A method for heat-setting wall coverings using low-melting-point adhesives

Abstract

This application provides a method for heat-sealing and grading of wall coverings using low-melting-point adhesives, comprising: acquiring back-side data of a textile material matrix; analyzing the back-side data to determine a distribution scheme for the heat-sensitive adhesive material and generating initial distribution parameters; adjusting the distribution of the heat-sensitive adhesive material according to the initial distribution parameters to generate optimized back-side adhesive data and outputting preprocessing results; extracting operation parameters from the preprocessing results, performing targeted processing to generate processed matrix data and outputting heat-treatment information; performing heat treatment according to the heat-treatment information to generate a heat-treated matrix state; extracting relevant parameters from the heat-treated matrix state, performing an adhesive process and generating curing bonding information; and adjusting cooling parameters according to the curing bonding information to generate dimensionally stable textile material information.

Description

Technical Field

[0001] This invention relates to the field of manufacturing technology of wall coverings for buildings, and in particular to a method for heat-setting wall coverings using low-melting-point adhesives. Background Technology

[0002] Current nonwoven fabric processing methods still have significant shortcomings in achieving material bonding and morphological stability. Many traditional processes often struggle to balance the smoothness of the material surface with the robustness of the internal structure, leading to problems such as delamination, deformation, or uneven surfaces during product use. These issues not only affect the product's appearance and feel but also reduce its actual lifespan, especially in scenarios requiring the application of tensile or frictional forces, where the defects become particularly noticeable.

[0003] A deeper technical challenge lies in the precise control of temperature and time during the thermal bonding process. Thermal bonding is the core step in nonwoven fabric processing to achieve a strong bond between fibers. However, if the temperature is too high or the time is too long, the fibers may over-melt, damaging the material's original flexibility and structure. Conversely, if the temperature is insufficient or the time is too short, the adhesive will not be able to fully penetrate the fiber gaps, resulting in a weak bond and easy delamination of the material. This stringent requirement for thermal bonding conditions makes it difficult to guarantee the stability of the processing technology. Specifically, in the production of nonwoven fabric products such as wallcovering base fabric, the backing needs to be tightly bonded to the fiber layer through a thermal bonding process to ensure the stability of the overall structure. However, in practice, fluctuations in thermal bonding conditions often lead to uneven bonding in some areas, or even localized over-melting while other areas remain unbonded. For example, on a production line, products from the same batch may experience uneven temperature distribution, causing some areas of the base fabric's backing fibers to loosen and fall off, while other areas become stiff due to overheating, severely affecting the overall consistency and performance of the product. Summary of the Invention

[0004] This invention provides a method for heat-setting wall coverings using low-melting-point adhesives, mainly comprising: The process involves: acquiring backside data of the textile material matrix; analyzing the backside data to determine the distribution scheme of the heat-sensitive adhesive material and generating initial distribution parameters; adjusting the distribution of the heat-sensitive adhesive material according to the initial distribution parameters to generate optimized backside adhesive data and outputting preprocessing results; extracting operation parameters from the preprocessing results and performing targeted processing to generate processed matrix data and outputting information for heat treatment; performing heat treatment according to the information for heat treatment to generate a heat-treated matrix state; extracting relevant parameters from the heat-treated matrix state, performing the bonding process and generating curing bonding information; adjusting cooling parameters according to the curing bonding information to generate dimensionally stable textile material information; extracting backside adhesive data from the textile material information, and using detection technology to determine whether the uniformity meets a preset threshold. If it does, a storage sequence is generated, completing the backside adhesive optimization process.

[0005] Furthermore, the acquisition of back-side data of the textile material matrix includes: acquiring back-side data of the textile material matrix through image acquisition technology, wherein the textile material matrix is ​​a non-woven fabric basic structure; performing layered analysis on the back-side data to generate a back-side roughness report, the report including surface unevenness indicators; determining the distribution scheme of the heat-sensitive adhesive material based on the fiber interlacing structure characteristics, and outputting initial distribution parameters, the initial distribution parameters including specific values ​​of concentration and zoning; determining the applicability of the initial distribution parameters by analyzing the back-side roughness report and fiber interlacing structure characteristics to ensure the accuracy of subsequent adjustments; and recording the analysis process of the back-side data to form a traceable data record for subsequent optimization reference.

[0006] Furthermore, adjusting the distribution of the thermal adhesive material according to the initial distribution parameters includes: using a proportioning control system to adjust the distribution ratio of the thermal adhesive material according to the initial distribution parameters; monitoring the uniformity of the mixing and combing in real time to generate optimized back-side adhesive data, wherein the data is the adjusted distribution value; recording the penetration change of the low-temperature adhesive material and outputting the back-side pretreatment result, wherein the result includes pretreatment status information; determining whether the distribution adjustment has achieved the expected effect by monitoring the uniformity of the mixing and combing; and adjusting subsequent processing parameters according to the penetration change of the low-temperature adhesive material to ensure the stability of the pretreatment result.

[0007] Furthermore, the step of extracting operational parameters from the pretreatment results includes: extracting napping operational parameters from the pretreatment results and inputting them into the carding device to perform targeted carding; processing weak areas in the fiber interlacing structure to generate napped textile material matrix data, wherein the data represents post-processing attributes; recording changes in back surface roughness and outputting information for heat treatment, including temperature tolerance; determining whether the carding effect meets expectations by analyzing the changes in back surface roughness; and adjusting the parameter settings for subsequent heat treatment based on the temperature tolerance to ensure the safety and effectiveness of the processing.

[0008] Furthermore, the heat treatment based on the heat treatment information includes: determining whether there is a non-textured area on the back side based on the heat treatment information; if confirmed, loading temperature settings and time control parameters through a heating and pressing device; generating execution data based on the melt penetration depth requirements, the data being operation instructions; outputting the substrate state after heat treatment, the state including physical properties; determining whether the heat treatment effect has achieved the expected target by analyzing the physical properties; and recording parameter changes during the heat treatment process based on the execution data to provide data support for subsequent optimization.

[0009] Furthermore, the extraction of relevant parameters from the heat-treated matrix state includes: extracting the type of low-temperature adhesive material and auxiliary strength parameters from the heat-treated matrix state; monitoring the curing rate through a temperature control system and performing a fiber surface bonding process; generating curing bonding information, which is bonding strength data; recording uniformity detection results and outputting bonding data to be cooled, which is the state to be processed; performing thermal bonding under set process conditions; determining whether the bonding process meets process requirements by analyzing the uniformity detection results; and adjusting subsequent cooling parameters based on the curing bonding information.

[0010] Furthermore, adjusting the cooling parameters based on the curing and bonding information includes: loading cooling parameters through a cooling system for the bonding data to be cooled; adjusting the cooling rate based on the curing rate monitoring results to stabilize the melt penetration depth; generating dimensionally stable flat piled textile material information, the information being the processed dimensions and surface properties; outputting the final finished product data to be tested, the data including overall attribute parameters; determining whether the cooling effect meets expectations by analyzing the overall attribute parameters; and recording parameter changes during the cooling process based on the cooling rate adjustment results to provide a basis for subsequent process optimization.

[0011] Furthermore, the step of extracting back-side adhesive data from the textile material information includes: extracting back-side adhesive data from the final finished product data to be tested; determining whether the uniformity meets a preset threshold using layered scanning technology; if it does, confirming no delamination and generating a storage sequence; if it does not, recording the specific location and data characteristics of the uneven area; forming a traceable processing record based on the storage sequence; determining the final effect of the optimization process by analyzing the uniformity results of the back-side adhesive data; and adjusting subsequent detection parameters based on the results of the layered scanning technology to ensure detection accuracy.

[0012] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses a method for optimizing the back-side heat-sensitive bonding of nonwoven fabric substrates. It acquires back-side data of the textile material substrate using image acquisition technology and performs layered analysis to generate a back-side roughness report including surface unevenness indicators. The initial distribution concentration and zoning parameters of the heat-sensitive bonding material are determined based on the fiber interlacing structure characteristics. Subsequently, a ratio control system is used to adjust the distribution ratio in real time and monitor the uniformity of mixing and carding and the penetration changes of the low-temperature bonding material, generating optimized back-side bonding pretreatment results. The napping operation parameters are extracted from the pretreatment results to perform targeted carding, improving weak areas of the fiber interlacing structure, and recording the roughness. The process involves several steps: First, the system outputs information about the area to be heat-treated. Based on this information, the system identifies the non-pilly areas and then precisely controls the temperature and time using a heating and pressing device to achieve stable melt penetration depth, resulting in a heat-treated matrix. Next, it extracts the type of low-temperature adhesive material and auxiliary strength parameters, monitors the curing rate to complete fiber surface bonding, and generates curing bonding information. Based on the bonding strength and uniformity test results, the cooling rate is adjusted to stabilize the melt penetration depth, ultimately obtaining a dimensionally stable, smooth, piled textile material. Finally, a layered scanning technique is used to threshold the uniformity of the back-side adhesive. If the requirements are met, a stored sequence is generated to complete the optimization process. This method effectively solves the technical problems of uneven distribution of thermally sensitive adhesive on the back of nonwoven fabrics, insufficient adhesive strength, poor dimensional stability, and easy delamination, significantly improving the uniformity of back-side adhesive, bonding strength, and overall product quality stability. Attached Figure Description

[0013] Figure 1 This is a flowchart of a method for heat-setting a wall covering using a low-melting-point adhesive, according to the present invention.

[0014] Figure 2 This is a schematic diagram of a method for heat-setting a wall covering using a low-melting-point adhesive according to the present invention.

[0015] Figure 3 This is another schematic diagram of a method for heat-setting wall coverings using low-melting-point adhesive according to the present invention.

[0016] Figure 4 This is another schematic diagram of a method for heat-setting wall coverings using low-melting-point adhesive according to the present invention.

[0017] Figure 5 This is another schematic diagram of a method for heat-setting wall coverings using low-melting-point adhesive according to the present invention.

[0018] Figure 6 This is another schematic diagram of a method for heat-setting wall coverings using low-melting-point adhesive according to the present invention.

[0019] Figure 7 This is another schematic diagram of a method for heat-setting wall coverings using low-melting-point adhesive according to the present invention.

[0020] Figure 8 This is another schematic diagram of a method for heat-setting wall coverings using low-melting-point adhesive according to the present invention.

[0021] Figure 9 This is another schematic diagram of a method for heat-setting wall coverings using low-melting-point adhesive according to the present invention.

[0022] Figure 10 This is another schematic diagram of a method for heat-setting wall coverings using low-melting-point adhesive according to the present invention.

[0023] Figure 11 This is another schematic diagram of a method for heat-setting wall coverings using low-melting-point adhesive according to the present invention.

[0024] Figure 12 This is another schematic diagram of a method for heat-setting wall coverings using low-melting-point adhesive according to the present invention.

[0025] Figure 13 This is another schematic diagram of a method for heat-setting wall coverings using low-melting-point adhesive according to the present invention. Detailed Implementation

[0026] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0027] like Figures 1-13 This embodiment of a method for heat-setting wall coverings using low-melting-point adhesives may specifically include: S101. Obtain back side data of the textile material matrix, analyze the back side data to determine the distribution scheme of the heat-sensitive adhesive material, and generate initial distribution parameters.

[0028] S1, Obtain back side data of the textile material matrix.

[0029] In one embodiment, image acquisition technology is used to acquire data of the back side of the textile material substrate, which is a non-woven fabric base structure. A high-resolution camera is used to capture the back side image to ensure that the data clarity meets the preset standard.

[0030] S2, Analyze the back side data to determine the distribution scheme of the heat-sensitive adhesive material.

[0031] Specifically, the back side data is analyzed in layers to generate a back side roughness report, which includes surface unevenness indicators. The distribution scheme of the heat-sensitive adhesive material is determined in combination with the fiber interlacing structure characteristics.

[0032] S21, perform layered analysis on the back side data to generate a back side roughness report, which includes surface unevenness indicators.

[0033] For example, image processing algorithms are used to layer the back image. For instance, grayscale conversion is first applied to convert the image to a single channel, and then the Sobel operator is used to detect edges and calculate the surface roughness index. This index is obtained by statistically analyzing the edge pixel density. The higher the density, the greater the roughness. A report is generated to record these index values.

[0034] S22, Determine the distribution scheme of the heat-sensitive adhesive material based on the characteristics of the fiber interlaced structure.

[0035] In one possible implementation, fiber interlacing structure features are extracted, such as using Canny edge detection to identify fiber outlines, calculating the number of interlacing points, adjusting the distribution scheme according to the interlacing density, and adding adhesive material coverage in densely interlaced areas to improve bonding strength.

[0036] S3 generates the initial distribution parameters.

[0037] It should be noted that the output initial distribution parameters include concentration and zoning. For example, the concentration is set to 5 grams per square meter, and the zoning is divided into high-density areas and low-density areas.

[0038] In one embodiment, for the analysis process in S2, different types of nonwoven fabrics are considered, such as polyester fiber matrix. When the back data is layered, the surface and bottom images are separated first. The roughness report shows that the unevenness index is 0.15. Combined with the fiber interlacing characteristics, the distribution scheme prioritizes covering the edge area to avoid the risk of delamination caused by uneven adhesion. This can improve the overall durability of the material.

[0039] Specifically, in the polyester fiber scenario, after the Sobel operator is applied in S21, the edge density calculation formula is the average pixel gradient value. After the report is generated, it is used in S22. When the number of staggered points exceeds the threshold, the solution adjusts the concentration to 7 grams per square meter, and the area is planned as a grid distribution to ensure uniform penetration of the thermal adhesive.

[0040] For example, for cotton nonwoven fabric substrates with high back roughness and an unevenness index of 0.2, combined with sparse fiber interlacing, the distribution scheme reduces the concentration to 3 grams per square meter, focusing on the central area. This can stabilize the melting depth, reduce material waste, and improve flatness.

[0041] In one embodiment, for the extension of S2, temperature sensitivity is taken into consideration. During the layered analysis, the thermal conductivity coefficient is calculated additionally. The thermal conductivity coefficient refers to the heat transfer rate of the material. It is estimated by measuring the fiber gaps in the image. The larger the gap, the lower the coefficient. Then, when determining the distribution scheme, if the coefficient is less than 0.5, the concentration of the adhesive material in the low coefficient area is increased. This can optimize the heat treatment effect and avoid local overheating that could cause fiber damage.

[0042] Specifically, the thermal conductivity coefficient is calculated by statistically analyzing the percentage of pixels in the fiber gaps, multiplying the percentage by the standard thermal conductivity of 0.03, and using this value to support the regional planning of the distribution scheme, ensuring that the distribution of the thermally sensitive adhesive material matches the thermal properties of the substrate, and improving the uniformity of bonding.

[0043] For example, in a high-humidity environment, the nonwoven fabric matrix has a gap ratio of 0.4 and a coefficient of 0.012. The scheme adjustment area is planned as a ring distribution with a concentration of 4 grams per square meter, which can enhance the penetration stability and bring better dimensional stability.

[0044] S102. Adjust the distribution of the thermal adhesive material according to the initial distribution parameters, generate optimized back-side adhesive data, and output the preprocessing results.

[0045] Based on the initial distribution parameters, the distribution of the heat-sensitive adhesive material is adjusted using a ratio control system. The uniformity of the mixing and combing is monitored in real time, and optimized back-side adhesive data is generated. The data is the adjusted distribution value. At the same time, the penetration change of the low-temperature adhesive material is recorded, and the back-side pretreatment results are output. The results include pretreatment status information.

[0046] S21, Adjust the distribution of the thermal adhesive material according to the initial distribution parameters.

[0047] In one embodiment, the initial distribution parameters include concentration and zoning. First, the concentration value is decomposed into multiple intervals, for example, the overall concentration is divided into low concentration area and high concentration area. Then, according to the zoning, the back of the textile material matrix is ​​divided into grid-like areas, with each grid corresponding to a concentration interval. The injection amount of the heat-sensitive adhesive material is controlled by an automatic adjustment valve, so that less material is injected into the low concentration area to avoid over-penetration, and more material is injected into the high concentration area to enhance the adhesive strength. After this adjustment, the distribution is more uniform.

[0048] S211 decomposes the concentration value into multiple intervals.

[0049] Specifically, when decomposing concentration values, a concentration threshold is first set, for example, 5%. Values ​​below the threshold are classified as low concentrations, and values ​​above the threshold are classified as high concentrations. This decomposition is based on the viscosity characteristics of the material to ensure that subsequent adjustments do not lead to uneven distribution.

[0050] S212, according to the regional planning, the back of the textile material matrix is ​​divided into a grid-like area.

[0051] In one possible implementation, the zoning uses a uniform grid division method, dividing the back side into 10 by 10 grids, each grid being 1 square centimeter in size. Higher concentrations are assigned to grids with high fiber interlacing density to match material requirements.

[0052] S213 controls the injection amount of heat-sensitive adhesive material through an automatic regulating valve.

[0053] For example, the automatic regulating valve opens and closes in real time based on feedback signals. For instance, when a certain mesh concentration is detected to be too low, the valve increases its opening to inject more material, which can stabilize the distribution and improve the bonding efficiency.

[0054] S22, real-time monitoring of the uniformity of the mixed combing.

[0055] In one embodiment, the uniformity monitoring of the mixed combing is achieved by capturing fiber distribution images through an optical sensor, collecting data once per second, and calculating the standard deviation of fiber density in the image. If the standard deviation is less than a preset value, such as 0.5, it is considered uniform; otherwise, an adjustment is triggered. This real-time monitoring can correct deviations in a timely manner and improve the overall quality.

[0056] S221 captures fiber distribution images using an optical sensor.

[0057] Specifically, the optical sensor is mounted above the combing device and uses an infrared light source to capture images of the back side, highlighting the interwoven fiber structure and avoiding interference from ambient light.

[0058] S222, calculate the standard deviation of fiber density in the image.

[0059] It should be noted that the fiber density standard deviation calculation process is as follows: first, the image is converted to grayscale, then the density value is calculated for each pixel. The density value is equal to the pixel grayscale divided by the average grayscale of the area, and then the standard deviation of all density values ​​is calculated. This process ensures accurate monitoring.

[0060] S23, Generate optimized back-side adhesion data, wherein the data is the adjusted distribution value.

[0061] This step, based on the aforementioned adjustments and monitoring results, directly summarizes the adjusted concentration and regional values ​​to form a data table, for example, an average concentration of 8% and a regional coverage rate of 95%.

[0062] S24, simultaneously recording the changes in the penetration of the low-temperature adhesive material.

[0063] In one embodiment, the low-temperature adhesive material permeation change recording measures the depth of the material penetrating the fiber layer using a permeation sensor, recording the change value once per minute, for example, from an initial depth of 2 mm to 3 mm. This recording helps with subsequent optimization.

[0064] S241 measures the depth of material penetrating the fiber layer using a permeation sensor.

[0065] For example, a permeation sensor uses the principle of ultrasound, emitting wave signals that are reflected back in time to calculate depth, thus making the measurement non-contact and accurate.

[0066] S25, Output the backside preprocessing result, the result including preprocessing status information.

[0067] The output integrates all data to form a report file, which includes uniformity indicators and penetration status, for use as input in the next process.

[0068] In one embodiment, for concentration adjustment, different fiber types are considered, such as polyester and polypropylene fibers. The concentration range is set to 3% to 7% in polyester fibers to prevent excessive water absorption due to high absorbency, and to 4% to 8% in polypropylene fibers to enhance durability. This differentiated adjustment improves adaptability.

[0069] For example, when monitoring uniformity, if the standard deviation exceeds a threshold, the combing process is automatically paused and the material is re-injected. For instance, if the standard deviation is 0.6, an additional 1% concentration is injected, and the concentration is recalculated to below 0.4. This prevents defect accumulation and improves product consistency.

[0070] In one possible implementation, when recording permeation changes, temperature factors are considered, such as a permeation rate of 0.5 mm per minute at 20 degrees Celsius. The recorded changes are stable, ensuring that the adhesion does not affect the integrity of the fiber structure.

[0071] Specifically, for the generated data, the adjusted values, such as the concentration being optimized from the initial 6% to 7.2% and the regional planning coverage from 80% to 92%, were obtained through multiple iterations to ensure effective optimization.

[0072] It should be noted that the pre-processing status information in the output includes uniformity and penetration uniformity. This information can guide the subsequent napping operation and avoid the appearance of nap-free areas.

[0073] In one embodiment, for real-time monitoring, multi-sensor fusion, such as combining optical and permeation sensors, is used to calculate a comprehensive uniformity score. If the score is higher than 85, the optimization is complete. This fusion improves accuracy and reduces errors.

[0074] For example, when adjusting the distribution, for high-density fiber areas, the planned grid was subdivided into 5x5, and the injection concentration was increased to 9%. The results showed that the penetration change was small, the uniformity was improved by 20%, and the adhesion effect was better.

[0075] Specifically, when decomposing concentration ranges, if the threshold is set to 4.5%, the injection volume in the low range is halved and the injection volume in the high range is doubled, thus achieving a balanced distribution and preventing local overload.

[0076] In one possible implementation, after capturing the image, the standard deviation is calculated using the square root formula, which is the square root of the sum of the squares of all density deviations divided by the number of points. For example, if there are 100 points and the sum of deviations is 25, then the standard deviation is 0.5, indicating uniformity.

[0077] It should be noted that when measuring depth, the ultrasonic time difference is 0.01 seconds, which corresponds to 1 millimeter. This allows for recording changes with an accuracy of 0.1 millimeters, which helps to track minute variations.

[0078] For example, in the case of polypropylene fiber, after adjusting the data concentration to 8.5%, the penetration increased from 1.8 mm to 2.5 mm, and the output status information showed that it was ready. This optimization reduces the risk of delamination and improves the durability of textile materials.

[0079] The above steps achieve optimized back-side adhesion.

[0080] S103. Extract the operation parameters from the preprocessing results, perform targeted processing to generate processed matrix data and output the information to be heat-treated.

[0081] S1. Extract operation parameters from the preprocessing results, perform targeted processing to generate processed matrix data and output information to be heat-treated.

[0082] S11, extract the napping operation parameters from the back pretreatment results and input them into the combing device to perform targeted combing.

[0083] In one embodiment, the napping operation parameters include the combing speed and the carding density. First, the fiber interlacing density distribution in the pre-processing results is identified by layer scanning, and areas with a density lower than the average value are marked as weak areas. Then, the local speed of the combing device is adjusted according to the location of these areas. For example, the speed is reduced to 80% of the standard value in the weak areas to enhance the fiber stretching effect and avoid excessive damage to the surrounding structure.

[0084] S12 processes weak areas in the interwoven fiber structure to generate the matrix data of the textile material after napping.

[0085] In one embodiment, a progressive combing method is used to treat weak areas. First, the boundary of the area is lightly combed to fix the fiber ends, and then the contact time of the needle cloth is increased in the central area, for example, from the initial 5 seconds to 10 seconds, to ensure that the fibers are uniformly napped. The generated data includes a napping height distribution map, in which the height uniformity is improved by 15%, which helps to apply the temperature evenly in the subsequent heat treatment.

[0086] S13 records the change in back surface roughness and outputs information for heat treatment.

[0087] For example, the change in back surface roughness is measured before and after by a surface scanner, and the roughness index is calculated to decrease from the initial Ra value of 2.5 micrometers to 1.8 micrometers. The output information includes temperature tolerance thresholds, such as a tolerance limit of 150 degrees Celsius, to guide the parameter setting of the heating and pressing device.

[0088] In one embodiment, step S1 is applied to the backside optimization of the nonwoven fabric substrate. The extracted parameters first focus on identifying weak areas in the fiber interlacing structure. These areas often have insufficient strength due to unevenness during material production. Through targeted combing, not only is the napping uniformity improved, but the overall mechanical properties are also enhanced. For example, in the case of high-density fibers, the combing speed is adjusted to 300 revolutions per minute. After the weak areas are treated, the napping height reaches 2 mm. The roughness change record shows that the flatness is improved by 20%. The heat treatment information emphasizes resistance to prevent excessive melting. This treatment can bring better adhesion stability and reduce the risk of delamination.

[0089] For example, in low-density fiber applications, when performing carding after parameter extraction, the carding roller density is set to 80 teeth per square centimeter, and the cylinder carding density is increased to 1200-1600 teeth. For the treatment of weak areas, emphasis is placed on multiple gentle passes, such as 3 cycles, with the tensile force gradually increasing to 1.2 times the standard force each time. The generated data reflects that the matrix thickness increases by 0.5 mm after napping, and the roughness is optimized from 3.0 microns to 2.0 microns. The output information includes a tolerance range of 120 to 140 degrees Celsius, which is helpful in controlling the penetration depth during heat treatment and ensuring the dimensional stability of the material.

[0090] In one embodiment, the progressive combing in step S12 involves first calculating the area ratio of weak areas. If the ratio exceeds 10%, these areas are processed first to balance the overall structure. For example, in a scenario where the area ratio is 15%, the combing time is allocated to 60% of the total time. As a result, the uniformity index in the napping data reaches 95%. This is connected with the roughness record in S13, forming a complete chain from processing to output, which improves the heat treatment preparation efficiency of the textile material matrix.

[0091] S104. Perform heat treatment according to the heat treatment information to generate a heat-treated matrix state; extract relevant parameters from the heat-treated matrix state, perform the bonding process, and generate curing bonding information.

[0092] S1, perform heat treatment according to the heat treatment information to generate the heat-treated matrix state.

[0093] S11, for the information to be heat treated, determine whether there is a non-textured area on the back side. If it is confirmed to be correct, load the temperature setting and time control parameters through the heating and pressing device, and generate execution data in combination with the melt penetration depth requirements. The data is an operation command, and output the state of the substrate after heat treatment. The state includes physical properties.

[0094] In one embodiment, to determine whether there are nap-free areas on the back side, the data of the textile material matrix after napping is first obtained from the heat treatment information. This data includes post-treatment properties and changes in back surface roughness. Then, it is compared with a preset nap-free threshold. If the roughness of all areas is higher than the threshold, it is confirmed to be correct. Next, the temperature is set to 120 degrees Celsius, the time is controlled to 30 seconds, and the melt penetration depth is set to 0.5 mm using a heating and pressing device. The generated operation instructions include gradual heating and uniform pressing, thereby obtaining the heat-treated matrix state, whose physical properties such as surface smoothness and fiber density are improved. This method ensures that the back side of the nonwoven matrix avoids uneven melting during heat treatment, which is beneficial to improving overall adhesion stability.

[0095] In one embodiment, for nonwoven fabrics with different fiber densities, the temperature setting can be adjusted to 100 degrees Celsius, the time control is 45 seconds, the melt penetration depth requirement is 0.3 mm, and the operation instructions emphasize gradual heating to adapt to weak areas. In this way, the physical properties of the resulting matrix state are enhanced in terms of temperature resistance, which is beneficial to the uniformity of the subsequent bonding process.

[0096] For example, in the application of polyester fiber nonwoven fabric, after confirming the non-pilly area, the loading temperature is set to 130 degrees Celsius, the time is 25 seconds, and the penetration depth is 0.4 mm. After execution, the matrix condition shows that the fiber interlacing structure is more compact. The beneficial effect is to reduce the risk of material deformation during heat treatment.

[0097] S2, extract relevant parameters from the state of the substrate after heat treatment, perform the bonding process and generate curing bonding information.

[0098] S21, extract the type of low-temperature adhesive material and auxiliary strength parameters from the substrate state after heat treatment, monitor the curing rate through the temperature control system, perform the fiber surface bonding process, generate curing bonding information, the information being bonding strength data, record the uniformity test results, output the bonding data to be cooled, the data being the state to be processed, and perform thermal bonding under the set process conditions.

[0099] In one embodiment, a low-temperature adhesive material, such as a thermosensitive resin, is extracted from the heat-treated matrix state, and auxiliary strength parameters, such as fiber tensile strength, are obtained. Then, the curing temperature is maintained at 80 degrees Celsius by a temperature control system, and the curing rate is monitored as 2% of the bonding progress per minute. During the fiber surface bonding process, it is ensured that the thermosensitive resin uniformly covers the interlaced structure, and the bonding strength data in the generated curing bonding information reaches 5 Newtons per square centimeter. At the same time, the uniformity test results are recorded, showing a deviation of less than 10%. The cooling bonding data includes the temperature distribution under the untreated state. In this way, thermal bonding under the set process conditions can form a stable back structure.

[0100] In one embodiment, for cotton-blended nonwoven fabric, the extracted low-temperature adhesive is a water-based heat-sensitive adhesive, the auxiliary strength parameter is adjusted to 4 Newtons per square centimeter, the temperature control system monitors the curing rate at 1.5% per minute, the fiber surface bonding process focuses on strengthening weak areas, the curing bonding information shows a bonding strength of 6 Newtons per square centimeter, the uniformity test result deviation is less than 5%, and the bonding data before cooling reflects a more uniform state before treatment, which is beneficial to improving the durability of the final textile material.

[0101] For example, when processing high-density nonwoven fabrics, after extracting the material type, the curing rate is monitored and bonding is performed. The bonding strength data can reach 7 Newtons per square centimeter, and the uniformity results are good. The beneficial effect is that the back bonding is optimized and delamination is prevented.

[0102] S105. Adjust the cooling parameters according to the curing and bonding information to generate dimensionally stable textile material information; extract back bonding data from the textile material information, and determine whether the uniformity meets the preset threshold through detection technology. If it does, generate a storage sequence to complete the back bonding optimization process.

[0103] S1, adjust the cooling parameters according to the curing and bonding information to generate dimensionally stable textile material information.

[0104] In one embodiment, the cured bonding information includes bond strength data and uniformity test results. These data originate from the previous fiber surface bonding process. The bond strength data reflects the degree of curing of the low-temperature adhesive material within the interwoven fiber structure, while the uniformity test results record the level of uniformity in the bond distribution. The specific process for adjusting the cooling parameters based on this information involves first evaluating the curing rate. If the curing rate is too fast, it may lead to uneven material shrinkage; therefore, the cooling rate needs to be reduced to extend the cooling time and ensure stable melt penetration depth.

[0105] S11, Extract the curing rate value from the curing bonding information. This value is obtained by monitoring the temperature control system. For example, during the bonding process, the curing rate is expressed as a percentage change per minute. If the curing rate is 5%, it means that the material cures rapidly under these conditions.

[0106] S12. Based on the extracted curing rate value, calculate the adjusted cooling parameters, which include the cooling rate and the cooling time. The calculation method is to use a preset formula, where the cooling rate is equal to the curing rate multiplied by an adjustment coefficient. This adjustment coefficient is set to 0.8 based on an empirical value to avoid dimensional deformation caused by excessively rapid cooling. Then, the cooling time is 1.5 times the total processing time to ensure sufficient stability.

[0107] S13. Apply the adjusted cooling parameters to the cooling system. After these parameters are applied, the system will gradually reduce the temperature, for example, from 80 degrees to room temperature, at a rate controlled at 2 degrees per minute, thereby generating dimensionally stable textile material information, which includes the processed dimensions and surface properties, such as a length change rate of less than 1% and a 20% improvement in surface smoothness.

[0108] For example, when the textile material is a non-woven fabric matrix, if the curing bonding information shows that the bonding strength is at a medium level, adjusting the cooling parameters can prevent local shrinkage caused by uneven distribution of the heat-sensitive adhesive material. The beneficial effect is to improve the overall durability of the material and avoid dimensional instability problems in subsequent use.

[0109] In one possible implementation, for textile materials with different fiber interlacing structures, such as high-density matrices, the curing bonding information may show higher bonding strength data. In this case, when adjusting the cooling parameters, the adjustment coefficient can be set to 0.7 to further slow down the cooling rate to match the high-density characteristics of the material. In this way, the surface properties of the generated textile material information will be more uniform, the dimensional stability performance will be better, and it will help extend the service life of the material.

[0110] S2, extract back adhesion data from the textile material information, determine whether the uniformity meets the preset threshold through detection technology, and if it does, generate a storage sequence to complete the back adhesion optimization process.

[0111] Specifically, back-side bonding data is extracted from the information on dimensionally stable textile materials. This data includes adjusted distribution values ​​and pre-treatment status information, derived from the post-treatment dimensions and surface properties generated in the previous step. The detection technology employs a layered scanning method to determine whether the uniformity meets a preset threshold, set at 95%, meaning the bonding coverage must reach this level.

[0112] S21. Extract the distribution values ​​from the back adhesion data, such as concentration and regional planning parameters. If the concentration is evenly distributed in the range of 0.5 to 0.8, it indicates preliminary compliance.

[0113] S22, use layered scanning technology to scan the back of the material. Layered scanning is to acquire data layer by layer through image acquisition equipment and calculate the unevenness index. If the index is lower than the threshold of 0.1, the uniformity is met.

[0114] S23. If the uniformity is met, a storage sequence is generated. This sequence is a digitally encoded record of all processing parameters, completing the back-side bonding optimization process.

[0115] For example, in textile materials with a nonwoven fabric base structure, if the extracted back adhesive data shows uniform regional planning, and layer scanning determines that there is no delamination, the generated storage sequence can facilitate subsequent traceability. The beneficial effect is to ensure the reliability and repeatability of the processing and avoid quality fluctuations.

[0116] S106. The acquisition of back-side data of the textile material matrix includes: acquiring back-side data of the textile material matrix through image acquisition technology, wherein the textile material matrix is ​​a non-woven fabric base structure; performing layered analysis on the back-side data to generate a back-side roughness report, the report including surface unevenness index; determining the distribution scheme of the heat-sensitive adhesive material in combination with the fiber interlacing structure characteristics, and outputting initial distribution parameters, the initial distribution parameters including specific values ​​of concentration and zoning; determining the applicability of the initial distribution parameters by analyzing the back-side roughness report and fiber interlacing structure characteristics to ensure the accuracy of subsequent adjustments; recording the analysis process of the back-side data to form a traceable data record for subsequent optimization reference.

[0117] Data on the back side of a textile material matrix is ​​acquired using image acquisition technology, wherein the textile material matrix is ​​a non-woven fabric base structure.

[0118] In one embodiment, a high-resolution camera is used to scan the back of the nonwoven fabric substrate to capture surface texture images, ensuring that the data resolution reaches the 0.1 mm level, thus forming an initial image dataset.

[0119] The back surface data is subjected to layered analysis to generate a back surface roughness report, which includes surface unevenness indicators.

[0120] For example, when an image dataset is input into a layered processing module, the surface layer and the inner layer data are first separated, and an edge detection algorithm is used to identify uneven regions. Edge detection algorithms identify boundaries with significant brightness changes in an image by calculating pixel gradients. For instance, the Sobel operator calculates gradient values ​​in the horizontal and vertical directions, and then the gradient magnitudes are synthesized to determine edge strength.

[0121] Specifically, the gradient of the neighborhood of each pixel is calculated, and if the magnitude exceeds a threshold, it is marked as an edge, thereby quantifying the surface roughness index. For example, the average roughness value Ra is calculated as the arithmetic mean of all height deviations. For instance, the height difference is measured in a 10 cm square area and averaged to obtain Ra = 2.5 micrometers.

[0122] It should be noted that this stratified analysis can improve the accuracy of the report because it distinguishes the surface microstructure and avoids the errors caused by overall averaging.

[0123] In one embodiment, for high-density nonwoven fabrics, adjusting the layer depth parameter to 3 layers generates a report showing an unevenness index of 3.2, which is 15% more accurate than the 2-layer analysis and is beneficial for subsequent bonding optimization.

[0124] The distribution scheme of the thermosensitive adhesive material is determined by combining the characteristics of the fiber interlaced structure, and the initial distribution parameters are output, including the specific values ​​of concentration and regional planning.

[0125] For example, based on the aforementioned roughness report, fiber cross-density features are extracted, and a clustering algorithm is used to group the regions. The clustering algorithm refers to the K-means method, which uses fiber points as data points and assigns them to K clusters based on distance. For example, K=5 is set, and the center of each cluster is calculated to represent the densely cross-densed area.

[0126] Specifically, a 15% concentration of thermosensitive adhesive material is allocated to the high-density clusters, with the area planned to cover 80% of the back surface area, and output parameters such as a concentration of 15% and a planned value of 0.8 for zones 1-4.

[0127] It should be noted that this combination ensures that the distribution plan is highly targeted and reduces material waste.

[0128] In one embodiment, for a low-interlacing nonwoven fabric, adjusting the K value to 3, the output concentration to 10%, and the zone planning to 0.6 resulted in an experimental improvement of 20% in adhesion uniformity, which is beneficial to heat treatment stability.

[0129] By analyzing the back surface roughness report and fiber interlacing structure characteristics, the applicability of the initial distribution parameters is determined, ensuring the accuracy of subsequent adjustments.

[0130] For example, compare the unevenness index in the comparison report with the fiber density threshold. If the index is below 2.0 and the density is above 50%, the parameter is confirmed to be applicable; otherwise, adjust the concentration by 5%.

[0131] The analysis process of the backside data is recorded to form a traceable data record for subsequent optimization reference.

[0132] In one embodiment, all analysis steps are logged in a database, including timestamps and parameter values, for easy traceability.

[0133] S107. Adjusting the distribution of the thermal adhesive material according to the initial distribution parameters includes: using a proportioning control system to adjust the distribution ratio of the thermal adhesive material according to the initial distribution parameters; monitoring the uniformity of the mixing and combing in real time to generate optimized back-side adhesive data, wherein the data is the adjusted distribution value; recording the penetration change of the low-temperature adhesive material and outputting the back-side pretreatment result, wherein the result includes pretreatment status information; judging whether the distribution adjustment has achieved the expected effect by monitoring the uniformity of the mixing and combing; and adjusting the subsequent processing parameters according to the penetration change of the low-temperature adhesive material to ensure the stability of the pretreatment result.

[0134] S21, a proportioning control system is used to adjust the distribution ratio of the thermal adhesive material according to the initial distribution parameters.

[0135] In one embodiment, the initial distribution parameters include concentration and zoning. Based on these parameters, the proportioning control system first reads the concentration value, for example, setting it to 5 grams of thermosensitive adhesive per square meter of textile matrix. Then, it divides the zoning into a central zone and an edge zone, with the central zone receiving 60% and the edge zone receiving 40%. Next, the system adjusts the injection volume of the thermosensitive adhesive via a proportional valve to ensure a higher proportion of material is injected into the central zone. This adjustment results in a more uniform distribution, which is beneficial to the stability of the subsequent penetration process and avoids uneven adhesion caused by localized over-concentration or under-concentration.

[0136] S22, Real-time monitoring of the uniformity of the mixed combing is performed to generate optimized back-side adhesion data, wherein the data is the adjusted distribution value.

[0137] S221 collects uniformity indicators during the blending and combing process in real time, including fiber density distribution and adhesive material coverage, by scanning the back side data once per second using a sensor.

[0138] S222: Calculate the deviation value based on the collected uniformity index. If the deviation value exceeds 0.5%, trigger the fine-tuning mechanism to redistribute the proportion of adhesive material.

[0139] S223, generate adjusted distribution values, such as optimizing the central area coverage from the initial 55% to 62% and the edge area coverage from 45% to 38%, and output these values ​​as optimized back adhesive data.

[0140] In one embodiment, for a nonwoven fabric matrix, monitoring the uniformity of the blending involves using optical sensors to detect the density at fiber interlacing points. If a weak area is detected with a density below 10% of the average, the system automatically increases the amount of adhesive material injected into that area to 1.2 times the initial parameter. This improves overall uniformity and reduces the risk of delamination during subsequent heat treatment.

[0141] Specifically, when treating cotton fiber matrices, increasing uniformity from an initial 78% to 92% is beneficial for improving material durability. In another embodiment, for polyester fiber matrices, monitoring focuses on the effect of carding speed. If the speed is 50 meters per minute and the uniformity deviation is 3%, the speed is reduced to 45 meters per minute, while the distribution values ​​are adjusted to ensure minimal permeability variation. This adjustment results in uniform coverage and enhances the physical property stability of the matrix.

[0142] S23, record the permeation changes of the low-temperature adhesive material and output the back pretreatment results, including pretreatment status information.

[0143] In one embodiment, the permeation changes are recorded by a thickness sensor, for example, an initial permeation depth of 0.2 mm, adjusted to 0.3 mm, and these changes are output as pre-processing status information.

[0144] S24, by monitoring the uniformity of the mixed combing, it is determined whether the distribution adjustment has achieved the expected effect.

[0145] S241. Compare the real-time uniformity with a preset threshold, for example, if the threshold is 85%, and the current uniformity is 88%, then it is determined that the expectation has been met.

[0146] If S242 is not achieved, the proportional adjustment in S21 is executed repeatedly until the uniformity is stable.

[0147] S243, after confirmation, record the judgment result as the basis for subsequent parameters.

[0148] In one embodiment, when monitoring the uniformity of the blending and combing process, a continuous sampling method is used, collecting data every 10 seconds. If the uniformity exceeds a threshold of 85% three consecutive times, it is considered that the expected effect has been achieved. This method is beneficial for timely feedback and avoids excessive adjustments that could lead to material waste.

[0149] Specifically, in the nonwoven fabric matrix treatment, if the initial uniformity is 80% and reaches 87% after two adjustments, then the adjustment is stopped to ensure stable distribution.

[0150] S25. Adjust the subsequent processing parameters according to the permeation changes of the low-temperature adhesive material to ensure the stability of the pretreatment results.

[0151] S251, analyze the trend of penetration change. For example, if the penetration rate drops from 0.1 mm per minute to 0.05 mm per minute, increase the subsequent combing time by 5%.

[0152] S252, adjust parameters including temperature setting to 50 degrees Celsius and time control to 10 minutes.

[0153] S253 verifies the stability after adjustment. Repeated tests show that the permeation depth fluctuation is less than 0.02 mm, ensuring the reliability of the pretreatment results.

[0154] In one embodiment, the low-temperature adhesive penetration change record showed an initial change rate of 2%, which was adjusted to 1%. Based on this, the combing intensity of subsequent napping operations was adjusted from medium to low to avoid excessive penetration leading to fiber damage. This adjustment helps maintain matrix integrity and improves heat treatment efficiency.

[0155] Specifically, in the case of cotton nonwoven fabrics, adjusting the permeation change parameter from 0.15 mm improves stability by 20% and reduces the appearance of non-pilly areas. In another embodiment, for synthetic fiber matrices, if the permeation change is negative, indicating rewetting, the cooling time is extended to 15 minutes to ensure that the uniformity threshold meets the requirements. This approach, starting from the permeation trend, supports the overall back-side bonding optimization process.

[0156] S108. Extracting operation parameters from the pretreatment results includes: extracting napping operation parameters from the pretreatment results and inputting them into the carding device to perform targeted carding; processing weak areas in the fiber interlacing structure to generate napped textile material matrix data, wherein the data is the post-processed attribute; recording back surface roughness changes and outputting heat treatment information, wherein the information includes temperature tolerance; determining whether the carding effect meets expectations by analyzing the back surface roughness changes; and adjusting the parameter settings for subsequent heat treatment based on the temperature tolerance to ensure the safety and effectiveness of the processing.

[0157] Extracting operation parameters from the preprocessing results includes: The napping operation parameters are extracted from the preprocessing results and input into the carding device to perform targeted carding.

[0158] In one embodiment, the preprocessing result includes preprocessing status information, wherein the napping operation parameters such as combing speed and needle density are preset. By directly reading these parameters and transmitting them to the combing device, the device automatically adjusts the roller speed to match the parameters and realizes the combing process.

[0159] The weak areas in the fiber interlacing structure are processed to generate the matrix data of the textile material after napping, and the data is the processed attributes.

[0160] Specifically, the carding device performs enhanced carding on weak areas in the fiber interlacing structure, such as areas where the fiber density is below average, by increasing the needle punching frequency to strengthen these areas.

[0161] S21 identifies weak areas in the fiber interlaced structure.

[0162] The combing device uses built-in sensors to scan the fiber distribution and calculate the number of fiber interlacing points in each area. If the number of interlacing points is less than a threshold, such as 10 points per square centimeter, it is marked as weak.

[0163] S22, increases combing intensity for weak areas.

[0164] For the marked area, the device adjusts the needle plate pressure depth to 2 mm and repeats the combing 3 times to increase the fiber interlacing density.

[0165] S23 generates the matrix data of the textile material after napping.

[0166] After combing is completed, the processed attributes, such as the average fiber height of 1.5 mm, are collected and stored as a data file.

[0167] For example, this treatment ensures that weak areas are reinforced, preventing fractures during subsequent heat treatment and improving the overall durability of the material.

[0168] Record the changes in back surface roughness and output information for heat treatment, including temperature tolerance.

[0169] Before and after combing, the changes were recorded using a roughness measuring instrument. For example, if the initial roughness was 5 micrometers, it would be reduced to 3 micrometers after processing. Based on this, the upper limit of temperature tolerance was calculated to be 150 degrees Celsius, and the output was an information report.

[0170] By analyzing the changes in the roughness of the back side, it can be determined whether the combing effect meets expectations.

[0171] It should be noted that roughness change analysis involves comparing data before and after. If the change rate exceeds 20%, the effect is as expected; otherwise, the data needs to be re-analyzed.

[0172] S31, calculate the roughness change rate.

[0173] The rate of change is equal to the roughness after treatment minus the initial roughness, then divided by the initial roughness, and multiplied by 100 to obtain the percentage.

[0174] S32, compare the rate of change with the expected threshold.

[0175] The expected threshold is 20%. If the rate of change is greater than this value, the effect is considered good; otherwise, it is marked as unqualified.

[0176] S33, Generate an effect assessment report.

[0177] The report includes the rate of change and a conclusion on whether the conditions are met, to guide the next steps.

[0178] In one possible implementation, for a nonwoven fabric matrix, if the initial roughness is high, such as 6 micrometers, it is reduced to 2 micrometers after treatment, with a change rate of 66.7%, which far exceeds the threshold. This indicates that combing effectively improves surface uniformity and reduces the risk of heat treatment.

[0179] For example, in cotton fiber applications, if the change rate is only 15%, which is below the threshold, the effect is considered insufficient, which is beneficial for timely adjustment of combing parameters and avoids material waste.

[0180] Based on the temperature tolerance, adjust the parameter settings for subsequent heat treatment to ensure the safety and effectiveness of the treatment process.

[0181] Preferably, if the temperature tolerance is 140 degrees Celsius, the heat treatment temperature is set to 130 degrees Celsius and the time is controlled at 5 minutes to ensure that the upper limit of tolerance is not exceeded.

[0182] S41, read the temperature tolerance value.

[0183] Extract tolerance values, such as 150 degrees Celsius, from the output information as a basis for adjustment.

[0184] S42, set the heat treatment temperature to 90% of the tolerance.

[0185] For example, if the tolerance is 150 degrees Celsius, the temperature is set to 135 degrees Celsius to allow for a safety margin.

[0186] S43, adjust time parameters based on temperature setting.

[0187] The higher the temperature, the shorter the time; for example, 135 degrees Celsius corresponds to 4 minutes, to prevent overheating and damage to the fibers.

[0188] S44, verify the safety of the adjusted parameters.

[0189] The simulation operation confirmed that the material did not deform under the parameters, thus ensuring its effectiveness.

[0190] In one embodiment, for a polyester fiber matrix with a tolerance of 160 degrees Celsius, the temperature is adjusted to 144 degrees Celsius for 3 minutes. This setting reduces the risk of over-melting and improves the uniformity of adhesion.

[0191] For example, in wool blends, where the tolerance is low (e.g., 120 degrees Celsius), adjusting the temperature to 108 degrees Celsius and extending the time to 6 minutes is beneficial for gentle handling, enhancing material strength without damage.

[0192] Understandably, these adjustments ensure a stable heat treatment process and optimize back-side adhesion.

[0193] S109. The heat treatment based on the heat treatment information includes: determining whether there is a non-textured area on the back side based on the heat treatment information; if it is confirmed to be correct, loading temperature setting and time control parameters through a heating and pressing device; generating execution data based on the melt penetration depth requirement, the data being an operation instruction; outputting the substrate state after heat treatment, the state including physical properties; determining whether the heat treatment effect has reached the expected target by analyzing the physical properties; and recording parameter changes during the heat treatment process based on the execution data to provide data support for subsequent optimization.

[0194] Based on the information to be heat-treated, determine whether there is a non-textured area on the back side.

[0195] In one embodiment, the napping operation parameters and back surface roughness change data are extracted from the heat treatment information. These data are compared, and if the roughness changes in all areas show napping coverage, it is confirmed that there are no napped areas.

[0196] If everything is confirmed to be correct, the temperature setting and time control parameters are applied through the heating and pressing device.

[0197] For example, the temperature is set to 120 degrees Celsius and the time is controlled to 30 seconds, and this is applied directly to the device.

[0198] Execution data is generated based on the required melt penetration depth; this data consists of operation instructions.

[0199] In one possible implementation, the melt penetration depth requirement means that the heat-sensitive adhesive material needs to penetrate to a depth of at least 2 mm into the interwoven fiber structure to ensure uniform adhesion. First, the required heat input is calculated based on the concentration and zoning in the initial distribution parameters. Then, combined with temperature tolerance data, specific operating instructions are generated, such as setting the pressure value of the heating and pressing device to 5 Newtons per square centimeter.

[0200] In one embodiment, for different textile material matrices, such as cotton nonwoven fabric, the required melt penetration depth is adjusted to 1.5 mm. When generating execution data, the pre-processing status information is first extracted from the back pre-processing results, the penetration rate is calculated, and then the operation instructions are output, including extending the time to 45 seconds. The beneficial effect is to improve the uniform distribution of the adhesive material and reduce the risk of delamination in weak areas.

[0201] For example, in the case of polyester nonwoven fabric, the required melt penetration depth is 2.5 mm. The process of generating execution data involves integrating the interwoven fiber structure characteristics and calculating the thermal conductivity coefficient. The thermal conductivity coefficient is obtained by the ratio of the material's thermal conductivity to its thickness, i.e., thermal conductivity divided by thickness. The resulting value is used to adjust the temperature setting. The final operation command is to raise the temperature to 140 degrees Celsius and the pressure to 6 Newtons per square centimeter. This ensures a stable melting process and avoids overheating that could damage the fibers.

[0202] Output the state of the substrate after heat treatment, including its physical properties.

[0203] Specifically, the substrate condition shows improved surface smoothness, and physical properties such as a thickness of 3 mm and a strength of 20 Newtons.

[0204] By analyzing the physical properties, it can be determined whether the heat treatment effect has achieved the expected goal.

[0205] In one embodiment, the physical properties include thickness, strength, and uniformity. First, the thickness is measured to see if it is within the range of 2.5 to 3.5 mm. Second, the strength is tested to see if it exceeds 15 Newtons. Finally, the uniformity is checked by layer scanning. If all indicators meet the requirements, the expected goal is considered to have been achieved.

[0206] For example, when analyzing the physical properties of a nonwoven fabric matrix, first compare it with a preset target, such as a strength target of 18 Newtons. If the actual strength is 19 Newtons, then the effect is confirmed to be good. The beneficial effect is to identify problems in a timely manner and ensure consistent product quality.

[0207] In one possible implementation, after adjusting the distribution scheme of the thermal adhesive material, the analysis of physical properties involves calculating the change in the roughness index. The roughness index is the difference obtained by subtracting the initial value from the average surface roughness. If the difference is less than 0.5, it is determined that the target has been achieved, which helps to optimize subsequent processes.

[0208] Based on the execution data, the parameter changes during the heat treatment process are recorded to provide data support for subsequent optimization.

[0209] In one embodiment, the temperature and time parameters in the execution data are monitored in real time during the process, such as the temperature fluctuating from 120 degrees Celsius to 125 degrees Celsius. These changes are recorded to form log data, which is used to adjust the initial distribution parameters for the next time.

[0210] For example, in continuous production scenarios, when recording parameter changes, first store the deviation between the actual value and the required value of melt penetration depth, such as a deviation of 0.2 mm, and then analyze the reasons, such as uneven heating, to provide data support for modifying the proportioning control system settings. The beneficial effect is to improve overall process efficiency and reduce material waste.

[0211] In one possible implementation, for different batches of nonwoven fabric, the recording process involves monitoring the fusion curing rate. Parameter changes, such as extending the time from 30 seconds to 35 seconds, generate optimization suggestions, such as increasing the cooling rate, thus ensuring more precise back-side bonding optimization.

[0212] S1010. Extracting relevant parameters from the heat-treated matrix state includes: extracting the type of low-temperature adhesive material and auxiliary strength parameters from the heat-treated matrix state; monitoring the curing rate through a temperature control system and performing a fiber surface bonding process; generating curing bonding information, which is bonding strength data; recording uniformity detection results and outputting cooling bonding data, which is the state to be processed; performing thermal bonding under set process conditions; determining whether the bonding process meets process requirements by analyzing the uniformity detection results; and adjusting subsequent cooling parameters based on the curing bonding information.

[0213] In one embodiment, step 1 involves extracting the type of low-temperature adhesive material and auxiliary strength parameters from the heat-treated matrix state.

[0214] Specifically, by scanning the matrix state data after heat treatment, a pre-stored material property library is directly read to extract the types of low-temperature adhesive materials, such as polyvinyl alcohol-based adhesives, as well as auxiliary strength parameters, such as tensile strength value of 5 MPa.

[0215] Step 11: Based on the type of low-temperature adhesive material extracted, confirm its melting point range as 80-100 degrees Celsius to support subsequent curing monitoring.

[0216] In one embodiment, step 2 involves monitoring the curing rate through a temperature control system to perform the fiber surface bonding process. Specifically, step 21 involves the temperature control system employing a PID controller to monitor temperature changes in real time. The PID controller is a feedback mechanism based on proportional, integral, and derivative calculations, used to maintain temperature stability during the curing process. For example, it adjusts the heating power by calculating the error value to ensure the curing rate remains between 0.5 and 1.0 mm / min.

[0217] Step 22: Based on the monitoring results, perform the fiber surface bonding process, uniformly apply the low-temperature bonding material to the fiber surface to form a preliminary bonding layer.

[0218] Step 23: Record the curing rate data as input to generate subsequent bonding information.

[0219] For example, in nonwoven fabric substrate scenarios, when the curing rate is monitored at 0.8 mm / min, the fiber surface bonding process can effectively reduce bubble formation and improve bonding uniformity.

[0220] In one embodiment, step 3 involves generating solidification bonding information, which is bonding strength data.

[0221] Specifically, using the curing rate data recorded in step 2, the bonding strength data is calculated, such as estimating the average bonding strength as 6 MPa using the strength formula.

[0222] In one embodiment, step 4 involves recording the uniformity detection results and outputting the data to be cooled and combined, which is the data to be processed.

[0223] Specifically, uniformity detection results, such as 95% coverage, are recorded through optical scanning, and then the data to be cooled and combined is output, including the current temperature status of 80 degrees Celsius.

[0224] In one embodiment, step 5 involves thermal bonding under set process conditions.

[0225] Specifically, thermal bonding is performed under the set conditions of 90 degrees Celsius and 10 minutes to strengthen the bond.

[0226] In one embodiment, step 6 involves analyzing the uniformity detection results to determine whether the bonding process meets the process requirements. Specifically, step 61 involves performing a threshold comparison on the uniformity detection results recorded in step 4; for example, if the coverage rate is greater than 90%, it is determined that the requirements are met.

[0227] Step 62: If the condition is not met, generate an alarm signal and adjust the preceding parameters.

[0228] Step 63: Output the judgment result as the basis for adjustment.

[0229] For example, in the treatment of textile material matrix, when the analysis of uniformity test results shows a coverage rate of 92%, it is judged to meet the process requirements, which is beneficial to ensure that there is no delamination on the back side and improve the overall durability.

[0230] In another embodiment, for high-density nonwoven fabric scenarios, the analysis in step 6 can be extended to multi-layer scanning to determine whether the uniformity exceeds a threshold of 95%. If it does, it is confirmed that the bonding process has optimized the stability of the fiber interlacing structure.

[0231] In one embodiment, step 7 involves adjusting subsequent cooling parameters based on the cured bonding information. Specifically, step 71 involves calculating a cooling rate adjustment value based on the cured bonding information generated in step 3, such as the bonding strength data of 6 MPa.

[0232] Step 72: Adjust the cooling parameters, for example, change the cooling rate from 2 degrees Celsius / min to 1.5 degrees Celsius / min, in order to stabilize the melt penetration depth.

[0233] Step 73: Apply the adjusted parameters to perform cooling to ensure dimensional stability.

[0234] For example, in the application of low-temperature adhesive materials, adjusting the cooling parameters according to the curing bonding information can reduce deformation caused by thermal stress, which is beneficial for producing smooth napped textile materials and improving product uniformity and durability.

[0235] In another embodiment, for scenarios with high auxiliary strength parameters, step 7 can infer the cooling curve from the bonding strength data and adjust it to a gradual cooling mode, such as slowly cooling from 90 degrees Celsius to room temperature, to optimize the back bonding effect.

[0236] For example, regarding the curing rate monitoring in step 2, in the nonwoven fabric matrix, the PID controller uses a proportional term to quickly respond to temperature deviations, an integral term to eliminate steady-state errors, and a derivative term to predict changing trends, ensuring a stable curing rate during the fiber surface bonding process. This avoids local overheating, improves the accuracy of bonding strength data, and is beneficial for uniformity detection in subsequent steps.

[0237] In one embodiment, the judgment process in step 6 can start from the distribution density analysis of the uniformity detection results. For example, if the detection results show that the uniform coverage area accounts for 93%, and the requirement is met, the cooling parameters can be adjusted to reduce the risk of delamination.

[0238] For example, the adjustment in step 7 is based on curing bonding information. If the strength data is higher than the threshold, the cooling parameter is set to slow cooling, which is beneficial to maintaining the integrity of the fiber structure and improves the optimization efficiency of back bonding in the field of textile materials.

[0239] S1011, The step of adjusting the cooling parameters based on the curing and bonding information includes: loading cooling parameters through a cooling system for the bonding data to be cooled; adjusting the cooling rate based on the curing rate monitoring results to stabilize the melt penetration depth; generating dimensionally stable flat piled textile material information, the information being the processed dimensions and surface properties; outputting the final finished product data to be tested, the data including overall attribute parameters; determining whether the cooling effect meets expectations by analyzing the overall attribute parameters; and recording parameter changes during the cooling process based on the cooling rate adjustment results to provide a basis for subsequent process optimization.

[0240] For the combined data to be cooled, cooling parameters are applied through the cooling system.

[0241] In one embodiment, after receiving the data to be cooled, the cooling system immediately loads preset cooling parameters, including initial cooling temperature and wind speed settings, to ensure a smooth transition of the material from the heat treatment state.

[0242] For example, the data to be cooled includes bond strength data and uniformity test results. This data is directly used to initialize the cooling process to avoid structural instability caused by sudden temperature changes in the material.

[0243] The cooling rate is adjusted based on the solidification rate monitoring results to stabilize the melt penetration depth.

[0244] For example, the curing rate monitoring results show the current curing progress. For instance, when the rate is below 0.5 mm / min, cooling needs to be accelerated to lock in the penetration depth.

[0245] In one possible implementation, the specific process of adjusting the cooling rate includes: calculating the current penetration depth deviation based on monitoring results; if the deviation exceeds 2 mm, increasing the cooling rate from the initial 5 degrees Celsius / minute to 8 degrees Celsius / minute to stabilize the depth within a preset range.

[0246] Specifically, the solidification bonding information is bonding strength data, which is correlated with the cooling rate to ensure that the penetration depth is not too shallow due to excessively rapid cooling or too deep due to excessively slow cooling.

[0247] For example, in the treatment of textile matrix materials, when the curing rate monitoring results indicate that the fiber surface bonding process has reached 80% completion, the cooling rate is adjusted to 6 degrees Celsius / minute to stabilize the melt penetration depth between 1 and 3 millimeters. This can prevent dimensional deformation and improve flatness.

[0248] In one embodiment, considering different types of heat-sensitive adhesive materials, such as polyester-based materials, the cooling rate is adjusted to 4 degrees Celsius / minute, while for polyurethane-based materials, it is set to 7 degrees Celsius / minute to match their curing characteristics and ensure uniform penetration depth.

[0249] For example, through real-time feedback loops, if the monitoring results show fluctuations in the curing rate, the cooling rate is dynamically fine-tuned in increments of 0.5-1 degrees Celsius per minute, stabilizing the depth change to no more than 0.2 millimeters, thereby improving the material's durability.

[0250] Generate information on dimensionally stable, flat, piled textile materials, the information being the processed dimensions and surface properties.

[0251] In one embodiment, based on the adjusted cooling rate, the processed dimensions, such as a length shrinkage rate of less than 1%, and surface properties, such as a roughness index of less than 0.3, are calculated, and corresponding information is generated.

[0252] Output the final finished product data to be tested, which includes overall attribute parameters.

[0253] For example, the generated information can be directly packaged into finished product data, including parameters for dimensional stability and surface flatness.

[0254] By analyzing the overall attribute parameters, it can be determined whether the cooling effect meets expectations.

[0255] For example, the overall attribute parameters include dimensional deviation and surface uniformity. If the dimensional deviation is less than 0.5 mm and the uniformity is higher than 95%, the cooling effect is considered to have met expectations.

[0256] In one possible implementation, the analysis process involves comparing parameters with thresholds, such as surface roughness indices. If these indices are below the initial roughness report threshold, the results are considered good.

[0257] Specifically, the overall property parameters are derived from the back pretreatment results and heat treatment status. These parameters are used for quantitative judgment. For example, after the penetration depth stabilizes, the analysis shows that cooling reduces the roughness change by 10%, achieving the expected smoothness effect.

[0258] For example, in the case of a non-woven fabric base structure, when the overall property parameters show that the temperature tolerance has increased to 150 degrees Celsius, the cooling is considered successful, thus avoiding delamination.

[0259] In one embodiment, for the parameters of different regional planning, the size stability rate of the eastern region was analyzed to be 98% and that of the western region was 97%, which on average exceeded the expected threshold of 95%, thus confirming the effect.

[0260] For example, by observing the parameter trend graph, if the attribute parameter curve is smooth and has no peak, it can be determined that cooling has optimized the adhesion uniformity and improved the product qualification rate.

[0261] Based on the cooling rate adjustment results, the parameter changes during the cooling process are recorded to provide a basis for subsequent process optimization.

[0262] For example, when the cooling rate is adjusted from 5 degrees Celsius / minute to 7 degrees Celsius / minute, the temperature drop curve and the stabilization time of the penetration depth are recorded, such as when the drop curve is linear and the stabilization time is less than 5 minutes.

[0263] In one possible implementation, the recording process includes storing serialized data, such as sampling parameter changes every minute, to form a log file for analyzing the causes of deviations.

[0264] Specifically, the parameter change records are linked to the back adhesive data. For example, the adjustment results show that after the concentration distribution is optimized, the change range is reduced by 20%, which provides a basis for the next batch of regional planning.

[0265] For example, in a distribution scheme for heat-sensitive adhesive materials, if the record shows that adjusting the cooling rate reduced the melt penetration depth fluctuation by 0.1 mm, it is used to optimize the initial distribution parameters and improve uniformity.

[0266] In one embodiment, for changes in the penetration of the low-temperature adhesive material, the penetration depth was recorded as 2 mm before cooling and 1.8 mm after adjustment, providing a basis for adjusting the time control parameters.

[0267] For example, after recording parameter changes, it was found that the wind speed setting affected the curing rate by 0.2 mm / min, which could optimize the loading of the subsequent heating and pressing device and improve the overall back-side bonding optimization process efficiency.

[0268] S1012. Extracting back-side adhesive data from the textile material information includes: extracting back-side adhesive data from the final finished product data to be tested; determining whether the uniformity meets a preset threshold using layered scanning technology; if it does, confirming no delamination and generating a storage sequence; if it does not, recording the specific location and data characteristics of the uneven area; forming a traceable processing record based on the storage sequence; determining the final effect of the optimization process by analyzing the uniformity results of the back-side adhesive data; and adjusting subsequent detection parameters based on the results of the layered scanning technology to ensure detection accuracy.

[0269] S1, extract the back adhesion data from the final finished product data to be tested.

[0270] In one embodiment, step S1 involves extracting back-side adhesion data from the final product data to be tested. Specifically, this includes step S11, reading the overall attribute parameters from the final product data to be tested, which includes back-side adhesion-related values ​​such as concentration distribution and zoning data. Step S12, separating the back-side adhesion data portion to ensure that the extracted data completely corresponds to the states after preprocessing and heat treatment. Through this extraction, adhesion information for subsequent judgment can be directly obtained, forming the starting point of the data chain.

[0271] S2 uses layered scanning technology to determine whether the uniformity meets the preset threshold.

[0272] In one embodiment, step S2 uses layered scanning technology to determine whether the uniformity meets a preset threshold. Specifically, step S21 involves activating the layered scanning device to image the back-side adhesive data layer by layer. Layered scanning technology uses optical or ultrasonic methods to decompose the material thickness direction into multiple layers, capturing the distribution density and continuity of the adhesive material at each layer. For example, the preset threshold is a uniformity index of 0.85, indicating that the distribution deviation of each layer does not exceed 15%. Step S22 involves calculating the adhesive uniformity value of each layer, quantifying it by comparing the deviation of the distribution density from the average value. Step S23 involves comparing the calculation results with the preset threshold. If all layers meet the threshold, the overall uniformity is deemed acceptable. This layered scanning can accurately identify hidden non-uniformities, improve the reliability of detection, and help avoid the risk of subsequent delamination.

[0273] In one possible implementation, for a nonwoven fabric substrate with a thickness of 2 mm, the scanning resolution is set to 0.1 mm layers, and the threshold is adjusted to 0.9 to accommodate the fine fiber structure and ensure accurate judgment.

[0274] For example, in a high-concentration adhesive scenario, the scan shows that the uniformity of layer 1 is 0.92 and that of layer 2 is 0.88. If the threshold is 0.85, it meets the requirements, effectively supporting the improvement of the accuracy of the optimization process.

[0275] S3, if the condition is met, then it is confirmed that there is no delamination phenomenon, and a storage sequence is generated.

[0276] In one embodiment, if the condition is met in step S3, it is confirmed that there is no delamination and a storage sequence is generated. Specifically, this includes step S31, verifying the absence of delamination based on the uniformity judgment result, i.e., checking whether the connections between layers are continuous and without breaks. Step S32, compiling the uniformity data and confirmation result into a sequence format for storage.

[0277] S4. If not satisfied, record the specific location and data characteristics of the uneven area.

[0278] In one embodiment, if the non-uniformity is not met in step S4, the specific location and data characteristics of the non-uniform region are recorded. Specifically, this includes step S41, locating the coordinates of the non-uniform region, such as marking the x and y positions using a scanning coordinate system. Step S42, extracting data features including deviation values ​​and the range of influence. This recording facilitates targeted optimization, avoids overall rework, and improves production efficiency.

[0279] In one possible implementation, for regions where the deviation exceeds the threshold of 0.15, the location is recorded as the matrix center area and the characteristic is a low concentration of 0.2, which effectively guides subsequent adjustments.

[0280] S5. Based on the stored sequence, a traceable processing record is formed.

[0281] In one embodiment, step S5 involves forming a traceable processing record based on the stored sequence, specifically including step S51, integrating the sequence data into a timestamp record to ensure that each step is traceable.

[0282] S6. By analyzing the uniformity results of the back adhesive data, the final effect of the optimization process is determined.

[0283] In one embodiment, step S6 determines the final effect of the optimization treatment by analyzing the uniformity results of the back-side adhesive data. Specifically, this includes step S61, summarizing the uniformity values ​​to calculate an overall effect index, for example, using a weighted average method, where the weights are based on layer depth, and an effect index higher than 0.9 indicates successful optimization. Step S62, comparing the roughness changes before and after to confirm the degree of adhesion enhancement. This analysis ensures the quantification of the treatment effect, which is beneficial for continuous process improvement.

[0284] In one possible implementation, a uniformity result of 0.95 corresponds to excellent performance for a napped substrate, demonstrating the effectiveness of the thermal adhesive distribution scheme.

[0285] For example, in a low-temperature bonding scenario, analysis showed that uniformity improved from an initial 0.7 to 0.93, ultimately achieving a stable tolerance temperature of 80 degrees Celsius and effectively preventing delamination. In another embodiment, for different fiber interlacing structures, analysis incorporated penetration depth data; a uniformity of 0.88 indicated moderate effectiveness, and adjustments improved it to 0.92, demonstrating the adaptability of the optimization treatment. This multi-faceted analysis, from distribution to strength support, forms a consistent optimization evaluation, which is beneficial for ensuring the quality of textile materials.

[0286] S7. Based on the results of the layered scanning technology, adjust the subsequent detection parameters to ensure detection accuracy.

[0287] In one embodiment, step S7 adjusts subsequent detection parameters based on the results of the layered scanning technology to ensure detection accuracy. Specifically, step S71 updates the threshold and resolution based on the scanning deviation; if the deviation is large, the scanning density is increased. This adjustment closely follows the back-side bonding optimization process, improving the overall process accuracy.

[0288] Those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for heat-setting and shaping wall coverings using low-melting-point adhesives, characterized in that, include: Data on the back side of the textile material matrix is ​​obtained, and the back side data is analyzed to determine the distribution scheme of the heat-sensitive adhesive material and generate initial distribution parameters. The distribution of the thermal adhesive material is adjusted according to the initial distribution parameters to generate optimized back-side adhesive data and output the preprocessing results. Operational parameters are extracted from the preprocessing results, and targeted processing is performed to generate processed matrix data and output heat treatment information. Heat treatment is performed according to the heat treatment information to generate the heat-treated matrix state. Relevant parameters are extracted from the heat-treated matrix state, the bonding process is executed, and curing bonding information is generated. Adjust the cooling parameters based on the curing and bonding information to generate dimensionally stable textile material information; Backside adhesion data is extracted from the textile material information. The uniformity is then determined using detection technology to determine if it meets a preset threshold. If it does, a storage sequence is generated to complete the backside adhesion optimization process.

2. The method for heat-setting and shaping of wall coverings using low-melting-point adhesive as described in claim 1, characterized in that, The acquisition of back-side data of the textile material matrix includes: Data of the back side of a textile material matrix is ​​acquired using image acquisition technology, wherein the textile material matrix is ​​a non-woven fabric basic structure. The back surface data is subjected to layered analysis to generate a back surface roughness report, which includes surface unevenness indicators. The distribution scheme of the thermosensitive adhesive material is determined by combining the characteristics of the fiber interlaced structure, and the initial distribution parameters are output, including the specific values ​​of concentration and regional planning. By analyzing the back surface roughness report and fiber interlacing structure characteristics, the applicability of the initial distribution parameters is determined, ensuring the accuracy of subsequent adjustments; The analysis process of the backside data is recorded to form a traceable data record for subsequent optimization reference.

3. The method for heat-setting and shaping of wall coverings using low-melting-point adhesive as described in claim 1, characterized in that, The step of adjusting the distribution of the thermal adhesive material according to the initial distribution parameters includes: A proportioning control system is used to adjust the distribution ratio of the thermosensitive adhesive material according to the initial distribution parameters. Real-time monitoring of the uniformity of the mixed combing is performed to generate optimized back-side adhesion data, which is an adjusted distribution value. Record the permeation changes of the low-temperature adhesive material and output the back pretreatment results, including pretreatment status information; By monitoring the uniformity of the mixed combing, it can be determined whether the distribution adjustment has achieved the expected effect; Based on the permeation changes of the low-temperature adhesive material, the subsequent processing parameters are adjusted to ensure the stability of the pretreatment results.

4. The method for heat-setting and shaping of wall coverings using low-melting-point adhesive as described in claim 1, characterized in that, The extraction of operation parameters from the preprocessing result includes: The napping operation parameters are extracted from the preprocessing results and input into the carding device to perform targeted carding; The weak areas in the fiber interlacing structure are processed to generate the matrix data of the textile material after napping, and the data is the processed attributes. Record the changes in back surface roughness and output information for heat treatment, including temperature tolerance. By analyzing the changes in the roughness of the back surface, it can be determined whether the combing effect meets expectations; Based on the temperature tolerance, adjust the parameter settings for subsequent heat treatment to ensure the safety and effectiveness of the treatment process.

5. The method for heat-setting and shaping of wall coverings using low-melting-point adhesive as described in claim 1, characterized in that, The heat treatment based on the heat treatment information includes: Based on the information regarding the heat treatment to be performed, determine whether there is a non-textured area on the back side; If everything is confirmed to be correct, the temperature setting and time control parameters are applied through the heating and pressing device; Execution data is generated based on the melt penetration depth requirements; this data consists of operation instructions. Output the state of the substrate after heat treatment, the state including physical properties; By analyzing the physical properties, it can be determined whether the heat treatment effect has achieved the expected goal; Based on the execution data, the parameter changes during the heat treatment process are recorded to provide data support for subsequent optimization.

6. The method for heat-setting and shaping of wall coverings using low-melting-point adhesive as described in claim 1, characterized in that, The extraction of relevant parameters from the matrix state after heat treatment includes: Extract the types of low-temperature adhesive materials and auxiliary strength parameters from the matrix state after heat treatment; The curing rate is monitored by a temperature control system to perform the fiber surface bonding process. Generate curing bonding information, wherein the information is bonding strength data; Record the uniformity test results and output the data to be cooled and combined, which is the data to be processed. Perform thermal bonding under the specified process conditions; By analyzing the uniformity test results, it can be determined whether the bonding process meets the process requirements; Adjust subsequent cooling parameters based on the solidification and bonding information.

7. The method for heat-setting and shaping of wall coverings using low-melting-point adhesive as described in claim 1, characterized in that, The step of adjusting the cooling parameters based on the solidification bonding information includes: For the combined data to be cooled, cooling parameters are applied through the cooling system; Adjust the cooling rate based on the solidification rate monitoring results to stabilize the melt penetration depth; Generate dimensionally stable flat piled textile material information, wherein the information is the processed size and surface properties; Output the final finished product data to be tested, including overall attribute parameters; By analyzing the overall attribute parameters, it can be determined whether the cooling effect meets expectations; Based on the cooling rate adjustment results, the parameter changes during the cooling process are recorded to provide a basis for subsequent process optimization.

8. The method for heat-setting and shaping of wall coverings using low-melting-point adhesive as described in claim 1, characterized in that, The extraction of back-side adhesive data from the textile material information includes: Extract the back adhesive data from the final product data to be tested; The uniformity is determined using layered scanning technology to see if it meets a preset threshold. If the conditions are met, it is confirmed that there is no delamination phenomenon, and a storage sequence is generated; If it does not meet the requirements, record the specific location and data characteristics of the uneven area; Based on the stored sequence, a traceable processing record is formed; The final effect of the optimization process was determined by analyzing the uniformity results of the back-side adhesive data. Based on the results of the layered scanning technique, subsequent detection parameters are adjusted to ensure detection accuracy.

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