Resin infusion process monitoring system based on ultrasonic waves

Abstract

The invention relates to the technical field of resin infusion monitoring, in particular to a resin infusion process monitoring system based on ultrasonic waves, which is provided with a data acquisition module, a pre-analysis module, an area identification module, an infusion analysis module and an infusion regulation and control module. Feature monitoring areas are marked through an area identification module, an ultrasonic feedback curve of each feature monitoring area is constructed through a perfusion analysis module, a coupling target moment of each feature monitoring area is determined, perfusion time sequence analysis is performed on each feature monitoring area to obtain an area perfusion sequence, and flow characterization parameters are determined through a perfusion regulation and control module. Therefore, whether the perfusion pressure parameters are optimized or not is judged. According to the method, the characteristic monitoring area is marked according to the actual characteristics of the mold, the ultrasonic monitoring points are subjected to conjoint analysis so as to adaptively adjust the technological parameters of the pouring process, and the reliability of monitoring the resin pouring process is improved.

Description

Technical Field

[0001] This invention relates to the field of resin infusion monitoring technology, and more particularly to an ultrasonic-based resin infusion process monitoring system. Background Technology

[0002] Resin-based composite materials are high-performance materials formed by adding reinforcing materials such as glass fiber, carbon fiber, and aramid fiber to the resin matrix. They possess characteristics including lightweight and high strength, corrosion resistance, high design flexibility, and fatigue resistance, and are widely used in various fields. Vacuum infusion is a closed-mold molding technology for composite material manufacturing. Its core is to uniformly infuse resin into pre-laid reinforcing materials using vacuum negative pressure, achieving efficient wetting. The resin flow behavior during the infusion process, including the propulsion path and speed of the flow front, and the final wetting integrity of the fibers, directly determines the internal quality of the molded component, such as the presence of defects like dry spots and cavitation, and the final mechanical properties. To ensure process stability and product quality, real-time and accurate online monitoring of the resin infusion process is crucial. Traditional monitoring methods mainly rely on monitoring the injection port and drainage... The monitoring of air inlet pressure and flow rate has significant lag and uncertainty. The introduction of embedded sensors, such as optical fibers, itself constitutes foreign matter inclusions, altering the continuity of the fiber structure and potentially becoming stress concentration points. This negatively impacts key mechanical properties of composite materials, such as structural integrity and fatigue performance. Furthermore, the embedding operation increases the complexity and time required for layup, and sensors are typically not reusable, leading to high per-piece production costs. Ultrasonic technology, due to its excellent penetration ability across various materials and sensitivity to interface conditions, can be used for resin infusion monitoring. However, the geometric complexity of the mold itself makes it difficult to detect potential anomalies in the infusion process through single-point ultrasonic feedback monitoring, affecting the efficiency and reliability of resin infusion process monitoring. Therefore, improving the reliability of resin infusion process monitoring is an urgent technical problem to be solved.

[0003] For example, Chinese patent application publication number CN120912560A discloses a dynamic prevention and control method for semi-dry yarn defects in the wind turbine blade infusion process. This method includes: collecting temperature and humidity data from the production environment in real time via a sensor network; monitoring dynamic changes during the infusion process, covering the mold area and resin flow path; generating an initial environmental dataset containing environmental fluctuation characteristics; acquiring fiber wetting status information in real time during the infusion process based on the corrected flow distribution dataset; analyzing high-risk areas of semi-dry yarn defects that may lead to incomplete fiber wetting; determining key defect prevention and control locations; acquiring local image data of fiber wetting using image acquisition equipment for the determined defect prevention and control locations; identifying the characteristics of semi-dry yarn defects through image processing technology; and accurately determining the distribution range and severity level of the defects.

[0004] The following problems still exist in the existing technology: Existing technologies do not take into account the geometric complexity of the mold itself, which makes it difficult to obtain potential anomalies in the injection process through single-point ultrasonic feedback monitoring. Existing technologies cannot mark characteristic monitoring areas according to the actual characteristics of the mold, cannot perform joint analysis of various ultrasonic monitoring points, and cannot adaptively adjust the process parameters of the injection process, thus affecting the reliability of resin injection process monitoring. Summary of the Invention

[0005] To address these issues, the present invention provides an ultrasonic-based resin injection process monitoring system to overcome the problems of existing technologies that cannot mark characteristic monitoring areas according to the actual characteristics of the mold, cannot perform joint analysis of various ultrasonic monitoring points, and cannot adaptively adjust the process parameters of the injection process, thus affecting the reliability of resin injection process monitoring.

[0006] To achieve the above objectives, the present invention provides an ultrasonic-based resin infusion process monitoring system, comprising: The data acquisition module is used to acquire several ultrasonic reflection signals at each monitoring point on the resin injection mold and the surface point cloud data of the resin injection mold. The pre-analysis module, which is connected to the data acquisition module, is used to determine the global distortion parameters based on the surface point cloud data of the resin injection mold, so as to determine whether there is an abnormal risk in the resin injection. The region identification module is connected to the data acquisition module and the pre-analysis module respectively, and is used to divide the resin injection mold into several monitoring regions, and determine the warping tendency parameters based on the surface point cloud data of each monitoring region to mark the feature monitoring region. The perfusion analysis module is connected to the data acquisition module and the region identification module respectively. It is used to construct the ultrasonic feedback curve of each feature monitoring region based on several ultrasonic reflection signals, determine the coupling target time of each feature monitoring region according to the ultrasonic feedback curve, and perform perfusion time series analysis on each feature monitoring region based on each coupling target time to obtain the region perfusion sequence. The perfusion control module, connected to the perfusion analysis module, is used to acquire ultrasonic feedback curves of each characteristic monitoring area according to the regional perfusion sequence, determine flow characterization parameters based on the comparison between each ultrasonic feedback curve, and determine whether to optimize the perfusion pressure parameters. The increase in the perfusion pressure parameters is determined based on the global distortion parameters and the flow characterization parameters.

[0007] Furthermore, the pre-analysis module is used to determine global distortion parameters based on the surface point cloud data of the resin injection mold, wherein, The pre-analysis module acquires several surface point cloud data of the resin injection mold, and determines the variance of the feature surface point cloud data as the global distortion parameter. The feature surface point cloud data is surface point cloud data perpendicular to the horizontal plane.

[0008] Furthermore, the pre-analysis module is used to determine whether there are any abnormal risks in the resin infusion process, wherein, The pre-analysis module determines that there is an abnormal risk in the resin injection based on the judgment result that the global distortion parameter of the resin injection mold exceeds the preset global distortion parameter threshold.

[0009] Furthermore, the region identification module is used to mark the feature monitoring region according to the warping tendency parameter, wherein, The region identification module acquires surface point cloud data of each monitoring region, determines the mean curvature of each monitoring region based on the surface point cloud data, and determines the mean curvature as the warping tendency parameter of the monitoring region. The region identification module marks the monitoring region as a feature monitoring region based on the determination result that the warping tendency parameter of the monitoring region exceeds the preset warping tendency parameter threshold.

[0010] Furthermore, the perfusion analysis module is used to determine several feedback trend parameters based on the ultrasonic feedback curve, wherein, The perfusion analysis module is used to construct the ultrasonic feedback curve of the characteristic monitoring area based on the ultrasonic reflection signals at several acquisition times, and to determine the absolute value of the difference between the intensity parameters of the ultrasonic reflection signals at adjacent acquisition times as the feedback trend parameter.

[0011] Furthermore, the perfusion analysis module is used to determine the coupling target time of each feature monitoring region, wherein, The perfusion analysis module is used to determine the time period consisting of adjacent acquisition times that meet the coupling conditions as the characteristic acquisition time period, and to determine the latter acquisition time among the adjacent acquisition times as the coupling target time of the characteristic monitoring area. The coupling condition is that the feedback trend parameter at adjacent acquisition times exceeds a preset feedback trend parameter threshold. The ultrasonic feedback curve is constructed with time as the horizontal axis and the intensity parameter of the ultrasonic reflected signal as the vertical axis.

[0012] Furthermore, the perfusion analysis module is used to perform perfusion time-series analysis on each characteristic monitoring area to obtain the regional perfusion sequence, wherein, The perfusion analysis module obtains the coupling target time of each feature monitoring region, and sorts the feature monitoring regions corresponding to the coupling target time according to the time sequence to obtain the regional perfusion sequence.

[0013] Furthermore, the perfusion control module is used to determine several ultrasonic feedback curve segments, wherein, The perfusion control module obtains the characteristic acquisition time period corresponding to the coupling target time of any two adjacent characteristic monitoring areas in the regional perfusion sequence, and determines the ultrasonic feedback curve segment of the characteristic acquisition time period. The ultrasonic feedback curve segment of the preceding feature monitoring area in two adjacent feature monitoring areas is used to determine the first ultrasonic feedback curve segment, and the ultrasonic feedback curve segment of the following feature monitoring area in the two adjacent feature monitoring areas is used to determine the second ultrasonic feedback curve segment.

[0014] Furthermore, the infusion control module is used to determine flow characterization parameters based on the comparison between various ultrasonic feedback curves, wherein, The infusion control module is used to calculate the ratio of the second flow characteristic to the first flow characteristic, and to determine the ratio as the flow characterization parameter. The first flow characteristic is the average slope of the first ultrasonic feedback curve segment, and the second flow characteristic is the average slope of the second ultrasonic feedback curve segment.

[0015] Furthermore, the infusion control module is used to determine whether to optimize the infusion pressure parameters, wherein, The infusion control module determines to optimize the infusion pressure parameters based on the determination result that there are flow characterization parameters in the regional infusion sequence that meet the conditions for infusion pressure optimization. The increase in infusion pressure is positively correlated with the global distortion parameter and negatively correlated with the mean of the flow characterization parameter. The injection pressure optimization condition is that the flow characterization parameters do not exceed the preset flow characterization parameter threshold.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention sets up a data acquisition module, a pre-analysis module, a region identification module, a pouring analysis module, and a pouring control module. The data acquisition module acquires several ultrasonic reflection signals from each monitoring point on the resin pouring mold and surface point cloud data of the resin pouring mold. The pre-analysis module determines global distortion parameters based on the surface point cloud data of the resin pouring mold to determine whether there is an abnormal risk in the resin pouring. The region identification module determines warping tendency parameters based on the surface point cloud data of each monitoring region to mark the characteristic monitoring regions. The pouring analysis module constructs the ultrasonic distortion parameters for each characteristic monitoring region. The ultrasonic feedback curve is used to determine the coupling target time of each characteristic monitoring area. Based on the coupling target time, the injection time sequence of each characteristic monitoring area is analyzed to obtain the regional injection sequence. The injection control module obtains the ultrasonic feedback curve of each characteristic monitoring area according to the regional injection sequence. Based on the comparison between each ultrasonic feedback curve, the flow characterization parameters are determined to determine whether the injection pressure parameters need to be optimized. Thus, the characteristic monitoring area is marked according to the actual characteristics of the mold, and the joint analysis of each ultrasonic monitoring point is carried out to adaptively adjust the injection process parameters and improve the reliability of resin injection process monitoring.

[0017] In particular, this invention uses a pre-analysis module to determine global distortion parameters to assess the potential for abnormal risks in resin infusion. This involves acquiring point cloud data of the entire mold surface through 3D scanning and taking a sequence of height coordinates along the direction of gravity (perpendicular to the horizontal plane). This sequence characterizes the depth variation and overall undulation of the mold cavity. The statistical variance of this height sequence, calculated using global distortion parameters, characterizes the complexity of the mold's shape. A larger global distortion parameter indicates a more complex mold shape, making the resin more prone to flow separation, stagnation, and even cavitation due to path twists and abrupt changes in cross-section. This necessitates more refined subsequent analysis. Before the resin injection process begins, the geometric complexity of the mold is predicted, which solves the pain point of traditional monitoring systems when facing unknown or new molds, such as blind allocation of monitoring resources and inability to distinguish between priorities. This improves the economy and reliability of the monitoring system. For molds with relatively simple structures and low risks, it can avoid excessive investment in sensors and computing resources, reducing production costs. For molds with relatively complex structures and high risks, it can initiate a high-precision monitoring process, accurately focusing limited monitoring resources on the key areas where defects are most likely to occur. In this way, it can determine the risk of abnormal resin injection and improve the reliability of resin injection process monitoring.

[0018] In particular, this invention uses a region identification module to determine warpage tendency parameters based on surface point cloud data of the monitoring area to mark the feature monitoring area. It can be understood that the warpage tendency parameter is the mean curvature of the monitoring area. The larger the warpage tendency parameter, the more severe the curvature, such as acute angles or small-radius rounded corners; the smaller the warpage tendency parameter, the flatter the area. During resin injection, high-curvature areas, such as concave corners and convex ridges, can alter the local boundary conditions of resin flow. Flow dead zones and cavitation are easily formed in concave corners, while radial flow or frontal splitting may occur at convex ridges. These are high-risk areas for defects. By determining the warpage tendency parameter, the key local geometric features affecting resin flow are converted into digital signals, realizing the transformation from continuous three-dimensional morphology to discrete risk areas. This solves the problems of monitoring blind spots and resource waste caused by relying on manual experience in traditional monitoring, improving the effectiveness of monitoring and the reliability of defect capture. It lays a data foundation for subsequent process diagnosis and control, and further enables the marking of feature monitoring areas based on the actual characteristics of the mold, improving the reliability of resin injection process monitoring.

[0019] In particular, this invention uses an infusion analysis module to determine the coupling target time for each characteristic monitoring area based on the ultrasonic feedback curve. It is understood that when the resin has not reached the monitoring point, the ultrasonic wave undergoes near total reflection at the mold-air interface, and the signal strength remains relatively stable. When the resin arrives, the interface abruptly becomes the mold-resin interface, and the increased acoustic impedance matching leads to a sharp drop in reflected energy, which manifests as a steep falling edge on the ultrasonic feedback curve. By calculating the feedback trend parameter of the signal strength at adjacent acquisition times in real time, the instantaneous rate of change of this falling edge is quantified. When the rate of change exceeds a preset threshold, it indicates a physical abrupt change in the interface properties. This moment is determined as the critical node for acoustic coupling between the resin and the mold, and is recorded as the coupling target time, avoiding the influence of sensor base... To prevent misjudgments caused by line drift or environmental noise, continuous analog signals are converted into discrete event timestamps with clear physical meaning. The ultrasonic feedback curve is processed in real time by the infusion analysis module to accurately locate the key time point when the resin arrives at each feature monitoring area, i.e., the coupling target time. This breaks through the limitation of traditional monitoring methods that can only determine whether filling has occurred. By capturing the instantaneous change characteristics of signal intensity, the anti-interference capability and time accuracy of resin front arrival time detection are improved. This provides reliable time reference data for subsequent analysis of flow timing, calculation of local flow velocity, and diagnosis of flow anomalies. It also provides a time sequence information basis for early warning of defects such as flow stagnation and flow around. In this way, the coupling target time of each feature monitoring area is obtained, improving the reliability of resin infusion process monitoring.

[0020] In particular, this invention determines flow characterization parameters based on the comparison between various ultrasonic feedback curves through the injection control module, in order to determine whether the injection pressure parameters need to be optimized. It can be understood that under stable flow drive, the resin will continuously fill along the pressure gradient direction from near to far and from upstream to downstream. Therefore, the temporal sequence directly maps the spatial connectivity and flow path. By sorting the feature monitoring areas according to the temporal order of their coupled target times, the trajectory of the resin flow in space is characterized. By quantifying the relative changes in the flow state of adjacent monitoring areas, i.e., the flow characterization parameters, it is possible to identify developing process anomalies such as flow stagnation and uneven flow rate in real time and accurately before the resin filling is completed. This shortens the response time from problem discovery to correction, prevents further expansion or even formation of defects, improves the first-time molding pass rate of products, and reduces the waste of materials and time. Furthermore, it enables joint analysis of various ultrasonic monitoring points to adaptively adjust the injection process parameters, thereby improving the reliability of resin injection process monitoring. Attached Figure Description

[0021] Figure 1 This is a functional block diagram of an ultrasonic-based resin infusion process monitoring system according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the logic of the pre-analysis module in this invention for determining whether there is an abnormal risk in resin injection. Figure 3 This is a flowchart illustrating the logic of the region identification module marking feature monitoring region in an embodiment of the present invention. Figure 4 This is a flowchart illustrating the logic of the infusion control module in an embodiment of the present invention for determining whether to optimize the infusion pressure parameters. Detailed Implementation

[0022] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0023] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0024] It should be noted that in the description of this invention, the terms "upper," "lower," "inner," "outer," etc., which indicate the direction or positional relationship, are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0025] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0026] Please see Figure 1 The diagram shown is a functional block diagram of an ultrasonic-based resin infusion process monitoring system according to an embodiment of the present invention. The ultrasonic-based resin infusion process monitoring system of the present invention includes: The data acquisition module is used to acquire several ultrasonic reflection signals at each monitoring point on the resin injection mold and the surface point cloud data of the resin injection mold. Specifically, the structure of the data acquisition module is not specifically limited in the embodiments of the present invention. Preferably, it can acquire several ultrasonic reflection signals by an ultrasonic probe sensor solidified on the outer surface of the resin injection mold, and acquire surface point cloud data of the resin injection mold by a lidar scanning device. Of course, other forms can also be used, which will not be elaborated here. The several monitoring points for acquiring ultrasonic reflection signals can be evenly distributed on the resin injection mold.

[0027] Specifically, the outer surface of the resin injection mold can be treated first. The outer surface of the mold to be monitored can be polished and filled to make the surface smooth and flat. Using glue with fast curing properties, the sensor can be fixed to a specific point on the outer surface of the mold, so that the receiving and transmitting plane of the sensor is in close contact with the outer surface of the mold. A composite material layer is then laid. Materials used in the composite material molding process, including release cloth, fiberglass cloth, carbon fiber cloth, balsa wood core, foam core, release film, flow guide net, and vacuum bag film, are laid on the inner surface of the mold. After vacuuming, resin is injected.

[0028] The pre-analysis module, which is connected to the data acquisition module, is used to determine the global distortion parameters based on the surface point cloud data of the resin injection mold, so as to determine whether there is an abnormal risk in the resin injection. Specifically, the embodiments of the present invention do not specifically limit the structure of the pre-analysis module. Preferably, it can be a microprocessor to determine whether there is an abnormal risk in resin infusion. Of course, other forms can also be used, which will not be elaborated here.

[0029] The region identification module is connected to the data acquisition module and the pre-analysis module respectively, and is used to divide the resin injection mold into several monitoring regions, and determine the warping tendency parameters based on the surface point cloud data of each monitoring region to mark the feature monitoring region. Specifically, the embodiments of the present invention do not impose specific limitations on the structure of the region identification module. Preferably, it can be a microprocessor used to mark the feature monitoring region. Of course, other forms can also be adopted, which will not be elaborated here.

[0030] Specifically, the monitoring area can be evenly divided, with a monitoring point at the center of each monitoring area to obtain ultrasonic reflected signals.

[0031] The perfusion analysis module is connected to the data acquisition module and the region identification module respectively. It is used to construct the ultrasonic feedback curve of each feature monitoring region based on several ultrasonic reflection signals, determine the coupling target time of each feature monitoring region according to the ultrasonic feedback curve, and perform perfusion time series analysis on each feature monitoring region based on each coupling target time to obtain the region perfusion sequence. Specifically, the embodiments of the present invention do not impose specific limitations on the structure of the perfusion analysis module. Preferably, it can be a processor used in a computer to construct ultrasonic feedback curves, determine the coupling target time, and obtain regional perfusion sequences. Of course, other forms can also be used, which will not be elaborated here.

[0032] There are no restrictions on the method for constructing the ultrasonic feedback curves for each feature monitoring area. For example, the ultrasonic feedback curves can be fitted using MATLAB correlation fitting software, which will not be elaborated further here.

[0033] The perfusion control module, connected to the perfusion analysis module, is used to acquire ultrasonic feedback curves of each characteristic monitoring area according to the regional perfusion sequence, determine flow characterization parameters based on the comparison between each ultrasonic feedback curve, and determine whether to optimize the perfusion pressure parameters. The increase in the perfusion pressure parameters is determined based on the global distortion parameters and the flow characterization parameters.

[0034] Specifically, the embodiments of the present invention do not specifically limit the structure of the infusion control module. Preferably, it can be a microprocessor used to determine whether to optimize the infusion pressure parameters. Of course, other forms can also be used, which will not be elaborated here.

[0035] Specifically, the pre-analysis module is used to determine global distortion parameters based on the surface point cloud data of the resin injection mold, wherein, The pre-analysis module acquires several surface point cloud data of the resin injection mold, and determines the variance of the feature surface point cloud data as the global distortion parameter. The feature surface point cloud data is surface point cloud data perpendicular to the horizontal plane.

[0036] Please see Figure 2 The diagram shown is a flowchart illustrating the logic of the pre-analysis module in this embodiment of the invention for determining whether there is an abnormal risk in resin injection. The pre-analysis module is used to determine whether there is an abnormal risk in resin injection. The pre-analysis module determines that there is an abnormal risk in resin injection based on the judgment result that the global distortion parameter of the resin injection mold exceeds the preset global distortion parameter threshold. Based on the determination that the global distortion parameters of the resin injection mold do not exceed the preset global distortion parameter threshold, it is determined that there is no abnormal risk in the resin injection.

[0037] Specifically, the preset threshold for global distortion parameters is the product of the reference value of global distortion parameters and the distortion factor. The reference value of global distortion parameters is the average value of global distortion parameters under the same working conditions in historical data. The distortion factor can be set by those skilled in the art according to the accuracy requirements of resin injection process monitoring. The higher the accuracy requirement, the smaller the value should be. The value range can be [1.05, 1.15], preferably 1.1.

[0038] Specifically, this embodiment of the invention uses a pre-analysis module to determine global distortion parameters to assess whether there are any abnormal risks in resin injection. Understandably, point cloud data of the entire mold surface is obtained through 3D scanning, and a sequence of height coordinates along the direction of gravity (perpendicular to the horizontal plane) is taken. This sequence characterizes the depth variation and overall undulation of the mold cavity. The statistical variance of this height sequence, calculated using global distortion parameters, characterizes the complexity of the mold's shape. A larger global distortion parameter indicates a more complex mold shape, making it more prone to flow separation, stagnation, and even cavitation due to path twists and abrupt changes in cross-section when resin flows within it. This necessitates more refined subsequent analysis. This system predicts the geometric complexity of the mold before the resin injection process begins, solving the problem of traditional monitoring systems that blindly allocate monitoring resources and fail to prioritize tasks when facing unknown or novel molds. This improves the economy and reliability of the monitoring system. For molds with relatively simple structures and low risks, it avoids excessive investment in sensors and computing resources, reducing production costs. For molds with relatively complex structures and high risks, it initiates a high-precision monitoring process, accurately focusing limited monitoring resources on the key areas most likely to have defects. In this way, it enables the determination of abnormal risks in resin injection and improves the reliability of monitoring the resin injection process.

[0039] Please see Figure 3The diagram shown is a logical flowchart of the region identification module marking feature monitoring regions according to an embodiment of the present invention. The region identification module is used to mark feature monitoring regions according to the warping tendency parameters. The region identification module acquires surface point cloud data of each monitoring region, determines the mean curvature of each monitoring region based on the surface point cloud data, and determines the mean curvature as the warping tendency parameter of the monitoring region. The region identification module marks the monitoring region as a feature monitoring region based on the determination result that the warping tendency parameter of the monitoring region exceeds the preset warping tendency parameter threshold. Based on the determination result that the warping tendency parameter of the monitoring area does not exceed the preset warping tendency parameter threshold, the monitoring area is not marked.

[0040] Specifically, the preset warp tendency parameter threshold is the product of the warp tendency parameter reference value and the warp factor. The warp tendency parameter reference value is the average value of the warp tendency parameter under the same working conditions in historical data. The warp factor can be set by those skilled in the art according to the accuracy requirements of resin injection process monitoring. The higher the accuracy requirement, the smaller the value should be. The value range can be [1.05, 1.15], preferably 1.1.

[0041] Specifically, in this embodiment of the invention, the warp tendency parameter is determined by the region identification module based on the surface point cloud data of the monitoring area to mark the feature monitoring area. It can be understood that the warp tendency parameter is the mean curvature of the monitoring area. The larger the warp tendency parameter, the more severe the curvature at that point, such as acute angles or small radius rounded corners. The smaller the warp tendency parameter, the flatter the area. During the resin injection process, high curvature areas, such as concave corners and convex ridges, will change the local boundary conditions of resin flow. Flow dead zones and cavitation are easily formed in concave corners, while radial flow or front splitting may occur at convex ridges. These are high-incidence areas of defects. By determining the warp tendency parameter, the key local geometric features affecting resin flow are converted into digital signals, realizing the transformation from continuous three-dimensional morphology to discrete risk areas. This solves the problems of monitoring blind spots and resource waste caused by relying on manual experience in traditional monitoring, improves the effectiveness of monitoring and the reliability of defect capture, and lays a data foundation for subsequent process diagnosis and control. Furthermore, it realizes the marking of feature monitoring areas according to the actual characteristics of the mold, improving the reliability of resin injection process monitoring.

[0042] Specifically, the perfusion analysis module is used to determine several feedback trend parameters based on the ultrasonic feedback curve, wherein, The perfusion analysis module is used to construct the ultrasonic feedback curve of the characteristic monitoring area based on the ultrasonic reflection signals at several acquisition times, and to determine the absolute value of the difference between the intensity parameters of the ultrasonic reflection signals at adjacent acquisition times as the feedback trend parameter.

[0043] Specifically, the interval between adjacent acquisition times can be set by those skilled in the art based on the accuracy requirements of monitoring the resin infusion process. The higher the accuracy requirement, the shorter the interval should be. The value range can be [5, 20], with the unit of interval being seconds. Preferably, it can be 10 seconds.

[0044] Specifically, the perfusion analysis module is used to determine the coupling target time of each feature monitoring region, wherein, The perfusion analysis module is used to determine the time period consisting of adjacent acquisition times that meet the coupling conditions as the characteristic acquisition time period, and to determine the latter acquisition time among the adjacent acquisition times as the coupling target time of the characteristic monitoring area. The coupling condition is that the feedback trend parameter at adjacent acquisition times exceeds a preset feedback trend parameter threshold. The ultrasonic feedback curve is constructed with time as the horizontal axis and the intensity parameter of the ultrasonic reflected signal as the vertical axis.

[0045] Specifically, the preset feedback trend parameter threshold is the product of the feedback trend parameter reference value and the feedback factor. The feedback trend parameter reference value is the average value of the feedback trend parameter under the same working conditions in historical data. The feedback factor can be set by those skilled in the art according to the accuracy requirements of resin injection process monitoring. The higher the accuracy requirement, the smaller the value should be. The value range can be [1.05, 1.2], preferably 1.15.

[0046] Specifically, in this embodiment of the invention, the injection analysis module determines the coupling target time for each characteristic monitoring area based on the ultrasonic feedback curve. It can be understood that when the resin has not reached the monitoring point, the ultrasonic wave undergoes near total reflection at the mold-air interface, and the signal strength remains relatively stable. When the resin arrives, the interface abruptly becomes the mold-resin interface, and the increased acoustic impedance matching leads to a sharp drop in reflected energy, which manifests as a steep falling edge on the ultrasonic feedback curve. By calculating the feedback trend parameter of the signal strength at adjacent acquisition times in real time, the instantaneous rate of change of this falling edge is quantified. When the rate of change exceeds a preset threshold, it indicates a physical abrupt change in the interface properties. This moment is determined as the critical node for acoustic coupling between the resin and the mold, and is recorded as the coupling target time, avoiding errors caused by sensing... To prevent misjudgments caused by baseline drift or environmental noise, continuous analog signals are converted into discrete event timestamps with clear physical meaning. The ultrasonic feedback curve is processed in real time by the infusion analysis module to accurately locate the key time point when the resin arrives at each characteristic monitoring area, i.e., the coupling target time. This breaks through the limitation of traditional monitoring methods that can only determine whether filling has occurred. By capturing the instantaneous change characteristics of signal intensity, the anti-interference capability and time accuracy of resin front arrival time detection are improved. This provides reliable time reference data for subsequent analysis of flow timing, calculation of local flow velocity, and diagnosis of flow anomalies. It also provides a time-series information basis for early warning of defects such as flow stagnation and flow around. In this way, the coupling target time of each characteristic monitoring area is obtained, improving the reliability of resin infusion process monitoring.

[0047] Specifically, the perfusion analysis module is used to perform perfusion time-series analysis on each characteristic monitoring area to obtain the regional perfusion sequence, wherein, The perfusion analysis module obtains the coupling target time of each feature monitoring region, and sorts the feature monitoring regions corresponding to the coupling target time according to the time sequence to obtain the regional perfusion sequence.

[0048] Specifically, the perfusion control module is used to determine several ultrasonic feedback curve segments, wherein, The perfusion control module obtains the characteristic acquisition time period corresponding to the coupling target time of any two adjacent characteristic monitoring areas in the regional perfusion sequence, and determines the ultrasonic feedback curve segment of the characteristic acquisition time period. The ultrasonic feedback curve segment of the preceding feature monitoring area in two adjacent feature monitoring areas is used to determine the first ultrasonic feedback curve segment, and the ultrasonic feedback curve segment of the following feature monitoring area in the two adjacent feature monitoring areas is used to determine the second ultrasonic feedback curve segment.

[0049] Specifically, the infusion control module is used to determine flow characterization parameters based on the comparison between various ultrasonic feedback curves, wherein, The infusion control module is used to calculate the ratio of the second flow characteristic to the first flow characteristic, and to determine the ratio as the flow characterization parameter. The first flow characteristic is the average slope of the first ultrasonic feedback curve segment, and the second flow characteristic is the average slope of the second ultrasonic feedback curve segment.

[0050] Please see Figure 4 The diagram shown is a flowchart illustrating the logic of the infusion control module in an embodiment of the present invention for determining whether to optimize the infusion pressure parameters. The infusion control module is used to determine whether to optimize the infusion pressure parameters. The infusion control module determines to optimize the infusion pressure parameters based on the determination result that there are flow characterization parameters in the regional infusion sequence that meet the conditions for infusion pressure optimization. The increase in infusion pressure is positively correlated with the global distortion parameter and negatively correlated with the mean of the flow characterization parameter. Based on the determination that there are no flow characterization parameters in the regional injection sequence that meet the conditions for injection pressure optimization, it is determined that the injection pressure parameters will not be optimized. The injection pressure optimization condition is that the flow characterization parameters do not exceed the preset flow characterization parameter threshold.

[0051] Specifically, the preset threshold for the flow characterization parameter is the product of the reference value of the flow characterization parameter and the flow factor. The reference value of the flow characterization parameter is the average value of the flow characterization parameter under the same working conditions in historical data. The flow factor can be calculated by those skilled in the art based on the average value of several historical experimental data, and the value range can be [1.05, 1.25]. Preferably, it can be 1.1.

[0052] Specifically, the increase in injection pressure is calculated as: (Global distortion parameter / Reference value of global distortion parameter × Distortion influence factor) + (Reference value of mean of flow characterization parameter / Mean of flow characterization parameter × Flow influence factor). The reference value of the mean of flow characterization parameter is the average value of the mean of flow characterization parameter under the same working conditions in historical data. The distortion influence factor and flow influence factor can be selected by those skilled in the art based on the degree of influence of the mean of global distortion parameter and the mean of flow characterization parameter on the calculation results in historical data. Distortion influence factor + flow influence factor = 1. Preferably, the distortion influence factor can be 0.6 and the flow influence factor can be 0.4. In particular, the injection pressure after the increase does not exceed the preset upper limit of injection pressure. The preset upper limit of injection pressure can be calculated by those skilled in the art based on the average of several experimental data.

[0053] Specifically, in this embodiment of the invention, the flow characterization parameters are determined by the injection control module based on the comparison between various ultrasonic feedback curves to determine whether the injection pressure parameters need to be optimized. It can be understood that under stable flow drive, the resin will continuously fill along the pressure gradient direction from near to far and from upstream to downstream. Therefore, the temporal sequence directly maps the spatial connectivity and flow path. By sorting the feature monitoring areas according to the temporal order of their coupled target times, the trajectory of the resin flow in space is characterized. By quantifying the relative changes in the flow state of adjacent monitoring areas, i.e., the flow characterization parameters, it is possible to identify developing process anomalies such as flow stagnation and uneven flow rate in real time and accurately before the resin filling is completed. This shortens the response time from problem discovery to correction, prevents further expansion or even formation of defects, improves the first-time molding pass rate of products, and reduces the waste of materials and time. Furthermore, it enables joint analysis of various ultrasonic monitoring points to adaptively adjust the injection process parameters and improve the reliability of resin injection process monitoring.

[0054] Specifically, it can be understood that selecting the characteristic curve segment representing the abrupt change in the state of the resin upon arrival, i.e., the characteristic acquisition period, physically reflects the average rate of signal change when the resin arrives at that point, and is positively correlated with the local apparent flow velocity of the resin at that point. By calculating the ratio of the average slope of the downstream point to the upstream point, i.e., the flow characterization parameter, the relative change of flow velocity in space is quantified. The smaller the flow characterization parameter, the more it indicates that the flow has abnormally decelerated downstream, the greater the risk of flow stagnation, and the greater the need to increase the injection pressure to increase the flow driving force to overcome the resistance. The larger the global distortion parameter, the more complex the mold, and the higher the upper limit of the allowable pressure adjustment. The smaller the flow characterization parameter, the more severe the current stagnation, and the greater the adjustment force required. Thus, it is possible to perform joint analysis of each ultrasonic monitoring point to adaptively adjust the process parameters of the injection process and improve the reliability of resin injection process monitoring.

[0055] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles 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 all fall within the scope of protection of the present invention.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A resin infusion process monitoring system based on ultrasound, characterized in that, include: The data acquisition module is used to acquire several ultrasonic reflection signals at each monitoring point on the resin injection mold and the surface point cloud data of the resin injection mold. The pre-analysis module, which is connected to the data acquisition module, is used to determine the global distortion parameters based on the surface point cloud data of the resin injection mold in order to determine whether there is an abnormal risk in the resin injection. The region identification module is connected to the data acquisition module and the pre-analysis module respectively, and is used to divide the resin injection mold into several monitoring regions, and determine the warping tendency parameters based on the surface point cloud data of each monitoring region to mark the feature monitoring region. The perfusion analysis module is connected to the data acquisition module and the region identification module respectively. It is used to construct the ultrasonic feedback curve of each feature monitoring region based on several ultrasonic reflection signals, determine the coupling target time of each feature monitoring region according to the ultrasonic feedback curve, and perform perfusion time series analysis on each feature monitoring region based on each coupling target time to obtain the region perfusion sequence. The perfusion control module, connected to the perfusion analysis module, is used to acquire ultrasonic feedback curves of each characteristic monitoring area according to the regional perfusion sequence, determine flow characterization parameters based on the comparison between each ultrasonic feedback curve, and determine whether to optimize the perfusion pressure parameters. The increase in the perfusion pressure parameters is determined based on the global distortion parameters and the flow characterization parameters.

2. The ultrasonic-based resin infusion process monitoring system according to claim 1, characterized in that, The pre-analysis module is used to determine global distortion parameters based on the surface point cloud data of the resin injection mold, wherein, The pre-analysis module acquires several surface point cloud data of the resin injection mold, and determines the variance of the feature surface point cloud data as the global distortion parameter. The feature surface point cloud data is surface point cloud data perpendicular to the horizontal plane.

3. The ultrasonic-based resin infusion process monitoring system according to claim 2, characterized in that, The pre-analysis module is used to determine whether there are any abnormal risks in the resin infusion process. The pre-analysis module determines that there is an abnormal risk in resin injection based on the judgment result that the global distortion parameter of the resin injection mold exceeds the preset global distortion parameter threshold.

4. The ultrasonic-based resin infusion process monitoring system according to claim 3, characterized in that, The region identification module is used to mark the feature monitoring region according to the warping tendency parameter, wherein... The region identification module acquires surface point cloud data of each monitoring region, determines the mean curvature of each monitoring region based on the surface point cloud data, and determines the mean curvature as the warping tendency parameter of the monitoring region. The region identification module marks the monitoring region as a feature monitoring region based on the determination result that the warping tendency parameter of the monitoring region exceeds the preset warping tendency parameter threshold.

5. The ultrasonic-based resin infusion process monitoring system according to claim 4, characterized in that, The perfusion analysis module is used to determine several feedback trend parameters based on the ultrasonic feedback curve, wherein... The perfusion analysis module is used to construct the ultrasonic feedback curve of the characteristic monitoring area based on the ultrasonic reflection signals at several acquisition times, and to determine the absolute value of the difference between the intensity parameters of the ultrasonic reflection signals at adjacent acquisition times as the feedback trend parameter.

6. The ultrasonic-based resin infusion process monitoring system according to claim 5, characterized in that, The perfusion analysis module is used to determine the coupling target time of each feature monitoring region, wherein, The perfusion analysis module is used to determine the time period consisting of adjacent acquisition times that meet the coupling conditions as the characteristic acquisition time period, and to determine the latter acquisition time among the adjacent acquisition times as the coupling target time of the characteristic monitoring area. The coupling condition is that the feedback trend parameter at adjacent acquisition times exceeds a preset feedback trend parameter threshold. The ultrasonic feedback curve is constructed with time as the horizontal axis and the intensity parameter of the ultrasonic reflected signal as the vertical axis.

7. The ultrasonic-based resin infusion process monitoring system according to claim 6, characterized in that, The perfusion analysis module is used to perform perfusion time-series analysis on each characteristic monitoring area to obtain the regional perfusion sequence, wherein, The perfusion analysis module obtains the coupling target time of each feature monitoring region, and sorts the feature monitoring regions corresponding to the coupling target time according to the time sequence to obtain the regional perfusion sequence.

8. The ultrasonic-based resin infusion process monitoring system according to claim 7, characterized in that, The infusion control module is used to determine several ultrasonic feedback curve segments, wherein... The perfusion control module obtains the characteristic acquisition time period corresponding to the coupling target time of any two adjacent characteristic monitoring areas in the regional perfusion sequence, and determines the ultrasonic feedback curve segment of the characteristic acquisition time period. The ultrasonic feedback curve segment of the preceding feature monitoring area in two adjacent feature monitoring areas is used to determine the first ultrasonic feedback curve segment, and the ultrasonic feedback curve segment of the following feature monitoring area in the two adjacent feature monitoring areas is used to determine the second ultrasonic feedback curve segment.

9. The ultrasonic-based resin infusion process monitoring system according to claim 8, characterized in that, The infusion control module is used to determine flow characterization parameters based on the comparison between various ultrasonic feedback curves, wherein... The infusion control module is used to calculate the ratio of the second flow characteristic to the first flow characteristic, and to determine the ratio as the flow characterization parameter. The first flow characteristic is the average slope of the first ultrasonic feedback curve segment, and the second flow characteristic is the average slope of the second ultrasonic feedback curve segment.

10. The ultrasonic-based resin infusion process monitoring system according to claim 9, characterized in that, The infusion control module is used to determine whether to optimize the infusion pressure parameters, wherein... The infusion control module determines to optimize the infusion pressure parameters based on the determination result that there are flow characterization parameters in the regional infusion sequence that meet the conditions for infusion pressure optimization. The increase in infusion pressure is positively correlated with the global distortion parameter and negatively correlated with the mean of the flow characterization parameter. The injection pressure optimization condition is that the flow characterization parameters do not exceed the preset flow characterization parameter threshold.

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