degree and the instability threshold; 2. The intelligent detection system for the integrally stamped composite spring according to claim 1, wherein When the state of the composite spring is that the detection abnormality degree is greater than or equal to the preset detection abnormality degree and the abnormality correlation degree is greater than or equal to the preset abnormality correlation degree, the processing and analysis module determines that the processing method is to reduce and adjust the fiber winding angle; The reduction value of the fiber winding angle has a positive correlation with the abnormality coefficient.

3. The intelligent detection system for the integrally stamped composite spring according to claim 2, wherein, When the state of the composite spring is that the detection abnormality degree is less than the preset detection abnormality degree or the abnormality correlation degree is less than the preset abnormality correlation degree, the processing and analysis module determines that the processing method is secondary detection analysis.

4. The intelligent detection system for the integrally stamped composite spring according to claim 3, characterized in that, When the working condition complexity is less than the preset working condition complexity and the multi-directional uniformity degree is greater than or equal to the preset multi-directional uniformity degree, the secondary detection module determines that the simulation method is to determine the simulation application point according to the distance coefficient and the action similarity.

5. The intelligent detection system for the integrally stamped composite spring according to claim 4, wherein, When the working condition complexity is greater than or equal to the preset working condition complexity or the multi-directional uniformity degree is less than the preset multi-directional uniformity degree, the secondary detection module determines that the simulation method is to determine the application simulation method according to the feature point category; If the feature point category is a type of feature point with an action duration coefficient less than the preset action duration coefficient or a change response value greater than or equal to the preset change response value, the application simulation method is intermittent application; If the feature point category is a type of feature point with an action duration coefficient greater than or equal to the preset action duration coefficient and a change response value less than the preset change response value, the application simulation method is continuous application.

6. The intelligent detection system for the integrally stamped composite spring according to claim 5, characterized in that, The secondary detection module determines the application setting method according to the use influence threshold, including: If the use influence threshold is greater than or equal to the preset use influence threshold, the application setting method is span adjustment detection; If the use influence threshold is less than the preset use influence threshold, the application setting method is constant threshold detection.

7. The intelligent detection system for the integrally stamped composite spring according to claim 6, characterized in that, The non-destructive detection module determines the non-destructive detection method according to the deformation coefficient and the relaxation dislocation coefficient, including: If the deformation coefficient is greater than or equal to the preset deformation coefficient or the relaxation dislocation coefficient is greater than or equal to the preset relaxation dislocation coefficient, the non-destructive testing method is to set non-destructive testing points in association; If the deformation coefficient is less than the preset deformation coefficient and the relaxation dislocation coefficient is less than the preset relaxation dislocation coefficient, the non-destructive testing method is to set non-destructive testing points uniformly.

8. The intelligent detection system of the integrally stamped composite spring according to claim 7, characterized in that, When the detection point correlation degree of the non-destructive testing module is greater than or equal to the preset detection point correlation degree and the instability threshold is greater than or equal to the preset instability threshold, it is determined that the adjustment method is to reduce the adjustment for the gradient reference value; The reduction value of the gradient reference value has a positive correlation with the comprehensive evaluation coefficient.

9. The intelligent detection system for the integrally stamped composite spring according to claim 8, wherein, When the detection point correlation degree of the non-destructive testing module is less than the preset detection point correlation degree or the instability threshold is less than the preset instability threshold, it is determined that the adjustment method is to perform local strengthening according to the defect coefficient.

10. A detection method for an intelligent detection system of a composite spring formed by integral stamping according to any one of claims 1 to 9, characterized in that, Including: Obtain the operation monitoring data and conduct a primary detection on the composite spring to obtain the detection data; Determine the state of the composite spring according to the detection abnormality degree and the abnormality correlation degree, and determine the processing method as secondary detection analysis or adjustment for the fiber winding angle according to the state of the composite spring; In the secondary detection analysis, determine the simulation method according to the working condition complexity and the multi-directional uniformity degree, and determine the application setting method according to the use influence threshold; The simulation method is to determine the simulation application point according to the distance coefficient and the action similarity or determine the application simulation method according to the characteristic point category. The application simulation method is intermittent application or continuous application, and the application setting method is span adjustment detection or constant threshold detection; Under the preset detection conditions, determine the non-destructive testing method as setting non-destructive testing points in association or uniformly setting non-destructive testing points according to the deformation coefficient and the relaxation dislocation coefficient, and determine the adjustment method as adjusting for the gradient reference value or performing local strengthening according to the defect coefficient according to the detection point correlation degree and the instability threshold.

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