Automatic welding method for automobile sound-absorbing cotton

Abstract

The invention relates to the technical field of image processing, and discloses an automatic welding method for automobile sound-absorbing cotton. The method comprises the following steps: pretreating a sound-absorbing cotton raw material through a precise cutting system and high-temperature treatment; positioning is conducted through a mechanical arm and visual recognition, and coordinate data are obtained; planning a welding path based on the coordinate data; performing welding by using ultrasonic equipment; evaluating the quality through three-dimensional scanning and acoustic detection; and the defects are accurately repaired, and a finished product assembly is obtained. By integrating precise positioning, intelligent path planning, double-frequency ultrasonic welding, multi-dimensional quality detection and precise defect repairing, full automation and high-quality control of the sound-absorbing cotton welding process are achieved, and the first-pass yield and acoustic performance stability of products are improved.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to an automatic welding method for automobile sound-absorbing cotton. Background Art

[0002] At present, the automotive industry has increasingly stringent requirements for in-car noise control. As a key material for in-car sound insulation and noise reduction, the installation quality of sound-absorbing cotton directly affects the acoustic environment inside the car. Traditional methods of installing automotive sound-absorbing cotton mainly use manual pasting and mechanical fixing. These methods have problems such as low positioning accuracy, uneven connection strength, and low construction efficiency. With the development of automation technology, some manufacturers have begun to use hot melt welding technology to fix sound-absorbing cotton, but most of the existing welding equipment is designed for a single frequency, which is difficult to adapt to sound-absorbing materials of different thicknesses and densities. In addition, welding quality inspection still mainly relies on manual sampling, which makes it difficult to achieve comprehensive and accurate quality assessment and timely repair.

[0003] The shortcomings of the existing technology are mainly manifested in the following aspects: First, it is difficult to accurately adjust the welding parameters according to the material characteristics, resulting in unstable welding strength; second, there is a lack of real-time monitoring and closed-loop control of the welding process, and the welding quality fluctuates greatly; third, the means of evaluating the quality of the product after welding are single, and it is difficult to fully detect the geometric shape and acoustic performance of the product; fourth, there is a lack of effective local repair methods after defects are found, which often leads to the scrapping of the entire part, resulting in material waste and increased production costs. These problems have seriously restricted the improvement of the welding quality of automotive sound-absorbing cotton and the improvement of production efficiency. Summary of the invention

[0004] The present application provides an automatic welding method for automobile sound-absorbing cotton, which is used to achieve full automation and high-quality control of the sound-absorbing cotton welding process by integrating technologies such as precise positioning, intelligent path planning, dual-frequency ultrasonic welding, multi-dimensional quality inspection and precise defect repair, thereby improving the product's first-time pass rate and acoustic performance stability.

[0005] In a first aspect, the present application provides an automatic welding method for automobile sound-absorbing cotton, which comprises: pre-treating polyester fiber sound-absorbing cotton raw materials through a precision cutting system and a high-frequency hot air circulation furnace to obtain pre-treated sound-absorbing cotton materials; modularly positioning the pre-treated sound-absorbing cotton materials through a six-axis robotic arm in cooperation with visual recognition to obtain a three-dimensional space coordinate data packet; planning and optimizing welding paths according to the three-dimensional space coordinate data packet to obtain a digital welding instruction package; performing welding operations according to the digital welding instruction package through a dual-frequency ultrasonic generator and a dual closed-loop feedback control system to obtain a welded sound-absorbing cotton component; performing quality inspection on the welded sound-absorbing cotton component through a three-dimensional laser scanning and acoustic feature analysis system to obtain a quality assessment report; and repairing defects through point enhancement welding technology and nano-scale polymer repair agents according to the quality assessment report to obtain a finished automobile sound-absorbing cotton component.

[0006] In the technical solution provided by this application, the accurate size and shape of the sound-absorbing cotton raw materials are ensured by pretreatment through a precision cutting system and a high-frequency hot air circulation furnace, and the materials are made to reach the best welding state through high-frequency hot air treatment, which significantly improves the stability of subsequent welding quality. The modular positioning technology combining a six-axis robotic arm and visual recognition realizes high-precision spatial positioning of flexible sound-absorbing cotton materials, effectively overcomes the problem that traditional positioning methods are difficult to deal with material deformation, and provides accurate three-dimensional spatial coordinate data packets for welding path planning. In the welding path planning link, this solution uses an intelligent optimization algorithm to optimize the path in multiple dimensions, considers factors such as material properties, welding strength and energy efficiency, and generates the optimal digital welding instruction package, which greatly improves welding efficiency and quality. The application of a dual-frequency ultrasonic generator enables the system to automatically select the most suitable ultrasonic frequency according to the thickness of the material, and combines a dual closed-loop feedback control system to realize real-time monitoring and parameter adjustment of the welding process, effectively preventing over-welding and under-welding, and ensuring the consistency of welding quality. The introduction of a three-dimensional laser scanning and acoustic feature analysis system realizes all-round quality inspection of welded products, which can not only evaluate the geometric accuracy of the product, but also test the sound absorption performance. The application of spot reinforcement welding technology and nano-scale polymer repair agents enables accurate repair of defects found during inspection, avoiding the scrapping of the entire part, significantly reducing production costs and improving material utilization. This solution has made innovative contributions in the application of intelligent algorithms, including the precise modeling and correction of material deformation by the flexible material deformation compensation algorithm, the intelligent balance of multi-dimensional parameters by the welding path optimization algorithm, the dynamic response of the adaptive welding control algorithm to the real-time welding state, and the accurate evaluation of sound absorption performance by the acoustic characteristics analysis algorithm in quality assessment, thus achieving intelligent and high-quality control of the sound-absorbing cotton welding process. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0008] Figure 1 This is a schematic diagram of an embodiment of the automatic welding method of automobile sound-absorbing cotton in the embodiment of the present application; Figure 2 This is a schematic diagram of the process of pre-treating polyester fiber sound-absorbing cotton raw materials through a precision cutting system and a high-frequency hot air circulation furnace in an embodiment of the present application. DETAILED DESCRIPTION

[0009] The embodiment of the present application provides an automatic welding method for automobile sound-absorbing cotton. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0010] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of the automatic welding method of automobile sound-absorbing cotton in the embodiment of the present application includes: Step S101, pre-treating the polyester fiber sound-absorbing cotton raw material through a precision cutting system and a high-frequency hot air circulation furnace to obtain a pre-treated sound-absorbing cotton material; Step S102: modular positioning is performed by a six-axis robot arm in cooperation with visual recognition according to the pre-processed sound-absorbing cotton material to obtain a three-dimensional space coordinate data packet; Step S103, performing welding path planning and optimization according to the three-dimensional space coordinate data packet to obtain a digital welding instruction packet; Step S104, performing welding operation through a dual-frequency ultrasonic generator and a dual closed-loop feedback control system according to the digital welding instruction package to obtain a welded sound-absorbing cotton component; Step S105, performing quality inspection on the welded sound-absorbing cotton component by means of a three-dimensional laser scanning and acoustic characteristic analysis system to obtain a quality assessment report; Step S106: repair defects by using spot reinforcement welding technology and nano-polymer repair agent according to the quality assessment report to obtain a finished automobile sound-absorbing cotton component.

[0011] It is understandable that the execution subject of the present application may be an automatic welding system for automobile sound-absorbing cotton, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0012] Specifically, the polyester fiber sound-absorbing cotton raw material is pretreated by a precision cutting system and a high-frequency hot air circulation furnace. During the pretreatment process, the computer-aided design system accurately calculates the geometric dimensions of the sound-absorbing cotton raw material and generates the geometric parameters of the sound-absorbing cotton suitable for the body structure. The precision cutting system cuts the raw material into a specific shape based on these parameters, and the high-frequency hot air circulation furnace heats the cut sound-absorbing cotton to 120±5℃ for 8-12 minutes to melt the fiber material. The vacuum suction device then removes moisture impurities from the material and reduces the moisture content to below 0.5%. The modified polyvinyl alcohol and nano-scale titanium dioxide composite reinforcement are evenly coated on the surface of the sound-absorbing cotton by the coating device to form a pretreated sound-absorbing cotton material. The six-axis robot arm cooperates with the visual recognition system for modular positioning. The high-definition CCD camera captures the surface feature points of the pretreated sound-absorbing cotton material and generates the surface feature information of the material. The feature matching algorithm matches this information with the preset three-dimensional model to obtain the initial position data. The flexible material deformation compensation algorithm corrects the deformation error of the initial position data and calculates the corrected position information with an accuracy of ±0.2mm. The distributed micro-pneumatic suction cup array applies 15-20kPa negative pressure to the material to ensure stable positioning of the material. At the same time, the thermal imaging sensor monitors the temperature distribution on the surface of the material and generates temperature parameter feedback data. The corrected position information and temperature parameter feedback data are integrated to generate a three-dimensional space coordinate data package.

[0013] According to the three-dimensional space coordinate data package, the welding path is planned and optimized. The three-dimensional space coordinate data package is combined with the structural parameters of the vehicle sound-absorbing cotton to calculate the preliminary welding path. The system uses historical welding quality data to perform multi-dimensional optimization of the preliminary welding path, taking into account factors such as welding strength and energy efficiency, and generates an optimized welding path. Subsequently, the system decomposes the overall welding task into multiple sub-areas to form welding sub-area data. Each sub-area is further subdivided into main welding points and auxiliary welding points to form a welding point distribution map. The system performs differential calibration of the energy input of each welding point according to the material thickness parameters, increases the energy input by 20% for areas with a thickness greater than 8mm, and reduces the energy input by 15% for areas with a thickness less than 3mm, forming a welding energy distribution curve. The system sets intermittent cooling points to mark the welding energy distribution curve for heat control, sets a cooling point for every 25cm of welding, and cools for 3 seconds to form a complete digital welding instruction package.

[0014] According to the digital welding instruction package, the dual-frequency ultrasonic generator and the dual closed-loop feedback control system perform the welding operation. According to the material thickness parameters in the welding instruction package, the 20kHz or 40kHz ultrasonic frequency is selected to generate the welding frequency configuration parameters. The titanium alloy transducer converts and transmits the ultrasonic energy to the welding point. The real-time impedance matching technology collects the acoustic impedance data of the welding point once every millisecond to form a welding impedance data stream. The system dynamically adjusts the ultrasonic power output (150-450W) and pressure (0.3-0.8MPa) to obtain the adaptive welding control value. The temperature monitor and deformation sensor perform dual closed-loop detection on the welding process. When the temperature exceeds 160°C or the deformation exceeds the standard, the system automatically reduces the power output or increases the cooling time. After the welding is completed, the rapid cooling device performs directional cooling on the welding area to form a welded sound-absorbing cotton component with a high-strength structure.

[0015] The three-dimensional laser scanning and acoustic characteristic analysis system performs quality inspection on the welded sound-absorbing cotton components. The multi-angle positioning device rotates the component 360 degrees, the laser transmitter projects light on the surface of the component, and the optical receiver collects the surface reflected light beam data to form the original reflected light signal. The triangulation method calculates the surface point depth based on the original reflected light signal to form a surface coordinate data group. The sparse point filtering algorithm removes outliers to obtain the optimized coordinate set and reconstructs it into a three-dimensional point cloud model of the component. The system compares this model with the theoretical model to generate deformation deviation data. The acoustic characteristic analyzer transmits a preset 500Hz-8000Hz frequency sound wave to the component, receives the reflected wave data, calculates the sound absorption coefficient (range 0.7-0.95) through spectrum analysis, and generates sound absorption function parameters. The micro-tensile sensor array applies a 25-50N test force to the key welding points to obtain welding strength data. The system comprehensively analyzes the deformation deviation data, sound absorption function parameters and welding strength data to generate a quality assessment report.

[0016] Defect repair is performed according to the quality assessment report. The parser classifies and analyzes the defect information in the report and generates a defect type location database. For areas with insufficient welding strength, a small diameter (2mm) high-energy ultrasonic focusing head is used for local reinforcement, increasing the welding strength by 20-30% to form a strength reinforcement area. For areas with tiny cracks, a precision jetting device sprays a nano-scale polymer repair agent, which can be cured within 60 seconds to form a crack repair area. For areas with slight deformation, shape memory thermal adjustment technology uses 80-100℃ hot air combined with vacuum negative pressure to restore the material to its ideal shape, forming a shape recovery area. After the system performs edge trimming and surface dust removal on the material, it applies an acoustic performance enhancement coating (nanoporous silica and elastomer composite material, thickness 0.1-0.2mm) to improve the product's sound absorption performance by 5-8%, forming a finished automotive sound-absorbing cotton component.

[0017] Taking the sound-absorbing cotton of the inner door panel of a certain model of SUV as an example, the original polyester fiber sound-absorbing cotton was cut into an irregular shape of 85cm×60cm. After being treated in a high-frequency hot air circulation furnace, the moisture content was reduced from the original 2.8% to 0.42%. The six-axis robotic arm can achieve a positioning accuracy of ±0.18mm by identifying the 8 preset feature points on the material. The welding path planning divides the overall task into three sub-areas: front door, rear door, and pillar, with a total of 57 welding points. The energy input differentiation adjustment in the thickness change area increases the welding strength by 22.4%. During the welding process, the real-time impedance matching technology dynamically adjusts the ultrasonic power, with an average fluctuation range of ±35W, ensuring welding stability. Quality inspection found that there were 3 tiny cracks and 1 point of insufficient welding strength in the rear door area. After the nano-polymer repair, the sound absorption coefficient of this area was restored to 0.82, meeting the consistency requirements of the overall sound absorption performance. The average sound absorption coefficient of the finished automotive sound-absorbing cotton component in the 100-3000Hz frequency band reached 0.87, meeting the NVH performance standards of this model.

[0018] In the embodiment of the present application, the accurate size and shape of the sound-absorbing cotton raw material are ensured by pretreatment through a precision cutting system and a high-frequency hot air circulation furnace, and the material is made to reach the optimal welding state through high-frequency hot air treatment, which significantly improves the stability of subsequent welding quality. The modular positioning technology combining the six-axis robot arm and visual recognition realizes the high-precision spatial positioning of the flexible sound-absorbing cotton material, effectively overcomes the problem that the traditional positioning method is difficult to deal with material deformation, and provides an accurate three-dimensional spatial coordinate data packet for welding path planning. In the welding path planning link, this scheme adopts an intelligent optimization algorithm to optimize the path in multiple dimensions, considers factors such as material properties, welding strength and energy efficiency, and generates the optimal digital welding instruction package, which greatly improves the welding efficiency and quality. The application of the dual-frequency ultrasonic generator enables the system to automatically select the most suitable ultrasonic frequency according to the material thickness, and combines the dual closed-loop feedback control system to realize real-time monitoring and parameter adjustment of the welding process, effectively prevents over-welding and under-welding, and ensures the consistency of welding quality. The introduction of three-dimensional laser scanning and acoustic feature analysis system realizes the all-round quality inspection of the finished welded products, which can not only evaluate the geometric accuracy of the product, but also test the sound absorption performance. The application of spot reinforcement welding technology and nano-scale polymer repair agents enables accurate repair of defects found during inspection, avoiding the scrapping of the entire part, significantly reducing production costs and improving material utilization. This solution has made innovative contributions in the application of intelligent algorithms, including the precise modeling and correction of material deformation by the flexible material deformation compensation algorithm, the intelligent balance of multi-dimensional parameters by the welding path optimization algorithm, the dynamic response of the adaptive welding control algorithm to the real-time welding state, and the accurate evaluation of sound absorption performance by the acoustic characteristics analysis algorithm in quality assessment, thus achieving intelligent and high-quality control of the sound-absorbing cotton welding process.

[0019] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) The dimensions of the polyester fiber sound-absorbing cotton raw material are accurately calculated through a computer-aided design system to obtain the geometric parameters of the sound-absorbing cotton; (2) According to the geometric parameters of the sound-absorbing cotton, the polyester fiber sound-absorbing cotton raw material is cut into shapes using precision cutting equipment to obtain cut sound-absorbing cotton that matches the vehicle body structure; (3) The cut sound-absorbing cotton is subjected to a preset temperature treatment for a predetermined time in a high-frequency hot air circulation furnace to obtain a molten sound-absorbing cotton; (4) removing moisture and impurities from the molten sound-absorbing cotton by a vacuum suction device to obtain dry sound-absorbing cotton with a moisture content of less than 0.5%; (5) The surface of the dry sound-absorbing cotton is uniformly coated with modified polyvinyl alcohol and nano-scale titanium dioxide composite reinforcing agent by a coating device to obtain a pretreated sound-absorbing cotton material.

[0020] Specifically, Figure 2 The figure is a flow chart of pre-processing the polyester fiber sound-absorbing cotton raw material by a precision cutting system and a high-frequency hot air circulation furnace in the embodiment of the present application. The computer-aided design system accurately calculates the size of the polyester fiber sound-absorbing cotton raw material and obtains the geometric parameters of the sound-absorbing cotton. The computer-aided design system performs precise matching calculations based on the interior three-dimensional model in the vehicle structure feature database, and converts the internal space data of the vehicle body into the precise size parameters required for the sound-absorbing cotton. The calculation system obtains the vehicle body interior CAD model, extracts the coordinates of the key installation points, and then considers the elastic deformation characteristics of the sound-absorbing material to perform compensation calculations to generate the geometric contour line and thickness distribution map of the sound-absorbing cotton. The system will specially mark the edge area, fixed point position and functional area of ​​the sound-absorbing cotton. The marking information is included in the geometric parameters of the sound-absorbing cotton to ensure that the key areas can be accurately identified in the subsequent processing process. According to the calculated geometric parameters of the sound-absorbing cotton, the precision cutting equipment cuts the polyester fiber sound-absorbing cotton raw material into a shape to obtain the cut sound-absorbing cotton that matches the vehicle body structure. The precision cutting equipment reads the contour line data in the geometric parameters, converts it into a cutting trajectory instruction, and drives the cutting head to move along the specified path. During the cutting process, the real-time visual monitoring system tracks the cutting status to ensure that the cutting accuracy is within ±0.5mm. The system will also make dynamic adjustments based on the tension state of the raw materials to avoid cutting deformation. For areas of different thickness and density, the cutting equipment will adjust the cutting speed and pressure to ensure that the edges are flat and smooth. After cutting, the equipment will check the cutting results and record the deviation data between the actual cutting size and the designed size to provide a reference for subsequent processes.

[0021] The cut sound-absorbing cotton is treated at a preset temperature for a predetermined time by a high-frequency hot air circulation furnace to obtain a molten sound-absorbing cotton. The high-frequency hot air circulation furnace sets the processing temperature to 120±5℃ and the processing time to 8-12 minutes according to the material characteristic parameter table. The specific time is automatically adjusted according to the material thickness and density. Multiple groups of temperature sensors are set inside the hot air circulation furnace to form a temperature field distribution network to monitor the heating condition of the material in real time to ensure the overall temperature is uniform. The circulating air system blows hot air from multiple angles to the material according to the preset airflow pattern to avoid local overheating or underheating. During the treatment process, the temperature control algorithm continuously collects temperature data, and adjusts the heating power through the PID control method to control the temperature fluctuation within the set range. During the heating process, the polyester fiber on the surface of the material begins to soften and melt, forming a surface state suitable for subsequent welding, and the internal structure of the material maintains the original sound-absorbing characteristics. After temperature treatment, the molten state sound-absorbing cotton is then removed of moisture and impurities by a vacuum suction device to obtain dry sound-absorbing cotton with a moisture content of less than 0.5%. The vacuum suction device adopts a regional adsorption method. According to the thickness and density distribution map of the material, different intensities of negative pressure are applied to different areas to ensure uniform suction effect. During the suction process, the moisture content detection sensor monitors the humidity changes in the exhaust gas in real time, and calculates the moisture content in the material by the humidity change rate. When the moisture content is detected to drop to a stable value and is lower than 0.5%, the system automatically ends the suction process. At the same time, the suction device is equipped with a particulate filtration system to capture and remove tiny impurity particles in the material to improve the purity of the material. The negative pressure intensity of the suction device is dynamically adjusted as the moisture content decreases to avoid excessive compression and deformation of the material structure.

[0022] The surface of the dry sound-absorbing cotton is uniformly coated with modified polyvinyl alcohol and nano-scale titanium dioxide composite reinforcing agent by a coating device to obtain a pre-treated sound-absorbing cotton material. The coating device mixes modified polyvinyl alcohol and nano-scale titanium dioxide in a ratio of 1:2 to form a composite reinforcing agent. The coating process adopts precision spray technology, and through multiple independently controllable nozzle arrays, different amounts of reinforcing agent are applied to different areas according to the surface profile and thickness distribution map of the material. The spray control system adjusts the injection pressure and flow rate of the nozzle in real time to ensure that the reinforcing agent forms a uniform coating on the surface of the material, and the thickness is controlled within the range of 0.1-0.2mm. After the coating is completed, the infrared scanning system detects the uniformity of the coating, identifies the uneven coating areas, and the system automatically marks these areas and performs additional coating. After the coated sound-absorbing cotton material is cured at low temperature for a short time, a thin film with good adhesion and durability is formed on the surface, that is, the preparation of the pre-treated sound-absorbing cotton material is completed.

[0023] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Capture the feature points of the pre-treated sound-absorbing cotton material through a high-definition CCD camera to obtain the surface feature information of the material; (2) Perform three-dimensional model matching on the pre-treated sound-absorbing cotton material based on the material surface feature information through a feature matching algorithm to obtain initial position data; (3) Correct the deformation error of the initial position data through the flexible material deformation compensation algorithm to obtain the corrected position information; (4) applying negative pressure to the pre-treated sound-absorbing cotton material through a distributed micro-pneumatic suction cup array to obtain a material in a stable fixed state; (5) Monitor the surface temperature distribution of the material in a stable fixed state through a thermal imaging sensor to obtain temperature parameter feedback data; (6) The coordinates are calculated and integrated based on the corrected position information and temperature parameter feedback data to obtain a three-dimensional space coordinate data packet.

[0024] Specifically, the feature points of the pre-treated sound-absorbing cotton material are captured by a high-definition CCD camera to obtain the surface feature information of the material. The high-definition CCD camera is a charge-coupled device image sensor with high resolution and low noise characteristics, and can capture tiny surface feature changes. During the capture process, the camera scans the surface of the sound-absorbing cotton from multiple angles to obtain feature data such as surface texture, edge contour, preset marking points, etc. The image processing unit performs noise reduction, contrast enhancement and edge sharpening on the original image to convert the blurred image into a clear feature point data set. The feature point extraction algorithm (such as Harris corner point detection or SIFT algorithm) identifies key feature points from the processed image, including material edge intersection points, reserved hole center points and surface texture feature points, to form a material surface feature information data set. According to the material surface feature information, the pre-treated sound-absorbing cotton material is matched with a three-dimensional model by a feature matching algorithm to obtain the initial position data. The feature matching algorithm is a calculation method that matches the captured feature points with the preset three-dimensional model, and mainly adopts a matching strategy combining the RANSAC (random sample consistency) algorithm and the ICP (iterative closest point) algorithm. The algorithm selects stable and reliable feature points from the feature information, and then matches them with the corresponding points in the three-dimensional model to calculate the transformation matrix. The RANSAC algorithm selects the best matching point set and removes abnormal points through multiple random sampling, while the ICP algorithm continuously optimizes the transformation matrix through iteration to minimize the error between the actual point cloud and the ideal model. The matching process involves the calculation of the rotation matrix and the translation vector to obtain the initial position data of the material in space, including the coordinate origin position, posture angle and spatial distribution information of the feature points.

[0025] The deformation error of the initial position data is corrected by the flexible material deformation compensation algorithm to obtain the corrected position information. The flexible material deformation compensation algorithm is a compensation method designed specifically for the non-rigid deformation of soft materials (such as sound-absorbing cotton) during the positioning process. The algorithm is based on finite element analysis and material mechanics model, taking into account the elastic properties of the material, the influence of gravity and the support state, to correct the initial position data. The mathematical model of the deformation compensation algorithm can be expressed as follows:

[0026] in, represents the corrected position coordinates, represents the initial position coordinates, is the weight coefficient of the jth deformation factor, is the deformation displacement function, E is the material elastic modulus tensor, G is the gravity field vector, S is the support state matrix. According to the thickness and density differences of different areas of the sound-absorbing cotton, the algorithm calculates the deformation compensation amount by area, forms a global deformation compensation field, and outputs the corrected position information.

[0027] The pre-treated sound-absorbing cotton material is fixed by applying negative pressure through a distributed micro-pneumatic suction cup array to obtain a material in a stable fixed state. The distributed micro-pneumatic suction cup array is a fixing device composed of multiple independently controlled small vacuum suction cups, which are arranged according to the shape characteristics and rigidity distribution of the material. Each suction cup is usually 5-15mm in diameter and connected to the central vacuum system through an independent control valve. The control unit calculates the optimal suction cup layout and negative pressure distribution plan based on the corrected position information, and applies different sizes of negative pressure to different areas of the material (usually in the range of 15-20kPa). The pressure distribution follows the principle of high pressure in the rigid area of ​​the material and low pressure in the flexible area to avoid material deformation. The suction cup activation sequence has also been optimized, first fixing the key support points, and then gradually expanding to the surrounding areas to ensure that the material will not wrinkle or stretch during the fixing process. When all suction cups reach the set negative pressure value and stabilize for a period of time, the material enters a stable fixed state.

[0028] The surface temperature distribution of the material in a stable fixed state is monitored by a thermal imaging sensor to obtain temperature parameter feedback data. A thermal imaging sensor is a device that can detect the infrared radiation intensity on the surface of an object and convert it into a temperature distribution map, with a resolution of usually 0.05°C. The sensor scans the entire surface of the fixed sound-absorbing cotton material to obtain the temperature field distribution data on the surface of the material. The data processing unit converts the original thermal image into a standardized temperature matrix, records the temperature value and spatial position of each pixel, and forms a temperature gradient map. The temperature parameter feedback data contains information such as the average temperature of the material surface, the position of the highest temperature point, the position of the lowest temperature point, the temperature gradient size, and the temperature field uniformity index. These data reflect the thermal state and internal structural characteristics of the material, and have important guiding significance for the setting of subsequent welding parameters. Coordinate calculation and integration are performed based on the corrected position information and temperature parameter feedback data to obtain a three-dimensional space coordinate data packet. The coordinate calculation integration process is the process of fusing the geometric position data and thermal characteristic data of the material, and integrating information of different dimensions into a unified three-dimensional coordinate system through a data fusion algorithm. The fusion process establishes a mapping relationship between the geometric coordinate system and the thermal imaging coordinate system, and then marks the temperature data as an additional attribute of each spatial point of the material. The integrated data contains multi-dimensional information such as the spatial coordinates, temperature value, material thickness, material density, etc. of each feature point. The center of mass position, spindle direction and boundary coordinates of key functional areas of the material are also calculated during the data sorting process. These information together constitute a three-dimensional spatial coordinate data package, which serves as the basic data for subsequent welding path planning.

[0029] Taking the positioning of the sound-absorbing cotton under the rear seat of a certain type of SUV as an example, the high-definition CCD camera scans the pre-processed sound-absorbing cotton at multiple angles, identifies 142 edge contour points, 8 reserved hole center points, and 326 surface texture feature points, forming complete material surface feature information. The feature matching algorithm compares these points with the standard three-dimensional model in the vehicle model database. The RANSAC algorithm selects the best matching point set of 429 after 50 iterations. The ICP algorithm performs 9 rounds of optimization on this basis and calculates the initial position data with a rotation angle of 2.3° on the X axis, 1.8° on the Y axis, and 0.4° on the Z axis, and a translation vector of (12.5mm, -8.3mm, 5.2mm). The flexible material deformation compensation algorithm detects that the material has a drooping deformation of about 4.2mm in the middle area due to its own weight. The deformation compensation amount is calculated based on the elastic modulus E=0.28MPa and the influence of the gravity field, and the initial position data is corrected to obtain the precise position coordinates after compensation. The distributed micro-pneumatic suction cup array activates 32 suction cups according to the corrected position information, applying an average negative pressure of 18.5 kPa to the material, with a slightly lower pressure of 16 kPa in the edge area and a higher pressure of 19.5 kPa in the center area, so that the material is fixed in the ideal position. The thermal imaging sensor scans the fixed material and finds that the average surface temperature of the material is 26.3 ° C, the highest point temperature of 28.7 ° C is located in the area that has been fully heat treated before, and the lowest point temperature of 24.1 ° C is located in the weak edge area. The temperature field distribution diagram shows that the internal structure of the material is uniform. The coordinate calculation integration process merges the geometric position data with the temperature field data to generate a three-dimensional space coordinate data package containing multi-dimensional attributes, marking the boundary coordinates of 8 key welding areas and the best welding entry point.

[0030] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Perform preliminary path calculation on the welding area based on the three-dimensional space coordinate data package and the vehicle model sound-absorbing cotton structural parameters to obtain the preliminary welding path; (2) Multi-dimensional optimization of the preliminary welding path is performed based on historical welding quality data to obtain the optimized welding path; (3) The overall welding task is progressively divided into zones according to the optimized welding path to obtain welding sub-area data; (4) Mark the main welding points and auxiliary welding points of each sub-area according to the welding sub-area data to obtain a welding point distribution map; (5) Differentiate the energy input of each weld point according to the weld point distribution map combined with the material thickness parameter to obtain the welding energy distribution curve; (6) The welding energy distribution curve is heat-controlled and marked by setting intermittent cooling points to obtain a digital welding instruction package.

[0031] Specifically, a preliminary path calculation is performed on the welding area according to the three-dimensional spatial coordinate data packet and the vehicle model sound-absorbing cotton structural parameters to obtain a preliminary welding path. The three-dimensional spatial coordinate data packet is the precise spatial position information generated by the previous modular positioning link, which contains the position, posture and feature point distribution of the sound-absorbing cotton. The vehicle model sound-absorbing cotton structural parameters are the material specification information from the vehicle design database, including thickness distribution map, density distribution map, stress area identification and welding requirements. The path calculation process extracts the standard welding point requirements for the sound-absorbing cotton of this model from the vehicle database, and then maps these theoretical welding points to the actual located sound-absorbing cotton, taking into account the position deviation and deformation factors of the actual material, and using the shortest path algorithm (such as the improved Algorithm) calculates the optimal connection path between welding points. Algorithms and standards Compared with the algorithm, the weight of material properties is increased, the deformation-prone areas and high-stress areas are avoided, and a welding path more suitable for soft materials is generated. The algorithm is a heuristic pathfinding algorithm that determines the shortest path by evaluating the function f(n) = g(n) + h(n), where g(n) represents the actual cost from the starting point to the current node and h(n) represents the estimated cost from the current node to the target node. The algorithm mainly considers the spatial distance factor and searches for the geometric shortest path in a grid or graph structure. Algorithm in standard Based on the algorithm, key improvements have been made to meet the special needs of soft materials. The algorithm modifies the evaluation function and introduces a material property weight factor so that it not only considers the spatial distance, but also comprehensively considers the elastic modulus, thickness distribution and stress condition of the material. This improvement enables the algorithm to intelligently avoid easily deformed areas and high stress areas, even if these areas may provide shorter paths geometrically. It can generate welding paths that are more suitable for the material properties of soft sound-absorbing cotton, effectively reducing the risk of material deformation and stress concentration during welding; secondly, by comprehensively considering multidimensional factors such as path length, material thickness and heat accumulation, it achieves a balance between welding quality and efficiency; it can adaptively adjust the path strategy according to the material properties of different regions, making the welding process more stable and reliable. The preliminary welding path formed contains the welding point coordinate sequence and connection line information. The preliminary welding path is multi-dimensionally optimized through historical welding quality data to obtain the optimized welding path. Historical welding quality data refers to the historical welding records and quality evaluation results of sound-absorbing cotton of the same or similar models. These data are stored in the welding quality database and contain multi-dimensional information such as welding parameters, environmental conditions, material status and quality scores. The multi-dimensional optimization process uses machine learning methods to find the best welding strategy by analyzing the correlation between various parameter combinations and welding quality in historical data. The optimization algorithm will consider multiple evaluation dimensions such as welding strength, energy efficiency, welding time and sound absorption performance, and comprehensively score each candidate path. The artificial neural network model will predict the quality results of different path selections based on the input preliminary welding path and current material state parameters, and give optimization suggestions. The optimization process will not only adjust the order and path of the welding points, but also fine-tune the welding parameters of specific areas based on historical experience. After multiple rounds of iterative optimization, the optimized welding path with the best comprehensive performance under multiple evaluation dimensions is obtained.

[0032] According to the optimized welding path, the overall welding task is partitioned and progressively divided to obtain the welding sub-area data. Partition progressive division is to decompose the overall welding task into several relatively independent sub-areas in order to achieve more refined welding control and heat management. The division process takes into account factors such as the material's geometric shape, thickness change, stress conditions and functional requirements, and divides sub-areas with clear boundaries and relatively uniform interiors through the region growing algorithm or watershed segmentation algorithm. The material properties in each sub-area are similar, and similar welding parameters are suitable. Progressive division is to plan the welding sequence between regions, usually from inside to outside, from thick to thin, from key structures to secondary areas, to avoid heat accumulation and material deformation during welding. The welding sub-area data contains information such as the boundary coordinates, area size, average thickness, material density and adjacent area relationship of each sub-area, as well as the welding priority ranking and recommended welding parameters of each area.

[0033] According to the welding sub-area data, the main and auxiliary welding points of each sub-area are marked to obtain the welding point distribution map. The main welding point refers to the key welding position that bears the main connection strength, which is usually set in the part with large force and important structure; the auxiliary welding point is the secondary welding position that assists fixation and enhances the overall stability. The welding point marking process identifies the key stress points according to the stress analysis results of the material and marks them as main welding points; then, according to the material deformation prediction model, the auxiliary welding points are added in the areas prone to deformation or separation. The spatial distribution of the main and auxiliary welding points follows the principle of "main point skeleton and auxiliary point filling", ensuring that the main welding points form the skeleton structure of the overall connection, and the auxiliary welding points fill the gaps between the skeletons to provide uniform support. The welding point density distribution is dynamically adjusted according to the importance of the area, with a higher density in important functional areas and a lower density in general areas. The welding point distribution map forms a complete data set containing the spatial coordinates, type identification (main / auxiliary), priority and recommended welding parameters of each welding point.

[0034] The energy input of each weld point is differentiated and calibrated according to the weld point distribution map combined with the material thickness parameter to obtain the welding energy distribution curve. The material thickness parameter refers to the thickness distribution data of the sound-absorbing cotton in different areas, which is usually provided by the design specifications or obtained through the pre-order scanning measurement. Differential calibration means that different welding energy parameters are assigned to each weld point according to the material characteristics of different areas. The calibration process adopts the thickness-energy mapping model. The thicker the area, the higher the energy input is required to ensure full penetration, while the thinner the area, the lower the energy is required to avoid burn-through. The calibration algorithm considers multiple factors such as the thermal conductivity, melting point, and density of the material to calculate the most suitable energy input value. For areas with a thickness greater than 8mm, the standard energy is increased by 20%; for areas with a thickness less than 3mm, the standard energy is reduced by 15%. The welding energy distribution curve is an energy control curve that changes with the welding process. It describes the energy input value of each weld point at each time point during the welding process, including parameters such as power size, duration, and waveform characteristics.

[0035] The heat control marking of the welding energy distribution curve is performed by setting intermittent cooling points to obtain a digital welding instruction package. Intermittent cooling points refer to short pauses inserted during the welding process to allow heat to dissipate and avoid overheating damage to the material. The heat control marking process predicts the temperature changes in each area during the welding process through the heat accumulation model, and then inserts cooling points before the temperature is about to exceed the safety threshold. Usually, a cooling point is set after each 25cm length of welding is completed, and the cooling time is 3 seconds. The distribution of cooling points also considers the thermal conductivity and heat dissipation conditions of the material, and increases the density of cooling points in areas with fast heat accumulation. After the heat control marking is completed, all welding parameters are integrated into a digital welding instruction package, which contains complete welding execution data, such as welding point coordinate sequence, welding path, energy input curve, cooling point location and duration, welding speed adjustment and other information. The digital welding instruction package is a structured data file that can be directly read and executed by the welding execution system.

[0036] Taking the welding of the sound-absorbing cotton in the trunk of a certain car as an example, the three-dimensional spatial coordinate data package contains the precise location information of the sound-absorbing cotton after positioning, and a total of 145 feature points are marked. Combined with the structural parameters of the sound-absorbing cotton in the trunk of this model in the vehicle database (material thickness varies from 6 to 14 mm, the edge area is thinner, and the central load-bearing area is thicker), the improved A* algorithm is used to calculate the preliminary welding path connecting all necessary points, with a total length of 267 cm and 68 welding points. Nearly 200 groups of welding records of the same type of sound-absorbing cotton were extracted from the historical quality database. Through multi-dimensional optimization algorithm analysis, it was found that under the current thickness distribution, the welding energy in the central area is low, resulting in a high frequency of insufficient strength problems, and the edge area is prone to over-welding. Based on this, the preliminary path is optimized and adjusted, the welding sequence is adjusted to expand from the center to the outside, and the welding parameters of the key points are fine-tuned to form an optimized welding path. The overall welding task is divided into three main sub-areas: front, middle and rear. Each area is further divided into 2-3 secondary areas, forming a total of 8 welding sub-areas. Each area is sorted according to importance and assigned welding priority. During the welding point marking process, the stress area at the bottom of the luggage compartment was marked as 13 main welding points, and 42 auxiliary welding points were assigned to the surrounding and connection areas to form a complete welding point distribution map. When calibrating the energy distribution, considering the average thickness of the bottom area of ​​12mm, the welding energy is set to 118% of the standard value; the average thickness of the edge area is only 7mm, and the welding energy is reduced to 90% of the standard value to generate a complete energy distribution curve. Through heat accumulation analysis, 11 intermittent cooling points are set on the welding path, mainly distributed in the central area where heat is not easy to dissipate. The cooling time varies from 3 to 5 seconds, and a digital welding instruction package containing all parameters is generated.

[0037] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Select the frequency of the dual-frequency ultrasonic generator according to the material thickness parameter in the digital welding instruction package to obtain the welding frequency configuration parameters; (2) According to the welding frequency configuration parameters, the ultrasonic energy is converted through the titanium alloy transducer to obtain the welding energy conduction data; (3) The acoustic impedance of the welding point is collected at the millisecond level through real-time impedance matching technology to obtain the welding impedance data stream; (4) Dynamically adjust the ultrasonic power output and pressure parameters according to the welding impedance data stream to obtain adaptive welding control values; (5) The welding status is double-closed-loop detected by the temperature monitor and deformation sensor to obtain real-time status information of the welding process; (6) The welded area is subjected to directional cooling treatment by means of a rapid cooling device to obtain a welded sound-absorbing cotton component.

[0038] Specifically, the frequency of the dual-frequency ultrasonic generator is selected according to the material thickness parameter in the digital welding instruction package to obtain the welding frequency configuration parameters. The dual-frequency ultrasonic generator is a device that can simultaneously or selectively output 20kHz and 40kHz ultrasonic frequencies, providing the most suitable welding energy for sound-absorbing cotton materials of different thicknesses and densities. The digital welding instruction package contains the material thickness data of each welding point position, and the optimal welding frequency is determined by the thickness-frequency mapping rule. Generally speaking, for areas with a thickness greater than 10mm, a 20kHz low-frequency ultrasonic wave is selected, which has strong penetration and large welding depth; for areas with a thickness less than 5mm, a 40kHz high-frequency ultrasonic wave is selected, which has concentrated energy and high precision; for medium-thickness areas of 5-10mm, the appropriate frequency or a combination of the two frequencies is selected based on a comprehensive judgment of the material density and fiber direction. The frequency selection algorithm also considers the functional importance and stress conditions of the welding point, and adopts a more conservative frequency selection strategy for key functional points. The generated welding frequency configuration parameters contain detailed information such as frequency selection, power level, waveform characteristics and duration of each welding point.

[0039] According to the welding frequency configuration parameters, the ultrasonic energy is converted through the titanium alloy transducer to obtain the welding energy conduction data. The titanium alloy transducer is a device that converts electrical energy into mechanical vibration energy. Titanium alloy is used because of its excellent acoustic properties and durability. The piezoelectric ceramic composite drive unit inside the transducer generates high-frequency vibrations under the action of the electric field, amplifies the tiny vibrations to a practical level through a mechanical amplifier, and then conducts the energy to the material contact surface through the welding head. During the energy conversion process, the frequency control module accurately adjusts the power supply output frequency according to the welding frequency configuration parameters, and the power control module adjusts the output power according to the material characteristics (usually in the range of 150-450W). During the welding energy conduction process, multiple sensors monitor the working status of the transducer in real time, including parameters such as amplitude, frequency stability, output power and temperature. These data together constitute the welding energy conduction data, reflecting the efficiency and quality of energy transfer from the generator to the material.

[0040] The acoustic impedance of the welding point is collected at the millisecond level through real-time impedance matching technology to obtain the welding impedance data stream. Acoustic impedance refers to the degree of resistance of the material to the propagation of sound waves, which changes dynamically with the change of the melting state of the material during the welding process. The real-time impedance matching technology collects the acoustic impedance data of the welding point every millisecond through a special sensor installed on the welding head to form an impedance change curve with high time resolution. The data acquisition system uses a high-speed A / D converter to convert the analog signal into a digital signal, and forms a welding impedance data stream after noise reduction and signal enhancement processing. These data reflect the dynamic changes in the internal state of the material during the welding process, including the characteristic impedance values ​​at different stages such as the beginning of melting, the melting process, and cooling and solidification. The impedance data processing unit will perform feature extraction and pattern recognition on the raw data, identify key impedance change points, such as the melting start point, the optimal welding point, and the solidification completion point, to provide a basis for subsequent parameter adjustments.

[0041] The ultrasonic power output and pressure parameters are dynamically adjusted according to the welding impedance data stream to obtain the adaptive welding control value. Adaptive welding control is the process of dynamically adjusting welding parameters according to the real-time welding status, mainly based on the characteristic changes in the impedance data stream. The control algorithm determines whether the current welding state is overheated, insufficient or moderate by analyzing the slope, fluctuation range and stability of the impedance change curve. When a sudden increase in impedance is detected, indicating that the material is not fully melted, the algorithm will increase the power output (floating in the range of 150-450W); when the impedance drops rapidly, indicating that the material may be overheated, the power output is reduced. At the same time, the pressure control system also dynamically adjusts the welding pressure (in the range of 0.3-0.8MPa) according to the impedance data to ensure good material contact but not over-compression. The adaptive control algorithm adopts the PID (proportional-integral-differential) control strategy, comprehensively considering the deviation, change rate and cumulative error of the current impedance value from the ideal impedance curve, and calculates the optimal power and pressure adjustment. The adjusted parameter values ​​form the adaptive welding control value, which is updated every millisecond to achieve precise control of the welding process. The temperature monitor and deformation sensor are used to perform double closed-loop detection of the welding state to obtain real-time status information of the welding process. Double closed-loop detection refers to a detection method that monitors two key parameters, temperature and deformation, at the same time, providing more comprehensive and reliable status information than single parameter monitoring. The temperature monitor uses a high-precision infrared sensor or thermocouple to measure the surface temperature and temperature distribution of the welding area in real time, and the data sampling rate is usually 100Hz. The deformation sensor uses a laser displacement sensor or a linear variable differential transformer (LVDT) to monitor the thickness change and plane deformation of the material during the welding process, with an accuracy of up to 0.01mm. The double closed-loop detection system synchronizes the temperature data and the deformation data in time and aligns them in space to form a complete state matrix. When the temperature exceeds 160°C (the safety upper limit of the sound-absorbing cotton material) or the deformation exceeds the preset threshold, the control system triggers a protection mechanism to automatically reduce the power output or increase the cooling time. The real-time status information of the welding process also includes data such as the welding head position, welding speed, completed welding length, and estimated remaining time, providing all-round monitoring for the entire welding process.

[0042] The welding area is subjected to directional cooling by a rapid cooling device to obtain a welded sound-absorbing cotton component. The rapid cooling device is a device that can accurately cool a specific area. It is mainly composed of a cooling airflow generator, a temperature control unit and a directional guide structure. After welding is completed, the cooling device will give priority to cooling the high-temperature area according to the position and temperature distribution of the welding point. During the cooling process, the temperature monitor continuously tracks the temperature changes in the welding area, and the cooling control algorithm adjusts the cold air flow and direction according to the target cooling curve to ensure that the temperature reduction rate is within the set range (usually 10-15℃ / second) to avoid internal stress accumulation and deformation caused by too fast cooling. The direction and intensity of the cooling airflow are dynamically adjusted according to the shape and thickness distribution of the material to ensure cooling uniformity. When the temperature of the welding area drops to the safety threshold (usually 40-50℃), the cooling device stops working and the completed welding point forms a stable connection structure. The welding area treated with directional cooling has high strength and stability, and the welded sound-absorbing cotton component has the shape, strength and functional characteristics required by the design.

[0043] Taking the welding of the sound-absorbing cotton in the front door lining of a medium-sized SUV as an example, the digital welding instruction package contains detailed material thickness parameters, showing that the thickness of the sound-absorbing cotton varies between 3-15mm, with the thickest being 15mm in the door handle area and the thinnest being only 3mm in the edge area. The frequency selection algorithm selects 20kHz low-frequency ultrasonic waves for the door handle area and 40kHz high-frequency ultrasonic waves for the edge area according to the thickness distribution map, and adopts a dual-frequency alternating mode for the intermediate transition area, generating a frequency configuration parameter table containing 53 welding points in total. According to the frequency configuration parameters, the output power of the titanium alloy transducer is set to 420W in the door handle area, and is reduced to 180W in the edge area, with the amplitude adjusted within the range of 25-40μm. During the welding process, the real-time impedance matching system collects the impedance data of the welding point every millisecond, and the records show that the initial impedance of the door handle area is 1250 ohms, and the impedance drops to 850 ohms during the melting process and stabilizes at 980 ohms; the edge area starts from 820 ohms and drops to 620 ohms at the lowest. The adaptive control system adjusted the power in real time according to the impedance change. The door handle area detected that the impedance dropped too fast, and the power was dynamically reduced from the initial 420W to 385W; while the impedance of the edge area suddenly rose in the middle of welding, and the power was increased to 210W accordingly to ensure full melting. The highest temperature recorded by the double closed-loop detection system occurred in the middle welding area, reaching 152°C, which was lower than the safety threshold of 160°C; the maximum deformation was 0.82mm, which occurred in the transition area between the edge and the middle. After welding is completed, the rapid cooling device performs directional cooling on the areas with higher temperatures, and the cooling rate is controlled at 12°C / second, and the cooling is stopped when the temperature drops to 48°C. The completed sound-absorbing cotton component perfectly maintains the design shape, the strength tests of the welding points all meet the design requirements, the edge sealing is good, and the core execution stage of the automatic welding of automotive sound-absorbing cotton is completed.

[0044] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Use a high-precision 3D laser scanner to scan the entire surface of the welded sound-absorbing cotton component to obtain a 3D point cloud model of the component; (2) Compare and analyze the component 3D point cloud model with the theoretical model to obtain deformation deviation data; (3) Using an acoustic characteristic analyzer, a preset frequency sound wave is emitted to the welded sound-absorbing cotton component to obtain acoustic reflection wave data; (4) Analyze the sound absorption characteristics of the welded sound-absorbing cotton components based on the acoustic reflection wave data to obtain the sound absorption function parameters; (5) Use a micro-tensile sensor array to perform sampling strength tests on key welding points of welded sound-absorbing cotton components to obtain welding strength data; (6) A comprehensive analysis is conducted based on the deformation deviation data, sound absorption function parameters and welding strength data to obtain a quality assessment report.

[0045] Specifically, the welded sound-absorbing cotton component is scanned on its entire surface by a high-precision 3D laser scanner to obtain a 3D point cloud model of the component. A high-precision 3D laser scanner is an optical measuring device that can collect geometric shape data on the surface of an object, and its resolution can usually reach 0.05mm. During the scanning process, the multi-angle positioning device rotates the sound-absorbing cotton component 360 degrees to ensure full coverage without blind spots. The laser transmitter projects structured light onto the surface of the component, and the optical receiver collects the reflected light and calculates the spatial position of the surface point by the triangulation principle. The original reflected light signal is filtered, denoised and calibrated to be converted into a high-precision 3D coordinate point set. The point cloud data processing algorithm removes outliers from the collected surface coordinate data set, identifies and deletes outliers through sparse point filtering technology, and improves the accuracy of the point cloud data. The processed point cloud data generates a complete 3D point cloud model of the component through a 3D reconstruction algorithm, which accurately reflects the actual shape, size and surface characteristics of the sound-absorbing cotton component.

[0046] The deformation deviation data is obtained by comparing and analyzing the three-dimensional point cloud model of the component with the theoretical model. The theoretical model refers to the standard three-dimensional model created in the product design stage, which contains the ideal shape and size of the sound-absorbing cotton component. The comparison analysis uses the iterative closest point (ICP) algorithm to align the actual scanned three-dimensional point cloud model with the theoretical model for optimal matching. The alignment process includes two stages: coarse registration and fine registration. The coarse registration quickly finds the approximate position based on feature point matching, and the fine matching criterion fine-tunes the relative position through iterative optimization to minimize the overall error between the two models. After the alignment is completed, the deviation distance of each point is calculated to form a deviation distribution map. The deformation analysis algorithm identifies the key deformation areas and deformation types, including plane bending, thickness compression, edge deformation, etc., and calculates the maximum deviation, average deviation and standard deviation of each area. The deformation deviation data also includes the overall size change rate, the deformation severity of the key functional area and the position mark of the deviation exceeding the standard area. These data together constitute a comprehensive evaluation of the product shape quality. The acoustic characteristic analyzer emits a preset frequency sound wave to the welded sound-absorbing cotton component to obtain the acoustic reflection wave data. The acoustic characteristic analyzer is a professional device that can generate, receive and analyze sound waves. It is used to test the acoustic properties of materials. During the test, the sound wave transmitter emits frequency sound waves in the range of 500Hz-8000Hz to the sound-absorbing cotton component, covering the main frequency band of automobile noise. The sound wave receiver captures the sound waves reflected from the surface of the component and converts the analog signal into a digital signal. The acoustic data acquisition system records the incident wave and reflected wave at each test frequency point, and calculates the reflection ratio and phase difference. To ensure the accuracy of the measurement, the test environment needs to control the background noise below 30dB, and the test positions are evenly distributed according to the geometric shape of the component. Generally, 20-30 test points are selected. The original reflected wave data is processed by time domain filtering and frequency domain analysis to generate a complete acoustic reflection characteristic data set, which contains the reflection intensity, phase information and spatial distribution characteristics of each frequency point.

[0047] According to the acoustic reflection wave data, the sound absorption characteristics of the welded sound-absorbing cotton components are analyzed to obtain the sound absorption function parameters. Sound absorption characteristic analysis is the process of evaluating the sound insulation performance of sound-absorbing cotton materials. The acoustic reflection wave data is converted into the time-frequency domain by a spectrum analyzer to obtain a frequency distribution spectrum, which intuitively displays the reflection characteristics of each frequency point. The energy attenuation calculation is based on the energy ratio of the incident sound wave to the reflected sound wave, and the sound absorption coefficient of each frequency band is calculated to reflect the material's absorption capacity for sound waves of different frequencies. The sound absorption coefficient calculation formula is α= 1-(Ir / Ii), where α is the sound absorption coefficient, Ir is the intensity of the reflected sound wave, and Ii is the intensity of the incident sound wave. The original sound absorption coefficient data is converted into a standardized sound absorption curve through normalization processing, which is convenient for comparison with industry standards. The sound absorption characteristic analysis also includes special analysis of key frequency points (such as 500Hz, 1000Hz, 2000Hz, and 4000Hz), which are important indicators for evaluating the sound absorption performance of automobiles. By calculating the acoustic impedance of the standardized sound absorption curve and key frequency sound absorption indicators, a material acoustic impedance characteristic map is generated. These data are integrated into a complete sound absorption function parameter set to evaluate the acoustic performance of the product.

[0048] The key welding points of the welded sound-absorbing cotton components are sampled for strength testing through a micro-tensile sensor array to obtain welding strength data. The micro-tensile sensor array is a test device composed of multiple small force sensors, which can apply standardized tensile tests to multiple welding points simultaneously or sequentially. The test process is carried out according to the preset sampling plan, and strength tests are carried out at the key welding points, typical welding points and randomly selected points of the product. The sampling coverage rate is usually 15%-20% of the total welding points. A standard test force of 25-50N is applied to each test point, and the force value and displacement when the welding point begins to deform or break are recorded. The test data processing includes maximum bearing force calculation, elastic deformation interval analysis and plastic deformation feature analysis to generate strength-deformation curves for each test point. The welding strength data not only includes absolute strength values, but also strength uniformity scores, weakness distribution maps and strength margin estimates, which fully reflect the structural strength status of the product.

[0049] A comprehensive analysis is performed based on the deformation deviation data, sound absorption function parameters and welding strength data to obtain a quality assessment report. The comprehensive analysis adopts a multi-index weighted evaluation method, normalizing various types of data and assigning different weights according to their importance. The deformation deviation data mainly affects the assembly matching and appearance quality of the product, the sound absorption function parameters are directly related to the realization of the core functions of the product, and the welding strength data determines the service life and reliability of the product. The data fusion algorithm maps these three types of indicator data into a unified scoring system, calculates the comprehensive quality score, and performs A / B / C three-level quality assessment based on the preset threshold. The quality assessment report also contains a detailed defect list, marking the areas that need to be repaired and their defect types, such as areas with excessive deformation, areas with insufficient sound absorption performance, and areas with substandard welding strength. For each defect type, the assessment report gives specific repair suggestions, including reinforcement methods, repair materials and operating parameters.

[0050] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) The defect information in the quality assessment report is classified and analyzed by the parser to obtain a defect type and location database; (2) Based on the defect type and location database, the area with insufficient welding strength is locally reinforced by a small-diameter high-energy ultrasonic focused head to obtain a strength-reinforced area; (3) Spraying a nano-scale polymer repair agent on the tiny crack area in the welded sound-absorbing cotton component through a precision spraying device to obtain a crack repair area; (4) Using shape memory thermal adjustment technology, hot air negative pressure treatment is performed on the slightly deformed area of ​​the welded sound-absorbing cotton component to obtain a shape recovery area; (5) Edge trimming and surface dust removal are performed on the welded sound-absorbing cotton component according to the strength reinforcement area, crack repair area and shape recovery area to obtain a repaired component; (6) The repaired component is surface treated with an acoustic performance enhancing coating to obtain a finished automotive sound-absorbing cotton component.

[0051] Specifically, the defect information in the quality assessment report is classified and analyzed by the parser to obtain a defect type location database. The parser is a software tool dedicated to text and data structured processing, which can extract key information from the quality assessment report and convert it into a standardized data format. The parsing process performs natural language processing on the report text, identifies the defect description paragraph, and then extracts key information such as defect type, location coordinates, severity, and repair suggestions. Defect classification follows the preset classification system and is mainly divided into categories such as insufficient welding strength, material cracks, shape deformation, and substandard sound absorption performance. The location information is converted from the coordinate description in the report to the precise location in the standard three-dimensional coordinate system. The parser also performs correlation analysis on adjacent or related defects to identify defect concentration areas and potential systemic problems. The generated defect type location database contains information such as the type identification, precise location coordinates, impact range, severity, and recommended repair methods of each defect, providing data support for subsequent targeted repairs. According to the defect type location database, the area with insufficient welding strength is locally reinforced by a small-diameter high-energy ultrasonic focused head to obtain a strength reinforcement area. The small-diameter high-energy ultrasonic focusing head is a specially designed ultrasonic welding tool with a diameter of about 2mm. It can focus ultrasonic energy to a very small area to achieve precise local reinforcement. The repair process selects areas with insufficient welding strength from the defect database, sorts them according to the severity, and prioritizes the points with the largest strength deviation. The focusing head positioning system accurately locates the point to be repaired according to the defect coordinates, adjusts the welding angle so that the energy direction is perpendicular to the original welding line, and increases the connection strength. The welding parameters are dynamically adjusted according to the degree of defect, and the energy input is increased by 20-30% in areas with severe strength deficiency and 10-15% in areas with slight strength deficiency. The reinforcement process adopts a pulsed energy input method, with each pulse lasting 0.1-0.3 seconds and an interval of 0.5-1.0 seconds to avoid excessive energy concentration and overheating damage to the material. After the reinforcement is completed, the treated area is quickly cooled and shaped to consolidate the welded structure and form a reinforcement area with strength that meets the requirements.

[0052] The crack repair area is obtained by spraying nano-scale polymer repair agent on the tiny crack area in the welded sound-absorbing cotton component through a precision spraying device. The precision spraying device is a device that can accurately control the spraying position, angle and amount, usually including a high-precision mobile platform, a micro-control valve and a pressure control system. The nano-scale polymer repair agent is a special material with good fluidity and permeability. It can penetrate into tiny cracks and quickly solidify to form an elastic connection consistent with the performance of the raw materials. The repair process extracts crack information from the defect database, including crack location, length, width and depth. The spraying device calculates the optimal spraying angle and pressure according to the crack characteristics. Usually, low-pressure and high-angle spraying is selected for deep cracks, and high-pressure and low-angle spraying is selected for surface cracks. The amount of repair agent is accurately calculated according to the crack volume to ensure full filling but no overflow. After the spraying is completed, the curing reaction of the repair agent is triggered by ultraviolet light or thermal catalysis. The curing time is usually controlled within 60 seconds to form a repair area that is closely combined with the raw materials.

[0053] The shape memory heat adjustment technology is used to treat the slightly deformed areas in the welded sound-absorbing cotton components with hot air negative pressure to obtain the shape recovery area. Shape memory heat adjustment technology is a technology that uses the plasticity of the sound-absorbing cotton material in the heated state and the shape memory characteristics after cooling, combined with negative pressure molding to achieve deformation correction. The processing process extracts the deformation area information from the defect database, including the deformation type, deformation degree and ideal shape data. The heat adjustment device sets the hot air temperature (80-100℃), air flow velocity and heating time according to the deformation characteristics, and accurately heats the deformation area to make the material soften but not melt. At the same time, the negative pressure molding system generates a negative pressure mold or negative pressure distribution according to the ideal shape data, applies appropriate negative pressure (usually 5-15kPa), and pulls the softened material back to the ideal position. When the material reaches the target shape, it maintains the negative pressure state for rapid cooling. After the temperature drops to room temperature, the material solidifies in the correction position to form an area that restores the normal shape. The shape recovery process monitors the surface temperature and deformation degree of the material in real time, and dynamically adjusts the parameters to ensure the correction effect.

[0054] The welded sound-absorbing cotton components are edge trimmed and surface dust removed according to the strength reinforcement area, crack repair area and shape recovery area to obtain repaired components. Edge trimming refers to the precise finishing of the component edges, removal of burrs, excess materials and irregular edges, and ensuring that the shape of the component meets the design requirements. The trimming tool selects the appropriate trimming tool, sanding head or laser trimmer according to the edge type, and performs precise trimming with reference to the edge profile data of the three-dimensional model. The dust removal process uses low-pressure airflow combined with electrostatic adsorption technology to remove dust, fiber debris and processing residues on the surface and inside of the component. During the cleaning process, the airflow pressure and electrostatic intensity will be adjusted according to the material characteristics to ensure thorough cleaning without damaging the material. After the edge trimming and surface dust removal are completed, the component is fully inspected to ensure that all repair areas are flat and smooth, there are no obvious seam marks, and the appearance and shape meet the design requirements to form a repaired sound-absorbing cotton component.

[0055] The repaired components are surface treated with an acoustic performance enhancement coating to obtain finished automotive sound-absorbing cotton components. Acoustic performance enhancement coating is a specially developed functional coating material composed of nanoporous silica and elastomer composite materials, which can improve the sound absorption performance, durability and anti-aging ability of the material. The coating thickness is usually controlled within the range of 0.1-0.2mm to ensure that the sound absorption performance is enhanced without affecting the elasticity and softness of the material. The coating process adopts a uniform spraying or dipping method, and the best coating method is selected according to the geometric shape and functional requirements of the component. The coating system dynamically adjusts the coating formula and thickness according to the acoustic function requirements of different areas, increases the coating concentration in key sound absorption areas, and maintains the standard concentration in general areas. The coating curing adopts low-temperature rapid curing technology to avoid high temperature from damaging the sound-absorbing cotton material. After the coating is completed, the components are subjected to aging tests and sound absorption performance tests to ensure that the coating is uniform, firmly adhered and the function meets the standards. The finished automotive sound-absorbing cotton components will be accompanied by a complete quality traceability QR code to record the product's production parameters, test data and repair information, and realize quality monitoring throughout the life cycle.

[0056] In a specific embodiment, the process of performing the step of scanning the entire surface of the welded sound-absorbing cotton component by a high-precision three-dimensional laser scanner may specifically include the following steps: (1) The welded sound-absorbing cotton components are rotated 360 degrees by a multi-angle positioning device to obtain a full-range scanning preparation state; (2) According to the full-range scanning preparation state, the laser transmitter is used to project light onto the surface of the welded sound-absorbing cotton component to obtain the surface reflection beam data; (3) Collect and organize the surface reflected light beam data through an optical receiver to obtain the original reflected light signal; (4) Calculate the depth of the surface points of the welded sound-absorbing cotton component by triangulation based on the original reflected light signal to obtain a surface coordinate data set; (5) Using the sparse point filtering algorithm to remove outliers from the surface coordinate data set, we obtain the optimized coordinate set; (6) Perform three-dimensional space reconstruction based on the optimized coordinate set to obtain a three-dimensional point cloud model of the component.

[0057] Specifically, the welded sound-absorbing cotton component is rotated 360 degrees by a multi-angle positioning device to obtain a full-range scanning preparation state. The multi-angle positioning device is a mechanical device specially designed for accurately rotating and positioning welded sound-absorbing cotton components during the scanning process. It consists of a rotating platform and an adjustable clamp, which can firmly fix the component and achieve smooth 360-degree rotation. The positioning process uses a non-invasive clamping mechanism to mount the component on the platform to avoid deformation of the soft material. The rotational motion is controlled by a high-precision servo motor, and the rotation is usually positioned at an incremental angle of 10-15 degrees to ensure sufficient overlap between adjacent scanning positions. At the same time, the vertical positioning is adjusted to capture data from different height angles to form a scanning position matrix covering the entire component surface. This comprehensive positioning strategy eliminates blind spots and shadow areas, and obtains the full-range scanning preparation state required for complete geometric data acquisition.

[0058] According to the full-range scanning preparation state, the laser transmitter is used to project light on the surface of the welded sound-absorbing cotton component to obtain the surface reflection beam data. The laser transmitter generates light of a precise wavelength (usually 650nm) and projects it onto the surface of the component in a predefined pattern, usually grid lines, dots or special coding patterns. These patterns are deformed according to the surface geometry, producing distortions that directly correspond to the surface contour. The emission system dynamically adjusts the light intensity according to the reflective properties of the material, using higher intensities for darker or more absorbent areas and lower intensities for highly reflective areas. The laser emission sequence follows a predetermined pattern to ensure complete coverage of the surface, and the emission angle and pattern density vary according to the complexity of the component and the required resolution. The generated reflected beam data contains encoded information about how the structured light pattern interacts and deforms with the three-dimensional surface of the component.

[0059] The optical receiver collects and organizes the data of the reflected light beam from the surface to obtain the original reflected light signal. The optical receiver consists of a high-resolution digital camera equipped with special optical filters that isolate the laser wavelength from the ambient light. These cameras capture the deformed light pattern at a resolution typically between 2-5 megapixels, capable of detecting subtle surface changes. The captured images undergo initial processing, including noise reduction, contrast enhancement, and background removal, to isolate the structured light pattern. The receiver system is synchronized with the laser transmitter to match each captured image with its corresponding emission pattern and position. Multiple cameras located at different angles capture the same projection pattern simultaneously, providing redundant data and improving accuracy and reliability. The processed image data forms the original reflected light signal, which contains the original information about the pattern deformation caused by the surface geometry of the component.

[0060] The depth of the surface points of the welded sound-absorbing cotton components is calculated by triangulation based on the original reflected light signal to obtain a surface coordinate data set. The triangulation calculation process is based on the principle of geometric optics and determines the depth by measuring the triangle formed by the two ends of the known baseline (laser transmitter and optical receiver) and the target point. Its mathematical model can be expressed as:

[0061] in, Indicates the depth value of the target point. B is the baseline distance between the transmitter and the receiver, f is the focal length of the receiver lens, is the observed mode shift, is the angle between the emitted light and the baseline, are the coordinates of the mode position on the reference plane, is the image center coordinate. For sound-absorbing cotton components with complex curved surfaces, the material reflection characteristic correction factor and surface normal vector are introduced in the calculation to further optimize the depth calculation accuracy. The calculation process is performed independently for each identified feature point to form a surface coordinate data set containing three-dimensional coordinates, which directly reflects the geometric characteristics of the surface of the sound-absorbing cotton component.

[0062] The surface coordinate data set is filtered out of outliers by the sparse point filtering algorithm to obtain an optimized coordinate set. The sparse point filtering algorithm is specifically used to handle measurement outliers caused by light scattering, material reflection or sensor noise. The algorithm meshes the entire point cloud, performs local statistical analysis in each grid cell, and calculates the spatial distribution characteristics of the points. For each point, its local neighborhood characteristics are calculated based on the K-nearest neighbor (KNN) method, including average distance, standard deviation and local curvature. Points that significantly deviate from local statistical characteristics are marked as potential outliers. The algorithm uses a combination of distance threshold method, density clustering method and local plane fitting method to comprehensively evaluate the reliability of each point. Points with scores below the threshold are eliminated, while boundary points and feature points are retained through special processing. The filtering intensity is dynamically adjusted according to the material properties and scanning quality to ensure that real details are retained while removing noise. The processed data forms an optimized coordinate set with higher geometric accuracy and consistency. Three-dimensional space reconstruction is performed based on the optimized coordinate set to obtain a three-dimensional point cloud model of the component. The 3D reconstruction process converts discrete point coordinates into a continuous surface model, and organizes the point cloud into spatial indexes through spatial partitioning techniques (such as octrees) to improve the efficiency of subsequent processing. The surface reconstruction algorithm uses the moving least squares method (MLS) to smooth local areas and reduce the impact of residual noise. For areas with slight data missing, the surface interpolation algorithm is used to fill the gaps to ensure the integrity of the model. The reconstruction process takes into account the characteristics of the sound-absorbing cotton material, and uses special edge-preserving processing for feature areas such as edges and corners to avoid loss of details due to excessive smoothing. The generated 3D point cloud model accurately expresses the geometric shape, size and surface characteristics of the sound-absorbing cotton component, providing basic data for subsequent deformation analysis and quality assessment.

[0063] In a specific embodiment, the process of performing the step of analyzing the sound absorption characteristics of the welded sound-absorbing cotton component according to the acoustic reflection wave data may specifically include the following steps: (1) Use a spectrum analyzer to convert the acoustic reflection wave data into time-frequency domain to obtain a frequency distribution spectrum; (2) Evaluate the sound wave energy loss in each frequency band through energy attenuation calculation based on the frequency distribution spectrum to obtain a frequency band attenuation coefficient table; (3) The frequency band attenuation coefficient table is normalized and converted through normalization processing to obtain a standardized sound absorption curve; (4) Extract and compare the sound absorption capacity at different frequency points based on the standardized sound absorption curve to obtain the key frequency sound absorption index; (5) Calculate the acoustic impedance of key frequency sound absorption indicators through impedance analysis to obtain the material acoustic impedance characteristic spectrum; (6) Data fusion is performed based on the material acoustic resistance characteristic map and the standardized sound absorption curve to obtain the sound absorption function parameters.

[0064] Specifically, the acoustic reflection wave data is converted into time-frequency domain by a spectrum analyzer to obtain a frequency distribution spectrum. A spectrum analyzer is a device dedicated to acoustic signal processing, which can convert acoustic wave signals in the time domain into frequency domain representation. The conversion process uses a fast Fourier transform algorithm to decompose the original reflection wave data in the time series into amplitude and phase information of different frequency components. Before processing, the original data is first processed by a window function to reduce spectrum leakage. Hanning window or Hamming window is commonly used, and the window length is usually set to 8192 or 16384 points to obtain sufficient frequency resolution. After FFT calculation, a complex spectrum containing frequency points in the range of 500Hz to 8000Hz is obtained. Subsequently, the power spectral density is calculated to obtain a frequency distribution spectrum reflecting the energy distribution of each frequency component. The spectrum diagram is usually expressed in a logarithmic scale, with the horizontal axis being the frequency and the vertical axis being the energy intensity, which intuitively shows the reflection characteristics of the material to sound waves of different frequencies.

[0065] According to the frequency distribution spectrum, the energy loss of sound waves in each frequency band is evaluated through energy attenuation calculation to obtain a frequency band attenuation coefficient table. Energy attenuation calculation is the basis for determining the sound absorption performance of materials. The core is to calculate the energy difference between the incident sound wave and the reflected sound wave. The calculation process divides the frequency range into several frequency bands, usually divided by 1 / 3 octave or 1 / 1 octave, such as 500Hz, 630Hz, 800Hz, 1000Hz and other standard frequency points. In each frequency band, the ratio of the incident sound wave energy to the reflected sound wave energy is calculated to obtain the energy reflection coefficient. This ratio is then used to calculate the energy absorption coefficient, that is, the proportion of the energy absorbed by the sound wave by the material to the total energy of the incident sound wave. For sound-absorbing cotton components with complex shapes, the influence of the incident angle must also be considered. Usually, multi-angle data is collected and the average absorption coefficient at different incident angles is calculated. A frequency band attenuation coefficient table containing the absorption coefficient of each frequency band is formed to fully reflect the sound absorption characteristics of the material in the acoustic spectrum.

[0066] The frequency band attenuation coefficient table is standardized and converted through normalization processing to obtain a standardized sound absorption curve. The purpose of normalization processing is to eliminate the influence of differences in test conditions and make the data measured in different batches and different environments comparable. The processing process selects a reference state, such as the ideal sound absorption performance under standard test conditions or an industry standard curve. Then, the original attenuation coefficient is linearly or nonlinearly transformed to map the data to the 0-1 interval. Commonly used transformation methods include maximum-minimum normalization method and Z-score normalization method. For different frequency bands, different weights are given according to their importance in acoustic evaluation. Generally, automotive interior sound-absorbing materials have a higher weight for the mid-high frequency (1000-4000Hz) segment. The normalized data is plotted into a continuous curve to form a standardized sound absorption curve, with the horizontal axis being the frequency and the vertical axis being the standardized sound absorption coefficient. The shape of the curve intuitively reflects the distribution of the sound absorption characteristics of the material on the sound spectrum.

[0067] According to the standardized sound absorption curve, the sound absorption capacity of different frequency points is extracted and compared to obtain the key frequency sound absorption index. Key frequency points refer to frequencies that are of special importance in the automotive acoustic environment, usually including the human voice frequency band (500-2000Hz), engine noise frequency band (100-500Hz) and wind noise frequency band (2000-6000Hz). The index extraction process selects the sound absorption coefficient values ​​corresponding to these key frequency points from the standardized sound absorption curve, and then compares and analyzes them with the target threshold. The comparative analysis not only focuses on the absolute sound absorption value, but also focuses on the shape characteristics of the sound absorption curve, such as peak position, bandwidth, slope, etc. These characteristics are crucial to evaluating the sound absorption effect of materials in practical applications. The feature extraction algorithm extracts key feature parameters from the curve by finding local extreme points and calculating the center of gravity of spectral energy. The key frequency sound absorption index formed is a set of comprehensive evaluation indicators that include the sound absorption capacity of characteristic frequency points and the description of curve characteristics, providing a quantitative basis for the evaluation of the acoustic performance of materials. The acoustic impedance of the key frequency sound absorption index is calculated by impedance analysis to obtain the material acoustic impedance characteristic spectrum. Acoustic impedance is a physical quantity that measures the degree of resistance of a material to the transmission of sound waves, and directly determines the reflection and transmission characteristics of sound waves at the material interface. Impedance calculation is based on key frequency sound absorption indicators and sound wave propagation principles. The surface acoustic impedance is inferred through the sound absorption coefficient to calculate the characteristic impedance in complex form, including the real part (acoustic resistance) and the imaginary part (acoustic reactance). The calculation takes into account the influence of physical parameters such as material density, porosity, and flow resistance on impedance to form a composite impedance value at each frequency point. The acoustic impedance characteristic spectrum uses frequency as the horizontal axis and the modulus and phase of the composite impedance as the vertical axis. It comprehensively describes the impedance characteristics of the material in the acoustic spectrum, and reflects the reflection, absorption, and transmission behavior of the material to sound waves of different frequencies.

[0068] Data fusion is performed based on the material acoustic impedance characteristic spectrum and the standardized sound absorption curve to obtain the sound absorption function parameters. Data fusion is the process of integrating two complementary acoustic characteristic representations into a set of comprehensive evaluation parameters. The fusion process extracts key features from the acoustic impedance characteristic spectrum, such as impedance peak frequency, valley frequency, bandwidth and uniformity. These features are then mapped and associated with the key indicators of the standardized sound absorption curve to establish a corresponding relationship between the acoustic impedance characteristics and the sound absorption performance. The fusion algorithm uses weighted average method, multi-attribute decision method or fuzzy logic method to comprehensively consider the importance and mutual influence of different acoustic indicators to generate sound absorption function parameters. These parameters include multi-dimensional indicators such as average sound absorption coefficient, sound absorption bandwidth, frequency selectivity, impedance matching and acoustic stability, which fully reflect the acoustic performance characteristics of the welded sound-absorbing cotton components.

[0069] In a specific embodiment, the process of performing the step of evaluating the loss of sound wave energy in each frequency band by energy attenuation calculation according to the frequency distribution spectrum may specifically include the following steps: (1) The frequency distribution spectrum is segmented by a frequency band divider to obtain multiple independent frequency band intervals; (2) Calculate the energy ratio of the incident sound wave to the reflected sound wave for each independent frequency band to obtain the original energy ratio data; (3) Compare the original energy ratio data with the sound absorption performance of the standard reference material through comparative calibration to obtain the target sound absorption capacity value; (4) Mathematically model the attenuation characteristics of each frequency band according to the target sound absorption capacity value to obtain the frequency band attenuation characteristic curve; (5) By compensating for interference factors, the frequency band attenuation characteristic curve is corrected for environmental noise and measurement errors to obtain a corrected attenuation curve; (6) Discretely sample the sound absorption coefficient of each frequency band according to the corrected attenuation curve to obtain a frequency band attenuation coefficient table.

[0070] Specifically, the frequency distribution spectrum is segmented by a frequency band divider to obtain multiple independent frequency band intervals. The frequency band divider is a frequency domain processing tool specifically used for acoustic data analysis, which can divide broadband acoustic data into narrow frequency bands for easy analysis. The division process uses the standard octave method to divide the frequency range of 500Hz to 8000Hz into multiple analysis intervals according to international standards. Common division methods include 1 / 1 octave division (such as 500Hz, 1000Hz, 2000Hz, etc.) and 1 / 3 octave division (such as 500Hz, 630Hz, 800Hz, 1000Hz, etc.). During the division process, a bandpass filter group is applied to the original spectrum data, and the center frequency and bandwidth of each filter are strictly set according to the acoustic standard. The filtered data is organized into frequency band intervals that are independent of each other but cover the complete spectrum. Each interval contains detailed spectral characteristics within the frequency band, laying the foundation for the subsequent accurate analysis of the sound absorption performance of each frequency band.

[0071] The energy ratio of the incident sound wave to the reflected sound wave is calculated for each independent frequency band to obtain the original energy ratio data. Energy ratio calculation is a direct method to evaluate the sound absorption performance of materials. The energy of the incident sound wave in the frequency band is compared with the energy of the reflected sound wave. During the calculation process, the incident sound pressure level and the reflected sound pressure level of each frequency band are extracted from the test records, the sound pressure level is converted into energy value, and then the ratio of the two is calculated. For plane wave incident conditions, the energy ratio is directly proportional to the square ratio of the sound pressure; for complex incident conditions, the sound field type, incident angle and sound wave propagation characteristics must also be considered. In the processing, special attention is paid to the continuity of the data at the boundaries of each frequency band, and an interpolation algorithm is used to ensure a smooth transition of the data at the junction of the frequency bands. The original energy ratio data obtained is a set of numerical values ​​representing the sound energy reflection of each frequency band. The lower the value, the better the sound absorption performance of the corresponding frequency band.

[0072] The target sound absorption capacity value is obtained by comparing the original energy ratio data with the sound absorption performance of the standard reference material through comparative calibration. Comparative calibration is a key step to eliminate measurement system errors and improve data reliability. The calibration process uses standard reference materials with known acoustic properties as the benchmark. These materials are usually standard sound-absorbing panels certified by national laboratories. When comparing, the original energy ratio data of the test sample is compared with the data of the reference material under the same test conditions, and the difference between the two in each frequency band is calculated. This difference comparison eliminates the influence of the deviation of the measurement system itself and highlights the difference in sound absorption performance of the tested material relative to the standard material. The calibration algorithm considers the importance weights of different frequency bands and gives higher weights to the key frequency bands of automotive interiors (such as the human voice frequency band 1000-3000Hz). After this relative comparison processing, the target sound absorption capacity value obtained more objectively reflects the sound absorption performance of the material in the actual application environment. According to the target sound absorption capacity value, the attenuation characteristics of each frequency band are mathematically modeled to obtain the frequency band attenuation characteristic curve. Mathematical modeling is the process of converting discrete sound absorption data points into continuous function expressions, with the aim of describing the sound absorption behavior of the material over the entire spectrum. Modeling uses methods such as polynomial fitting, spline interpolation or Bezier curves to select the most suitable function form according to the distribution characteristics of data points. The modeling process preprocesses the target sound absorption capacity value, including removing outliers, smoothing and data normalization. Then, through optimization algorithms such as the least squares method, the best fitting parameters are found to minimize the error between the modeling curve and the measured data points. When modeling, special attention is paid to the continuity and smoothness of the curve at the junction of each frequency band. If necessary, the segmented modeling method is used to ensure that the curve accurately reflects the changes in the sound absorption characteristics of the material in different frequency bands. The frequency band attenuation characteristic curve after modeling is a continuous function that can predict the sound absorption performance of the material at any frequency point.

[0073] The frequency band attenuation characteristic curve is corrected for environmental noise and measurement errors through interference factor compensation to obtain the corrected attenuation curve. Interference factor compensation is an important part of improving measurement accuracy, mainly targeting factors such as environmental noise, measurement system errors and sample state fluctuations during the test process. The compensation process identifies the main interference sources, including background noise, reflection surface interference, sound wave scattering and sensor errors. For environmental noise, noise baseline extraction and signal denoising technology are used to remove noise components from the original curve. Measurement error compensation is based on system calibration data to systematically correct the curve. The compensation algorithm also considers the influence of factors such as material surface state, temperature and humidity conditions and installation methods on the measurement results. By establishing a correlation model between interference factors and measurement deviations, the curve is corrected in a targeted manner. After comprehensive interference factor compensation processing, the corrected attenuation curve is closer to the actual sound absorption performance characteristics of the material under ideal conditions.

[0074] According to the corrected attenuation curve, the sound absorption coefficient of each frequency band is discretized and sampled to obtain the frequency band attenuation coefficient table. Discrete sampling is to convert the continuous curve function into a practical data table form, which is convenient for subsequent evaluation and application. The sampling process is based on the acoustic evaluation standard, and the sound absorption coefficient value is extracted at the standard frequency points (such as 125Hz, 250Hz, 500Hz, 1000Hz, 2000Hz, 4000Hz, 8000Hz, etc.). When sampling, the importance distribution of the frequency band is considered, and more dense sampling points are used in the key frequency band to ensure that the sound absorption characteristics of the important frequency range are accurately expressed. The sampling results are formatted to form a standardized data table containing frequency points and corresponding sound absorption coefficients. This kind of tabular data is convenient for comparison with design requirements, and is also convenient for presentation and analysis in the quality assessment report. The frequency band attenuation coefficient table has become an important basis for evaluating the welding quality of sound-absorbing cotton, which directly reflects the impact of welding technology on the sound absorption function of the product.

[0075] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An automatic welding method for automobile sound-absorbing cotton, characterized in that: The automatic welding method for automobile sound-absorbing cotton comprises: The polyester fiber sound-absorbing cotton raw material is pretreated by a precision cutting system and a high-frequency hot air circulation furnace to obtain a pretreated sound-absorbing cotton material; According to the pre-processed sound-absorbing cotton material, modular positioning is performed through a six-axis robotic arm in conjunction with visual recognition to obtain a three-dimensional space coordinate data packet; Perform welding path planning and optimization based on the three-dimensional space coordinate data package to obtain a digital welding instruction package; The welding operation is performed according to the digital welding instruction package through a dual-frequency ultrasonic generator and a dual closed-loop feedback control system to obtain a welded sound-absorbing cotton component; The quality of the welded sound-absorbing cotton components is inspected through 3D laser scanning and acoustic characteristic analysis systems to obtain a quality assessment report; According to the quality assessment report, defects are repaired through point reinforcement welding technology and nano-level polymer repair agents to obtain finished automotive sound-absorbing cotton components.

2. The automatic welding method for automobile sound-absorbing cotton according to claim 1 is characterized in that: The polyester fiber sound-absorbing cotton raw material is pretreated by a precision cutting system and a high-frequency hot air circulation furnace to obtain a pretreated sound-absorbing cotton material, including: The dimensions of the polyester fiber sound-absorbing cotton raw material are accurately calculated by a computer-aided design system to obtain geometric parameters of the sound-absorbing cotton; According to the geometric parameters of the sound-absorbing cotton, the polyester fiber sound-absorbing cotton raw material is cut into shapes by precision cutting equipment to obtain cut sound-absorbing cotton that matches the vehicle body structure; The cut sound-absorbing cotton is subjected to a preset temperature treatment for a predetermined time in a high-frequency hot air circulation furnace to obtain a molten sound-absorbing cotton; The molten sound-absorbing cotton is subjected to a vacuum suction device to remove moisture and impurities to obtain a dry sound-absorbing cotton having a moisture content of less than 0.5%; The surface of the dry sound-absorbing cotton is uniformly coated with modified polyvinyl alcohol and nano-scale titanium dioxide composite reinforcing agent by a coating device to obtain the pretreated sound-absorbing cotton material.

3. The automatic welding method for automobile sound-absorbing cotton according to claim 1 is characterized in that: The pre-processed sound-absorbing cotton material is modularly positioned by a six-axis robotic arm in conjunction with visual recognition to obtain a three-dimensional space coordinate data packet, including: Capturing feature points of the pre-treated sound-absorbing cotton material by a high-definition CCD camera to obtain surface feature information of the material; According to the surface feature information of the material, a feature matching algorithm is used to perform three-dimensional model matching on the pre-treated sound-absorbing cotton material to obtain initial position data; Performing deformation error correction on the initial position data by using a flexible material deformation compensation algorithm to obtain corrected position information; Applying negative pressure to fix the pretreated sound-absorbing cotton material through a distributed micro-pneumatic suction cup array to obtain a material in a stable fixed state; Monitor the surface temperature distribution of the material in the stable fixed state by using a thermal imaging sensor to obtain temperature parameter feedback data; Coordinate calculation and integration are performed based on the corrected position information and the temperature parameter feedback data to obtain the three-dimensional space coordinate data packet.

4. The automatic welding method for automobile sound-absorbing cotton according to claim 1 is characterized in that: The welding path planning and optimization is performed according to the three-dimensional space coordinate data packet to obtain a digital welding instruction packet, including: Performing a preliminary path calculation on the welding area according to the three-dimensional space coordinate data packet and the vehicle model sound-absorbing cotton structure parameters to obtain a preliminary welding path; Perform multi-dimensional optimization on the preliminary welding path through historical welding quality data to obtain an optimized welding path; According to the optimized welding path, the overall welding task is progressively divided into zones to obtain welding sub-area data; Marking main welding points and auxiliary welding points of each sub-area according to the welding sub-area data to obtain a welding point distribution map; Differentiately calibrate the energy input of each welding point according to the welding point distribution map combined with the material thickness parameter to obtain a welding energy distribution curve; The welding energy distribution curve is heat-controlled and marked by setting intermittent cooling points to obtain the digital welding instruction package.

5. The automatic welding method for automobile sound-absorbing cotton according to claim 1 is characterized in that: The method of performing welding operation according to the digital welding instruction package through a dual-frequency ultrasonic generator and a dual closed-loop feedback control system to obtain a welded sound-absorbing cotton component includes: Selecting the frequency of the dual-frequency ultrasonic generator according to the material thickness parameter in the digital welding instruction package to obtain the welding frequency configuration parameter; According to the welding frequency configuration parameters, ultrasonic energy is converted through a titanium alloy transducer to obtain welding energy conduction data; The acoustic impedance of the welding point is collected at the millisecond level through real-time impedance matching technology to obtain the welding impedance data stream; Dynamically adjusting ultrasonic power output and pressure parameters according to the welding impedance data stream to obtain an adaptive welding control value; The temperature monitor and deformation sensor are used to perform double closed-loop detection of the welding status to obtain real-time status information of the welding process; The welded area is subjected to directional cooling treatment by a rapid cooling device to obtain the welded sound-absorbing cotton component.

6. The automatic welding method for automobile sound-absorbing cotton according to claim 1 is characterized in that: The three-dimensional laser scanning and acoustic characteristic analysis system is used to perform quality inspection on the welded sound-absorbing cotton components to obtain a quality assessment report, including: The welded sound-absorbing cotton component is scanned on its entire surface by a high-precision three-dimensional laser scanner to obtain a three-dimensional point cloud model of the component; Comparing and analyzing the three-dimensional point cloud model of the component with the theoretical model, the deformation deviation data is obtained; The acoustic characteristic analyzer emits a preset frequency sound wave to the welded sound-absorbing cotton component to obtain acoustic reflection wave data; Analyzing the sound absorption characteristics of the welded sound-absorbing cotton component according to the acoustic reflection wave data to obtain sound absorption function parameters; Performing sampling strength test on key welding points of the welded sound-absorbing cotton components by using a micro-tensile sensor array to obtain welding strength data; The quality assessment report is obtained by performing a comprehensive analysis based on the deformation deviation data, the sound absorption function parameters and the welding strength data.

7. The automatic welding method for automobile sound-absorbing cotton according to claim 1 is characterized in that: The defect repair is performed by point strengthening welding technology and nano-scale polymer repair agent according to the quality assessment report to obtain a finished automobile sound-absorbing cotton component, including: Classify and analyze the defect information in the quality assessment report through a parser to obtain a defect type location database; According to the defect type position database, locally reinforce the area with insufficient welding strength by using a small-diameter high-energy ultrasonic focusing head to obtain a strength-reinforced area; Spraying the nano-scale polymer repair agent on the tiny crack area in the welded sound-absorbing cotton component by a precision spraying device to obtain a crack repair area; The slightly deformed area of ​​the welded sound-absorbing cotton component is subjected to hot air negative pressure treatment by shape memory thermal adjustment technology to obtain a shape recovery area; Perform edge trimming and surface dust removal on the welded sound-absorbing cotton component according to the strength reinforcement area, the crack repair area and the shape recovery area to obtain a repaired component; The repaired component is surface treated by an acoustic performance enhancing coating to obtain the finished automobile sound-absorbing cotton component.

8. The automatic welding method for automobile sound-absorbing cotton according to claim 6 is characterized in that: The high-precision three-dimensional laser scanner is used to scan the entire surface of the welded sound-absorbing cotton component to obtain a three-dimensional point cloud model of the component, including: The welded sound-absorbing cotton component is rotated 360 degrees by a multi-angle positioning device to obtain a full-range scanning preparation state; According to the omni-directional scanning preparation state, a laser transmitter is used to project light onto the surface of the welded sound-absorbing cotton component to obtain surface reflected light beam data; The optical receiver collects and organizes the surface reflected light beam data to obtain an original reflected light signal; According to the original reflected light signal, depth calculation is performed on the surface points of the welded sound-absorbing cotton component by triangulation to obtain a surface coordinate data group; Using a sparse point filtering algorithm to remove outliers from the surface coordinate data set, to obtain an optimized coordinate set; A three-dimensional space reconstruction process is performed according to the optimized coordinate set to obtain a three-dimensional point cloud model of the component.

9. The automatic welding method for automobile sound-absorbing cotton according to claim 6 is characterized in that: The sound absorption characteristic analysis of the welded sound-absorbing cotton component is performed according to the acoustic reflection wave data to obtain the sound absorption function parameters, including: Performing time-frequency domain conversion on the acoustic reflection wave data by means of a spectrum analyzer to obtain a frequency distribution spectrum; According to the frequency distribution spectrum, the sound wave energy loss of each frequency band is evaluated by energy attenuation calculation to obtain a frequency band attenuation coefficient table; The frequency band attenuation coefficient table is normalized and converted by normalization processing to obtain a standardized sound absorption curve; The sound absorption capacity at different frequency points is extracted and compared according to the standardized sound absorption curve to obtain the key frequency sound absorption index; The acoustic impedance of the key frequency sound absorption index is calculated by impedance analysis to obtain a material acoustic impedance characteristic spectrum; The sound absorption function parameters are obtained by performing data fusion according to the material acoustic resistance characteristic map and the standardized sound absorption curve.

10. The automatic welding method for automobile sound-absorbing cotton according to claim 9 is characterized in that: The energy loss of the sound wave in each frequency band is evaluated by energy attenuation calculation according to the frequency distribution spectrum to obtain a frequency band attenuation coefficient table, including: The frequency distribution spectrum is segmented by a frequency band divider to obtain a plurality of independent frequency band intervals; The energy ratio of the incident sound wave to the reflected sound wave is calculated for each independent frequency band to obtain the original energy ratio data; By comparative calibration, the original energy ratio data is compared with the sound absorption performance of the standard reference material to obtain a target sound absorption capacity value; Mathematically modeling the attenuation characteristics of each frequency band according to the target sound absorption capacity value to obtain a frequency band attenuation characteristic curve; The attenuation characteristic curve of the frequency band is corrected for environmental noise and measurement errors by compensating for interference factors to obtain a corrected attenuation curve; Discrete sampling is performed on the sound absorption coefficient of each frequency band according to the corrected attenuation curve to obtain the frequency band attenuation coefficient table.

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